TwinEQTL: ultrafast and powerful association analysis for eQTL and GWAS in twin studies

Abstract

We develop a computationally efficient alternative, TwinEQTL, to a linear mixed-effects model for twin genome-wide association study data. Instead of analyzing all twin samples together with linear mixed-effects model, TwinEQTL first splits twin samples into 2 independent groups on which multiple linear regression analysis can be validly performed separately, followed by an appropriate meta-analysis-like approach to combine the 2 nonindependent test results. Through mathematical derivations, we prove the validity of TwinEQTL algorithm and show that the correlation between 2 dependent test statistics at each single-nucleotide polymorphism is independent of its minor allele frequency. Thus, the correlation is constant across all single-nucleotide polymorphisms. Through simulations, we show empirically that TwinEQTL has well controlled type I error with negligible power loss compared with the gold-standard linear mixed-effects models. To accommodate expression quantitative loci analysis with twin subjects, we further implement TwinEQTL into an R package with much improved computational efficiency. Our approaches provide a significant leap in terms of computing speed for genome-wide association study and expression quantitative loci analysis with twin samples.