Evidence map›Paper›PMID 35438198›Full record

ArticleGenetic epidemiology2022

Gene-level association analysis of ordinal traits with functional ordinal logistic regressions.

Chi-Yang Chiu, Shuqi Wang, Bingsong Zhang, Yutong Luo, Claire Simpson, Wei Zhang, Alexander F Wilson, Joan E Bailey-Wilson, Elvira Agron, Emily Y Chew and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genetic epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025
    Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Chi-Yang ChiuDivision of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA.ORCID 0000-0002-4837-3194
Shuqi WangDepartment of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA.
Bingsong ZhangDepartment of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA.
Yutong LuoDepartment of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA.
Claire SimpsonDepartment of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
Wei ZhangAcademy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
Alexander F WilsonComputational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, USA.
Joan E Bailey-WilsonComputational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, USA.
Elvira AgronNational Eye Institute, National Institute of Health, Bethesda, Maryland, USA.
Emily Y ChewNational Eye Institute, National Institute of Health, Bethesda, Maryland, USA.
Jun ZhangDepartment of Computer Science and Engineering Technology, University of Maryland Eastern Shore, Princess Anne, Maryland, USA.
Momiao XiongHuman Genetics Center, University of Texas-Houston, Houston, Texas, USA.ORCID 0000-0003-0635-5796
Ruzong FanComputational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, USA.ORCID 0000-0002-7603-2135

Funding

Development of statistical genetics methodologyZIAHG000153 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI BAILEY-WILSON, JOAN ELLEN · 2009 to 2022
$5.0M
Age-Related Eye Disease Follow-up Study (AREDS) Follow-up StudyZIAEY000489 · NEI · NATIONAL EYE INSTITUTE · PI CHEW, EMILY Y · 2009 to 2025
$639k
Intramural NIH HHS Z99 EY999999
6 · The paper itself

Abstract

In this paper, we develop functional ordinal logistic regression (FOLR) models to perform gene-based analysis of ordinal traits. In the proposed FOLR models, genetic variant data are viewed as stochastic functions of physical positions and the genetic effects are treated as a function of physical positions. The FOLR models are built upon functional data analysis which can be revised to analyze the ordinal traits and high dimension genetic data. The proposed methods are capable of dealing with dense genotype data which is usually encountered in analyzing the next-generation sequencing data. The methods are flexible and can analyze three types of genetic data: (1) rare variants only, (2) common variants only, and (3) a combination of rare and common variants. Simulation studies show that the likelihood ratio test statistics of the FOLR models control type I errors well and have good power performance. The proposed methods achieve the goals of analyzing ordinal traits directly, reducing high dimensionality of dense genetic variants, being computationally manageable, facilitating model convergence, properly controlling type I errors, and maintaining high power levels. The FOLR models are applied to analyze Age-Related Eye Disease Study data, in which two genes are found to strongly associate with four ordinal traits.

Indexed as

Genetic TestingModels, GeneticComputer SimulationGenetic VariationGenotypeHumansLogistic ModelsPhenotypeassociation mappingcomplex diseasefunctional data analysisordinal traitsrare variantssequence data

Identifiers

PMID35438198
PMCPMC11550389

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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.