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- W4319736482 abstract "Understanding the biology that underlies artificial selection in animal breeding programs is of significant interest. Recent and ongoing polygenic selection is difficult to map because it does not leave well-defined signatures on the genome. When populations have temporally-stratified genomic data, we can use genome-wide association (GWAS) models to map selection during the sampled period using Generation Proxy Selection Mapping (GPSM). Here, we demonstrate the power of GPSM in two large datasets from American beef cattle populations with 11,759,568 imputed SNPs. We identify ongoing polygenic selection at 294 unique loci. We observe that GPSM has high power to detect minuscule allele frequency shifts over short timescales (<10 years). GPSM loci localized near genes, in annotated epigenetic marks, or in predicted functional genomic regions. We also observed minimal overlap in loci identified by GPSM and other traditional sweep detection methods." @default.
- W4319736482 created "2023-02-11" @default.
- W4319736482 date "2022-12-31" @default.
- W4319736482 modified "2023-09-26" @default.
- W4319736482 title "372. Leveraging GWAS models to map selection on complex traits in massive datasets" @default.
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- W4319736482 doi "https://doi.org/10.3920/978-90-8686-940-4_372" @default.
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