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Mol Biol Evol. 2017 Nov 1;34(11):2996-3005. doi: 10.1093/molbev/msx209.

Detecting Long-Term Balancing Selection Using Allele Frequency Correlation.

Author information

1
Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
2
Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
3
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
4
Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

Abstract

Balancing selection occurs when multiple alleles are maintained in a population, which can result in their preservation over long evolutionary time periods. A characteristic signature of this long-term balancing selection is an excess number of intermediate frequency polymorphisms near the balanced variant. However, the expected distribution of allele frequencies at these loci has not been extensively detailed, and therefore existing summary statistic methods do not explicitly take it into account. Using simulations, we show that new mutations which arise in close proximity to a site targeted by balancing selection accumulate at frequencies nearly identical to that of the balanced allele. In order to scan the genome for balancing selection, we propose a new summary statistic, β, which detects these clusters of alleles at similar frequencies. Simulation studies show that compared with existing summary statistics, our measure has improved power to detect balancing selection, and is reasonably powered in non-equilibrium demographic models and under a range of recombination and mutation rates. We compute β on 1000 Genomes Project data to identify loci potentially subjected to long-term balancing selection in humans. We report two balanced haplotypes-localized to the genes WFS1 and CADM2-that are strongly linked to association signals for complex traits. Our approach is computationally efficient and applicable to species that lack appropriate outgroup sequences, allowing for well-powered analysis of selection in the wide variety of species for which population data are rapidly being generated.

KEYWORDS:

balancing selection; human evolution; selection scans

PMID:
28981714
PMCID:
PMC5850717
DOI:
10.1093/molbev/msx209
[Indexed for MEDLINE]
Free PMC Article

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