Last updated: 2020-12-04
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Knit directory: IITA_2020GS/
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| Rmd | 79b6430 | wolfemd | 2020-12-04 | Update the analysis of rate-of-gain. Include regressions and output |
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| Rmd | 9718666 | wolfemd | 2020-12-03 | Refresh BLUPs and GBLUPs with trials harvested so far. Include |
Follow outlined GenomicPredictionChecklist and previous pipeline to process cassavabase data for ultimate genomic prediction.
Below we will clean and format training data.
This will update genomic predictions relative to the earlier analysis done in Sept. 2020.
Downloaded all IITA field trials with studyYear 2018, 2019, 2020.
DatabaseDownload_2020Dec03/ uploaded to Cassavabase FTP server.2018 trials: probably redundant to those previously downloaded in July 2019 for the genomic prediction of GS C4. In case some trials weren’t harvested as of July 2019, use the 2018 trials downloaded here instead of the ones from 2019.
2019 trials: All trials harvested and uploaded as of now (Dec. 3rd, 2020) are to be added to refresh the genomic predictions.
2020 trials: If any current trials already have e.g. disease data, will use it.
Downloaded all IITA field trials.
DatabaseDownload_2020Sep15/ uploaded to Cassavabase FTP server.Possible database bug? The entire >500Mb phenotype dataset for IITA downloaded without a problem. However, I’m getting an “server error” message trying to download the corresponding meta-data in one chunk.
Solution: combine meta-data downloaded for “all” trials in July 2019, with meta-data download for the 2018-2020 period done Dec 3rd, 2020. Feed joined file to readDBdata().
metadata19 <- read.csv("ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/DatabaseDownload_72419/2019-07-24T144144metadata_download.csv",
na.strings = c("#VALUE!", NA, ".", "", " ", "-", "\""), stringsAsFactors = F)
metadata20 <- read.csv("ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/DatabaseDownload_2020Dec03/2020-12-03T094057metadata_download.csv",
na.strings = c("#VALUE!", NA, ".", "", " ", "-", "\""), stringsAsFactors = F)
metadata19 %>% # remove lines for trials in the 2020 download
filter(!studyName %in% metadata20$studyName) %>% bind_rows(metadata20) %>% # ensure no duplicate lines
distinct %>% # write to disk
write.csv(., here::here("output", "all_iita_metadata_Dec2020.csv"), row.names = F)rm(list = ls())
library(tidyverse)
library(magrittr)
source(here::here("code", "gsFunctions.R"))Read DB data directly from the Cassavabase FTP server.
## From September
dbdata_sep2020 <- readDBdata(phenotypeFile = paste0("ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/",
"DatabaseDownload_2020Sep15/2020-09-15T185453phenotype_download.csv"), metadataFile = here::here("output",
"all_iita_metadata.csv"))
## New, December download
dbdata_dec2020 <- readDBdata(phenotypeFile = paste0("ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/",
"DatabaseDownload_2020Dec03/2020-12-03T102130phenotype_download.csv"), metadataFile = here::here("output",
"all_iita_metadata_Dec2020.csv"))
# dbdata_dec2020 %>% filter(!is.na(plantingDate)) %>%
# count(studyName,studyYear,locationName) dbdata_dec2020 %>%
# select(studyYear,studyDbId) table(unique(dbdata_dec2020$studyName) %in%
# unique(dbdata_sep2020$studyName)) FALSE TRUE 42 371
# 42 'new trials' table(dbdata_dec2020$observationUnitDbId %in%
# dbdata_sep2020$observationUnitDbId) FALSE TRUE 5823 175964
# length(dbdata_dec2020$observationUnitDbId)==length(unique(dbdata_dec2020$observationUnitDbId))
# any of the 'new' plots register as being for trials already in the sep data?
# yes, 1 trial (see below)
# table(unique(dbdata_dec2020$studyName[!dbdata_dec2020$observationUnitDbId %in%
# dbdata_sep2020$observationUnitDbId]) %in% unique(dbdata_sep2020$studyName))
# FALSE TRUE 42 1
### probably doesn't matter, just replace the old with the new
dbdata <- bind_rows(dbdata_sep2020 %>% mutate(replicate = as.integer(replicate),
rowNumber = as.integer(rowNumber), colNumber = as.integer(colNumber)) %>% mutate(across(contains("CO_334"),
as.numeric)) %>% anti_join(dbdata_dec2020 %>% distinct(observationUnitDbId, studyName,
studyYear, studyDbId)), dbdata_dec2020)Make TrialType Variable
dbdata <- makeTrialTypeVar(dbdata)
dbdata %>% count(TrialType) TrialType n
1 AYT 53226
2 CET 70458
3 Conservation 997
4 CrossingBlock 1546
5 ExpCET 1865
6 GeneticGain 51905
7 NCRP 3872
8 PYT 61254
9 SN 155596
10 UYT 74378
11 <NA> 77651Looking at the studyName’s of trials getting NA for TrialType, which can’t be classified at present.
