A Simple Test Identifies Selection on Complex Traits. Beissinger, T., Kruppa, J., Cavero, D., Ha, N., Erbe, M., & Simianer, H. Genetics, 209(1):321–333, 2018.
doi  abstract   bibtex   
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 uses 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 [Formula: see text] which can be used to test for significant evidence of selection on a trait. Our test requires pre- and postselection genotypic data but only a single time point with phenotypic information. Simulations demonstrate that [Formula: see text] 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.
@article{Beissinger2018Simple,
 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 uses 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 [Formula: see text] which can be used to test for significant evidence of selection on a trait. Our test requires pre- and postselection genotypic data but only a single time point with phenotypic information. Simulations demonstrate that [Formula: see text] 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.},
 author = {Beissinger, Tim and Kruppa, Jochen and Cavero, David and Ha, Ngoc-Thuy and Erbe, Malena and Simianer, Henner},
 year = {2018},
 title = {A Simple Test Identifies Selection on Complex Traits},
 keywords = {gen;postdoc},
 pages = {321--333},
 volume = {209},
 number = {1},
 issn = {1943-2631},
 journal = {Genetics},
 doi = {10.1534/genetics.118.300857},
 file = {http://www.ncbi.nlm.nih.gov/pubmed/29545467},
 file = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5937188},
 howpublished = {refereed}
}

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