Multi-PGS enhances polygenic prediction by combining 937 polygenic scores

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Standard

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

Harvard

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, 4702. https://doi.org/10.1038/s41467-023-40330-w

APA

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, [4702]. https://doi.org/10.1038/s41467-023-40330-w

Vancouver

Albiñana C, Zhu Z, Schork AJ, Ingason A, Aschard H, Brikell I o.a. Multi-PGS enhances polygenic prediction by combining 937 polygenic scores. Nature Communications. 2023;14. 4702. https://doi.org/10.1038/s41467-023-40330-w

Author

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. / Multi-PGS enhances polygenic prediction by combining 937 polygenic scores. I: Nature Communications. 2023 ; Bind 14.

Bibtex

@article{a193fbeaf78b44e0a2035738f33d3eb1,

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.",

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.}",

note = "Publisher Copyright: {\textcopyright} 2023, Springer Nature Limited.",

year = "2023",

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

language = "English",

volume = "14",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "nature publishing group",

}

RIS

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.

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