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

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. and Day, Ian N. M. (2017) HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics. Bioinformatics, 33 1: 79-86. doi:10.1093/bioinformatics/btw565


Author 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.
Title HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics
Journal name Bioinformatics   Check publisher's open access policy
ISSN 1460-2059
1367-4803
Publication date 2017-01-01
Year available 2016
Sub-type Article (original research)
DOI 10.1093/bioinformatics/btw565
Open Access Status Not yet assessed
Volume 33
Issue 1
Start page 79
End page 86
Total pages 8
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Collection year 2018
Language eng
Formatted 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 metaanalyses (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).
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
UQ Diamantina Institute Publications
 
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