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

Jie Zheng; Santiago Rodriguez; Charles Laurin; Denis Baird; Lea Trela-Larsen; Mesut A Erzurumluoglu; Yi Zheng; Jon White; Claudia Giambartolomei; Delilah Zabaneh; Richard Morris; Meena Kumari; Juan P Casas; Aroon D Hingorani; David M Evans; Tom R Gaunt; Ian N M Day; UCLEB Consortium

doi:10.1093/bioinformatics/btw565

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

Jie Zheng, Santiago Rodriguez, Charles Laurin, Denis Baird, Lea Trela-Larsen, Mesut A Erzurumluoglu, Yi Zheng, Jon White, Claudia Giambartolomei, Delilah Zabaneh, Richard Morris, Meena Kumari, Juan P Casas, Aroon D Hingorani, David M Evans, Tom R Gaunt, Ian N M Day, UCLEB Consortium

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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<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.

Original language	English
Journal	BIOINFORMATICS
DOIs	https://doi.org/10.1093/bioinformatics/btw565
Publication status	Published - 1 Sept 2016

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HAPRAP: a haplotype-based_ZHENG_Accepted26Aug2016_GOLD VoRFinal published version, 486 KBLicence: CC BY

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Zheng, J., Rodriguez, S., Laurin, C., Baird, D., Trela-Larsen, L., Erzurumluoglu, M. A., Zheng, Y., White, J., Giambartolomei, C., Zabaneh, D., Morris, R., Kumari, M., Casas, J. P., Hingorani, A. D., Evans, D. M., Gaunt, T. R., Day, I. N. M., & UCLEB Consortium (2016). HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics. BIOINFORMATICS. https://doi.org/10.1093/bioinformatics/btw565

@article{91538cdea42443f1b7a94bef95b48567,

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

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<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.",

author = "Jie Zheng and Santiago Rodriguez and Charles Laurin and Denis Baird and Lea Trela-Larsen and Erzurumluoglu, {Mesut A} and Yi Zheng and Jon White and Claudia Giambartolomei and Delilah Zabaneh and Richard Morris and Meena Kumari and Casas, {Juan P} and Hingorani, {Aroon D} and Evans, {David M} and Gaunt, {Tom R} and Day, {Ian N M} and {UCLEB Consortium}",

note = "{\textcopyright} The Author(s) 2016. Published by Oxford University Press.",

year = "2016",

month = sep,

day = "1",

doi = "10.1093/bioinformatics/btw565",

language = "English",

journal = "BIOINFORMATICS",

issn = "1367-4803",

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Zheng, J, Rodriguez, S, Laurin, C, Baird, D, Trela-Larsen, L, Erzurumluoglu, MA, Zheng, Y, White, J, Giambartolomei, C, Zabaneh, D , Morris, R, Kumari, M, Casas, JP, Hingorani, AD, Evans, DM, Gaunt, TR, Day, INM & UCLEB Consortium 2016, 'HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics', BIOINFORMATICS. https://doi.org/10.1093/bioinformatics/btw565

TY - JOUR

T1 - HAPRAP

T2 - a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics

AU - Zheng, Jie

AU - Rodriguez, Santiago

AU - Laurin, Charles

AU - Baird, Denis

AU - Trela-Larsen, Lea

AU - Erzurumluoglu, Mesut A

AU - Zheng, Yi

AU - White, Jon

AU - Giambartolomei, Claudia

AU - Zabaneh, Delilah

AU - Morris, Richard

AU - Kumari, Meena

AU - Casas, Juan P

AU - Hingorani, Aroon D

AU - Evans, David M

AU - Gaunt, Tom R

AU - Day, Ian N M

AU - UCLEB Consortium

PY - 2016/9/1

Y1 - 2016/9/1

N2 - 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.

AB - 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.

U2 - 10.1093/bioinformatics/btw565

DO - 10.1093/bioinformatics/btw565

M3 - Article

C2 - 27591082

SN - 1367-4803

JO - BIOINFORMATICS

JF - BIOINFORMATICS

ER -

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

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