Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies.
paper
Cited
Public
- Authors
- de Vlaming, Ronald; Okbay, Aysu; Rietveld, Cornelius A; Johannesson, Magnus; Magnusson, Patrik K E; Uitterlinden, André G; van Rooij, Frank J A; Hofman, Albert; Groenen, Patrick J F; Thurik, A Roy; Koellinger, Philipp D
- Year
- 2017
- Journal
- PLoS genetics
- PMID
- 28095416
- DOI
- 10.1371/journal.pgen.1006495
- PMCID
- PMC5240919
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 (MetaGAP) calculator (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 the MetaGAP calculator with actual results obtained in the GWAS literature. Specifically, we use genomic-relatedness-matrix restricted maximum likelihood 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.
Theoretical predictions of power per causal SNP (upper panel) and out-of-sample R2 of the PGS (lower panel), for total sample size (x-axis) and cross-study genetic correlation (y-axis).Factor levels: 50 studies, 100k independent SNPs, and hSNP2=50% arising from a subset of 1k independent SNPs.
Theoretical predictions of power per causal SNP (upper panel) and out-of-sample R2 of the PGS (lower panel), for a trait that across studies has SNP heritability (x-axis) and cross-study genetic correlation (y-axis).Factor levels: 50 studies, sample size 5,000 individuals per study, 100k independent SNPs, and heritability arising from a subset of 1k independent SNPs.
Theoretical predictions of power per causal SNP (upper panel) and out-of-sample R2 of the PGS (lower panel), for a trait with GWAS results from the number of studies (x-axis) with cross-study genetic correlation (y-axis).Factor levels: total sample size 250,000 individuals, 100k independent SNPs, and hSNP2=50% arising from a subset of 1k independent SNPs.
Theoretical predictions of out-of-sample R2 of the PGS, for the SNP heritability in the hold-out sample (x-axis) and the SNP heritability in the discovery samples (y-axis).Factor levels: 50 studies, sample size 5,000 individuals per study, cross-study genetic correlation 0.8, 100k independent SNPs, and heritability arising from a subset of 1k independent SNPs.
Theoretical predictions of power per causal SNP, for total sample size (x-axis) and CGR between two sets of studies (y-axis).Factor levels: 2 sets of 50 studies, CGR equal to 1 within both sets, 100k independent SNPs, and hSNP2=50% arising from a subset of 1k independent SNPs.
No entities extracted from this document yet.
No uploaded files.
Not in any collection.
| Citation | PMID | DOI | Status |
|---|---|---|---|
| BenjaminDJ, CesariniD, ChabrisCF, GlaeserEL, LaibsonDI, GuðnasonV, et al The Promises and Pitfalls of Genoeconomics. Annu Rev Econom. 2012;4:627–662. 10.1146/annurev-economics-080511-110939 23482589PMC3592970 | — | — | — |
| BhattacharjeeS, RajaramanP, JacobsKB, WheelerWA, MelinBS, HartgeP, 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 22560090PMC3376551 | — | — | — |
| BrownBC, the Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, YeCJ, PriceAL, ZaitlenN. Transethnic Genetic-Correlation Estimates from Summary Statistics. Am J Hum Genet. 2016;99:76–88. 10.1016/j.ajhg.2016.05.001 27321947PMC5005434 | — | — | — |
| Bulik-SullivanBK, FinucaneHK, AnttilaV, GusevA, DayFR, LohPR, et al An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47:1236–1241. 10.1038/ng.3406 26414676PMC4797329 | — | — | — |
| Bulik-SullivanBK, LohPR, FinucaneHK, RipkeS, YangJ, PattersonN, et al LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–295. 10.1038/ng.3211 25642630PMC4495769 | — | — | — |
| DaetwylerHD, VillanuevaB, WoolliamsJA. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLOS ONE. 2008;3:e3395 10.1371/journal.pone.0003395 18852893PMC2561058 | — | — | — |
| DudbridgeF. Power and predictive accuracy of polygenic risk scores. PLOS Genet. 2013;9:e1003348 10.1371/journal.pgen.1003348 23555274PMC3605113 | — | — | — |
| EelesRA, Kote-JaraiZ, Al OlamaAA, GilesGG, GuyM, SeveriG, 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 19767753PMC2846760 | — | — | — |
| EhretGB, MunroePB, RiceKM, BochudM, JohnsonAD, ChasmanDI, et al Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478:103–109. 10.1038/nature10405 21909115PMC3340926 | — | — | — |
| EichlerEE, FlintJ, GibsonG, KongA, LealSM, MooreJH, et al Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet. 2010;11:446–450. 10.1038/nrg2809 20479774PMC2942068 | — | — | — |
