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Detecting Gene-Gene Interactions Associated with Multiple Complex Traits with U-Statistics.

Ming Li1, Changshuai Wei1, Yalu Wen1

  • 11Department of Epidemiology and Biostatistics, Indiana University at Bloomington, Bloomington, IN 47405, U.S.A; 2Department of Epidemiology and Biostatistics, University of North Texas Health Science Center, Fort Worth, TX 76107, U.S.A; 3Department of Statistics, University of Auckland, Auckland 1010, New Zealand; 4Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, P.R. China; 5Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, U.S.A.

Current Genomics
|May 9, 2017
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Summary
This summary is machine-generated.

Analyzing multiple disease measurements together in genetic studies boosts the power to find disease-associated variants. This approach, applied to Nicotine Dependence (ND), identified key genetic markers.

Keywords:
Nicotine dependencePleiotropyPopulation-based association studies

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Area of Science:

  • Genetics
  • Biostatistics
  • Complex Disease Research

Background:

  • Complex diseases manifest through diverse physical, behavioral, and psychological phenotypes.
  • Analyzing single phenotypes limits genetic association studies; joint analysis of multiple phenotypes offers advantages.
  • Gene-gene interactions are crucial in complex disease etiology and genetic discovery.

Purpose of the Study:

  • To propose a novel statistical method for joint association analysis of multiple phenotypes and multiple genetic loci.
  • To enhance the power of detecting disease-associated variants by simultaneously analyzing correlated phenotypes.
  • To investigate gene-gene interactions within complex disease genetic association studies.

Main Methods:

  • Development of a stepwise U-test for joint association analysis.
  • Simulations to evaluate the power of the proposed method compared to single-phenotype analysis.
  • Application of the method to Nicotine Dependence (ND) phenotypes using the SAGE dataset.

Main Results:

  • Simulations confirmed that joint analysis of multiple phenotypes yields higher power, particularly when genes influence multiple traits.
  • The stepwise U-test identified two single nucleotide polymorphisms (SNPs), rs10508649 and rs2491397, associated with ND.
  • Significant associations were replicated in independent datasets, demonstrating robustness.

Conclusions:

  • Joint analysis of multiple phenotypes is a powerful strategy for genetic association studies of complex diseases.
  • The proposed stepwise U-test effectively identifies genetic variants contributing to multiple disease phenotypes.
  • This approach advances the discovery of genetic underpinnings for complex conditions like Nicotine Dependence.