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Read the pedigree I downloaded for IITA
library(tidyverse); library(magrittr)
<-read_delim(here::here("data/DatabaseDownload_2021May04","pedigree.txt"),
peddelim = "\t")
<-readxl::read_xlsx(here::here("data/DatabaseDownload_2021Aug08","Pedigree.xlsx"))
ped_tms20%<>%
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 distinct
Number of full-sib families?
%>% distinct(Female_Parent,Male_Parent) %>% nrow() ped
[1] 1826
Summarize distribution of full-sib family sizes
%>%
ped count(Female_Parent,Male_Parent) %>% arrange(desc(n)) %>% summary(.$n)
Female_Parent Male_Parent n
Length:1826 Length:1826 Min. : 1.000
Class :character Class :character 1st Qu.: 1.000
Mode :character Mode :character Median : 2.000
Mean : 5.059
3rd Qu.: 6.000
Max. :276.000
%>% head ped
# A tibble: 6 × 3
Accession Female_Parent Male_Parent
<chr> <chr> <chr>
1 TMS13F1002P0001 IITA-TMS-IBA930265 IITA-TMS-MOK980068
2 TMS13F1002P0002 IITA-TMS-IBA930265 IITA-TMS-MOK980068
3 TMS13F1002P0003 IITA-TMS-IBA930265 IITA-TMS-MOK980068
4 TMS13F1002P0004 IITA-TMS-IBA930265 IITA-TMS-MOK980068
5 TMS13F1002P0005 IITA-TMS-IBA930265 IITA-TMS-MOK980068
6 TMS13F1002P0006 IITA-TMS-IBA930265 IITA-TMS-MOK980068
Goal is to identify DNA samples names for listed accessions and parents in the pedigree. Important to choose same samples used in genomic predictions where possible, esp. phenotyped training clones.
However, there may be non-phenotyped clones that are genotyped genomic selection progeny, which I still want in my analysis.
First with the union of the parent and accession IDs in the pedigree.
<-union(ped$Accession,union(ped$Female_Parent,ped$Male_Parent)) %>%
pednamestibble(germplasmName=.)
$germplasmName %>% length # number of names in ped pednames
[1] 9568
Add 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 670
The 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.
<-readRDS(here::here("output","IITA_ExptDesignsDetected_2021Aug08.rds"))
dbdata<-dbdata %>%
phenos2genosdistinct(GID,germplasmName,FullSampleName) %>%
filter(!is.na(FullSampleName))
%>% head phenos2genos
# 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_A35750
How 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 674
I 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.
<-read.table(here::here("data",
gids_in_fam"chr1_RefPanelAndGSprogeny_ReadyForGP_72719.fam"),
stringsAsFactors = F, header = F)$V2
length(gids_in_fam)
[1] 21856
Split 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] 2420
There 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_fambind_rows(ped_tms20 %>%
select(Accession_name,Lab_ID) %>%
rename(germplasmName=Accession_name,
FullSampleName=Lab_ID) %>%
mutate(FullSampleName=gsub("\\.","_",FullSampleName)))
%>% head gids_in_fam
# 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 TMS15F1179P0024
%>%
gids_in_fam filter(grepl("TMS16|TMS17|TMS18|TMS19|TMS20",FullSampleName)) %>% nrow()
[1] 3713
There are 3713 “TMS16” though “TMS20” clones genotyped.
<-pednames %>%
pednames2genosinner_join(phenos2genos) %>%
bind_rows(pednames %>%
anti_join(phenos2genos) %>%
inner_join(gids_in_fam))
%>% count(Cohort) pednames2genos
# 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 674
Are there germplasmName in the pednames2genos
match table with multiple DNA samples?
%>% count(germplasmName) %>% arrange(desc(n)) pednames2genos
# 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 rows
Of course there are. Will need to pick.
Which pednames have BLUPs?
<-readRDS(file=here::here("output","IITA_blupsForModelTraining_twostage_asreml_2021Aug09.rds"))
blups%>%
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 670
Now make a pedigree with both Accession and parent names matching the genos (FullSampleName) rather than phenos (germplasmName).
