Fitness landscapes of human microsatellites.
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, m...
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Public Library of Science (PLoS)
2024-12-01
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| Series: | PLoS Genetics |
| Online Access: | https://doi.org/10.1371/journal.pgen.1011524 |
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| author | Ryan J Haasl Bret A Payseur |
| author_facet | Ryan J Haasl Bret A Payseur |
| author_sort | Ryan J Haasl |
| collection | DOAJ |
| description | 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. |
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| institution | Kabale University |
| issn | 1553-7390 1553-7404 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Public Library of Science (PLoS) |
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| spelling | doaj-art-c983a8be781740e2ae1beea942d49e822025-02-05T05:31:01ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042024-12-012012e101152410.1371/journal.pgen.1011524Fitness landscapes of human microsatellites.Ryan J HaaslBret A PayseurAdvances 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.https://doi.org/10.1371/journal.pgen.1011524 |
| spellingShingle | Ryan J Haasl Bret A Payseur Fitness landscapes of human microsatellites. PLoS Genetics |
| title | Fitness landscapes of human microsatellites. |
| title_full | Fitness landscapes of human microsatellites. |
| title_fullStr | Fitness landscapes of human microsatellites. |
| title_full_unstemmed | Fitness landscapes of human microsatellites. |
| title_short | Fitness landscapes of human microsatellites. |
| title_sort | fitness landscapes of human microsatellites |
| url | https://doi.org/10.1371/journal.pgen.1011524 |
| work_keys_str_mv | AT ryanjhaasl fitnesslandscapesofhumanmicrosatellites AT bretapayseur fitnesslandscapesofhumanmicrosatellites |