Multi-PGS enhances polygenic prediction by combining 937 polygenic scores
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Multi-PGS enhances polygenic prediction by combining 937 polygenic scores. / Albiñana, Clara; Zhu, Zhihong; Schork, Andrew J.; Ingason, Andrés; Aschard, Hugues; Brikell, Isabell; Bulik, Cynthia M.; Petersen, Liselotte V.; Agerbo, Esben; Grove, Jakob; Nordentoft, Merete; Hougaard, David M.; Werge, Thomas; Børglum, Anders D.; Mortensen, Preben Bo; McGrath, John J.; Neale, Benjamin M; Privé, Florian; Vilhjálmsson, Bjarni J.
I: Nature Communications, Bind 14, 4702, 2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Multi-PGS enhances polygenic prediction by combining 937 polygenic scores
AU - Albiñana, Clara
AU - Zhu, Zhihong
AU - Schork, Andrew J.
AU - Ingason, Andrés
AU - Aschard, Hugues
AU - Brikell, Isabell
AU - Bulik, Cynthia M.
AU - Petersen, Liselotte V.
AU - Agerbo, Esben
AU - Grove, Jakob
AU - Nordentoft, Merete
AU - Hougaard, David M.
AU - Werge, Thomas
AU - Børglum, Anders D.
AU - Mortensen, Preben Bo
AU - McGrath, John J.
AU - Neale, Benjamin M
AU - Privé, Florian
AU - Vilhjálmsson, Bjarni J.
N1 - Publisher Copyright: © 2023, Springer Nature Limited.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
U2 - 10.1038/s41467-023-40330-w
DO - 10.1038/s41467-023-40330-w
M3 - Journal article
C2 - 37543680
AN - SCOPUS:85166599056
VL - 14
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
M1 - 4702
ER -
ID: 362278567