<-ped %>%
ped2genosrename(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))
%>% distinct# %>% filter(grepl("TMS20",germplasmName)) ped2genos
# 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 rows
In the end, considering only pedigree entries where the entire trio (offspring + both parents) are genotyped, the pedigree has 6257 entries to check.
%>% count(Cohort,DamID,SireID) %>%
ped2genos ggplot(.,aes(x=Cohort,y=n,fill=Cohort)) +
geom_boxplot(notch = T) + theme_bw() +
ggtitle("Distribution of family sizes (genotyped only)")
Version | Author | Date |
---|---|---|
1c03315 | wolfemd | 2021-08-11 |
%>%
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 8
%>%
ped2genos select(FullSampleName,DamID,SireID) %>%
write.table(.,file=here::here("output","ped2genos.txt"),row.names=F, col.names=F, quote=F)
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:
AllChrom_RefPanelAndGSprogeny_ReadyForGP_2021Aug08
) to only lines in the pedigree.--indep-pairwise 100 25 0.25
stringent, but somewhat arbitrary--genome
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.
<-read.table(file=here::here("output","ped2genos.txt"),
ped2checkheader = F, stringsAsFactors = F)
<-union(ped2check$V1,union(ped2check$V2,ped2check$V3)) %>%
pednamestibble(FID=0,IID=.)
write.table(pednames,file=here::here("output","pednames2keep.txt"),
row.names = F, col.names = F, quote = F)
Checked plink’s order-of-operations and combining –keep and –indep-pairwise in the same filter call should result in the correct ordering: first subset samples, then LD prune.
cd ~/IITA_2021GS/1.9-x86_64-beta3.30:$PATH;
export PATH=/programs/plink-
plink --bfile output/AllChrom_RefPanelAndGSprogeny_ReadyForGP_2021Aug08 \
--keep output/pednames2keep.txt \100 25 0.25 \
--indep-pairwise
--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)
<-fread(here::here("output/",
genome"pednames_Prune100_25_pt25.genome"),
stringsAsFactors = F,header = T) %>%
as_tibble
<-read.table(file=here::here("output","ped2genos.txt"),
ped2checkheader = 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 14
<-genome %>%
ped2check_genomesemi_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.genome
library(tidyverse); library(magrittr);
<-readRDS(file=here::here("output","ped2check_genome.rds"))
ped2check_genome%<>%
ped2check_genome select(IID1,IID2,Z0,Z1,Z2,PI_HAT)
<-read.table(file=here::here("output","ped2genos.txt"),
ped2checkheader = 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",
==DamID & PI_HAT>0.6 & Z0<0.3 & Z2>0.32~"Confirm",
SireIDTRUE~"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",
==DamID & PI_HAT>0.6 & Z0<0.3 & Z2>0.32~"Confirm",
SireIDTRUE~"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 0.09
%>%
ped2check 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",
=="Confirm") %>%
ConfirmMaleParentcount(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
I’m only interested in families / trios that are confirmed. Remove any without both parents confirmed.
<-ped2check %>%
correctedpedfilter(ConfirmFemaleParent=="Confirm",
=="Confirm") %>%
ConfirmMaleParentselect(-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
81 I974766:250300468 I980196:250164025 13
82 MM970043:250300482 I030060:250090848 13
83 TMS13F1106P0006:250300932 2013_0423_9:250162755 13
84 2013_0108_7:250159843 2013_0307_20:250161028 12
