Multi-PGS enhances polygenic prediction by combining 937 polygenic scores
The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes...
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| Published in | Nature communications Vol. 14; no. 1; pp. 4702 - 11 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
05.08.2023
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2041-1723 2041-1723 |
| DOI | 10.1038/s41467-023-40330-w |
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| Abstract | The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction
R
2
increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.
Polygenic scores (PGS) have high potential for clinical use but are currently underpowered for many applications. Here, the authors develop an approach that leverages an agnostic library of hundreds of PGS to increase prediction of complex diseases and other traits. This multi-PGS framework is ideal for emerging biobank data. |
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| AbstractList | The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.Polygenic scores (PGS) have high potential for clinical use but are currently underpowered for many applications. Here, the authors develop an approach that leverages an agnostic library of hundreds of PGS to increase prediction of complex diseases and other traits. This multi-PGS framework is ideal for emerging biobank data. The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks. The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R 2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks. Polygenic scores (PGS) have high potential for clinical use but are currently underpowered for many applications. Here, the authors develop an approach that leverages an agnostic library of hundreds of PGS to increase prediction of complex diseases and other traits. This multi-PGS framework is ideal for emerging biobank data. The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks. Abstract The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R 2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks. The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks. The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R 2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks. |
| ArticleNumber | 4702 |
| Author | Hougaard, David M. Grove, Jakob Aschard, Hugues Albiñana, Clara Ingason, Andrés Neale, Benjamin M. Schork, Andrew J. Petersen, Liselotte V. Børglum, Anders D. Nordentoft, Merete Mortensen, Preben Bo Zhu, Zhihong Brikell, Isabell Agerbo, Esben McGrath, John J. Werge, Thomas Vilhjálmsson, Bjarni J. Bulik, Cynthia M. Privé, Florian |
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University, Center for Genomics and Personalized Medicine, Aarhus University – sequence: 15 givenname: Preben Bo orcidid: 0000-0002-5230-9865 surname: Mortensen fullname: Mortensen, Preben Bo organization: The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, National Centre for Register-Based Research, Aarhus University – sequence: 16 givenname: John J. orcidid: 0000-0002-4792-6068 surname: McGrath fullname: McGrath, John J. organization: National Centre for Register-Based Research, Aarhus University, Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Queensland Brain Institute, University of Queensland – sequence: 17 givenname: Benjamin M. orcidid: 0000-0003-1513-6077 surname: Neale fullname: Neale, Benjamin M. organization: Analytic and Translational Genetics Unit, Massachusetts General Hospital, Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard – sequence: 18 givenname: Florian orcidid: 0000-0002-3138-2078 surname: Privé fullname: Privé, Florian