= GWAS QC pipeline = (((#MediaWiki # Removed all bad samples (taken from Mathieu). #*

p-link --bfile GvNL_250111 --remove GvNL_bad_samples.txt --make-bed --out GvNL_good_samples

# Update family information #*

p-link --bfile GvNL_samples --update-ids GvNL_family_update.txt  --make-bed --out GvNL_good_fam

# Set trio relationships #*

p-link --bfile GvNL_good_fam --update-parents GvNL_parents_update.txt  --make-bed --out GvNL_good_fam_par

# Manual check for suspicious individuals (not in trios) and remove them #*

p-link --bfile GvNL_good_fam_par --remove GvNL_suspicious.txt  --make-bed --out GvNL_good_fam_par_susp

#* After checking suspicious individuals with Mathieu, turns out it was 1 typo and 1 filtered low call rate (93%). Therefore, only the low callrate individual was screened out after all. # Check and update sex information ## PLINK sex check ##*

p-link --bfile GvNL_good_fam_par_susp --check-sex --make-bed

## Crosscheck with information from Genome Studio provided by Mathieu ##* 100% match between plink and Genome Studio when sex information is within plink threshold ##* 2 pairs of parents swapped ##* 1 individual tagged as male instead of female by both Genome Studio and plink. Tagged as suspicious in fail-sexcheck-qc.txt ## Update sex information for children and swapped individuals ##*

p-link --bfile GvNL_good_fam_par_susp --update-sex GvNL_sex_update.txt --make-bed --out GvNL_raw_final

# Identification of individuals with elevated missing data rates or outlying heterozygosity rate (See Anderson, NP2010, p.1569) => How does this compare with the PLINK suggested approach [[http://pngu.mgh.harvard.edu/~purcell/plink/thresh.shtml here]] ## Get missing data information:

p-link --bfile GvNL_raw_final --missing --out GvNL_raw_final

## Get heterozygocity information:

p-link --bfile GvNL_raw_final --het --out GvNL_raw_final

## Calculate the observed heterozygosity rate per individual using the formula (N(NM) − O(Hom))/N(NM) and plot the missing SNPs vs heterozygocity rate for eyeball inspection. ##* In R:

het <- read.table("GvNL_raw_final.het", head=TRUE)
mis <- read.table("GvNL_raw_final.imiss", head=TRUE)
het$HET_RATE = (het$"N.NM." - het$"O.HOM.")/het$"N.NM."
plot (mis$F_MISS,het$HET_RATE, xlab="Missing SNPs", ylab="Heterozygocity Rate")

## No samples with callrate < 95% (Filtered in lab) ## Put samples where heterozygocity rate is outside 3 standard dev from the mean (Anderson, NP2010, p.1569) in fail-het-3sd-qc.txt, along with their deviation from the mean ##* In R:

het_fail = subset(het, (het$HET_RATE < mean(het$HET_RATE)-3*sd(het$HET_RATE)) | (het$HET_RATE > mean(het$HET_RATE)+3*sd(het$HET_RATE)))
het_fail$HET_DST = (het_fail$HET_RATE-mean(het$HET_RATE))/sd(het$HET_RATE)
write.table(het_fail, "fail-het-qc.txt", row.names=FALSE)

##* A total of 5 individuals failed. Worst failure has a distance of -3.74sd from the mean. # Check inheritance and duplicates ## Prune SNPs for LD ##*

p-link --bfile GvNL_raw_final --indep-pairwise 50 5 0.2 --out GvNL_raw_final

## Generate pairwise Identity-By-State (IBS) metrics using pruned SNPs only ##*

p-link --bfile GvNL_raw_final --extract GvNL_raw_final.prune.in --genome --out GvNL_raw_final

## Check trio inheritance based on IBS: Check the Pi_HAT value for the individuals (unrelated individuals =~ 0, parent <-> child =~ 0.5, siblings =~0.5, twins =~ 1.0). Done with homemade perl script. ##* 2 Families are not real families (G34c (child) is wrong and A56b (mother) is wrong) ##* Duplicated family: R24 == R25 ## Check trio inheritance based on Mendelian segregation ##*

 p-link --file GvNL_raw_final --me 0.05 0.1 --make-bed --out GvNL_inheritance

##* A diff between GvNL_raw_final.fam and GvNL_inheritance.fam confirms IBS version: families G34 and A56 have been removed. }}}