Multi-polygenic score approach to trait prediction

E Krapohl; H Patel; S Newhouse; C J Curtis; S von Stumm; P S Dale; D Zabaneh; Gerome Breen; P F O'Reilly; R Plomin

doi:10.1038/mp.2017.163

Multi-polygenic score approach to trait prediction

E Krapohl, H Patel, S Newhouse, C J Curtis, S von Stumm, P S Dale, D Zabaneh, Gerome Breen, P F O'Reilly, R Plomin

University of New Mexico

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Abstract

A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWAS), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWAS, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWAS of cognitive, medical, and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body-mass index (BMI), and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK-representative sample of 6,710 unrelated adolescents. The MPS approach predicted 11% variance in educational achievement, 4.8% in general cognitive ability, and 5.4% in BMI in an independent test set, improving prediction by 1.1%, 1.1%, and 1.6% variance explained over the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene-environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions.

Original language	English
Pages (from-to)	1368-1374
Journal	Molecular Psychiatry
Volume	23
Issue number	5
Early online date	8 Aug 2017
DOIs	https://doi.org/10.1038/mp.2017.163
Publication status	Published - 1 May 2018

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title = "Multi-polygenic score approach to trait prediction",

abstract = "A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWAS), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWAS, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWAS of cognitive, medical, and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body-mass index (BMI), and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK-representative sample of 6,710 unrelated adolescents. The MPS approach predicted 11% variance in educational achievement, 4.8% in general cognitive ability, and 5.4% in BMI in an independent test set, improving prediction by 1.1%, 1.1%, and 1.6% variance explained over the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene-environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions. ",

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AU - Dale, P S

AU - Zabaneh, D

AU - Breen, Gerome

AU - O'Reilly, P F

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N2 - A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWAS), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWAS, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWAS of cognitive, medical, and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body-mass index (BMI), and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK-representative sample of 6,710 unrelated adolescents. The MPS approach predicted 11% variance in educational achievement, 4.8% in general cognitive ability, and 5.4% in BMI in an independent test set, improving prediction by 1.1%, 1.1%, and 1.6% variance explained over the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene-environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions.

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Multi-polygenic score approach to trait prediction

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