Here is the list of trials I am not including.
dbdata %>% filter(is.na(TrialType)) %$% unique(studyName) %>% write.csv(., file = here::here("output",
"iita_trials_NOT_identifiable_Dec2020.csv"), row.names = F)Wrote to disk a CSV in the output/ sub-directory.
Should any of these trials have been included?
Especially the following new trials (post 2018)?
dbdata %>% filter(is.na(TrialType), as.numeric(studyYear) > 2018) %$% unique(studyName) [1] "18Hawaii_Parents" "19CB1IB"
[3] "19CVS12Chitala" "19CVS12Mkondezi"
[5] "19flowexpPGR22UB" "19flowexpRedLight22UB"
[7] "19flowLightIntensityUB" "19flowPGRFeminizationIB"
[9] "19flowPGRFreqIB" "19flowPGRRatioIB"
[11] "19flowPGRRtflwrIB" "19GhanaGermplasmUB"
[13] "19GRCgermplasmUB" "19.GS.C1.C2.C3.SelGain.AB"
[15] "19HarvTimeKabangwe" "19LocalGermplasmUB"
[17] "19SN5968Chitala" "2019GXEBUKEMBA"
[19] "2019GXEMUHANGA" "2019GXENGOMA"
[21] "2019GXENYAGATARE" "2019GXERUBIRIZI"
[23] "2019GXERUBONA" "20CSV12Chitala"
[25] "20CSV12Mkondezi" "20GRCgermplasmIB"
[27] "20LocalGermplasmIB" "20PTY49Kabangwe"
[29] "Hawaii_IITA_seed_2019" "Hawaii_seed_Asia_2019"
[31] "Hawaii_seed_CIAT_2019" dbdata %<>% filter(!is.na(TrialType))
dbdata %>% group_by(programName) %>% summarize(N = n())# A tibble: 1 x 2
programName N
<chr> <int>
1 IITA 475097# 475097 plots (~155K are seedling nurseries which will be excluded from most
# analyses)Making a table of abbreviations for renaming. Since July 2019 version: added chromometer traits (L, a, b) and added branching levels count (BRLVLS) at IYR’s request.
traitabbrevs <- tribble(~TraitAbbrev, ~TraitName, "CMD1S", "cassava.mosaic.disease.severity.1.month.evaluation.CO_334.0000191",
"CMD3S", "cassava.mosaic.disease.severity.3.month.evaluation.CO_334.0000192",
"CMD6S", "cassava.mosaic.disease.severity.6.month.evaluation.CO_334.0000194",
"CMD9S", "cassava.mosaic.disease.severity.9.month.evaluation.CO_334.0000193",
"CGM", "Cassava.green.mite.severity.CO_334.0000033", "CGMS1", "cassava.green.mite.severity.first.evaluation.CO_334.0000189",
"CGMS2", "cassava.green.mite.severity.second.evaluation.CO_334.0000190", "DM",
"dry.matter.content.percentage.CO_334.0000092", "PLTHT", "plant.height.measurement.in.cm.CO_334.0000018",
"BRNHT1", "first.apical.branch.height.measurement.in.cm.CO_334.0000106", "BRLVLS",
"branching.level.counting.CO_334.0000079", "SHTWT", "fresh.shoot.weight.measurement.in.kg.per.plot.CO_334.0000016",
"RTWT", "fresh.storage.root.weight.per.plot.CO_334.0000012", "RTNO", "root.number.counting.CO_334.0000011",
"TCHART", "total.carotenoid.by.chart.1.8.CO_334.0000161", "LCHROMO", "L.chromometer.value.CO_334.0002065",
"ACHROMO", "a.chromometer.value.CO_334.0002066", "BCHROMO", "b.chromometer.value.CO_334.0002064",
"NOHAV", "plant.stands.harvested.counting.CO_334.0000010")
traitabbrevs %>% rmarkdown::paged_table()Run function renameAndSelectCols() to rename columns and remove everything unecessary
dbdata <- renameAndSelectCols(traitabbrevs, indata = dbdata, customColsToKeep = "TrialType")Standard code, recycled… should be a function?