85 2013_0214_4:250160658 2013_10063_19:250164239 12
86 2013_0307_20:250161028 2013_0153_11:250160352 12
87 2013_0307_7:250160603 2013_10309_1:250164479 12
88 2013_0333_3:250162424 2013_0153_11:250160352 12
89 I010046:250300307 I980002:250300438 12
90 I020129:250090842 I950971:250300591 12
91 I020285:250300276 I980002:250300438 12
92 I030055A:250300218 I961632:250300546 12
93 I051553:250300205 I070004:250164022 12
94 I920429:250164041 B9200061:250304482 12
95 I930007:250090827 2013_0307_16:250160612 12
96 I930134:250164039 I020540:250300283 12
97 I930134:250164039 I070258:250300140 12
98 I940006:250090826 I000211:250300361 12
99 I950306:250164034 I950971:250300591 12
100 I971228:250164027 I011412:250300323 12
101 M980004:250164026 I950971:250300591 12
102 MM970806:250164028 I030060:250090848 12
103 MM990268:250300436 I011412:250300323 12
104 TMEB419:250253865 I972205:250301855 12
105 TMEB419:250253865 I974766:250300468 12
106 2013_0008_6:250162074 2013_0087_2:250159576 11
107 2013_0108_7:250159843 2013_0107_10:250159835 11
108 2013_0108_7:250159843 2013_0436_5:250162760 11
109 2013_0212_2:250159907 2013_10120_1:250164341 11
110 2013_0212_32:250159937 2013_0423_9:250162755 11
111 2013_0307_8:250160604 2013_0212_2:250159907 11
112 2013_0307_8:250160604 2013_10020_4:250164717 11
113 2013_0333_3:250162424 2013_0024_2:250162894 11
114 2013_10059_6:250164095 2013_0436_5:250162760 11
115 I000070:250300358 2013_0053_10:250162218 11
116 I010046:250300307 I010903:250300322 11
117 I010903:250300322 I000211:250300361 11
118 I030055A:250300218 I020431:250300292 11
119 I930007:250090827 I940018:250164035 11
120 I940006:250090826 I000214:250300403 11
121 M980004:250164026 I971228:250164027 11
122 M980068:250300452 I930265:250164038 11
123 MM970043:250300482 I020431:250300292 11
124 MM970806:250164028 I993073:250300423 11
125 TMS13F1106P0006:250300932 2013_0088_8:250159760 11
126 Z930151:250164040 I020540:250300283 11
127 2013_0108_7:250159843 2013_0333_3:250162424 10
128 2013_0108_7:250159843 2013_10084_6:250164723 10
129 2013_0212_2:250159907 2013_0332_42:250161115 10
130 2013_0212_2:250159907 2013_10063_7:250164227 10
131 2013_0212_32:250159937 2013_0436_5:250162760 10
132 2013_0333_17:250162439 2013_0153_11:250160352 10
133 2013_0333_17:250162439 TMS13F1391P0039:250465621 10
134 2013_0333_3:250162424 2013_0160_3:250160398 10
135 2013_0333_3:250162424 2013_0212_55:250159960 10
136 2013_0436_4:250162759 2013_10063_19:250164239 10
137 2013_10063_19:250164239 2013_10303_2:250164467 10
138 2013_10069_24:250164153 2013_0307_16:250160612 10
139 2013_10303_3:250164468 2013_0381_5:250162690 10
140 I010046:250300307 I070004:250164022 10
141 I020285:250300276 I010903:250300322 10
142 I030055A:250300218 MM970806:250164028 10
143 I030060A:250300227 2013_0343_22:250162550 10
144 I051740:250300206 I993073:250300423 10
145 I970425:250300465 I993073:250300423 10
146 M980068:250300452 I010903:250300322 10
147 M980068:250300452 I011412:250300323 10
148 TMS14F1292P0015:250304248 TMS14F1229P0002:250303379 10
149 Z930151:250164040 I980196:250164025 10
150 2013_0088_7:250159759 2013_0307_16:250160612 9
151 2013_0108_7:250159843 2013_10306_3:250164474 9
152 2013_0212_32:250159937 2013_0050_8:250169056 9
153 2013_0212_32:250159937 2013_0333_3:250162424 9
154 2013_0212_32:250159937 2013_0336_23:250162476 9
