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An Adaptive Genetic Association Test Using Double Kernel Machines.

Xiang Zhan1, Michael P Epstein2, Debashis Ghosh3

  • 1Department of Statistics, Pennsylvania State University, University Park, PA 16802, U.S.A. Tel.: +1-8143213493.

Statistics in Biosciences
|December 8, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new gene pathway analysis method, the double kernel machine (DKM) test, to identify key genes and assess overall genetic effects in disease studies. The DKM test improves power and reduces noise in gene expression profiling.

Keywords:
Double kernel machineGarrote kernel machineLeast squares kernel machineSubset testingThresholding

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Gene set-based approaches are popular for linking genetic variants to disease outcomes.
  • Existing methods often suffer from noise and reduced power due to including non-differentially expressed genes.
  • Identifying key genes within a pathway is crucial for understanding disease mechanisms.

Purpose of the Study:

  • To propose an adaptive association test, the double kernel machine (DKM) test.
  • To enable simultaneous selection of important genes within a pathway and testing of the overall genetic pathway effect.
  • To provide a flexible and unified framework for modeling genetic pathway effects.

Main Methods:

  • The proposed DKM procedure utilizes garrote kernel machines (GKM) for subset selection.
  • It employs the least squares kernel machine (LSKM) test to assess the effect of the selected gene subset.
  • The kernel machine framework allows for flexible, multi-dimensional modeling, incorporating both parametric and nonparametric components.

Main Results:

  • The DKM approach effectively selects important genes within a pathway.
  • It accurately tests the overall genetic pathway effect, improving upon existing methods.
  • Demonstrated utility in simulated data and a neuroimaging genetics study.

Conclusions:

  • The DKM test offers a powerful and flexible tool for gene set-based association studies.
  • It enhances the ability to identify disease-related genetic pathways and key contributing genes.
  • This method advances the analysis of complex genetic data in disease research.