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Title: A simple test identifies selection on complex traits in breeding and experimentally-evolved populations

Author
item Beissinger, Timothy
item KRUPPA, JOCHEN - Georg August University
item CAVERO, DAVID - Lohmann & Rauschler Gmbh & Co Kg
item HA, NGOC-THUY - Georg August University
item ERBE, MALENA - Georg August University
item SIMIANER, HENNER - Georg August University

Submitted to: bioRxiv
Publication Type: Pre-print Publication
Publication Acceptance Date: 12/21/2017
Publication Date: 12/21/2017
Citation: Beissinger, T.M., Kruppa, J., Cavero, D., Ha, N., Erbe, M., Simianer, H. 2017. A simple test identifies selection on complex traits in breeding and experimentally-evolved populations. bioRxiv. Article 238295. https://doi.org/10.1101/238295.
DOI: https://doi.org/10.1101/238295

Interpretive Summary: Important agricultural traits such as yield, plant height, and disease resistance are often controlled by many genes of small effects. However, the majority of genetic tools for understanding these traits focus on major genes with large effects. This discrepancy limits our ability to fully understand and leverage the genetics of agricultural plants and animals. We have developed an approach that enables scientists to better understand the genetics behind breeding and improvement of traits controlled by genes with small effects. Ultimately, our new method has the potential to improve strategies for breeding and develop improved varieties of crops such as corn and farm animals such as chickens.

Technical Abstract: Important traits in agricultural, natural, and human populations are increasingly being shown to be under the control of many genes that individually contribute only a small proportion of genetic variation. However, the majority of modern tools in quantitative and population genetics, including genome wide association studies and selection mapping protocols, are designed to identify individual genes with large effects. We have developed an approach to identify traits that have been under selection and are controlled by large numbers of loci. In contrast to existing methods, our technique utilizes additive effects estimates from all available markers, and relates these estimates to allele frequency change over time. Using this information, we generate a composite statistic, denoted G, which can be used to test for significant evidence of selection on a trait. Our test requires pre- and post-selection genotypic data but only a single time point with phenotypic information. Simulations demonstrate that G is powerful for identifying selection, particularly in situations where the trait being tested is controlled by many genes, which is precisely the scenario where classical approaches for selection mapping are least powerful. We apply this test to breeding populations of maize and chickens, where we demonstrate the successful identification of selection on traits that are documented to have been under selection.