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Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Biostatistics

Background:

  • Kernel machine (KM) tests are effective for set-based association tests of rare variants.
  • Standard KM methodology is limited to single time point phenotypes, not longitudinal data.
  • Existing methods lack rare-variant tests for longitudinal family samples, necessitating new approaches.

Purpose of the Study:

  • To introduce a novel association test for rare variants using longitudinal phenotype data.
  • To accommodate both population and family-based genetic samples.
  • To address the gap in rare-variant testing for longitudinal family data.

Main Methods:

  • Developed KM regression within a linear mixed model framework.
  • Introduced a population-based version (L-KM) and a family-based version (LF-KM).
  • Applied the methods to simulated population and family data, and real data from GAW18.

Main Results:

  • L-KM demonstrated good Type I error control and increased power in population studies.
  • L-KM showed inflated Type I error in family samples; LF-KM maintained desired Type I error rates.
  • LF-KM exhibited superior power performance in family-based simulations and analysis of GAW18 data.

Conclusions:

  • Proposed a robust method for rare-variant association testing with longitudinal phenotypes in population and family samples.
  • The LF-KM approach provides the best power performance compared to existing methods in simulation studies.
  • The method is applicable to genetic association studies with complex longitudinal family data.