Adjustment for local ancestry in genetic association analysis of admixed populations

• Published in: Bioinformatics (Oxford, England) Vol. 27; no. 5; pp. 670 - 677
• Main Authors: XUEXIA WANG, XIAOFENG ZHU, HUAIZHEN QIN, COOPER, Richard S, EWENS, Warren J, CHUN LI, MINGYAO LI
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.03.2011
• Subjects: Biological and medical sciences; Computer Simulation; Fundamental and applied biological sciences. Psychology; Gene Frequency; General aspects; Genetic Predisposition to Disease; Genetics, Population - methods; Genome-Wide Association Study - methods; Genotype; Humans; Likelihood Functions; Logistic Models; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Original Papers; Polymorphism, Single Nucleotide; Genetics; Population; Analysis
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btq709

Admixed populations offer a unique opportunity for mapping diseases that have large disease allele frequency differences between ancestral populations. However, association analysis in such populations is challenging because population stratification may lead to association with loci unlinked to the disease locus. We show that local ancestry at a test single nucleotide polymorphism (SNP) may confound with the association signal and ignoring it can lead to spurious association. We demonstrate theoretically that adjustment for local ancestry at the test SNP is sufficient to remove the spurious association regardless of the mechanism of population stratification, whether due to local or global ancestry differences among study subjects; however, global ancestry adjustment procedures may not be effective. We further develop two novel association tests that adjust for local ancestry. Our first test is based on a conditional likelihood framework which models the distribution of the test SNP given disease status and flanking marker genotypes. A key advantage of this test lies in its ability to incorporate different directions of association in the ancestral populations. Our second test, which is computationally simpler, is based on logistic regression, with adjustment for local ancestry proportion. We conducted extensive simulations and found that the Type I error rates of our tests are under control; however, the global adjustment procedures yielded inflated Type I error rates when stratification is due to local ancestry difference.