Verify the pedigree entries
Marnin Wolfe
2021-May-12
Last updated: 2021-08-11
Checks: 7 0
Knit directory: IITA_2021GS/
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Unstaged changes:
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/03-validatePedigree.Rmd) and HTML (docs/03-validatePedigree.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e4df79f | wolfemd | 2021-08-11 | Completed IITA_2021GS pipeline including imputation and genomic prediction. Last bit of cross-validation and cross-prediction finishes in 24 hrs. |
| html | 934141c | wolfemd | 2021-07-14 | Build site. |
| html | e66bdad | wolfemd | 2021-06-10 | Build site. |
| Rmd | a8452ba | wolfemd | 2021-06-10 | Initial build of the entire page upon completion of all |
Previous step
- Get BLUPs combining all trial data: Combine data from all trait-trials to get BLUPs for downstream genomic prediction. Fit mixed-model to multi-trial dataset and extract BLUPs, de-regressed BLUPs and weights. Include two rounds of outlier removal.
Read pedigree
Read the pedigree I downloaded for IITA
library(tidyverse); library(magrittr)
ped<-read_delim(here::here("data/DatabaseDownload_2021May04","pedigree.txt"),
delim = "\t")
ped_tms20<-readxl::read_xlsx(here::here("data/DatabaseDownload_2021Aug08","Pedigree.xlsx"))
ped %<>%
select(-Cross_Type) %>%
bind_rows(ped_tms20 %>%
select(Accession_name,Pedigree) %>%
rename(Accession=Accession_name) %>%
separate(Pedigree,c("Female_Parent","Male_Parent"),"/"))
#rm(ped_tms20)Filter: Keep only complete pedigree records.
ped %<>%
filter(!is.na(Female_Parent),
!is.na(Male_Parent),
Female_Parent!="?",
Male_Parent!="?") %>%
distinctNumber of full-sib families?
ped %>% distinct(Female_Parent,Male_Parent) %>% nrow()[1] 1826Summarize distribution of full-sib family sizes
ped %>%
count(Female_Parent,Male_Parent) %>% arrange(desc(n)) %>% summary(.$n) Female_Parent Male_Parent n
Length:1826 Length:1826 Min. : 1.000
Class :character Class :character 1st Qu.: 1.000
Mode :character Mode :character Median : 2.000
Mean : 5.059
3rd Qu.: 6.000
Max. :276.000 ped %>% head# A tibble: 6 × 3
Accession Female_Parent Male_Parent
<chr> <chr> <chr>
1 TMS13F1002P0001 IITA-TMS-IBA930265 IITA-TMS-MOK980068
2 TMS13F1002P0002 IITA-TMS-IBA930265 IITA-TMS-MOK980068
3 TMS13F1002P0003 IITA-TMS-IBA930265 IITA-TMS-MOK980068
4 TMS13F1002P0004 IITA-TMS-IBA930265 IITA-TMS-MOK980068
5 TMS13F1002P0005 IITA-TMS-IBA930265 IITA-TMS-MOK980068
6 TMS13F1002P0006 IITA-TMS-IBA930265 IITA-TMS-MOK980068Match SNP-to-pedigree names
Goal is to identify DNA samples names for listed accessions and parents in the pedigree. Important to choose same samples used in genomic predictions where possible, esp. phenotyped training clones.
However, there may be non-phenotyped clones that are genotyped genomic selection progeny, which I still want in my analysis.
First with the union of the parent and accession IDs in the pedigree.
pednames<-union(ped$Accession,union(ped$Female_Parent,ped$Male_Parent)) %>%
tibble(germplasmName=.)
pednames$germplasmName %>% length # number of names in ped[1] 9568Add a “Cohort” variable corresponding to the genetic groups or cycles in the germplasm.
pednames %<>%
mutate(Cohort=NA,
Cohort=ifelse(grepl("TMS20",germplasmName,ignore.case = T),"TMS20",
ifelse(grepl("TMS19",germplasmName,ignore.case = T),"TMS19",
ifelse(grepl("TMS18",germplasmName,ignore.case = T),"TMS18",
ifelse(grepl("TMS17",germplasmName,ignore.case = T),"TMS17",
ifelse(grepl("TMS16",germplasmName,ignore.case = T),"TMS16",
ifelse(grepl("TMS15",germplasmName,ignore.case = T),"TMS15",
ifelse(grepl("TMS14",germplasmName,ignore.case = T),"TMS14",
ifelse(grepl("TMS13|2013_",germplasmName,ignore.case = T),"TMS13","GGetc")))))))))
pednames %>%
count(Cohort)# A tibble: 9 × 2
Cohort n
<chr> <int>
1 GGetc 400
2 TMS13 2629
3 TMS14 1981
4 TMS15 1240
5 TMS16 6
6 TMS17 70
7 TMS18 2394
8 TMS19 178
9 TMS20 670The names in the pedigree downloaded from cassavabase should match the names in the germplasmName variable in the plot-basis pheno data, also downloaded from cassavabase. From that plot-basis data, make a data.frame of the unique germplasmName-to-FullSampleName matches, where FullSampleName is the column with names matching VCF files / DNA samples.
