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A generalized association test based on U statistics.

Changshuai Wei1, Qing Lu2

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A new generalized similarity U (GSU) test effectively identifies genetic variants associated with complex phenotypes in sequencing studies. This robust method, applied to Alzheimer's disease data, pinpointed key genes like APOE, enhancing genetic association analysis.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Second-generation sequencing is crucial for genetic association studies aiming to link genetic variants to phenotypes.
  • Phenotypes can range from simple disease status to complex, high-dimensional outcomes.
  • Testing associations between complex genotypes and phenotypes presents a significant statistical challenge.

Purpose of the Study:

  • To develop and validate a novel statistical test for assessing associations between complex genetic objects and phenotypes.
  • To introduce a similarity-based approach, the generalized similarity U (GSU) test, for genetic association studies.
  • To enhance the power and robustness of genetic association analyses using next-generation sequencing data.

Main Methods:

  • Developed the generalized similarity U (GSU) test, a similarity-based statistical method.
  • Utilized theoretical analysis to establish the properties of the GSU test.
  • Proposed and implemented Laplacian Kernel-based similarity within the GSU framework for improved performance.
  • Created a C++ package for whole genome sequencing (WGS) data analysis using GSU.

Main Results:

  • The GSU test demonstrated superior power and robustness compared to existing methods in simulations.
  • A whole genome sequencing scan identified significant associations between genes APOE, APOC1, and TOMM40 and imaging phenotypes in Alzheimer's disease data.
  • The developed C++ package is available for public use, facilitating WGS data analysis.

Conclusions:

  • The GSU test provides a powerful and robust approach for genetic association studies with complex phenotypes.
  • Laplacian Kernel-based similarity enhances the GSU test's effectiveness.
  • The study successfully identified novel genetic associations relevant to Alzheimer's disease neuroimaging phenotypes.