ArticleGenetic epidemiology2013
Statistical testing of shared genetic control for potentially related traits.
Article in Genetic epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 8 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
83 citing papers in PubMed, 8 syntheses or guidelines pooled it, 125 citations in OpenAlex.
- Interrogating DNA methylation associated with Lewy body pathology in a cross brain-region and multi-cohort study.Nature communications · 2026Pooled it
- Gene-level analysis reveals the genetic aetiology and therapeutic targets of schizophrenia.Nature human behaviour · 2025Pooled it
- Genome-Wide Association and Two-Sample Mendelian Randomization Analyses of Plasma Ghrelin and Gastrointestinal Cancer Risk.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023Pooled it
- Genome-wide meta-analysis of iron status biomarkers and the effect of iron on all-cause mortality in HUNT.Communications biology · 2022Pooled it
- Smoking Status and Type 2 Diabetes, and Cardiovascular Disease: A Comprehensive Analysis of Shared Genetic Etiology and Causal Relationship.Frontiers in endocrinology · 2022Pooled it
- Prostate cancer risk SNP rs10993994 is a trans-eQTL for SNHG11 mediated through MSMB.Human molecular genetics · 2020Pooled it
- Shared genetic architecture and casual relationship between leptin levels and type 2 diabetes: large-scale cross-trait meta-analysis and Mendelian randomization analysis.BMJ open diabetes research & care · 2020Pooled it
- Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.PLoS genetics · 2014Pooled it
- Shared genetics of IgA nephropathy and cardiometabolic diseases revealed by integrative genomic analysis.Renal failure · 2026Article
- Article
- Cross-trait mapping of shared susceptibility across inflammatory bowel disease and spondyloarthropathies.Journal of translational medicine · 2026Article
- Integrating genetic data with biological insight: A practical guide to cis-Mendelian randomization.American journal of human genetics · 2026Review
- Genetic and functional insights into long noncoding RNAs in schizophrenia.Molecular psychiatry · 2026Article
- Early and late RNA eQTL are driven by different genetic mechanisms.Nature communications · 2026Article
- Article
- Beyond the baseline: mapping the context-specific regulatory landscape of disease.Trends in genetics : TIG · 2026Review
- Genetic Architecture of Trans-Laminar Cribrosa Pressure Difference and Primary Open-Angle Glaucoma.Investigative ophthalmology & visual science · 2026Article
- Article
- Trans-ancestry genome-wide analyses of bipolar disorder in East Asian and European populations improve genetic discovery.Nature neuroscience · 2026Article
- Multiple trait association analysis revealed common genetic loci between lung cancer and heart failure.Cardio-oncology (London, England) · 2026Article
23 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Integration of data from genome-wide single nucleotide polymorphism (SNP) association studies of different traits should allow researchers to disentangle the genetics of potentially related traits within individually associated regions. Formal statistical colocalisation testing of individual regions requires selection of a set of SNPs summarising the association in a region. We show that the SNP selection method greatly affects type 1 error rates, with published studies having used methods expected to result in substantially inflated type 1 error rates. We show that either avoiding variable selection and instead testing the most informative principal components or integrating over variable selection using Bayesian model averaging can help control type 1 error rates. Application to data from Graves' disease and Hashimoto's thyroiditis reveals a common genetic signature across seven regions shared between the diseases, and indicates that in five of six regions associated with Graves' disease and not Hashimoto's thyroiditis, this more likely reflects genuine absence of association with the latter rather than lack of power. Our examination, by simulation, of the performance of colocalisation tests and associated software will foster more widespread adoption of formal colocalisation testing. Given the increasing availability of large expression and genetic association datasets from disease-relevant tissue and purified cell populations, coupled with identification of regulatory sequences by projects such as ENCODE, colocalisation analysis has the potential to reveal both shared genetic signatures of related traits and causal disease genes and tissues.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.