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Bioinformatics. 2017 Jan 1;33(1):79-86. doi: 10.1093/bioinformatics/btw565. Epub 2016 Sep 1.

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

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MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol BS8 6BN, UK.
School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK.
Department of Health Sciences, Genetic Epidemiology Group, University of Leicester, Leicester LE1 7RH, UK.
Dedman College of Humanities and Sciences, Southern Methodist University, Dallas, TX 750235, USA.
Department of Genetics, Environment and Evolution, University College London Genetics Institute, London WC1E 6BT, UK.
Department of Primary Care & Population Health, University College London, Royal Free Campus, London NW3 2PF, UK.
Centre for Clinical Pharmacology, University College London, London WC1E 6BT, UK, Division of Medicine.
University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Australia, QLD 4102.



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 < 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).


The HAPRAP package and documentation are available at CONTACT: : or information: Supplementary data are available at Bioinformatics online.

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