Tree-guided Bayesian inference of population structures.
BACKGROUND
Inferring population structures using genetic data sampled from a group of individuals is a challenging task. Many methods either consider a fixed population number or ignore the correlation between populations. As a result, they can lose sensitivity and specificity in detecting subtle stratifications. In addition, when a large number of genetic markers are used, many existing algorithms perform rather inefficiently.


RESULTS
We propose a new Bayesian method to infer population structures using multiple unlinked single nucleotide polymorphisms (SNPs). Our approach explicitly considers the population correlation through a tree hierarchy, and treat the population number as a random variable. Using both simulated and real datasets of worldwide samples, we demonstrate that an incorporated tree can consistently improve the power in detecting subtle population stratifications. A tree-based model often involves a large number of unknown parameters, and the corresponding estimation procedure can be highly inefficient. We further implement a partition method to analytically integrate out all nuisance parameters in the tree. As a result, our method can analyze large SNP datasets with significantly improved convergence rate.


BACKGROUND
http://www.stat.psu.edu/~yuzhang/tips.tar.
Zhang
Yu
Y
Department of Statistics, The Pennsylvania State University, State College, PA, USA. yuzhang@stat.psu.edu
D000465:Algorithms; D001483:Base Sequence; D001499:Bayes Theorem; D003198:Computer Simulation; D004252:DNA Mutational Analysis / Q000379:methods*; D005828:Genetics, Population; D008957:Models, Genetic; D008969:Molecular Sequence Data; D009154:Mutation / Q000235:genetics; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods*