Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations
Abstract
Fine-scale rates of meiotic recombination vary by orders of magnitude across the genome and differ between species and even populations. Studying cross-population differences has been stymied by the confounding effects of demographic history. To address this problem, we developed a demography-aware method to infer fine-scale recombination rates and applied it to 26 diverse human populations, inferring population-specific recombination maps. These maps recapitulate many aspects of the history of these populations including signatures of the trans-Atlantic slave trade and the Iberian colonization of the Americas. We also investigated modulators of the local recombination rate, finding further evidence that Polycomb group proteins and the trimethylation of H3K27 elevate recombination rates. Further differences in the recombination landscape across the genome and between populations are driven by variation in the gene that encodes the DNA binding protein PRDM9, and we quantify the weak effect of meiotic drive acting to remove its binding sites.
- Authors:
-
- Univ. of California, Berkeley, CA (United States). Graduate Group in Computational Biology
- Univ. of California, Berkeley, CA (United States). Computer Science Division and Department of Statistics; Chan Zuckerberg Biohub, San Francisco, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab (LBNL); Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); National Institutes of Health (NIH); Packard Fellowship for Science and Engineering
- OSTI Identifier:
- 1626017
- Grant/Contract Number:
- AC02-05CH11231; R01-GM108805
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Science Advances
- Additional Journal Information:
- Journal Volume: 5; Journal Issue: 10; Journal ID: ISSN 2375-2548
- Publisher:
- AAAS
- Country of Publication:
- United States
- Language:
- English
- Subject:
- Science & Technology - Other Topics
Citation Formats
Spence, Jeffrey P., and Song, Yun S. Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations. United States: N. p., 2019.
Web. doi:10.1126/sciadv.aaw9206.
Spence, Jeffrey P., & Song, Yun S. Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations. United States. https://doi.org/10.1126/sciadv.aaw9206
Spence, Jeffrey P., and Song, Yun S. Tue .
"Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations". United States. https://doi.org/10.1126/sciadv.aaw9206. https://www.osti.gov/servlets/purl/1626017.
@article{osti_1626017,
title = {Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations},
author = {Spence, Jeffrey P. and Song, Yun S.},
abstractNote = {Fine-scale rates of meiotic recombination vary by orders of magnitude across the genome and differ between species and even populations. Studying cross-population differences has been stymied by the confounding effects of demographic history. To address this problem, we developed a demography-aware method to infer fine-scale recombination rates and applied it to 26 diverse human populations, inferring population-specific recombination maps. These maps recapitulate many aspects of the history of these populations including signatures of the trans-Atlantic slave trade and the Iberian colonization of the Americas. We also investigated modulators of the local recombination rate, finding further evidence that Polycomb group proteins and the trimethylation of H3K27 elevate recombination rates. Further differences in the recombination landscape across the genome and between populations are driven by variation in the gene that encodes the DNA binding protein PRDM9, and we quantify the weak effect of meiotic drive acting to remove its binding sites.},
doi = {10.1126/sciadv.aaw9206},
journal = {Science Advances},
number = 10,
volume = 5,
place = {United States},
year = {Tue Oct 01 00:00:00 EDT 2019},
month = {Tue Oct 01 00:00:00 EDT 2019}
}
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