Abstract
Motivation Genome-wide, epigenome-wide and gene-environment association studies are plagued with the problems of confounding and causality. Although those problems have received considerable attention in each application field, no consensus have emerged on which approaches are the most appropriate to solve this problem. Current methods use approximate heuristics for estimating confounders, and often ignore correlation between confounders and primary variables, resulting in suboptimal power and precision.
Results In this study, we developed a least-squares estimation theory of confounder estimation using latent factor models, providing a unique framework for several categories of genomic data. Based on statistical learning methods, the proposed algorithms are fast and efficient, and can be proven to provide optimal solutions mathematically. In simulations, the algorithms outperformed commonly used methods based on principal components and surrogate variable analysis. In analysis of methylation profiles and genotypic data, they provided new insights on the molecular basis of diseases and adaptation of humans to their environment.
Availability and implementation Software is available in the R package lfmm at https://bcm-uga.github.io/lfmm/.
