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HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics

Zheng, Jie and Rodriguez, Santiago and Laurin, Charles and Baird, Denis and Trela-Larsen, Lea and Erzurumluoglu, Mesut A and Zheng, Yi and White, Jon and Giambartolomei, Claudia and Zabaneh, Delilah and Morris, Richard and Kumari, Meena and Casas, Juan P and Hingorani, Aroon D and Evans, David M and Gaunt, Tom R and Day, Ian NM (2017) 'HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics.' Bioinformatics, 33 (1). 79 - 86. ISSN 1367-4803

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Abstract

Motivation Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients (r2 ) of the variants. However, haplotypes rather than pairwise r2 , are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this article, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel. Results Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits height data, HAPRAP performs well with a small training sample size (N < 2000) while other methods become suboptimal. Moreover, HAPRAP’s performance is not affected substantially by single nucleotide polymorphisms (SNPs) with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization).

Item Type: Article
Subjects: H Social Sciences > HA Statistics
R Medicine > R Medicine (General)
Divisions: Faculty of Social Sciences > Institute for Social and Economic Research
Depositing User: Elements
Date Deposited: 04 Sep 2018 12:32
Last Modified: 04 Sep 2018 12:32
URI: http://repository.essex.ac.uk/id/eprint/22930

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