organization: The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, National Centre for Register-Based Research, Aarhus University – sequence: 19 givenname: Bjarni J. orcidid: 0000-0003-2277-9249 surname: Vilhjálmsson fullname: Vilhjálmsson, Bjarni J. email: bjv.ncrr@au.dk organization: The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, National Centre for Register-Based Research, Aarhus University, Bioinformatics Research Centre, Aarhus University, Novo Nordisk Foundation Center for Genomic Mechanisms, Broad Institute of MIT and Harvard |
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| Cites_doi | 10.1177/1403494810395825 10.1038/s41593-021-00826-4 10.1038/s41588-017-0009-4 10.1038/s41562-019-0566-x 10.1093/nar/gky1120 10.1093/hmg/ddz187 10.1038/s41588-019-0344-8 10.1038/s41467-019-12653-0 10.1038/s41588-018-0144-6 10.1016/j.ajhg.2022.01.007 10.1038/ng.3643 10.1002/gepi.21614 10.1038/s41588-018-0269-7 10.1038/s41588-018-0183-z 10.1016/j.jacc.2018.07.079 10.1007/s10654-018-0356-1 10.1038/s41588-018-0090-3 10.1016/j.ajhg.2021.04.014 10.1016/j.ajhg.2019.11.001 10.1038/s41562-021-01110-y 10.1016/j.ajhg.2017.03.004 10.1093/bioinformatics/btz633 10.1038/s41467-017-02769-6 10.1038/ng.3211 10.1038/s41588-019-0481-0 10.1038/s41467-019-13848-1 10.1038/s41593-018-0326-7 10.1038/ng.2653 10.1016/j.cct.2018.09.015 10.1176/appi.ajp.22010003 10.3389/fmed.2021.711208 10.1038/ng.3406 10.1038/s41588-021-00978-w 10.1038/ng.3190 10.1093/ehjci/jeaa297 10.1038/s41467-019-11112-0 10.1038/s41467-021-27930-0 10.1038/s41588-021-00783-5 10.1038/s41588-018-0320-8 10.1038/s41467-019-08535-0 10.1093/bioinformatics/bty185 10.1038/s41467-019-09718-5 10.1371/journal.pgen.1009697 10.1038/mp.2017.163 10.18637/jss.v033.i01 10.1038/mp.2017.196 10.1038/s41588-019-0379-x 10.1016/j.xhgg.2022.100136 10.1101/2022.01.19.476997 10.1038/s41467-021-24485-y 10.5281/zenodo.7988493 10.1093/bioinformatics/btaa520 10.1093/bioinformatics/btaa1029 10.1145/2939672.2939785 10.1101/2020.11.30.20237768 |
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| PublicationDate_xml | – month: 08 year: 2023 text: 2023-08-05 day: 05 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Nature communications |
| PublicationTitleAbbrev | Nat Commun |
| PublicationTitleAlternate | Nat Commun |
| PublicationYear | 2023 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | Inouye (CR1) 2018; 72 Privé (CR6) 2022; 109 Lambert (CR45) 2021; 53 Privé, Vilhjálmsson, Aschard, Blum (CR15) 2019; 105 Maier (CR8) 2018; 9 Martin (CR40) 2017; 100 Friedman, Hastie, Tibshirani (CR53) 2010; 33 CR38 Smith (CR41) 2021; 24 Zhou, Zhao (CR19) 2021; 17 Khera (CR3) 2018; 50 Albiñana (CR32) 2021; 108 Rohde, Nyegaard, Kjolby, Sørensen (CR13) 2021; 8 Ge, Chen, Ni, Feng, Smoller (CR18) 2019; 10 Grove (CR14) 2019; 51 Abdellaoui, Verweij (CR24) 2021; 5 Pedersen (CR22) 2017; 23 Lloyd-Jones (CR17) 2019; 10 Watanabe (CR27) 2019; 51 McCaw (CR36) 2022; 13 Bliddal, Broe, Pottegård, Olsen, Langhoff-Roos (CR34) 2018; 33 Lam (CR50) 2020; 36 Raisi-Estabragh, Harvey, Neubauer, Petersen (CR42) 2021; 22 CR46 Turley (CR9) 2018; 50 Abraham (CR11) 2019; 10 Hasseris (CR37) 2023; 180 Martin (CR4) 2019; 51 Duncan (CR5) 2019; 10 (CR43) 2013; 45 Grotzinger (CR10) 2019; 3 Bulik-Sullivan (CR39) 2015; 47 Lee, Goddard, Wray, Visscher (CR56) 2012; 36 Loh (CR54) 2015; 47 CR16 CR57 Howard (CR30) 2019; 22 Demontis (CR35) 2019; 51 CR51 Loh, Kichaev, Gazal, Schoech, Price (CR55) 2018; 50 Wray (CR29) 2018; 50 Mors, Perto, Mortensen (CR33) 2011; 39 Bulik-Sullivan (CR47) 2015; 47 Thornton (CR48) 2018; 74 Ferkingstad (CR44) 2021; 53 Buniello (CR26) 2019; 47 McCarthy (CR49) 2016; 48 Krapohl (CR7) 2018; 23 