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knitr::opts_chunk$set(message = FALSE) 
library(tidyverse)

-- Attaching packages ------------------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --

v ggplot2 3.3.2     v purrr   0.3.4
v tibble  3.0.2     v dplyr   1.0.0
v tidyr   1.1.0     v stringr 1.4.0
v readr   1.3.1     v forcats 0.5.0

-- Conflicts ---------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(patchwork)
library(sjPlot)

Learn more about sjPlot with 'browseVignettes("sjPlot")'.

library(ggsci)
library(dabestr)

Loading required package: magrittr


Attaching package: 'magrittr'

The following object is masked from 'package:purrr':

    set_names

The following object is masked from 'package:tidyr':

    extract

library(dabestr)
library(cowplot)


********************************************************

Note: As of version 1.0.0, cowplot does not change the

  default ggplot2 theme anymore. To recover the previous

  behavior, execute:
  theme_set(theme_cowplot())

********************************************************


Attaching package: 'cowplot'

The following objects are masked from 'package:sjPlot':

    plot_grid, save_plot

The following object is masked from 'package:patchwork':

    align_plots

library(ggsignif)
library(ggforce)
library(lme4)

Loading required package: Matrix


Attaching package: 'Matrix'

The following objects are masked from 'package:tidyr':

    expand, pack, unpack

library(directlabels)
library(lmerTest)


Attaching package: 'lmerTest'

The following object is masked from 'package:lme4':

    lmer

The following object is masked from 'package:stats':

    step

library(dotwhisker)
library(pals)
theme_set(theme_cowplot())
library(RColorBrewer)

Data loading

npg_col = pal_npg("nrc")(9)
col_list <- c(`Wild`=npg_col[8],
   Landrace = npg_col[3],
  `Old cultivar`=npg_col[2],
  `Modern cultivar`=npg_col[4])

pav_table <- read_tsv('./data/soybean_pan_pav.matrix_gene.txt.gz')

nbs <- read_tsv('./data/Lee.NBS.candidates.lst', col_names = c('Name', 'Class'))
nbs

# A tibble: 486 x 2
   Name                   Class
   <chr>                  <chr>
 1 UWASoyPan00953.t1      CN   
 2 GlymaLee.13G222900.1.p CN   
 3 GlymaLee.18G227000.1.p CN   
 4 GlymaLee.18G080600.1.p CN   
 5 GlymaLee.20G036200.1.p CN   
 6 UWASoyPan01876.t1      CN   
 7 UWASoyPan04211.t1      CN   
 8 GlymaLee.19G105400.1.p CN   
 9 GlymaLee.18G085100.1.p CN   
10 GlymaLee.11G142600.1.p CN   
# ... with 476 more rows

# have to remove the .t1s 
nbs$Name <- gsub('.t1','', nbs$Name)
nbs_pav_table <- pav_table %>% filter(Individual %in% nbs$Name)

names <- c()
presences <- c()

for (i in seq_along(nbs_pav_table)){
  if ( i == 1) next
  thisind <- colnames(nbs_pav_table)[i]
  pavs <- nbs_pav_table[[i]]
  presents <- sum(pavs)
  names <- c(names, thisind)
  presences <- c(presences, presents)
}
nbs_res_tibb <- new_tibble(list(names = names, presences = presences))

Warning: The `nrow` argument of `new_tibble()` can't be missing as of tibble 2.0.0.
`x` must be a scalar integer.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.

# let's make the same table for all genes too
names <- c()
presences <- c()

for (i in seq_along(pav_table)){
  if ( i == 1) next
  thisind <- colnames(pav_table)[i]
  pavs <- pav_table[[i]]
  presents <- sum(pavs)
  names <- c(names, thisind)
  presences <- c(presences, presents)
}
res_tibb <- new_tibble(list(names = names, presences = presences))

groups <- read_csv('./data/Table_of_cultivar_groups.csv')
groups <- rename(groups, Group = `Group in violin table`)
groups <- groups %>% 
  mutate(Group = str_replace_all(Group, 'landrace', 'Landrace')) %>%
  mutate(Group = str_replace_all(Group, 'Old_cultivar', 'Old cultivar')) %>%
  mutate(Group = str_replace_all(Group, 'Modern_cultivar', 'Modern cultivar')) %>%
  mutate(Group = str_replace_all(Group, 'Wild-type', 'Wild'))

groups$Group <-
  factor(
    groups$Group,
    levels = c('Wild',
               'Landrace',
               'Old cultivar',
               'Modern cultivar')
  )
groups

# A tibble: 1,069 x 3
   `Data-storage-ID` `PI-ID`   Group   
   <chr>             <chr>     <fct>   
 1 SRR1533284        PI416890  Landrace
 2 SRR1533282        PI323576  Landrace
 3 SRR1533292        PI157421  Landrace
 4 SRR1533216        PI594615  Landrace
 5 SRR1533239        PI603336  Landrace
 6 USB-108           PI165675  Landrace
 7 HNEX-13           PI253665D Landrace
 8 USB-382           PI603549  Landrace
 9 SRR1533236        PI587552  Landrace
10 SRR1533332        PI567293  Landrace
# ... with 1,059 more rows

nbs_joined_groups <-
  inner_join(nbs_res_tibb, groups, by = c('names' = 'Data-storage-ID'))
all_joined_groups <-
    inner_join(res_tibb, groups, by = c('names' = 'Data-storage-ID'))

Linking with yield

Can we link the trajectory of NLR genes with the trajectory of yield across the history of soybean breeding? let’s make a simple regression for now

Yield

yield <- read_tsv('./data/yield.txt')
yield_join <- inner_join(nbs_res_tibb, yield, by=c('names'='Line'))

yield_join %>% ggplot(aes(x=presences, y=Yield)) + geom_hex() + geom_smooth() +
  xlab('NLR gene count')

Protein

protein <- read_tsv('./data/protein_phenotype.txt')
protein_join <- left_join(nbs_res_tibb, protein, by=c('names'='Line')) %>% filter(!is.na(Protein))

protein_join %>% ggplot(aes(x=presences, y=Protein)) + geom_hex() + geom_smooth() +
  xlab('NLR gene count')

summary(lm(Protein ~ presences, data = protein_join))


Call:
lm(formula = Protein ~ presences, data = protein_join)

Residuals:
     Min       1Q   Median       3Q      Max 
-11.8479  -2.1274  -0.3336   1.9959  10.0949 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -7.98158    7.24125  -1.102    0.271    
presences    0.11786    0.01624   7.258 8.07e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.106 on 960 degrees of freedom
Multiple R-squared:  0.05203,   Adjusted R-squared:  0.05104 
F-statistic: 52.69 on 1 and 960 DF,  p-value: 8.075e-13

Seed weight

Let’s look at seed weight:

seed_weight <- read_tsv('./data/Seed_weight_Phenotype.txt', col_names = c('names', 'wt'))
seed_join <- left_join(nbs_res_tibb, seed_weight) %>% filter(!is.na(wt))

seed_join %>% filter(wt > 5) %>%  ggplot(aes(x=presences, y=wt)) + geom_hex() + geom_smooth() +
  ylab('Seed weight') +
  xlab('NLR gene count')

summary(lm(wt ~ presences, data = seed_join))


Call:
lm(formula = wt ~ presences, data = seed_join)

Residuals:
     Min       1Q   Median       3Q      Max 
-12.2910  -2.8692   0.1462   2.7771  19.6962 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 91.40656   14.67990   6.227 8.28e-10 ***
presences   -0.17636    0.03298  -5.348 1.21e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.714 on 690 degrees of freedom
Multiple R-squared:  0.0398,    Adjusted R-squared:  0.0384 
F-statistic:  28.6 on 1 and 690 DF,  p-value: 1.213e-07

Oil content

And now let’s look at the oil phenotype:

oil <- read_tsv('./data/oil_phenotype.txt')
oil_join <- left_join(nbs_res_tibb, oil, by=c('names'='Line')) %>% filter(!is.na(Oil))
oil_join

# A tibble: 962 x 3
   names presences   Oil
   <chr>     <dbl> <dbl>
 1 AB-01       445  17.6
 2 AB-02       454  16.8
 3 BR-24       455  20.6
 4 ESS         454  20.9
 5 For         448  21  
 6 HN001       448  23.6
 7 HN002       444  18.5
 8 HN003       446  17.5
 9 HN004       442  18.9
10 HN005       440  15.5
# ... with 952 more rows

oil_join %>%  ggplot(aes(x=presences, y=Oil)) + geom_hex() + geom_smooth() +
  xlab('NLR gene count')

summary(lm(Oil ~ presences, data = oil_join))


Call:
lm(formula = Oil ~ presences, data = oil_join)

Residuals:
     Min       1Q   Median       3Q      Max 
-10.4376  -1.9081   0.4846   2.2401   9.0361 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 118.03941    7.31646   16.13   <2e-16 ***
presences    -0.22591    0.01641  -13.77   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.139 on 960 degrees of freedom
Multiple R-squared:  0.1649,    Adjusted R-squared:  0.1641 
F-statistic: 189.6 on 1 and 960 DF,  p-value: < 2.2e-16

OK there are many, many outliers here. Clearly I’ll have to do something fancier - for example, using the first two PCs as covariates might get rid of some of those outliers.

Boxplots per group

Yield

nbs_joined_groups %>% 
  filter(!is.na(Group)) %>% 
  inner_join(yield, by=c('names'='Line')) %>% 
  ggplot(aes(x=Group, y=Yield, fill = Group)) + 
  geom_boxplot() +
  scale_fill_manual(values = col_list) + 
  theme_minimal_hgrid() +
  theme(axis.text.x = element_text(size=12),
        axis.text.y = element_text(size=12)) +
  geom_signif(comparisons = list(c('Old cultivar', 'Modern cultivar')), 
              map_signif_level = T) +
  guides(fill=FALSE) +
  ylab('Yield') +
  xlab('Accession group')

And let’s check the dots:

nbs_joined_groups %>% 
  filter(!is.na(Group)) %>% 
  inner_join(yield_join, by = 'names') %>% 
  ggplot(aes(y=presences.x, x=Yield, color=Group)) +
  geom_point() + 
  scale_color_manual(values = col_list) + 
  theme_minimal_hgrid() +
  theme(axis.text.x = element_text(size=12),
        axis.text.y = element_text(size=12)) +  
  ylab('NLR gene count')

nbs_joined_groups %>% 
  filter(!is.na(Group)) %>% 
  inner_join(yield_join, by = 'names') %>% 
  filter(Group != 'Landrace') %>% 
  ggplot(aes(x=presences.x, y=Yield, color=Group)) +
  geom_point() + 
  scale_color_manual(values = col_list) + 
  theme_minimal_hgrid() +
  geom_smooth() +
  theme(axis.text.x = element_text(size=12),
        axis.text.y = element_text(size=12)) +  
  xlab('NLR gene count')

## Protein

protein vs. the four groups:

nbs_joined_groups %>% 
  filter(!is.na(Group)) %>% 
  inner_join(protein, by=c('names'='Line')) %>% 
  ggplot(aes(x=Group, y=Protein, fill = Group)) + 
  geom_boxplot() +
  scale_fill_manual(values = col_list) + 
  theme_minimal_hgrid() +
  theme(axis.text.x = element_text(size=12),
        axis.text.y = element_text(size=12)) +
  geom_signif(comparisons = list(c('Wild', 'Landrace'),
                                 c('Old cultivar', 'Modern cultivar')), 
              map_signif_level = T) +
  guides(fill=FALSE) +
  ylab('Protein') +
  xlab('Accession group')

Seed weight

And seed weight:

nbs_joined_groups %>% 
  filter(!is.na(Group)) %>% 
  inner_join(seed_join) %>% 
  ggplot(aes(x=Group, y=wt, fill = Group)) + 
  geom_boxplot() +
  scale_fill_manual(values = col_list) + 
  theme_minimal_hgrid() +
  theme(axis.text.x = element_text(size=12),
        axis.text.y = element_text(size=12)) +
  geom_signif(comparisons = list(c('Wild', 'Landrace'),
                                 c('Old cultivar', 'Modern cultivar')), 
              map_signif_level = T) +
  guides(fill=FALSE) +
  ylab('Seed weight') +
  xlab('Accession group')

Wow, that’s breeding!

Oil content

And finally, Oil content:

nbs_joined_groups %>% 
  filter(!is.na(Group)) %>% 
  inner_join(oil_join, by = 'names') %>% 
  ggplot(aes(x=Group, y=Oil, fill = Group)) + 
  geom_boxplot() +
  scale_fill_manual(values = col_list) + 
  theme_minimal_hgrid() +
  theme(axis.text.x = element_text(size=12),
        axis.text.y = element_text(size=12)) +
  geom_signif(comparisons = list(c('Wild', 'Landrace'),
                                 c('Old cultivar', 'Modern cultivar')), 
              map_signif_level = T) +
  guides(fill=FALSE) +
  ylab('Oil content') +
  xlab('Accession group')

Oha, a single star. That’s p < 0.05!

Let’s redo the above hexplot, but also color the dots by group.

nbs_joined_groups %>% 
  filter(!is.na(Group)) %>% 
  inner_join(oil_join, by = 'names') %>% 
  ggplot(aes(x=presences.x, y=Oil, color=Group)) +
  geom_point() + 
  scale_color_manual(values = col_list) + 
  theme_minimal_hgrid() +
  theme(axis.text.x = element_text(size=12),
        axis.text.y = element_text(size=12)) +  
  xlab('NLR gene count')

Oha, so it’s the Wilds that drag this out a lot.

Let’s remove them and see what it looks like:

nbs_joined_groups %>% 
  filter(!is.na(Group)) %>% 
  inner_join(oil_join, by = 'names') %>% 
  filter(Group %in% c('Old cultivar', 'Modern cultivar')) %>% 
  ggplot(aes(x=presences.x, y=Oil, color=Group)) +
  geom_point() + 
  scale_color_manual(values = col_list) + 
  theme_minimal_hgrid() +
  theme(axis.text.x = element_text(size=12),
        axis.text.y = element_text(size=12)) +  
  xlab('NLR gene count') +
  geom_smooth()

Let’s remove that one outlier:

nbs_joined_groups %>% 
  filter(!is.na(Group)) %>% 
  inner_join(oil_join, by = 'names') %>% 
  filter(Group %in% c('Old cultivar', 'Modern cultivar')) %>% 
  filter(Oil > 13) %>% 
  ggplot(aes(x=presences.x, y=Oil, color=Group)) +
  geom_point() + 
  scale_color_manual(values = col_list) + 
  theme_minimal_hgrid() +
  theme(axis.text.x = element_text(size=12),
        axis.text.y = element_text(size=12)) +  
  xlab('NLR gene count') +
  geom_smooth()

Does the above oil content boxplot become different if we exclude the one outlier? I’d bet so

nbs_joined_groups %>% 
  filter(!is.na(Group)) %>% 
  inner_join(oil_join, by = 'names') %>% 
  filter(names != 'USB-393') %>% 
  ggplot(aes(x=Group, y=Oil, fill = Group)) + 
  geom_boxplot() +
  scale_fill_manual(values = col_list) + 
  theme_minimal_hgrid() +
  theme(axis.text.x = element_text(size=12),
        axis.text.y = element_text(size=12)) +
  geom_signif(comparisons = list(c('Wild', 'Landrace'),
                                 c('Old cultivar', 'Modern cultivar')), 
              map_signif_level = T) +
  guides(fill=FALSE) +
  ylab('Oil content') +
  xlab('Accession group')

Nope, still significantly higher in modern cultivars!

Mixed modeling

Alright here’s my hypothesis: There’s a link between cultivar status (Old, Wild, Landrace, Modern), r-gene count, and yield, but it’s ‘hidden’ by country differences.

Great tutorial here: https://ourcodingclub.github.io/tutorials/mixed-models

So we’ll have to build some lme4 models!

Normalising NLR gene counts

nbs_joined_groups$presences2 <- scale(nbs_joined_groups$presences, center=T, scale=T)
hist(nbs_joined_groups$presences2)

Oil

oil_nbs_joined_groups <- nbs_joined_groups %>% inner_join(oil_join, by = 'names') 
oil_nbs_joined_groups$Oil2 <- scale(oil_nbs_joined_groups$Oil, center=T, scale=T)

basic.lm <- lm(Oil2 ~ presences2, data=oil_nbs_joined_groups)

ggplot(oil_nbs_joined_groups, aes(x = presences2, y = Oil2)) +
  geom_point() +
  geom_smooth(method = "lm")

Hm looks messy, you can see two groups

plot(basic.lm, which = 1)

which is confirmed by the messy line

plot(basic.lm, which = 2)

and this garbage qqplot.

So let’s build an lmer model!

mixed.lmer <- lmer(Oil2 ~ presences2 + (1|Group), data=oil_nbs_joined_groups)
summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Oil2 ~ presences2 + (1 | Group)
   Data: oil_nbs_joined_groups

REML criterion at convergence: 1872.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.5879 -0.5672  0.0869  0.6631  3.2111 

Random effects:
 Groups   Name        Variance Std.Dev.
 Group    (Intercept) 1.3349   1.1554  
 Residual             0.4075   0.6384  
Number of obs: 951, groups:  Group, 4

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)  
(Intercept)  -0.04360    0.57867   2.99844  -0.075   0.9447  
presences2   -0.05350    0.02394 947.27006  -2.234   0.0257 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
           (Intr)
presences2 -0.004

So the Variance for Group is 1.3349, that means it’s 1.3349/(1.3349+0.4075) *100 = 76% of the variance is explained by the four groups!

plot(mixed.lmer)

qqnorm(resid(mixed.lmer))
qqline(resid(mixed.lmer))

These still look fairly bad - better than before, but the QQ plot still isn’t on the line.

Let’s quickly check yield too

Yield

yield_nbs_joined_groups <- nbs_joined_groups %>% inner_join(yield_join, by = 'names') 
yield_nbs_joined_groups$Yield2 <-scale(yield_nbs_joined_groups$Yield, center=T, scale=T)

yield_all_joined_groups <- all_joined_groups %>% inner_join(yield_join, by = 'names')

mixed.lmer <- lmer(Yield2 ~ presences2 + (1|Group), data=yield_nbs_joined_groups)
summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield2 ~ presences2 + (1 | Group)
   Data: yield_nbs_joined_groups

REML criterion at convergence: 2060.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1643 -0.6819  0.0316  0.6948  2.8002 

Random effects:
 Groups   Name        Variance Std.Dev.
 Group    (Intercept) 0.6466   0.8041  
 Residual             0.8600   0.9274  
Number of obs: 761, groups:  Group, 3

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)   0.23641    0.46910   1.98335   0.504 0.664692    
presences2   -0.15364    0.04172 757.46580  -3.683 0.000247 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
           (Intr)
presences2 0.025

Percentage explained by breeding group: 0.6466 / (0.6466+0.8600)*100 = 42%

plot(mixed.lmer)

qqnorm(resid(mixed.lmer))
qqline(resid(mixed.lmer))

p-value of 0.000247 for the normalised presences while accounting for the breeding group, that’s beautiful.

ggplot(yield_nbs_joined_groups, aes(x = presences2, y = Yield2)) +
      facet_wrap(~Group, nrow=1) +   # a panel for each mountain range
      geom_point(alpha = 0.5) +
      theme_classic() +
      geom_line(data = cbind(yield_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) + 
      theme_minimal_hgrid() +
      theme(legend.position = "none") +
      xlab('Scaled and centered NLR gene count') +
      ylab('Scaled and centered yield') +
      scale_color_manual(values=as.vector(isol(40)))

Making the breeding group fixed

We have < 10 possible factors in the group, so making that fixed instead of random

# this doesn't work because you need at least one random effect
# mixed.lmer <- lmer(Yield2 ~ presences2 + Group, data=yield_nbs_joined_groups)

Adding country

We should also add the country the plant is from as a random effect, that definitely has an influence too (perhaps a stronger one???)

Yield

country <- read_csv('./data/Cultivar_vs_country.csv')

names(country) <- c('names', 'PI-ID', 'Country')

yield_country_nbs_joined_groups <- yield_nbs_joined_groups %>% inner_join(country)

yield_country_all_joined_groups <- yield_all_joined_groups %>% inner_join(country)

I need a summary table of sample sizes:

table(yield_country_nbs_joined_groups$Group)


           Wild        Landrace    Old cultivar Modern cultivar 
              0             656              33              52

And a summary histogram:

yield_country_nbs_joined_groups %>% ggplot(aes(x=presences.x, fill=Group)) + 
  geom_histogram(bins=25) +
  xlab(expression(paste('Yield [Mg ', ha^-1, ']'))) +
  ylab('Count') +
  facet_wrap(~Group) +
  scale_fill_manual(values = col_list) +
  theme(legend.position = "none")

mixed.lmer <- lmer(Yield2 ~ presences2 + (1|Group) + (1|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield2 ~ presences2 + (1 | Group) + (1 | Country)
   Data: yield_country_nbs_joined_groups

REML criterion at convergence: 1957

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-3.09429 -0.56737  0.03072  0.65680  2.89981 

Random effects:
 Groups   Name        Variance Std.Dev.
 Country  (Intercept) 0.3807   0.6170  
 Group    (Intercept) 0.4178   0.6464  
 Residual             0.7614   0.8726  
Number of obs: 741, groups:  Country, 40; Group, 3

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)   
(Intercept)   0.07150    0.40194   2.28533   0.178  0.87336   
presences2   -0.11258    0.04116 726.98206  -2.735  0.00639 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
           (Intr)
presences2 0.051

Nice! Yield is negatively correlated with the number of NLR genes when accounting for breeding group AND country

ggplot(yield_country_nbs_joined_groups, aes(x = presences2, y = Yield2, colour = Country)) +
      facet_wrap(~Group, nrow=1) +   # a panel for each mountain range
      geom_point(alpha = 0.5) +
      theme_classic() +
      geom_line(data = cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) + 
      theme_minimal_hgrid() +
      theme(legend.position = "none") +
      xlab('Scaled and centered NLR gene count') +
      ylab('Scaled and centered yield') +
      scale_color_manual(values=as.vector(isol(40)))

Some diagnostics:

plot(mixed.lmer)

qqnorm(resid(mixed.lmer))
qqline(resid(mixed.lmer))

Hm, the qqplot looks slightly worse than when I use maturity group alone, interesting!

BIG DISCLAIMER: Currently, I treat country and group not as nested variables, they’re independent. I think that is the way it should be in this case but I’m thinking.

Making the breeding group fixed

Since we have too few factors in the breeding groups we have to make that fixed, not random

mixed.lmer <- lmer(Yield2 ~ presences2 + Group + (1|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield2 ~ presences2 + Group + (1 | Country)
   Data: yield_country_nbs_joined_groups

REML criterion at convergence: 1951.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1864 -0.5700  0.0305  0.6525  2.8982 

Random effects:
 Groups   Name        Variance Std.Dev.
 Country  (Intercept) 0.3776   0.6145  
 Residual             0.7616   0.8727  
Number of obs: 741, groups:  Country, 40

Fixed effects:
                      Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)           -0.15265    0.13488  33.06645  -1.132  0.26588    
presences2            -0.11149    0.04117 726.45969  -2.708  0.00693 ** 
GroupOld cultivar     -0.29578    0.16164 734.95182  -1.830  0.06768 .  
GroupModern cultivar   1.00458    0.22335 360.40725   4.498 9.28e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) prsnc2 GrpOlc
presences2   0.119              
GrpOldcltvr -0.112 -0.008       
GrpMdrncltv -0.194  0.065  0.156

Non-normalised yield

Let’s see whether the ‘raw’ values perform the same.

mixed.lmer <- lmer(Yield ~ presences.x + (1|Group) + (1|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield ~ presences.x + (1 | Group) + (1 | Country)
   Data: yield_country_nbs_joined_groups

REML criterion at convergence: 1679.6

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-3.09429 -0.56737  0.03072  0.65680  2.89981 

Random effects:
 Groups   Name        Variance Std.Dev.
 Country  (Intercept) 0.2602   0.5101  
 Group    (Intercept) 0.2856   0.5345  
 Residual             0.5205   0.7215  
Number of obs: 741, groups:  Country, 40; Group, 3

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)   9.011013   2.481360 677.994843   3.631 0.000303 ***
presences.x  -0.015192   0.005555 726.982171  -2.735 0.006389 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr)
presences.x -0.991

Oh, lower p-values for the intercept

ggplot(yield_country_nbs_joined_groups, aes(x = presences.x, y = Yield, colour = Country)) +
      facet_wrap(~Group, nrow=1) +   # a panel for each mountain range
      geom_point(alpha = 0.5) +
      theme_classic() +
      geom_line(data = cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) + 
      theme_minimal_hgrid() +
      theme(legend.position = "none") +
      xlab('NLR gene count') +
      xlab(expression(paste('Yield [Mg ', ha^-1, ']'))) +
      scale_color_manual(values=as.vector(isol(40)))

plot(mixed.lmer)

qqnorm(resid(mixed.lmer))
qqline(resid(mixed.lmer))

Making the breeding group fixed

mixed.lmer <- lmer(Yield ~ presences.x + Group + (1|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield ~ presences.x + Group + (1 | Country)
   Data: yield_country_nbs_joined_groups

REML criterion at convergence: 1675.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1864 -0.5700  0.0305  0.6525  2.8982 

Random effects:
 Groups   Name        Variance Std.Dev.
 Country  (Intercept) 0.2581   0.5081  
 Residual             0.5206   0.7216  
Number of obs: 741, groups:  Country, 40

Fixed effects:
                       Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)            8.760056   2.465570 726.660933   3.553 0.000406 ***
presences.x           -0.015045   0.005556 726.459065  -2.708 0.006929 ** 
GroupOld cultivar     -0.244554   0.133648 734.951816  -1.830 0.067680 .  
GroupModern cultivar   0.830604   0.184672 360.407255   4.498 9.28e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) prsnc. GrpOlc
presences.x -0.999              
GrpOldcltvr  0.003 -0.008       
GrpMdrncltv -0.074  0.065  0.156

Oh, lower p-values for the intercept

ggplot(yield_country_nbs_joined_groups, aes(x = presences.x, y = Yield, colour = Country)) +
      facet_wrap(~Group, nrow=1) +   # a panel for each mountain range
      geom_point(alpha = 0.5) +
      theme_classic() +
      geom_line(data = cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) + 
      theme_minimal_hgrid() +
      theme(legend.position = "none") +
      xlab('NLR gene count') +
    xlab(expression(paste('Yield [Mg ', ha^-1, ']'))) +
      scale_color_manual(values=as.vector(isol(40)))

plot(mixed.lmer)

qqnorm(resid(mixed.lmer))
qqline(resid(mixed.lmer))

plot(resid(mixed.lmer))

These are the final numbers for the paper.

Plotting effect of each covariate

(re.effects <- plot_model(mixed.lmer, type = "re", show.values = TRUE))

#lmerTest breaks these other packages so I better unload it and reload only lme4
detach("package:lmerTest", unload=TRUE)

yield_country_nbs_joined_groups_renamed <- yield_country_nbs_joined_groups
names(yield_country_nbs_joined_groups_renamed) <- c('names', 'presences.x', 'PI-ID', 'Group', 'presences2', 'presences.y', 'Yield', 'Yield2', 'Country')
mixed.lmer <- lmer(Yield2 ~ presences2 + Group + (1|Country), data=yield_country_nbs_joined_groups_renamed)

dwplot(mixed.lmer,
       vline = geom_vline(xintercept = 0, colour = "grey60", linetype = 2))

library(stargazer)
stargazer(mixed.lmer, type = "text",
          digits = 3,
          star.cutoffs = c(0.05, 0.01, 0.001),
          digit.separator = "")


==================================================
                          Dependent variable:     
                     -----------------------------
                                Yield2            
--------------------------------------------------
presences2                     -0.111**           
                                (0.041)           
                                                  
GroupOld cultivar               -0.296            
                                (0.162)           
                                                  
GroupModern cultivar           1.005***           
                                (0.223)           
                                                  
Constant                        -0.153            
                                (0.135)           
                                                  
--------------------------------------------------
Observations                      741             
Log Likelihood                 -975.906           
Akaike Inf. Crit.              1963.812           
Bayesian Inf. Crit.            1991.460           
==================================================
Note:                *p<0.05; **p<0.01; ***p<0.001

library(ggeffects)
ggpredict(mixed.lmer, terms = c("presences2",  'Group'), type = "re") %>% 
   plot() +
   theme_minimal()

Let’s also plot that for non-normalised data

mixed.lmer <- lmer(Yield ~ presences.x + Group + (1|Country), data=yield_country_nbs_joined_groups_renamed)
ggpredict(mixed.lmer, terms = c("presences.x",  'Group'), type = "re") %>% 
   plot() +
   theme_minimal_hgrid() +
  xlab('NLR count') +
  ylab(expression(paste('Yield [Mg ', ha^-1, ']')))

# alright back to regular programming
library(lmerTest)

More complex models

If I add random slopes to either groups not much changes, I do get warnings indicating that there’s not much in the data:

mixed.lmer <- lmer(Yield2 ~ presences2 + (presences2|Group) + (1|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield2 ~ presences2 + (presences2 | Group) + (1 | Country)
   Data: yield_country_nbs_joined_groups

REML criterion at convergence: 1954.8

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-3.09789 -0.56422  0.04471  0.67067  2.88976 

Random effects:
 Groups   Name        Variance Std.Dev. Corr
 Country  (Intercept) 0.3920   0.6261       
 Group    (Intercept) 0.3858   0.6211       
          presences2  0.0310   0.1761   0.30
 Residual             0.7564   0.8697       
Number of obs: 741, groups:  Country, 40; Group, 3

Fixed effects:
            Estimate Std. Error       df t value Pr(>|t|)
(Intercept)  0.02964    0.38964  2.29148   0.076    0.945
presences2  -0.22515    0.12370  1.66243  -1.820    0.235

Correlation of Fixed Effects:
           (Intr)
presences2 0.269

mixed.lmer <- lmer(Yield2 ~ presences2 + Group + (1 + presences2|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield2 ~ presences2 + Group + (1 + presences2 | Country)
   Data: yield_country_nbs_joined_groups

REML criterion at convergence: 1951.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1904 -0.5715  0.0314  0.6551  2.9003 

Random effects:
 Groups   Name        Variance Std.Dev. Corr
 Country  (Intercept) 0.397511 0.63048      
          presences2  0.002137 0.04623  1.00
 Residual             0.761491 0.87263      
Number of obs: 741, groups:  Country, 40

Fixed effects:
                     Estimate Std. Error       df t value Pr(>|t|)    
(Intercept)           -0.1470     0.1387  28.1820  -1.060   0.2982    
presences2            -0.1162     0.0430  30.8335  -2.702   0.0111 *  
GroupOld cultivar     -0.3010     0.1615 733.1173  -1.863   0.0628 .  
GroupModern cultivar   1.0093     0.2192 166.8461   4.605 8.15e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) prsnc2 GrpOlc
presences2   0.334              
GrpOldcltvr -0.109 -0.017       
GrpMdrncltv -0.188  0.044  0.158
convergence code: 0
boundary (singular) fit: see ?isSingular

Oh, a significant p-value, let’s plot plot that and compare with he previous plot:

ggplot(yield_country_nbs_joined_groups, aes(x = presences2, y = Yield2, colour = Country)) +
      facet_wrap(~Group, nrow=1) +   # a panel for each mountain range
      geom_point(alpha = 0.5) +
      theme_classic() +
      geom_line(data = cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) + 
      theme_minimal_hgrid() +
      theme(legend.position = "none") +
      xlab('Scaled and centered NLR gene count') +
      ylab('Scaled and centered yield') +
      scale_color_manual(values=as.vector(isol(40)))

Quite similar, mostly downwards trajectories for each country.

Let’s do that non-normalised:

mixed.lmer <- lmer(Yield ~ presences.x + Group + (1 + presences.x|Country), data=yield_country_nbs_joined_groups)

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
unable to evaluate scaled gradient

Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge: degenerate Hessian with 2 negative eigenvalues

Warning: Model failed to converge with 1 negative eigenvalue: -9.3e+04

summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield ~ presences.x + Group + (1 + presences.x | Country)
   Data: yield_country_nbs_joined_groups

REML criterion at convergence: 1675.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1863 -0.5679  0.0294  0.6532  2.8982 

Random effects:
 Groups   Name        Variance  Std.Dev.  Corr 
 Country  (Intercept) 5.191e-01 0.7204930      
          presences.x 2.402e-07 0.0004901 -0.98
 Residual             5.206e-01 0.7215284      
Number of obs: 741, groups:  Country, 40

Fixed effects:
                      Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)            8.73673    2.46492 709.51652   3.544 0.000419 ***
presences.x           -0.01499    0.00555 412.58876  -2.702 0.007186 ** 
GroupOld cultivar     -0.24420    0.13365 733.16976  -1.827 0.068096 .  
GroupModern cultivar   0.83046    0.18493 218.59478   4.491 1.15e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) prsnc. GrpOlc
presences.x -0.999              
GrpOldcltvr  0.002 -0.007       
GrpMdrncltv -0.076  0.067  0.156
convergence code: 0
unable to evaluate scaled gradient
Model failed to converge: degenerate  Hessian with 2 negative eigenvalues

ggplot(yield_country_nbs_joined_groups, aes(x = presences.x, y = Yield, colour = Country)) +
      facet_wrap(~Group, nrow=1) +   # a panel for each mountain range
      geom_point(alpha = 0.5) +
      theme_classic() +
      geom_line(data = cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) + 
      theme_minimal_hgrid() +
      theme(legend.position = "none") +
      xlab('NLR gene count') +
    ylab(expression(paste('Yield [Mg ', ha^-1, ']'))) +
      scale_color_manual(values=as.vector(isol(40)))

Quite similar, mostly downwards trajectories for each country.

And now both random slopes:

mixed.lmer <- lmer(Yield2 ~ presences2 + (presences2|Group) + (1 + presences2|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield2 ~ presences2 + (presences2 | Group) + (1 + presences2 |  
    Country)
   Data: yield_country_nbs_joined_groups

REML criterion at convergence: 1953.7

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-3.11214 -0.56909  0.04459  0.66469  2.91084 

Random effects:
 Groups   Name        Variance Std.Dev. Corr
 Country  (Intercept) 0.42704  0.6535       
          presences2  0.01045  0.1022   0.81
 Group    (Intercept) 0.37523  0.6126       
          presences2  0.04201  0.2050   0.18
 Residual             0.75392  0.8683       
Number of obs: 741, groups:  Country, 40; Group, 3

Fixed effects:
            Estimate Std. Error       df t value Pr(>|t|)
(Intercept)  0.03595    0.38772  2.35869   0.093    0.933
presences2  -0.23848    0.14300  1.96304  -1.668    0.240

Correlation of Fixed Effects:
           (Intr)
presences2 0.231

ggplot(yield_country_nbs_joined_groups, aes(x = presences2, y = Yield2, colour = Country)) +
      facet_wrap(~Group, nrow=1) +   # a panel for each mountain range
      geom_point(alpha = 0.5) +
      theme_classic() +
      geom_line(data = cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) + 
      theme_minimal_hgrid() +
      theme(legend.position = "none") +
      xlab('Scaled and centered NLR gene count') +
      ylab('Scaled and centered yield') +
      scale_color_manual(values=as.vector(isol(40)))

Yeah, nah

Oil

I’m removing the wilds from the other phenotypes to make the models comparable with the yield model - the yield model uses Landrace as baseline, if I keep Wild in then the baseline is different!

oil_country_nbs_joined_groups <- oil_nbs_joined_groups %>% inner_join(country)
oil_country_nbs_joined_groups <- oil_country_nbs_joined_groups %>% filter(Group != 'Wild')
mixed.lmer <- lmer(Oil ~ presences.x + Group + (1|Country), data=oil_country_nbs_joined_groups)
summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Oil ~ presences.x + Group + (1 | Country)
   Data: oil_country_nbs_joined_groups

REML criterion at convergence: 3543.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.4112 -0.5544  0.1126  0.6528  3.0813 

Random effects:
 Groups   Name        Variance Std.Dev.
 Country  (Intercept) 0.7957   0.892   
 Residual             5.0326   2.243   
Number of obs: 789, groups:  Country, 41

Fixed effects:
                      Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)           30.65976    7.34188 782.49728   4.176  3.3e-05 ***
presences.x           -0.02802    0.01654 783.56323  -1.694 0.090583 .  
GroupOld cultivar      1.12205    0.36849 784.98582   3.045 0.002404 ** 
GroupModern cultivar   1.71759    0.48119  99.35821   3.569 0.000553 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) prsnc. GrpOlc
presences.x -0.999              
GrpOldcltvr -0.002 -0.003       
GrpMdrncltv -0.075  0.066  0.153

No significance here.

tab_model(mixed.lmer, p.val='kr')

	Oil
Predictors	Estimates	CI	p
(Intercept)	30.66	16.22 – 45.10	<0.001
presences.x	-0.03	-0.06 – 0.00	0.091
Group [Old cultivar]	1.12	0.40 – 1.85	0.002
Group [Modern cultivar]	1.72	0.73 – 2.70	0.001
Random Effects
σ²	5.03
τ₀₀ _Country	0.80
ICC	0.14
N _Country	41
Observations	789
Marginal R² / Conditional R²	0.053 / 0.182

oilmod <- mixed.lmer

table(oil_country_nbs_joined_groups$Group)


           Wild        Landrace    Old cultivar Modern cultivar 
              0             677              41              71

Protein

protein_nbs_joined_groups <- nbs_joined_groups %>% inner_join(protein_join, by = 'names') 
#protein_nbs_joined_groups$Protein2 <- scale(protein_nbs_joined_groups$Protein, center=T, scale=T)
protein_country_nbs_joined_groups <- protein_nbs_joined_groups %>% inner_join(country)
#protein_country_nbs_joined_groups <- rename(protein_country_nbs_joined_groups, Group=`Group in violin table`)
protein_country_nbs_joined_groups <- protein_country_nbs_joined_groups %>% filter(Group != 'Wild')

mixed.lmer <- lmer(Protein ~ presences.x + Group + (1|Country), data=protein_country_nbs_joined_groups)
summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Protein ~ presences.x + Group + (1 | Country)
   Data: protein_country_nbs_joined_groups

REML criterion at convergence: 3867.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.7191 -0.6983 -0.0562  0.5934  3.5741 

Random effects:
 Groups   Name        Variance Std.Dev.
 Country  (Intercept) 0.6582   0.8113  
 Residual             7.6769   2.7707  
Number of obs: 789, groups:  Country, 41

Fixed effects:
                      Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)           34.21818    9.03231 784.99934   3.788 0.000163 ***
presences.x            0.02180    0.02033 784.80098   1.072 0.283955    
GroupOld cultivar     -1.54464    0.45281 784.14871  -3.411 0.000680 ***
GroupModern cultivar  -1.06476    0.55283  74.32151  -1.926 0.057927 .  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) prsnc. GrpOlc
presences.x -1.000              
GrpOldcltvr -0.003 -0.001       
GrpMdrncltv -0.083  0.075  0.141

No significance here.

tab_model(mixed.lmer, p.val='kr')

	Protein
Predictors	Estimates	CI	p
(Intercept)	34.22	16.44 – 52.00	<0.001
presences.x	0.02	-0.02 – 0.06	0.285
Group [Old cultivar]	-1.54	-2.44 – -0.65	0.001
Group [Modern cultivar]	-1.06	-2.22 – 0.09	0.071
Random Effects
σ²	7.68
τ₀₀ _Country	0.66
ICC	0.08
N _Country	41
Observations	789
Marginal R² / Conditional R²	0.025 / 0.102

protmod <- mixed.lmer

table(protein_country_nbs_joined_groups$Group)


           Wild        Landrace    Old cultivar Modern cultivar 
              0             677              41              71

Seed weight

seed_nbs_joined_groups <- nbs_joined_groups %>% inner_join(seed_join, by = 'names') 
#seed_nbs_joined_groups$wt2 <- scale(seed_nbs_joined_groups$wt, center=T, scale=T)
seed_country_nbs_joined_groups <- seed_nbs_joined_groups %>% inner_join(country)
#seed_country_nbs_joined_groups <- rename(seed_country_nbs_joined_groups, Group = `Group in violin table`)
seed_country_nbs_joined_groups <- seed_country_nbs_joined_groups %>% filter(Group != 'Wild')
mixed.lmer <- lmer(wt ~ presences.x + Group + (1|Country), data=seed_country_nbs_joined_groups)
summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: wt ~ presences.x + Group + (1 | Country)
   Data: seed_country_nbs_joined_groups

REML criterion at convergence: 3608.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.8823 -0.6388  0.0149  0.6015  4.6946 

Random effects:
 Groups   Name        Variance Std.Dev.
 Country  (Intercept)  1.353   1.163   
 Residual             17.156   4.142   
Number of obs: 633, groups:  Country, 38

Fixed effects:
                       Estimate Std. Error         df t value Pr(>|t|)  
(Intercept)           15.329943  15.266389 627.894885   1.004   0.3157  
presences.x           -0.004727   0.034360 624.361120  -0.138   0.8906  
GroupOld cultivar      1.554124   0.778695 579.732427   1.996   0.0464 *
GroupModern cultivar   1.754191   0.925435   6.832254   1.896   0.1009  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) prsnc. GrpOlc
presences.x -1.000              
GrpOldcltvr -0.013  0.009       
GrpMdrncltv -0.104  0.096  0.137

Again, no significance here.

tab_model(mixed.lmer, p.val='kr')

	wt
Predictors	Estimates	CI	p
(Intercept)	15.33	-14.75 – 45.41	0.317
presences.x	-0.00	-0.07 – 0.06	0.891
Group [Old cultivar]	1.55	0.02 – 3.09	0.047
Group [Modern cultivar]	1.75	-0.23 – 3.74	0.082
Random Effects
σ²	17.16
τ₀₀ _Country	1.35
ICC	0.07
N _Country	38
Observations	633
Marginal R² / Conditional R²	0.018 / 0.090

seedmod <- mixed.lmer

table(seed_country_nbs_joined_groups$Group)


           Wild        Landrace    Old cultivar Modern cultivar 
              0             548              31              54

The final yield model

This is the final yield model for the paper

mixed.lmer <- lmer(Yield ~ presences.x + Group + (1|Country), data=yield_country_nbs_joined_groups)
summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield ~ presences.x + Group + (1 | Country)
   Data: yield_country_nbs_joined_groups

REML criterion at convergence: 1675.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1864 -0.5700  0.0305  0.6525  2.8982 

Random effects:
 Groups   Name        Variance Std.Dev.
 Country  (Intercept) 0.2581   0.5081  
 Residual             0.5206   0.7216  
Number of obs: 741, groups:  Country, 40