| EvangelouE, FellayJ, ColomboS, Martinez-PicadoJ, ObelN, GoldsteinDB, 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 21490045PMC4806701 | — | — | — |
| EvangelouE, IoannidisJPA. Meta-analysis methods for genome-wide association studies and beyond. Nat Rev Genet. 2013;14:379–389. 10.1038/nrg3472 23657481 | — | — | — |
| HanB, EskinE. Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. Am J Hum Genet. 2011;88:586–598. 10.1016/j.ajhg.2011.04.014 21565292PMC3146723 | — | — | — |
| HarrisSE, HagenaarsSP, DaviesG, HillWD, LiewaldDCM, RitchieSJ, et al Molecular genetic contributions to self-rated health. Int J Epidemiol. 2016;advance access:dyw219. 10.1093/ije/dyw219 27864402PMC5837683 | — | — | — |
| Lango AllenH, EstradaK, LettreG, BerndtSI, WeedonMN, RivadeneiraF, et al Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010;467:832–838. 10.1038/nature09410 20881960PMC2955183 | — | — | — |
| LebrecJJ, StijnenT, Van HouwelingenHC. Dealing with heterogeneity between cohorts in genomewide SNP association studies. Stat Appl Genet Mol Biol. 2010;9:8 10.2202/1544-6115.1503 20196758 | — | — | — |
| LeeS, TeslovichTM, BoehnkeM, LinX. 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 23768515PMC3710762 | — | — | — |
| LeeSH, YangJ, GoddardME, VisscherPM, WrayNR. 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 22843982PMC3463125 | — | — | — |
| LiMX, YeungJMY, ChernySS, ShamPC. 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 22143225PMC3325408 | — | — | — |
| LockeAE, KahaliB, BerndtSI, JusticeAE, PersTH, DayFR, et al Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206. 10.1038/nature14177 25673413PMC4382211 | — | — | — |
| MaherB. Personal genomes: the case of the missing heritability. Nature. 2008;456:18–21. 10.1038/456018a 18987709 | — | — | — |
| ManolioTA, CollinsFS, CoxNJ, GoldsteinDB, HindorffLA, HunterDJ, et al Finding the missing heritability of complex diseases. Nature. 2009;461:747–753. 10.1038/nature08494 19812666PMC2831613 | — | — | — |
| MorrisAP. Transethnic meta-analysis of genomewide association studies. Genet Epidemiol. 2011;35:809–822. 10.1002/gepi.20630 22125221PMC3460225 | — | — | — |
| NallsMA, PankratzN, LillCM, DoCB, HernandezDG, SaadM, et al Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease. Nat Genet. 2014;46:989–993. 10.1038/ng.3043 25064009PMC4146673 | — | — | — |
| OkbayA, BaselmansBML, De NeveJE, TurleyP, NivardMG, FontanaMA, 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 27089181PMC4884152 | — | — | — |
| OkbayA, BeauchampJP, FontanaMA, LeeJJ, PersTH, RietveldCA, et al Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016;533:539–542. 10.1038/nature17671 27225129PMC4883595 | — | — | — |
| RietveldCA, CesariniD, BenjaminDJ, KoellingerPD, De NeveJE, TiemeierH, et al Molecular genetics and subjective well-being. Proc Natl Acad Sci USA. 2013;110:9692–9697. 10.1073/pnas.1222171110 23708117PMC3683731 | — | — | — |
| RietveldCA, MedlandSE, DerringerJ, YangJ, EskoT, MartinNW, et al GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science. 2013;340:1467–1471. 10.1126/science.1235488 23722424PMC3751588 | — | — | — |
| RipkeS, NealeBM, 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 25056061PMC4112379 | — | — | — |
| ShamPC, PurcellSM. Statistical power and significance testing in large-scale genetic studies. Nat Rev Genet. 2014;15:335–346. 10.1038/nrg3706 24739678 | — | — | — |
| ShiJ, LeeS. 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 26916671PMC4996751 | — | — | — |
| SpeliotesEK, WillerCJ, BerndtSI, MondaKL, ThorleifssonG, JacksonAU, 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 20935630PMC3014648 | — | — | — |
| VisscherPM, BrownMA, McCarthyMI, YangJ. Five years of GWAS discovery. Am J Hum Genet. 2012;90:7–24. 10.1016/j.ajhg.2011.11.029 22243964PMC3257326 | — | — | — |
| VisscherPM, HemaniG, VinkhuyzenAAE, ChenGB, LeeSH, WrayNR, 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 24721987PMC3983037 | — | — | — |
| WeedonMN, Lango AllenH, LindgrenCM, WallaceC, EvansDM, ManginoM, et al Genome-wide association analysis identifies 20 loci that influence adult height. Nat Genet. 2008;40:575–583. 10.1038/ng.121 18391952PMC2681221 | — | — | — |
| WenX, StephensM. 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 26413181PMC4583155 | — | — | — |