155 2013_0212_55:250159960 2013_0154_8:250160392 9
156 2013_0333_17:250162439 2013_10020_4:250164717 9
157 2013_10303_1:250164466 2013_10122_3:250164747 9
158 I020129:250090842 I930007:250090827 9
159 I030060A:250300227 2013_0343_2:250162499 9
160 I930007:250090827 2013_10020_1:250164714 9
161 I930007:250090827 I070004:250164022 9
162 I930007:250090827 I970290:250090804 9
163 I940006:250090826 I993073:250300423 9
164 I972205:250301855 2013_0053_10:250162218 9
165 KALESO:250304590 I000211:250300361 9
166 KALESO:250304590 I011412:250300323 9
167 MM970806:250164028 I020431:250300292 9
168 MM970806:250164028 I030055A:250300218 9
169 MM970806:250164028 I930007:250090827 9
170 TMEB419:250253865 I30572:250253643 9
171 TMS13F1106P0006:250300932 2013_10063_9:250164229 9
172 2013_0212_2:250159907 2013_10084_6:250164723 8
173 2013_0307_20:250161028 2013_0109_9:250160030 8
174 2013_0307_4:250160600 2013_0381_5:250162690 8
175 2013_0307_4:250160600 2013_10084_6:250164723 8
176 2013_0307_8:250160604 2013_0333_27:250162449 8
177 2013_0333_3:250162424 2013_0212_2:250159907 8
178 2013_0436_4:250162759 2013_0307_4:250160600 8
179 2013_10020_2:250164715 2013_0307_20:250161028 8
180 2013_10059_6:250164095 2013_0107_6:250159831 8
181 I000211:250300361 I020129:250090842 8
182 I030060A:250300227 2013_0053_15:250162223 8
183 I030075:250300232 I030007:250300231 8
184 I920429:250164041 I930134:250164039 8
185 I930007:250090827 2013_0343_2:250162499 8
186 I930007:250090827 I960860:250164032 8
187 I930007:250090827 I971228:250164027 8
188 I930007:250090827 I974580:250300467 8
189 I930134:250164039 I993073:250300423 8
190 I950279:250164033 I960860:250164032 8
191 TMEB419:250253865 2013_0343_2:250162499 8
192 TMS13F1106P0006:250300932 2013_0008_20:250162088 8
193 TMS13F1106P0006:250300932 2013_0212_55:250159960 8
194 TMS14F1157P0002:250301832 TMS14F1243P0019:250303486 8
195 TMS14F1234P0001:250303393 TMEB693:250253991 8
196 Z930151:250164040 I020131:250300295 8
197 Z930151:250164040 I930134:250164039 8
198 2013_0079_2:250159538 2013_10063_19:250164239 7
199 2013_0108_7:250159843 2013_0028_7:250162328 7
200 2013_0108_7:250159843 2013_0088_8:250159760 7
201 2013_0108_7:250159843 2013_0333_14:250162435 7
202 2013_0154_10:250160394 2013_0212_32:250159937 7
203 2013_0154_10:250160394 2013_0212_58:250159963 7
204 2013_0212_2:250159907 2013_0045_19:250169030 7
205 2013_0212_2:250159907 2013_0079_7:250159543 7
206 2013_0212_2:250159907 2013_0423_9:250162755 7
207 2013_0212_32:250159937 2013_0436_12:250162767 7
208 2013_0307_20:250161028 2013_0107_10:250159835 7
209 2013_0307_20:250161028 TMS13F1106P0006:250300932 7
210 2013_0333_27:250162449 2013_0212_2:250159907 7
211 2013_0333_3:250162424 2013_0212_32:250159937 7
212 2013_0333_3:250162424 2013_0214_4:250160658 7
213 2013_10063_19:250164239 2013_0307_6:250160602 7
214 2013_10063_19:250164239 2013_0381_5:250162690 7
215 2013_10309_1:250164479 2013_10063_19:250164239 7
216 I000070:250300358 2013_0160_5:250160400 7
217 I071313:250164024 I950971:250300591 7
218 I30572:250253643 TMEB419:250253865 7
219 I930007:250090827 2013_0343_22:250162550 7
220 I930007:250090827 I070593:250300163 7
221 I972205:250301855 2013_10020_1:250164714 7
222 MM990268:250300436 I000211:250300361 7
223 TMEB419:250253865 2013_0053_10:250162218 7
224 TMEB419:250253865 2013_10020_1:250164714 7
