Multi-PGS enhances polygenic prediction by combining 937 polygenic scores

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

doi:10.1038/s41467-023-40330-w

Multi-PGS enhances polygenic prediction by combining 937 polygenic scores

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

^*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

13 Citationer (Scopus)

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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 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.

Originalsprog	Engelsk
Artikelnummer	4702
Tidsskrift	Nature Communications
Vol/bind	14
Nummer	1
ISSN	2041-1723
DOI	https://doi.org/10.1038/s41467-023-40330-w
Status	Udgivet - aug. 2023

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10.1038/s41467-023-40330-wLicens: CC BY

s41467-023-40330-wForlagets udgivne version, 1,24 MBLicens: CC BY

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Albiñana, C., Zhu, Z., Schork, A. J., Ingason, A., Aschard, H., Brikell, I., Bulik, C. M., Petersen, L. V., Agerbo, E., Grove, J., Nordentoft, M., Hougaard, D. M., Werge, T., Børglum, A. D., Mortensen, P. B., McGrath, J. J., Neale, B. M., Privé, F., & Vilhjálmsson, B. J. (2023). Multi-PGS enhances polygenic prediction by combining 937 polygenic scores. Nature Communications, 14(1), Artikel 4702. https://doi.org/10.1038/s41467-023-40330-w

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title = "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 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. ",

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author = "Clara Albi{\~n}ana and Zhihong Zhu and Schork, {Andrew J} and Andr{\'e}s Ingason and Hugues Aschard and Isabell Brikell and Bulik, {Cynthia M} and Petersen, {Liselotte V} and Esben Agerbo and Jakob Grove and Merete Nordentoft and Hougaard, {David M} and Thomas Werge and B{\o}rglum, {Anders D} and Mortensen, {Preben Bo} and McGrath, {John J} and Neale, {Benjamin M} and Florian Priv{\'e} and Vilhj{\'a}lmsson, {Bjarni J}",

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doi = "10.1038/s41467-023-40330-w",

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Albiñana, C , Zhu, Z, Schork, AJ, Ingason, A, Aschard, H, Brikell, I, Bulik, CM, Petersen, LV , Agerbo, E , Grove, J, Nordentoft, M, Hougaard, DM, Werge, T, Børglum, AD , Mortensen, PB , McGrath, JJ, Neale, BM, Privé, F & Vilhjálmsson, BJ 2023, 'Multi-PGS enhances polygenic prediction by combining 937 polygenic scores', Nature Communications, bind 14, nr. 1, 4702. https://doi.org/10.1038/s41467-023-40330-w

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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

PY - 2023/8

Y1 - 2023/8

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.

KW - Attention Deficit Disorder with Hyperactivity/genetics

KW - Genome-Wide Association Study

KW - Humans

KW - Multifactorial Inheritance/genetics

KW - Phenotype

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U2 - 10.1038/s41467-023-40330-w

DO - 10.1038/s41467-023-40330-w

M3 - Journal article

C2 - 37543680

SN - 2041-1723

VL - 14

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 4702

ER -

Multi-PGS enhances polygenic prediction by combining 937 polygenic scores

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