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Group association test using a hidden Markov model.

Yichen Cheng1, James Y Dai2, Charles Kooperberg2

  • 1Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA ycheng@fredhutch.org.

Biostatistics (Oxford, England)
|October 1, 2015
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Summary
This summary is machine-generated.

This study introduces a new group association test for genomic data, improving power when only a few features are truly associated with an outcome. The method models feature associations using a mixture model and hidden Markov chain for better genomic association analysis.

Keywords:
Finite mixture modelGenome-wide association studyModified likelihood ratio test

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Group association tests are crucial in the genomic era due to the large number of individual genomic features.
  • Existing methods often lack the power to detect associations when only a subset of features within a group are truly associated with a trait.
  • A substantial proportion of genomic features within a functional unit may not be associated with the trait of interest.

Purpose of the Study:

  • To develop a novel group association testing method that explicitly accounts for the mixture of associated and non-associated features within a group.
  • To improve the power of detecting associations in genomic studies, particularly when effect sizes are small and only a few features are relevant.
  • To provide a robust statistical framework for analyzing group-level genomic associations.

Main Methods:

  • Feature-level associations are estimated using generalized linear models.
  • A hidden Markov chain models the sequence of estimated feature associations.
  • A modified likelihood ratio test is developed, incorporating a penalty term for global association testing.
  • The asymptotic distribution of the test statistic under the null hypothesis is derived.

Main Results:

  • The proposed method demonstrates strong performance in simulations and data applications compared to existing group association tests.
  • The method is particularly effective when a small fraction of features within a group are associated with the outcome.
  • Posterior probabilities of association for individual features are obtained, aiding in follow-up studies.

Conclusions:

  • The proposed mixture model and hidden Markov chain approach offers a powerful and flexible framework for group association testing in genomics.
  • This method enhances the ability to identify relevant genomic features and functional units associated with traits.
  • The approach provides valuable insights for feature selection and understanding complex genetic architectures.