CoChat
Sign Up
Cited by 8Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and Animals

Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies

Open access
de Vlaming, Ronald, Okbay, Aysu, Rietveld, Cornelius A., Johannesson, Magnus, Magnusson, Patrik K. E., Uitterlinden, André G., van Rooij, Frank J. A., Hofman, Albert +3 more·2017·PLOS Genetics
DOI 10.1371/journal.pgen.1006495PMC PMC5240919

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

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 R 2 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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pgen.1006495

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 41

  1. [1]Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014; 46: 1173– 1186. 10.1038/ng.3097 25282103 PMC4250049 · DOI · Paper page
  2. [2]Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015; 518: 197– 206. 10.1038/nature14177 25673413 PMC4382211 · DOI · Paper page
  3. [3]Eeles RA, Kote-Jarai Z, Al Olama AA, Giles GG, Guy M, Severi G, et al Identification of seven new prostate cancer susceptibility loci through a genome-wide association study. Nat Genet. 2009; 41: 1116– 1121. 10.1038/ng.450 19767753 PMC2846760 · DOI
  4. [4]Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, Chasman DI, et al Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011; 478: 103– 109. 10.1038/nature10405 21909115 PMC3340926 · DOI · Paper page
  5. [5]Ripke S, Neale BM, the Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014; 511: 421– 427. 10.1038/nature13595 25056061 PMC4112379 · DOI · Paper page
  6. [6]Rietveld CA, Cesarini D, Benjamin DJ, Koellinger PD, De Neve JE, Tiemeier H, et al Molecular genetics and subjective well-being. Proc Natl Acad Sci USA. 2013; 110: 9692– 9697. 10.1073/pnas.1222171110 23708117 PMC3683731 · DOI · Paper page
  7. [7]Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, et al Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016; 533: 539– 542. 10.1038/nature17671 27225129 PMC4883595 · DOI · Paper page
  8. [8]Okbay A, Baselmans BML, De Neve JE, Turley P, Nivard MG, Fontana MA, et al Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat Genet. 2016; 48: 624– 633. 10.1038/ng.3552 27089181 PMC4884152 · DOI · Paper page
  9. [10]Benjamin DJ, Cesarini D, Chabris CF, Glaeser EL, Laibson DI, Guðnason V, et al The Promises and Pitfalls of Genoeconomics. Annu Rev Econom. 2012; 4: 627– 662. 10.1146/annurev-economics-080511-110939 23482589 PMC3592970 · DOI
  10. [12]Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al Finding the missing heritability of complex diseases. Nature. 2009; 461: 747– 753. 10.1038/nature08494 19812666 PMC2831613 · DOI · Paper page
  11. [13]Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, et al Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet. 2010; 11: 446– 450. 10.1038/nrg2809 20479774 PMC2942068 · DOI
  12. [14]Zuk O, Hechter E, Sunyaev SR, Lander ES. The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci USA. 2012; 109: 1193– 1198. 10.1073/pnas.1119675109 22223662 PMC3268279 · DOI · Paper page
  13. [15]Wray NR, Yang J, Hayes BJ, Price AL, Goddard ME, Visscher PM. Pitfalls of predicting complex traits from SNPs. Nat Rev Genet. 2013; 14: 507– 515. 10.1038/nrg3457 23774735 PMC4096801 · DOI
  14. [16]Witte JS, Visscher PM, Wray NR. The contribution of genetic variants to disease depends on the ruler. Nat Rev Genet. 2014; 15: 765– 776. 10.1038/nrg3786 25223781 PMC4412738 · DOI
  15. [18]Evangelou E, Fellay J, Colombo S, Martinez-Picado J, Obel N, Goldstein DB, et al Impact of phenotype definition on genome-wide association signals: empirical evaluation in human immunodeficiency virus type 1 infection. Am J Epidemiol. 2011; 173: 1336– 1342. 10.1093/aje/kwr024 21490045 PMC4806701 · DOI
  16. [19]Wray NR, Lee SH, Kendler KS. Impact of diagnostic misclassification on estimation of genetic correlations using genome-wide genotypes. Eur J Hum Genet. 2012; 20: 668– 674. 10.1038/ejhg.2011.257 22258521 PMC3355255 · DOI
  17. [20]Wray NR, Lee SH, the Cross-Disorder Group of the Psychiatric Genomics Consortium. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet. 2013; 45: 984– 994. 10.1038/ng.2711 23933821 PMC3800159 · DOI · Paper page
  18. [21]Lee S, Teslovich TM, Boehnke M, Lin X. General framework for meta-analysis of rare variants in sequencing association studies. Am J Hum Genet. 2013; 93: 42– 53. 10.1016/j.ajhg.2013.05.010 23768515 PMC3710762 · DOI