dbdata <- dbdata %>% mutate(CMD1S = ifelse(CMD1S < 1 | CMD1S > 5, NA, CMD1S), CMD3S = ifelse(CMD3S <
1 | CMD3S > 5, NA, CMD3S), CMD6S = ifelse(CMD6S < 1 | CMD1S > 5, NA, CMD6S),
CMD9S = ifelse(CMD9S < 1 | CMD1S > 5, NA, CMD9S), CGM = ifelse(CGM < 1 | CGM >
5, NA, CGM), CGMS1 = ifelse(CGMS1 < 1 | CGMS1 > 5, NA, CGMS1), CGMS2 = ifelse(CGMS2 <
1 | CGMS2 > 5, NA, CGMS2), DM = ifelse(DM > 100 | DM <= 0, NA, DM), RTWT = ifelse(RTWT ==
0 | NOHAV == 0 | is.na(NOHAV), NA, RTWT), SHTWT = ifelse(SHTWT == 0 | NOHAV ==
0 | is.na(NOHAV), NA, SHTWT), RTNO = ifelse(RTNO == 0 | NOHAV == 0 | is.na(NOHAV),
NA, RTNO), NOHAV = ifelse(NOHAV == 0, NA, NOHAV), NOHAV = ifelse(NOHAV >
42, NA, NOHAV), RTNO = ifelse(!RTNO %in% 1:10000, NA, RTNO))dbdata <- dbdata %>% mutate(HI = RTWT/(RTWT + SHTWT))I anticipate this will not be necessary as it will be computed before or during data upload.
For calculating fresh root yield:
dbdata <- dbdata %>% mutate(PlotSpacing = ifelse(programName != "IITA", 1, ifelse(studyYear <
2013, 1, ifelse(TrialType %in% c("CET", "GeneticGain", "ExpCET"), 1, 0.8))))
maxNOHAV_byStudy <- dbdata %>% group_by(programName, locationName, studyYear, studyName,
studyDesign) %>% summarize(MaxNOHAV = max(NOHAV, na.rm = T)) %>% ungroup() %>%
mutate(MaxNOHAV = ifelse(MaxNOHAV == "-Inf", NA, MaxNOHAV))
write.csv(maxNOHAV_byStudy %>% arrange(studyYear), file = here::here("output", "maxNOHAV_byStudy.csv"),
row.names = F)# I log transform yield traits to satisfy homoskedastic residuals assumption of
# linear mixed models
dbdata <- left_join(dbdata, maxNOHAV_byStudy) %>% mutate(RTWT = ifelse(NOHAV > MaxNOHAV,
NA, RTWT), SHTWT = ifelse(NOHAV > MaxNOHAV, NA, SHTWT), RTNO = ifelse(NOHAV >
MaxNOHAV, NA, RTNO), HI = ifelse(NOHAV > MaxNOHAV, NA, HI), FYLD = RTWT/(MaxNOHAV *
PlotSpacing) * 10, DYLD = FYLD * (DM/100), logFYLD = log(FYLD), logDYLD = log(DYLD),
logTOPYLD = log(SHTWT/(MaxNOHAV * PlotSpacing) * 10), logRTNO = log(RTNO), PropNOHAV = NOHAV/MaxNOHAV)
# remove non transformed / per-plot (instead of per area) traits
dbdata %<>% select(-RTWT, -SHTWT, -RTNO, -FYLD, -DYLD)dbdata <- dbdata %>% mutate(MCMDS = rowMeans(.[, c("CMD1S", "CMD3S", "CMD6S", "CMD9S")],
na.rm = T)) %>% select(-CMD1S, -CMD3S, -CMD6S, -CMD9S)This step is mostly copy-pasted from previous processing of IITA-specific data.
Uses 3 flat files, which are available e.g. here. Specifically, IITA_GBStoPhenoMaster_33018.csv, GBSdataMasterList_31818.csv and NRCRI_GBStoPhenoMaster_40318.csv. I copy them to the data/ sub-directory for the current analysis.
In addition, DArT-only samples are now expected to also have phenotypes. Therefore, checking for matches in new flatfiles, deposited in the data/ (see code below).
