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Comparing the Efficacy of SNP Filtering Methods for Identifying a Single Causal SNP in a Known Association Region
Author(s) -
Spencer Amy Victoria,
Cox Angela,
Walters Kevin
Publication year - 2014
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1111/ahg.12043
Subject(s) - linkage disequilibrium , tag snp , single nucleotide polymorphism , snp , genetic association , minor allele frequency , genetics , sample size determination , genome wide association study , biology , linkage (software) , allele frequency , statistics , allele , mathematics , genotype , gene
Summary Genome‐wide association studies have successfully identified associations between common diseases and a large number of single nucleotide polymorphisms (SNPs) across the genome. We investigate the effectiveness of several statistics, including p ‐values, likelihoods, genetic map distance and linkage disequilibrium between SNPs, in filtering SNPs in several disease‐associated regions. We use simulated data to compare the efficacy of filters with different sample sizes and for causal SNPs with different minor allele frequencies (MAFs) and effect sizes, focusing on the small effect sizes and MAFs likely to represent the majority of unidentified causal SNPs. In our analyses, of all the methods investigated, filtering on the ranked likelihoods consistently retains the true causal SNP with the highest probability for a given false positive rate. This was the case for all the local linkage disequilibrium patterns investigated. Our results indicate that when using this method to retain only the top 5% of SNPs, even a causal SNP with an odds ratio of 1.1 and MAF of 0.08 can be retained with a probability exceeding 0.9 using an overall sample size of 50,000.