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PopCluster: an algorithm to identify genetic variants with ethnicity-dependent effects.

Anastasia Gurinovich1, Harold Bae2, John J Farrell3

  • 1Bioinformatics Program, Boston University, Boston, MA, USA.

Bioinformatics (Oxford, England)
|January 10, 2019
PubMed
Summary

PopCluster identifies population-specific genetic effects without needing prior ethnicity data. This algorithm enhances genome-wide association studies for better diagnostic and drug discovery applications.

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Area of Science:

  • Genetics
  • Population Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) increasingly include diverse populations.
  • Analyzing diverse datasets as a whole may obscure population-specific genetic variant effects.
  • Identifying population-specific effects is crucial for diagnostic tests and drug discovery.

Purpose of the Study:

  • To introduce PopCluster, an algorithm for discovering subsets of individuals with statistically different genetic variant effects.
  • To provide a framework for analyzing genotype data without pre-existing ethnicity information.

Main Methods:

  • PopCluster integrates logistic regression, principal component analysis, and hierarchical clustering.
  • A recursive bottom-up tree parsing procedure is employed.
  • The algorithm directly analyzes genotype data.

Main Results:

  • PopCluster demonstrates a stable low false positive rate (approximately 4%) and high true positive rate (over 80%) in simulations.
  • The algorithm successfully identified ethnicity-dependent heterogeneity in the association of rs3764814 (USP42) with longevity in a real-world genetic study.

Conclusions:

  • PopCluster is an effective tool for uncovering population-specific genetic associations within diverse cohorts.
  • The algorithm facilitates more precise genetic analyses, aiding in the development of targeted diagnostics and therapeutics.