dbdata<-readRDS(here::here("output","IITA_ExptDesignsDetected_2021Aug08.rds"))
phenos2genos<-dbdata %>%
distinct(GID,germplasmName,FullSampleName) %>%
filter(!is.na(FullSampleName))
phenos2genos %>% head# A tibble: 6 × 3
germplasmName FullSampleName GID
<chr> <chr> <chr>
1 IITA-TMS-IBA30572 I30572:250253643 I30572:250253643
2 IITA-TMS-IBA30572 IITA-TMS-IBA30572_A35468 IITA-TMS-IBA30572_A35468
3 IITA-TMS-IBA30572 IITA-TMS-IBA30572_A35591 IITA-TMS-IBA30572_A35591
4 IITA-TMS-IBA30572 IITA-TMS-IBA30572_A35546 IITA-TMS-IBA30572_A35546
5 IITA-TMS-IBA30572 IITA-TMS-IBA30572_A35594 IITA-TMS-IBA30572_A35594
6 IITA-TMS-IBA30572 IITA-TMS-IBA30572_A35750 IITA-TMS-IBA30572_A35750How many from each cohort in the pednames match a germplasmName with a FullSampleName in the plot-basis trial data?
pednames %>%
inner_join(phenos2genos) %>%
count(Cohort)# A tibble: 9 × 2
Cohort n
<chr> <int>
1 GGetc 302
2 TMS13 2467
3 TMS14 1536
4 TMS15 867
5 TMS16 1
6 TMS17 36
7 TMS18 1400
8 TMS19 178
9 TMS20 674I want haplotypes of genotyped progeny in the pedigree even if they aren’t phenotyped.
The *.fam file for the RefPanelAndGSprogeny VCF I will ultimately use to extract haplotypes and do predictions with is already in the data/ directory because it’s used in the standard match-genos-to-phenos step.
gids_in_fam<-read.table(here::here("data",
"chr1_RefPanelAndGSprogeny_ReadyForGP_72719.fam"),
stringsAsFactors = F, header = F)$V2
length(gids_in_fam)[1] 21856Split the names based on a “:” for the GBS samples, and “_A” seems to work (for IITA) to split the DArT sample ID from the germplasmName for DArTseqLD samples. The suffix / ID added to DNA sample names at DARt seems to vary depending on what gets submitted to them. GBS-era samples (almost) 100% had the “:” separator.
gids_in_fam %<>%
tibble(FullSampleName=.) %>%
separate(FullSampleName,c("germplasmName","DNA_ID"),":|_A",remove = F) %>%
select(-DNA_ID)
gids_in_fam %>%
filter(grepl("TMS18",FullSampleName)) %>% nrow()[1] 2420There are also TMS20 in the latest DArT report and the DB sourced ped contains matches to the DARt sample names.
gids_in_fam<-gids_in_fam %>%
bind_rows(ped_tms20 %>%
select(Accession_name,Lab_ID) %>%
rename(germplasmName=Accession_name,
FullSampleName=Lab_ID) %>%
mutate(FullSampleName=gsub("\\.","_",FullSampleName)))
gids_in_fam %>% head# A tibble: 6 × 2
FullSampleName germplasmName
<chr> <chr>
1 TMS15F1142P0004:250465388 TMS15F1142P0004
2 TMS15F1021P0003:250464911 TMS15F1021P0003
3 TMS15F1276P0003:250465968 TMS15F1276P0003
4 TMS15F1282P0001:250466007 TMS15F1282P0001
5 TMS15F1035P0002:250465011 TMS15F1035P0002
6 TMS15F1179P0024:250465452 TMS15F1179P0024gids_in_fam %>%
filter(grepl("TMS16|TMS17|TMS18|TMS19|TMS20",FullSampleName)) %>% nrow()[1] 3713There are 3713 “TMS16” though “TMS20” clones genotyped.
pednames2genos<-pednames %>%
inner_join(phenos2genos) %>%
bind_rows(pednames %>%
anti_join(phenos2genos) %>%
inner_join(gids_in_fam))
pednames2genos %>% count(Cohort) # A tibble: 9 × 2
Cohort n
<chr> <int>
1 GGetc 304
2 TMS13 2469
3 TMS14 1538
4 TMS15 901
5 TMS16 1
6 TMS17 36
7 TMS18 1400
8 TMS19 178
9 TMS20 674Are there germplasmName in the pednames2genos match table with multiple DNA samples?
pednames2genos %>% count(germplasmName) %>% arrange(desc(n))# A tibble: 7,185 × 2
germplasmName n
<chr> <int>
1 IITA-TMS-IBA000070 17
2 TMEB419 16
3 TMS13F1160P0004 16
4 IITA-TMS-IBA30572 15
5 IITA-TMS-IBA980581 14
6 TMS13F1053P0010 10
7 IITA-TMS-IBA982101 9
8 TMS13F1343P0022 8
9 IITA-TMS-IBA070593 7
10 TMEB693 6
# … with 7,175 more rowsOf course there are. Will need to pick.
Which pednames have BLUPs?