225 TMS14F1085P0003:250303128 TMS14F1213P0002:250302239 7
226 TMS14F1240P0005:250303462 TMS14F1283P0002:250303981 7
227 TMS15F1318P0024:250476735 TMS15F1080P0004:250465178 7
228 2013_0008_6:250162074 2013_10086_3:250164329 6
229 2013_0053_15:250162223 2013_0307_16:250160612 6
230 2013_0079_2:250159538 2013_0307_10:250160606 6
231 2013_0108_7:250159843 TMS13F1106P0006:250300932 6
232 2013_0154_10:250160394 2013_0212_79:250160650 6
233 2013_0212_32:250159937 2013_0109_9:250160030 6
234 2013_0212_55:250159960 2013_0153_11:250160352 6
235 2013_0216_10:250160702 2013_0423_9:250162755 6
236 2013_0307_20:250161028 2013_0333_17:250162439 6
237 2013_0307_20:250161028 2013_0333_3:250162424 6
238 2013_0333_17:250162439 2013_0045_19:250169030 6
239 2013_0333_2:250162423 2013_0153_11:250160352 6
240 2013_0333_3:250162424 2013_0028_7:250162328 6
241 2013_0333_3:250162424 2013_0212_79:250160650 6
242 2013_0333_3:250162424 2013_10309_1:250164479 6
243 2013_10084_6:250164723 2013_0307_8:250160604 6
244 2013_10122_3:250164747 2013_10063_19:250164239 6
245 I000355:250300360 I030060:250090848 6
246 I011797:250090770 I011368:250300318 6
247 I020129:250090842 MM964500:250300562 6
248 I051740:250300206 I000211:250300361 6
249 I930007:250090827 2013_0053_10:250162218 6
250 I930265:250164038 I030060:250090848 6
251 I940006:250090826 I960869:250300548 6
252 I972205:250301855 2013_0307_16:250160612 6
253 I972205:250301855 I070593:250300163 6
254 KALESO:250304590 I996069:250300420 6
255 TMEB419:250253865 2013_0307_16:250160612 6
256 TMS13F1227P0119_A19491 TMS14F1284P0001:250304001 6
257 TMS13F1300P0075:250465619 2013_0079_7:250159543 6
258 TMS14F1085P0003:250303128 TMS14F1196P0005:250302110 6
259 TMS14F1176P0006:250301993 TMS14F1174P0011:250301981 6
260 TMS15F1021P0028:250464932 TMS15F1329P0005:250476750 6
261 TMS15F1069P0007:250521086 TMS15F1351P0003:250466280 6
262 TMS15F1132P0013:250475590 TMS15F1153P0009:250465409 6
263 2013_0008_6:250162074 2013_0154_8:250160392 5
264 2013_0008_6:250162074 2013_0333_2:250162423 5
265 2013_0079_2:250159538 2013_0307_6:250160602 5
266 2013_0088_7:250159759 2013_0343_22:250162550 5
267 2013_0108_7:250159843 2013_0107_6:250159831 5
268 2013_0111_12:250160049 2013_10063_9:250164229 5
269 2013_0154_10:250160394 2013_0212_2:250159907 5
270 2013_0154_10:250160394 2013_0333_14:250162435 5
271 2013_0212_2:250159907 2013_0079_5:250159541 5
272 2013_0212_2:250159907 2013_0333_2:250162423 5
273 2013_0212_2:250159907 2013_0333_3:250162424 5
274 2013_0212_32:250159937 2013_10120_1:250164341 5
275 2013_0212_55:250159960 2013_10084_6:250164723 5
276 2013_0212_7:250159912 2013_10063_19:250164239 5
277 2013_0214_14:250160668 2013_0008_20:250162088 5
278 2013_0307_20:250161028 2013_0212_55:250159960 5
279 2013_0307_20:250161028 TMS13F1391P0039:250465621 5
280 2013_0307_4:250160600 2013_0214_14:250160668 5
281 2013_0307_8:250160604 2013_0332_42:250161115 5
282 2013_0333_3:250162424 2013_0087_2:250159576 5
283 2013_0333_3:250162424 2013_0332_42:250161115 5
284 2013_10020_2:250164715 TMS13F1106P0006:250300932 5
285 2013_10063_19:250164239 2013_10124_2:250164352 5
286 2013_10303_1:250164466 2013_0381_5:250162690 5
287 2013_10303_2:250164467 2013_0381_5:250162690 5
288 2013_10303_2:250164467 2013_10084_6:250164723 5
289 2013_10303_3:250164468 2013_10084_6:250164723 5
290 I000070:250300358 2013_0343_22:250162550 5
