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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

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Published on: June 21, 2018

A hybrid likelihood model for sequence-based disease association studies.

Yun-Ching Chen1, Hannah Carter, Jennifer Parla

  • 1Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, USA.

Plos Genetics
|January 30, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hybrid statistical model for analyzing rare genetic variants in common diseases. The new method improves power for identifying disease-associated gene sets, particularly in complex genetic studies.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Genomic association studies

Background:

  • Whole-exome sequencing generates vast amounts of rare and novel genetic variants.
  • Classical single-marker association analysis struggles with rare variants.
  • Burden-style collapsing methods are common but have limitations.

Purpose of the Study:

  • To develop a new hybrid statistical model for rare variant association analysis.
  • To improve the power of detecting disease-associated gene sets.
  • To address challenges posed by rare variants in case-control studies.

Main Methods:

  • Proposed a hybrid likelihood model combining burden tests and variant position distribution tests.
  • Evaluated the model using extensive simulations.
  • Applied the model to empirical data from the Dallas Heart Study and a bipolar disorder sequencing study.

Main Results:

  • The hybrid model demonstrated consistently good power in simulations and empirical data.
  • The model showed particular strength when applied to gene sets, clustered variants, and when protective variants were present.
  • Analysis of bipolar disorder data identified seven nominally significant gene sets, including the MAPK signaling pathway.

Conclusions:

  • The proposed hybrid model offers a powerful approach for rare variant association studies.
  • This method enhances the ability to identify gene sets and pathways implicated in common diseases.
  • The findings have implications for understanding the genetic architecture of complex disorders like bipolar disorder.