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Integration of Functional Annotation Score into GWAS

Here we use childhood asthma as an example. Following are detailed code and procedure to integrate functional annotation score (CADD/Eigen) into GWAS.

1. Download GWAS summary statistics

This GWAS result is perfomed by Nealelab based on UkBiobank data. See http://www.nealelab.is/uk-biobank.

wget https://www.dropbox.com/s/kp9bollwekaco0s/20002_1111.gwas.imputed_v3.both_sexes.tsv.bgz?dl=0 -O 20002_1111.gwas.imputed_v3.both_sexes.tsv.bgz
rename 20002_1111.gwas.imputed_v3.both_sexes.tsv.bgz 20002_1111.gwas.imputed_v3.both_sexes.tsv.gz
gunzip 20002_1111.gwas.imputed_v3.both_sexes.tsv.gz

2. QC control

To accomodate availabity of meta function score Eigen, we excluded InDel variants and SNPs on X-chromosome. In addition, we excluded SNPs with minor allele frequency less than 5%.

3. Obatin CADD/Eigen score

We use ANNOVAR to efficiently obatin Eigen scores on each SNP. ANNOVAR can be freely downloaded at http://download.openbioinformatics.org/annovar_download_form.php. Unfortunately Eigen is not included in ANNOVAR by defualt, we need to download it manually from ANNOVAR database.

tar xvfz annovar.latest.tar.gz 
perl annotate_variation.pl -buildver hg19 -downdb -webfrom annovar -eigen humandb/
perl annotate_variation.pl -filter asthma.variants.avinput humandb/ -dbtype eigen -build hg19

CADD score can be easily computed by running scripts from https://cadd.gs.washington.edu/download.

4. Integration Methods

Now your gwas dataset should contain at least the following 7 columns chr,pos,ref,alt,tstat,pval,anno.

Function.R contains four integration methods: inverse-variance meta-analysis, Fisher's method, straitified FDR and weighted p-value approach. For meta-analysis and Fisher's method to run, we must obtain p-values for functional annotations scores. However, the functional annoations are

i) not necessarily normal

ii) negative values indicate "less deleterious" and therefore should have large p-values.

Instead we use an inverse-normal transformation of the raw annotation scores.

# Inverse-normal transformation
gwas$anno.inv <- IVT(gwas$anno,gwas$tstat)

# Meta Analysis
gwas$meta.p <- Meta(gwas$tstat, gwas$anno.inv)

# Fisher
gwas$fisher.p <- Fisher(gwas$pval, gwas$anno.inv)

# sFDR
gwas$sfdr.p <- sFDR(gwas$pval, gwas$anno, c=0.05, gamma=0.05,nbins=2)

# wFDR
gwas$wfdr.p <- wFDR(gwas$pval, gwas$anno)