blups<-readRDS(file=here::here("output","IITA_blupsForModelTraining_twostage_asreml_2021Aug09.rds"))
blups %>%
select(Trait,blups) %>%
unnest(blups) %>%
distinct(GID) %$% GID -> gidWithBLUPs
pednames %>%
inner_join(phenos2genos) %>%
filter(FullSampleName %in% gidWithBLUPs) %>%
count(Cohort)# A tibble: 7 × 2
Cohort n
<chr> <int>
1 GGetc 302
2 TMS13 2447
3 TMS14 1536
4 TMS15 845
5 TMS17 36
6 TMS18 1399
7 TMS19 178# if there are any blups for a germplasmName
# keep only the FullSampleName/GID associated
# else keep all
pednames2genos %<>%
mutate(HasBLUPs=ifelse(GID %in% gidWithBLUPs,T,F)) %>%
nest(DNAsamples=-c(germplasmName,Cohort)) %>%
mutate(AnyBLUPs=map_lgl(DNAsamples,~any(.$HasBLUPs)),
DNAsamples=ifelse(AnyBLUPs==T,
map(DNAsamples,~filter(.,HasBLUPs==TRUE)),
DNAsamples)) %>%
select(-AnyBLUPs) %>%
unnest(DNAsamples)
# Among all remaining
# Select only one GID to use for each germplasmName
pednames2genos %<>%
group_by(germplasmName) %>%
slice(1) %>%
ungroup()
pednames2genos %>%
count(Cohort)# A tibble: 9 × 2
Cohort n
<chr> <int>
1 GGetc 200
2 TMS13 2425
3 TMS14 1519
4 TMS15 894
5 TMS16 1
6 TMS17 36
7 TMS18 1262
8 TMS19 178
9 TMS20 670Now make a pedigree with both Accession and parent names matching the genos (FullSampleName) rather than phenos (germplasmName).
ped2genos<-ped %>%
rename(germplasmName=Accession) %>%
inner_join(pednames2genos %>%
select(-GID,-HasBLUPs)) %>%
left_join(pednames2genos %>%
select(-GID,-Cohort,-HasBLUPs) %>%
rename(Female_Parent=germplasmName,
DamID=FullSampleName)) %>%
left_join(pednames2genos %>%
select(-GID,-Cohort,-HasBLUPs) %>%
rename(Male_Parent=germplasmName,
SireID=FullSampleName))
ped2genos %<>%
filter(!is.na(FullSampleName),
!is.na(DamID),
!is.na(SireID))
ped2genos %>% distinct# %>% filter(grepl("TMS20",germplasmName))# A tibble: 6,257 × 7
germplasmName Female_Parent Male_Parent Cohort FullSampleName DamID SireID
<chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 TMS13F1002P00… IITA-TMS-IBA9… IITA-TMS-MO… TMS13 2013_0002_1:2… I930… M9800…
2 TMS13F1002P00… IITA-TMS-IBA9… IITA-TMS-MO… TMS13 2013_0002_2:2… I930… M9800…
3 TMS13F1002P00… IITA-TMS-IBA9… IITA-TMS-MO… TMS13 2013_0002_3:2… I930… M9800…
4 TMS13F1002P00… IITA-TMS-IBA9… IITA-TMS-MO… TMS13 2013_0002_4:2… I930… M9800…
5 TMS13F1002P00… IITA-TMS-IBA9… IITA-TMS-MO… TMS13 2013_0002_5:2… I930… M9800…
6 TMS13F1002P00… IITA-TMS-IBA9… IITA-TMS-MO… TMS13 2013_0002_6:2… I930… M9800…
7 TMS13F1002P00… IITA-TMS-IBA9… IITA-TMS-MO… TMS13 2013_0002_7:2… I930… M9800…
8 TMS13F1002P00… IITA-TMS-IBA9… IITA-TMS-MO… TMS13 2013_0002_8:2… I930… M9800…
9 TMS13F1002P00… IITA-TMS-IBA9… IITA-TMS-MO… TMS13 2013_0002_9:2… I930… M9800…
10 TMS13F1002P00… IITA-TMS-IBA9… IITA-TMS-MO… TMS13 2013_0002_10:… I930… M9800…
# … with 6,247 more rowsIn the end, considering only pedigree entries where the entire trio (offspring + both parents) are genotyped, the pedigree has 6257 entries to check.
ped2genos %>% count(Cohort,DamID,SireID) %>%
ggplot(.,aes(x=Cohort,y=n,fill=Cohort)) +
geom_boxplot(notch = T) + theme_bw() +
ggtitle("Distribution of family sizes (genotyped only)")
ped2genos %>%
count(Cohort,DamID,SireID) %$% summary(n) Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 1.000 3.000 5.298 7.000 78.000 Number of families with at least 10 genotyped members, by cohort:
ped2genos %>%
count(Cohort,DamID,SireID) %>%
filter(n>=10) %>%
count(Cohort)# A tibble: 5 × 2
Cohort n
<chr> <int>
1 TMS13 95
2 TMS14 55
3 TMS15 11
4 TMS18 27
5 TMS20 8ped2genos %>%
select(FullSampleName,DamID,SireID) %>%
write.table(.,file=here::here("output","ped2genos.txt"),row.names=F, col.names=F, quote=F)Run plink IBD (–genome)
Alternative to the below: Could compute everything manually based on mendelian rules. Kinship coefficients directly from the relationship matrix used for prediction would also be useful, for example, the estimated inbreeding coefficient of an individual is 1/2 the relationship of its parents.