Fixed effects:
                       Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)            8.760056   2.465570 726.660933   3.553 0.000406 ***
presences.x           -0.015045   0.005556 726.459065  -2.708 0.006929 ** 
GroupOld cultivar     -0.244554   0.133648 734.951816  -1.830 0.067680 .  
GroupModern cultivar   0.830604   0.184672 360.407255   4.498 9.28e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) prsnc. GrpOlc
presences.x -0.999              
GrpOldcltvr  0.003 -0.008       
GrpMdrncltv -0.074  0.065  0.156

ggplot(yield_country_nbs_joined_groups, aes(x = presences.x, y = Yield, colour = Country)) +
      facet_wrap(~Group, nrow=1) +   # a panel for each mountain range
      geom_point(alpha = 0.5) +
      geom_line(data = cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer)), aes(y = pred), size = 1) + 
      theme_minimal_hgrid() +
      theme(legend.position = "none") +
      xlab('NLR gene count') +
  ylab(expression(paste('Yield [Mg ', ha^-1, ']'))) +
      scale_color_manual(values=as.vector(isol(40)))

newdat <-cbind(yield_country_nbs_joined_groups, pred = predict(mixed.lmer))

newdat %>% mutate(Country2 = case_when ( Country == 'USA' ~ 'USA',
                                              Country == 'China' ~ 'China',
                                              Country == 'Korea' ~ 'Korea',
                                              Country == 'Japan' ~ 'Japan',
                                              Country == 'Russia' ~ 'Russia',
                                              TRUE ~ '')) %>% 
  ggplot(aes(x = presences.x, y = pred, colour = Country)) +
      facet_wrap(~Group, nrow=1) +   # a panel for each mountain range
      geom_line( size = 1) +
      theme_minimal_hgrid() +
      theme(legend.position = "none") +
      xlab('NLR gene count') +
  ylab(expression(paste('Yield [Mg ', ha^-1, ']'))) +
      scale_color_manual(values=as.vector(isol(40)))+
      geom_point(aes(y = Yield),alpha = 0.5) +
      geom_dl(aes(label = Country2), method='last.bumpup') +
      xlim(c(430, 480))

Let’s just use 6 groups - 5 main countries plus the rest

#remove that ugly yellow
mycol <- c(brewer.pal(n = 8, name = "Accent")[1:3], brewer.pal(n = 8, name = "Accent")[5:8])

newdat %>% mutate(Country2 = case_when ( Country == 'USA' ~ 'USA',
                                              Country == 'China' ~ 'China',
                                              Country == 'Korea' ~ 'Korea',
                                              Country == 'Japan' ~ 'Japan',
                                              Country == 'Russia' ~ 'Russia',
                                              TRUE ~ 'Rest')) %>% 
  mutate(Country2 = factor(Country2, levels=c('China', 'Japan', 'Korea', 'Russia', 'USA', 'Rest'))) %>% 
  ggplot(aes(x = presences.x, y = pred, color = Country2)) +
      facet_wrap(~Group, nrow=1) +   # a panel for each mountain range
      geom_point(aes(y = Yield, color=Country2),alpha = 0.8, size=2) +
      geom_line(aes(y=pred, group=Country, color=Country2), size = 1.5) +
      theme_minimal_hgrid() +
      xlab('NLR gene count') +
      ylab(expression(paste('Yield [Mg ', ha^-1, ']'))) +
      scale_color_manual(values=mycol) +
      xlim(c(430, 480)) +
  labs(color = "Country")

Let’s try another color scheme

# I want only every second, stronger color of the Paired scheme
mycol <- brewer.pal(n = 12, name = "Paired")[seq(2, 12, 2)]

newdat %>% mutate(Country2 = case_when ( Country == 'USA' ~ 'USA',
                                              Country == 'China' ~ 'China',
                                              Country == 'Korea' ~ 'Korea',
                                              Country == 'Japan' ~ 'Japan',
                                              Country == 'Russia' ~ 'Russia',
                                              TRUE ~ 'Rest')) %>% 
  mutate(Country2 = factor(Country2, levels=c('China', 'Japan', 'Korea', 'Russia', 'USA', 'Rest'))) %>% 
  ggplot(aes(x = presences.x, y = pred, color = Country2)) +
      facet_wrap(~Group, nrow=1) +   # a panel for each mountain range
      geom_point(aes(y = Yield, color=Country2),alpha = 0.8, size=2) +
      geom_line(aes(y=pred, group=Country, color=Country2), size = 1.5) +
      theme_minimal_hgrid() +
      xlab('NLR gene count') +
      ylab(expression(paste('Yield [Mg ', ha^-1, ']'))) +
      scale_color_manual(values=mycol) +
      xlim(c(430, 480)) +
  labs(color = "Country") +
  theme(panel.spacing = unit(0.9, "lines"),
        axis.text.x = element_text(size=10))

OK that’s much better, nice and strong colors.

plot(mixed.lmer)

qqnorm(resid(mixed.lmer))
qqline(resid(mixed.lmer))

plot(resid(mixed.lmer))

detach("package:lmerTest", unload=TRUE)

yield_country_nbs_joined_groups_renamed <- yield_country_nbs_joined_groups
names(yield_country_nbs_joined_groups_renamed) <- c('names', 'Count', 'PI-ID', 'Group', 'presences2.x', 'presences.y', 'Yield', 'Yield2.x', 'Country')
mixed.lmer <- lmer(Yield ~ `Count` + Group + (1|Country), data=yield_country_nbs_joined_groups_renamed)
yield_country_nbs_joined_groups_renamed

# A tibble: 741 x 9
   names Count `PI-ID` Group presences2.x[,1] presences.y Yield Yield2.x[,1]
   <chr> <dbl> <chr>   <fct>            <dbl>       <dbl> <dbl>        <dbl>
 1 AB-01   445 PI4580~ Land~           -0.119         445  1.54      -0.774 
 2 AB-02   454 PI6037~ Land~            1.35          454  1.3       -1.06  
 3 For     448 PI5486~ Mode~            0.371         448  3.34       1.40  
 4 HN001   448 PI5186~ Mode~            0.371         448  3.54       1.64  
 5 HN005   440 PI4041~ Land~           -0.935         440  2.15      -0.0366
 6 HN006   448 PI4077~ Land~            0.371         448  2.06      -0.145 
 7 HN007   442 PI4242~ Land~           -0.609         442  1.64      -0.653 
 8 HN008   439 PI4376~ Land~           -1.10          439  2.51       0.399 
 9 HN009   445 PI4950~ Land~           -0.119         445  1.85      -0.399 
10 HN010   447 PI4689~ Land~            0.207         447  2.8        0.749 
# ... with 731 more rows, and 1 more variable: Country <chr>

dwplot(mixed.lmer,
       vline = geom_vline(xintercept = 0, colour = "grey60", linetype = 2))

library(stargazer)
stargazer(mixed.lmer, type = "text",
          digits = 3,
          star.cutoffs = c(0.05, 0.01, 0.001),
          digit.separator = "")


==================================================
                          Dependent variable:     
                     -----------------------------
                                 Yield            
--------------------------------------------------
Count                          -0.015**           
                                (0.006)           
                                                  
GroupOld cultivar               -0.245            
                                (0.134)           
                                                  
GroupModern cultivar           0.831***           
                                (0.185)           
                                                  
Constant                       8.760***           
                                (2.466)           
                                                  
--------------------------------------------------
Observations                      741             
Log Likelihood                 -837.565           
Akaike Inf. Crit.              1687.131           
Bayesian Inf. Crit.            1714.779           
==================================================
Note:                *p<0.05; **p<0.01; ***p<0.001

library(ggeffects)
ggpredict(mixed.lmer, terms = c("Count",  'Group'), type = "re") %>% 
   plot() +
   theme_minimal_hgrid() +
   xlab('NLR count') +  
   theme(plot.title=element_blank())

plot_model(mixed.lmer, type = "re", sort.est = TRUE) + theme(plot.title=element_blank())

plot_model(mixed.lmer, data=yield_country_nbs_joined_groups_renamed) +
  theme_minimal_hgrid() +
  theme(plot.title=element_blank())

plot_model(mixed.lmer, type = "pred", terms = c("Count", "Group")) +
  theme_minimal_hgrid() +
  xlab('NLR count') + 
  ylab((expression(paste('Yield [Mg ', ha^-1, ']')))) +
  theme(plot.title=element_blank())

tab_model(mixed.lmer, p.val='kr', digits=3)

	Yield
Predictors	Estimates	CI	p
(Intercept)	8.760	3.915 – 13.606	<0.001
Count	-0.015	-0.026 – -0.004	0.007
Group [Old cultivar]	-0.245	-0.507 – 0.018	0.068
Group [Modern cultivar]	0.831	0.463 – 1.199	<0.001
Random Effects
σ²	0.52
τ₀₀ _Country	0.26
ICC	0.33
N _Country	40
Observations	741
Marginal R² / Conditional R²	0.070 / 0.378

σ measures the random effect variance I think, 0.52 is pretty good (this can easily be >1), but more useful to compare models with each other which I don’t do here.

intraclass-correlation coefficient (ICC) measures how the proportion of variance explained by the grouping structure, in this case, country

Let’s compare all models in one table:

tab_model(mixed.lmer, oilmod, protmod, seedmod, digits=3 )

	Yield			Oil			Protein			wt
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	8.760	3.928 – 13.592	<0.001	30.660	16.270 – 45.050	<0.001	34.218	16.515 – 51.921	<0.001	15.330	-14.592 – 45.252	0.315
Count	-0.015	-0.026 – -0.004	0.007
Group [Old cultivar]	-0.245	-0.507 – 0.017	0.067	1.122	0.400 – 1.844	0.002	-1.545	-2.432 – -0.657	0.001	1.554	0.028 – 3.080	0.046
Group [Modern cultivar]	0.831	0.469 – 1.193	<0.001	1.718	0.774 – 2.661	<0.001	-1.065	-2.148 – 0.019	0.054	1.754	-0.060 – 3.568	0.058
presences.x				-0.028	-0.060 – 0.004	0.090	0.022	-0.018 – 0.062	0.284	-0.005	-0.072 – 0.063	0.891
Random Effects
σ²	0.52			5.03			7.68			17.16
τ₀₀	0.26 _Country			0.80 _Country			0.66 _Country			1.35 _Country
ICC	0.33			0.14			0.08			0.07
N	40 _Country			41 _Country			41 _Country			38 _Country
Observations	741			789			789			633
Marginal R² / Conditional R²	0.070 / 0.378			0.053 / 0.182			0.025 / 0.102			0.018 / 0.090

We still need the same model for ALL genes

What if we just see a general gene shrinkage, not just NLR-genes?

library('lmerTest')
mixed.lmer <- lmer(Yield ~ presences.x + Group + (1|Country), data=yield_country_all_joined_groups)
summary(mixed.lmer)

Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Yield ~ presences.x + Group + (1 | Country)
   Data: yield_country_all_joined_groups

REML criterion at convergence: 1689.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.4280 -0.5759  0.0421  0.6532  2.7847 

Random effects:
 Groups   Name        Variance Std.Dev.
 Country  (Intercept) 0.2556   0.5056  
 Residual             0.5261   0.7253  
Number of obs: 741, groups:  Country, 40

Fixed effects:
                       Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)           3.635e+00  9.289e+00  7.219e+02   0.391   0.6956    
presences.x          -3.196e-05  1.921e-04  7.221e+02  -0.166   0.8679    
GroupOld cultivar    -2.475e-01  1.343e-01  7.351e+02  -1.843   0.0658 .  
GroupModern cultivar  8.599e-01  1.864e-01  3.577e+02   4.613 5.53e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) prsnc. GrpOlc
presences.x -1.000              
GrpOldcltvr -0.004  0.003       
GrpMdrncltv -0.127  0.124  0.156

OK good, so all genes don’t have a statistically significant correlation.

tab_model(mixed.lmer, p.val='kr', digits=3)

	Yield
Predictors	Estimates	CI	p
(Intercept)	3.635	-14.615 – 21.886	0.696
presences.x	-0.000	-0.000 – 0.000	0.868
Group [Old cultivar]	-0.247	-0.512 – 0.017	0.066
Group [Modern cultivar]	0.860	0.488 – 1.231	<0.001
Random Effects
σ²	0.53
τ₀₀ _Country	0.26
ICC	0.33
N _Country	40
Observations	741
Marginal R² / Conditional R²	0.063 / 0.369

newdat <-cbind(yield_country_all_joined_groups, pred = predict(mixed.lmer))

newdat %>% mutate(Country2 = case_when ( Country == 'USA' ~ 'USA',
                                              Country == 'China' ~ 'China',
                                              Country == 'Korea' ~ 'Korea',
                                              Country == 'Japan' ~ 'Japan',
                                              Country == 'Russia' ~ 'Russia',
                                              TRUE ~ '')) %>% 
  ggplot(aes(x = presences.x/1000, y = pred, colour = Country)) +
      facet_wrap(~Group, nrow=1) +   # a panel for each mountain range
      geom_line( size = 1) +
      theme_minimal_hgrid() +
      theme(legend.position = "none") +
      xlab('Gene count (1000s)') +
  ylab(expression(paste('Yield [Mg ', ha^-1, ']'))) +
      scale_color_manual(values=as.vector(isol(40)))+
      geom_point(aes(y = Yield),alpha = 0.5) +
      geom_dl(aes(label = Country2), method='last.bumpup')+
      xlim(c(47.900, 49.700))

With better color scheme:

newdat %>% mutate(
  Country2 = case_when (
  Country == 'USA' ~ 'USA',
  Country == 'China' ~ 'China',
  Country == 'Korea' ~ 'Korea',
  Country == 'Japan' ~ 'Japan',
  Country == 'Russia' ~ 'Russia',
  TRUE ~ 'Rest'
  )
  ) %>%
  mutate(Country2 = factor(
  Country2,
  levels = c('China', 'Japan', 'Korea', 'Russia', 'USA', 'Rest')
  )) %>%
  ggplot(aes(x = presences.x / 1000, y = pred, color = Country2)) +
  facet_wrap( ~ Group, nrow = 1) +   # a panel for each mountain range
  geom_point(aes(y = Yield, color = Country2),
  alpha = 0.8,
  size = 2) +
  geom_line(aes(y = pred, group = Country, color = Country2), size = 1.5) +
  theme_minimal_hgrid() +
  xlab('Gene count') +
  ylab(expression(paste('Yield [Mg ', ha ^ -1, ']'))) +
  scale_color_manual(values = mycol) +
  xlim(c(47.900, 49.700)) +
  labs(color = "Country") +
  theme(panel.spacing = unit(0.9, "lines"),
  axis.text.x = element_text(size = 10))

Let’s join the countries into continents and then run everything again

library(countrycode)

yield_country_nbs_joined_groups$continent <- countrycode(sourcevar = yield_country_nbs_joined_groups$Country,
                                                         origin = 'country.name',
                                                         destination = 'continent')

Warning in countrycode(sourcevar = yield_country_nbs_joined_groups$Country, : Some values were not matched unambiguously: Costa, ND

yield_country_nbs_joined_groups <- yield_country_nbs_joined_groups %>% mutate(continent2 = case_when (
  Country == 'USA' ~ 'North America',
  Country == 'Canada' ~ 'North America',
  continent == 'Americas' ~ 'South America',
  TRUE ~ continent
  ))

mixed.ranslope <- lmer(Yield ~ presences.y + ( 1 + presences.y | continent2) +  Group, data = yield_country_nbs_joined_groups, REML = F) 
summary(mixed.ranslope)

Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
  method [lmerModLmerTest]
Formula: Yield ~ presences.y + (1 + presences.y | continent2) + Group
   Data: yield_country_nbs_joined_groups

     AIC      BIC   logLik deviance df.resid 
  1674.3   1711.1   -829.2   1658.3      722 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.91426 -0.66710  0.05709  0.71067  2.92556 

Random effects:
 Groups     Name        Variance  Std.Dev. Corr 
 continent2 (Intercept) 5.592e-01 0.747764      
            presences.y 5.203e-06 0.002281 -1.00
 Residual               5.601e-01 0.748425      
Number of obs: 730, groups:  continent2, 6

Fixed effects:
                       Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)            9.087919   2.547824  16.517818   3.567  0.00247 ** 
presences.y           -0.015453   0.005795  10.175080  -2.667  0.02330 *  
GroupOld cultivar     -0.284306   0.137565 727.141346  -2.067  0.03912 *  
GroupModern cultivar   0.822251   0.177554  24.727294   4.631 9.93e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) prsnc. GrpOlc
presences.y -0.998              
GrpOldcltvr  0.008 -0.013       
GrpMdrncltv -0.037  0.018  0.147
convergence code: 0
boundary (singular) fit: see ?isSingular

tab_model(mixed.ranslope, digits=3)#, p.val='kr', digits=3)

	Yield
Predictors	Estimates	CI	p
(Intercept)	9.088	4.094 – 14.082	<0.001
presences.y	-0.015	-0.027 – -0.004	0.008
Group [Old cultivar]	-0.284	-0.554 – -0.015	0.039
Group [Modern cultivar]	0.822	0.474 – 1.170	<0.001
Random Effects
σ²	0.56
τ₀₀ _continent2	0.56
τ₁₁ _{continent2.presences.y}	0.00
ρ₀₁ _continent2	-1.00
N _continent2	6
Observations	730
Marginal R² / Conditional R²	0.097 / NA

no_na <- yield_country_nbs_joined_groups %>% filter(!is.na(continent))

