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Journal article

Detecting selection from linked sites using an f-model

  • Galimberti, Marco Swiss Institute of Bioinformatics, Fribourg, Switzerland
  • Leuenberger, Christoph Department of Mathematics, University of Fribourg, Fribourg, Switzerland
  • Wolf, Beat iCoSys, University of Applied Sciences Western Switzerland, Fribourg, Switzerland,
  • Szilágyi, Sándor Miklós Department of Informatics, University of Medicine, Pharmacy, Science and Technology of Târgu Mureş, Târgu Mureş, 540139, Romania,
  • Foll, Matthieu International Agency for Research on Cancer (IARC/WHO), Section of Genetics, Lyon, France
  • Wegmann, Daniel Swiss Institute of Bioinformatics, Fribourg, Switzerland - Department of Biology, University of Fribourg, Fribourg, Switzerland
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    2020
Published in:
  • Genetics. - 2020, vol. 216, no. 4, p. 1205–1215
English Allele frequencies vary across populations and loci, even in the presence of migration. While most differences may be due to genetic drift, divergent selection will further increase differentiation at some loci. Identifying those is key in studying local adaptation, but remains statistically challenging. A particularly elegant way to describe allele frequency differences among populations connected by migration is the F- model, which measures differences in allele frequencies by population specific FST coefficients. This model readily accounts for multiple evolutionary forces by partitioning FST coefficients into locus- and population-specific components reflecting selection and drift, respectively. Here we present an extension of this model to linked loci by means of a hidden Markov model (HMM), which characterizes the effect of selection on linked markers through correlations in the locus specific component along the genome. Using extensive simulations, we show that the statistical power of our method is up to twofold higher than that of previous implementations that assume sites to be independent. We finally evidence selection in the human genome by applying our method to data from the Human Genome Diversity Project (HGDP).
Faculty
Faculté des sciences et de médecine
Department
Département de Biologie
Language
  • English
Classification
Biological sciences
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/309070
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