48
views
0
recommends
+1 Recommend
3 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found

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

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Author and article information

          Journal
          Genetics
          Genetics
          genetics
          genetics
          genetics
          Genetics
          Genetics Society of America
          0016-6731
          1943-2631
          August 2016
          26 May 2016
          : 203
          : 4
          : 1871-1883
          Affiliations
          [* ]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
          []Department of Biological Sciences and Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
          Author notes
          [1 ]Corresponding author: Research Centre Foulum, Aarhus University, Blichers Allé 1, Postbox 50, 8830 Tjele, Denmark. E-mail: pso@ 123456mbg.au.dk
          Article
          PMC4981283 PMC4981283 4981283 187161
          10.1534/genetics.116.187161
          4981283
          27235308
          54e236d0-42d9-46e3-be6c-c86ebc970662
          Copyright © 2016 by the Genetics Society of America
          History
          : 15 January 2016
          : 19 May 2016
          Page count
          Figures: 4, Tables: 2, Equations: 5, References: 56, Pages: 13
          Categories
          Investigations
          Genetics of Complex Traits
          Custom metadata
          highlight-article

          best linear unbiased prediction, Drosophila Genetic Reference Population,startle response,starvation resistance,chill coma recovery time,genomic selection,GenPred,shared data resource,genomic feature models

          Comments

          Comment on this article