Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data
Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery sam...
| Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Instituto de Salud Carlos III (ISCIII) |
| Repositorio: | Repisalud |
| Idioma: | inglés |
| OAI Identifier: | oai:repisalud.isciii.es:20.500.12105/7901 |
| Acceso en línea: | http://hdl.handle.net/20.500.12105/7901 |
| Access Level: | acceso abierto |
| Palabra clave: | Algorithms Computer Simulation Genome-Wide Association Study Humans Linkage Disequilibrium Multifactorial Inheritance Polymorphism, Single Nucleotide Risk Factors Genetic Predisposition to Disease Models, Genetic |
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Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level DataShi, JianxinPark, Ju-HyunDuan, JubaoBerndt, Sonja TMoy, WintonYu, KaiSong, LeiWheeler, WilliamHua, XingSilverman, DebraGarcia-Closas, MontserratHsiung, Chao AgnesFigueroa, Jonine DCortessis, Victoria KMalats, NuriaKaragas, Margaret RVineis, PaoloChang, I-ShouLin, DongxinZhou, BaosenSeow, AdelineMatsuo, KeitaroHong, Yun-ChulCaporaso, Neil EWolpin, BrianJacobs, EricPetersen, Gloria MKlein, Alison PLi, DonghuiRisch, HarveySanders, Alan RHsu, LiSchoen, Robert EBrenner, HermannStolzenberg-Solomon, RachaelGejman, PabloLan, QingRothman, NathanielAmundadottir, Laufey TLandi, Maria TeresaLevinson, Douglas FChanock, Stephen JChatterjee, NilanjanAlgorithmsComputer SimulationGenome-Wide Association StudyHumansLinkage DisequilibriumMultifactorial InheritancePolymorphism, Single NucleotideRisk FactorsGenetic Predisposition to DiseaseModels, GeneticRecent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner's-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner's curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25-50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner's curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10-5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.Public Library of Science (PLOS)NIH - National Cancer Institute (NCI) (Estados Unidos)Deutsche Forschungsgemeinschaft (Alemania)Federal Ministry of Education & Research (Alemania)NIH - National Heart, Lung, and Blood Institute (NHLBI) (Estados Unidos)20192019-07-1220162016-12-0120162016-12-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/20.500.12105/7901reponame:Repisaludinstname:Instituto de Salud Carlos III (ISCIII)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:repisalud.isciii.es:20.500.12105/79012026-06-12T12:43:37Z |
| dc.title.none.fl_str_mv |
Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data |
| title |
Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data |
| spellingShingle |
Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data Shi, Jianxin Algorithms Computer Simulation Genome-Wide Association Study Humans Linkage Disequilibrium Multifactorial Inheritance Polymorphism, Single Nucleotide Risk Factors Genetic Predisposition to Disease Models, Genetic |
| title_short |
Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data |
| title_full |
Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data |
| title_fullStr |
Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data |
| title_full_unstemmed |
Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data |
| title_sort |
Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data |
| dc.creator.none.fl_str_mv |
Shi, Jianxin Park, Ju-Hyun Duan, Jubao Berndt, Sonja T Moy, Winton Yu, Kai Song, Lei Wheeler, William Hua, Xing Silverman, Debra Garcia-Closas, Montserrat Hsiung, Chao Agnes Figueroa, Jonine D Cortessis, Victoria K Malats, Nuria Karagas, Margaret R Vineis, Paolo Chang, I-Shou Lin, Dongxin Zhou, Baosen Seow, Adeline Matsuo, Keitaro Hong, Yun-Chul Caporaso, Neil E Wolpin, Brian Jacobs, Eric Petersen, Gloria M Klein, Alison P Li, Donghui Risch, Harvey Sanders, Alan R Hsu, Li Schoen, Robert E Brenner, Hermann Stolzenberg-Solomon, Rachael Gejman, Pablo Lan, Qing Rothman, Nathaniel Amundadottir, Laufey T Landi, Maria Teresa Levinson, Douglas F Chanock, Stephen J Chatterjee, Nilanjan |
