Detecting Epistatic Selection with Partially Observed Genotype Data Using Copula Graphical Models

TL;DR

This paper introduces a novel statistical method using copula graphical models to detect epistatic selection in high-dimensional genotype data, effectively handling missing data and applied to plant genomes.

Contribution

The authors develop a penalized Gaussian copula graphical model approach for reconstructing networks of epistatic interactions from genotype data, including an efficient multi-core implementation.

Findings

Successfully applied to simulated data, Arabidopsis thaliana, and maize datasets.

Demonstrates ability to identify epistatic interactions beyond linkage disequilibrium.

Provides an open-source R package for broader use.

Abstract

Recombinant Inbred Lines derived from divergent parental lines can display extensive segregation distortion and long-range linkage disequilibrium (LD) between distant loci. These genomic signatures are consistent with epistatic selection during inbreeding. Epistatic interactions affect growth and fertility traits or even cause complete lethality. Detecting epistasis is challenging as multiple testing approaches are under-powered and true long-range LD is difficult to distinguish from drift. Here we develop a method for reconstructing an underlying network of genomic signatures of high-dimensional epistatic selection from multi-locus genotype data. The network captures the conditionally dependent short- and long-range LD structure and thus reveals "aberrant" marker-marker associations that are due to epistatic selection rather than gametic linkage. The network estimation relies on penalized Gaussian copula graphical models, which accounts for a large number of markers p and a small number of individuals n. A multi-core implementation of our algorithm makes it feasible to estimate the graph in high-dimensions also in the presence of significant portions of missing data. We demonstrate the efficiency of the proposed method on simulated datasets as well as on genotyping data in A.thaliana and maize. In addition, we implemented the method in the R package netgwas which is freely available at https://CRAN.R-project.org/package=netgwas.