  19. [24]Brown BC, the Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Ye CJ, Price AL, Zaitlen N. Transethnic Genetic-Correlation Estimates from Summary Statistics. Am J Hum Genet. 2016; 99: 76– 88. 10.1016/j.ajhg.2016.05.001 27321947 PMC5005434 · DOI · Paper page
  20. [25]Wray NR, Goddard ME, Visscher PM. Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res. 2007; 17: 1520– 1528. 10.1101/gr.6665407 17785532 PMC1987352 · DOI
  21. [26]Lebrec JJ, Stijnen T, Van Houwelingen HC. Dealing with heterogeneity between cohorts in genomewide SNP association studies. Stat Appl Genet Mol Biol. 2010; 9: 8 10.2202/1544-6115.1503 20196758 · DOI
  22. [27]Morris AP. Transethnic meta-analysis of genomewide association studies. Genet Epidemiol. 2011; 35: 809– 822. 10.1002/gepi.20630 22125221 PMC3460225 · DOI
  23. [29]Bhattacharjee S, Rajaraman P, Jacobs KB, Wheeler WA, Melin BS, Hartge P, et al A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits. Am J Hum Genet. 2012; 90: 821– 835. 10.1016/j.ajhg.2012.03.015 22560090 PMC3376551 · DOI
  24. [30]Wen X, Stephens M. Bayesian methods for genetic association analysis with heterogeneous subgroups: from meta-analyses to gene-environment interactions. Ann Appl Stat. 2014; 8: 176– 203. 10.1214/13-AOAS695 26413181 PMC4583155 · DOI
  25. [31]Shi J, Lee S. A novel random effect model for GWAS meta-analysis and its application to trans-ethnic meta-analysis. Biometrics. 2016; 72: 945– 954. 10.1111/biom.12481 26916671 PMC4996751 · DOI
  26. [32]Evangelou E, Ioannidis JPA. Meta-analysis methods for genome-wide association studies and beyond. Nat Rev Genet. 2013; 14: 379– 389. 10.1038/nrg3472 23657481 · DOI · Paper page
  27. [35]Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, et al Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010; 42: 565– 569. 10.1038/ng.608 20562875 PMC3232052 · DOI · Paper page
  28. [36]Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011; 88: 76– 82. 10.1016/j.ajhg.2010.11.011 21167468 PMC3014363 · DOI · Paper page
  29. [37]Lee SH, Yang J, Goddard ME, Visscher PM, Wray NR. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics. 2012; 28: 2540– 2542. 10.1093/bioinformatics/bts474 22843982 PMC3463125 · DOI · Paper page
  30. [39]Bulik-Sullivan BK, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015; 47: 1236– 1241. 10.1038/ng.3406 26414676 PMC4797329 · DOI · Paper page
  31. [40]Daetwyler HD, Villanueva B, Woolliams JA. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLOS ONE. 2008; 3: e3395 10.1371/journal.pone.0003395 18852893 PMC2561058 · DOI
  32. [41]Li MX, Yeung JMY, Cherny SS, Sham PC. Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum Genet. 2012; 131: 747– 756. 10.1007/s00439-011-1118-2 22143225 PMC3325408 · DOI · Paper page
  33. [42]Yang J, Bakshi A, Zhu Z, Hemani G, Vinkhuyzen AAE, Lee SH, et al Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet. 2015; 47: 1114– 1120. 10.1038/ng.3390 26323059 PMC4589513 · DOI · Paper page
  34. [43]Yang J, Weedon MN, Purcell SM, Lettre G, Estrada K, Willer CJ, et al Genomic inflation factors under polygenic inheritance. Eur J Hum Genet. 2011; 19: 807– 812. 10.1038/ejhg.2011.39 21407268 PMC3137506 · DOI
  35. [45]Visscher PM, Hemani G, Vinkhuyzen AAE, Chen GB, Lee SH, Wray NR, et al Statistical power to detect genetic (co) variance of complex traits using SNP data in unrelated samples. PLOS Genet. 2014; 10: e1004269 10.1371/journal.pgen.1004269 24721987 PMC3983037 · DOI
  36. [46]Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, et al Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010; 467: 832– 838. 10.1038/nature09410 20881960 PMC2955183 · DOI · Paper page
  37. [47]Weedon MN, Lango Allen H, Lindgren CM, Wallace C, Evans DM, Mangino M, et al Genome-wide association analysis identifies 20 loci that influence adult height. Nat Genet. 2008; 40: 575– 583. 10.1038/ng.121 18391952 PMC2681221 · DOI · Paper page
  38. [48]Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010; 42: 937– 948. 10.1038/ng.686 20935630 PMC3014648 · DOI · Paper page
  39. [49]Willer CJ, Speliotes EK, Loos RJF, Li S, Lindgren CM, Heid IM, et al Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet. 2008; 41: 25– 34. 10.1038/ng.287 19079261 PMC2695662 · DOI · Paper page
  40. [50]Rietveld CA, Medland SE, Derringer J, Yang J, Esko T, Martin NW, et al GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science. 2013; 340: 1467– 1471. 10.1126/science.1235488 23722424 PMC3751588 · DOI · Paper page
  41. [51]Harris SE, Hagenaars SP, Davies G, Hill WD, Liewald DCM, Ritchie SJ, et al Molecular genetic contributions to self-rated health. Int J Epidemiol. 2016;advance access:dyw219. 10.1093/ije/dyw219 27864402 PMC5837683 · DOI

