@article{Zheng2016, 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 (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}, author = {Zheng, J. and Rodriguez, S. and Laurin, C. and Trela-Larsen, L. and Ma, Erzurumluoglu and Zheng, Y. and White, J. and Baird, D. and Zabaneh, D. and Giambartolomei, C. and Morris, R. and Hingorani, A. D. and Ucleb, Consortium and Erzurumluoglu, Mesut A. and Jp, Casas and Kumari, Meena and Ad, Hingorani and Casas, J. P. and Dm, Evans and Evans, D. M. and Gaunt, Tom R. and Tr, Gaunt and Day, I. N. and In, Day}, month = {dec}, title = {HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics.}, url = {http://bioinformatics.oxfordjournals.org/content/early/2016/10/02/bioinformatics.btw565}, year = {2016} }