PLINK1.9 pipeline to use:
- Subset whole-pop. plink file (
AllChrom_RefPanelAndGSprogeny_ReadyForGP_2021Aug08) to only lines in the pedigree. - LD-prune
--indep-pairwise 100 25 0.25stringent, but somewhat arbitrary - Compute IBD-relationships
--genome - Parent-offspring relationships determination (see below)
Determine parent-offspring relationship status based on plink IBD:
should have a kinship \(\hat{\pi} \approx 0.5\).
Three standard IBD probabilities are defined for each pair; the probability of sharing zero (Z0), one (Z1) or two (Z2) alleles at a randomly chosen locus IBD.
The expectation for siblings in terms of these probabilities is Z0=0.25, Z1=0.5 and Z2=0.25.
The expectation for parent-offspring pairs is Z0=0, Z1=1 and Z2=0.
Based on work I did in 2016 (never published), declare a parent-offspring pair where: Z0<0.313 and Z1>0.668.
ped2check<-read.table(file=here::here("output","ped2genos.txt"),
header = F, stringsAsFactors = F)
pednames<-union(ped2check$V1,union(ped2check$V2,ped2check$V3)) %>%
tibble(FID=0,IID=.)
write.table(pednames,file=here::here("output","pednames2keep.txt"),
row.names = F, col.names = F, quote = F)Checked plink’s order-of-operations and combining –keep and –indep-pairwise in the same filter call should result in the correct ordering: first subset samples, then LD prune.
cd ~/IITA_2021GS/
export PATH=/programs/plink-1.9-x86_64-beta3.30:$PATH;
plink --bfile output/AllChrom_RefPanelAndGSprogeny_ReadyForGP_2021Aug08 \
--keep output/pednames2keep.txt \
--indep-pairwise 100 25 0.25 \
--genome \
--out output/pednames_Prune100_25_pt25;Creates a 2GB *.genome, >6000 samples samples worth of pairwise relationships.
#cd /home/jj332_cas/marnin/implementGMSinCassava/
#export PATH=/programs/plink-1.9-x86_64-beta3.30:$PATH;
#plink --bfile output/AllChrom_RefPanelAndGSprogeny_ReadyForGP_2021Aug08 \
# --indep-pairwise 100 25 0.25 --out output/Prune100_25_pt25;
#plink --bfile output/AllChrom_RefPanelAndGSprogeny_ReadyForGP_2021Aug08 \
# --extract output/Prune100_25_pt25.prune.in --genome \
# --out output/AllChrom_RefPanelAndGSprogeny_ReadyForGP_2021Aug08_Prune100_25_pt25
# That wastefully creates a >40GB `*.genome` file with all pairwise relationships.
# Brute force solution is to read that, grab the needed relationships, and delete it....library(tidyverse); library(magrittr); library(data.table)
genome<-fread(here::here("output/",
"pednames_Prune100_25_pt25.genome"),
stringsAsFactors = F,header = T) %>%
as_tibble
ped2check<-read.table(file=here::here("output","ped2genos.txt"),
header = F, stringsAsFactors = F)
head(genome)
# A tibble: 6 × 14
# FID1 IID1 FID2 IID2 RT EZ Z0 Z1 Z2 PI_HAT PHE DST
# <int> <chr> <int> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
# 1 0 TMS15F1… 0 TMS15F… OT 0 1 0 0 0 -1 0.720
# 2 0 TMS15F1… 0 TMS15F… OT 0 0.854 0.103 0.0434 0.0948 -1 0.763
# 3 0 TMS15F1… 0 TMS15F… OT 0 0.868 0.132 0 0.0661 -1 0.718
# 4 0 TMS15F1… 0 TMS15F… OT 0 1 0 0 0 -1 0.726
# 5 0 TMS15F1… 0 TMS15F… OT 0 0.407 0.593 0 0.296 -1 0.775
# 6 0 TMS15F1… 0 TMS15F… OT 0 1 0 0 0 -1 0.715
dim(genome)
# [1] 20470401 14ped2check_genome<-genome %>%
semi_join(ped2check %>% rename(IID1=V1,IID2=V2)) %>%
bind_rows(genome %>% semi_join(ped2check %>% rename(IID1=V2,IID2=V1))) %>%
bind_rows(genome %>% semi_join(ped2check %>% rename(IID1=V1,IID2=V3))) %>%
bind_rows(genome %>% semi_join(ped2check %>% rename(IID1=V3,IID2=V1)))
saveRDS(ped2check_genome,file=here::here("output","ped2check_genome.rds"))cd ~/IITA_2021GS/output/;
rm pednames_Prune100_25_pt25.genomeVerify and Correct Pedigree
library(tidyverse); library(magrittr);
ped2check_genome<-readRDS(file=here::here("output","ped2check_genome.rds"))
ped2check_genome %<>%
select(IID1,IID2,Z0,Z1,Z2,PI_HAT)
