ArticlePLoS genetics2017
Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies.
Article in PLoS genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 7 of them syntheses that pooled it.
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Who cites it
53 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Meta-analysis of African ancestry genome-wide association studies identified novel locus and validates multiple loci associated with kidney function.BMC genomics · 2023Pooled it
- Overcoming attenuation bias in regressions using polygenic indices.Nature communications · 2023Pooled it
- Genome-wide Association Meta-analysis of Childhood and Adolescent Internalizing Symptoms.Journal of the American Academy of Child and Adolescent Psychiatry · 2022Pooled it
- Interpretation of risk loci from genome-wide association studies of Alzheimer's disease.The Lancet. Neurology · 2020Pooled it
- Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences.Nature genetics · 2019Pooled it
- Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry.Human molecular genetics · 2018 · on this mapPooled it
- Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.Nature genetics · 2018Pooled it
- All of Us diversity and scale yield context-dependent improvements in polygenic prediction.Nature genetics · 2026Article
- Genetic underpinnings of chills from art and music.PLoS genetics · 2026Article
- The predicament of heritable confounders.Nature genetics · 2026Review
- Genomic meta-analyses of binge-eating behavior and anorexia nervosa yield insights into the unique and shared biology of eating disorder phenotypes.Nature. Mental health · 2026Article
- Preschool musicality is associated with school-age communication abilities through genes related to rhythmicity.NPJ science of learning · 2025Article
- The contribution of gametic phase disequilibrium to the heritability of complex traits.Nature genetics · 2025Article
- All of Us diversity and scale improve polygenic prediction contextually with greatest improvements for under-represented populations.bioRxiv : the preprint server for biology · 2025Article
- Associations between common genetic variants and income provide insights about the socio-economic health gradient.Nature human behaviour · 2025Article
- The impact of self-report inaccuracy in the UK Biobank and its interplay with selective participation.Nature human behaviour · 2025Article
- Secure and federated quantitative trait loci mapping with privateQTL.Cell genomics · 2025Article
- Valid inference for machine learning-assisted genome-wide association studies.Nature genetics · 2024Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Interpretation of 10 years of Alzheimer's disease genetic findings in the perspective of statistical heterogeneity.Briefings in bioinformatics · 2024Review
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11 authors.
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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 (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.
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