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Modeling Heterogeneity in the Genetic Architecture of Ethnically Diverse Groups Using Random Effect Interaction
Yogasudha Veturi1,2, Gustavo de Los Campos3,4,5, Nengjun Yi2
1Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania 19104 yveturi@upenn.edu.
Genetics
|February 24, 2019
Summary
Genetic architecture of complex traits differs across human populations. This study quantifies genetic effect heterogeneity between European-Americans and African-Americans, revealing significant variation for traits like HDL, impacting genetic discovery.
Area of Science:
- Population Genetics
- Statistical Genetics
- Human Genomics
Background:
- Genome-wide association studies (GWAS) predominantly use Caucasian data, leading to poor replication in diverse populations.
- Replication failure may stem from statistical issues or fundamental differences in trait genetic architecture across ethnic groups.
Purpose of the Study:
- To investigate and quantify the extent of genetic effect heterogeneity for complex human traits between distinct subpopulations.
- To develop and apply statistical methods for assessing how genetic effects vary across ethnic groups.
Main Methods:
- Utilized Bayesian random effect interaction models to study genetic effect heterogeneity.
- Employed shrinkage and variable selection techniques for robust estimation.
- Analyzed four complex traits (standing height, HDL, LDL, serum urate) in European-Americans (EAs) and African-Americans (AAs).
Main Results:
- Estimated correlations of genetic effects between EAs and AAs were substantially below 1 (0.50-0.73) for all traits.
- Significant effect heterogeneity was observed, varying by trait and SNP set.
- Height exhibited less heterogeneity compared to HDL, which showed greater variation, suggesting lifestyle influences.
Conclusions:
- Genetic effect heterogeneity is a significant factor in complex human traits across different ethnic subpopulations.
- The developed methodology provides valuable insights into population-specific genetic architectures.
- Understanding effect heterogeneity is crucial for improving the generalizability of GWAS findings and advancing precision medicine.
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