Abstract
We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × 10-12).
Highlights
Testing for the joint effect of single-nucleotide polymorphisms (SNPs) located in a gene is a popular alternative to single-marker tests
Single SNP methods are underpowered because single SNPs have typically small effect sizes or small minor allele frequencies (MAFs)
For the Genetic Analysis Workshop 17 (GAW17), we studied the performance of this approach for the sequence data on the families [2]
Summary
Testing for the joint effect of single-nucleotide polymorphisms (SNPs) located in a gene is a popular alternative to single-marker tests. Single SNP methods are underpowered because single SNPs have typically small effect sizes (common variants) or small minor allele frequencies (MAFs) (rare variants). We have proposed a method consisting of 2 steps [1,2]: (a) the dimensionality of the genetic data is reduced, and gene-specific summaries are produced, and (b) these summaries are introduced as covariates in the model for the phenotype. To model the correlation among SNPs within a gene, we use a generalized linear mixed model for the SNPs. A gene-level random effect captures the correlation within each gene. The empirical Bayes estimates of the random effects per subject and gene are used as summary measures of the SNPs data and are included in the phenotype model to test for association
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