291 I000203:250300407 I30572:250253643 5
292 I000350:250300359 I070004:250164022 5
293 I020129:250090842 I971149:250300466 5
294 I970425:250300465 I010903:250300322 5
295 I972205:250301855 2013_10122_5:250164749 5
296 M940583:250164037 I030060:250090848 5
297 M980068:250300452 I996016:250090791 5
298 TMEB419:250253865 2013_0088_7:250159759 5
299 TMEB419:250253865 MM965280:250300556 5
300 TMS14F1085P0003:250303128 TMS14F1213P0007:250302244 5
301 TMS14F1113P0002:250301450 TMS14F1283P0008:250303987 5
302 TMS14F1138P0005:250301674 TMS14F1240P0005:250303462 5
303 TMS14F1138P0005:250301674 TMS14F1244P0006:250303493 5
304 TMS14F1241P0002:250303465 TMS14F1213P0007:250302244 5
305 TMS14F1284P0001:250304001 2013_0160_5:250160400 5
306 TMS15F1153P0009:250465409 TMS15F1092P0016:250465193 5
307 TMS15F1177P0004:250476294 TMS15F1154P0002:250518934 5
308 TMS15F1398P0008:250466574 TMS15F1079P0025:250465175 5
309 Z930151:250164040 I030060:250090848 5
310 2013_0008_6:250162074 2013_10124_2:250164352 4
311 2013_0079_2:250159538 2013_0307_4:250160600 4
312 2013_0108_7:250159843 2013_0307_11:250160607 4
313 2013_0154_10:250160394 2013_0107_8:250159833 4
314 2013_0154_10:250160394 2013_0202_46:250160176 4
315 2013_0154_10:250160394 2013_0216_26:250160718 4
316 2013_0212_2:250159907 2013_10145_11:250164626 4
317 2013_0212_32:250159937 2013_0107_6:250159831 4
318 2013_0212_32:250159937 2013_0111_12:250160049 4
319 2013_0212_32:250159937 2013_0153_11:250160352 4
320 2013_0212_55:250159960 2013_0381_5:250162690 4
321 2013_0212_55:250159960 2013_0420_6:250162738 4
322 2013_0212_7:250159912 2013_10084_6:250164723 4
323 2013_0214_29:250160683 2013_0107_8:250159833 4
324 2013_0214_29:250160683 2013_0307_20:250161028 4
325 2013_0216_10:250160702 2013_0324_2:250161035 4
326 2013_0307_11:250160607 2013_10122_3:250164747 4
327 2013_0307_20:250161028 2013_0324_2:250161035 4
328 2013_0307_8:250160604 2013_0088_8:250159760 4
329 2013_0307_8:250160604 2013_0212_55:250159960 4
330 2013_0332_27:250161100 2013_10122_3:250164747 4
331 2013_0333_17:250162439 2013_0332_42:250161115 4
332 2013_0333_17:250162439 2013_0336_23:250162476 4
333 2013_0333_17:250162439 2013_10084_6:250164723 4
334 2013_0333_3:250162424 2013_0108_7:250159843 4
335 2013_10020_2:250164715 2013_10303_1:250164466 4
336 2013_10084_6:250164723 2013_0212_2:250159907 4
337 2013_10084_6:250164723 2013_0212_79:250160650 4
338 2013_10122_3:250164747 2013_0381_5:250162690 4
339 2013_10303_2:250164467 2013_10124_2:250164352 4
340 2013_10306_3:250164474 2013_0008_20:250162088 4
341 I030060A:250300227 I090516:250099187 4
342 I071378:250300142 I950986:250300582 4
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511 I972205:250301855 I011797:250090770 2
512 I972205:250301855 TMS15F1021P0013:250464921 2
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514 TMEB419:250253865 2013_0343_22:250162550 2
515 TMEB419:250253865 TMS14F1016P0006:250302439 2
516 TMEB419:250253865 TMS14F1284P0001:250304001 2
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518 TMS13F1106P0006:250300932 2013_0212_58:250159963 2
519 TMS14F1035P0004:250302641 TMS14F1016P0006:250302439 2
520 TMS14F1063P0003:250302894 TMS14F1154P0001:250301818 2
521 TMS14F1113P0002:250301450 TMS14F1213P0007:250302244 2
522 TMS14F1157P0002:250301832 TMS14F1189P0001:250302072 2
523 TMS14F1166P0002:250301867 TMS14F1107P0004:250301360 2