ped2check<-read.table(file=here::here("output","ped2genos.txt"),
header = F, stringsAsFactors = F) %>%
rename(FullSampleName=V1,DamID=V2,SireID=V3)
ped2check %<>%
select(FullSampleName,DamID,SireID) %>%
inner_join(ped2check_genome %>%
rename(FullSampleName=IID1,DamID=IID2) %>%
bind_rows(ped2check_genome %>%
rename(FullSampleName=IID2,DamID=IID1))) %>%
distinct %>%
mutate(ConfirmFemaleParent=case_when(Z0<0.32 & Z1>0.67~"Confirm",
SireID==DamID & PI_HAT>0.6 & Z0<0.3 & Z2>0.32~"Confirm",
TRUE~"Reject")) %>%
select(-Z0,-Z1,-Z2,-PI_HAT) %>%
inner_join(ped2check_genome %>%
rename(FullSampleName=IID1,SireID=IID2) %>%
bind_rows(ped2check_genome %>%
rename(FullSampleName=IID2,SireID=IID1))) %>%
distinct %>%
mutate(ConfirmMaleParent=case_when(Z0<0.32 & Z1>0.67~"Confirm",
SireID==DamID & PI_HAT>0.6 & Z0<0.3 & Z2>0.32~"Confirm",
TRUE~"Reject")) %>%
select(-Z0,-Z1,-Z2,-PI_HAT)ped2check %>%
count(ConfirmFemaleParent,ConfirmMaleParent) %>% mutate(Prop=round(n/sum(n),2)) ConfirmFemaleParent ConfirmMaleParent n Prop
1 Confirm Confirm 4473 0.72
2 Confirm Reject 715 0.12
3 Reject Confirm 442 0.07
4 Reject Reject 576 0.09ped2check %>%
mutate(Cohort=NA,
Cohort=ifelse(grepl("TMS20",FullSampleName,ignore.case = T),"TMS20",
ifelse(grepl("TMS19",FullSampleName,ignore.case = T),"TMS19",
ifelse(grepl("TMS18",FullSampleName,ignore.case = T),"TMS18",
ifelse(grepl("TMS17",FullSampleName,ignore.case = T),"TMS17",
ifelse(grepl("TMS16",FullSampleName,ignore.case = T),"TMS16",
ifelse(grepl("TMS15",FullSampleName,ignore.case = T),"TMS15",
ifelse(grepl("TMS14",FullSampleName,ignore.case = T),"TMS14",
ifelse(grepl("TMS13|2013_",FullSampleName,
ignore.case = T),"TMS13","GGetc"))))))))) %>%
filter(ConfirmFemaleParent=="Confirm",
ConfirmMaleParent=="Confirm") %>%
count(Cohort,name = "BothParentsConfirmed") Cohort BothParentsConfirmed
1 GGetc 20
2 TMS13 1786
3 TMS14 1303
4 TMS15 589
5 TMS17 11
6 TMS18 592
7 TMS19 40
8 TMS20 132I’m only interested in families / trios that are confirmed. Remove any without both parents confirmed.
correctedped<-ped2check %>%
filter(ConfirmFemaleParent=="Confirm",
ConfirmMaleParent=="Confirm") %>%
select(-contains("Confirm"))correctedped %>%
count(SireID,DamID) %>% arrange(desc(n)) SireID DamID n
1 I020129:250090842 I011412:250300323 77
2 MM990477:250090809 I011412:250300323 48
3 I970290:250090804 I940237:250164036 46
4 I011797:250090770 I070004:250164022 45
5 TMEB117:250253666 I961632:250300546 45
6 I940006:250090826 I030075:250300232 41
7 I980002:250300438 I30572:250253643 40
8 I930007:250090827 I970425:250300465 39
9 I980510:250303662 I980505:250090767 38
10 I071313:250164024 I011412:250300323 37
11 TMEB419:250253865 TMEB419:250253865 35
12 I020233:250300288 I011412:250300323 33
13 I30572:250253643 I980505:250090767 31
14 I010046:250300307 I940237:250164036 30
15 I020540:250300283 I011412:250300323 29
16 I970290:250090804 I950306:250164034 28
17 I930007:250090827 I071313:250164024 26
18 I011412:250300323 I070004:250164022 25
19 I940006:250090826 B9200068:250304480 25
20 M940583:250164037 I010046:250300307 25
21 M980068:250300452 MM964500:250300562 24
22 I010903:250300322 I030060:250090848 23
23 I930007:250090827 I974766:250300468 23
24 TMEB778:250254008 TMEB693:250253991 22
25 I930007:250090827 I000211:250300361 21
26 I940006:250090826 MM964500:250300562 21
27 TMEB419:250253865 I020285:250300276 21
28 I010903:250300322 I970353:250090805 20
29 I980581:250253626 I980002:250300438 20
30 I010903:250300322 I020129:250090842 19
31 I071313:250164024 I970290:250090804 19
32 I950279:250164033 I063046:250300177 19
33 I980002:250300438 I020285:250300276 19
34 I000355:250300360 I020129:250090842 18
35 I010903:250300322 I972205:250301855 18
36 I030075:250300232 I010046:250300307 18
