Author
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Beissinger, Timothy |
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KRUPPA, JOCHEN - Georg August University |
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CAVERO, DAVID - Lohmann & Rauschler Gmbh & Co Kg |
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HA, NGOC-THUY - Georg August University |
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ERBE, MALENA - Georg August University |
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SIMIANER, HENNER - Georg August University |
Submitted to: Genetics
Publication Type: Peer Reviewed Journal Publication Acceptance Date: 3/10/2018 Publication Date: 3/14/2018 Citation: Beissinger, T.M., Kruppa, J., Cavero, D., Ha, N., Erbe, M., Simianer, H. 2018. A simple test identifies selection on complex traits. Genetics. 209(1):321-333. https://doi.org/10.1534/genetics.118.300857. DOI: https://doi.org/10.1534/genetics.118.300857 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 target the identification of 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 genotypic data from multiple time points 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. |