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Multiple Sources of Uncertainty Confound Inference of Historical Human Generation Times.
Ragsdale, Aaron P; Thornton, Kevin R.
Afiliação
  • Ragsdale AP; Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI, USA.
  • Thornton KR; Department of Ecology and Evolutionary Biology, University of California, Irvine, CA, USA.
Mol Biol Evol ; 40(8)2023 08 03.
Article em En | MEDLINE | ID: mdl-37450583
Wang et al. (2023) recently proposed an approach to infer the history of human generation intervals from changes in mutation profiles over time. As the relative proportions of different mutation types depend on the ages of parents, binning variants by the time they arose allows for the inference of changes in average paternal and maternal generation intervals. Applying this approach to published allele age estimates, Wang et al. (2023) inferred long-lasting sex differences in average generation times and surprisingly found that ancestral generation times of West African populations remained substantially higher than those of Eurasian populations extending tens of thousands of generations into the past. Here, we argue that the results and interpretations in Wang et al. (2023) are primarily driven by noise and biases in input data and a lack of validation using independent approaches for estimating allele ages. With the recent development of methods to reconstruct genome-wide gene genealogies, coalescence times, and allele ages, we caution that downstream analyses may be strongly influenced by uncharacterized biases in their output.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Incerteza Limite: Female / Humans / Male Idioma: En Revista: Mol Biol Evol Assunto da revista: BIOLOGIA MOLECULAR Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Incerteza Limite: Female / Humans / Male Idioma: En Revista: Mol Biol Evol Assunto da revista: BIOLOGIA MOLECULAR Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos