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HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics
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 t...
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| Published in: | Bioinformatics (Oxford, England) England), 2017-01, Vol.33 (1), p.79-86 |
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| creator | 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 |
| description | 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 |
| doi_str_mv | 10.1093/bioinformatics/btw565 |
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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 http://apps.biocompute.org.uk/haprap/ CONTACT: : jie.zheng@bristol.ac.uk or tom.gaunt@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.</description><identifier>ISSN: 1367-4803</identifier><identifier>ISSN: 1367-4811</identifier><identifier>ISSN: 1460-2059</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btw565</identifier><identifier>PMID: 27591082</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>bioinformatics ; Chromosome Mapping - methods ; gall bladder ; Gene Frequency ; Genome-Wide Association Study ; Genotype ; Haplotypes ; Humans ; Linkage Disequilibrium ; loci ; meta-analysis ; Original Papers ; Polymorphism, Single Nucleotide ; prediction ; Quantitative Trait, Heritable ; quantitative traits ; regression analysis ; Sample Size ; single nucleotide polymorphism ; Software</subject><ispartof>Bioinformatics (Oxford, England), 2017-01, Vol.33 (1), p.79-86</ispartof><rights>The Author 2016. Published by Oxford University Press.</rights><rights>The Author 2016. Published by Oxford University Press. 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c444t-30afbaaeb948cd2546bdf4ef346a5275be3d5d869f7e7a750f0cae7b065a97313</citedby><cites>FETCH-LOGICAL-c444t-30afbaaeb948cd2546bdf4ef346a5275be3d5d869f7e7a750f0cae7b065a97313</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5544112/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5544112/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,882,27905,27906,53772,53774</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27591082$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Ratsch, Gunnar</contributor><creatorcontrib>Zheng, Jie</creatorcontrib><creatorcontrib>Rodriguez, Santiago</creatorcontrib><creatorcontrib>Laurin, Charles</creatorcontrib><creatorcontrib>Baird, Denis</creatorcontrib><creatorcontrib>Trela-Larsen, Lea</creatorcontrib><creatorcontrib>Erzurumluoglu, Mesut A</creatorcontrib><creatorcontrib>Zheng, Yi</creatorcontrib><creatorcontrib>White, Jon</creatorcontrib><creatorcontrib>Giambartolomei, Claudia</creatorcontrib><creatorcontrib>Zabaneh, Delilah</creatorcontrib><creatorcontrib>Morris, Richard</creatorcontrib><creatorcontrib>Kumari, Meena</creatorcontrib><creatorcontrib>Casas, Juan P</creatorcontrib><creatorcontrib>Hingorani, Aroon D</creatorcontrib><creatorcontrib>Evans, David M</creatorcontrib><creatorcontrib>Gaunt, Tom R</creatorcontrib><creatorcontrib>Day, Ian N M</creatorcontrib><creatorcontrib>UCLEB Consortium</creatorcontrib><title>HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics</title><title>Bioinformatics (Oxford, England)</title><addtitle>Bioinformatics</addtitle><description>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 http://apps.biocompute.org.uk/haprap/ CONTACT: : jie.zheng@bristol.ac.uk or tom.gaunt@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.</description><subject>bioinformatics</subject><subject>Chromosome Mapping - methods</subject><subject>gall bladder</subject><subject>Gene Frequency</subject><subject>Genome-Wide Association Study</subject><subject>Genotype</subject><subject>Haplotypes</subject><subject>Humans</subject><subject>Linkage Disequilibrium</subject><subject>loci</subject><subject>meta-analysis</subject><subject>Original Papers</subject><subject>Polymorphism, Single Nucleotide</subject><subject>prediction</subject><subject>Quantitative Trait, Heritable</subject><subject>quantitative traits</subject><subject>regression analysis</subject><subject>Sample Size</subject><subject>single nucleotide