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Testing for association with multiple traits in generalized estimation equations, with application to neuroimaging

Yiwei Zhang1, Zhiyuan Xu1, Xiaotong Shen2

  • 1Division of Biostatistics, School of Public Health, Minneapolis, MN 55455, USA.

Neuroimage
|April 8, 2014
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Summary

This study introduces new adaptive statistical tests for detecting genetic associations with multiple traits, improving power in complex studies like Alzheimer's Disease Neuroimaging Initiative (ADNI). These methods offer enhanced performance across diverse data types and situations.

Keywords:
GEEGWASNeuroimaging geneticsScore testStatistical powerSum of powered score (SPU) testaSPU test

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Area of Science:

  • Biostatistics
  • Statistical Genetics
  • Neuroimaging Genetics

Background:

  • Increasing need for powerful statistical tests to detect multiple trait-single locus associations in complex studies.
  • Limitations of existing methods like MANOVA for discrete traits and lack of data adaptiveness.
  • Data collected in Alzheimer's Disease Neuroimaging Initiative (ADNI) includes genetic, neuroimaging, and neuropsychological data.

Purpose of the Study:

  • To propose a class of data-adaptive weighted tests within the generalized estimating equations (GEE) framework.
  • To develop a highly adaptive test for selecting the most powerful weighted test across various scenarios.
  • To analytically demonstrate relationships between existing and proposed statistical tests.

Main Methods:

  • Development of data-adaptive weights and weighted tests using generalized estimating equations (GEE).
  • Proposal of a highly adaptive test to optimize power by selecting from weighted tests.
  • Analytical derivations to establish relationships among statistical tests.

Main Results:

  • Proposed GEE-based Score test and adaptive test show high and complementary performance.
  • Existing statistical tests are identified as special cases of the proposed framework.
  • Simulation studies confirm superior power properties of the new methods compared to existing ones.

Conclusions:

  • The GEE-based Score test and the proposed adaptive test are recommended for their robust and versatile performance.
  • The new methods are applicable to various trait types and can incorporate covariates.
  • The findings provide valuable tools for analyzing complex genetic and phenotypic data, particularly in neuroimaging genetics.