37 I980581:250253626 I30572:250253643 18
38 MM970646:250300486 I030060:250090848 18
39 I960963:250300550 I070258:250300140 17
40 I972205:250301855 2013_0343_22:250162550 17
41 MM970806:250164028 I011412:250300323 17
42 2013_0307_20:250161028 2013_10084_6:250164723 16
43 I010046:250300307 M940583:250164037 16
44 I020129:250090842 TMEB419:250253865 16
45 I071313:250164024 I970353:250090805 16
46 I972205:250301855 2013_0088_7:250159759 16
47 I980581:250253626 Z930151:250164040 16
48 2013_0008_6:250162074 2013_0336_23:250162476 15
49 2013_10063_19:250164239 2013_0307_10:250160606 15
50 2013_10063_19:250164239 2013_10084_6:250164723 15
51 I000070:250300358 I961632:250300546 15
52 I000211:250300361 I930134:250164039 15
53 I030055A:250300218 I020540:250300283 15
54 I030075:250300232 I020129:250090842 15
55 I071313:250164024 I020285:250300276 15
56 I930007:250090827 I972205:250301855 15
57 I930134:250164039 Z930151:250164040 15
58 Z930151:250164040 MM970806:250164028 15
59 2013_0108_7:250159843 2013_0307_8:250160604 14
60 2013_0333_3:250162424 2013_10020_4:250164717 14
61 I011371:250090769 I020431:250300292 14
62 I020129:250090842 M980004:250164026 14
63 I030060A:250300227 2013_0053_10:250162218 14
64 I071313:250164024 MM970806:250164028 14
65 I930007:250090827 2013_0088_7:250159759 14
66 I970290:250090804 MM970016:250164029 14
67 MM970043:250300482 I030055A:250300218 14
68 MM970043:250300482 I993073:250300423 14
69 MM970043:250300482 MM964500:250300562 14
70 MM970043:250300482 Z930151:250164040 14
71 TMEB419:250253865 I940237:250164036 14
72 2013_0088_7:250159759 2013_0053_15:250162223 13
73 2013_0111_12:250160049 2013_0307_8:250160604 13
74 I000211:250300361 B9200068:250304480 13
75 I930007:250090827 I020285:250300276 13
76 I940006:250090826 I000345:250090783 13
77 I950279:250164033 I070126:250164023 13
78 I972205:250301855 2013_0053_15:250162223 13
79 I972205:250301855 2013_0343_2:250162499 13
80 I972205:250301855 B9200068:250304480 13
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253 I972205:250301855 I070593:250300163 6
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521 TMS14F1113P0002:250301450 TMS14F1213P0007:250302244 2
522 TMS14F1157P0002:250301832 TMS14F1189P0001:250302072 2
523 TMS14F1166P0002:250301867 TMS14F1107P0004:250301360 2
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562 TMS18F1173P0009_A18858 TMS18F1026P0014_A18745 2
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721 TMS14F1113P0002:250301450 TMS14F1247P0007:250303518 1
722 TMS14F1138P0005:250301674 TMS14F1171P0004:250301946 1
723 TMS14F1138P0005:250301674 TMS14F1228P0013:250303377 1
724 TMS14F1157P0002:250301832 TMS14F1113P0003:250301451 1
725 TMS14F1157P0002:250301832 TMS14F1244P0006:250303493 1
726 TMS14F1166P0002:250301867 TMS14F1222P0003:250303322 1
727 TMS14F1166P0002:250301867 TMS14F1229P0004:250303381 1
728 TMS14F1166P0002:250301867 TMS14F1247P0003:250303514 1
729 TMS14F1166P0002:250301867 TMS14F1283P0013:250303992 1
730 TMS14F1174P0011:250301981 TMS14F1176P0010:250301997 1
731 TMS14F1174P0011:250301981 TMS14F1247P0003:250303514 1
732 TMS14F1176P0006:250301993 TMS14F1153P0003:250301810 1
733 TMS14F1176P0006:250301993 TMS14F1247P0007:250303518 1
734 TMS14F1176P0010:250301997 TMS14F1174P0011:250301981 1
735 TMS14F1176P0010:250301997 TMS14F1222P0003:250303322 1
736 TMS14F1195P0008:250302099 TMS14F1122P0003:250301540 1
737 TMS14F1240P0004:250303461 TMS14F1213P0007:250302244 1
738 TMS14F1240P0005:250303462 TMS14F1122P0003:250301540 1
739 TMS14F1240P0005:250303462 TMS14F1213P0007:250302244 1
740 TMS14F1240P0005:250303462 TMS14F1228P0013:250303377 1
741 TMS14F1240P0005:250303462 TMS14F1283P0015:250303994 1
742 TMS14F1241P0002:250303465 TMS14F1154P0002:250301819 1