Privé, Aschard, Ziyatdinov, Blum (CR52) 2018; 34 CR28 CR25 CR23 Lambert, Abraham, Inouye (CR2) 2019; 28 Baselmans (CR31) 2019; 51 CR21 CR20 Chung (CR12) 2019; 10 40330_CR46 D Demontis (40330_CR35) 2019; 51 E Ferkingstad (40330_CR44) 2021; 53 SM Smith (40330_CR41) 2021; 24 AV Khera (40330_CR3) 2018; 50 BK Bulik-Sullivan (40330_CR47) 2015; 47 M Lam (40330_CR50) 2020; 36 K Watanabe (40330_CR27) 2019; 51 P Turley (40330_CR9) 2018; 50 BML Baselmans (40330_CR31) 2019; 51 CB Pedersen (40330_CR22) 2017; 23 W Chung (40330_CR12) 2019; 10 P-R Loh (40330_CR55) 2018; 50 RM Maier (40330_CR8) 2018; 9 NR Wray (40330_CR29) 2018; 50 40330_CR38 F Privé (40330_CR15) 2019; 105 SA Lambert (40330_CR2) 2019; 28 L Duncan (40330_CR5) 2019; 10 E Krapohl (40330_CR7) 2018; 23 SA Lambert (40330_CR45) 2021; 53 ZR McCaw (40330_CR36) 2022; 13 A Abdellaoui (40330_CR24) 2021; 5 GTEx Consortium. (40330_CR43) 2013; 45 F Privé (40330_CR52) 2018; 34 S McCarthy (40330_CR49) 2016; 48 SH Lee (40330_CR56) 2012; 36 40330_CR28 G Abraham (40330_CR11) 2019; 10 J Grove (40330_CR14) 2019; 51 M Bliddal (40330_CR34) 2018; 33 A Buniello (40330_CR26) 2019; 47 T Ge (40330_CR18) 2019; 10 40330_CR25 40330_CR20 P-R Loh (40330_CR54) 2015; 47 40330_CR21 40330_CR23 G Zhou (40330_CR19) 2021; 17 PD Rohde (40330_CR13) 2021; 8 F Privé (40330_CR6) 2022; 109 O Mors (40330_CR33) 2011; 39 M Inouye (40330_CR1) 2018; 72 AR Martin (40330_CR4) 2019; 51 B Bulik-Sullivan (40330_CR39) 2015; 47 40330_CR57 C Albiñana (40330_CR32) 2021; 108 40330_CR16 LR Lloyd-Jones (40330_CR17) 2019; 10 40330_CR51 DM Howard (40330_CR30) 2019; 22 S Hasseris (40330_CR37) 2023; 180 AR Martin (40330_CR40) 2017; 100 Z Raisi-Estabragh (40330_CR42) 2021; 22 J Friedman (40330_CR53) 2010; 33 AD Grotzinger (40330_CR10) 2019; 3 LM Thornton (40330_CR48) 2018; 74 |
| References_xml | – volume: 39 start-page: 54 year: 2011 end-page: 57 ident: CR33 article-title: The Danish psychiatric central research register publication-title: Scand. J. Public Health doi: 10.1177/1403494810395825 – volume: 24 start-page: 737 year: 2021 end-page: 745 ident: CR41 article-title: An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank publication-title: Nat. Neurosci. doi: 10.1038/s41593-021-00826-4 – volume: 50 start-page: 229 year: 2018 end-page: 237 ident: CR9 article-title: Multi-trait analysis of genome-wide association summary statistics using MTAG publication-title: Nat. Genet. doi: 10.1038/s41588-017-0009-4 – volume: 3 start-page: 513 year: 2019 end-page: 525 ident: CR10 article-title: Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits publication-title: Nat. Hum. Behav. doi: 10.1038/s41562-019-0566-x – volume: 47 start-page: D1005 year: 2019 end-page: D1012 ident: CR26 article-title: The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gky1120 – volume: 28 start-page: R133 year: 2019 end-page: R142 ident: CR2 article-title: Towards clinical utility of polygenic risk scores publication-title: Hum. Mol. Genet. doi: 10.1093/hmg/ddz187 – ident: CR16 – ident: CR51 – volume: 51 start-page: 431 year: 2019 end-page: 444 ident: CR14 article-title: Identification of common genetic risk variants for autism spectrum disorder publication-title: Nat. Genet. doi: 10.1038/s41588-019-0344-8 – volume: 10 year: 2019 ident: CR17 article-title: Improved polygenic prediction by Bayesian multiple regression on summary statistics publication-title: Nat. Commun. doi: 10.1038/s41467-019-12653-0 – volume: 50 start-page: 906 year: 2018 end-page: 908 ident: CR55 article-title: Mixed-model association for biobank-scale datasets publication-title: Nat. Genet. doi: 10.1038/s41588-018-0144-6 – volume: 109 start-page: 373 year: 2022 ident: CR6 