Using Disease Risk Scores to Account for Population Stratification in Rare Variant Association Studies
Date Issued
2015
Date
2015
Author(s)
Li, C.H.
Abstract
Background: In genetic studies, we often collect unrelated cases and controls and compare allele frequencies between the two groups. However, cases and controls may come from different ancestral populations, and the allele frequencies of the two groups cannot be compared directly. Researchers usually use markers unlinked to the gene of interest to construct principal components. By using tens of important principal components as covariates in the logistic regression, we can adjust for the ancestral difference between the cases and the controls. Method: The cost of next-generation sequencing is still high. Many studies cannot afford to the cost of whole-genome sequencing, and may only afford to sequence a chromosomal region of interest. In this study, we discuss the situation that only a 500 kb (kilo base pairs) region can be sequenced. We use disease risk scores to account for population stratification in the sequence kernel association test. Result: According to the Monte Carlo simulations, using disease risk scores in the sequence kernel association test can adjust for population stratification more efficiently, compared with the conventional approach of using principal component scores. Suggestion: If researchers have a sequenced region longer than 500 kb, we suggest using common single-nucleotide polymorphisms (with minor allele frequency > 5%) far from the gene of interest to construct disease risk scores, and adjusting the disease risk scores in the sequence kernel association test to account for the population stratification.
Subjects
next-generation sequencing
case-control study
population stratification
rare variants
sequence kernel association test
principal component analysis
disease risk score
Type
thesis
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