Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Ordered multinomial regression for genetic association analysis of ordinal phenotypes at Biobank scale.

Genetic epidemiology - 1 Apr 2020

German Christopher A, Sinsheimer Janet S, Klimentidis Yann C, Zhou Hua, Zhou Jin J

Abstract excerpt

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...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Biological Specimen Banks

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Computer Simulation

Share to Hypertension

Share to Models, Genetic

Share to Pulmonary Disease, Chronic Obstructive

Share to Regression Analysis

Share to Respiratory Function Tests

Source

Open in PubMed Open full text

Identifiers

PMID: 31879980
DOI: 10.1002/gepi.22276
PMCID: PMC8256450

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Ordered multinomial regression for genetic association analysis of ordinal phenotypes at Biobank scale.PMID 31879980Read ↗

Testing for association between ordinal traits and genetic variants in pedigree-structured samples by collapsing and kernel methods.Semantic proximity Read ↗

Gene-level association analysis of bivariate ordinal traits with functional regressions.Semantic proximity Read ↗

Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypesSemantic proximity Read ↗

Scalable mixed model methods for set-based association studies on large-scale categorical data analysis and its application to exome-sequencing data in UK Biobank.Semantic proximity Read ↗

Semiparametric Allelic Tests for Mapping Multiple Phenotypes: Binomial Regression and Mahalanobis Distance.Semantic proximity Read ↗

Quantile Regression for biomarkers in the UK BiobankSemantic proximity Read ↗

Genetic association tests in family samples for multi-category phenotypes.Semantic proximity Read ↗

Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes.Semantic proximity Read ↗

A powerful approach to sub-phenotype analysis in population-based genetic association studies.Semantic proximity Read ↗

Gene-level association analysis of ordinal traits with functional ordinal logistic regressions.Semantic proximity Read ↗

Implicit bias of encoded variables: frameworks for addressing structured bias in EHR-GWAS data.Semantic proximity Read ↗

Uncovering hidden gene-trait patterns through biclustering analysis of the UK BiobankSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.