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Scanning the human genome for “signatures” of positive selection: Transformative opportunities and ethical obligations
Author(s) -
Hernandez Margarita,
Perry George H.
Publication year - 2021
Publication title -
evolutionary anthropology: issues, news, and reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.401
H-Index - 85
eISSN - 1520-6505
pISSN - 1060-1538
DOI - 10.1002/evan.21893
Subject(s) - selection (genetic algorithm) , evolutionary biology , biology , genomics , genome , adaptation (eye) , data science , genetics , computer science , artificial intelligence , gene , neuroscience
The relationship history of evolutionary anthropology and genetics is complex. At best, genetics is a beautifully integrative part of the discipline. Yet this integration has also been fraught, with punctuated, disruptive challenges to dogma, periodic reluctance by some members of the field to embrace results from analyses of genetic data, and occasional over‐assertions of genetic definitiveness by geneticists. At worst, evolutionary genetics has been a tool for reinforcing racism and colonialism. While a number of genetics/genomics papers have disproportionately impacted evolutionary anthropology, here we highlight the 2002 presentation of an elegantly powerful approach for identifying “signatures” of past positive selection from haplotype‐based patterns of genetic variation. Together with technological advances in genotyping methods, this article transformed our field by facilitating genome‐wide “scans” for signatures of past positive selection in human populations. This approach helped researchers test longstanding evolutionary anthropology hypotheses while simultaneously providing opportunities to develop entirely new ones. Genome‐wide scans for signatures of positive selection have since been conducted in diverse worldwide populations, with striking findings of local adaptation and convergent evolution. Yet there are ethical considerations with respect to the ubiquity of these studies and the cross‐application of the genome‐wide scan approach to existing datasets, which we also discuss.