Bayesian spatial modeling of genetic population structure

• Published in: Computational statistics Vol. 23; no. 1; pp. 111 - 129
• Main Authors: Corander, Jukka, Sirén, Jukka, Arjas, Elja
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 2008; Springer Nature B.V
• Subjects: Algorithms; Bayesian analysis; Biologists; Biology; Computational mathematics; Datasets; Economic Theory/Quantitative Economics/Mathematical Methods; Learning; Markov analysis; Mathematics and Statistics; Monte Carlo simulation; Original Paper; Population; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Software; Statistics; Studies; Statistical learning theory; Unsupervised classification; Bayesian inference; Genetic structure; Spatial modeling
• ISSN: 0943-4062; 1613-9658
• DOI: 10.1007/s00180-007-0072-x

Natural populations of living organisms often have complex histories consisting of phases of expansion and decline, and the migratory patterns within them may fluctuate over space and time. When parts of a population become relatively isolated, e.g., due to geographical barriers, stochastic forces reshape certain DNA characteristics of the individuals over generations such that they reflect the restricted migration and mating/reproduction patterns. Such populations are typically termed as genetically structured and they may be statistically represented in terms of several clusters between which DNA variations differ clearly from each other. When detailed knowledge of the ancestry of a natural population is lacking, the DNA characteristics of a sample of current generation individuals often provide a wealth of information in this respect. Several statistical approaches to model-based clustering of such data have been introduced, and in particular, the Bayesian approach to modeling the genetic structure of a population has attained a vivid interest among biologists. However, the possibility of utilizing spatial information from sampled individuals in the inference about genetic clusters has been incorporated into such analyses only very recently. While the standard Bayesian hierarchical modeling techniques through Markov chain Monte Carlo simulation provide flexible means for describing even subtle patterns in data, they may also result in computationally challenging procedures in practical data analysis. Here we develop a method for modeling the spatial genetic structure using a combination of analytical and stochastic methods. We achieve this by extending a novel theory of Bayesian predictive classification with the spatial information available, described here in terms of a colored Voronoi tessellation over the sample domain. Our results for real and simulated data sets illustrate well the benefits of incorporating spatial information to such an analysis.