Detecting selection from linked sites using an F-model

Galimberti, Marco (Swiss Institute of Bioinformatics, University of Fribourg, Fribourg, Switzerland) ; Leuenberger, Christoph (University of Fribourg, Fribourg, Switzerland) ; Wolf, Beat (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Szilagyi, Sandor M. (University of Medicine, Pharmacy, Science and Technology of Targu Mures, Romania) ; Foll, Matthieu (International Agency or Research on Cancer (IARC/WHO), Lyon, France) ; Wegmann, Daniel (Swiss Institute of Bioinformatics, University of Fribourg, Fribourg, Switzerland)

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 two-fold 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).


Keywords:
Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
iCoSys - Institut des systèmes complexes
Date:
2020-10
Pagination:
11 p.
Published in:
Genetics
Numeration (vol. no.):
2020, vol. 216, no. 2
DOI:
ISSN:
0016-6731
Appears in Collection:

Note: The file is under embargo until: 2021-10-31


 Record created 2020-10-27, last modified 2020-11-03

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