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Meta-GWAS Accuracy and Power (MetaGAP) calculator shows that hiding heritability is partially due to imperfect genetic correlations across studies

Ronald de Vlaming, Aysu Okbay, Cornelius A. Rietveld, Magnus Johannesson, Patrik K.E. Magnusson, André G. Uitterlinden, Frank J.A. van Rooij, Albert Hofman, Patrick J.F. Groenen, A. Roy Thurik, Philipp D. Koellinger
doi: https://doi.org/10.1101/048322
Ronald de Vlaming
1 Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands.
2 Department of Applied Economics, Erasmus School of Economics, Rotterdam, the Netherlands.
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Aysu Okbay
1 Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands.
2 Department of Applied Economics, Erasmus School of Economics, Rotterdam, the Netherlands.
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Cornelius A. Rietveld
1 Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands.
2 Department of Applied Economics, Erasmus School of Economics, Rotterdam, the Netherlands.
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Magnus Johannesson
3 Department of Economics, Stockholm School of Economics, Stockholm, Sweden.
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Patrik K.E. Magnusson
4 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
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André G. Uitterlinden
1 Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands.
5 Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.
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Frank J.A. van Rooij
5 Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.
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Albert Hofman
1 Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands.
5 Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.
6 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, the United States of America.
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Patrick J.F. Groenen
1 Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands.
7 Econometric Institute, Erasmus School of Economics, Rotterdam, the Netherlands.
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A. Roy Thurik
1 Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands.
2 Department of Applied Economics, Erasmus School of Economics, Rotterdam, the Netherlands.
8 Montpellier Business School, Montpellier, France.
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Philipp D. Koellinger
1 Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands.
2 Department of Applied Economics, Erasmus School of Economics, Rotterdam, the Netherlands.
9 Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam, the Netherlands.
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Abstract

Large-scale genome-wide association results are typically obtained from a fixed-effects meta-analysis of GWAS summary statistics from multiple studies spanning different regions and/or time periods. This approach averages the estimated effects of genetic variants across studies. In case genetic effects are heterogeneous across studies, the statistical power of a GWAS and the predictive accuracy of polygenic scores are attenuated, contributing to the so-called ‘missing heritability’. Here, we describe the online Meta-GWAS Accuracy and Power calculator (MetaGAP; available at www.devlaming.eu) which quantifies this attenuation based on a novel multi-study framework. By means of simulation studies, we show that under a wide range of genetic architectures, the statistical power and predictive accuracy provided by this calculator are accurate. We compare the predictions from MetaGAP with actual results obtained in the GWAS literature. Specifically, we use genomic-relatedness-matrix restricted maximum likelihood (GREML) to estimate the SNP heritability and cross-study genetic correlation of height, BMI, years of education, and self-rated health in three large samples. These estimates are used as input parameters for the MetaGAP calculator. Results from the calculator suggest that cross-study heterogeneity has led to attenuation of statistical power and predictive accuracy in recent large-scale GWAS efforts on these traits (e.g., for years of education, we estimate a relative loss of 51–62% in the number of genome-wide significant loci and a relative loss in polygenic score R2 of 36–38%). Hence, cross-study heterogeneity contributes to the missing heritability.

Author Summary Large-scale genome-wide association studies are uncovering the genetic architecture of traits which are affected by many genetic variants. Such studies typically meta-analyze association results from multiple studies spanning different regions and/or time periods. GWAS results do not yet capture a large share of the total proportion of trait variation attributable to genetic variation. The origins of this so-called ‘missing heritability’ have been strongly debated. One factor exacerbating the missing heritability is heterogeneity in the effects of genetic variants across studies. Its influence on statistical power to detect associated genetic variants and the accuracy of polygenic predictions is poorly understood. In the current study, we derive the precise effects of heterogeneity in genetic effects across studies on both the statistical power to detect associated genetic variants as well as the accuracy of polygenic predictions. We provide an online calculator, available at www.devlaming.eu, which accounts for these effects. By means of this calculator, we show that imperfect genetic correlations between studies substantially decrease statistical power and predictive accuracy and, thereby, contribute to the missing heritability. The MetaGAP calculator helps researchers to gauge how sensitive their results will be to heterogeneity in genetic effects across studies. If strong heterogeneity is expected, random-instead of fixed-effects meta-analysis methods should be used.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 13, 2016.
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Meta-GWAS Accuracy and Power (MetaGAP) calculator shows that hiding heritability is partially due to imperfect genetic correlations across studies
Ronald de Vlaming, Aysu Okbay, Cornelius A. Rietveld, Magnus Johannesson, Patrik K.E. Magnusson, André G. Uitterlinden, Frank J.A. van Rooij, Albert Hofman, Patrick J.F. Groenen, A. Roy Thurik, Philipp D. Koellinger
bioRxiv 048322; doi: https://doi.org/10.1101/048322
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Meta-GWAS Accuracy and Power (MetaGAP) calculator shows that hiding heritability is partially due to imperfect genetic correlations across studies
Ronald de Vlaming, Aysu Okbay, Cornelius A. Rietveld, Magnus Johannesson, Patrik K.E. Magnusson, André G. Uitterlinden, Frank J.A. van Rooij, Albert Hofman, Patrick J.F. Groenen, A. Roy Thurik, Philipp D. Koellinger
bioRxiv 048322; doi: https://doi.org/10.1101/048322

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