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Haplotype‐Based Methods for Detecting Uncommon Causal Variants With Common SNPs
Author(s) -
Lin WanYu,
Yi Nengjun,
Zhi Degui,
Zhang Kui,
Gao Guimin,
Tiwari Hemant K.,
Liu Nianjun
Publication year - 2012
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21650
Subject(s) - haplotype , haplotype estimation , single nucleotide polymorphism , linkage disequilibrium , biology , genetics , matching (statistics) , snp , allele frequency , allele , statistics , mathematics , gene , genotype
Detecting uncommon causal variants (minor allele frequency [MAF] < 5%) is difficult with commercial single‐nucleotide polymorphism (SNP) arrays that are designed to capture common variants (MAF > 5%). Haplotypes can provide insights into underlying linkage disequilibrium (LD) structure and can tag uncommon variants that are not well tagged by common variants. In this work, we propose a wei‐SIMc‐matching test that inversely weights haplotype similarities with the estimated standard deviation of haplotype counts to boost the power of similarity‐based approaches for detecting uncommon causal variants. We then compare the power of the wei‐SIMc‐matching test with that of several popular haplotype‐based tests, including four other similarity‐based tests, a global score test for haplotypes ( global ), a test based on the maximum score statistic over all haplotypes ( max ), and two newly proposed haplotype‐based tests for rare variant detection. With systematic simulations under a wide range of LD patterns, the results show that wei‐SIMc‐matching and global are the two most powerful tests. Among these two tests, wei‐SIMc‐matching has reliable asymptotic P ‐values, whereas global needs permutations to obtain reliable P ‐values when the frequencies of some haplotype categories are low or when the trait is skewed. Therefore, we recommend wei‐SIMc‐matching for detecting uncommon causal variants with surrounding common SNPs, in light of its power and computational feasibility.