Weighted Pseudolikelihood for SNP set Analysis with Multiple Secondary Outcomes in Case-Control Genetic Association Studies

We propose a weighted pseudolikelihood method for analyzing the association of a SNP set, example, SNPs in a gene or a genetic pathway or network, with multiple secondary phenotypes in case-control genetic association studies. To boost analysis power, we assume that the SNP-specific effects are shar...

Full description

Saved in:
Bibliographic Details
Published in:Biometrics 2017-12, Vol.73 (4), p.1210-1220
Main Authors: Sofer, Tamar, Schifano, Elizabeth D., Christiani, David C., Lin, Xihong
Format: Article
Language:English
Subjects:
Bayesian analysis
Biased sampling
BIOMETRIC METHODOLOGY
Case-Control Studies
Computer Simulation
Genetic Association Studies - statistics & numerical data
Genetics
High‐dimensional data
Humans
Likelihood Functions
Lung cancer
Lung Neoplasms
Mathematical models
Parameter estimation
Phenotype
Polymorphism, Single Nucleotide
Regression analysis
Regression models
Single-nucleotide polymorphism
Smoking
SNP set analysis
Sparsity
Statistical analysis
Variable selection
Variance component test
Weighted BIC
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We propose a weighted pseudolikelihood method for analyzing the association of a SNP set, example, SNPs in a gene or a genetic pathway or network, with multiple secondary phenotypes in case-control genetic association studies. To boost analysis power, we assume that the SNP-specific effects are shared across all secondary phenotypes using a scaled mean model. We estimate regression parameters using Inverse Probability Weighted (IPW) estimating equations obtained from the weighted pseudolikelihood, which accounts for case-control sampling to prevent potential ascertainment bias. To test the effect of a SNP set, we propose a weighted variance component pseudo-score test. We also propose a penalized IPW pseudolikelihood method for selecting a subset of SNPs that are associated with the multiple secondary phenotypes. We show that the proposed variable selection procedure has the oracle properties and is robust to misspecification of the correlation structure among secondary phenotypes. We select the tuning parameter using a weighted Bayesian Informationlike Criterion (wBIC). We evaluate the finite sample performance of the proposed methods via simulations, and illustrate the methods by the analysis of the multiple secondary smoking behavior outcomes in a lung cancer case-control genetic association study.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.12680