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

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 ([Formula: see t...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2017-01, Vol.33 (1), p.79-86
Main Authors: Zheng, Jie, Rodriguez, Santiago, Laurin, Charles, Baird, Denis, Trela-Larsen, Lea, Erzurumluoglu, Mesut A, Zheng, Yi, White, Jon, Giambartolomei, Claudia, Zabaneh, Delilah, Morris, Richard, Kumari, Meena, Casas, Juan P, Hingorani, Aroon D, Evans, David M, Gaunt, Tom R, Day, Ian N M
Format: Article
Language:English
Subjects:
bioinformatics
Chromosome Mapping - methods
gall bladder
Gene Frequency
Genome-Wide Association Study
Genotype
Haplotypes
Humans
Linkage Disequilibrium
loci
meta-analysis
Original Papers
Polymorphism, Single Nucleotide
prediction
Quantitative Trait, Heritable
quantitative traits
regression analysis
Sample Size
single nucleotide polymorphism
Software
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Summary: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 ([Formula: see text]) of the variants. However, haplotypes rather than pairwise [Formula: see text], 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. 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 
ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btw565