Whole-genome modeling accurately predicts quantitative traits in plants

TL;DR

This study demonstrates that whole-genome modeling can accurately predict quantitative traits in plants, supporting the infinitesimal model and revealing many small-effect mutations influence phenotypes, which benefits breeding and adaptation research.

Contribution

The paper introduces the novel WhoGEM method, providing experimental validation for the infinitesimal model in plant quantitative trait prediction.

Findings

Phenotypes can be predicted using genome-wide admixture patterns.

Many small-effect mutations underlie quantitative traits.

Supports the infinitesimal model for plant adaptation.

Abstract

Understanding the relationship between genomic variation and variation in phenotypes for quantitative traits such as physiology, yield, fitness or behavior, will provide important insights for both predicting adaptive evolution and for breeding schemes. A particular question is whether the genetic variation that influences quantitative phenotypes is typically the result of one or two mutations of large effect, or multiple mutations of small effect. In this paper we explore this issue using the wild model legume Medicago truncatula. We show that phenotypes, such as quantitative disease resistance, can be well-predicted using genome-wide patterns of admixture, from which it follows that there must be many mutations of small effect. Our findings prove the potential of our novel 'whole-genome modeling' -WhoGEM- method and experimentally validate, for the first time, the infinitesimal model as a mechanism for adaptation of quantitative phenotypes in plants. This insight can accelerate breeding and biomedicine research programs.