ArticleBMC genomics2018
A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Large-scale pleiotropic analysis across cancers reveals shared genetic mechanisms and identifies novel functional genes.Briefings in bioinformatics · 2026Article
- Disentangling Shared and Differential Genetic Architectures Between COVID-19 and Other Respiratory Disorders-A Genome-Wide Multi-Omics Framework.International journal of molecular sciences · 2026Article
- Divided-and-combined association test for pleiotropic effects with GWAS summary statistics.BMC genomics · 2026Article
- Sparse matrix factorization robust to sample sharing across GWASs reveals interpretable genetic components.American journal of human genetics · 2025Article
- Addressing overfitting bias due to sample overlap in polygenic risk scoring.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Two-stage strategy using denoising autoencoders for robust reference-free genotype imputation with missing input genotypes.Journal of human genetics · 2024Review
- Epistasis and pleiotropy-induced variation for plant breeding.Plant biotechnology journal · 2024Review
- Inflation of polygenic risk scores caused by sample overlap and relatedness: Examples of a major risk of bias.American journal of human genetics · 2024Article
- Genetic factors associated with suicidal behaviors and alcohol use disorders in an American Indian population.Molecular psychiatry · 2024Article
- The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders.Nature human behaviour · 2024Article
- A cross-trait study of lung cancer and its related respiratory diseases based on large-scale exome sequencing population.Translational lung cancer research · 2024Article
- simmrd: An open-source tool to perform simulations in Mendelian randomization.Genetic epidemiology · 2024Article
- Article
- Multi-response Mendelian randomization: Identification of shared and distinct exposures for multimorbidity and multiple related disease outcomes.American journal of human genetics · 2023Article
- Similarity and diversity of genetic architecture for complex traits between East Asian and European populations.BMC genomics · 2023Article
- Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data.PLoS genetics · 2023Article
- An autoimmune pleiotropic SNP modulates IRF5 alternative promoter usage through ZBTB3-mediated chromatin looping.Nature communications · 2023Article
- MRBEE: A novel bias-corrected multivariable Mendelian Randomization method.Research square · 2023Article
- graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data.Frontiers in genetics · 2023Article
- EraSOR: a software tool to eliminate inflation caused by sample overlap in polygenic score analyses.GigaScience · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundThere is considerable evidence that many complex traits have a partially shared genetic basis, termed pleiotropy. It is therefore useful to consider integrating genome-wide association study (GWAS) data across several traits, usually at the summary statistic level. A major practical challenge arises when these GWAS have overlapping subjects. This is particularly an issue when estimating pleiotropy using methods that condition the significance of one trait on the signficance of a second, such as the covariate-modulated false discovery rate (cmfdr).
resultsWe propose a method for correcting for sample overlap at the summary statistic level. We quantify the expected amount of spurious correlation between the summary statistics from two GWAS due to sample overlap, and use this estimated correlation in a simple linear correction that adjusts the joint distribution of test statistics from the two GWAS. The correction is appropriate for GWAS with case-control or quantitative outcomes. Our simulations and data example show that without correcting for sample overlap, the cmfdr is not properly controlled, leading to an excessive number of false discoveries and an excessive false discovery proportion. Our correction for sample overlap is effective in that it restores proper control of the false discovery rate, at very little loss in power.
conclusionsWith our proposed correction, it is possible to integrate GWAS summary statistics with overlapping samples in a statistical framework that is dependent on the joint distribution of the two GWAS.
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