Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories inDrosophila melanogaster

Abstract

Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, plant and animal breeding, and adaptive evolution. However, this is difficult for populations of unrelated individuals when the number of causal variants is low relative to the total number of polymorphisms and causal variants individually have small effects on the traits. We hypothesized that mapping molecular polymorphisms to genomic features such as genes and their gene ontology categories could increase the accuracy of genomic prediction models. We developed a genomic feature best linear unbiased prediction (GFBLUP) model that implements this strategy and applied it to three quantitative traits (startle response, starvation resistance, and chill coma recovery) in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel. Our results indicate that subsetting markers based on genomic features increases the predictive ability relative to the standard genomic best linear unbiased prediction (GBLUP) model. Both models use all markers, but GFBLUP allows differential weighting of the individual genetic marker relationships, whereas GBLUP weighs the genetic marker relationships equally. Simulation studies show that it is possible to further increase the accuracy of genomic prediction for complex traits using this model, provided the genomic features are enriched for causal variants. Our GFBLUP model using prior information on genomic features enriched for causal variants can increase the accuracy of genomic predictions in populations of unrelated individuals and provides a formal statistical framework for leveraging and evaluating information across multiple experimental studies to provide novel insights into the genetic architecture of complex traits.

Genomic Analysis of Genotype-by-Social Environment Interaction for Drosophila melanogaster Aggressive Behavior

“…We found low PAs (± SE) for normally socialized flies (PA = 0.18±0.03), socially isolated flies (PA = 0.25±0.04) and GSEI (PA = 0.24±0.03) (Table S3). Similar low predictive abilities for the GBLUP NULL model were observed previously for other quantitative traits measured in the DGRP (Ober et al 2012(Ober et al , 2015Edwards et al 2016). To put this in perspective, the maximum predictive ability is H 2 = r 2 = PA 2 (Mrode 2005;Goddard 2009).…”

“…Therefore, the broad sense heritabilities of line means for the socialized and socially isolated flies are, respectively H 2 = 0.76 and H 2 = 0.73. This increases the power of association mapping as well as predictive ability (Edwards et al 2016).…”

“…Extending GBLUP by fitting multiple genetic components has been shown to increase predictive ability (Speed and Balding 2014;Tucker et al 2015). In particular, fitting multiple genetic components can be used to partition genetic variance by pathway (Edwards et al 2015), or to test the predictive ability of predefined sets of genes using genomic feature BLUP (GF-BLUP) (Edwards et al 2016;Sarup et al 2016;Fang et al 2017). The second set of genomic analyses therefore included genomic prediction models where the aims were to identify sets of genes predictive of the trait variation, but also to investigate means to predict aggression across social rearing conditions.…”

Covariance Association Test (CVAT) Identifies Genetic Markers Associated with Schizophrenia in Functionally Associated Biological Processes

“…We used a two-component partitioning (Equation 13), fitting two genetic effects jointly. Multiple genetic components can be fitted (Speed and Balding 2014); however, if the correlation between the multiple correlation matrices is high, uncertainties of the variance components increase (Gusev et al 2014;Edwards et al 2016), which affects the accuracy of the marker effects. Therefore, using marker effects from multigenetic components might decrease the power of CVAT because the marker effects are estimated with bias.…”

“…Therefore, we extend CVAT, allowing the genetic effects to have different distributions caused by different underlying partitioning. This is obtained using our genomic feature BLUP (GFBLUP) in which an additional variance component is added to the traditional GBLUP (Edwards et al 2016;Sarup et al 2016):…”