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  • Gustavo de los Campos, University of Alabama at Birmingham, United States
  • Daniel Sorensen
  • Daniel Gianola, Department of Animal Sciences, University of Wisconsin, United States
Whole-genome regression models have become ubiquitous for analysis and prediction of complex traits. In human genetics, these methods are commonly used for inferences about genetic parameters. This is so despite the fact that some of the assumptions commonly adopted for data analysis are at odds with important quantitative genetic principles. In this article we develop theory that leads to a precise definition of parameters arising in regression models using genomic data. Our approach is framed within the classical quantitative genetics paradigm. We discuss how these parameters relate to statistical parameters, indicate potential inferential problems and provide a limited set of simulations where some statistical properties of likelihood-based estimates are assessed.
Original languageEnglish
Publication year17 Aug 2014
Number of pages3
Publication statusPublished - 17 Aug 2014
Event10th World Congress on Genetics Applied to Livestock Production (WCGALP) - The Westin Bayshore, 1601 Bayshore Drive, Vancouver, BC V6G 2V4, Vancouver, Canada
Duration: 17 Aug 201422 Aug 2014
Conference number: 10th

Conference

Conference10th World Congress on Genetics Applied to Livestock Production (WCGALP)
Number10th
LocationThe Westin Bayshore, 1601 Bayshore Drive, Vancouver, BC V6G 2V4
CountryCanada
CityVancouver
Period17/08/201422/08/2014

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