Determination of Nonlinear Genetic Architecture using Compressed Sensing

TL;DR

This paper presents a novel compressed sensing-based statistical method to reconstruct complex nonlinear genetic models, including epistasis, from GWAS data, assuming sparsity in gene interactions, and demonstrates its effectiveness on real and simulated genomes.

Contribution

It introduces a generalized compressed sensing approach for nonlinear genetic architecture reconstruction, extending linear models to include gene-gene interactions with theoretical and empirical validation.

Findings

Method effectively reconstructs nonlinear genetic models.

Performance nearly optimal as per theoretical analysis.

Validated on real and simulated human genome data.

Abstract

We introduce a statistical method that can reconstruct nonlinear genetic models (i.e., including epistasis, or gene-gene interactions) from phenotype-genotype (GWAS) data. The computational and data resource requirements are similar to those necessary for reconstruction of linear genetic models (or identification of gene-trait associations), assuming a condition of generalized sparsity, which limits the total number of gene-gene interactions. An example of a sparse nonlinear model is one in which a typical locus interacts with several or even many others, but only a small subset of all possible interactions exist. It seems plausible that most genetic architectures fall in this category. Our method uses a generalization of compressed sensing (L1-penalized regression) applied to nonlinear functions of the sensing matrix. We give theoretical arguments suggesting that the method is nearly optimal in performance, and demonstrate its effectiveness on broad classes of nonlinear genetic models using both real and simulated human genomes.