article-title: Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort publication-title: Am. J. Hum. Genet. doi: 10.1016/j.ajhg.2022.01.007 – volume: 48 start-page: 1279 year: 2016 end-page: 1283 ident: CR49 article-title: A reference panel of 64,976 haplotypes for genotype imputation publication-title: Nat. Genet. doi: 10.1038/ng.3643 – volume: 36 start-page: 214 year: 2012 end-page: 224 ident: CR56 article-title: A better coefficient of determination for genetic profile analysis publication-title: Genet. Epidemiol. doi: 10.1002/gepi.21614 – volume: 51 start-page: 63 year: 2019 end-page: 75 ident: CR35 article-title: Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder publication-title: Nat. Genet. doi: 10.1038/s41588-018-0269-7 – volume: 50 start-page: 1219 year: 2018 end-page: 1224 ident: CR3 article-title: Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations publication-title: Nat. Genet. doi: 10.1038/s41588-018-0183-z – ident: CR25 – ident: CR21 – ident: CR46 – volume: 72 start-page: 1883 year: 2018 end-page: 1893 ident: CR1 article-title: Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention publication-title: J. Am. Coll. Cardiol. doi: 10.1016/j.jacc.2018.07.079 – volume: 33 start-page: 27 year: 2018 end-page: 36 ident: CR34 article-title: The Danish medical birth register publication-title: Eur. J. Epidemiol. doi: 10.1007/s10654-018-0356-1 – volume: 50 start-page: 668 year: 2018 end-page: 681 ident: CR29 article-title: Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression publication-title: Nat. Genet. doi: 10.1038/s41588-018-0090-3 – volume: 108 start-page: 1001 year: 2021 end-page: 1011 ident: CR32 article-title: Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction publication-title: Am. J. Hum. Genet. doi: 10.1016/j.ajhg.2021.04.014 – volume: 105 start-page: 1213 year: 2019 end-page: 1221 ident: CR15 article-title: Making the most of clumping and thresholding for polygenic scores publication-title: Am. J. Hum. Genet. doi: 10.1016/j.ajhg.2019.11.001 – ident: CR57 – volume: 5 start-page: 686 year: 2021 end-page: 694 ident: CR24 article-title: Dissecting polygenic signals from genome-wide association studies on human behaviour publication-title: Nat. Hum. Behav. doi: 10.1038/s41562-021-01110-y – volume: 100 start-page: 635 year: 2017 end-page: 649 ident: CR40 article-title: Human demographic history impacts genetic risk prediction across diverse populations publication-title: Am. J. Hum. Genet. doi: 10.1016/j.ajhg.2017.03.004 – volume: 36 start-page: 930 year: 2020 end-page: 933 ident: CR50 article-title: RICOPILI: Rapid Imputation for COnsortias PIpeLIne publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz633 – volume: 9 year: 2018 ident: CR8 article-title: Improving genetic prediction by leveraging genetic correlations among human diseases and traits publication-title: Nat. Commun. doi: 10.1038/s41467-017-02769-6 – volume: 47 start-page: 291 year: 2015 end-page: 295 ident: CR47 article-title: LD Score regression distinguishes confounding from polygenicity in genome-wide association studies publication-title: Nat. Genet. doi: 10.1038/ng.3211 – volume: 51 start-page: 1339 year: 2019 end-page: 1348 ident: CR27 article-title: A global overview of pleiotropy and genetic architecture in complex traits publication-title: Nat. Genet. doi: 10.1038/s41588-019-0481-0 – volume: 10 year: 2019 ident: CR11 article-title: Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke publication-title: Nat. Commun. doi: 10.1038/s41467-019-13848-1 – volume: 22 start-page: 343 year: 2019 end-page: 352 ident: CR30 article-title: Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions publication-title: Nat. Neurosci. doi: 10.1038/s41593-018-0326-7 – volume: 45 start-page: 580 year: 2013 end-page: 585 ident: CR43 article-title: The Genotype-Tissue Expression (GTEx) project publication-title: Nat. Genet. doi: 10.1038/ng.2653 – volume: 74 start-page: 61 year: 2018 end-page: 69 ident: CR48 article-title: The Anorexia Nervosa Genetics Initiative (ANGI): Overview and methods publication-title: Contemp. Clin. Trials doi: 10.1016/j.cct.2018.09.015 – volume: 180 start-page: 200 year: 2023 end-page: 208 ident: CR37 article-title: Polygenic risk and episode polarity among individuals with bipolar disorder publication-title: Am. J. Psychiatry doi: 10.1176/appi.ajp.22010003 – volume: 8 start-page: 711208 year: 2021 ident: CR13 article-title: Multi-trait genomic risk stratification for type 2 diabetes publication-title: Front. Med. doi: 10.3389/fmed.2021.711208 – volume: 47 start-page: 1236 year: 2015 end-page: 1241 ident: CR39 article-title: An atlas of genetic correlations across human diseases and traits publication-title: Nat. Genet. doi: 10.1038/ng.3406 – volume: 53 start-page: 1712 year: 2021 end-page: 1721 ident: CR44 article-title: Large-scale integration of the plasma proteome with genetics and disease publication-title: Nat. Genet. doi: 10.1038/s41588-021-00978-w – volume: 47 start-page: 284 year: 2015 end-page: 290 ident: CR54 article-title: Efficient Bayesian mixed-model analysis increases association power in large cohorts publication-title: Nat. Genet. doi: 10.1038/ng.3190 – ident: CR23 – volume: 22 start-page: 251 year: 2021 end-page: 258 ident: CR42 article-title: Cardiovascular magnetic resonance imaging in the UK Biobank: a major international health research resource publication-title: Eur. Heart J. Cardiovasc. Imaging doi: 10.1093/ehjci/jeaa297 – volume: 10 year: 2019 ident: CR5 article-title: Analysis of polygenic risk score usage and performance in diverse human populations publication-title: Nat. Commun. doi: 10.1038/s41467-019-11112-0 – volume: 13 year: 2022 ident: CR36 article-title: DeepNull models non-linear covariate effects to improve phenotypic prediction and association power publication-title: Nat. Commun. doi: 10.1038/s41467-021-27930-0 – volume: 53 start-page: 420 year: 2021 end-page: 425 ident: CR45 article-title: The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation publication-title: Nat. Genet. doi: 10.1038/s41588-021-00783-5 – volume: 51 start-page: 445 year: 2019 end-page: 451 ident: CR31 article-title: Multivariate genome-wide analyses of the well-being spectrum publication-title: Nat. Genet. doi: 10.1038/s41588-018-0320-8 – ident: CR38 – volume: 10 start-page: 1 year: 2019 end-page: 11 ident: CR12 article-title: Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes publication-title: Nat. Commun. doi: 10.1038/s41467-019-08535-0 – volume: 34 start-page: 2781 year: 2018 end-page: 2787 ident: CR52 article-title: Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty185 – volume: 10 year: 2019 ident: CR18 article-title: Polygenic prediction via Bayesian regression and continuous shrinkage priors publication-title: Nat. Commun. doi: 10.1038/s41467-019-09718-5 – volume: 17 start-page: e1009697 year: 2021 ident: CR19 article-title: A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics publication-title: PLoS Genet. doi: 10.1371/journal.pgen.1009697 – volume: 23 start-page: 1368 year: 2018 end-page: 1374 ident: CR7 article-title: Multi-polygenic score approach to trait prediction publication-title: Mol. Psychiatry doi: 10.1038/mp.2017.163 – volume: 33 start-page: 1 year: 2010 end-page: 22 ident: CR53 article-title: Regularization paths for generalized linear models