Analyzing Genetic Association Studies with an Extended Propensity Score Approach
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Huaqing Zhao
Abstract
Propensity scores are commonly used to address confounding in observational studies. However, they have not been previously adapted to deal with bias in genetic association studies. We propose an extension of our previous method (Zhao et al., 2009) that uses a multilevel propensity score approach and allows one to estimate the effect of a genotype under an additive model and also simultaneously adjusts for confounders such as genetic ancestry and patient and disease characteristics. Using simulation studies, we demonstrate that this extended genetic propensity score (eGPS) can adequately adjust and consistently correct for bias due to confounding in a variety of circumstances. Under all simulation scenarios, the eGPS method yields estimates with bias close to 0 (mean=0.018, standard error=0.01). Our method also preserves statistical properties such as coverage probability, Type I error, and power. We illustrate this approach in a population-based genetic association study of testicular germ cell tumors and KITLG and SPRY4 susceptibility genes. We conclude that our method provides a novel and broadly applicable analytic strategy for obtaining less biased and more valid estimates of genetic associations.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- Large-scale Parentage Inference with SNPs: an Efficient Algorithm for Statistical Confidence of Parent Pair Allocations
- ExactDAS: An Exact Test Procedure for the Detection of Differential Alternative Splicing in Microarray Experiments
- Incorporating Genomic Annotation into a Hidden Markov Model for DNA Methylation Tiling Array Data
- Variational Bayes Procedure for Effective Classification of Tumor Type with Microarray Gene Expression Data
- Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates
- Empirical Bayesian Selection of Hypothesis Testing Procedures for Analysis of Sequence Count Expression Data
- Analyzing Genetic Association Studies with an Extended Propensity Score Approach
- Genotype Copy Number Variations using Gaussian Mixture Models: Theory and Algorithms
- Estimators of the local false discovery rate designed for small numbers of tests
- A PAUC-based Estimation Technique for Disease Classification and Biomarker Selection
- Comparison of Targeted Maximum Likelihood and Shrinkage Estimators of Parameters in Gene Networks
- DNA Pooling and Statistical Tests for the Detection of Single Nucleotide Polymorphisms
