Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

Abstract

1.AbstractGenome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary data has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate (FDR) can be a more powerful approach as sample sizes increase and many associations are expected in each study. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions. We relax the distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary data from any continuous distribution (“Flexible cFDR”). Our method is iterative, whereby additional layers of auxiliary data can be leveraged in turn. Through simulations we show that flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional data to find additional genetic associations for asthma, which we validated in the larger UK Biobank data resource.

fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS

“…The methods used to estimate the cumulative densities in equation ( 3) vary across approaches. In the original cFDR approach they are estimated using empirical cumulative density functions 1,10,11 whilst in Flexible cFDR they are estimated using kernel density estimation 9 .…”

“…Motivated by the enrichment of GWAS SNPs in particular functional genomic annotations 14 , Flexible cFDR was developed to extend the usage of the cFDR approach to the accelerating field of functional genomics 9 . However, at-present no cFDR methodology exists that permits binary auxiliary data, meaning that the approach cannot currently be used to leverage auxiliary data with a binary representation, such as whether SNPs are synonymous or non-synonymous or whether they reside in regions of the genome with specific activity states.…”