Abstract
Motivation: Over the last decade, more diverse populations have been included in genome-wide association studies. If a genetic variant has a varying effect on a phenotype in different populations, genome-wide association studies applied to a dataset as a whole may not pinpoint such differences. It is especially important to be able to identify population-specific effects of genetic variants in studies that would eventually lead to development of diagnostic tests or drug discovery. Results: In this paper, we propose PopCluster: an algorithm to automatically discover subsets of individuals in which the genetic effects of a variant are statistically different. PopCluster provides a simple framework to directly analyze genotype data without prior knowledge of subjects' ethnicities. PopCluster combines logistic regression modeling, principal component analysis, hierarchical clustering and a recursive bottom-up tree parsing procedure. The evaluation of PopCluster suggests that the algorithm has a stable low false positive rate (∼4%) and high true positive rate (>80%) in simulations with large differences in allele frequencies between cases and controls. Application of PopCluster to data from genetic studies of longevity discovers ethnicity-dependent heterogeneity in the association of rs3764814 (USP42) with the phenotype.
Original language | English (US) |
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Pages (from-to) | 3046-3054 |
Number of pages | 9 |
Journal | Bioinformatics |
Volume | 35 |
Issue number | 17 |
DOIs | |
State | Published - Sep 1 2019 |
ASJC Scopus subject areas
- Statistics and Probability
- Biochemistry
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics