Evidence map›Paper›PMID 31502722›Full record

ArticleGenetic epidemiology2019

Gene-based association analysis of survival traits via functional regression-based mixed effect cox models for related samples.

Chi-Yang Chiu, Bingsong Zhang, Shuqi Wang, Jingyi Shao, M'Hamed Lajmi Lakhal-Chaieb, Richard J Cook, Alexander F Wilson, Joan E Bailey-Wilson, Momiao Xiong, Ruzong Fan

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Genetic epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Chi-Yang ChiuDivision of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee.
Bingsong ZhangDepartment of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia.
Shuqi WangDepartment of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia.
Jingyi ShaoDepartment of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia.
M'Hamed Lajmi Lakhal-ChaiebDepartment de Mathematiques et de Statistique, Universite Laval, Quebec, Quebec, Canada.
Richard J CookDepartment of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada.ORCID 0000-0002-1414-4908
Alexander F WilsonComputational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland.
Joan E Bailey-WilsonComputational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland.
Momiao XiongDepartment of Biostatistics, Human Genetics Center, University of Texas-Houston, Houston, Texas.
Ruzong FanDepartment of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia.ORCID 0000-0002-7603-2135

Funding

Genetic Epidemiology of Complex TraitsZ01HG200327 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI BAILEY-WILSON, JOAN ELLEN · 2005 to 2008
$1.3M
Development of Statistical Genetics MethodologyZ01HG000153 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI BAILEY-WILSON, JOAN ELLEN · 1999 to 2008
$616k
Intramural NIH HHS Z01 HG000153Intramural NIH HHS Z01 HG200327Intramural NIH HHS Z99 HG999999
6 · The paper itself

Abstract

The importance to integrate survival analysis into genetics and genomics is widely recognized, but only a small number of statisticians have produced relevant work toward this study direction. For unrelated population data, functional regression (FR) models have been developed to test for association between a quantitative/dichotomous/survival trait and genetic variants in a gene region. In major gene association analysis, these models have higher power than sequence kernel association tests. In this paper, we extend this approach to analyze censored traits for family data or related samples using FR based mixed effect Cox models (FamCoxME). The FamCoxME model effect of major gene as fixed mean via functional data analysis techniques, the local gene or polygene variations or both as random, and the correlation of pedigree members by kinship coefficients or genetic relationship matrix or both. The association between the censored trait and the major gene is tested by likelihood ratio tests (FamCoxME FR LRT). Simulation results indicate that the LRT control the type I error rates accurately/conservatively and have good power levels when both local gene or polygene variations are modeled. The proposed methods were applied to analyze a breast cancer data set from the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). The FamCoxME provides a new tool for gene-based analysis of family-based studies or related samples.

Indexed as

Genetic Association StudiesModels, GeneticSurvival AnalysisComputer SimulationGenetic VariationHumansPedigreePhenotypeProportional Hazards ModelsRegression Analysisassociation studycommon variantscomplex diseasesfunctional data analysismixed effect Cox modelsrare variants

Identifiers

PMID31502722
PMCPMC6829032

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Registered trials

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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.