title: Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila 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.
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, EH25 9RG Scotland, United Kingdom.
authors.forename: Stefan M
authors.initials: SM
authors.lastname: Edwards
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark.
authors.forename: Izel F
authors.initials: IF
authors.lastname: Sørensen
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark.
authors.forename: Pernille
authors.initials: P
authors.lastname: Sarup
authors.affiliation: Department of Biological Sciences and Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695.
authors.forename: Trudy F C
authors.initials: TF
authors.lastname: Mackay
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark pso@mbg.au.dk.
authors.forename: Peter
authors.initials: P
authors.lastname: Sørensen
mesh_terms: D000818:Animals; D004331:Drosophila melanogaster / Q000235:genetics*; D063990:Gene Ontology; D005819:Genetic Markers; D049750:Genome, Insect; D023281:Genomics; D005838:Genotype; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci / Q000235:genetics*
chemical_list: D005819:Genetic Markers
keywords: Drosophila Genetic Reference Population; GenPred; best linear unbiased prediction; chill coma recovery time; genomic feature models; genomic selection; shared data resource; startle response; starvation resistance