newdat <-cbind(no_na, pred = predict(mixed.ranslope))
newdat

         names presences.x     PI-ID           Group  presences2 presences.y
1        AB-01         445  PI458020        Landrace -0.11893490         445
2        AB-02         454  PI603713        Landrace  1.34996459         454
3          For         448  PI548645 Modern cultivar  0.37069826         448
4        HN001         448  PI518664 Modern cultivar  0.37069826         448
5        HN005         440  PI404166        Landrace -0.93499018         440
6        HN006         448 PI407788A        Landrace  0.37069826         448
7        HN007         442  PI424298        Landrace -0.60856807         442
8        HN008         439  PI437655        Landrace -1.09820123         439
9        HN009         445 PI495017C        Landrace -0.11893490         445
10       HN010         447  PI468915        Landrace  0.20748721         447
11       HN011         441  PI507354        Landrace -0.77177912         441
12       HN012         443  PI567305        Landrace -0.44535701         443
13      HN016B         449  PI567387        Landrace  0.53390932         449
14      HN017B         444  PI437725        Landrace -0.28214596         444
15       HN018         443  PI437690        Landrace -0.44535701         443
16       HN021         449  PI209332        Landrace  0.53390932         449
17       HN022         444 PI404198B        Landrace -0.28214596         444
18       HN023         442 PI424608A        Landrace -0.60856807         442
19       HN025         445 PI567516C        Landrace -0.11893490         445
20       HN026         440  PI612611        Landrace -0.93499018         440
21       HN028         441  PI572239 Modern cultivar -0.77177912         441
22       HN030         447  PI598124 Modern cultivar  0.20748721         447
23       HN031         442 PI79691-4        Landrace -0.60856807         442
24       HN032         443   PI86006        Landrace -0.44535701         443
25       HN033         449   PI87617        Landrace  0.53390932         449
26       HN034         440 PI87631-1        Landrace -0.93499018         440
27       HN035         451  PI196175        Landrace  0.86033142         451
28       HN036         444  PI200471        Landrace -0.28214596         444
29       HN037         449  PI200508        Landrace  0.53390932         449
30       HN038         449  PI248515        Landrace  0.53390932         449
31       HN041         446  PI398593        Landrace  0.04427615         446
32       HN042         447  PI398595        Landrace  0.20748721         447
33       HN043         451  PI398610        Landrace  0.86033142         451
34       HN044         451  PI398614        Landrace  0.86033142         451
35       HN047         443  PI407729        Landrace -0.44535701         443
36       HN048         445  PI407965        Landrace -0.11893490         445
37       HN049         447 PI408105A        Landrace  0.20748721         447
38       HN051         451  PI424078        Landrace  0.86033142         451
39       HN054         442 PI437169B        Landrace -0.60856807         442
40       HN055         447  PI437679        Landrace  0.20748721         447
41       HN056         444 PI437863A        Landrace -0.28214596         444
42       HN057         444  PI438258        Landrace -0.28214596         444
43       HN058         454  PI458515        Landrace  1.34996459         454
44       HN059         443 PI464920B        Landrace -0.44535701         443
45       HN060         442  PI467312        Landrace -0.60856807         442
46       HN061         448  PI471938        Landrace  0.37069826         448
47       HN062         439 PI475783B        Landrace -1.09820123         439
48       HN064         442  PI518751 Modern cultivar -0.60856807         442
49       HN067         446  PI548317    Old cultivar  0.04427615         446
50       HN068         445  PI548349    Old cultivar -0.11893490         445
51       HN069         445  PI548415    Old cultivar -0.11893490         445
52       HN070         441  PI548511 Modern cultivar -0.77177912         441
53       HN071         440  PI548657 Modern cultivar -0.93499018         440
54       HN072         444  PI549031        Landrace -0.28214596         444
55       HN073         442  PI552538 Modern cultivar -0.60856807         442
56       HN074         444  PI561271        Landrace -0.28214596         444
57       HN075         443  PI567230        Landrace -0.44535701         443
58       HN076         442 PI567336B        Landrace -0.60856807         442
59       HN077         443  PI567343        Landrace -0.44535701         443
60       HN078         446  PI567354        Landrace  0.04427615         446
61       HN079         445  PI567357        Landrace -0.11893490         445
62       HN080         445  PI567383        Landrace -0.11893490         445
63       HN081         449  PI567519        Landrace  0.53390932         449
64       HN082         439  PI567611        Landrace -1.09820123         439
65       HN083         442  PI567651        Landrace -0.60856807         442
66       HN084         441  PI567690        Landrace -0.77177912         441
67       HN085         443  PI567719        Landrace -0.44535701         443
68       HN086         441  PI567731        Landrace -0.77177912         441
69       HN088         438  PI593258 Modern cultivar -1.26141229         438
70       HN089         450  PI594012        Landrace  0.69712037         450
71       HN090         446 PI594512A        Landrace  0.04427615         446
72       HN091         450  PI594599        Landrace  0.69712037         450
73       HN092         449  PI597387 Modern cultivar  0.53390932         449
74       HN093         446  PI603154        Landrace  0.04427615         446
75       HN094         445  PI603170        Landrace -0.11893490         445
76       HN095         443  PI603175        Landrace -0.44535701         443
77       HN096         446 PI603176A        Landrace  0.04427615         446
78       HN097         436  PI603497        Landrace -1.58783439         436
79       HN098         439 PI605869A        Landrace -1.09820123         439
81       HN105         446  PI438471        Landrace  0.04427615         446
82       HN106         441  PI417091        Landrace -0.77177912         441
83       HN107         449  PI417015        Landrace  0.53390932         449
84       HN108         448  PI229343        Landrace  0.37069826         448
86     HNEX-02         452   PI68508        Landrace  1.02354248         452
87     HNEX-04         448   PI68600        Landrace  0.37069826         448
88     HNEX-06         450  PI800471        Landrace  0.69712037         450
89     HNEX-08         448 PI90566-1        Landrace  0.37069826         448
90     HNEX-09         452 PI91730-1        Landrace  1.02354248         452
91     HNEX-10         450  PI189930        Landrace  0.69712037         450
92     HNEX-11         448  PI227328        Landrace  0.37069826         448
93     HNEX-13         455 PI253665D        Landrace  1.51317564         455
94     HNEX-14         456  PI283331        Landrace  1.67638670         456
95     HNEX-16         450  PI297515        Landrace  0.69712037         450
96     HNEX-17         453  PI297544        Landrace  1.18675353         453
97     HNEX-18         444  PI361064        Landrace -0.28214596         444
98     HNEX-19         444 PI361066A        Landrace -0.28214596         444
99     HNEX-20         453  PI370059        Landrace  1.18675353         453
100    HNEX-21         450 PI384469A        Landrace  0.69712037         450
101    HNEX-23         444  PI391594        Landrace -0.28214596         444
102    HNEX-24         449  PI404157        Landrace  0.53390932         449
103    HNEX-25         450  PI407710        Landrace  0.69712037         450
104    HNEX-26         448  PI407720        Landrace  0.37069826         448
105    HNEX-27         439 PI424159B        Landrace -1.09820123         439
106    HNEX-28         447 PI424195B        Landrace  0.20748721         447
107    HNEX-29         442  PI427099        Landrace -0.60856807         442
108    HNEX-30         452  PI436682        Landrace  1.02354248         452
109    HNEX-32         454 PI437851A        Landrace  1.34996459         454
110    HNEX-33         445  PI438151        Landrace -0.11893490         445
111    HNEX-34         446  PI438206        Landrace  0.04427615         446
112    HNEX-35         440  PI445830        Landrace -0.93499018         440
113    HNEX-36         447  PI445837        Landrace  0.20748721         447
114    HNEX-37         448  PI458511        Landrace  0.37069826         448
115    HNEX-38         450  PI468377        Landrace  0.69712037         450
116    HNEX-39         448 PI561319A        Landrace  0.37069826         448
117    HNEX-40         448  PI561377        Landrace  0.37069826         448
118    HNSM-02         457  PI407985        Landrace  1.83959775         457
119    HNSM-03         455 PI408319C        Landrace  1.51317564         455
120    HNSM-04         455  PI157432        Landrace  1.51317564         455
121    HNSM-05         450  PI200543        Landrace  0.69712037         450
122    HNSM-06         460  PI200553        Landrace  2.32923092         460
123    HNSM-07         451  PI398666        Landrace  0.86033142         451
124    HNSM-08         451  PI398775        Landrace  0.86033142         451
125    HNSM-09         454  PI398791        Landrace  1.34996459         454
126    HNSM-10         453  PI398946        Landrace  1.18675353         453
127    HNSM-11         451  PI399004        Landrace  0.86033142         451
128    HNSM-12         444 PI424237A        Landrace -0.28214596         444
129    HNSM-13         448 PI424237B        Landrace  0.37069826         448
130    HNSM-14         451  PI424477        Landrace  0.86033142         451
131    HNSM-15         450  PI398440        Landrace  0.69712037         450
132    HNSM-16         453  PI399079        Landrace  1.18675353         453
133    HNSM-17         455  PI408029        Landrace  1.51317564         455
134    HNSM-18         450  PI408097        Landrace  0.69712037         450
135    HNSM-19         456  PI408111        Landrace  1.67638670         456
136    HNSM-20         451  PI408132        Landrace  0.86033142         451
137    HNSM-21         452 PI273483D        Landrace  1.02354248         452
138    HNSM-23         455  PI427106        Landrace  1.51317564         455
139    HNSM-24         449 PI427105B        Landrace  0.53390932         449
140    HNSM-25         445  PI525453 Modern cultivar -0.11893490         445
141     HNY-07         448 PI567425B        Landrace  0.37069826         448
142     HNY-08         444  PI597473        Landrace -0.28214596         444
143     HNY-09         439 PI567671A        Landrace -1.09820123         439
144     HNY-10         437 PI567614B        Landrace -1.42462334         437
145     HNY-26         444  STODDARD Modern cultivar -0.28214596         444
146     HNY-42         446  PI340042        Landrace  0.04427615         446
147     HNY-45         441  PI553045 Modern cultivar -0.77177912         441
148     HNY-51         440  PI548352    Old cultivar -0.93499018         440
149     HNY-52         441  PI548379    Old cultivar -0.77177912         441
150     HNY-53         451 PI360955A        Landrace  0.86033142         451
151     HNY-55         442  PI248399        Landrace -0.60856807         442
152 SRR1533216         465  PI594615        Landrace  3.14528619         465
153 SRR1533220         448  PI196166        Landrace  0.37069826         448
154 SRR1533221         449  PI467343        Landrace  0.53390932         449
155 SRR1533229         455  PI416971        Landrace  1.51317564         455
156 SRR1533230         448  PI594777        Landrace  0.37069826         448
157 SRR1533231         453  PI548312    Old cultivar  1.18675353         453
158 SRR1533235         449  PI507355        Landrace  0.53390932         449
159 SRR1533236         457  PI587552        Landrace  1.83959775         457
160 SRR1533239         461  PI603336        Landrace  2.49244197         461
161 SRR1533243         445  PI602991        Landrace -0.11893490         445
162 SRR1533245         446  PI407801        Landrace  0.04427615         446
163 SRR1533248         453  PI398296        Landrace  1.18675353         453
164 SRR1533257         443  PI548417    Old cultivar -0.44535701         443
165 SRR1533266         445  PI438498        Landrace -0.11893490         445
166 SRR1533269         443  PI153262        Landrace -0.44535701         443
167 SRR1533271         445  PI603318        Landrace -0.11893490         445
168 SRR1533272         451  PI437653        Landrace  0.86033142         451
169 SRR1533278         450 PI567189A        Landrace  0.69712037         450
171 SRR1533284         454  PI416890        Landrace  1.34996459         454
172 SRR1533285         449  PI587752        Landrace  0.53390932         449
173 SRR1533286         448  PI424391        Landrace  0.37069826         448
174 SRR1533288         451  PI603357        Landrace  0.86033142         451
175 SRR1533290         449  PI437944        Landrace  0.53390932         449
176 SRR1533292         463  PI157421        Landrace  2.81886408         463
177 SRR1533295         451  PI567364        Landrace  0.86033142         451
178 SRR1533297         442  PI548298    Old cultivar -0.60856807         442
179 SRR1533301         450  PI423954        Landrace  0.69712037         450
180 SRR1533302         447  PI587848        Landrace  0.20748721         447
181 SRR1533303         452  PI587666        Landrace  1.02354248         452
182 SRR1533310         448 PI317334A        Landrace  0.37069826         448
183 SRR1533312         456  PI548456    Old cultivar  1.67638670         456
184 SRR1533314         450 PI588053A        Landrace  0.69712037         450
185 SRR1533315         449  PI594629        Landrace  0.53390932         449
186 SRR1533319         438  PI567258        Landrace -1.26141229         438
187 SRR1533324         450  PI317336        Landrace  0.69712037         450
188 SRR1533326         451  PI594579        Landrace  0.86033142         451
189 SRR1533328         445 PI253658B        Landrace -0.11893490         445
190 SRR1533332         453  PI567293        Landrace  1.18675353         453
191 SRR1533333         448  PI407849        Landrace  0.37069826         448
192 SRR1533336         439   PI89138        Landrace -1.09820123         439
193 SRR1533338         448  PI417398        Landrace  0.37069826         448
194 SRR1533341         445  PI243541        Landrace -0.11893490         445
195 SRR1533351         448  PI548634 Modern cultivar  0.37069826         448
196 SRR1533356         444  PI553047 Modern cultivar -0.28214596         444
197 SRR1533362         439  PI533655 Modern cultivar -1.09820123         439
198 SRR1533378         441  PI548311    Old cultivar -0.77177912         441
199 SRR1533384         440  PI548512 Modern cultivar -0.93499018         440
200 SRR1533415         445  PI548573 Modern cultivar -0.11893490         445
201 SRR1533423         448  PI536635 Modern cultivar  0.37069826         448
202 SRR1533441         451  PI548985 Modern cultivar  0.86033142         451
203 SRR1533442         455  PI548477    Old cultivar  1.51317564         455
205    USB-007         443 PI266806C        Landrace -0.44535701         443
206    USB-012         448 PI437265D        Landrace  0.37069826         448
207    USB-014         448  PI468908        Landrace  0.37069826         448
208    USB-016         443  PI506862        Landrace -0.44535701         443
209    USB-023         448  PI548313    Old cultivar  0.37069826         448
210    USB-024         451  PI548325    Old cultivar  0.86033142         451
211    USB-029         451  PI548452    Old cultivar  0.86033142         451
212    USB-035         450  PI549018        Landrace  0.69712037         450
213    USB-036         441  PI549026        Landrace -0.77177912         441
214    USB-037         450 PI549041A        Landrace  0.69712037         450
215    USB-038         449  PI559932 Modern cultivar  0.53390932         449
216    USB-039         448  PI567171        Landrace  0.37069826         448
217    USB-040         442  PI567426        Landrace -0.60856807         442
218    USB-041         452  PI567558        Landrace  1.02354248         452
219    USB-044         450  PI578495        Landrace  0.69712037         450
220    USB-047         454  PI594307        Landrace  1.34996459         454
221    USB-048         447  PI597476 Modern cultivar  0.20748721         447
222    USB-049         444 PI603426G        Landrace -0.28214596         444
223    USB-050         449  PI603442        Landrace  0.53390932         449
224    USB-058         441  PI548658 Modern cultivar -0.77177912         441
227    USB-061         441   PI54591        Landrace -0.77177912         441
228    USB-062         448 PI54608-1        Landrace  0.37069826         448
229    USB-063         442   PI54614        Landrace -0.60856807         442
230    USB-064         445 PI54615-1        Landrace -0.11893490         445
231    USB-065         445   PI58955        Landrace -0.11893490         445
232    USB-066         442   PI62203        Landrace -0.60856807         442
233    USB-067         438 PI68521-1        Landrace -1.26141229         438
234    USB-068         443 PI68604-1        Landrace -0.44535701         443
235    USB-069         444 PI68732-1        Landrace -0.28214596         444
236    USB-070         445   PI70080        Landrace -0.11893490         445
237    USB-071         447 PI70466-3        Landrace  0.20748721         447
238    USB-072         445   PI71465        Landrace -0.11893490         445
239    USB-073         446   PI80837        Landrace  0.04427615         446
240    USB-074         442   PI81041        Landrace -0.60856807         442
241    USB-075         451   PI83881        Landrace  0.86033142         451
242    USB-076         441   PI83942        Landrace -0.77177912         441
243    USB-077         450   PI84631        Landrace  0.69712037         450
244    USB-078         449   PI84637        Landrace  0.53390932         449
245    USB-079         445   PI84656        Landrace -0.11893490         445
246    USB-081         447   PI84973        Landrace  0.20748721         447
247    USB-084         446   PI86904        Landrace  0.04427615         446
248    USB-085         441 PI86972-2        Landrace -0.77177912         441
249    USB-086         451   PI87620        Landrace  0.86033142         451
250    USB-087         445   PI88313        Landrace -0.11893490         445
251    USB-088         446   PI88468        Landrace  0.04427615         446
252    USB-089         444 PI89005-5        Landrace -0.28214596         444
253    USB-090         441   PI89775        Landrace -0.77177912         441
254    USB-091         444  PI90479P        Landrace -0.28214596         444
255    USB-092         442   PI90486        Landrace -0.60856807         442
256    USB-093         442 PI91100-3        Landrace -0.60856807         442
257    USB-094         446 PI91159-4        Landrace  0.04427615         446
258    USB-096         444   PI92651        Landrace -0.28214596         444
259    USB-097         448 PI94159-3        Landrace  0.37069826         448
260    USB-098         440   PI95860        Landrace -0.93499018         440
261    USB-100         443  PI103088        Landrace -0.44535701         443
262    USB-101         437  PI123440        Landrace -1.42462334         437
264    USB-103         439  PI153281        Landrace -1.09820123         439
265    USB-104         441  PI154189        Landrace -0.77177912         441
266    USB-106         444  PI159925        Landrace -0.28214596         444
267    USB-107         450  PI165563        Landrace  0.69712037         450
268    USB-108         456  PI165675        Landrace  1.67638670         456
269    USB-109         442  PI171428        Landrace -0.60856807         442
270    USB-110         443  PI171451        Landrace -0.44535701         443
271    USB-112         442  PI179935        Landrace -0.60856807         442
272    USB-113         449  PI189873        Landrace  0.53390932         449
273    USB-115         450  PI209333        Landrace  0.69712037         450
274    USB-116         443  PI209334        Landrace -0.44535701         443
275    USB-117         445  PI232992        Landrace -0.11893490         445
276    USB-119         449 PI253661B        Landrace  0.53390932         449
277    USB-122         449  PI291294        Landrace  0.53390932         449
278    USB-123         443 PI291309D        Landrace -0.44535701         443
279    USB-124         447 PI291310C        Landrace  0.20748721         447
280    USB-125         449  PI297505        Landrace  0.53390932         449
281    USB-126         440  PI297520        Landrace -0.93499018         440
282    USB-127         438  PI324924 Modern cultivar -1.26141229         438
283    USB-128         443  PI342434        Landrace -0.44535701         443
284    USB-129         443 PI342619A        Landrace -0.44535701         443
285    USB-131         442 PI361066B        Landrace -0.60856807         442
286    USB-132         441  PI361070        Landrace -0.77177912         441
287    USB-133         442  PI361080        Landrace -0.60856807         442
288    USB-134         442 PI372403B        Landrace -0.60856807         442
289    USB-135         444  PI372418        Landrace -0.28214596         444
290    USB-139         445  PI378658        Landrace -0.11893490         445
291    USB-140         449  PI378663        Landrace  0.53390932         449
292    USB-141         445 PI378680E        Landrace -0.11893490         445
293    USB-143         446  PI379618        Landrace  0.04427615         446
294    USB-144         447  PI391577        Landrace  0.20748721         447
295    USB-145         446  PI391583        Landrace  0.04427615         446
296    USB-146         446  PI398633        Landrace  0.04427615         446
297    USB-147         450  PI398965        Landrace  0.69712037         450
298    USB-148         443  PI404187        Landrace -0.44535701         443
299    USB-149         448  PI407701        Landrace  0.37069826         448
300    USB-150         441 PI407708A        Landrace -0.77177912         441
301    USB-151         446  PI407742        Landrace  0.04427615         446
302    USB-152         440  PI416751        Landrace -0.93499018         440
303    USB-153         449  PI416838        Landrace  0.53390932         449
304    USB-154         449  PI417215        Landrace  0.53390932         449
305    USB-155         448  PI417242        Landrace  0.37069826         448
306    USB-156         444 PI417345B        Landrace -0.28214596         444
307    USB-157         446  PI417381        Landrace  0.04427615         446
308    USB-159         447  PI417500        Landrace  0.20748721         447
309    USB-160         438  PI417529        Landrace -1.26141229         438
310    USB-161         444  PI417581        Landrace -0.28214596         444
311    USB-162         450  PI423926        Landrace  0.69712037         450
312    USB-166         441 PI424195A        Landrace -0.77177912         441
313    USB-167         445  PI430595        Landrace -0.11893490         445
314    USB-168         443  PI436684        Landrace -0.44535701         443
315    USB-169         447 PI437110A        Landrace  0.20748721         447
316    USB-170         441 PI437112A        Landrace -0.77177912         441
317    USB-171         442 PI437127A        Landrace -0.60856807         442
318    USB-173         448  PI437160        Landrace  0.37069826         448
319    USB-174         440 PI437165A        Landrace -0.93499018         440
320    USB-175         449  PI437240        Landrace  0.53390932         449
321    USB-177         440 PI437376A        Landrace -0.93499018         440
322    USB-178         445  PI437485        Landrace -0.11893490         445
323    USB-179         446 PI437500A        Landrace  0.04427615         446
324    USB-180         442  PI437505        Landrace -0.60856807         442
325    USB-182         445  PI437662        Landrace -0.11893490         445
326    USB-183         443 PI437685D        Landrace -0.44535701         443
327    USB-184         454 PI437695A        Landrace  1.34996459         454
328    USB-185         437  PI437776        Landrace -1.42462334         437
329    USB-186         446 PI437788A        Landrace  0.04427615         446
330    USB-187         450  PI437793        Landrace  0.69712037         450
331    USB-188         446 PI437814A        Landrace  0.04427615         446
332    USB-189         447  PI437838        Landrace  0.20748721         447
333    USB-191         449 PI437956B        Landrace  0.53390932         449
334    USB-192         446 PI437991B        Landrace  0.04427615         446
335    USB-193         443 PI438019B        Landrace -0.44535701         443
336    USB-195         443  PI438083        Landrace -0.44535701         443
337    USB-196         449 PI438112B        Landrace  0.53390932         449
338    USB-198         441 PI438230A        Landrace -0.77177912         441
339    USB-199         438 PI438239B        Landrace -1.26141229         438
340    USB-201         439  PI438309        Landrace -1.09820123         439
341    USB-202         444  PI438323        Landrace -0.28214596         444
342    USB-203         438  PI438336        Landrace -1.26141229         438
343    USB-204         447  PI438347        Landrace  0.20748721         447
344    USB-206         450 PI438496B        Landrace  0.69712037         450
345    USB-207         446 PI438496C        Landrace  0.04427615         446
346    USB-208         441  PI438500        Landrace -0.77177912         441
347    USB-209         445 PI445824A        Landrace -0.11893490         445
348    USB-210         445  PI458505        Landrace -0.11893490         445
349    USB-212         449  PI458510        Landrace  0.53390932         449
350    USB-213         446  PI464896        Landrace  0.04427615         446
351    USB-215         447  PI464923        Landrace  0.20748721         447
352    USB-216         442  PI467347        Landrace -0.60856807         442
353    USB-217         449 PI468408B        Landrace  0.53390932         449
354    USB-218         444  PI475820        Landrace -0.28214596         444
355    USB-220         442 PI476352B        Landrace -0.60856807         442
356    USB-221         443  PI479735        Landrace -0.44535701         443
357    USB-222         442  PI490766        Landrace -0.60856807         442
