ArticleHuman molecular genetics2017
Quantifying the extent to which index event biases influence large genetic association studies.
Article in Human molecular genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reassessing the association of MUC5B with survival in idiopathic pulmonary fibrosis.Annals of human genetics · 2023Pooled it
- Bayesian Mendelian randomization methods for index trait bias correction in subsequent trait genome-wide association studies.HGG advances · 2026Article
- Impact of control selection strategies on GWAS results: a study of prostate cancer in the UK biobank.Briefings in bioinformatics · 2026Article
- Polygenic risk score and 20-year prostate cancer-specific mortality and survival.Communications medicine · 2026Article
- Limited overlap between genetic effects on disease susceptibility and disease survival.Nature genetics · 2025Article
- Identification of effect modifiers using a stratified Mendelian randomization algorithmic framework.European journal of epidemiology · 2025Article
- Analyzing longitudinal trait trajectories using GWAS identifies genetic variants for kidney function decline.Nature communications · 2024Article
- Human height: a model common complex trait.Annals of human biology · 2023Review
- Strategies to investigate and mitigate collider bias in genetic and Mendelian randomisation studies of disease progression.PLoS genetics · 2023Review
- Adjusting for collider bias in genetic association studies using instrumental variable methods.Genetic epidemiology · 2022Article
- Inheritance of a common androgen synthesis variant allele is associated with female COVID susceptibility in UK Biobank.European journal of endocrinology · 2022Article
- A robust method for collider bias correction in conditional genome-wide association studies.Nature communications · 2022Article
- Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.Scientific reports · 2021Article
- Associations of TCF7L2 rs11196218 (A/G) and GLP-1R rs761386 (C/T) Gene Polymorphisms with Obesity in Chinese Population.Diabetes, metabolic syndrome and obesity : targets and therapy · 2021Article
- Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity.Diabetes · 2020Article
- Criteria for evaluating risk prediction of multiple outcomes.Statistical methods in medical research · 2020Article
- Collider bias undermines our understanding of COVID-19 disease risk and severity.Nature communications · 2020Article
- Genome-wide association study of self-reported walking pace suggests beneficial effects of brisk walking on health and survival.Communications biology · 2020Article
- The Māori and Pacific specific CREBRF variant and adult height.International journal of obesity (2005) · 2020Article
- Survival Bias in Mendelian Randomization Studies: A Threat to Causal Inference.Epidemiology (Cambridge, Mass.) · 2019Article
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
As genetic association studies increase in size to 100 000s of individuals, subtle biases may influence conclusions. One possible bias is 'index event bias' (IEB) that appears due to the stratification by, or enrichment for, disease status when testing associations between genetic variants and a disease-associated trait. We aimed to test the extent to which IEB influences some known trait associations in a range of study designs and provide a statistical framework for assessing future associations. Analyzing data from 113 203 non-diabetic UK Biobank participants, we observed three (near TCF7L2, CDKN2AB and CDKAL1) overestimated (body mass index (BMI) decreasing) and one (near MTNR1B) underestimated (BMI increasing) associations among 11 type 2 diabetes risk alleles (at P < 0.05). IEB became even stronger when we tested a type 2 diabetes genetic risk score composed of these 11 variants (-0.010 standard deviations BMI per allele, P = 5 × 10- 4), which was confirmed in four additional independent studies. Similar results emerged when examining the effect of blood pressure increasing alleles on BMI in normotensive UK Biobank samples. Furthermore, we demonstrated that, under realistic scenarios, common disease alleles would become associated at P < 5 × 10- 8 with disease-related traits through IEB alone, if disease prevalence in the sample differs appreciably from the background population prevalence. For example, some hypertension and type 2 diabetes alleles will be associated with BMI in sample sizes of >500 000 if the prevalence of those diseases differs by >10% from the background population. In conclusion, IEB may result in false positive or negative genetic associations in very large studies stratified or strongly enriched for/against disease cases.
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