A Simple Test Identifies Selection on Complex Traits

Genetics: A Periodical Record of Investigations Bearing on Heredity and Variation

article

Tim Beissinger¹  Jochen Kruppa⁴  David Cavero⁶  Ngoc-Thuy Ha⁴  Malena Erbe⁷  Henner Simianer⁴ 

[1] Plant Genetics Research Unit, U. S. Department of Agriculture–Agricultural Research Service, Columbia, Missouri 65211;Division of Biological Sciences,University of Missouri, Columbia, Missouri 65211;Division of Plant Sciences, University of Missouri, Columbia, Missouri 65211;Center for Integrated Breeding Research, University of Göttingen, 37075, Germany;Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, 30559, Germany;H&N International, 27472 Cuxhaven, Germany,;Institute for Animal Breeding, Bavarian State Research Centre for Agriculture, 85586 Grub, Germany

关键词: chickens;    complex traits;    maize;    selection;    GenPred;    Shared Data Resources;    Genomic Selection;   

DOI  :  10.1534/genetics.118.300857

学科分类：医学（综合）

来源: Genetics Society of America

【 摘 要 】

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 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 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.

【 授权许可】

CC BY