| WillerCJ, LiY, AbecasisGR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–2191. 10.1093/bioinformatics/btq340 20616382PMC2922887 | — | — | — |
| WillerCJ, SpeliotesEK, LoosRJF, LiS, LindgrenCM, HeidIM, 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 19079261PMC2695662 | — | — | — |
| WitteJS, VisscherPM, WrayNR. The contribution of genetic variants to disease depends on the ruler. Nat Rev Genet. 2014;15:765–776. 10.1038/nrg3786 25223781PMC4412738 | — | — | — |
| WoodAR, EskoT, YangJ, VedantamS, PersTH, GustafssonS, 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 25282103PMC4250049 | — | — | — |
| WrayNR, GoddardME, VisscherPM. Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res. 2007;17:1520–1528. 10.1101/gr.6665407 17785532PMC1987352 | — | — | — |
| WrayNR, LeeSH, KendlerKS. 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 22258521PMC3355255 | — | — | — |
| WrayNR, LeeSH, 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 23933821PMC3800159 | — | — | — |
| WrayNR, MaierR. Genetic basis of complex genetic disease: the contribution of disease heterogeneity to missing heritability. Curr Epidemiol Rep. 2014;1:220–227. 10.1007/s40471-014-0023-3 | — | — | — |
| WrayNR, YangJ, HayesBJ, PriceAL, GoddardME, VisscherPM. Pitfalls of predicting complex traits from SNPs. Nat Rev Genet. 2013;14:507–515. 10.1038/nrg3457 23774735PMC4096801 | — | — | — |
| YangJ, BakshiA, ZhuZ, HemaniG, VinkhuyzenAAE, LeeSH, 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 26323059PMC4589513 | — | — | — |
| YangJ, BenyaminB, McEvoyBP, GordonS, HendersAK, NyholtDR, et al Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42:565–569. 10.1038/ng.608 20562875PMC3232052 | — | — | — |
| YangJ, LeeSH, GoddardME, VisscherPM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82. 10.1016/j.ajhg.2010.11.011 21167468PMC3014363 | — | — | — |
| YangJ, WeedonMN, PurcellSM, LettreG, EstradaK, WillerCJ, et al Genomic inflation factors under polygenic inheritance. Eur J Hum Genet. 2011;19:807–812. 10.1038/ejhg.2011.39 21407268PMC3137506 | — | — | — |
| YangJ, ZaitlenNA, GoddardME, VisscherPM, PriceAL. Advantages and pitfalls in the application of mixed-model association methods. Nat Genet. 2014;46:100–106. 10.1038/ng.2876 24473328PMC3989144 | — | — | — |
| ZukO, HechterE, SunyaevSR, LanderES. The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci USA. 2012;109:1193–1198. 10.1073/pnas.1119675109 22223662PMC3268279 | — | — | — |
In this knowledge base
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| The predicament of heritable confounders. | Cai N et al. | — | 2026 | → |
| Associations between common genetic variants and income provide insights about the socio-economic health gradient. | Kweon H et al. | — | 2025 | → |
| Preschool musicality is associated with school-age communication abilities through genes related to rhythmicity. | de Hoyos L et al. | — | 2025 | → |
| Secure and federated quantitative trait loci mapping with privateQTL. | Choi YA et al. | — | 2025 | → |
| The contribution of gametic phase disequilibrium to the heritability of complex traits. | Zhang Y et al. | — | 2025 | → |
| The impact of self-report inaccuracy in the UK Biobank and its interplay with selective participation. | Schoeler T et al. | — | 2025 | → |
| A Genetic Analysis of Current Medication Use in the UK Biobank. | Rohde PD | — | 2024 | → |
| Evaluating three strategies of genome-wide association analysis for integrating data from multiple populations. | Zhong Z et al. | — | 2024 | → |
| Interpretation of 10 years of Alzheimer's disease genetic findings in the perspective of statistical heterogeneity. | Gao S et al. | — | 2024 | → |
| The goldmine of GWAS summary statistics: a systematic review of methods and tools. | Kontou PI et al. | — | 2024 | → |
| Valid inference for machine learning-assisted genome-wide association studies. | Miao J et al. | — | 2024 | → |
| Cohort profile: Genetic data in the German Socio-Economic Panel Innovation Sample (SOEP-G). | Koellinger PD et al. | — | 2023 | → |
| Guidelines for Evaluating the Comparability of Down-Sampled GWAS Summary Statistics. | Williams CM et al. | — | 2023 | → |
| Low and differential polygenic score generalizability among African populations due largely to genetic diversity. | Majara L et al. | — | 2023 | → |
