Gene-based analysis of bi-variate survival traits via functional regressions with applications to eye diseases

Bingsong Zhang; Chi-Yang Chiu; Fang Yuan; Tian Sang; Richard J Cook; Alexander F Wilson; Joan E Bailey-Wilson; Emily Y Chew; Momiao Xiong; Ruzong Fan

doi:10.1002/gepi.22381

Gene-based analysis of bi-variate survival traits via functional regressions with applications to eye diseases

Genet Epidemiol. 2021 Jul;45(5):455-470. doi: 10.1002/gepi.22381. Epub 2021 Mar 1.

Authors

Bingsong Zhang¹, Chi-Yang Chiu^{2

3}, Fang Yuan⁴, Tian Sang^{1

5}, Richard J Cook⁶, Alexander F Wilson³, Joan E Bailey-Wilson³, Emily Y Chew⁷, Momiao Xiong⁸, Ruzong Fan^{1

3}

Affiliations

¹ Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA.
² Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
³ Computational and Statistical Genomics Branch, National Human Genome, Research Institute, National Institutes of Health (NIH), Baltimore, Maryland, USA.
⁴ Department of Biochemistry and Molecular Biology, School of Basic Medicine, Kunming Medical University, Kunming, People's Republic of China.
⁵ School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, China.
⁶ Department of Statistics and Actuarial Science, Waterloo, Ontario, Canada.
⁷ Division of Epidemiology and Clinical Applications, National Eye Institute, NIH, Bethesda, Maryland, USA.
⁸ Human Genetics Center, University of Texas-Houston, Houston, Texas, USA.

PMID: 33645812
DOI: 10.1002/gepi.22381

Abstract

Genetic studies of two related survival outcomes of a pleiotropic gene are commonly encountered but statistical models to analyze them are rarely developed. To analyze sequencing data, we propose mixed effect Cox proportional hazard models by functional regressions to perform gene-based joint association analysis of two survival traits motivated by our ongoing real studies. These models extend fixed effect Cox models of univariate survival traits by incorporating variations and correlation of multivariate survival traits into the models. The associations between genetic variants and two survival traits are tested by likelihood ratio test statistics. Extensive simulation studies suggest that type I error rates are well controlled and power performances are stable. The proposed models are applied to analyze bivariate survival traits of left and right eyes in the age-related macular degeneration progression.

Keywords: association study; common variants; complex diseases; functional data analysis; mixed effect Cox models; rare variants.

Publication types

Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Eye Diseases* / genetics
Genetic Association Studies
Genetic Variation*
Humans
Models, Genetic
Phenotype