743 TMS14F1247P0007:250303518 TMS14F1107P0004:250301360 1
744 TMS14F1255P0005:250303597 2013_0307_4:250160600 1
745 TMS14F1255P0005:250303597 2013_10303_2:250164467 1
746 TMS14F1255P0005:250303597 TMS14F1288P0001:250304117 1
747 TMS14F1256P0002:250303615 TMS14F1283P0002:250303981 1
748 TMS14F1288P0001:250304117 TMS14F1244P0006:250303493 1
749 TMS14F1300P0002:250300902 TMS15F1463P0054_A19558 1
750 TMS14F1300P0002:250300902 TMS15F1466P0195_A19530 1
751 TMS14F1300P0002:250300902 TMS15F1467P0033_A19532 1
752 TMS14F1310P0004:250301178 TMS14F1035P0004:250302641 1
753 TMS15F1001P0001:250464861 TMS15F1269P0008:250465951 1
754 TMS15F1021P0028:250464932 TMS15F1305P0017:250466080 1
755 TMS15F1021P0028:250464932 TMS15F1318P0009:250476726 1
756 TMS15F1072P0031:250521122 TMS15F1079P0025:250465175 1
757 TMS15F1079P0020:250465173 2013_0307_8:250160604 1
758 TMS15F1109P0003:250465275 TMS15F1100P0005:250465254 1
759 TMS15F1124P0001:250465316 TMS15F1351P0003:250466280 1
760 TMS15F1132P0003:250465370 TMS15F1021P0002:250464910 1
761 TMS15F1132P0013:250475590 TMS15F1196P0010:250465572 1
762 TMS15F1142P0012:250475597 TMS15F1329P0005:250476750 1
763 TMS15F1153P0009:250465409 TMS15F1021P0002:250464910 1
764 TMS15F1153P0009:250465409 TMS15F1073P0005:250465136 1
765 TMS15F1153P0009:250465409 TMS15F1080P0004:250465178 1
766 TMS15F1153P0009:250465409 TMS15F1393P0006:250466543 1
767 TMS15F1154P0002:250518934 TMS15F1351P0003:250466280 1
768 TMS15F1156P0014:250518954 TMS15F1021P0002:250464910 1
769 TMS15F1156P0014:250518954 TMS15F1072P0005:250465127 1
770 TMS15F1156P0014:250518954 TMS15F1079P0020:250465173 1
771 TMS15F1156P0014:250518954 TMS15F1111P0011:250475547 1
772 TMS15F1156P0014:250518954 TMS15F1132P0013:250475590 1
773 TMS15F1159P0001:250518960 TMS15F1318P0022:250476734 1
774 TMS15F1159P0006:250465430 I980505:250090767 1
775 TMS15F1159P0006:250465430 TMS15F1021P0002:250464910 1
776 TMS15F1177P0004:250476294 TMS15F1318P0009:250476726 1
777 TMS15F1196P0010:250465572 TMS15F1153P0009:250465409 1
778 TMS15F1196P0010:250465572 TMS15F1154P0002:250518934 1
779 TMS15F1196P0010:250465572 TMS15F1154P0015:250518942 1
780 TMS15F1196P0010:250465572 TMS15F1156P0014:250518954 1
781 TMS15F1305P0007:250466076 TMS15F1142P0006:250465390 1
782 TMS15F1305P0017:250466080 TMS15F1079P0020:250465173 1
783 TMS15F1305P0017:250466080 TMS15F1159P0006:250465430 1
784 TMS15F1305P0017:250466080 TMS15F1329P0005:250476750 1
785 TMS15F1310P0019:250467021 TMS15F1021P0002:250464910 1
786 TMS15F1310P0019:250467021 TMS15F1196P0010:250465572 1
787 TMS15F1318P0009:250476726 TMS15F1329P0005:250476750 1
788 TMS15F1318P0024:250476735 TMS15F1153P0009:250465409 1
789 TMS15F1318P0024:250476735 TMS15F1156P0014:250518954 1
790 TMS15F1318P0024:250476735 TMS15F1160P0010:250518971 1
791 TMS15F1326P0004:250467063 TMS15F1079P0020:250465173 1
792 TMS15F1326P0004:250467063 TMS15F1195P0010:250465562 1
793 TMS15F1329P0005:250476750 I930007:250090827 1
794 TMS15F1351P0003:250466280 TMS15F1396P0001:250466560 1
795 TMS15F1365P0003:250466326 TMS15F1159P0006:250465430 1
796 TMS15F1367P0001:250475193 TMS15F1021P0003:250464911 1
797 TMS15F1367P0001:250475193 TMS15F1069P0007:250521086 1
798 TMS15F1367P0001:250475193 TMS15F1318P0009:250476726 1
799 TMS15F1396P0001:250466560 TMS15F1351P0003:250466280 1
800 TMS15F1396P0004:250466562 TMS15F1160P0010:250518971 1
801 TMS15F1397P0001:250466568 TMS15F1153P0009:250465409 1
802 TMS15F1398P0008:250466574 TMS15F1130P0007:250465327 1
803 TMS15F1424P0004:250466799 TMS15F1099P0004:250465252 1
804 TMS15F1463P0054_A19558 TMS14F1301P0013:250300938 1
805 TMS18F1024P0005_A19030 TMS18F1047P0024_A18757 1
806 TMS18F1173P0009_A18858 TMS18F1289P0016_A18967 1correctedped %>%
count(SireID,DamID) %$% summary(n) Min. 1st Qu. Median Mean 3rd Qu. Max.
