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

S. M. Edwards, I. F. Sorensen, P. Sarup, T. F. C. Mackay, P. Sorensen

Research output: Contribution to journalArticlepeer-review

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 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.
Original languageEnglish
Pages (from-to)1871-1883
JournalGenetics
Volume203
Issue number4
Early online date27 May 2016
DOIs
Publication statusPublished - 1 Aug 2016

Keywords

  • Genomic feature models
  • best linear unbiased prediction
  • Drosophila Genetic Reference 17 Population
  • startle response
  • starvation resistance
  • chill coma recovery time

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