Abstract

The ability to predict quantitative trait phenotypes from molecular polymorphism data will revolutionize evolutionary biology, medicine and human biology, and animal and plant breeding. Efforts to map quantitative trait loci have yielded novel insights into the biology of quantitative traits, but the combination of individually significant quantitative trait loci typically has low predictive ability. Utilizing all segregating variants can give good predictive ability in plant and animal breeding populations, but gives little insight into trait biology. Here, we used the Drosophila Genetic Reference Panel to perform both a genome wide association analysis and genomic prediction for the fitness-related trait chill coma recovery time. We found substantial total genetic variation for chill coma recovery time, with a genetic architecture that differs between males and females, a small number of molecular variants with large main effects, and evidence for epistasis. Although the top additive variants explained 36% (17%) of the genetic variance among lines in females (males), the predictive ability using genomic best linear unbiased prediction and a relationship matrix using all common segregating variants was very low for females and zero for males. We hypothesized that the low predictive ability was due to the mismatch between the infinitesimal genetic architecture assumed by the genomic best linear unbiased prediction model and the true genetic architecture of chill coma recovery time. Indeed, we found that the predictive ability of the genomic best linear unbiased prediction model is markedly improved when we combine quantitative trait locus mapping with genomic prediction by only including the top variants associated with main and epistatic effects in the relationship matrix. This trait-associated prediction approach has the advantage that it yields biologically interpretable prediction models.

Highlights

  • The ability to accurately predict phenotypes for quantitative traits from genome wide polymorphism data will revolutionize evolutionary biology, medicine and human biology, as well as breeding of agriculturally important plant and animal species

  • We find substantial total genetic variation for chill coma recovery time, with a genetic architecture that differs between males and females and includes alleles with large additive effects as well evidence for epistasis [21]

  • We find significant genetic variation (P = 1.07 x 10−52 for the between line variance and P = 1.40 x 10−6 for the sex by line interaction variance) for chill coma recovery time (Fig 1), with a broad sense heritability (± SE) of H^ 2 = 0.35 (± 0.04) (S2 Table), similar to that previously reported for 157 Drosophila melanogaster Genetic Reference Panel (DGRP) lines [17]

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Summary

Introduction

The ability to accurately predict phenotypes for quantitative traits from genome wide polymorphism data will revolutionize evolutionary biology, medicine and human biology, as well as breeding of agriculturally important plant and animal species. The most commonly used experimental approach to dissect the genotype-phenotype map has been to identify individual quantitative trait loci (QTLs) by linkage to, or association with, segregating molecular markers in mapping populations [1]. These studies evaluate the null hypothesis that variants are not associated with differences in trait means, and rejection of the null hypothesis gives biological insight into genes and genetic networks affecting naturally segregating quantitative variation. When applied to human quantitative traits and complex diseases, this method explains a much greater fraction of the total heritability than single marker analyses [6, 7]. Genomic selection can massively increase genetic progress and is currently widely utilized in applied breeding programs

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