1.00 1.00 3.00 5.55 7.00 77.00 correctedped %>%
count(SireID,DamID) %>%
filter(n>=5) %>% arrange(desc(n)) %$% union(SireID,DamID) %>%
tibble(ParentsMoreThanFiveProg=.) %>%
mutate(Cohort=NA,
Cohort=ifelse(grepl("TMS20",ParentsMoreThanFiveProg,ignore.case = T),"TMS20",
ifelse(grepl("TMS19",ParentsMoreThanFiveProg,ignore.case = T),"TMS19",
ifelse(grepl("TMS18",ParentsMoreThanFiveProg,ignore.case = T),"TMS18",
ifelse(grepl("TMS17",ParentsMoreThanFiveProg,ignore.case = T),"TMS17",
ifelse(grepl("TMS16",ParentsMoreThanFiveProg,ignore.case = T),"TMS16",
ifelse(grepl("TMS15",ParentsMoreThanFiveProg,ignore.case = T),"TMS15",
ifelse(grepl("TMS14",ParentsMoreThanFiveProg,ignore.case = T),"TMS14",
ifelse(grepl("TMS13|2013_",ParentsMoreThanFiveProg,
ignore.case = T),"TMS13","GGetc"))))))))) %>%
count(Cohort, name="ParentsWithLeast5offspring")# A tibble: 4 × 2
Cohort ParentsWithLeast5offspring
<chr> <int>
1 GGetc 82
2 TMS13 77
3 TMS14 19
4 TMS15 13correctedped %>%
count(SireID,DamID) %>%
arrange(desc(n)) %$% union(SireID,DamID) %>%
tibble(Parents=.) %>%
mutate(Cohort=NA,
Cohort=ifelse(grepl("TMS20",Parents,ignore.case = T),"TMS20",
ifelse(grepl("TMS19",Parents,ignore.case = T),"TMS19",
ifelse(grepl("TMS18",Parents,ignore.case = T),"TMS18",
ifelse(grepl("TMS17",Parents,ignore.case = T),"TMS17",
ifelse(grepl("TMS16",Parents,ignore.case = T),"TMS16",
ifelse(grepl("TMS15",Parents,ignore.case = T),"TMS15",
ifelse(grepl("TMS14",Parents,ignore.case = T),"TMS14",
ifelse(grepl("TMS13|2013_",Parents,
ignore.case = T),"TMS13","GGetc"))))))))) %>%
count(Cohort, name="Parents")# A tibble: 5 × 2
Cohort Parents
<chr> <int>
1 GGetc 109
2 TMS13 117
3 TMS14 56
4 TMS15 72
5 TMS18 7correctedped %>%
mutate(Cohort=NA,
Cohort=ifelse(grepl("TMS20",FullSampleName,ignore.case = T),"TMS20",
ifelse(grepl("TMS19",FullSampleName,ignore.case = T),"TMS19",
ifelse(grepl("TMS18",FullSampleName,ignore.case = T),"TMS18",
ifelse(grepl("TMS17",FullSampleName,ignore.case = T),"TMS17",
ifelse(grepl("TMS16",FullSampleName,ignore.case = T),"TMS16",
ifelse(grepl("TMS15",FullSampleName,ignore.case = T),"TMS15",
ifelse(grepl("TMS14",FullSampleName,ignore.case = T),"TMS14",
ifelse(grepl("TMS13|2013_",FullSampleName,
ignore.case = T),"TMS13","GGetc"))))))))) %>%
count(Cohort, name="BothParentsConfirmed") Cohort BothParentsConfirmed
1 GGetc 20
2 TMS13 1786
3 TMS14 1303
4 TMS15 589
5 TMS17 11
6 TMS18 592
7 TMS19 40
8 TMS20 132–> Write verified pedigree
correctedped %>%
write.table(.,here::here("output","verified_ped.txt"),
row.names = F, col.names = T, quote = F)Next Step
- Preprocess data files: Prepare haplotype and dosage matrices, GRMs, pedigree and BLUPs, genetic map and recombination frequency matrix, for use in predictions.
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] magrittr_2.0.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[5] purrr_0.3.4 readr_2.0.0 tidyr_1.1.3 tibble_3.1.3
[9] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 here_1.0.1 lubridate_1.7.10 assertthat_0.2.1
[5] rprojroot_2.0.2 digest_0.6.27 utf8_1.2.2 R6_2.5.0
[9] cellranger_1.1.0 backports_1.2.1 reprex_2.0.1 evaluate_0.14
[13] highr_0.9 httr_1.4.2 pillar_1.6.2 rlang_0.4.11
[17] readxl_1.3.1 rstudioapi_0.13 whisker_0.4 jquerylib_0.1.4
[21] rmarkdown_2.10 labeling_0.4.2 bit_4.0.4 munsell_0.5.0
[25] broom_0.7.9 compiler_4.1.0 httpuv_1.6.1 modelr_0.1.8
[29] xfun_0.25 pkgconfig_2.0.3 htmltools_0.5.1.1 tidyselect_1.1.1
[33] fansi_0.5.0 crayon_1.4.1 tzdb_0.1.2 dbplyr_2.1.1
[37] withr_2.4.2 later_1.2.0 grid_4.1.0 jsonlite_1.7.2
[41] gtable_0.3.0 lifecycle_1.0.0 DBI_1.1.1 git2r_0.28.0
[45] scales_1.1.1 cli_3.0.1 stringi_1.7.3 vroom_1.5.4
[49] farver_2.1.0 fs_1.5.0 promises_1.2.0.1 xml2_1.3.2
[53] bslib_0.2.5.1 ellipsis_0.3.2 generics_0.1.0 vctrs_0.3.8
[57] tools_4.1.0 bit64_4.0.5 glue_1.4.2 hms_1.1.0
[61] parallel_4.1.0 yaml_2.2.1 colorspace_2.0-2 rvest_1.0.1
[65] knitr_1.33 haven_2.4.3 sass_0.4.0