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Heredity (Edinb). 2018 Jun;120(6):500-514. doi: 10.1038/s41437-017-0043-0. Epub 2018 Feb 10.

Effect of genetic architecture on the prediction accuracy of quantitative traits in samples of unrelated individuals.

Morgante F1,2,3, Huang W1,2,3,4,5, Maltecca C1,6, Mackay TFC7,8,9,10.

Author information

1
Program in Genetics, North Carolina State University, Raleigh, NC, 27695-7614, USA.
2
Department of Biological Sciences, North Carolina State University, Raleigh, NC, 27695-7614, USA.
3
W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC, 27695-7614, USA.
4
Initiative in Biological Complexity, North Carolina State University, Raleigh, NC, 27695-7614, USA.
5
Department of Animal Science, Michigan State University, East Lansing, MI, 48824, USA.
6
Department of Animal Science, North Carolina State University, Raleigh, NC, 27695-7621, USA.
7
Program in Genetics, North Carolina State University, Raleigh, NC, 27695-7614, USA. trudy_mackay@ncsu.edu.
8
Department of Biological Sciences, North Carolina State University, Raleigh, NC, 27695-7614, USA. trudy_mackay@ncsu.edu.
9
W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC, 27695-7614, USA. trudy_mackay@ncsu.edu.
10
Initiative in Biological Complexity, North Carolina State University, Raleigh, NC, 27695-7614, USA. trudy_mackay@ncsu.edu.

Abstract

Predicting complex phenotypes from genomic data is a fundamental aim of animal and plant breeding, where we wish to predict genetic merits of selection candidates; and of human genetics, where we wish to predict disease risk. While genomic prediction models work well with populations of related individuals and high linkage disequilibrium (LD) (e.g., livestock), comparable models perform poorly for populations of unrelated individuals and low LD (e.g., humans). We hypothesized that low prediction accuracies in the latter situation may occur when the genetics architecture of the trait departs from the infinitesimal and additive architecture assumed by most prediction models. We used simulated data for 10,000 lines based on sequence data from a population of unrelated, inbred Drosophila melanogaster lines to evaluate this hypothesis. We show that, even in very simplified scenarios meant as a stress test of the commonly used Genomic Best Linear Unbiased Predictor (G-BLUP) method, using all common variants yields low prediction accuracy regardless of the trait genetic architecture. However, prediction accuracy increases when predictions are informed by the genetic architecture inferred from mapping the top variants affecting main effects and interactions in the training data, provided there is sufficient power for mapping. When the true genetic architecture is largely or partially due to epistatic interactions, the additive model may not perform well, while models that account explicitly for interactions generally increase prediction accuracy. Our results indicate that accounting for genetic architecture can improve prediction accuracy for quantitative traits.

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