Background selection and biased gene conversion affect more than 95% of the human genome and bias demographic inferences
| Autor: | Alexandre Thiéry, Simon Aeschbacher, Laurent Excoffier, Fanny Pouyet |
|---|---|
| Přispěvatelé: | University of Zurich, Pouyet, Fanny |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
demography Genome genomic diversity Negative selection 2400 General Immunology and Microbiology Biology (General) Recombination Genetic Neutral Evolution GC-biased gene conversion Base Composition Natural selection General Neuroscience 2800 General Neuroscience natural selection Genomics General Medicine Mutation (genetic algorithm) 590 Animals (Zoology) Medicine Insight Human QH301-705.5 Science Gene Conversion Genetics and Molecular Biology Biology General Biochemistry Genetics and Molecular Biology Evolution Molecular 03 medical and health sciences 10127 Institute of Evolutionary Biology and Environmental Studies 1300 General Biochemistry Genetics and Molecular Biology evolution Humans Gene conversion Selection Genetic Models Genetic General Immunology and Microbiology Genome Human Genetics and Genomics Background selection recombination background selection 030104 developmental biology Evolutionary biology Mutation General Biochemistry 570 Life sciences biology Human genome |
| Zdroj: | eLife, Vol 7 (2018) Pouyet, Fanny; Aeschbacher, Simon; Thiéry, Alexandre Benoit Xavier; Excoffier, Laurent (2018). Background selection and biased gene conversion affect more than 95% of the human genome and bias demographic inferences. eLife, 7, pp. 1-21. eLife Sciences Publications 10.7554/eLife.36317 eLife |
| DOI: | 10.7892/boris.120988 |
| Popis: | Disentangling the effect on genomic diversity of natural selection from that of demography is notoriously difficult, but necessary to properly reconstruct the history of species. Here, we use high-quality human genomic data to show that purifying selection at linked sites (i.e. background selection, BGS) and GC-biased gene conversion (gBGC) together affect as much as 95% of the variants of our genome. We find that the magnitude and relative importance of BGS and gBGC are largely determined by variation in recombination rate and base composition. Importantly, synonymous sites and non-transcribed regions are also affected, albeit to different degrees. Their use for demographic inference can lead to strong biases. However, by conditioning on genomic regions with recombination rates above 1.5 cM/Mb and mutation types (C↔G, A↔T), we identify a set of SNPs that is mostly unaffected by BGS or gBGC, and that avoids these biases in the reconstruction of human history. |
| Databáze: | OpenAIRE |
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