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

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posted on 2016-12-01, 11:39 authored by J. Zheng, S. Rodriguez, C. Laurin, D. Baird, L. Trela-Larsen, Mesut A. Erzurumluoglu, Y. Zheng, J. White, C. Giambartolomei, D. Zabaneh, R. Morris, M. Kumari, J. P. Casas, A. D. Hingorani, UCLEB Consortium, D. M. Evans, T. R. Gaunt, I. N. Day
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 (r(2)) of the variants. However, haplotypes rather than pairwise r(2), are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this paper, 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 (GIANT) 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 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). AVAILABILITY: The HAPRAP package and documentation are available online: http://apps.biocompute.org.uk/haprap.

Funding

This work was financially supported by MRC Integrative Epidemiology Unit at the Universtiy of Bristol (MC_UU_12013/1-9).

History

Citation

Bioinformatics, 2016

Author affiliation

/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGY/School of Medicine/Department of Health Sciences

Version

  • VoR (Version of Record)

Published in

Bioinformatics

Publisher

Oxford University Press (OUP)

issn

1367-4803

eissn

1460-2059

Acceptance date

2016-08-26

Copyright date

2016

Available date

2016-12-01

Publisher version

http://bioinformatics.oxfordjournals.org/content/early/2016/10/02/bioinformatics.btw565

Language

en

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