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Detecting local haplotype sharing and haplotype association.

Hanli Xu1, Yongtao Guan2

  • 1U.S. Department of Agriculture/Agricultural Research Service Children's Nutrition Research Center, Baylor College of Medicine, Houston, Texas 77030 Department of Biomedical Engineering, Southeast University, Nanjing, Jiangsu 210000, China.

Genetics
|May 10, 2014
PubMed
Summary

A new method for haplotype association analysis infers ancestral haplotypes to identify genetic links to diseases. This approach efficiently analyzes large datasets and discovered novel gene-disease associations, including GRIK4 with coronary artery disease and rheumatoid arthritis.

Keywords:
LDassociationhaplotypelocal haplotype sharingtwo-layer HMM

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Haplotype association methods are crucial for identifying genetic variants underlying complex diseases.
  • Existing methods face challenges with computational complexity, phase uncertainty, and arbitrary haplotype definitions.

Purpose of the Study:

  • To present a novel, computationally efficient haplotype association method.
  • To demonstrate the method's power in identifying novel genetic associations with complex diseases.
  • To provide a scalable solution for analyzing large genetic datasets.

Main Methods:

  • Developed a statistical model for linkage disequilibrium (LD) to infer ancestral haplotypes and individual loadings.
  • Quantified local haplotype sharing between individuals at each marker.
  • Implemented a novel LD model fitting approach reducing complexity from quadratic to linear.
  • Integrated out phase uncertainty and avoided arbitrary haplotype specification.

Main Results:

  • The novel method successfully analyzed large datasets, fitting the LD model simultaneously across all samples.
  • Eight novel associations between seven gene regions and five disease phenotypes were discovered.
  • GRIK4 gene was found to be strongly associated with both coronary artery disease and rheumatoid arthritis.
  • The method demonstrated comparable statistical power to single-SNP analysis with integrated phase information.

Conclusions:

  • The presented haplotype association method is computationally efficient and scalable for big data analysis.
  • It overcomes limitations of existing methods by integrating phase uncertainty and avoiding arbitrary definitions.
  • The method successfully identified novel genetic associations, highlighting GRIK4's role in coronary artery disease and rheumatoid arthritis.
  • A freely available software package facilitates the application of this method in genetic research.