via coordinate descent publication-title: J. Stat. Softw. doi: 10.18637/jss.v033.i01 – volume: 23 start-page: 6 year: 2017 ident: CR22 article-title: The iPSYCH2012 case–cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders publication-title: Mol. Psychiatry doi: 10.1038/mp.2017.196 – ident: CR28 – volume: 51 start-page: 584 year: 2019 end-page: 591 ident: CR4 article-title: Clinical use of current polygenic risk scores may exacerbate health disparities publication-title: Nat. Genet. doi: 10.1038/s41588-019-0379-x – ident: CR20 – volume: 51 start-page: 445 year: 2019 ident: 40330_CR31 publication-title: Nat. Genet. doi: 10.1038/s41588-018-0320-8 – volume: 5 start-page: 686 year: 2021 ident: 40330_CR24 publication-title: Nat. Hum. Behav. doi: 10.1038/s41562-021-01110-y – volume: 23 start-page: 1368 year: 2018 ident: 40330_CR7 publication-title: Mol. Psychiatry doi: 10.1038/mp.2017.163 – ident: 40330_CR28 – volume: 3 start-page: 513 year: 2019 ident: 40330_CR10 publication-title: Nat. Hum. Behav. doi: 10.1038/s41562-019-0566-x – ident: 40330_CR46 doi: 10.1016/j.xhgg.2022.100136 – ident: 40330_CR38 doi: 10.1101/2022.01.19.476997 – volume: 45 start-page: 580 year: 2013 ident: 40330_CR43 publication-title: Nat. Genet. doi: 10.1038/ng.2653 – volume: 10 start-page: 1 year: 2019 ident: 40330_CR12 publication-title: Nat. Commun. doi: 10.1038/s41467-019-08535-0 – volume: 47 start-page: 1236 year: 2015 ident: 40330_CR39 publication-title: Nat. Genet. doi: 10.1038/ng.3406 – volume: 53 start-page: 420 year: 2021 ident: 40330_CR45 publication-title: Nat. Genet. doi: 10.1038/s41588-021-00783-5 – volume: 180 start-page: 200 year: 2023 ident: 40330_CR37 publication-title: Am. J. Psychiatry doi: 10.1176/appi.ajp.22010003 – volume: 47 start-page: 284 year: 2015 ident: 40330_CR54 publication-title: Nat. Genet. doi: 10.1038/ng.3190 – volume: 23 start-page: 6 year: 2017 ident: 40330_CR22 publication-title: Mol. Psychiatry doi: 10.1038/mp.2017.196 – volume: 109 start-page: 373 year: 2022 ident: 40330_CR6 publication-title: Am. J. Hum. Genet. doi: 10.1016/j.ajhg.2022.01.007 – volume: 51 start-page: 584 year: 2019 ident: 40330_CR4 publication-title: Nat. Genet. doi: 10.1038/s41588-019-0379-x – volume: 53 start-page: 1712 year: 2021 ident: 40330_CR44 publication-title: Nat. Genet. doi: 10.1038/s41588-021-00978-w – volume: 22 start-page: 343 year: 2019 ident: 40330_CR30 publication-title: Nat. Neurosci. doi: 10.1038/s41593-018-0326-7 – volume: 39 start-page: 54 year: 2011 ident: 40330_CR33 publication-title: Scand. J. Public Health doi: 10.1177/1403494810395825 – volume: 36 start-page: 214 year: 2012 ident: 40330_CR56 publication-title: Genet. Epidemiol. doi: 10.1002/gepi.21614 – volume: 33 start-page: 27 year: 2018 ident: 40330_CR34 publication-title: Eur. J. Epidemiol. doi: 10.1007/s10654-018-0356-1 – volume: 22 start-page: 251 year: 2021 ident: 40330_CR42 publication-title: Eur. Heart J. Cardiovasc. Imaging doi: 10.1093/ehjci/jeaa297 – ident: 40330_CR20 doi: 10.1038/s41467-021-24485-y – volume: 50 start-page: 668 year: 2018 ident: 40330_CR29 publication-title: Nat. Genet. doi: 10.1038/s41588-018-0090-3 – volume: 47 start-page: 291 year: 2015 ident: 40330_CR47 publication-title: Nat. Genet. doi: 10.1038/ng.3211 – volume: 50 start-page: 906 year: 2018 ident: 40330_CR55 publication-title: Nat. Genet. doi: 10.1038/s41588-018-0144-6 – ident: 40330_CR57 doi: 10.5281/zenodo.7988493 – volume: 51 start-page: 1339 year: 2019 ident: 40330_CR27 publication-title: Nat. Genet. doi: 10.1038/s41588-019-0481-0 – volume: 24 start-page: 737 year: 2021 ident: 40330_CR41 publication-title: Nat. Neurosci. doi: 10.1038/s41593-021-00826-4 – volume: 