| Meta-analysis of African ancestry genome-wide association studies identified novel locus and validates multiple loci associated with kidney function. | Kintu C et al. | — | 2023 | → |
| Overcoming attenuation bias in regressions using polygenic indices. | van Kippersluis H et al. | — | 2023 | → |
| Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology. | Wang Y et al. | — | 2023 | → |
| Polygenic risk prediction: why and when out-of-sample prediction R<sup>2</sup> can exceed SNP-based heritability. | Wang X et al. | — | 2023 | → |
| Rank concordance of polygenic indices. | Muslimova D et al. | — | 2023 | → |
| The identification of mediating effects using genome-based restricted maximum likelihood estimation. | Rietveld CA et al. | — | 2023 | → |
| Wrestling with Social and Behavioral Genomics: Risks, Potential Benefits, and Ethical Responsibility. | Meyer MN et al. | — | 2023 | → |
| Genome-wide Association Meta-analysis of Childhood and Adolescent Internalizing Symptoms. | Jami ES et al. | — | 2022 | → |
| Incarceration, polygenic risk, and depressive symptoms among males in late adulthood. | Liu H et al. | — | 2022 | → |
| Multivariate estimation of factor structures of complex traits using SNP-based genomic relationships. | De Vlaming R et al. | — | 2022 | → |
| Polygenic power calculator: Statistical power and polygenic prediction accuracy of genome-wide association studies of complex traits. | Wu T et al. | — | 2022 | → |
| Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology | Wang Y et al. | — | 2022 | — |
| Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. | Okbay A et al. | — | 2022 | → |
| sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies. | Nasirigerdeh R et al. | — | 2022 | → |
| Genome-Wide Association Study of 2,093 Cases With Idiopathic Polyneuropathy and 445,256 Controls Identifies First Susceptibility Loci. | Winsvold BS et al. | — | 2021 | → |
| Pathfinder: a gamified measure to integrate general cognitive ability into the biological, medical, and behavioural sciences. | Malanchini M et al. | — | 2021 | → |
| Detecting Genotype-Population Interaction Effects by Ancestry Principal Components. | Yu C et al. | — | 2020 | → |
| Familial Influences on Neuroticism and Education in the UK Biobank. | Cheesman R et al. | — | 2020 | → |
| Interpretation of risk loci from genome-wide association studies of Alzheimer's disease. | Andrews SJ et al. | — | 2020 | → |
| Population phenomena inflate genetic associations of complex social traits. | Morris TT et al. | — | 2020 | → |
| Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations. | Wang Y et al. | — | 2020 | → |
| Using genetics for social science. | Harden KP et al. | — | 2020 | → |
| Using imputed whole-genome sequence variants to uncover candidate mutations and genes affecting milking speed and temperament in Holstein cattle. | Chen SY et al. | — | 2020 | → |
| Developmental Change in Adolescent Delinquency: Modeling Time-Varying Effects of a Preventative Intervention and GABRA2 Halpotype Linked to Alcohol Use. | Schlomer GL et al. | — | 2019 | → |
| Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. | Karlsson Linnér R et al. | — | 2019 | → |
| Modeling Heterogeneity in the Genetic Architecture of Ethnically Diverse Groups Using Random Effect Interaction Models. | Veturi Y et al. | — | 2019 | → |
| A genome-wide association study for extremely high intelligence. | Zabaneh D et al. | — | 2018 | → |
| Big data hurdles in precision medicine and precision public health. | Prosperi M et al. | — | 2018 | → |
| Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. | Lee JJ et al. | — | 2018 | → |
| Genetic analysis of social-class mobility in five longitudinal studies. | Belsky DW et al. | — | 2018 | → |
| Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data. | DiPrete TA et al. | — | 2018 | → |
| Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. | Yengo L et al. | — | 2018 | → |
| The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome-wide association studies. | Lee JJ et al. | — | 2018 | → |
| Trans-Ethnic Polygenic Analysis Supports Genetic Overlaps of Lumbar Disc Degeneration With Height, Body Mass Index, and Bone Mineral Density. | Zhou X et al. | — | 2018 | → |
| Using nature to understand nurture. | Koellinger PD et al. | — | 2018 | → |
| Hidden heritability due to heterogeneity across seven populations. | Tropf FC et al. | — | 2017 | → |