524 TMS14F1166P0002:250301867 TMS14F1169P0001:250301880 2
525 TMS14F1174P0011:250301981 TMS14F1153P0003:250301810 2
526 TMS14F1174P0011:250301981 TMS14F1229P0004:250303381 2
527 TMS14F1174P0011:250301981 TMS14F1283P0002:250303981 2
528 TMS14F1176P0006:250301993 TMS14F1091P0008:250303196 2
529 TMS14F1176P0006:250301993 TMS14F1138P0005:250301674 2
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531 TMS14F1240P0004:250303461 TMS14F1283P0015:250303994 2
532 TMS14F1240P0005:250303462 TMS14F1174P0011:250301981 2
533 TMS14F1240P0005:250303462 TMS14F1276P0005:250303833 2
534 TMS14F1240P0005:250303462 TMS14F1283P0013:250303992 2
535 TMS14F1241P0002:250303465 TMS14F1107P0004:250301360 2
536 TMS14F1247P0007:250303518 TMS14F1229P0004:250303381 2
537 TMS14F1288P0001:250304117 TMS14F1091P0009:250303197 2
538 TMS14F1292P0010:250304243 TMS14F1154P0002:250301819 2
539 TMS15F1069P0007:250521086 TMS15F1333P0001:250467074 2
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541 TMS15F1132P0013:250475590 TMS15F1154P0015:250518942 2
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543 TMS15F1153P0009:250465409 TMS15F1109P0003:250465275 2
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547 TMS15F1156P0014:250518954 TMS15F1104P0001:250465265 2
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549 TMS15F1159P0001:250518960 TMS15F1276P0003:250465968 2
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557 TMS15F1396P0004:250466562 TMS15F1130P0012:250475583 2
558 TMS15F1397P0001:250466568 TMS15F1103P0006:250465263 2
559 TMS15F1398P0008:250466574 TMS15F1124P0001:250465316 2
560 TMS15F1398P0008:250466574 TMS15F1130P0002:250475578 2
561 TMS18F1092P0022_A18764 TMS18F1015P0015_A18729 2
562 TMS18F1173P0009_A18858 TMS18F1026P0014_A18745 2
563 TMS18F1173P0009_A18858 TMS18F1047P0024_A18757 2
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649 2013_10303_1:250164466 2013_10309_1:250164479 1
650 2013_10303_2:250164467 2013_0050_8:250169056 1
651 2013_10303_2:250164467 2013_0087_14:250159588 1
652 2013_10303_2:250164467 2013_10064_25:250164276 1
653 2013_10303_3:250164468 2013_0332_27:250161100 1
654 2013_10303_3:250164468 2013_0339_9:250162492 1
655 2013_10303_3:250164468 2013_10063_19:250164239 1
656 2013_10306_3:250164474 2013_0332_42:250161115 1
657 2013_10306_3:250164474 TMS13F1391P0039:250465621 1
658 2013_10309_1:250164479 2013_0307_11:250160607 1
659 2013_10309_1:250164479 2013_0307_6:250160602 1
660 I000070:250300358 2013_0053_15:250162223 1
661 I000070:250300358 2013_0053_2:250162210 1
662 I000070:250300358 TMS15F1318P0009:250476726 1
663 I000388:250300404 I070126:250164023 1
664 I011335:250300345 I011371:250090769 1
665 I011368:250300318 I011412:250300323 1
666 I011797:250090770 2013_0053_10:250162218 1
667 I030060:250090848 2013_10377_18:250164522 1
668 I030060A:250300227 2013_10020_1:250164714 1
669 I070004:250164022 I070004:250164022 1
670 I070337:250300150 2013_0053_2:250162210 1
671 I070337:250300150 2013_0160_5:250160400 1
672 I070337:250300150 2013_0343_26:250162554 1
673 I090516:250099187 2013_0160_5:250160400 1
674 I090516:250099187 TMS14F1284P0019:250304019 1
675 I090581:250099170 TMEB693:250253991 1
676 I30572:250253643 2013_0160_5:250160400 1
677 I30572:250253643 I30572:250253643 1
678 I9102325:250304510 I9102325:250304510 1