library(tidyverse); library(magrittr)
gbs2phenoMaster<-dbdata %>%
select(germplasmName) %>%
distinct %>%
left_join(read.csv(here::here("data","NRCRI_GBStoPhenoMaster_40318.csv"),
stringsAsFactors = F)) %>%
mutate(FullSampleName=ifelse(grepl("C2a",germplasmName,ignore.case = T) &
is.na(FullSampleName),germplasmName,FullSampleName)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName) %>%
bind_rows(dbdata %>%
select(germplasmName) %>%
distinct %>%
left_join(read.csv(here::here("data","IITA_GBStoPhenoMaster_33018.csv"),
stringsAsFactors = F)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName)) %>%
bind_rows(dbdata %>%
select(germplasmName) %>%
distinct %>%
left_join(read.csv(here::here("data","GBSdataMasterList_31818.csv"),
stringsAsFactors = F) %>%
select(DNASample,FullSampleName) %>%
rename(germplasmName=DNASample)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName)) %>%
bind_rows(dbdata %>%
select(germplasmName) %>%
distinct %>%
mutate(germplasmSynonyms=ifelse(grepl("^UG",germplasmName,ignore.case = T),
gsub("UG","Ug",germplasmName),germplasmName)) %>%
left_join(read.csv(here::here("data","GBSdataMasterList_31818.csv"),
stringsAsFactors = F) %>%
select(DNASample,FullSampleName) %>%
rename(germplasmSynonyms=DNASample)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName)) %>%
bind_rows(dbdata %>%
select(germplasmName) %>%
distinct %>%
mutate(germplasmSynonyms=ifelse(grepl("^TZ",germplasmName,
ignore.case = T),
gsub("TZ","",germplasmName),germplasmName)) %>%
left_join(read.csv(here::here("data","GBSdataMasterList_31818.csv"),
stringsAsFactors = F) %>%
select(DNASample,FullSampleName) %>%
rename(germplasmSynonyms=DNASample)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName)) %>%
distinct %>%
left_join(read.csv(here::here("data","GBSdataMasterList_31818.csv"),
stringsAsFactors = F) %>%
select(FullSampleName,OrigKeyFile,Institute) %>%
rename(OriginOfSample=Institute)) %>%
mutate(OrigKeyFile=ifelse(grepl("C2a",germplasmName,ignore.case = T),
ifelse(is.na(OrigKeyFile),"LavalGBS",OrigKeyFile),
OrigKeyFile),
OriginOfSample=ifelse(grepl("C2a",germplasmName,ignore.case = T),
ifelse(is.na(OriginOfSample),"NRCRI",OriginOfSample),
OriginOfSample))
## NEW: check for germName-DArT name matches
germNamesWithoutGBSgenos<-dbdata %>%
select(programName,germplasmName) %>%
distinct %>%
left_join(gbs2phenoMaster) %>%
filter(is.na(FullSampleName)) %>%
select(-FullSampleName)
germNamesWithDArT<-germNamesWithoutGBSgenos %>%
inner_join(read.table(here::here("data","chr1_RefPanelAndGSprogeny_ReadyForGP_72719.fam"), header = F, stringsAsFactors = F)$V2 %>%
grep("TMS16|TMS17|TMS18|TMS19|TMS20",.,value = T, ignore.case = T) %>%
tibble(dartName=.) %>%
separate(dartName,c("germplasmName","dartID"),"_",extra = 'merge',remove = F)) %>%
group_by(germplasmName) %>%
slice(1) %>%
ungroup() %>%
rename(FullSampleName=dartName) %>%
mutate(OrigKeyFile="DArTseqLD", OriginOfSample="IITA") %>%
select(-dartID)
print(paste0(nrow(germNamesWithDArT)," germNames with DArT-only genos"))[1] "2401 germNames with DArT-only genos"# [1] "2401 germNames with DArT-only genos"
# first, filter to just program-DNAorigin matches
germNamesWithGenos<-dbdata %>%
select(programName,germplasmName) %>%
distinct %>%
left_join(gbs2phenoMaster) %>%
filter(!is.na(FullSampleName))
print(paste0(nrow(germNamesWithGenos)," germNames with GBS genos"))[1] "9323 germNames with GBS genos"# [1] "9323 germNames with GBS genos"
# program-germNames with locally sourced GBS samples
germNamesWithGenos_HasLocalSourcedGBS<-germNamesWithGenos %>%
filter(programName==OriginOfSample) %>%
select(programName,germplasmName) %>%
semi_join(germNamesWithGenos,.) %>%
group_by(programName,germplasmName) %>% # select one DNA per germplasmName per program
slice(1) %>% ungroup()
print(paste0(nrow(germNamesWithGenos_HasLocalSourcedGBS)," germNames with local GBS genos"))[1] "8257 germNames with local GBS genos"# [1] "8257 germNames with local GBS genos"
