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Inference on the strength of balancing selection for epistatically interacting loci

Author

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  • Buzbas, Erkan Ozge
  • Joyce, Paul
  • Rosenberg, Noah A.

Abstract

Existing inference methods for estimating the strength of balancing selection in multi-locus genotypes rely on the assumption that there are no epistatic interactions between loci. Complex systems in which balancing selection is prevalent, such as sets of human immune system genes, are known to contain components that interact epistatically. Therefore, current methods may not produce reliable inference on the strength of selection at these loci. In this paper, we address this problem by presenting statistical methods that can account for epistatic interactions in making inference about balancing selection. A theoretical result due to Fearnhead (2006) is used to build a multi-locus Wright-Fisher model of balancing selection, allowing for epistatic interactions among loci. Antagonistic and synergistic types of interactions are examined. The joint posterior distribution of the selection and mutation parameters is sampled by Markov chain Monte Carlo methods, and the plausibility of models is assessed via Bayes factors. As a component of the inference process, an algorithm to generate multi-locus allele frequencies under balancing selection models with epistasis is also presented. Recent evidence on interactions among a set of human immune system genes is introduced as a motivating biological system for the epistatic model, and data on these genes are used to demonstrate the methods.

Suggested Citation

  • Buzbas, Erkan Ozge & Joyce, Paul & Rosenberg, Noah A., 2011. "Inference on the strength of balancing selection for epistatically interacting loci," Theoretical Population Biology, Elsevier, vol. 79(3), pages 102-113.
    • Handle: RePEc:eee:thpobi:v:79:y:2011:i:3:p:102-113
    DOI: 10.1016/j.tpb.2011.01.002
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    References listed on IDEAS

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    1. Buzbas Erkan Ozge & Joyce Paul & Abdo Zaid, 2009. "Estimation of Selection Intensity under Overdominance by Bayesian Methods," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-22, June.
    2. Brian S. Caffo, 2002. "Empirical supremum rejection sampling," Biometrika, Biometrika Trust, vol. 89(4), pages 745-754, December.
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    Cited by:

    1. Ferguson, Jake M. & Buzbas, Erkan Ozge, 2018. "Inference from the stationary distribution of allele frequencies in a family of Wright–Fisher models with two levels of genetic variability," Theoretical Population Biology, Elsevier, vol. 122(C), pages 78-87.
    2. Steinrücken, Matthias & Wang, Y.X. Rachel & Song, Yun S., 2013. "An explicit transition density expansion for a multi-allelic Wright–Fisher diffusion with general diploid selection," Theoretical Population Biology, Elsevier, vol. 83(C), pages 1-14.

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    1. Ferguson, Jake M. & Buzbas, Erkan Ozge, 2018. "Inference from the stationary distribution of allele frequencies in a family of Wright–Fisher models with two levels of genetic variability," Theoretical Population Biology, Elsevier, vol. 122(C), pages 78-87.
    2. Steinrücken, Matthias & Wang, Y.X. Rachel & Song, Yun S., 2013. "An explicit transition density expansion for a multi-allelic Wright–Fisher diffusion with general diploid selection," Theoretical Population Biology, Elsevier, vol. 83(C), pages 1-14.
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