Multi-PGS enhances polygenic prediction by combining 937 polygenic scores
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 p...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-08-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-40330-w |
| _version_ | 1827710255976415232 |
|---|---|
| author | Clara Albiñana Zhihong Zhu Andrew J. Schork Andrés Ingason Hugues Aschard Isabell Brikell Cynthia M. Bulik Liselotte V. Petersen Esben Agerbo Jakob Grove Merete Nordentoft David M. Hougaard Thomas Werge Anders D. Børglum Preben Bo Mortensen John J. McGrath Benjamin M. Neale Florian Privé Bjarni J. Vilhjálmsson |
| author_facet | Clara Albiñana Zhihong Zhu Andrew J. Schork Andrés Ingason Hugues Aschard Isabell Brikell Cynthia M. Bulik Liselotte V. Petersen Esben Agerbo Jakob Grove Merete Nordentoft David M. Hougaard Thomas Werge Anders D. Børglum Preben Bo Mortensen John J. McGrath Benjamin M. Neale Florian Privé Bjarni J. Vilhjálmsson |
| author_sort | Clara Albiñana |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T17:35:29Z |
| format | Article |
| id | doaj.art-65c6fd7da745423fbb7f56199da61c83 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-10T17:35:29Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-65c6fd7da745423fbb7f56199da61c832023-11-20T09:52:23ZengNature PortfolioNature Communications2041-17232023-08-0114111110.1038/s41467-023-40330-wMulti-PGS enhances polygenic prediction by combining 937 polygenic scoresClara Albiñana0Zhihong Zhu1Andrew J. Schork2Andrés Ingason3Hugues Aschard4Isabell Brikell5Cynthia M. Bulik6Liselotte V. Petersen7Esben Agerbo8Jakob Grove9Merete Nordentoft10David M. Hougaard11Thomas Werge12Anders D. Børglum13Preben Bo Mortensen14John J. McGrath15Benjamin M. Neale16Florian Privé17Bjarni J. Vilhjálmsson18The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCHNational Centre for Register-Based Research, Aarhus UniversityThe Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCHThe Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCHDepartment of Computational Biology, Institut Pasteur, Université de ParisThe Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCHDepartment of Medical Epidemiology and Biostatistics, Karolinska InstituteThe Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCHThe Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCHThe Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCHThe Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCHThe Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCHThe Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCHThe Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCHThe Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCHNational Centre for Register-Based Research, Aarhus UniversityAnalytic and Translational Genetics Unit, Massachusetts General HospitalThe Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCHThe Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCHAbstract 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.https://doi.org/10.1038/s41467-023-40330-w |
| spellingShingle | Clara Albiñana Zhihong Zhu Andrew J. Schork Andrés Ingason Hugues Aschard Isabell Brikell Cynthia M. Bulik Liselotte V. Petersen Esben Agerbo Jakob Grove Merete Nordentoft David M. Hougaard Thomas Werge Anders D. Børglum Preben Bo Mortensen John J. McGrath Benjamin M. Neale Florian Privé Bjarni J. Vilhjálmsson Multi-PGS enhances polygenic prediction by combining 937 polygenic scores Nature Communications |
| title | Multi-PGS enhances polygenic prediction by combining 937 polygenic scores |
| title_full | Multi-PGS enhances polygenic prediction by combining 937 polygenic scores |
| title_fullStr | Multi-PGS enhances polygenic prediction by combining 937 polygenic scores |
| title_full_unstemmed | Multi-PGS enhances polygenic prediction by combining 937 polygenic scores |
| title_short | Multi-PGS enhances polygenic prediction by combining 937 polygenic scores |
| title_sort | multi pgs enhances polygenic prediction by combining 937 polygenic scores |
| url | https://doi.org/10.1038/s41467-023-40330-w |
| work_keys_str_mv | AT claraalbinana multipgsenhancespolygenicpredictionbycombining937polygenicscores AT zhihongzhu multipgsenhancespolygenicpredictionbycombining937polygenicscores AT andrewjschork multipgsenhancespolygenicpredictionbycombining937polygenicscores AT andresingason multipgsenhancespolygenicpredictionbycombining937polygenicscores AT huguesaschard multipgsenhancespolygenicpredictionbycombining937polygenicscores AT isabellbrikell multipgsenhancespolygenicpredictionbycombining937polygenicscores AT cynthiambulik multipgsenhancespolygenicpredictionbycombining937polygenicscores AT liselottevpetersen multipgsenhancespolygenicpredictionbycombining937polygenicscores AT esbenagerbo multipgsenhancespolygenicpredictionbycombining937polygenicscores AT jakobgrove multipgsenhancespolygenicpredictionbycombining937polygenicscores AT meretenordentoft multipgsenhancespolygenicpredictionbycombining937polygenicscores AT davidmhougaard multipgsenhancespolygenicpredictionbycombining937polygenicscores AT thomaswerge multipgsenhancespolygenicpredictionbycombining937polygenicscores AT andersdbørglum multipgsenhancespolygenicpredictionbycombining937polygenicscores AT prebenbomortensen multipgsenhancespolygenicpredictionbycombining937polygenicscores AT johnjmcgrath multipgsenhancespolygenicpredictionbycombining937polygenicscores AT benjaminmneale multipgsenhancespolygenicpredictionbycombining937polygenicscores AT florianprive multipgsenhancespolygenicpredictionbycombining937polygenicscores AT bjarnijvilhjalmsson multipgsenhancespolygenicpredictionbycombining937polygenicscores |