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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

Modeling haplotype-haplotype interactions in case-control genetic association studies.

Li Zhang1, Ruitao Liu, Zhong Wang

  • 1Department of Quantitative Health Sciences, Cleveland Clinic Cleveland, OH, USA.

Frontiers in Genetics
|February 4, 2012
PubMed
Summary

This study introduces a new statistical model to analyze complex genetic interactions between haplotypes, crucial for understanding human diseases. The model helps identify how different genetic variations contribute to disease development and gene-environment interactions.

Keywords:
EM algorithmepistasishaplotypelinkage disequilibriumrisk haplotype

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Published on: July 27, 2021

Area of Science:

  • Genetics
  • Statistical Genetics
  • Human Disease Research

Background:

  • Haplotype analysis is vital for studying the genetic basis of human diseases.
  • Existing models lack robust methods for characterizing interactions between haplotypes from different chromosomal regions.

Purpose of the Study:

  • To develop and present a novel statistical model for testing haplotype-haplotype interactions in human diseases using a case-control design.
  • To characterize physiologically meaningful epistasis arising from interactions between haplotypes across different chromosomal regions.
  • To extend the model for investigating gene-environment interactions at the haplotype level.

Main Methods:

  • Formulated a statistical model on a contingency table for case-control genetic association studies.
  • Integrated quantitative genetic principles to partition epistasis into additive x additive, additive x dominance, dominance x additive, and dominance x dominance components.
  • Derived the Expectation-Maximization (EM) algorithm for estimating and testing these epistatic components.
  • Validated the method through simulation studies and application to a sarcoidosis genetic association project.

Main Results:

  • The developed model effectively characterizes complex genetic interactions between haplotypes from different chromosomal regions.
  • The model allows for the detailed partition and analysis of various epistatic components.
  • The extended method successfully investigated gene-environment interactions at the haplotype level.
  • The model's utility was demonstrated through its application to a real-world human genetics project (sarcoidosis).

Conclusions:

  • The new statistical model provides a powerful framework for analyzing haplotype-haplotype interactions in human diseases.
  • This approach enhances our understanding of the genetic architecture of complex diseases, including epistasis and gene-environment interactions.
  • The model and its validation offer a valuable tool for future genetic association studies and disease pathogenesis research.