Bayesian Analysis

Modeling Population Structure Under Hierarchical Dirichlet Processes

Lloyd T. Elliott, Maria De Iorio, Stefano Favaro, Kaustubh Adhikari, and Yee Whye Teh

Full-text: Open access

Abstract

We propose a Bayesian nonparametric model to infer population admixture, extending the hierarchical Dirichlet process to allow for correlation between loci due to linkage disequilibrium. Given multilocus genotype data from a sample of individuals, the proposed model allows inferring and classifying individuals as unadmixed or admixed, inferring the number of subpopulations ancestral to an admixed population and the population of origin of chromosomal regions. Our model does not assume any specific mutation process, and can be applied to most of the commonly used genetic markers. We present a Markov chain Monte Carlo (MCMC) algorithm to perform posterior inference from the model and we discuss some methods to summarize the MCMC output for the analysis of population admixture. Finally, we demonstrate the performance of the proposed model in a real application, using genetic data from the ectodysplasin-A receptor (EDAR) gene, which is considered to be ancestry-informative due to well-known variations in allele frequency as well as phenotypic effects across ancestry. The structure analysis of this dataset leads to the identification of a rare haplotype in Europeans. We also conduct a simulated experiment and show that our algorithm outperforms parametric methods.

Article information

Source
Bayesian Anal., Volume 14, Number 2 (2019), 313-339.

Dates
First available in Project Euclid: 19 May 2018

Permanent link to this document
https://projecteuclid.org/euclid.ba/1526695351

Digital Object Identifier
doi:10.1214/17-BA1093

Mathematical Reviews number (MathSciNet)
MR3934088

Zentralblatt MATH identifier
07045433

Keywords
admixture modeling Bayesian nonparametrics hierarchical Dirichlet process linkage disequilibrium population stratification single nucleotide polymorphism data MCMC algorithm

Rights
Creative Commons Attribution 4.0 International License.

Citation

Elliott, Lloyd T.; De Iorio, Maria; Favaro, Stefano; Adhikari, Kaustubh; Teh, Yee Whye. Modeling Population Structure Under Hierarchical Dirichlet Processes. Bayesian Anal. 14 (2019), no. 2, 313--339. doi:10.1214/17-BA1093. https://projecteuclid.org/euclid.ba/1526695351


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