358    USB-223         448  PI495020        Landrace  0.37069826         448
359    USB-226         448  PI497967        Landrace  0.37069826         448
360    USB-227         440  PI504288        Landrace -0.93499018         440
361    USB-228         448  PI506933        Landrace  0.37069826         448
362    USB-229         444  PI506942        Landrace -0.28214596         444
363    USB-230         446  PI507017        Landrace  0.04427615         446
364    USB-232         446  PI507088        Landrace  0.04427615         446
365    USB-233         446 PI507293B        Landrace  0.04427615         446
366    USB-234         445  PI507458        Landrace -0.11893490         445
367    USB-235         444  PI507467        Landrace -0.28214596         444
368    USB-236         445  PI507471        Landrace -0.11893490         445
369    USB-238         442  PI507480        Landrace -0.60856807         442
370    USB-240         440  PI514671        Landrace -0.93499018         440
371    USB-241         448  PI518727        Landrace  0.37069826         448
372    USB-242         447 PI532463B        Landrace  0.20748721         447
373    USB-245         438 PI538386A        Landrace -1.26141229         438
374    USB-246         437  PI540552 Modern cultivar -1.42462334         437
375    USB-255         445  PI548336    Old cultivar -0.11893490         445
376    USB-256         445  PI548356    Old cultivar -0.11893490         445
377    USB-258         440  PI548383    Old cultivar -0.93499018         440
378    USB-259         441  PI548400    Old cultivar -0.77177912         441
379    USB-260         436  PI548411    Old cultivar -1.58783439         436
380    USB-261         442  PI548427    Old cultivar -0.60856807         442
381    USB-263         451  PI548474    Old cultivar  0.86033142         451
382    USB-264         440  PI548479    Old cultivar -0.93499018         440
383    USB-265         436  PI548521 Modern cultivar -1.58783439         436
384    USB-266         446  PI548524 Modern cultivar  0.04427615         446
385    USB-268         436  PI548571 Modern cultivar -1.58783439         436
386    USB-269         442  PI548572 Modern cultivar -0.60856807         442
387    USB-270         435  PI548582 Modern cultivar -1.75104545         435
388    USB-271         442  PI548619 Modern cultivar -0.60856807         442
389    USB-272         444  PI548633 Modern cultivar -0.28214596         444
390    USB-276         445  PI548978 Modern cultivar -0.11893490         445
391    USB-277         447  PI549017        Landrace  0.20748721         447
392    USB-278         448 PI549021A        Landrace  0.37069826         448
393    USB-279         448  PI549028        Landrace  0.37069826         448
394    USB-281         450  PI549040        Landrace  0.69712037         450
395    USB-283         444 PI561318A        Landrace -0.28214596         444
396    USB-284         441  PI561371        Landrace -0.77177912         441
397    USB-285         446  PI561387        Landrace  0.04427615         446
398    USB-286         437 PI561389B        Landrace -1.42462334         437
399    USB-291         442  PI567173        Landrace -0.60856807         442
400    USB-293         443  PI567225        Landrace -0.44535701         443
401    USB-294         442  PI567226        Landrace -0.60856807         442
402    USB-295         446  PI567231        Landrace  0.04427615         446
403    USB-298         443  PI567307        Landrace -0.44535701         443
404    USB-301         444  PI567346        Landrace -0.28214596         444
405    USB-302         443 PI567352A        Landrace -0.44535701         443
406    USB-303         445  PI567353        Landrace -0.11893490         445
407    USB-304         447  PI567361        Landrace  0.20748721         447
408    USB-306         444  PI567407        Landrace -0.28214596         444
409    USB-307         449  PI567408        Landrace  0.53390932         449
410    USB-308         451 PI567410B        Landrace  0.86033142         451
411    USB-309         448 PI567415A        Landrace  0.37069826         448
412    USB-310         452  PI567416        Landrace  1.02354248         452
413    USB-311         447 PI567418A        Landrace  0.20748721         447
414    USB-312         443  PI567428        Landrace -0.44535701         443
415    USB-313         444 PI567435B        Landrace -0.28214596         444
416    USB-314         448  PI567439        Landrace  0.37069826         448
417    USB-315         448 PI567488A        Landrace  0.37069826         448
418    USB-316         452 PI567489A        Landrace  1.02354248         452
419    USB-317         447  PI567525        Landrace  0.20748721         447
420    USB-318         451  PI567532        Landrace  0.86033142         451
421    USB-319         450  PI567548        Landrace  0.69712037         450
422    USB-320         448  PI567576        Landrace  0.37069826         448
423    USB-322         442  PI567675        Landrace -0.60856807         442
424    USB-323         443  PI567685        Landrace -0.44535701         443
425    USB-324         448 PI567698A        Landrace  0.37069826         448
426    USB-325         445  PI567726        Landrace -0.11893490         445
427    USB-326         445  PI567746        Landrace -0.11893490         445
428    USB-327         446 PI567780B        Landrace  0.04427615         446
429    USB-328         445  PI567782 Modern cultivar -0.11893490         445
430    USB-330         442  PI574477        Landrace -0.60856807         442
431    USB-331         445 PI578375B        Landrace -0.11893490         445
432    USB-332         448  PI578412        Landrace  0.37069826         448
433    USB-333         450  PI578493        Landrace  0.69712037         450
434    USB-334         447 PI578499A        Landrace  0.20748721         447
435    USB-335         448  PI578503        Landrace  0.37069826         448
436    USB-336         444  PI578504        Landrace -0.28214596         444
437    USB-338         445 PI587588A        Landrace -0.11893490         445
438    USB-339         441 PI587588B        Landrace -0.77177912         441
439    USB-341         441 PI587712B        Landrace -0.77177912         441
440    USB-342         445  PI587804        Landrace -0.11893490         445
441    USB-343         444 PI587811A        Landrace -0.28214596         444
442    USB-345         437  PI592937        Landrace -1.42462334         437
443    USB-346         445  PI592940        Landrace -0.11893490         445
444    USB-347         442  PI592952        Landrace -0.60856807         442
445    USB-348         446  PI592960        Landrace  0.04427615         446
446    USB-351         443  PI593953        Landrace -0.44535701         443
447    USB-353         444 PI594170B        Landrace -0.28214596         444
448    USB-354         454 PI594456A        Landrace  1.34996459         454
449    USB-357         454  PI594880        Landrace  1.34996459         454
450    USB-358         444  PI594922 Modern cultivar -0.28214596         444
451    USB-361         449  PI597464        Landrace  0.53390932         449
452    USB-363         434 PI597478B Modern cultivar -1.91425650         434
453    USB-366         439 PI602502B        Landrace -1.09820123         439
454    USB-367         444  PI602993        Landrace -0.28214596         444
455    USB-368         446  PI603162        Landrace  0.04427615         446
456    USB-369         450  PI603290        Landrace  0.69712037         450
457    USB-370         442  PI603345        Landrace -0.60856807         442
458    USB-371         441  PI603389        Landrace -0.77177912         441
459    USB-372         447  PI603397        Landrace  0.20748721         447
460    USB-373         444  PI603399        Landrace -0.28214596         444
461    USB-374         444  PI603463        Landrace -0.28214596         444
462    USB-375         450  PI603488        Landrace  0.69712037         450
463    USB-376         443  PI603492        Landrace -0.44535701         443
464    USB-377         446  PI603494        Landrace  0.04427615         446
465    USB-378         441 PI603495B        Landrace -0.77177912         441
466    USB-381         445  PI603526        Landrace -0.11893490         445
467    USB-382         455  PI603549        Landrace  1.51317564         455
468    USB-383         436  PI603555        Landrace -1.58783439         436
469    USB-384         445  PI603556        Landrace -0.11893490         445
470    USB-385         441  PI603559        Landrace -0.77177912         441
471    USB-386         444  PI603675        Landrace -0.28214596         444
472    USB-387         440 PI603698J        Landrace -0.93499018         440
473    USB-388         445  PI603722        Landrace -0.11893490         445
474    USB-392         448  PI612730        Landrace  0.37069826         448
475    USB-393         455  PI612754 Modern cultivar  1.51317564         455
476    USB-411         448  PI567788 Modern cultivar  0.37069826         448
477    USB-412         435  FC3654-1        Landrace -1.75104545         435
478    USB-414         437   FC31571        Landrace -1.42462334         437
480    USB-416         444   PI54873        Landrace -0.28214596         444
481    USB-417         441   PI59845        Landrace -0.77177912         441
482    USB-418         439   PI60970        Landrace -1.09820123         439
483    USB-419         445 PI62202-2        Landrace -0.11893490         445
484    USB-421         440   PI68423        Landrace -0.93499018         440
485    USB-422         437   PI68436        Landrace -1.42462334         437
486    USB-423         444   PI68523        Landrace -0.28214596         444
487    USB-424         442 PI68679-2        Landrace -0.60856807         442
488    USB-425         439   PI68731        Landrace -1.09820123         439
489    USB-426         444   PI68736        Landrace -0.28214596         444
490    USB-427         440   PI68765        Landrace -0.93499018         440
491    USB-428         451   PI68815        Landrace  0.86033142         451
492    USB-429         446   PI70078        Landrace  0.04427615         446
493    USB-430         439   PI70201        Landrace -1.09820123         439
494    USB-431         440   PI70208        Landrace -0.93499018         440
495    USB-432         441 PI70242-2        Landrace -0.77177912         441
496    USB-433         442   PI79616        Landrace -0.60856807         442
498    USB-436         433   PI79832        Landrace -2.07746756         433
499    USB-437         436 PI79870-4        Landrace -1.58783439         436
500    USB-438         440 PI80466-1        Landrace -0.93499018         440
501    USB-440         441 PI81042-2        Landrace -0.77177912         441
502    USB-441         443   PI82302        Landrace -0.44535701         443
503    USB-442         440   PI83925        Landrace -0.93499018         440
505    USB-444         444   PI84660        Landrace -0.28214596         444
506    USB-445         447   PI84734        Landrace  0.20748721         447
507    USB-446         443   PI86084        Landrace -0.44535701         443
508    USB-447         450   PI86982        Landrace  0.69712037         450
509    USB-448         446   PI87076        Landrace  0.04427615         446
510    USB-449         446   PI87571        Landrace  0.04427615         446
511    USB-450         441   PI87618        Landrace -0.77177912         441
512    USB-451         440   PI87634        Landrace -0.93499018         440
513    USB-452         441   PI88306        Landrace -0.77177912         441
514    USB-453         444   PI88448        Landrace -0.28214596         444
515    USB-454         440   PI88479        Landrace -0.93499018         440
516    USB-455         444   PI88499        Landrace -0.28214596         444
517    USB-456         447   PI88508        Landrace  0.20748721         447
518    USB-459         442   PI90369        Landrace -0.60856807         442
519    USB-460         447 PI90406-1        Landrace  0.20748721         447
520    USB-462         436  PI90495N        Landrace -1.58783439         436
521    USB-463         437 PI90499-1        Landrace -1.42462334         437
522    USB-464         444   PI90577        Landrace -0.28214596         444
524    USB-466         445   PI90768        Landrace -0.11893490         445
525    USB-467         443   PI91083        Landrace -0.44535701         443
526    USB-468         441   PI91089        Landrace -0.77177912         441
527    USB-469         439   PI91120        Landrace -1.09820123         439
528    USB-470         449 PI91132-3        Landrace  0.53390932         449
529    USB-472         438   PI91725        Landrace -1.26141229         438
530    USB-473         442 PI91731-1        Landrace -0.60856807         442
531    USB-474         447   PI92601        Landrace  0.20748721         447
532    USB-475         442   PI92604        Landrace -0.60856807         442
533    USB-476         440   PI92618        Landrace -0.93499018         440
534    USB-477         435 PI92688-2        Landrace -1.75104545         435
535    USB-478         439 PI92718-2        Landrace -1.09820123         439
536    USB-479         442   PI92728        Landrace -0.60856807         442
537    USB-480         445   PI93055        Landrace -0.11893490         445
538    USB-481         445   PI93563        Landrace -0.11893490         445
539    USB-484         439  PI103079        Landrace -1.09820123         439
540    USB-485         433  PI123439        Landrace -2.07746756         433
541    USB-486         448  PI123587        Landrace  0.37069826         448
542    USB-488         452  PI165674        Landrace  1.02354248         452
543    USB-489         443  PI165929        Landrace -0.44535701         443
544    USB-490         447  PI171438        Landrace  0.20748721         447
545    USB-492         439  PI180532    Old cultivar -1.09820123         439
546    USB-494         440  PI192870        Landrace -0.93499018         440
547    USB-496         445  PI198067    Old cultivar -0.11893490         445
548    USB-497         454  PI200503        Landrace  1.34996459         454
549    USB-498         432  PI209331        Landrace -2.24067861         432
550    USB-499         434  PI219698        Landrace -1.91425650         434
551    USB-500         446  PI227320        Landrace  0.04427615         446
552    USB-502         437  PI235347        Landrace -1.42462334         437
553    USB-504         441 PI253652B        Landrace -0.77177912         441
554    USB-505         450 PI253656B        Landrace  0.69712037         450
555    USB-506         434  PI257429    Old cultivar -1.91425650         434
556    USB-510         431  PI290136        Landrace -2.40388967         431
557    USB-512         447  PI319527        Landrace  0.20748721         447
558    USB-518         439 PI370057B        Landrace -1.09820123         439
559    USB-520         450  PI377574        Landrace  0.69712037         450
560    USB-521         449 PI378670B        Landrace  0.53390932         449
561    USB-522         446 PI378682C        Landrace  0.04427615         446
562    USB-524         446  PI399058        Landrace  0.04427615         446
563    USB-525         435  PI404161        Landrace -1.75104545         435
564    USB-526         444  PI404182        Landrace -0.28214596         444
565    USB-527         447  PI404199        Landrace  0.20748721         447
566    USB-528         439 PI407659B        Landrace -1.09820123         439
567    USB-529         443  PI407717        Landrace -0.44535701         443
568    USB-530         446  PI408088        Landrace  0.04427615         446
569    USB-531         446  PI416768        Landrace  0.04427615         446
570    USB-533         444  PI417007        Landrace -0.28214596         444
571    USB-534         444  PI417077        Landrace -0.28214596         444
572    USB-537         439  PI417550        Landrace -1.09820123         439
573    USB-538         453  PI424005        Landrace  1.18675353         453
574    USB-540         452  PI430596        Landrace  1.02354248         452
575    USB-541         437 PI430598B        Landrace -1.42462334         437
576    USB-544         435  PI437123        Landrace -1.75104545         435
577    USB-545         440 PI437135B        Landrace -0.93499018         440
578    USB-546         441  PI437138        Landrace -0.77177912         441
579    USB-548         442 PI437168A        Landrace -0.60856807         442
580    USB-549         440 PI437211B        Landrace -0.93499018         440
581    USB-550         442  PI437287        Landrace -0.60856807         442
582    USB-551         438  PI437296        Landrace -1.26141229         438
583    USB-553         440  PI437321        Landrace -0.93499018         440
584    USB-554         435  PI437326        Landrace -1.75104545         435
585    USB-555         446  PI437347        Landrace  0.04427615         446
586    USB-556         440  PI437366        Landrace -0.93499018         440
587    USB-558         443 PI437427A        Landrace -0.44535701         443
588    USB-559         437  PI437431        Landrace -1.42462334         437
589    USB-560         448  PI437476        Landrace  0.37069826         448
590    USB-561         445 PI437477B        Landrace -0.11893490         445
591    USB-562         443  PI437487        Landrace -0.44535701         443
592    USB-563         432  PI437513        Landrace -2.24067861         432
593    USB-564         446  PI437562        Landrace  0.04427615         446
594    USB-565         437  PI437578        Landrace -1.42462334         437
595    USB-566         448 PI437594A        Landrace  0.37069826         448
596    USB-567         441 PI437609B        Landrace -0.77177912         441
597    USB-568         440 PI437615A        Landrace -0.93499018         440
598    USB-569         436  PI437634        Landrace -1.58783439         436
599    USB-570         441 PI437640A        Landrace -0.77177912         441
600    USB-571         443 PI437721B        Landrace -0.44535701         443
601    USB-573         443  PI437781        Landrace -0.44535701         443
602    USB-574         436  PI437798        Landrace -1.58783439         436
603    USB-575         434  PI437815        Landrace -1.91425650         434
604    USB-576         433 PI437875A        Landrace -2.07746756         433
605    USB-577         440 PI437995A        Landrace -0.93499018         440
606    USB-578         445  PI437999        Landrace -0.11893490         445
607    USB-579         437  PI438011        Landrace -1.42462334         437
608    USB-580         440  PI438047        Landrace -0.93499018         440
609    USB-581         442  PI438120        Landrace -0.60856807         442
610    USB-582         442  PI438195        Landrace -0.60856807         442
611    USB-583         438  PI438267        Landrace -1.26141229         438
612    USB-584         435  PI438310        Landrace -1.75104545         435
613    USB-585         448  PI438312        Landrace  0.37069826         448
614    USB-586         439  PI438329        Landrace -1.09820123         439
615    USB-587         438 PI438358B        Landrace -1.26141229         438
616    USB-589         447  PI438450        Landrace  0.20748721         447
617    USB-590         441  PI438492        Landrace -0.77177912         441
618    USB-591         438 PI438504B        Landrace -1.26141229         438
619    USB-592         447 PI438509A        Landrace  0.20748721         447
620    USB-593         442  PI464913        Landrace -0.60856807         442
621    USB-594         451 PI466749A        Landrace  0.86033142         451
622    USB-595         448  PI467313        Landrace  0.37069826         448
623    USB-596         462  PI468919        Landrace  2.65565302         462
624    USB-597         446 PI475812B        Landrace  0.04427615         446
625    USB-599         437  PI476344        Landrace -1.42462334         437
626    USB-602         444  PI476939        Landrace -0.28214596         444
627    USB-604         446  PI504495        Landrace  0.04427615         446
628    USB-607         433  PI507678    Old cultivar -2.07746756         433
629    USB-608         442  PI508083 Modern cultivar -0.60856807         442
630    USB-609         443  PI508269 Modern cultivar -0.44535701         443
631    USB-611         445  PI518711        Landrace -0.11893490         445
632    USB-612         444  PI518759        Landrace -0.28214596         444
633    USB-613         437  PI525454 Modern cultivar -1.42462334         437
634    USB-614         436  PI532454        Landrace -1.58783439         436
635    USB-615         441  PI533654 Modern cultivar -0.77177912         441
636    USB-616         440  PI534648 Modern cultivar -0.93499018         440
637    USB-617         438  PI539865 Modern cultivar -1.26141229         438
638    USB-619         440  PI540555 Modern cultivar -0.93499018         440
639    USB-620         441  PI543794 Modern cultivar -0.77177912         441
640    USB-628         448  PI548414    Old cultivar  0.37069826         448
641    USB-629         440  PI548440    Old cultivar -0.93499018         440
642    USB-630         446  PI548464    Old cultivar  0.04427615         446
643    USB-632         448  PI548472    Old cultivar  0.37069826         448
644    USB-633         447  PI548491    Old cultivar  0.20748721         447
645    USB-634         448  PI548497    Old cultivar  0.37069826         448
646    USB-635         434  PI548528 Modern cultivar -1.91425650         434
647    USB-637         431  PI548547 Modern cultivar -2.40388967         431
648    USB-640         435  PI548568 Modern cultivar -1.75104545         435
649    USB-642         442 PI549023B        Landrace -0.60856807         442
650    USB-650         436  PI561321        Landrace -1.58783439         436
651    USB-654         442 PI566998A        Landrace -0.60856807         442
652    USB-655         449 PI567056A        Landrace  0.53390932         449
653    USB-656         447 PI567060A        Landrace  0.20748721         447
654    USB-658         438 PI567170A        Landrace -1.26141229         438
655    USB-663         444  PI567265        Landrace -0.28214596         444
656    USB-664         444 PI567272A        Landrace -0.28214596         444
657    USB-666         446  PI567331        Landrace  0.04427615         446
658    USB-669         440 PI567366A        Landrace -0.93499018         440
659    USB-670         437 PI567370A        Landrace -1.42462334         437
660    USB-672         443 PI567381B        Landrace -0.44535701         443
661    USB-673         446  PI567390        Landrace  0.04427615         446
662    USB-674         440  PI567392        Landrace -0.93499018         440
663    USB-675         448 PI567394B        Landrace  0.37069826         448
664    USB-676         441  PI567395        Landrace -0.77177912         441
665    USB-677         438 PI567396B        Landrace -1.26141229         438
666    USB-678         440  PI567398        Landrace -0.93499018         440
667    USB-679         441 PI567404A        Landrace -0.77177912         441
668    USB-680         443  PI567405        Landrace -0.44535701         443
669    USB-682         436 PI567417B        Landrace -1.58783439         436
670    USB-684         441  PI567458        Landrace -0.77177912         441
671    USB-687         444  PI567503        Landrace -0.28214596         444
672    USB-688         442 PI567541B        Landrace -0.60856807         442
673    USB-689         457  PI567552        Landrace  1.83959775         457
674    USB-690         448  PI567556        Landrace  0.37069826         448
675    USB-691         441  PI567559        Landrace -0.77177912         441
676    USB-694         439 PI567582A        Landrace -1.09820123         439
677    USB-696         440 PI567593B        Landrace -0.93499018         440
678    USB-697         443  PI567599        Landrace -0.44535701         443
679    USB-699         447  PI567638        Landrace  0.20748721         447
680    USB-700         440 PI567648C        Landrace -0.93499018         440
681    USB-701         438  PI567721        Landrace -1.26141229         438
682    USB-703         446  PI567772        Landrace  0.04427615         446
683    USB-705         435 PI574478C        Landrace -1.75104545         435
684    USB-706         439 PI578316C        Landrace -1.09820123         439
685    USB-707         436 PI578329C Modern cultivar -1.58783439         436
686    USB-708         442  PI578330 Modern cultivar -0.60856807         442
687    USB-710         444  PI578490        Landrace -0.28214596         444
688    USB-711         445  PI583366 Modern cultivar -0.11893490         445
689    USB-712         445 PI587560A        Landrace -0.11893490         445
690    USB-713         438 PI587575A        Landrace -1.26141229         438
691    USB-715         438  PI587582        Landrace -1.26141229         438
692    USB-717         439 PI587668A        Landrace -1.09820123         439
693    USB-718         443 PI587788B        Landrace -0.44535701         443
694    USB-719         442  PI587794        Landrace -0.60856807         442
695    USB-729         440 PI588015B        Landrace -0.93499018         440
696    USB-730         433 PI588026C        Landrace -2.07746756         433
697    USB-732         441  PI592919        Landrace -0.77177912         441
698    USB-733         442  PI592928        Landrace -0.60856807         442
699    USB-734         442  PI592932        Landrace -0.60856807         442
700    USB-735         433  PI592936        Landrace -2.07746756         433
701    USB-737         439 PI594409B        Landrace -1.09820123         439
702    USB-740         447  PI594467        Landrace  0.20748721         447
703    USB-741         445 PI594470C        Landrace -0.11893490         445
704    USB-742         446  PI594487        Landrace  0.04427615         446
705    USB-745         447 PI594660A        Landrace  0.20748721         447
706    USB-748         445 PI594753A        Landrace -0.11893490         445
707    USB-751         447 PI594805A        Landrace  0.20748721         447
708    USB-752         442 PI594810B        Landrace -0.60856807         442
709    USB-755         441 PI594834A        Landrace -0.77177912         441
710    USB-756         452 PI594839A        Landrace  1.02354248         452
711    USB-758         436  PI594883        Landrace -1.58783439         436
712    USB-761         437  PI597402        Landrace -1.42462334         437