47 start-page: D1005 year: 2019 ident: 40330_CR26 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gky1120 – ident: 40330_CR25 – volume: 108 start-page: 1001 year: 2021 ident: 40330_CR32 publication-title: Am. J. Hum. Genet. doi: 10.1016/j.ajhg.2021.04.014 – ident: 40330_CR51 doi: 10.1093/bioinformatics/btaa520 – volume: 72 start-page: 1883 year: 2018 ident: 40330_CR1 publication-title: J. Am. Coll. Cardiol. doi: 10.1016/j.jacc.2018.07.079 – volume: 51 start-page: 431 year: 2019 ident: 40330_CR14 publication-title: Nat. Genet. doi: 10.1038/s41588-019-0344-8 – volume: 10 year: 2019 ident: 40330_CR17 publication-title: Nat. Commun. doi: 10.1038/s41467-019-12653-0 – ident: 40330_CR16 doi: 10.1093/bioinformatics/btaa1029 – volume: 8 start-page: 711208 year: 2021 ident: 40330_CR13 publication-title: Front. Med. doi: 10.3389/fmed.2021.711208 – volume: 17 start-page: e1009697 year: 2021 ident: 40330_CR19 publication-title: PLoS Genet. doi: 10.1371/journal.pgen.1009697 – volume: 13 year: 2022 ident: 40330_CR36 publication-title: Nat. Commun. doi: 10.1038/s41467-021-27930-0 – volume: 10 year: 2019 ident: 40330_CR18 publication-title: Nat. Commun. doi: 10.1038/s41467-019-09718-5 – volume: 34 start-page: 2781 year: 2018 ident: 40330_CR52 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty185 – volume: 74 start-page: 61 year: 2018 ident: 40330_CR48 publication-title: Contemp. Clin. Trials doi: 10.1016/j.cct.2018.09.015 – volume: 10 year: 2019 ident: 40330_CR11 publication-title: Nat. Commun. doi: 10.1038/s41467-019-13848-1 – volume: 10 year: 2019 ident: 40330_CR5 publication-title: Nat. Commun. doi: 10.1038/s41467-019-11112-0 – ident: 40330_CR21 doi: 10.1145/2939672.2939785 – volume: 51 start-page: 63 year: 2019 ident: 40330_CR35 publication-title: Nat. Genet. doi: 10.1038/s41588-018-0269-7 – volume: 105 start-page: 1213 year: 2019 ident: 40330_CR15 publication-title: Am. J. Hum. Genet. doi: 10.1016/j.ajhg.2019.11.001 – volume: 100 start-page: 635 year: 2017 ident: 40330_CR40 publication-title: Am. J. Hum. Genet. doi: 10.1016/j.ajhg.2017.03.004 – volume: 36 start-page: 930 year: 2020 ident: 40330_CR50 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz633 – volume: 48 start-page: 1279 year: 2016 ident: 40330_CR49 publication-title: Nat. Genet. doi: 10.1038/ng.3643 – volume: 28 start-page: R133 year: 2019 ident: 40330_CR2 publication-title: Hum. Mol. Genet. doi: 10.1093/hmg/ddz187 – ident: 40330_CR23 doi: 10.1101/2020.11.30.20237768 – volume: 50 start-page: 1219 year: 2018 ident: 40330_CR3 publication-title: Nat. Genet. doi: 10.1038/s41588-018-0183-z – volume: 33 start-page: 1 year: 2010 ident: 40330_CR53 publication-title: J. Stat. Softw. doi: 10.18637/jss.v033.i01 – volume: 50 start-page: 229 year: 2018 ident: 40330_CR9 publication-title: Nat. Genet. doi: 10.1038/s41588-017-0009-4 – volume: 9 year: 2018 ident: 40330_CR8 publication-title: Nat. Commun. doi: 10.1038/s41467-017-02769-6 |
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| Snippet | The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a... Abstract The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample... |
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| Title | Multi-PGS enhances polygenic prediction by combining 937 polygenic scores |
| URI | https://link.springer.com/article/10.1038/s41467-023-40330-w https://www.ncbi.nlm.nih.gov/pubmed/37543680 https://www.proquest.com/docview/2846402280 https://www.proquest.com/docview/2846924552 https://hal.science/hal-04145280 https://pubmed.ncbi.nlm.nih.gov/PMC10404269 https://doaj.org/article/65c6fd7da745423fbb7f56199da61c83 |
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