Second-order group knockoffs with applications to GWAS

TL;DR

This paper develops advanced second-order group knockoff methods tailored for GWAS data, improving variable selection accuracy and computational efficiency, with applications demonstrated on UK Biobank data.

Contribution

It introduces new algorithms and software for constructing second-order group knockoffs specifically suited for GWAS, enhancing multivariate analysis of correlated genetic variants.

Findings

Effective group knockoff algorithms for GWAS data

Significant computational savings with correlation matrix approximations

Successful application to UK Biobank albuminuria data

Abstract

Conditional testing via the knockoff framework allows one to identify -- among large number of possible explanatory variables -- those that carry unique information about an outcome of interest, and also provides a false discovery rate guarantee on the selection. This approach is particularly well suited to the analysis of genome wide association studies (GWAS), which have the goal of identifying genetic variants which influence traits of medical relevance. While conditional testing can be both more powerful and precise than traditional GWAS analysis methods, its vanilla implementation encounters a difficulty common to all multivariate analysis methods: it is challenging to distinguish among multiple, highly correlated regressors. This impasse can be overcome by shifting the object of inference from single variables to groups of correlated variables. To achieve this, it is necessary to construct "group knockoffs." While successful examples are already documented in the literature, this paper substantially expands the set of algorithms and software for group knockoffs. We focus in particular on second-order knockoffs, for which we describe correlation matrix approximations that are appropriate for GWAS data and that result in considerable computational savings. We illustrate the effectiveness of the proposed methods with simulations and with the analysis of albuminuria data from the UK Biobank. The described algorithms are implemented in an open-source Julia package Knockoffs.jl, for which both R and Python wrappers are available.