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Multi-SNP Haplotype Analysis Methods for Association Analysis.

Daniel O Stram1

  • 1Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 1540 Alcazar Street, Los Angeles, CA, 90032, USA. stram@usc.edu.

Methods in Molecular Biology (Clifton, N.J.)
|October 6, 2017
PubMed
Summary
This summary is machine-generated.

Haplotype analysis directly tests genetic associations with traits, offering better coverage than single SNPs and potentially identifying causal variants. This study explores practical methods for implementing haplotype analysis in genetic research.

Keywords:
Expectation-substitution methodsGenetic association testingHaplotype-specific risk estimationMaximum likelihoodPhase estimationUncertainty analysis

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Haplotype analysis is fundamental to genetic association studies, underpinning SNP imputation for trait association testing and meta-analyses.
  • Direct application of haplotypes in association testing enhances genotype coverage and can identify causal variants missed by single SNP analysis.

Purpose of the Study:

  • To review the rationale and statistical considerations for direct haplotype-based association testing.
  • To provide practical guidance for implementing haplotype association tests for candidate genes and genome-wide analyses.
  • To compare different statistical approaches for incorporating haplotype data into regression models.

Main Methods:

  • Discusses three methods for integrating SNP haplotype analysis into generalized linear regression models: imputed haplotype substitution, simultaneous maximum likelihood estimation, and a simplified ML approximation for case-control data.
  • Provides examples of haplotype analysis for candidate genes and genome-wide risk estimation.
  • Compares the performance of approximation-based methods against full ML approaches.

Main Results:

  • Simpler, approximation-based methods for haplotype analysis perform well in practice for candidate gene studies.
  • Practical implementation strategies for genome-wide haplotype risk estimation are described.
  • Computational shortcuts for intensive genome-wide analyses are presented.

Conclusions:

  • Haplotype analysis offers a powerful approach for genetic association studies, improving upon single SNP methods.
  • Practical and computationally efficient methods exist for applying haplotype analysis to both candidate regions and the entire genome.
  • The discussed methods facilitate robust association testing and risk estimation using haplotype data.