# the rest (program-germNames) with GBS but coming from a different breeding program
germNamesWithGenos_NoLocalSourcedGBS<-germNamesWithGenos %>%
filter(programName==OriginOfSample) %>%
select(programName,germplasmName) %>%
anti_join(germNamesWithGenos,.) %>%
# select one DNA per germplasmName per program
group_by(programName,germplasmName) %>%
slice(1) %>% ungroup()
print(paste0(nrow(germNamesWithGenos_NoLocalSourcedGBS)," germNames without local GBS genos"))[1] "167 germNames without local GBS genos"# [1] "167 germNames without local GBS genos"
genosForPhenos<-bind_rows(germNamesWithGenos_HasLocalSourcedGBS,
germNamesWithGenos_NoLocalSourcedGBS) %>%
bind_rows(germNamesWithDArT)
print(paste0(nrow(genosForPhenos)," total germNames with genos either GBS or DArT"))[1] "10825 total germNames with genos either GBS or DArT"# [1] "10825 total germNames with genos either GBS or DArT"
dbdata %<>%
left_join(genosForPhenos)
# Create a new identifier, GID
## Equals the value SNP data name (FullSampleName)
## else germplasmName if no SNP data
dbdata %<>%
mutate(GID=ifelse(is.na(FullSampleName),germplasmName,FullSampleName))# going to check against SNP data
# snps<-readRDS(file=url(paste0('ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/',
# 'DosageMatrix_RefPanelAndGSprogeny_ReadyForGP_73019.rds')))
# rownames_snps<-rownames(snps); rm(snps); gc() # current matches to SNP data
# dbdata %>% distinct(GID,germplasmName,FullSampleName) %>%
# semi_join(tibble(GID=rownames_snps)) %>% nrow() # 10707 dbdata %>%
# distinct(GID,germplasmName,FullSampleName) %>%
# semi_join(tibble(GID=rownames_snps)) %>%
# filter(grepl('TMS13|2013_',GID,ignore.case = F)) %>% nrow() # 2424 TMS13 dbdata
# %>% distinct(GID,germplasmName,FullSampleName) %>%
# semi_join(tibble(GID=rownames_snps)) %>% filter(grepl('TMS14',GID,ignore.case =
# F)) %>% nrow() # 2236 TMS14 dbdata %>%
# distinct(GID,germplasmName,FullSampleName) %>%
# semi_join(tibble(GID=rownames_snps)) %>% filter(grepl('TMS15',GID,ignore.case =
# F)) %>% nrow() # 2287 TMS15 dbdata %>%
# distinct(GID,germplasmName,FullSampleName) %>%
# semi_join(tibble(GID=rownames_snps)) %>% filter(grepl('TMS18',GID,ignore.case =
# F)) %>% nrow() # 2401 TMS18WARNING: User input required! If I had preselected locations before downloading, this wouldn’t have been necessary.
Based on previous locations used for IITA analysis, but adding based on list of locations used in IYR’s trial list data/2019_GS_PhenoUpload.csv: “Ago-Owu” wasn’t used last year.
dbdata %<>% filter(locationName %in% c("Abuja", "Ago-Owu", "Ibadan", "Ikenne", "Ilorin",
"Jos", "Kano", "Malam Madori", "Mokwa", "Ubiaja", "Umudike", "Warri", "Zaria"))
nrow(dbdata) # [1] 432408[1] 432408saveRDS(dbdata, file = here::here("output", "IITA_CleanedTrialData_2020Dec03.rds"))
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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_0.5.0 stringr_1.4.0 dplyr_1.0.2
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[9] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.19 haven_2.3.1 colorspace_2.0-0
[5] vctrs_0.3.5 generics_0.1.0 htmltools_0.5.0 yaml_2.2.1
[9] utf8_1.1.4 rlang_0.4.9 later_1.1.0.1 pillar_1.4.7
[13] withr_2.3.0 glue_1.4.2 DBI_1.1.0 dbplyr_2.0.0
[17] modelr_0.1.8 readxl_1.3.1 lifecycle_0.2.0 munsell_0.5.0
[21] gtable_0.3.0 cellranger_1.1.0 rvest_0.3.6 evaluate_0.14
[25] knitr_1.30 ps_1.4.0 httpuv_1.5.4 fansi_0.4.1
[29] broom_0.7.2 Rcpp_1.0.5 promises_1.1.1 backports_1.2.0
[33] scales_1.1.1 formatR_1.7 jsonlite_1.7.1 fs_1.5.0
[37] hms_0.5.3 digest_0.6.27 stringi_1.5.3 rprojroot_2.0.2
[41] grid_4.0.2 here_1.0.0 cli_2.2.0 tools_4.0.2
[45] crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1
[49] xml2_1.3.2 reprex_0.3.0 lubridate_1.7.9.2 rstudioapi_0.13
[53] assertthat_0.2.1 rmarkdown_2.5 httr_1.4.2 R6_2.5.0
[57] git2r_0.27.1 compiler_4.0.2