679 I91934:250304625 TMEB1:250253593 1
680 I91934:250304625 TMEB9:250253601 1
681 I930007:250090827 2013_0053_15:250162223 1
682 I930007:250090827 2013_10122_5:250164749 1
683 I930007:250090827 TMS15F1021P0013:250464921 1
684 I930007:250090827 TMS15F1130P0012:250475583 1
685 I930007:250090827 TMS15F1142P0012:250475597 1
686 I940006:250090826 I960557:250300551 1
687 I940239:250300602 I91934:250304625 1
688 I940330:250090830 I011663:250300298 1
689 I940330:250090830 I950379:250300567 1
690 I940561:250090825 I950971:250300591 1
691 I950379:250300567 I8902195:250304523 1
692 I972205:250301855 I070337:250300150 1
693 I972205:250301855 TMEB419:250253865 1
694 I972205:250301855 TMS15F1130P0012:250475583 1
695 I972205:250301855 TMS15F1142P0012:250475597 1
696 I972205:250301855 TMS15F1329P0005:250476750 1
697 I974779:250300473 I980002:250300438 1
698 I992123:250300419 I991702:250090772 1
699 I993073:250300423 I011412:250300323 1
700 M980068:250300452 M980068:250300452 1
701 MM970646:250300486 I930265:250164038 1
702 TMEB1:250253593 I4_2_1425:250304632 1
703 TMEB117:250253666 I011412:250300323 1
704 TMEB117:250253666 I950379:250300567 1
705 TMEB419:250253865 2013_10063_13:250164233 1
706 TMEB419:250253865 2013_10122_5:250164749 1
707 TMEB419:250253865 I940561:250090825 1
708 TMEB693:250253991 I070593:250300163 1
709 TMEB693:250253991 TMEB693:250253991 1
710 TMEB9:250253601 I30572:250253643 1
711 TMS13F1106P0006:250300932 2013_0212_72:250159977 1
712 TMS13F1106P0006:250300932 2013_0214_29:250160683 1
713 TMS13F1106P0006:250300932 2013_0216_15:250160707 1
714 TMS13F1106P0006:250300932 2013_0216_26:250160718 1
715 TMS13F1227P0119_A19491 2013_0053_15:250162223 1
716 TMS14F1035P0004:250302641 TMS14F1310P0004:250301178 1
717 TMS14F1063P0003:250302894 TMS14F1157P0002:250301832 1
718 TMS14F1113P0002:250301450 TMS14F1138P0005:250301674 1
719 TMS14F1113P0002:250301450 TMS14F1157P0002:250301832 1
720 TMS14F1113P0002:250301450 TMS14F1169P0001:250301880 1
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 1
%>%
correctedped 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)) %$% union(SireID,DamID) %>%
tibble(ParentsMoreThanFiveProg=.) %>%
mutate(Cohort=NA,
Cohort=ifelse(grepl("TMS20",ParentsMoreThanFiveProg,ignore.case = T),"TMS20",
ifelse(grepl("TMS19",ParentsMoreThanFiveProg,ignore.case = T),"TMS19",
ifelse(grepl("TMS18",ParentsMoreThanFiveProg,ignore.case = T),"TMS18",
ifelse(grepl("TMS17",ParentsMoreThanFiveProg,ignore.case = T),"TMS17",
ifelse(grepl("TMS16",ParentsMoreThanFiveProg,ignore.case = T),"TMS16",
ifelse(grepl("TMS15",ParentsMoreThanFiveProg,ignore.case = T),"TMS15",
ifelse(grepl("TMS14",ParentsMoreThanFiveProg,ignore.case = T),"TMS14",
ifelse(grepl("TMS13|2013_",ParentsMoreThanFiveProg,
ignore.case = T),"TMS13","GGetc"))))))))) %>%
count(Cohort, name="ParentsWithLeast5offspring")
# A tibble: 4 × 2
Cohort ParentsWithLeast5offspring
<chr> <int>
1 GGetc 82
2 TMS13 77
3 TMS14 19
4 TMS15 13
%>%
correctedped count(SireID,DamID) %>%
arrange(desc(n)) %$% 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 7
%>%
correctedped 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
%>%
correctedped write.table(.,here::here("output","verified_ped.txt"),
row.names = F, col.names = T, quote = F)
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_2.0.1 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