Multiple Trait Covariance Association Test Identifies Gene Ontology Categories Associated with Chill Coma Recovery Time in Drosophila melanogaster

Izel Fourie Sørensen,Peter Sørensen,Stefan M Edwards,Palle Duun Rohde

doi:10.1038/s41598-017-02281-3

Abstract

The genomic best linear unbiased prediction (GBLUP) model has proven to be useful for prediction of complex traits as well as estimation of population genetic parameters. Improved inference and prediction accuracy of GBLUP may be achieved by identifying genomic regions enriched for causal genetic variants. We aimed at searching for patterns in GBLUP-derived single-marker statistics, by including them in genetic marker set tests, that could reveal associations between a set of genetic markers (genomic feature) and a complex trait. GBLUP-derived set tests proved to be powerful for detecting genomic features, here defined by gene ontology (GO) terms, enriched for causal variants affecting a quantitative trait in a population with low degree of relatedness. Different set test approaches were compared using simulated data illustrating the impact of trait- and genomic feature-specific factors on detection power. We extended the most powerful single trait set test, covariance association test (CVAT), to a multiple trait setting. The multiple trait CVAT (MT-CVAT) identified functionally relevant GO categories associated with the quantitative trait, chill coma recovery time, in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel.

Highlights

It appears that markers associated with trait variation are not uniformly distributed throughout the genome, but enriched in genes that are connected in biological pathways[3,4,5,6,7]
Multiple feature sets can be fitted in the model, such as grouping markers based on their minor allele frequency[19, 20] or prior QTL information[16]
We derived a multiple trait genomic best linear unbiased prediction (GBLUP) set test (MT-covariance association test (CVAT)) and used it to identify genomic features associated with a quantitative trait phenotype, chill coma recovery time (CCRT), in the unrelated, sequenced inbred lines of the Drosophila Genetic Reference Panel (DGRP)

Summary

Introduction

It appears that markers associated with trait variation are not uniformly distributed throughout the genome, but enriched in genes that are connected in biological pathways[3,4,5,6,7]. We have previously evaluated a number of GBLUP-derived set tests on a binary outcome (i.e. disease trait) using high-density single nucleotide polymorphisms (SNPs) from genotyping arrays[19]. A multiple trait GBLUP model[21, 22] can be fitted This can potentially increase the accuracy of the total genomic value[21, 22] and thereby the single marker effect, which in turn will lead to more accurate test statistics for genetic marker sets, thereby increasing detection power of the set test. Different set tests were evaluated and compared using simulated data generated from DGRP genotypes, focussing on factors specific to genomic features (e.g. the number, location and effect sizes of the true causal variants in the feature) that influence the power of set tests to detect genomic features affecting the trait phenotype. We derived a multiple trait GBLUP set test (MT-CVAT) and used it to identify genomic features associated with a quantitative trait phenotype, chill coma recovery time (CCRT), in the unrelated, sequenced inbred lines of the DGRP

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Discussion

Conclusion