713    USB-762         449  PI602492        Landrace  0.53390932         449
714    USB-763         445 PI603174A        Landrace -0.11893490         445
715    USB-765         446 PI603308A        Landrace  0.04427615         446
716    USB-768         446  PI603356        Landrace  0.04427615         446
717    USB-769         441  PI603401        Landrace -0.77177912         441
718    USB-771         447  PI603408        Landrace  0.20748721         447
719    USB-772         440 PI603412A        Landrace -0.93499018         440
720    USB-773         446 PI603421B        Landrace  0.04427615         446
721    USB-776         447  PI603446        Landrace  0.20748721         447
722    USB-777         438 PI603451A        Landrace -1.26141229         438
723    USB-778         443 PI603457B        Landrace -0.44535701         443
724    USB-779         442 PI603477A        Landrace -0.60856807         442
725    USB-780         439  PI603490        Landrace -1.09820123         439
726    USB-781         442 PI603496B        Landrace -0.60856807         442
727    USB-782         444 PI603502A        Landrace -0.28214596         444
728    USB-783         452  PI603505        Landrace  1.02354248         452
729    USB-784         451  PI603510        Landrace  0.86033142         451
730    USB-786         445  PI603523        Landrace -0.11893490         445
731    USB-787         446  PI603532        Landrace  0.04427615         446
732    USB-788         450 PI603534B        Landrace  0.69712037         450
733    USB-792         446 PI603554B        Landrace  0.04427615         446
734    USB-793         442  PI603557        Landrace -0.60856807         442
735    USB-794         446  PI603574        Landrace  0.04427615         446
736    USB-795         446  PI603583        Landrace  0.04427615         446
737    USB-798         443 PI603687A        Landrace -0.44535701         443
738    USB-799         445  PI603696        Landrace -0.11893490         445
739    USB-801         442 PI603910C        Landrace -0.60856807         442
740    USB-802         443 PI603911B        Landrace -0.44535701         443
741    USB-803         441 PI605839A        Landrace -0.77177912         441
    Yield        Yield2       Country continent    continent2     pred
1    1.54 -0.7744148915         Korea      Asia          Asia 2.038163
2    1.30 -1.0646833992         China      Asia          Asia 1.885786
3    3.34  1.4025989168           USA  Americas North America 3.202444
4    3.54  1.6444893399           USA  Americas North America 3.202444
5    2.15 -0.0366491009         China      Asia          Asia 2.122816
6    2.06 -0.1454997913         Korea      Asia          Asia 1.987370
7    1.64 -0.6534696799         Korea      Asia          Asia 2.088955
8    2.51  0.3987536607         China      Asia          Asia 2.139747
9    1.85 -0.3994847356         China      Asia          Asia 2.038163
10   2.80  0.7494947743         China      Asia          Asia 2.004301
11   1.49 -0.8348874973         Japan      Asia          Asia 2.105885
12   1.86 -0.3873902145         China      Asia          Asia 2.072024
13   1.45 -0.8832655819         China      Asia          Asia 1.970440
14   1.69 -0.5929970741         China      Asia          Asia 2.055093
15   1.87 -0.3752956933         China      Asia          Asia 2.072024
16   2.10 -0.0971217067         Japan      Asia          Asia 1.970440
17   1.53 -0.7865094126         China      Asia          Asia 2.055093
18   1.84 -0.4115792568         Korea      Asia          Asia 2.088955
19   1.39 -0.9558327088         China      Asia          Asia 2.038163
20   2.58  0.4834153088         Korea      Asia          Asia 2.122816
21   4.38  2.6604291171           USA  Americas North America 3.298079
22   4.16  2.3943496516           USA  Americas North America 3.216106
23   1.96 -0.2664450029         China      Asia          Asia 2.088955
24   1.32 -1.0404943569         Japan      Asia          Asia 2.072024
25   2.28  0.1205796741         Korea      Asia          Asia 1.970440
26   3.11  1.1244249302         Japan      Asia          Asia 2.122816
27   0.77 -1.7056930206         Korea      Asia          Asia 1.936578
28   1.19 -1.1977231320         Japan      Asia          Asia 2.055093
29   1.88 -0.3632011721         Japan      Asia          Asia 1.970440
30   2.41  0.2778084492         Japan      Asia          Asia 1.970440
31   0.86 -1.5968423302         Korea      Asia          Asia 2.021232
32   1.00 -1.4275190340         Korea      Asia          Asia 2.004301
33   1.34 -1.0163053146         Korea      Asia          Asia 1.936578
34   1.71 -0.5688080318         Korea      Asia          Asia 1.936578
35   2.31  0.1568632376         China      Asia          Asia 2.072024
36   0.95 -1.4879916397         Korea      Asia          Asia 2.038163
37   2.18 -0.0003655374         Korea      Asia          Asia 2.004301
38   1.27 -1.1009669627         Korea      Asia          Asia 1.936578
39   3.54  1.6444893399        Russia    Europe        Europe 2.577572
40   1.54 -0.7744148915         China      Asia          Asia 2.004301
41   3.41  1.4872605649         China      Asia          Asia 2.055093
42   2.78  0.7253057320         China      Asia          Asia 2.055093
43   1.25 -1.1251560050         China      Asia          Asia 1.885786
44   3.07  1.0760468455         China      Asia          Asia 2.072024
45   2.02 -0.1938778759         China      Asia          Asia 2.088955
46   2.59  0.4955098300         Nepal      Asia          Asia 1.987370
47   2.39  0.2536194069         China      Asia          Asia 2.139747
48   4.05  2.2613099189 Former Serbia    Europe        Europe 3.399824
49   2.19  0.0117289837         China      Asia          Asia 1.736925
50   2.66  0.5801714781         Korea      Asia          Asia 1.753856
51   2.60  0.5076043512         China      Asia          Asia 1.753856
52   4.06  2.2734044401           USA  Americas North America 3.298079
53   2.75  0.6890221685           USA  Americas North America 3.311741
54   2.21  0.0359180260         China      Asia          Asia 2.055093
55   4.27  2.5273893844           USA  Americas North America 3.284417
56   0.79 -1.6815039783         China      Asia          Asia 2.055093
57   0.43 -2.1169067399         China      Asia          Asia 2.072024
58   1.25 -1.1251560050         China      Asia          Asia 2.088955
59   1.47 -0.8590765396         China      Asia          Asia 2.072024
60   1.23 -1.1493450473         China      Asia          Asia 2.021232
61   1.90 -0.3390121298         China      Asia          Asia 2.038163
62   2.32  0.1689577588         China      Asia          Asia 2.038163
63   2.38  0.2415248857         China      Asia          Asia 1.970440
64   2.23  0.0601070684         China      Asia          Asia 2.139747
65   1.76 -0.5083354260         China      Asia          Asia 2.088955
66   1.82 -0.4357682991         China      Asia          Asia 2.105885
67   1.34 -1.0163053146         China      Asia          Asia 2.072024
68   1.96 -0.2664450029         China      Asia          Asia 2.105885
69   4.00  2.2008373131           USA  Americas North America 3.339065
70   0.58 -1.9354889225         Korea      Asia          Asia 1.953509
71   0.61 -1.8992053591         China      Asia          Asia 2.021232
72   0.66 -1.8387327533         China      Asia          Asia 1.953509
73   4.48  2.7813743287           USA  Americas North America 3.188782
74   1.74 -0.5325244683         Korea      Asia          Asia 2.021232
75   1.66 -0.6292806376         Korea      Asia          Asia 2.038163
76   2.05 -0.1575943125         Korea      Asia          Asia 2.072024
77   1.14 -1.2581957378         Korea      Asia          Asia 2.021232
78   1.57 -0.7381313280         China      Asia          Asia 2.190539
79   0.79 -1.6815039783       Vietnam      Asia          Asia 2.139747
81   1.46 -0.8711710607        Sweden    Europe        Europe 2.526967
82   2.15 -0.0366491009         Japan      Asia          Asia 2.105885
83   2.94  0.9188180705         Japan      Asia          Asia 1.970440
84   2.05 -0.1575943125         Japan      Asia          Asia 1.987370
86   2.93  0.9067235493         China      Asia          Asia 1.919647
87   2.66  0.5801714781         China      Asia          Asia 1.987370
88   1.24 -1.1372505262         Japan      Asia          Asia 1.953509
89   2.76  0.7011166897         China      Asia          Asia 1.987370
90   2.76  0.7011166897         China      Asia          Asia 1.919647
91   2.70  0.6285495627        France    Europe        Europe 2.476361
92   3.09  1.1002358879         Japan      Asia          Asia 1.987370
93   3.21  1.2453701417         China      Asia          Asia 1.868855
94   2.81  0.7615892955       Morocco    Africa        Africa 2.045492
95   3.02  1.0155742398       Hungary    Europe        Europe 2.476361
96   3.29  1.3421263110        Russia    Europe        Europe 2.438407
97   3.46  1.5477331707       Romania    Europe        Europe 2.552270
98   2.90  0.8704399859       Romania    Europe        Europe 2.552270
99   3.51  1.6082057764        Russia    Europe        Europe 2.438407
100  3.10  1.1123304090        Russia    Europe        Europe 2.476361
101  3.57  1.6807729034         China      Asia          Asia 2.055093
102  3.35  1.4146934379        Russia    Europe        Europe 2.489013
103  3.63  1.7533400303         China      Asia          Asia 1.953509
104  3.42  1.4993550860         China      Asia          Asia 1.987370
105  3.46  1.5477331707         Korea      Asia          Asia 2.139747
106  2.92  0.8946290282       Hungary    Europe        Europe 2.514315
107  3.24  1.2816537052         China      Asia          Asia 2.088955
108  3.98  2.1766482708         China      Asia          Asia 1.919647
109  4.04  2.2492153978         China      Asia          Asia 1.885786
110  3.32  1.3784098745         China      Asia          Asia 2.038163
111  3.97  2.1645537497         China      Asia          Asia 2.021232
112  3.87  2.0436085381       Romania    Europe        Europe 2.602875
113  4.21  2.4548222574       Romania    Europe        Europe 2.514315
114  3.22  1.2574646629         China      Asia          Asia 1.987370
115  2.92  0.8946290282         China      Asia          Asia 1.953509
116  3.81  1.9710414112         China      Asia          Asia 1.987370
117  3.77  1.9226633265         Japan      Asia          Asia 1.987370
118  1.89 -0.3511066510         Korea      Asia          Asia 1.834994
119  1.50 -0.8227929761         Korea      Asia          Asia 1.868855
120  1.78 -0.4841463837         Korea      Asia          Asia 1.868855
121  1.52 -0.7986039338         Japan      Asia          Asia 1.953509
122  1.21 -1.1735340897         Japan      Asia          Asia 1.784202
123  1.86 -0.3873902145         Korea      Asia          Asia 1.936578
124  2.54  0.4350372242         Korea      Asia          Asia 1.936578
125  2.41  0.2778084492         Korea      Asia          Asia 1.885786
126  2.04 -0.1696888336         Korea      Asia          Asia 1.902717
127  2.08 -0.1213107490         Korea      Asia          Asia 1.936578
128  2.04 -0.1696888336         Korea      Asia          Asia 2.055093
129  1.43 -0.9074546242         Korea      Asia          Asia 1.987370
130  1.00 -1.4275190340         Korea      Asia          Asia 1.936578
131  1.98 -0.2422559606         Korea      Asia          Asia 1.953509
132  1.68 -0.6050915953         Korea      Asia          Asia 1.902717
133  1.85 -0.3994847356         Korea      Asia          Asia 1.868855
134  2.18 -0.0003655374         Korea      Asia          Asia 1.953509
135  2.27  0.1084851530         Korea      Asia          Asia 1.851925
136  2.01 -0.2059723971         Korea      Asia          Asia 1.936578
137  2.81  0.7615892955         Korea      Asia          Asia 1.919647
138  2.41  0.2778084492         China      Asia          Asia 1.868855
139  3.06  1.0639523244         China      Asia          Asia 1.970440
140  4.00  2.2008373131           USA  Americas North America 3.243430
141  2.17 -0.0124600586         China      Asia          Asia 1.987370
142  2.87  0.8341564224         China      Asia          Asia 2.055093
143  1.69 -0.5929970741         China      Asia          Asia 2.139747
144  1.41 -0.9316436665         China      Asia          Asia 2.173608
145  4.12  2.3459715670           USA  Americas North America 3.257093
146  1.20 -1.1856286108         Korea      Asia          Asia 2.021232
147  3.24  1.2816537052           USA  Americas North America 3.298079
148  2.74  0.6769276474         Korea      Asia          Asia 1.838510
149  1.63 -0.6655642011         China      Asia          Asia 1.821579
150  1.61 -0.6897532434        Sweden    Europe        Europe 2.463710
151  2.13 -0.0608381432        Serbia    Europe        Europe 2.577572
152  0.77 -1.7056930206         China      Asia          Asia 1.699548
153  1.06 -1.3549519070         Korea      Asia          Asia 1.987370
154  2.86  0.8220619012         China      Asia          Asia 1.970440
155  2.65  0.5680769569         Japan      Asia          Asia 1.868855
156  0.54 -1.9838670072         China      Asia          Asia 1.987370
157  1.61 -0.6897532434         China      Asia          Asia 1.618410
158  1.68 -0.6050915953         Japan      Asia          Asia 1.970440
159  1.83 -0.4236737779         China      Asia          Asia 1.834994
160  1.90 -0.3390121298         China      Asia          Asia 1.767271
161  1.88 -0.3632011721         China      Asia          Asia 2.038163
162  1.10 -1.3065738224         Korea      Asia          Asia 2.021232
163  2.89  0.8583454647         Korea      Asia          Asia 1.902717
164  2.45  0.3261865338         Italy    Europe        Europe 2.280615
165  1.55 -0.7623203703           USA  Americas North America 2.421179
166  1.80 -0.4599573414       Belgium    Europe        Europe 2.564921
167  1.63 -0.6655642011         China      Asia          Asia 2.038163
168  2.70  0.6285495627         China      Asia          Asia 1.936578
169  1.81 -0.4478628202       Vietnam      Asia          Asia 1.953509
171  1.56 -0.7502258492         Japan      Asia          Asia 1.885786
172  2.41  0.2778084492         China      Asia          Asia 1.970440
173  1.05 -1.3670464282         Korea      Asia          Asia 1.987370
174  1.11 -1.2944793012         China      Asia          Asia 1.936578
175  2.12 -0.0729326644         China      Asia          Asia 1.970440
176  2.80  0.7494947743         Korea      Asia          Asia 1.733410
177  1.91 -0.3269176087         China      Asia          Asia 1.936578
178  2.88  0.8462509436         China      Asia          Asia 1.804648
179  1.33 -1.0283998358         Japan      Asia          Asia 1.953509
180  1.95 -0.2785395240         China      Asia          Asia 2.004301
181  2.01 -0.2059723971         China      Asia          Asia 1.919647
182  2.54  0.4350372242         Japan      Asia          Asia 1.987370
183  1.18 -1.2098176531         Korea      Asia          Asia 1.567618
184  2.16 -0.0245545797         China      Asia          Asia 1.953509
185  1.47 -0.8590765396         China      Asia          Asia 1.970440
186  1.56 -0.7502258492         China      Asia          Asia 2.156678
187  1.78 -0.4841463837         Japan      Asia          Asia 1.953509
188  2.36  0.2173358434         China      Asia          Asia 1.936578
189  1.92 -0.3148230875         China      Asia          Asia 2.038163
190  2.93  0.9067235493         China      Asia          Asia 1.902717
191  2.22  0.0480125472         Korea      Asia          Asia 1.987370
192  2.33  0.1810522799         Korea      Asia          Asia 2.139747
193  3.59  1.7049619457         China      Asia          Asia 1.987370
194  1.16 -1.2340066954         Japan      Asia          Asia 2.038163
195  3.60  1.7170564669           USA  Americas North America 3.202444
196  2.59  0.4955098300           USA  Americas North America 3.257093
197  4.12  2.3459715670           USA  Americas North America 3.325403
198  2.29  0.1326741953        Canada  Americas North America 2.191521
199  4.18  2.4185386940           USA  Americas North America 3.311741
200  2.66  0.5801714781        Canada  Americas North America 3.243430
201  3.71  1.8500961996           USA  Americas North America 3.202444
202  1.52 -0.7986039338           USA  Americas North America 3.161458
203  1.61 -0.6897532434           USA  Americas North America 2.000252
205  2.11 -0.0850271855         China      Asia          Asia 2.072024
206  2.04 -0.1696888336       Moldova    Europe        Europe 2.501664
207  0.09 -2.5281204592         China      Asia          Asia 1.987370
208  1.41 -0.9316436665         Japan      Asia          Asia 2.072024
209  2.17 -0.0124600586        Russia    Europe        Europe 2.217358
210  2.21  0.0359180260        Russia    Europe        Europe 2.179403
211  1.54 -0.7744148915         Korea      Asia          Asia 1.652272
212  0.14 -2.4676478535         China      Asia          Asia 1.953509
213  2.32  0.1689577588         China      Asia          Asia 2.105885
214  0.63 -1.8750163168         China      Asia          Asia 1.953509
215  2.88  0.8462509436           USA  Americas North America 3.188782
216  1.77 -0.4962409049         China      Asia          Asia 1.987370
217  1.89 -0.3511066510         China      Asia          Asia 2.088955
218  2.53  0.4229427031         China      Asia          Asia 1.919647
219  2.17 -0.0124600586         China      Asia          Asia 1.953509
220  0.17 -2.4313642900         Japan      Asia          Asia 1.885786
221  2.94  0.9188180705         Korea      Asia          Asia 2.826552
222  2.03 -0.1817833548         China      Asia          Asia 2.055093
223  2.21  0.0359180260         China      Asia          Asia 1.970440
224  2.90  0.8704399859           USA  Americas North America 3.298079
227  2.39  0.2536194069         China      Asia          Asia 2.105885
228  2.13 -0.0608381432         China      Asia          Asia 1.987370
229  2.82  0.7736838166         China      Asia          Asia 2.088955
230  2.19  0.0117289837         China      Asia          Asia 2.038163
231  2.68  0.6043605204         China      Asia          Asia 2.038163
232  2.25  0.0842961107         China      Asia          Asia 2.088955
233  2.47  0.3503755761         China      Asia          Asia 2.156678
234  2.72  0.6527386050         China      Asia          Asia 2.072024
235  2.54  0.4350372242         China      Asia          Asia 2.055093
236  2.33  0.1810522799         China      Asia          Asia 2.038163
237  2.36  0.2173358434         China      Asia          Asia 2.004301
238  1.72 -0.5567135107         China      Asia          Asia 2.038163
239  2.62  0.5317933935         Japan      Asia          Asia 2.021232
240  1.86 -0.3873902145         Japan      Asia          Asia 2.088955
241  2.19  0.0117289837         Korea      Asia          Asia 1.936578
242  2.30  0.1447687165         Korea      Asia          Asia 2.105885
243  1.75 -0.5204299472         Korea      Asia          Asia 1.953509
244  2.36  0.2173358434         Korea      Asia          Asia 1.970440
245  2.75  0.6890221685         Korea      Asia          Asia 2.038163
246  1.63 -0.6655642011         Japan      Asia          Asia 2.004301
247  0.52 -2.0080560495         Korea      Asia          Asia 2.021232
248  2.78  0.7253057320         Korea      Asia          Asia 2.105885
249  2.63  0.5438879146         Korea      Asia          Asia 1.936578
250  2.82  0.7736838166         China      Asia          Asia 2.038163
251  2.70  0.6285495627         China      Asia          Asia 2.021232
252  1.65 -0.6413751588         China      Asia          Asia 2.055093
253  0.74 -1.7419765840         China      Asia          Asia 2.105885
254  2.31  0.1568632376         China      Asia          Asia 2.055093
255  3.13  1.1486139725         China      Asia          Asia 2.088955
256  2.01 -0.2059723971         China      Asia          Asia 2.088955
257  2.34  0.1931468011         China      Asia          Asia 2.021232
258  2.66  0.5801714781         China      Asia          Asia 2.055093
259  2.59  0.4955098300         Japan      Asia          Asia 1.987370
260  1.65 -0.6413751588         Korea      Asia          Asia 2.122816
261  2.22  0.0480125472         China      Asia          Asia 2.072024
262  0.96 -1.4758971186       Myanmar      Asia          Asia 2.173608
264  2.06 -0.1454997913       Belgium    Europe        Europe 2.615527
265  1.51 -0.8106984550   Netherlands    Europe        Europe 2.590224
266  0.99 -1.4396135551          Peru  Americas South America 1.945253
267  0.65 -1.8508272745         India      Asia          Asia 1.953509
268  2.12 -0.0729326644         China      Asia          Asia 1.851925
269  2.16 -0.0245545797         China      Asia          Asia 2.088955
270  1.51 -0.8106984550         Japan      Asia          Asia 2.072024
271  1.44 -0.8953601030         India      Asia          Asia 2.088955
272  1.43 -0.9074546242        France    Europe        Europe 2.489013
273  0.71 -1.7782601475         Japan      Asia          Asia 1.953509
274  1.53 -0.7865094126         Japan      Asia          Asia 2.072024
275  1.55 -0.7623203703         Japan      Asia          Asia 2.038163
276  2.62  0.5317933935         China      Asia          Asia 1.970440
277  3.08  1.0881413667         China      Asia          Asia 1.970440
278  2.17 -0.0124600586         China      Asia          Asia 2.072024
279  2.55  0.4471317454         China      Asia          Asia 2.004301
280  2.97  0.9551016340         China      Asia          Asia 1.970440
281  2.53  0.4229427031       Hungary    Europe        Europe 2.602875
282  2.47  0.3503755761  South Africa    Africa        Africa 3.145314
283  0.31 -2.2620409938         Japan      Asia          Asia 2.072024
284  0.47 -2.0685286553        Russia    Europe        Europe 2.564921
285  2.89  0.8583454647       Romania    Europe        Europe 2.577572
286  2.72  0.6527386050       Romania    Europe        Europe 2.590224
287  3.11  1.1244249302        Russia    Europe        Europe 2.577572
288  2.62  0.5317933935       Austria    Europe        Europe 2.577572
289  2.69  0.6164550416        Serbia    Europe        Europe 2.552270
290  2.88  0.8462509436       Ukraine    Europe        Europe 2.539618
291  2.89  0.8583454647        Russia    Europe        Europe 2.489013
292  2.86  0.8220619012        Russia    Europe        Europe 2.539618
293  1.21 -1.1735340897        Taiwan      Asia          Asia 2.021232
294  2.09 -0.1092162278         China      Asia          Asia 2.004301
295  3.16  1.1848975360         China      Asia          Asia 2.021232
296  1.03 -1.3912354705         Korea      Asia          Asia 2.021232
297  2.45  0.3261865338         Korea      Asia          Asia 1.953509
298  2.28  0.1205796741         China      Asia          Asia 2.072024
299  2.78  0.7253057320         China      Asia          Asia 1.987370
300  2.47  0.3503755761         China      Asia          Asia 2.105885
301  1.74 -0.5325244683         China      Asia          Asia 2.021232
302  2.70  0.6285495627         Japan      Asia          Asia 2.122816
303  1.93 -0.3027285664         Japan      Asia          Asia 1.970440
304  2.27  0.1084851530         Japan      Asia          Asia 1.970440
305  1.87 -0.3752956933         China      Asia          Asia 1.987370
306  1.56 -0.7502258492         China      Asia          Asia 2.055093
307  2.08 -0.1213107490         Japan      Asia          Asia 2.021232
308  1.39 -0.9558327088        Brazil  Americas South America 1.891624
309  1.73 -0.5446189895       Germany    Europe        Europe 2.628178
310  3.18  1.2090865783           USA  Americas North America 2.434841
311  1.49 -0.8348874973         Japan      Asia          Asia 1.953509
312  2.64  0.5559824358       Hungary    Europe        Europe 2.590224
313  1.81 -0.4478628202         China      Asia          Asia 2.038163
314  2.43  0.3019974915         China      Asia          Asia 2.072024
315  2.17 -0.0124600586        Russia    Europe        Europe 2.514315
316  2.40  0.2657139280        Russia    Europe        Europe 2.590224
317  3.11  1.1244249302       Georgia      Asia          Asia 2.088955
318  3.20  1.2332756206        Russia    Europe        Europe 2.501664
319  2.78  0.7253057320        Russia    Europe        Europe 2.602875
320  2.21  0.0359180260       Moldova    Europe        Europe 2.489013
321  3.18  1.2090865783        Russia    Europe        Europe 2.602875
322  2.84  0.7978728589        Russia    Europe        Europe 2.539618
323  3.53  1.6323948188        Russia    Europe        Europe 2.526967
324  2.97  0.9551016340        Russia    Europe        Europe 2.577572
325  1.15 -1.2461012166         China      Asia          Asia 2.038163
326  2.52  0.4108481819         China      Asia          Asia 2.072024
327  2.73  0.6648331262         China      Asia          Asia 1.885786
328  2.36  0.2173358434         China      Asia          Asia 2.173608
329  2.57  0.4713207877         China      Asia          Asia 2.021232
330  2.68  0.6043605204         China      Asia          Asia 1.953509
331  3.05  1.0518578032         China      Asia          Asia 2.021232
332  3.44  1.5235441283        Russia    Europe        Europe 2.514315
333  2.93  0.9067235493         China      Asia          Asia 1.970440
334  2.23  0.0601070684         China      Asia          Asia 2.021232
335  2.54  0.4350372242         China      Asia          Asia 2.072024
336  3.15  1.1728030148         China      Asia          Asia 2.072024
337  2.41  0.2778084492         China      Asia          Asia 1.970440
338  3.11  1.1244249302         China      Asia          Asia 2.105885
339  3.50  1.5961112553         China      Asia          Asia 2.156678
340  2.52  0.4108481819         China      Asia          Asia 2.139747
341  3.59  1.7049619457        France    Europe        Europe 2.552270
342  2.14 -0.0487436221       Algeria    Africa        Africa 2.323063
343  1.44 -0.8953601030     Australia   Oceania       Oceania 2.094109
344  1.75 -0.5204299472           USA  Americas North America 2.352869
345  2.05 -0.1575943125         China      Asia          Asia 2.021232
346  2.74  0.6769276474           USA  Americas North America 2.475828
347  1.68 -0.6050915953       Germany    Europe        Europe 2.539618
348  2.86  0.8220619012         China      Asia          Asia 2.038163
349  4.16  2.3943496516         China      Asia          Asia 1.970440
350  2.25  0.0842961107         China      Asia          Asia 2.021232
351  3.00  0.9913851974         China      Asia          Asia 2.004301
352  3.12  1.1365194513         China      Asia          Asia 2.088955
353  2.74  0.6769276474         China      Asia          Asia 1.970440
354  1.63 -0.6655642011         China      Asia          Asia 2.055093
355  2.91  0.8825345070    Kyrgyzstan      Asia          Asia 2.088955
356  2.09 -0.1092162278         China      Asia          Asia 2.072024
