Evidence mapPaperPMID 39775235Full record

ArticlePLoS genetics2024

Fitness landscapes of human microsatellites.

Ryan J Haasl, Bret A Payseur

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Article in PLoS genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Ryan J HaaslDepartment of Biology, University of Wisconsin-Platteville, Platteville, Wisconsin, United States of America.ORCID 0009-0002-8996-9418
Bret A PayseurLaboratory of Genetics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.ORCID 0000-0003-3109-4778

Funding

Evolution of Phenotypic Extremes and Mechanisms Governing InheritanceR35GM139412 · UNIVERSITY OF WISCONSIN-MADISON · 2025 to 2025
$971k
NHGRI NIH HHS R01 HG004498NIGMS NIH HHS R35 GM139412
6 · The paper itself

Abstract

Advances in DNA sequencing technology and computation now enable genome-wide scans for natural selection to be conducted on unprecedented scales. By examining patterns of sequence variation among individuals, biologists are identifying genes and variants that affect fitness. Despite this progress, most population genetic methods for characterizing selection assume that variants mutate in a simple manner and at a low rate. Because these assumptions are violated by repetitive sequences, selection remains uncharacterized for an appreciable percentage of the genome. To meet this challenge, we focus on microsatellites, repetitive variants that mutate orders of magnitude faster than single nucleotide variants, can harbor substantial variation, and are known to influence biological function in some cases. We introduce four general models of natural selection that are each characterized by just two parameters, are easily simulated, and are specifically designed for microsatellites. Using a random forests approach to approximate Bayesian computation, we fit these models to carefully chosen microsatellites genotyped in 200 humans from a diverse collection of eight populations. Altogether, we reconstruct detailed fitness landscapes for 43 microsatellites we classify as targets of selection. Microsatellite fitness surfaces are diverse, including a range of selection strengths, contributions from dominance, and variation in the number and size of optimal alleles. Microsatellites that are subject to selection include loci known to cause trinucleotide expansion disorders and modulate gene expression, as well as intergenic loci with no obvious function. The heterogeneity in fitness landscapes we report suggests that genome-scale analyses like those used to assess selection targeting single nucleotide variants run the risk of oversimplifying the evolutionary dynamics of microsatellites. Moreover, our fitness landscapes provide a valuable visualization of the selective dynamics navigated by microsatellites.

Indexed as

Genetic FitnessMicrosatellite RepeatsSelection, GeneticAllelesBayes TheoremEvolution, MolecularGenetics, PopulationGenome, HumanHumansModels, GeneticMutation

Identifiers

PMID39775235
PMCPMC11734926

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.