A classical likelihood based approach for admixture mapping using EM algorithm.
Several disease-mapping methods have been proposed recently, which use the information generated by recent admixture of populations from historically distinct geographic origins. These methods include both classic likelihood and Bayesian approaches. In this study we directly maximize the likelihood function from the hidden Markov Model for admixture mapping using the EM algorithm, allowing for uncertainty in model parameters, such as the allele frequencies in the parental populations. We determined the robustness of the proposed method by examining the ancestral allele frequency estimate and individual marker-location specific ancestry when the data were generated by different population admixture models and no learning sample was used. The proposed method outperforms a widely used Bayesian MCMC strategy for data generated from various population admixture models. The multipoint information content for ancestry was derived based on the map provided by Smith et al. (2004) and the associated statistical power was calculated. We examined the distribution of admixture LD across the genome for both real and simulated data and established a threshold for genome wide significance applicable to admixture mapping studies. The software ADMIXPROGRAM for performing admixture mapping is available from authors.
Zhu
Xiaofeng
X
Department of Preventive Medicine and Epidemiology, Loyola University Medical Center, 2160 S. First Ave, Maywood, IL 60153, USA. xzhu1@lumc.edu
Zhang
Shuanglin
S
Tang
Hua
H
Cooper
Richard
R
D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D005191:Family Characteristics; D005787:Gene Frequency; D005828:Genetics, Population; D006801:Humans; D016013:Likelihood Functions; D015810:Linkage Disequilibrium; D008390:Markov Chains; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide