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Sequence kernel association test for survival traits.

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  • 1Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America; Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America.

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|January 28, 2014
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Summary
This summary is machine-generated.

This study extends the sequence kernel association test (SKAT) for rare genetic variants to survival data using Cox regression. The modified test improves type I error control and statistical power for genetic association studies.

Keywords:
Cox proportional hazard modellikelihood ratio testrare variant analysisvariance component test

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Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Rare variant association tests are crucial for understanding genetic contributions to diseases.
  • The sequence kernel association test (SKAT) is a powerful omnibus test for rare variants in linear/logistic models.
  • Extending SKAT to survival data is essential for comprehensive genetic epidemiology.

Purpose of the Study:

  • To adapt the sequence kernel association test (SKAT) for survival phenotypes within a Cox regression framework.
  • To improve the type I error rate control and statistical power of rare variant association tests for survival data.
  • To enable the application of this extended SKAT in genetic meta-analyses.

Main Methods:

  • Modified the SKAT statistic by substituting score statistics with signed square-root likelihood ratio statistics in a Cox model.
  • Evaluated the performance of the modified test using simulation studies.
  • Applied the extended SKAT to time-to-obesity data from the Framingham Heart Study SNP Health Association Resource.

Main Results:

  • The modified SKAT demonstrated improved small-sample control of type I error compared to the standard score test in Cox models.
  • Simulation studies indicated superior statistical power for the extended SKAT over burden tests in most scenarios.
  • The test proved applicable to real-world genetic association studies with survival outcomes.

Conclusions:

  • The extended SKAT provides a robust and powerful method for analyzing rare genetic variants in survival data.
  • This approach enhances the ability to detect genetic associations with complex diseases and traits.
  • The method is suitable for both individual studies and meta-analyses in genetic epidemiology.