ArticleAmerican journal of human genetics2024
Inflation of polygenic risk scores caused by sample overlap and relatedness: Examples of a major risk of bias.
Article in American journal of human genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Polygenic risk scores in human genetics for study design discovery and translation.Human genetics · 2026Review
- Stratification by a polygenic risk score of common variation aids in Alzheimer's disease rare variant discovery.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Observational
- Cross-ancestry polygenic risk scores enhance Alzheimer's disease risk prediction in multiethnic cohorts.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Evidence standards for multi-ancestry polygenic prediction.Genome biology · 2026Review
- Polygenic scores as modifiers in Mendelian diseases.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2026Article
- PGS Browser: a public platform for personalized polygenic score analysis and interpretation.Nature communications · 2026Article
- A decade of discovery: Leveraging genomic data in the ABCD study to illuminate adolescent neurodevelopment.Developmental cognitive neuroscience · 2026Review
- The Contributions of Multiple Polygenic Scores in Predicting Liability for Major Depressive Disorder and Its Comorbidity with Alcohol Use Disorder.Behavior genetics · 2026Article
- Epilepsy concordance in monozygotic twins: the role of common genetic variants.Brain : a journal of neurology · 2026Article
- Methods for modeling gene-environment interplay using polygenic risk scores.Statistical applications in genetics and molecular biology · 2026Review
- The role of genomic-informed risk assessments in predicting dementia outcomes.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Cross-Ancestry Polygenic Risk Scores Enhance Alzheimer's Disease Risk Prediction in Multiethnic Cohorts.medRxiv : the preprint server for health sciences · 2025Article
- The Role of Genomic-Informed Risk Assessments in Predicting Dementia Outcomes.medRxiv : the preprint server for health sciences · 2025Article
- Predicting the direction of phenotypic difference.Nature communications · 2025Article
- Phenotypic heterogeneity in familial epilepsies is influenced by polygenic risk for generalized and focal epilepsies.Epilepsia · 2025Article
- Investigating the effect of polygenic background on epilepsy phenotype in 'monogenic' families.EBioMedicine · 2024Article
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9 authors.
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Abstract
Polygenic risk scores (PRSs) are an important tool for understanding the role of common genetic variants in human disease. Standard best practices recommend that PRSs be analyzed in cohorts that are independent of the genome-wide association study (GWAS) used to derive the scores without sample overlap or relatedness between the two cohorts. However, identifying sample overlap and relatedness can be challenging in an era of GWASs performed by large biobanks and international research consortia. Although most genomics researchers are aware of best practices and theoretical concerns about sample overlap and relatedness between GWAS and PRS cohorts, the prevailing assumption is that the risk of bias is small for very large GWASs. Here, we present two real-world examples demonstrating that sample overlap and relatedness is not a minor or theoretical concern but an important potential source of bias in PRS studies. Using a recently developed statistical adjustment tool, we found that excluding overlapping and related samples was equal to or more powerful than adjusting for overlap bias. Our goal is to make genomics researchers aware of the magnitude of risk of bias from sample overlap and relatedness and to highlight the need for mitigation tools, including independent validation cohorts in PRS studies, continued development of statistical adjustment methods, and tools for researchers to test their cohorts for overlap and relatedness with GWAS cohorts without sharing individual-level data.
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