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Gene-based association analysis for bivariate time-to-event data through functional regression with copula models.

Yue Wei1, Yi Liu2, Tao Sun1

  • 1Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania.

Biometrics
|October 19, 2019
PubMed
Summary

This study introduces a new statistical method for analyzing gene associations with two survival outcomes, like age-related macular degeneration (AMD) progression. The functional regression method effectively identifies gene regions linked to complex bilateral diseases.

Keywords:
AMD progressionbivariate time-to-eventcopulafunctional regressiongene-based association analysis

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Area of Science:

  • Genetics
  • Biostatistics
  • Ophthalmology

Background:

  • Gene-based association tests are crucial for survival analysis.
  • Existing methods are limited for bivariate survival outcomes.
  • Age-related macular degeneration (AMD) progression involves bilateral eye disease.

Purpose of the Study:

  • To develop a novel statistical method for gene-based association analysis of bivariate survival traits.
  • To identify gene regions associated with age-related macular degeneration (AMD) progression.

Main Methods:

  • Implemented a functional regression (FR) method within a copula framework.
  • Modeled variant effects using a functional linear model contributing to marginal survival functions.
  • Derived generalized score test statistics for bivariate survival traits and genetic regions.

Main Results:

  • The proposed method demonstrated robust type I error control and power in simulations.
  • Compared favorably against existing single-trait methods and marginal Cox FR models.
  • Successfully applied to the Age-related Eye Disease Study to identify AMD-associated gene regions.

Conclusions:

  • The novel FR copula method provides a powerful tool for gene-based association studies with bivariate survival outcomes.
  • This approach enhances the understanding of genetic contributions to complex diseases like AMD.
  • The method is effective for identifying disease-associated genetic regions in large-scale studies.