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Conditional likelihood methods for haplotype-based association analysis using matched case-control data.

Jinbo Chen1, Carmen Rodriguez

  • 1Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Boulevard, Rockville, Maryland 20852, USA. chenjin@mail.nih.gov

Biometrics
|December 15, 2007
PubMed
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This study introduces two statistical methods for inferring disease risk from genetic haplotypes using SNP genotype data in matched case-control studies. The methods improve accuracy in estimating haplotype-disease associations, crucial for understanding genetic susceptibility.

Area of Science:

  • Genetics
  • Epidemiology
  • Statistical genetics

Background:

  • Assessing disease susceptibility often involves analyzing haplotypes, which are combinations of alleles on a single chromosome.
  • Matched case-control studies are frequently used in genetic epidemiology to investigate disease risk factors.

Purpose of the Study:

  • To develop and evaluate statistical methods for inferring haplotype-related disease risk from single nucleotide polymorphism (SNP) genotype data.
  • To address the challenge of phase ambiguity, where haplotypes cannot be definitively inferred from genotype data.

Main Methods:

  • Proposed two conditional likelihood approaches for haplotype inference in matched case-control studies.
  • One method conditions on matching strata, while the other uses a joint likelihood approach.

Related Experiment Videos

  • Evaluated methods using simulation studies and applied them to a prostate cancer case-control study.

    • Main Results:

    • The joint-likelihood approach demonstrated higher efficiency, especially for analyzing haplotype-environment interactions.
    • The first approach (strata-conditional likelihood) showed greater robustness to model assumptions regarding diplotype distribution.
    • Both methods were successfully applied to a real-world matched case-control study.

    Conclusions:

    • The developed statistical methods provide robust tools for estimating haplotype-disease associations in matched case-control studies.
    • The choice between the two methods depends on specific study characteristics, such as the need for efficiency or robustness to model assumptions.
    • These methods enhance the ability to understand genetic susceptibility and gene-environment interactions in disease etiology.