Cited by

Sorted by citations

Authors
de Vlaming, R., Okbay, A., Rietveld, C. A., Johannesson, M., Magnusson, P. K. E., Uitterlinden, A. G., van Rooij, F. J. A., Hofman, A., Groenen, P. J. F., Thurik, A. R., & Koellinger, P. D.
Affiliations
  • Erasmus University Rotterdam (NL)
  • Stockholm School of Economics (SE)
  • Karolinska Institutet (SE)
  • Erasmus MC (NL)
ORCID
Venue
PLOS Genetics
Publisher
Public Library of Science (PLoS)
Published
2017-01-17
Volume
13
Issue
1
Pages
e1006495
ISSN
  • 1553-7390 (ISSN-L)
  • 1553-7404
DOI
10.1371/journal.pgen.1006495
PMC
PMC5240919
OpenAlex
W2935442247
Cited by
8
References
11
Citations by year
2025: 1 · 2024: 2 · 2023: 3 · 2018: 2
FWCI
0.36
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Genetics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • VU Research Portal (submittedVersion, other-oa)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • ERIM Top-Core Articles
Funders
  • European Research Council (647648)
  • NWO (016.165.004)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords
missing heritability, study heterogeneity, statistical power, GWAS summary statistics, heritability estimation, height, body mass index, years of schooling, self-rated health
MeSH terms
Data Accuracy, Humans, Software, Meta-Analysis as Topic, Genome-Wide Association Study

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review