Multi-polygenic score approach to trait prediction

A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWASs), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score...

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Bibliographic Details
Published in:Molecular psychiatry 2018-05, Vol.23 (5), p.1368-1374
Main Authors: Krapohl, E, Patel, H, Newhouse, S, Curtis, C J, von Stumm, S, Dale, P S, Zabaneh, D, Breen, G, O'Reilly, P F, Plomin, R
Format: Article
Language:English
Subjects:
Adolescent
Adolescents
Anthropometry
Body mass
Body Mass Index
Cognition
Cognitive ability
Computer Simulation
Deoxyribonucleic acid
Developmental genetics
DNA
Educational Status
Female
Forecasting - methods
Genetic Predisposition to Disease
Genetic research
Genetic Testing - methods
Genome-wide association studies
Genome-Wide Association Study - methods
Genomes
Humans
Male
Methods
Multifactorial Inheritance - genetics
Neurogenetics
Original
Phenotype
Phenotypes
Polygenic inheritance
Polymorphism, Single Nucleotide - genetics
Quantitative trait loci
Variance
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Summary:A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWASs), 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 GWASs, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWASs 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 6710 unrelated adolescents. The MPS approach predicted 10.9% variance in educational achievement, 4.8% in general cognitive ability and 5.4% in BMI in an independent test set, predicting 1.1%, 1.1%, and 1.6% more variance than 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.
ISSN:1359-4184
1476-5578
DOI:10.1038/mp.2017.163