357  2.49  0.3745646184         China      Asia          Asia 2.088955
358  2.55  0.4471317454         China      Asia          Asia 1.987370
359  1.16 -1.2340066954         India      Asia          Asia 1.987370
360  0.26 -2.3225135996         Japan      Asia          Asia 2.122816
361  0.55 -1.9717724860         Japan      Asia          Asia 1.987370
362  3.15  1.1728030148         Japan      Asia          Asia 2.055093
363  0.27 -2.3104190784         Japan      Asia          Asia 2.021232
364  1.30 -1.0646833992         Japan      Asia          Asia 2.021232
365  3.10  1.1123304090         Japan      Asia          Asia 2.021232
366  2.17 -0.0124600586         Japan      Asia          Asia 2.038163
367  2.29  0.1326741953         Japan      Asia          Asia 2.055093
368  2.85  0.8099673801         Japan      Asia          Asia 2.038163
369  1.28 -1.0888724416         Japan      Asia          Asia 2.088955
370  2.50  0.3866591396         China      Asia          Asia 2.122816
371  1.15 -1.2461012166         China      Asia          Asia 1.987370
372  2.59  0.4955098300         China      Asia          Asia 2.004301
373  2.21  0.0359180260         China      Asia          Asia 2.156678
374  3.32  1.3784098745           USA  Americas North America 3.352727
375  2.33  0.1810522799        Russia    Europe        Europe 2.255312
376  2.00 -0.2180669183         Korea      Asia          Asia 1.753856
377  3.12  1.1365194513         China      Asia          Asia 1.838510
378  2.89  0.8583454647         China      Asia          Asia 1.821579
379  2.75  0.6890221685         China      Asia          Asia 1.906233
380  2.21  0.0359180260         China      Asia          Asia 1.804648
381  1.13 -1.2702902589         Korea      Asia          Asia 1.652272
382  0.74 -1.7419765840        Taiwan      Asia          Asia 1.838510
383  3.55  1.6565838611           USA  Americas North America 3.366390
384  4.34  2.6120510325           USA  Americas North America 3.229768
385  3.10  1.1123304090        Canada  Americas North America 3.366390
386  2.21  0.0359180260        Canada  Americas North America 3.284417
387  3.29  1.3421263110           USA  Americas North America 3.380052
388  3.87  2.0436085381           USA  Americas North America 3.284417
389  3.36  1.4267879591           USA  Americas North America 3.257093
390  1.58 -0.7260368069           USA  Americas North America 3.243430
391  1.77 -0.4962409049         China      Asia          Asia 2.004301
392  1.80 -0.4599573414         China      Asia          Asia 1.987370
393  0.98 -1.4517080763         China      Asia          Asia 1.987370
394  1.04 -1.3791409493         China      Asia          Asia 1.953509
395  2.87  0.8341564224         China      Asia          Asia 2.055093
396  2.66  0.5801714781         China      Asia          Asia 2.105885
397  1.55 -0.7623203703         Japan      Asia          Asia 2.021232
398  1.56 -0.7502258492         Japan      Asia          Asia 2.173608
399  2.11 -0.0850271855         China      Asia          Asia 2.088955
400  2.23  0.0601070684       Moldova    Europe        Europe 2.564921
401  1.54 -0.7744148915        Russia    Europe        Europe 2.577572
402  0.88 -1.5726532878         China      Asia          Asia 2.021232
403  1.28 -1.0888724416         China      Asia          Asia 2.072024
404  1.50 -0.8227929761         China      Asia          Asia 2.055093
405  1.53 -0.7865094126         China      Asia          Asia 2.072024
406  0.77 -1.7056930206         China      Asia          Asia 2.038163
407  1.82 -0.4357682991         China      Asia          Asia 2.004301
408  1.34 -1.0163053146         China      Asia          Asia 2.055093
409  1.66 -0.6292806376         China      Asia          Asia 1.970440
410  2.46  0.3382810550         China      Asia          Asia 1.936578
411  1.91 -0.3269176087         China      Asia          Asia 1.987370
412  1.94 -0.2906340452         China      Asia          Asia 1.919647
413  2.49  0.3745646184         China      Asia          Asia 2.004301
414  1.70 -0.5809025530         China      Asia          Asia 2.072024
415  2.27  0.1084851530         China      Asia          Asia 2.055093
416  1.01 -1.4154245128         China      Asia          Asia 1.987370
417  2.44  0.3140920126         China      Asia          Asia 1.987370
418  1.85 -0.3994847356         China      Asia          Asia 1.919647
419  2.08 -0.1213107490         China      Asia          Asia 2.004301
420  2.92  0.8946290282         China      Asia          Asia 1.936578
421  1.69 -0.5929970741         China      Asia          Asia 1.953509
422  2.61  0.5196988723         China      Asia          Asia 1.987370
423  1.80 -0.4599573414         China      Asia          Asia 2.088955
424  1.55 -0.7623203703         China      Asia          Asia 2.072024
425  1.79 -0.4720518626         China      Asia          Asia 1.987370
426  1.97 -0.2543504817         China      Asia          Asia 2.038163
427  1.87 -0.3752956933         China      Asia          Asia 2.038163
428  2.20  0.0238235049         China      Asia          Asia 2.021232
429  3.01  1.0034797186        Canada  Americas North America 3.243430
430  3.26  1.3058427475         China      Asia          Asia 2.088955
431  2.80  0.7494947743         China      Asia          Asia 2.038163
432  2.07 -0.1334052702         China      Asia          Asia 1.987370
433  2.32  0.1689577588         China      Asia          Asia 1.953509
434  1.03 -1.3912354705         China      Asia          Asia 2.004301
435  2.17 -0.0124600586         China      Asia          Asia 1.987370
436  0.60 -1.9112998802         China      Asia          Asia 2.055093
437  1.54 -0.7744148915         China      Asia          Asia 2.038163
438  2.67  0.5922659993         China      Asia          Asia 2.105885
439  1.42 -0.9195491454         China      Asia          Asia 2.105885
440  1.64 -0.6534696799         China      Asia          Asia 2.038163
441  0.90 -1.5484642455         China      Asia          Asia 2.055093
442  2.29  0.1326741953         China      Asia          Asia 2.173608
443  2.87  0.8341564224         China      Asia          Asia 2.038163
444  2.43  0.3019974915         China      Asia          Asia 2.088955
445  2.42  0.2899029703         China      Asia          Asia 2.021232
446  2.60  0.5076043512         China      Asia          Asia 2.072024
447  1.66 -0.6292806376         Japan      Asia          Asia 2.055093
448  1.04 -1.3791409493         China      Asia          Asia 1.885786
449  1.41 -0.9316436665         China      Asia          Asia 1.885786
450  4.01  2.2129318343           USA  Americas North America 3.257093
451  1.02 -1.4033299916         China      Asia          Asia 1.970440
452  3.23  1.2695591840         Korea      Asia          Asia 3.046652
453  2.06 -0.1454997913         China      Asia          Asia 2.139747
454  1.93 -0.3027285664         China      Asia          Asia 2.055093
455  2.45  0.3261865338         Korea      Asia          Asia 2.021232
456  0.56 -1.9596779649         China      Asia          Asia 1.953509
457  1.86 -0.3873902145         China      Asia          Asia 2.088955
458  2.00 -0.2180669183         China      Asia          Asia 2.105885
459  1.85 -0.3994847356         China      Asia          Asia 2.004301
460  2.46  0.3382810550         China      Asia          Asia 2.055093
461  2.14 -0.0487436221         China      Asia          Asia 2.055093
462  2.31  0.1568632376         China      Asia          Asia 1.953509
463  2.18 -0.0003655374         China      Asia          Asia 2.072024
464  2.88  0.8462509436         China      Asia          Asia 2.021232
465  1.32 -1.0404943569         China      Asia          Asia 2.105885
466  1.90 -0.3390121298         China      Asia          Asia 2.038163
467  2.20  0.0238235049         China      Asia          Asia 1.868855
468  1.98 -0.2422559606         China      Asia          Asia 2.190539
469  1.81 -0.4478628202         China      Asia          Asia 2.038163
470  1.92 -0.3148230875         China      Asia          Asia 2.105885
471  1.78 -0.4841463837         China      Asia          Asia 2.055093
472  1.28 -1.0888724416         China      Asia          Asia 2.122816
473  1.36 -0.9921162723         China      Asia          Asia 2.038163
474  2.13 -0.0608381432         China      Asia          Asia 1.987370
475  0.51 -2.0201505706         China      Asia          Asia 2.691106
476  2.11 -0.0850271855           USA  Americas North America 3.202444
477  2.76  0.7011166897         China      Asia          Asia 2.207470
478  2.55  0.4471317454         China      Asia          Asia 2.173608
480  2.23  0.0601070684         China      Asia          Asia 2.055093
481  1.10 -1.3065738224         Japan      Asia          Asia 2.105885
482  2.06 -0.1454997913         China      Asia          Asia 2.139747
483  2.85  0.8099673801         China      Asia          Asia 2.038163
484  2.74  0.6769276474         China      Asia          Asia 2.122816
485  2.47  0.3503755761         China      Asia          Asia 2.173608
486  2.88  0.8462509436         China      Asia          Asia 2.055093
487  2.81  0.7615892955         China      Asia          Asia 2.088955
488  2.70  0.6285495627         China      Asia          Asia 2.139747
489  2.64  0.5559824358         China      Asia          Asia 2.055093
490  2.56  0.4592262665         China      Asia          Asia 2.122816
491  2.02 -0.1938778759         China      Asia          Asia 1.936578
492  2.24  0.0722015895         China      Asia          Asia 2.021232
493  2.56  0.4592262665         China      Asia          Asia 2.139747
494  2.69  0.6164550416         China      Asia          Asia 2.122816
495  2.66  0.5801714781         China      Asia          Asia 2.105885
496  2.58  0.4834153088         China      Asia          Asia 2.088955
498  2.19  0.0117289837         China      Asia          Asia 2.241331
499  2.91  0.8825345070         China      Asia          Asia 2.190539
500  2.00 -0.2180669183         Japan      Asia          Asia 2.122816
501  2.21  0.0359180260         Japan      Asia          Asia 2.105885
502  2.02 -0.1938778759         Korea      Asia          Asia 2.072024
503  2.52  0.4108481819         Japan      Asia          Asia 2.122816
505  1.81 -0.4478628202         Korea      Asia          Asia 2.055093
506  1.57 -0.7381313280         Korea      Asia          Asia 2.004301
507  1.45 -0.8832655819         Japan      Asia          Asia 2.072024
508  2.01 -0.2059723971         Korea      Asia          Asia 1.953509
509  1.79 -0.4720518626         Korea      Asia          Asia 2.021232
510  2.04 -0.1696888336         Korea      Asia          Asia 2.021232
511  2.80  0.7494947743         Korea      Asia          Asia 2.105885
512  2.85  0.8099673801         Japan      Asia          Asia 2.122816
513  2.91  0.8825345070         China      Asia          Asia 2.105885
514  2.14 -0.0487436221         China      Asia          Asia 2.055093
515  1.91 -0.3269176087         China      Asia          Asia 2.122816
516  2.60  0.5076043512         China      Asia          Asia 2.055093
517  3.04  1.0397632821         China      Asia          Asia 2.004301
518  1.80 -0.4599573414         China      Asia          Asia 2.088955
519  2.53  0.4229427031         China      Asia          Asia 2.004301
520  2.31  0.1568632376         China      Asia          Asia 2.190539
521  2.50  0.3866591396         China      Asia          Asia 2.173608
522  0.78 -1.6935984994         China      Asia          Asia 2.055093
524  1.06 -1.3549519070         China      Asia          Asia 2.038163
525  2.60  0.5076043512         Korea      Asia          Asia 2.072024
526  3.11  1.1244249302         China      Asia          Asia 2.105885
527  2.15 -0.0366491009         China      Asia          Asia 2.139747
528  2.41  0.2778084492         China      Asia          Asia 1.970440
529  1.41 -0.9316436665         Korea      Asia          Asia 2.156678
530  2.68  0.6043605204         China      Asia          Asia 2.088955
531  0.64 -1.8629217956         China      Asia          Asia 2.004301
532  2.65  0.5680769569         China      Asia          Asia 2.088955
533  2.97  0.9551016340         China      Asia          Asia 2.122816
534  2.18 -0.0003655374         China      Asia          Asia 2.207470
535  3.09  1.1002358879         China      Asia          Asia 2.139747
536  2.57  0.4713207877         China      Asia          Asia 2.088955
537  2.16 -0.0245545797         China      Asia          Asia 2.038163
538  2.24  0.0722015895         China      Asia          Asia 2.038163
539  1.99 -0.2301614394         China      Asia          Asia 2.139747
540  1.45 -0.8832655819       Myanmar      Asia          Asia 2.241331
541  1.66 -0.6292806376         China      Asia          Asia 1.987370
542  1.42 -0.9195491454         China      Asia          Asia 1.919647
543  0.71 -1.7782601475         India      Asia          Asia 2.072024
544  1.60 -0.7018477645         China      Asia          Asia 2.004301
545  2.09 -0.1092162278       Germany    Europe        Europe 2.331220
546  0.53 -1.9959615283     Indonesia      Asia          Asia 2.122816
547  0.77 -1.7056930206        Sweden    Europe        Europe 2.255312
548  1.78 -0.4841463837         Japan      Asia          Asia 1.885786
549  2.21  0.0359180260         Japan      Asia          Asia 2.258262
550  0.15 -2.4555533323      Pakistan      Asia          Asia 2.224400
551  1.45 -0.8832655819         Japan      Asia          Asia 2.021232
552  1.47 -0.8590765396         Japan      Asia          Asia 2.173608
553  2.74  0.6769276474         China      Asia          Asia 2.105885
554  1.63 -0.6655642011         China      Asia          Asia 1.953509
555  2.11 -0.0850271855       Germany    Europe        Europe 2.394477
556  2.24  0.0722015895        France    Europe        Europe 2.716738
557  2.23  0.0601070684         China      Asia          Asia 2.004301
558  3.04  1.0397632821        Russia    Europe        Europe 2.615527
559  1.71 -0.5688080318         Japan      Asia          Asia 1.953509
560  3.10  1.1123304090       Moldova    Europe        Europe 2.489013
561  1.25 -1.1251560050         Japan      Asia          Asia 2.021232
562  1.56 -0.7502258492         Korea      Asia          Asia 2.021232
563  3.04  1.0397632821       Georgia      Asia          Asia 2.207470
564  2.40  0.2657139280         China      Asia          Asia 2.055093
565  2.46  0.3382810550         China      Asia          Asia 2.004301
566  2.96  0.9430071128         China      Asia          Asia 2.139747
567  3.12  1.1365194513         China      Asia          Asia 2.072024
568  0.82 -1.6452204148         Korea      Asia          Asia 2.021232
569  1.15 -1.2461012166         Japan      Asia          Asia 2.021232
570  1.43 -0.9074546242         Japan      Asia          Asia 2.055093
571  3.28  1.3300317898         Japan      Asia          Asia 2.055093
572  2.52  0.4108481819        Russia    Europe        Europe 2.615527
573  0.44 -2.1048122187         Korea      Asia          Asia 1.902717
574  2.21  0.0359180260         China      Asia          Asia 1.919647
575  1.60 -0.7018477645         China      Asia          Asia 2.173608
576  3.32  1.3784098745        Russia    Europe        Europe 2.666132
577  2.68  0.6043605204        Russia    Europe        Europe 2.602875
578  1.60 -0.7018477645        Russia    Europe        Europe 2.590224
579  2.92  0.8946290282        Russia    Europe        Europe 2.577572
580  2.17 -0.0124600586       Moldova    Europe        Europe 2.602875
581  2.05 -0.1575943125       Moldova    Europe        Europe 2.577572
582  1.54 -0.7744148915       Moldova    Europe        Europe 2.628178
583  2.58  0.4834153088        Russia    Europe        Europe 2.602875
584  3.07  1.0760468455        Russia    Europe        Europe 2.666132
585  2.57  0.4713207877        Russia    Europe        Europe 2.526967
586  3.44  1.5235441283        Russia    Europe        Europe 2.602875
587  3.10  1.1123304090        Russia    Europe        Europe 2.564921
588  2.65  0.5680769569        Russia    Europe        Europe 2.640829
589  2.16 -0.0245545797        Russia    Europe        Europe 2.501664
590  2.40  0.2657139280        Russia    Europe        Europe 2.539618
591  2.23  0.0601070684        Russia    Europe        Europe 2.564921
592  2.73  0.6648331262        Russia    Europe        Europe 2.704087
593  0.70 -1.7903546687         China      Asia          Asia 2.021232
594  3.36  1.4267879591         China      Asia          Asia 2.173608
595  3.04  1.0397632821         China      Asia          Asia 1.987370
596  2.83  0.7857783378         China      Asia          Asia 2.105885
597  1.35 -1.0042107935         China      Asia          Asia 2.122816
598  3.93  2.1161756650         China      Asia          Asia 2.190539
599  3.63  1.7533400303         China      Asia          Asia 2.105885
600  3.38  1.4509770014         China      Asia          Asia 2.072024
601  3.25  1.2937482264         China      Asia          Asia 2.072024
602  1.88 -0.3632011721         China      Asia          Asia 2.190539
603  3.09  1.1002358879         China      Asia          Asia 2.224400
604  3.18  1.2090865783         China      Asia          Asia 2.241331
605  1.99 -0.2301614394         China      Asia          Asia 2.122816
606  2.33  0.1810522799         China      Asia          Asia 2.038163
607  3.08  1.0881413667         China      Asia          Asia 2.173608
608  3.04  1.0397632821         China      Asia          Asia 2.122816
609  2.83  0.7857783378         China      Asia          Asia 2.088955
610  3.81  1.9710414112         China      Asia          Asia 2.088955
611  2.63  0.5438879146         China      Asia          Asia 2.156678
612  3.21  1.2453701417         Korea      Asia          Asia 2.207470
613  2.01 -0.2059723971       Algeria    Africa        Africa 2.168857
614  2.48  0.3624700973       Algeria    Africa        Africa 2.307642
615  1.85 -0.3994847356      Bulgaria    Europe        Europe 2.628178
616  3.08  1.0881413667        Poland    Europe        Europe 2.514315
617  3.70  1.8380016784           USA  Americas North America 2.475828
618  2.37  0.2294303645           USA  Americas North America 2.516814
619  3.32  1.3784098745           USA  Americas North America 2.393855
620  2.51  0.3987536607         China      Asia          Asia 2.088955
621  2.17 -0.0124600586         China      Asia          Asia 1.936578
622  1.90 -0.3390121298         China      Asia          Asia 1.987370
623  0.37 -2.1894738668         China      Asia          Asia 1.750340
624  2.61  0.5196988723         China      Asia          Asia 2.021232
625  2.60  0.5076043512    Uzbekistan      Asia          Asia 2.173608
626  1.09 -1.3186683435       Vietnam      Asia          Asia 2.055093
627  1.87 -0.3752956933        Taiwan      Asia          Asia 2.021232
628  2.84  0.7978728589        Russia    Europe        Europe 2.407129
629  2.82  0.7736838166           USA  Americas North America 3.284417
630  2.45  0.3261865338           USA  Americas North America 3.270755
631  3.48  1.5719222130         China      Asia          Asia 2.038163
632  1.69 -0.5929970741        Taiwan      Asia          Asia 2.055093
633  4.34  2.6120510325           USA  Americas North America 3.352727
634  1.67 -0.6171861164         China      Asia          Asia 2.190539
635  4.20  2.4427277363           USA  Americas North America 3.298079
636  3.55  1.6565838611           USA  Americas North America 3.311741
637  3.35  1.4146934379           USA  Americas North America 3.339065
638  3.37  1.4388824802           USA  Americas North America 3.311741
639  2.94  0.9188180705           USA  Americas North America 3.298079
640  0.63 -1.8750163168         Japan      Asia          Asia 1.703064
641  0.88 -1.5726532878           USA  Americas North America 2.205184
642  1.32 -1.0404943569         China      Asia          Asia 1.736925
643  1.63 -0.6655642011         China      Asia          Asia 1.703064
644  1.48 -0.8469820184         China      Asia          Asia 1.719995
645  1.16 -1.2340066954           USA  Americas North America 2.095886
646  3.41  1.4872605649           USA  Americas North America 3.393714
647  2.31  0.1568632376           USA  Americas North America 3.434700
648  2.06 -0.1454997913           USA  Americas North America 3.380052
649  0.48 -2.0564341341         China      Asia          Asia 2.088955
650  2.73  0.6648331262         China      Asia          Asia 2.190539
651  0.92 -1.5242752032     Indonesia      Asia          Asia 2.088955
652  0.80 -1.6694094571     Indonesia      Asia          Asia 1.970440
653  1.43 -0.9074546242     Indonesia      Asia          Asia 2.004301
654  3.27  1.3179372687         China      Asia          Asia 2.156678
655  1.12 -1.2823847801         China      Asia          Asia 2.055093
656  1.85 -0.3994847356        Taiwan      Asia          Asia 2.055093
657  1.35 -1.0042107935         China      Asia          Asia 2.021232
658  2.08 -0.1213107490         China      Asia          Asia 2.122816
659  1.61 -0.6897532434         China      Asia          Asia 2.173608
660  1.53 -0.7865094126         China      Asia          Asia 2.072024
661  1.25 -1.1251560050         China      Asia          Asia 2.021232
662  1.26 -1.1130614839         China      Asia          Asia 2.122816
663  1.38 -0.9679272300         China      Asia          Asia 1.987370
664  1.12 -1.2823847801         China      Asia          Asia 2.105885
665  1.33 -1.0283998358         China      Asia          Asia 2.156678
666  1.20 -1.1856286108         China      Asia          Asia 2.122816
667  2.80  0.7494947743         China      Asia          Asia 2.105885
668  1.32 -1.0404943569         China      Asia          Asia 2.072024
669  1.94 -0.2906340452         China      Asia          Asia 2.190539
670  2.21  0.0359180260         China      Asia          Asia 2.105885
671  2.14 -0.0487436221         China      Asia          Asia 2.055093
672  2.20  0.0238235049         China      Asia          Asia 2.088955
673  2.71  0.6406440839         China      Asia          Asia 1.834994
674  1.93 -0.3027285664         China      Asia          Asia 1.987370
675  2.16 -0.0245545797         China      Asia          Asia 2.105885
676  2.41  0.2778084492         China      Asia          Asia 2.139747
677  2.57  0.4713207877         China      Asia          Asia 2.122816
678  2.11 -0.0850271855         China      Asia          Asia 2.072024
679  2.04 -0.1696888336         China      Asia          Asia 2.004301
680  1.95 -0.2785395240         China      Asia          Asia 2.122816
681  1.52 -0.7986039338         China      Asia          Asia 2.156678
682  2.15 -0.0366491009         China      Asia          Asia 2.021232
683  2.35  0.2052413222         China      Asia          Asia 2.207470
684  0.88 -1.5726532878         Nepal      Asia          Asia 2.139747
685  2.63  0.5438879146     Argentina  Americas South America 2.910513
686  2.28  0.1205796741     Argentina  Americas South America 2.803256
687  2.11 -0.0850271855         China      Asia          Asia 2.055093
688  3.38  1.4509770014           USA  Americas North America 3.243430
689  1.85 -0.3994847356         China      Asia          Asia 2.038163
690  1.83 -0.4236737779         China      Asia          Asia 2.156678
691  1.92 -0.3148230875         China      Asia          Asia 2.156678
692  1.49 -0.8348874973         China      Asia          Asia 2.139747
693  2.17 -0.0124600586         China      Asia          Asia 2.072024
694  1.86 -0.3873902145         China      Asia          Asia 2.088955
695  1.29 -1.0767779204         China      Asia          Asia 2.122816
696  1.61 -0.6897532434         China      Asia          Asia 2.241331
697  1.58 -0.7260368069         China      Asia          Asia 2.105885
698  1.22 -1.1614395685         China      Asia          Asia 2.088955
699  1.79 -0.4720518626         China      Asia          Asia 2.088955
700  2.93  0.9067235493         China      Asia          Asia 2.241331
701  2.21  0.0359180260         China      Asia          Asia 2.139747
702  1.72 -0.5567135107         China      Asia          Asia 2.004301
703  0.34 -2.2257574303         China      Asia          Asia 2.038163
704  0.25 -2.3346081207         China      Asia          Asia 2.021232
705  2.03 -0.1817833548         China      Asia          Asia 2.004301
706  0.37 -2.1894738668         China      Asia          Asia 2.038163
707  0.87 -1.5847478090         China      Asia          Asia 2.004301
708  1.23 -1.1493450473         China      Asia          Asia 2.088955
709  0.16 -2.4434588111         China      Asia          Asia 2.105885
710  0.08 -2.5402149804         China      Asia          Asia 1.919647
711  0.98 -1.4517080763         China      Asia          Asia 2.190539
712  1.48 -0.8469820184        Russia    Europe        Europe 2.640829
713  2.88  0.8462509436         China      Asia          Asia 1.970440
714  2.38  0.2415248857         Korea      Asia          Asia 2.038163
715  2.96  0.9430071128         China      Asia          Asia 2.021232
716  1.91 -0.3269176087         China      Asia          Asia 2.021232
717  1.78 -0.4841463837         China      Asia          Asia 2.105885
718  1.99 -0.2301614394         China      Asia          Asia 2.004301
719  2.33  0.1810522799         China      Asia          Asia 2.122816
720  2.51  0.3987536607         China      Asia          Asia 2.021232
721  2.18 -0.0003655374         China      Asia          Asia 2.004301
722  2.18 -0.0003655374         China      Asia          Asia 2.156678
723  2.40  0.2657139280         China      Asia          Asia 2.072024
724  2.50  0.3866591396         China      Asia          Asia 2.088955
725  2.67  0.5922659993         China      Asia          Asia 2.139747
726  2.54  0.4350372242         China      Asia          Asia 2.088955
727  1.78 -0.4841463837         China      Asia          Asia 2.055093
728  2.15 -0.0366491009         China      Asia          Asia 1.919647
729  1.92 -0.3148230875         China      Asia          Asia 1.936578
730  1.35 -1.0042107935         China      Asia          Asia 2.038163
731  1.76 -0.5083354260         China      Asia          Asia 2.021232
732  1.54 -0.7744148915         China      Asia          Asia 1.953509
733  1.74 -0.5325244683         China      Asia          Asia 2.021232
734  2.17 -0.0124600586         China      Asia          Asia 2.088955
735  2.33  0.1810522799         China      Asia          Asia 2.021232
736  2.20  0.0238235049         China      Asia          Asia 2.021232
737  2.00 -0.2180669183         China      Asia          Asia 2.072024
738  1.30 -1.0646833992         China      Asia          Asia 2.038163
739  1.54 -0.7744148915         Korea      Asia          Asia 2.088955
740  2.17 -0.0124600586         Korea      Asia          Asia 2.072024
741  2.26  0.0963906318       Vietnam      Asia          Asia 2.105885

