Abstract
We evaluate an approach to detect single-nucleotide polymorphisms (SNPs) that account for a linkage signal with covariate-based affected relative pair linkage analysis in a conditional-logistic model framework using all 200 replicates of the Genetic Analysis Workshop 17 family data set. We begin by combining the multiple known covariate values into a single variable, a propensity score. We also use each SNP as a covariate, using an additive coding based on the number of minor alleles. We evaluate the distribution of the difference between LOD scores with the propensity score covariate only and LOD scores with the propensity score covariate and a SNP covariate. The inclusion of causal SNPs in causal genes increases LOD scores more than the inclusion of noncausal SNPs either within causal genes or outside causal genes. We compare the results from this method to results from a family-based association analysis and conclude that it is possible to identify SNPs that account for the linkage signals from genes using a SNP-covariate-based affected relative pair linkage approach.
Highlights
Owing to the complexity of the genetic models underlying complex traits, model-free linkage methods, which do not require the specification of a disease model, are a popular choice
To identify single-nucleotide polymorphisms (SNPs) that may explain the observed linkage signals, several researchers have developed methods for an affected pair analysis [5,6,7,8,9,10] and for quantitative trait linkage analysis [11]. Among these studies, Houwing-Duistermaat et al [8] proposed using Olson’s conditional-logistic model with a genotype-based covariate to explain the linkage signals. They applied the method to three SNPs and five markers in the Genetic Analysis Workshop 14 data, and they confirmed a SNP that explained a linkage peak
The large numbers of SNPs from exome sequencing data, along with the identical-bydescent (IBD) allele sharing from fully informative markers in the Genetic Analysis Workshop 17 (GAW17) data set [12], provide a good opportunity to evaluate this approach; our purpose here is to evaluate this new method in depth
Summary
Owing to the complexity of the genetic models underlying complex traits, model-free linkage methods, which do not require the specification of a disease model, are a popular choice. To identify single-nucleotide polymorphisms (SNPs) that may explain the observed linkage signals, several researchers have developed methods for an affected pair analysis [5,6,7,8,9,10] and for quantitative trait linkage analysis [11] Among these studies, Houwing-Duistermaat et al [8] proposed using Olson’s conditional-logistic model with a genotype-based covariate to explain the linkage signals. They applied the method to three SNPs and five markers in the Genetic Analysis Workshop 14 data, and they confirmed a SNP that explained a linkage peak. The large numbers of SNPs from exome sequencing data, along with the identical-bydescent (IBD) allele sharing from fully informative markers in the Genetic Analysis Workshop 17 (GAW17) data set [12], provide a good opportunity to evaluate this approach; our purpose here is to evaluate this new method in depth
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