Calibrating Population Stratification in Association Analysis

Methods in Molecular Biology
Huaizhen Qin, Xiaofeng Zhu

Abstract

In genetic association studies, it is necessary to correct for population structure to avoid inference bias. During the past decade, prevailing corrections often only involved adjustments of global ancestry differences between sampled individuals. Nevertheless, population structure may vary across local genomic regions due to the variability of local ancestries associated with natural selection, migration, or random genetic drift. Adjusting for global ancestry alone may be inadequate when local population structure is an important confounding factor. In contrast, adjusting for local ancestry can more effectively prevent false positives due to local population structure. To more accurately locate disease genes, we recommend adjusting for local ancestries by interrogating local structure. In practice, locus-specific ancestries are usually unknown and must be inferred. For recently admixed populations with known reference ancestral populations, locus-specific ancestries can be inferred accurately using some hidden Markov model-based methods. However, SNP-wise ancestries cannot be accurately inferred when ancestral population information is not available. For such scenarios, we propose employing local principal components (PCs) to ...Continue Reading

Related Concepts

Genetics, Population
Markov Chains
Selection, Genetic
Computer Programs and Programming
Single Nucleotide Polymorphism
Principal Component Analysis
Evolution, Neutral
Candidate Gene Identification
Genes
Local

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