vignettes/sample_overlap.md

In josefin-werme/LAVA: Local Analysis of [co]Variant Association

LAVA TUTORIAL

Josefin Werme, CTG Lab, VU Amsterdam 2020-12-17

Obtaining sample overlap from LDSC

To correct for potential sample overlap, LAVA uses the known or estimated sampling correlation (i.e. the phenotypic correlation that is due to sample overlap). If unknown, the intercept from cross-trait LDSC can be used to as an estimation of the sampling correlation (see Bulik-Sullivan et al. 2015 for details)

This tutorial will show you how you can create create a sampling correlation matrix from the results of cross-trait LDSC.

1. Perform cross-trait LDSC for all pairs of phenotypes (including each phenotype with itself)

• see LDSC github page info on how to do this).

2. Add all the output to a single file (in bash)

FILES=($(ls *_rg.log))        # assuming the output format is [phenotype]_rg.log
N=$(echo ${#FILES[@]})          # and that all combinations of penotypes have been analysed

for I in ${FILES[@]}; do
        PHEN=$(echo $I | sed 's/_rg\.log//')

        # subset log files to relevant output
        tail -n$(($N+4)) $I | head -$((N+1)) > $PHEN.rg     # (adapt as necessary)

        # add to single data set
        if [[ $I == ${FILES[0]} ]]; then
            cat $PHEN.rg > all.rg       # only including the header for the first phenotypes
        else
            cat $PHEN.rg | sed '1d' >> all.rg
        fi
done

3. Extract the intercepts and create a sampling correlation matrix (in R)

scor = read.table("all.rg",header=T)              # read in
scor = scor[,c("p1","p2","gcov_int")]             # retain key headers
scor$p1 = gsub("_munge.sumstats.gz","",scor$p1)   # assuming the munged files have format [phenotype]_munge.sumstats.gz
scor$p2 = gsub("_munge.sumstats.gz","",scor$p2)   # (adapt as necessary)

phen = unique(scor$p1)                  # obtain list of all phenotypes (assuming all combinations have been analysed)
n = length(phen)
mat = matrix(NA,n,n)                    # create matrix
rownames(mat) = colnames(mat) = phen    # set col/rownames

for (i in phen) {
        for (j in phen) {
                mat[i,j] = subset(scor, p1==i & p2==j)$gcov_int
        }
}

if (!all(t(mat)==mat)) { mat[lower.tri(mat)] = t(mat)[lower.tri(mat)] }  # sometimes there might be small differences in gcov_int depending on which phenotype was analysed as the outcome / predictor
mat = round(cov2cor(mat),5)                       # standardise
write.table(mat, "sample.overlap.txt", quote=F)   # save

josefin-werme/LAVA documentation built on July 4, 2024, 8:11 p.m.