Marker-based estimation of heritability in immortal populations

TL;DR

This paper introduces a new mixed model approach at the individual plant level for estimating heritability in immortal populations, improving accuracy over traditional genotypic mean methods and enhancing genomic prediction.

Contribution

It proposes a novel methodology using mixed models at individual plant or plot level, addressing limitations of current approaches that require genotypic means.

Findings

Mixed models at individual level yield more realistic heritability estimates.

Standard errors are up to 13 times smaller with the new approach.

Genomic prediction accuracy increases by up to 49% using the proposed method.

Abstract

Heritability is a central parameter in quantitative genetics, both from an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within and between genotype variability. This approach estimates broad-sense heritability, and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here, is to use mixed models at individual plant or plot level. Using statistical arguments, simulations and real data we investigate the feasibility of both approaches, and how these affect genomic prediction with G-BLUP and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For GWAS on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.