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

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.

Environmental variation partitioned into separate heritable components

“…(); Sørensen et al. (), which counts the number of genetic markers in a particular GO term that are associated with the trait. The enrichment score was computed as, where is the number of markers within the GO term, is the i th single marker P ‐value, t 0 is the significance threshold (here ), and is an indicator function that takes the value one if the argument is satisfied.…”

“…Functional categories, here GO categories, were tested for enrichment of associated SNPs, that is SNPs with P-values < 1 x 10 −5 . We used a count-based set test approach (previously described by Rohde et al (2016); Sørensen et al (2017), which counts the number of genetic markers in a particular GO term that are associated with the trait. The enrichment score was computed as,…”

Comparative analyses of sperm DNA methylomes among human, mouse and cattle provide insights into epigenomic evolution and complex traits

“…Since the complex phenotypes being studied are highly polygenic or even omnigenic [66], we employed the following sum-based marker-set test approach to examine the enrichment of GWAS signals in a given genomic features (e.g., a list of HMRs or genes). Previous studies demonstrated that this approach had higher power or at least equal to many commonly used marker-set test methods (e.g., count-based, score-based and coviance-based) in human [67], Drosophila melanogaster [68] and livestocks [69][70][71], particularly in the highly polygenic traits.…”

“…This method is similar to the popular linkage disequilibrium (LD) score regression [72], it analysed the genome-wide polygenic signals rather than a subset of SNPs that pass a certain significance threshold. It controlled LD patterns among SNPs and SNP-set sizes through applying the following cyclical permutation strategy, as described previously [67,68]. Briefly, we first ordered the test statistics (i.e., t 2 ) for all markers on the basis of their physical positions (i.e., t 2 1 , t 2 2 , ⋯ t 2 mÀ1 , t 2 m ).…”

Prediction of complex phenotypes using theDrosophilametabolome

“…grouping genetic variants into functional pathways, can increase the prediction accuracy markedly. [33][34][35][36][37][38] Therefore, we investigated if similar benefits could be achieved by partitioning the metabolome.…”