Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies

TLDR

GWAS false positives can be controlled by mixed linear models that account for population structure and kinship, but this adjustment can reduce true positives; a modified MLMM approach includes multiple markers as covariates to partially mitigate this confounding. The study aims to eliminate confounding in GWAS by iteratively applying a fixed‑effect model and a random‑effect model. The method iteratively applies a fixed‑effect model that tests one marker while controlling for associated markers, and a random‑effect model that estimates those markers to define kinship, unifying p‑values at each step; this iterative procedure is called FarmCPU. FarmCPU outperforms existing methods in statistical power and achieves linear‑time computation, enabling analysis of datasets with half a million individuals and markers in under three days.

Abstract

False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days.