polymorphism</subject><subject>Software</subject><issn>1367-4803</issn><issn>1367-4811</issn><issn>1460-2059</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNqFUV1rFTEQDaLYevUnKHn0ZW2yyeyHD8KlaCsULH7gY5jsTnoju5t1k63035vLrVf7JAMzIXPmzBkOYy-leCNFq86sD35yYRkx-S6e2fQLKnjETqWq6kI3Uj4-voU6Yc9i_CGEAAHVU3ZS1tBK0ZSnjC6315-312858h3OQ0h3MxUWI_XcJ1oy-S3xkdIu9Dxv4zHlr5hX4sCdn3IP59lPN3yN-3zxffuFx3Uccbn7i43P2ROHQ6QX93XDvn14__X8srj6dPHxfHtVdFrrVCiBziKSbXXT9SXoyvZOk1O6QsiaLake-qZqXU011iCc6JBqKyrAtlZSbdi7A--82pH6jqa04GDmxe8FmYDePOxMfmduwq0B0FrKMhO8vidYws-VYjKjjx0NA04U1mhKJUC2uqyr_0JlowCEghwbBgdot4QYF3JHRVKYvZvmoZvm4Gaee_XvOcepP_ap3xJ3o8c</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Zheng, Jie</creator><creator>Rodriguez, Santiago</creator><creator>Laurin, Charles</creator><creator>Baird, Denis</creator><creator>Trela-Larsen, Lea</creator><creator>Erzurumluoglu, Mesut A</creator><creator>Zheng, Yi</creator><creator>White, Jon</creator><creator>Giambartolomei, Claudia</creator><creator>Zabaneh, Delilah</creator><creator>Morris, Richard</creator><creator>Kumari, Meena</creator><creator>Casas, Juan P</creator><creator>Hingorani, Aroon D</creator><creator>Evans, David M</creator><creator>Gaunt, Tom R</creator><creator>Day, Ian N M</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><scope>5PM</scope></search><sort><creationdate>20170101</creationdate><title>HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics</title><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</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c444t-30afbaaeb948cd2546bdf4ef346a5275be3d5d869f7e7a750f0cae7b065a97313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>bioinformatics</topic><topic>Chromosome Mapping - methods</topic><topic>gall bladder</topic><topic>Gene Frequency</topic><topic>Genome-Wide Association Study</topic><topic>Genotype</topic><topic>Haplotypes</topic><topic>Humans</topic><topic>Linkage Disequilibrium</topic><topic>loci</topic><topic>meta-analysis</topic><topic>Original Papers</topic><topic>Polymorphism, Single Nucleotide</topic><topic>prediction</topic><topic>Quantitative Trait, Heritable</topic><topic>quantitative traits</topic><topic>regression analysis</topic><topic>Sample Size</topic><topic>single nucleotide polymorphism</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zheng, Jie</creatorcontrib><creatorcontrib>Rodriguez, Santiago</creatorcontrib><creatorcontrib>Laurin, Charles</creatorcontrib><creatorcontrib>Baird, Denis</creatorcontrib><creatorcontrib>Trela-Larsen, Lea</creatorcontrib><creatorcontrib>Erzurumluoglu, Mesut A</creatorcontrib><creatorcontrib>Zheng, Yi</creatorcontrib><creatorcontrib>White, Jon</creatorcontrib><creatorcontrib>Giambartolomei, Claudia</creatorcontrib><creatorcontrib>Zabaneh, Delilah</creatorcontrib><creatorcontrib>Morris, Richard</creatorcontrib><creatorcontrib>Kumari, Meena</creatorcontrib><creatorcontrib>Casas, Juan P</creatorcontrib><creatorcontrib>Hingorani, Aroon D</creatorcontrib><creatorcontrib>Evans, David M</creatorcontrib><creatorcontrib>Gaunt, Tom R</creatorcontrib><creatorcontrib>Day, Ian N M</creatorcontrib><creatorcontrib>UCLEB Consortium</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics (Oxford, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zheng, Jie</au><au>Rodriguez, Santiago</au><au>Laurin, Charles</au><au>Baird, Denis</au><au>Trela-Larsen, Lea</au><au>Erzurumluoglu, Mesut A</au><au>Zheng, Yi</au><au>White, Jon</au><au>Giambartolomei, Claudia</au><au>Zabaneh, Delilah</au><au>Morris, Richard</au><au>Kumari, Meena</au><au>Casas, Juan P</au><au>Hingorani, Aroon D</au><au>Evans, David M</au><au>Gaunt, Tom R</au><au>Day, Ian N M</au><au>Ratsch, Gunnar</au><aucorp>UCLEB Consortium</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics</atitle><jtitle>Bioinformatics (Oxford, England)</jtitle><addtitle>Bioinformatics</addtitle><date>2017-01-01</date><risdate>2017</risdate><volume>33</volume><issue>1</issue><spage>79</spage><epage>86</epage><pages>79-86</pages><issn>1367-4803</issn><issn>1367-4811</issn><issn>1460-2059</issn><eissn>1367-4811</eissn><abstract>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 http://apps.biocompute.org.uk/haprap/ CONTACT: : jie.zheng@bristol.ac.uk or tom.gaunt@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>27591082</pmid><doi>10.1093/bioinformatics/btw565</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | bioinformatics Chromosome Mapping - methods gall bladder Gene Frequency Genome-Wide Association Study Genotype Haplotypes Humans Linkage Disequilibrium loci meta-analysis Original Papers Polymorphism, Single Nucleotide prediction Quantitative Trait, Heritable quantitative traits regression analysis Sample Size single nucleotide polymorphism Software |
| title | HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics |
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