Bayesian models for the analysis of genetic structure when populations are correlated
Author(s) -
Rui Fu,
Dipak K. Dey,
Kent E. Holsinger
Publication year - 2004
Publication title -
computer applications in the biosciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1460-2059
pISSN - 0266-7061
DOI - 10.1093/bioinformatics/bti178
Subject(s) - inference , correlation , allele frequency , population , biology , bayes' theorem , bayesian probability , allele , bayesian inference , population genetics , microsatellite , statistics , genetics , evolutionary biology , mathematics , computer science , artificial intelligence , gene , demography , geometry , sociology
Population allele frequencies are correlated when populations have a shared history or when they exchange genes. Unfortunately, most models for allele frequency and inference about population structure ignore this correlation. Recent analytical results show that among populations, correlations can be very high, which could affect estimates of population genetic structure. In this study, we propose a mixture beta model to characterize the allele frequency distribution among populations. This formulation incorporates the correlation among populations as well as extending the model to data with different clusters of populations.
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