| author |
Shi, Jianxin |
| author_facet |
Shi, Jianxin Park, Ju-Hyun Duan, Jubao Berndt, Sonja T Moy, Winton Yu, Kai Song, Lei Wheeler, William Hua, Xing Silverman, Debra Garcia-Closas, Montserrat Hsiung, Chao Agnes Figueroa, Jonine D Cortessis, Victoria K Malats, Nuria Karagas, Margaret R Vineis, Paolo Chang, I-Shou Lin, Dongxin Zhou, Baosen Seow, Adeline Matsuo, Keitaro Hong, Yun-Chul Caporaso, Neil E Wolpin, Brian Jacobs, Eric Petersen, Gloria M Klein, Alison P Li, Donghui Risch, Harvey Sanders, Alan R Hsu, Li Schoen, Robert E Brenner, Hermann Stolzenberg-Solomon, Rachael Gejman, Pablo Lan, Qing Rothman, Nathaniel Amundadottir, Laufey T Landi, Maria Teresa Levinson, Douglas F Chanock, Stephen J Chatterjee, Nilanjan |
| author_role |
author |
| author2 |
Park, Ju-Hyun Duan, Jubao Berndt, Sonja T Moy, Winton Yu, Kai Song, Lei Wheeler, William Hua, Xing Silverman, Debra Garcia-Closas, Montserrat Hsiung, Chao Agnes Figueroa, Jonine D Cortessis, Victoria K Malats, Nuria Karagas, Margaret R Vineis, Paolo Chang, I-Shou Lin, Dongxin Zhou, Baosen Seow, Adeline Matsuo, Keitaro Hong, Yun-Chul Caporaso, Neil E Wolpin, Brian Jacobs, Eric Petersen, Gloria M Klein, Alison P Li, Donghui Risch, Harvey Sanders, Alan R Hsu, Li Schoen, Robert E Brenner, Hermann Stolzenberg-Solomon, Rachael Gejman, Pablo Lan, Qing Rothman, Nathaniel Amundadottir, Laufey T Landi, Maria Teresa Levinson, Douglas F Chanock, Stephen J Chatterjee, Nilanjan |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
NIH - National Cancer Institute (NCI) (Estados Unidos) Deutsche Forschungsgemeinschaft (Alemania) Federal Ministry of Education & Research (Alemania) NIH - National Heart, Lung, and Blood Institute (NHLBI) (Estados Unidos) |
| dc.subject.none.fl_str_mv |
Algorithms Computer Simulation Genome-Wide Association Study Humans Linkage Disequilibrium Multifactorial Inheritance Polymorphism, Single Nucleotide Risk Factors Genetic Predisposition to Disease Models, Genetic |
| topic |
Algorithms Computer Simulation Genome-Wide Association Study Humans Linkage Disequilibrium Multifactorial Inheritance Polymorphism, Single Nucleotide Risk Factors Genetic Predisposition to Disease Models, Genetic |
| description |
Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner's-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner's curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25-50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner's curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10-5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure. |
| publishDate |
2016 |
| dc.date.none.fl_str_mv |
2016 2016-12-01 2016 2016-12-01 2019 2019-07-12 |
| dc.type.none.fl_str_mv |
journal article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/20.500.12105/7901 |
| url |
http://hdl.handle.net/20.500.12105/7901 |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Atribución-NoComercial-CompartirIgual 4.0 Internacional http://creativecommons.org/licenses/by-nc-sa/4.0/ |
| dc.rights.openaire.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Atribución-NoComercial-CompartirIgual 4.0 Internacional http://creativecommons.org/licenses/by-nc-sa/4.0/ |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Public Library of Science (PLOS) |
| publisher.none.fl_str_mv |
Public Library of Science (PLOS) |
| dc.source.none.fl_str_mv |
reponame:Repisalud instname:Instituto de Salud Carlos III (ISCIII) |
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Instituto de Salud Carlos III (ISCIII) |
| reponame_str |
Repisalud |
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Repisalud |
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|
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1869425063785136128 |
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15,811543 |