Recommendations for improving statistical inference in population genomics

Johri, Parul; Aquadro, Charles F.; Beaumont, Mark; Charlesworth, Brian; Excoffier, Laurent; Eyre-Walker, Adam; Keightley, Peter D.; Lynch, Michael; McVean, Gil; Payseur, Bret A.; Pfeifer, Susanne P.; Stephan, Wolfgang; Jensen, Jeffrey D. (2022). Recommendations for improving statistical inference in population genomics. PLoS biology, 20(5), pp. 1-23. Public Library of Science 10.1371/journal.pbio.3001669

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The field of population genomics has grown rapidly in response to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population genetic insights outpaced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous nonadaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting
model fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Department of Biology > Institute of Ecology and Evolution (IEE)
08 Faculty of Science > Department of Biology > Institute of Ecology and Evolution (IEE) > Population Genetics

UniBE Contributor:

Excoffier, Laurent

Subjects:

500 Science > 570 Life sciences; biology

ISSN:

1544-9173

Publisher:

Public Library of Science

Language:

English

Submitter:

Susanne Holenstein

Date Deposited:

11 Aug 2022 14:49

Last Modified:

23 Dec 2022 09:27

Publisher DOI:

10.1371/journal.pbio.3001669

PubMed ID:

35639797

BORIS DOI:

10.48350/171829

URI:

https://boris.unibe.ch/id/eprint/171829

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