ArticleGenetic epidemiology2020
Ordered multinomial regression for genetic association analysis of ordinal phenotypes at Biobank scale.
Article in Genetic epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Assessment of the functionality and usability of open-source rare variant analysis pipelines.Briefings in bioinformatics · 2025Pooled it
- Genome-wide association study of early ischaemic stroke risk in Brazilian individuals with sickle cell disease implicates ADAMTS2 and CDK18 and uncovers novel loci.British journal of haematology · 2023Pooled it
- Genome-wide association studies and QTL mapping for traits deviating from normal distribution.National science review · 2026Article
- Article
- Phenomapping in Heart Failure With Reduced Ejection Fraction to Identify Subpopulations With High Residual Risk: A VICTORIA Substudy.Circulation. Heart failure · 2026Article
- Polygenic risk scores: Navigating the future of precision medicine through economic, ethical, and scientific advancements.iScience · 2026Review
- Genetic Determinants of Leisure-Time Physical Activity in the Taiwanese Population: A Genome-Wide Association Study.Medicine and science in sports and exercise · 2025Article
- Large-Scale Gene-Smoking Interactions and Fine Mapping Study Identifies Multiple Novel Blood Pressure Loci in over 1 Million Individuals.medRxiv : the preprint server for health sciences · 2025Article
- Genome-wide association study on chronic postsurgical pain after abdominal surgeries in the UK Biobank.Anaesthesia · 2025Article
- The Genetic Variants Influencing Hypertension Prevalence Based on the Risk of Insulin Resistance as Assessed Using the Metabolic Score for Insulin Resistance (METS-IR).International journal of molecular sciences · 2024Article
- Predicting functional outcome in ischemic stroke patients using genetic, environmental, and clinical factors: a machine learning analysis of population-based prospective cohort study.Briefings in bioinformatics · 2024Article
- Cross-EHR validation of antidepressant response algorithm and links with genetics of psychiatric traits.medRxiv : the preprint server for health sciences · 2024Article
- Genome-wide association studies from spoken phenotypic descriptions: a proof of concept from maize field studies.G3 (Bethesda, Md.) · 2024Article
- Genetic interactions and pleiotropy in metabolic diseases: Insights from a comprehensive GWAS analysis.Journal of cellular and molecular medicine · 2024Article
- Genome-wide association analysis of hypertension and epigenetic aging reveals shared genetic architecture and identifies novel risk loci.Scientific reports · 2024Article
- An endothelial regulatory module links blood pressure regulation with elite athletic performance.PLoS genetics · 2024Article
- Mendelian randomisation and mediation analysis of self-reported walking pace and coronary artery disease.Scientific reports · 2024Article
- Genome-wide association study on pharmacological outcomes of musculoskeletal pain in UK Biobank.The pharmacogenomics journal · 2023Article
- Gene-level association analysis of bivariate ordinal traits with functional regressions.Genetic epidemiology · 2023Article
- Genome-wide Association Studies of Retinal Vessel Tortuosity Identify Numerous Novel Loci Revealing Genes and Pathways Associated With Ocular and Cardiometabolic Diseases.Ophthalmology science · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Logistic regression is the primary analysis tool for binary traits in genome-wide association studies (GWAS). Multinomial regression extends logistic regression to multiple categories. However, many phenotypes more naturally take ordered, discrete values. Examples include (a) subtypes defined from multiple sources of clinical information and (b) derived phenotypes generated by specific phenotyping algorithms for electronic health records (EHR). GWAS of ordinal traits have been problematic. Dichotomizing can lead to a range of arbitrary cutoff values, generating inconsistent, hard to interpret results. Using multinomial regression ignores trait value hierarchy and potentially loses power. Treating ordinal data as quantitative can lead to misleading inference. To address these issues, we analyze ordinal traits with an ordered, multinomial model. This approach increases power and leads to more interpretable results. We derive efficient algorithms for computing test statistics, making ordinal trait GWAS computationally practical for Biobank scale data. Our method is available as a Julia package OrdinalGWAS.jl. Application to a COPDGene study confirms previously found signals based on binary case-control status, but with more significance. Additionally, we demonstrate the capability of our package to run on UK Biobank data by analyzing hypertension as an ordinal trait.
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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.