newdat %>%
  ggplot(aes(x = presences.y, y = pred, color = continent2)) +
  facet_wrap( ~ Group, nrow = 1) +   # a panel for each mountain range
  geom_point(aes(y = Yield, color = continent2),
  alpha = 0.8,
  size = 2) +
  geom_line(aes(y = pred, group = Country, color = continent2), size = 1.5) +
  theme_minimal_hgrid() +
  xlab('Gene count') +
  ylab(expression(paste('Yield [Mg ', ha ^ -1, ']'))) +
  #scale_color_manual(values = mycol) +
  #xlim(c(47.900, 49.700)) +
  labs(color = "Continent") +
  theme(panel.spacing = unit(0.9, "lines"),
  axis.text.x = element_text(size = 10))

Let’s redo everything using basic lm()

Do we even need lme4? Let’s find out!

yield_country_nbs_joined_groups$Count <- yield_country_nbs_joined_groups$presences.y
yield_country_all_joined_groups$Count <- yield_country_all_joined_groups$presences.y

lm_model <- lm(Yield ~ Count +  Group, data = yield_country_nbs_joined_groups) 
summary(lm_model)


Call:
lm(formula = Yield ~ Count + Group, data = yield_country_nbs_joined_groups)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.47398 -0.52358  0.02648  0.53660  2.14648 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          10.985573   2.532389   4.338 1.64e-05 ***
Count                -0.019960   0.005694  -3.505 0.000483 ***
GroupOld cultivar    -0.198279   0.137005  -1.447 0.148255    
GroupModern cultivar  1.080136   0.111621   9.677  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.768 on 737 degrees of freedom
Multiple R-squared:  0.1412,    Adjusted R-squared:  0.1377 
F-statistic: 40.41 on 3 and 737 DF,  p-value: < 2.2e-16

clm_model <- lm(Yield ~ Count +  Group + Country, data = yield_country_nbs_joined_groups) 
summary(clm_model)


Call:
lm(formula = Yield ~ Count + Group + Country, data = yield_country_nbs_joined_groups)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.26158 -0.38952  0.00518  0.45393  2.09468 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)           9.254610   2.523097   3.668 0.000263 ***
Count                -0.015950   0.005634  -2.831 0.004776 ** 
GroupOld cultivar    -0.251301   0.136739  -1.838 0.066514 .  
GroupModern cultivar  0.791799   0.213969   3.701 0.000232 ***
CountryArgentina     -0.589333   0.692075  -0.852 0.394759    
CountryAustralia     -0.684933   0.833177  -0.822 0.411316    
CountryAustria        0.415317   0.832637   0.499 0.618080    
CountryBelgium       -0.290633   0.658267  -0.442 0.658978    
CountryBrazil        -0.734933   0.833177  -0.882 0.378035    
CountryBulgaria      -0.418484   0.832891  -0.502 0.615512    
CountryCanada        -0.137052   0.555958  -0.247 0.805356    
CountryChina         -0.027711   0.418161  -0.066 0.947184    
CountryCosta          0.781516   0.832891   0.938 0.348406    
CountryFormer Serbia  1.053517   0.859715   1.225 0.220828    
CountryFrance         0.309242   0.550833   0.561 0.574700    
CountryGeorgia        0.814491   0.658498   1.237 0.216543    
CountryGermany       -0.224383   0.555185  -0.404 0.686220    
CountryHungary        0.612692   0.550968   1.112 0.266509    
CountryIndia         -1.154871   0.551217  -2.095 0.036519 *  
CountryIndonesia     -1.244808   0.550968  -2.259 0.024172 *  
CountryItaly          0.512568   0.843810   0.607 0.543754    
CountryJapan         -0.335536   0.426151  -0.787 0.431336    
CountryKorea         -0.228364   0.425271  -0.537 0.591449    
CountryKyrgyzstan     0.705317   0.832637   0.847 0.397237    
CountryMoldova        0.020924   0.497790   0.042 0.966484    
CountryMorocco        0.828618   0.836543   0.991 0.322260    
CountryMyanmar       -1.111334   0.659327  -1.686 0.092328 .  
CountryND             0.304537   0.475083   0.641 0.521721    
CountryNepal         -0.445758   0.658337  -0.677 0.498568    
CountryNetherlands   -0.710633   0.832644  -0.853 0.393693    
CountryPakistan      -2.182284   0.833755  -2.617 0.009052 ** 
CountryPeru          -1.182783   0.832739  -1.420 0.155951    
CountryPoland         0.955067   0.833177   1.146 0.252066    
CountryRomania        1.152933   0.509938   2.261 0.024071 *  
CountryRussia         0.542843   0.431934   1.257 0.209256    
CountrySerbia         0.221267   0.658299   0.336 0.736882    
CountrySouth Africa  -0.590283   0.859663  -0.687 0.492535    
CountrySweden        -0.755849   0.591363  -1.278 0.201622    
CountryTaiwan        -0.650523   0.527472  -1.233 0.217885    
CountryUkraine        0.723167   0.832847   0.868 0.385525    
CountryUSA            0.355285   0.462399   0.768 0.442538    
CountryUzbekistan     0.315566   0.833050   0.379 0.704945    
CountryVietnam       -0.693258   0.550833  -1.259 0.208609    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.7211 on 698 degrees of freedom
Multiple R-squared:  0.2829,    Adjusted R-squared:  0.2398 
F-statistic: 6.557 on 42 and 698 DF,  p-value: < 2.2e-16

interlm_model <- lm(Yield ~ Count * Group, data = yield_country_nbs_joined_groups) 
summary(interlm_model)


Call:
lm(formula = Yield ~ Count * Group, data = yield_country_nbs_joined_groups)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.38311 -0.51569  0.01947  0.53258  2.13947 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                 9.62255    2.70414   3.558 0.000397 ***
Count                      -0.01690    0.00608  -2.779 0.005598 ** 
GroupOld cultivar          18.63259   11.02113   1.691 0.091333 .  
GroupModern cultivar        5.56130   10.01054   0.556 0.578692    
Count:GroupOld cultivar    -0.04234    0.02478  -1.709 0.087920 .  
Count:GroupModern cultivar -0.01012    0.02263  -0.447 0.654959    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.7674 on 735 degrees of freedom
Multiple R-squared:  0.1448,    Adjusted R-squared:  0.139 
F-statistic: 24.88 on 5 and 735 DF,  p-value: < 2.2e-16

tab_model(lm_model, p.val='kr', digits=3)

	Yield
Predictors	Estimates	CI	p
(Intercept)	10.986	6.014 – 15.957	<0.001
Count	-0.020	-0.031 – -0.009	<0.001
Group [Old cultivar]	-0.198	-0.467 – 0.071	0.148
Group [Modern cultivar]	1.080	0.861 – 1.299	<0.001
Observations	741
R² / R² adjusted	0.141 / 0.138

newdat <-cbind(yield_country_all_joined_groups, pred = predict(clm_model))

newdat %>% mutate(
  Country2 = case_when (
  Country == 'USA' ~ 'USA',
  Country == 'China' ~ 'China',
  Country == 'Korea' ~ 'Korea',
  Country == 'Japan' ~ 'Japan',
  Country == 'Russia' ~ 'Russia',
  TRUE ~ 'Rest'
  )
  ) %>%
  mutate(Country2 = factor(
  Country2,
  levels = c('China', 'Japan', 'Korea', 'Russia', 'USA', 'Rest')
  )) %>%
  ggplot(aes(x = Count, y = pred, color = Country2)) +
  facet_wrap( ~ Group, nrow = 1) +  
  geom_point(aes(y = Yield, color = Country2),
    alpha = 0.8,
    size = 2) +
  geom_line(aes(y=pred, group=interaction(Group, Country),colour=Country2), size=1.5)+
  theme_minimal_hgrid() +
  xlab('Gene count') +
  ylab(expression(paste('Yield [Mg ', ha ^ -1, ']'))) +
  scale_color_manual(values = mycol) +
  #xlim(c(47.900, 49.700)) +
  labs(color = "Country") +
  theme(panel.spacing = unit(0.9, "lines"),
  axis.text.x = element_text(size = 10))

Oh wow, that looks very similar.

plot_model(clm_model, type = "pred", terms = c("Count", "Group")) +
  theme_minimal_hgrid() +
  #scale_fill_manual(values = col_list) +
  #scale_color_manual(values = col_list) +
  xlab('NLR count') + 
  ylab((expression(paste('Yield [Mg ', ha^-1, ']')))) +
  theme(plot.title=element_blank())

qqnorm(resid(clm_model))
qqline(resid(clm_model))

plot(resid(clm_model))

plot_model(clm_model, show.values = TRUE, value.offset = .3, terms=c('GroupOld cultivar', 'Count', 'GroupModern cultivar'))

plot_model(clm_model, type = "std", show.values = TRUE, value.offset = .3,)

plot_model(clm_model, type = "std", show.values = TRUE, value.offset = .3,, terms=c('GroupOld cultivar', 'Count', 'GroupModern cultivar'))

sessionInfo()

R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17134)

Matrix products: default

locale:
[1] LC_COLLATE=English_Australia.1252  LC_CTYPE=English_Australia.1252   
[3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C                      
[5] LC_TIME=English_Australia.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] countrycode_1.1.1      lmerTest_3.1-2         ggeffects_0.16.0      
 [4] stargazer_5.2.2        RColorBrewer_1.1-2     pals_1.6              
 [7] dotwhisker_0.5.0       directlabels_2020.6.17 lme4_1.1-21           
[10] Matrix_1.2-18          ggforce_0.3.1          ggsignif_0.6.0        
[13] cowplot_1.0.0          dabestr_0.3.0          magrittr_1.5          
[16] ggsci_2.9              sjPlot_2.8.6           patchwork_1.0.0       
[19] forcats_0.5.0          stringr_1.4.0          dplyr_1.0.0           
[22] purrr_0.3.4            readr_1.3.1            tidyr_1.1.0           
[25] tibble_3.0.2           ggplot2_3.3.2          tidyverse_1.3.0       
[28] workflowr_1.6.2.9000  

loaded via a namespace (and not attached):
  [1] TH.data_1.0-10      minqa_1.2.4         colorspace_1.4-1   
  [4] ellipsis_0.3.1      sjlabelled_1.1.7    rprojroot_1.3-2    
  [7] estimability_1.3    ggstance_0.3.4      parameters_0.9.0   
 [10] fs_1.5.0.9000       dichromat_2.0-0     rstudioapi_0.11    
 [13] glmmTMB_1.0.2.1     hexbin_1.28.1       farver_2.0.3       
 [16] fansi_0.4.1         mvtnorm_1.1-1       lubridate_1.7.9    
 [19] xml2_1.3.2          codetools_0.2-16    splines_3.6.3      
 [22] knitr_1.29          sjmisc_2.8.5        polyclip_1.10-0    
 [25] jsonlite_1.7.1      nloptr_1.2.1        broom_0.5.6        
 [28] dbplyr_1.4.4        effectsize_0.4.0    mapproj_1.2.7      
 [31] compiler_3.6.3      httr_1.4.2          sjstats_0.18.0     
 [34] emmeans_1.4.5       backports_1.1.10    assertthat_0.2.1   
 [37] cli_2.0.2           later_1.1.0.1       tweenr_1.0.1       
 [40] htmltools_0.5.0     tools_3.6.3         coda_0.19-3        
 [43] gtable_0.3.0        glue_1.4.2          maps_3.3.0         
 [46] Rcpp_1.0.5          cellranger_1.1.0    vctrs_0.3.1        
 [49] nlme_3.1-148        insight_0.10.0      xfun_0.17          
 [52] ps_1.3.4            rvest_0.3.5         lifecycle_0.2.0    
 [55] getPass_0.2-2       MASS_7.3-51.6       zoo_1.8-8          
 [58] scales_1.1.1        hms_0.5.3           promises_1.1.1     
 [61] sandwich_2.5-1      TMB_1.7.16          yaml_2.2.1         
 [64] stringi_1.5.3       bayestestR_0.7.5    boot_1.3-25        
 [67] rlang_0.4.7         pkgconfig_2.0.3     evaluate_0.14      
 [70] lattice_0.20-41     labeling_0.3        processx_3.4.4     
 [73] tidyselect_1.1.0    plyr_1.8.6          R6_2.4.1           
 [76] generics_0.0.2      multcomp_1.4-13     DBI_1.1.0          
 [79] mgcv_1.8-31         pillar_1.4.4        haven_2.3.1        
 [82] whisker_0.4         withr_2.2.0         survival_3.2-3     
 [85] performance_0.5.1   modelr_0.1.8        crayon_1.3.4       
 [88] utf8_1.1.4          rmarkdown_2.3       grid_3.6.3         
 [91] readxl_1.3.1        blob_1.2.1          callr_3.4.4        
 [94] git2r_0.27.1        reprex_0.3.0        digest_0.6.25      
 [97] xtable_1.8-4        httpuv_1.5.4        numDeriv_2016.8-1.1
[100] munsell_0.5.0       quadprog_1.5-8

Linking yield with NLR PAV

Philipp Bayer

2020-09-22

Data loading

Linking with yield

Yield

Protein

Seed weight

Oil content

Boxplots per group

Yield

Seed weight

Oil content

Mixed modeling

Normalising NLR gene counts

Oil

Yield

Making the breeding group fixed

Adding country

Yield

Making the breeding group fixed

Non-normalised yield

Making the breeding group fixed

Plotting effect of each covariate

More complex models

Oil

Protein

Seed weight

The final yield model

We still need the same model for ALL genes

Let’s join the countries into continents and then run everything again

Let’s redo everything using basic lm()