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Related Experiment Videos

Likelihood-based inference for the genetic relative risk based on affected-sibling-pair marker data

B McKnight1, C Tierney, S P McGorray

  • 1Department of Biostatistics, University of Washington, Seattle 98195, USA. barb@biostat.washington.edu

Biometrics
|June 18, 1998
PubMed
Summary

This study introduces a new statistical method for genetic linkage analysis using affected sibling pairs. The model-based approach offers a practical alternative to nonparametric tests for disease inheritance, especially when the mode of inheritance is known.

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Area of Science:

  • Human Genetics
  • Statistical Genetics
  • Epidemiology

Background:

  • Affected sibling pair (ASP) analysis is a common method for genetic linkage analysis.
  • Traditional nonparametric ASP tests have limitations, particularly when the mode of inheritance is unknown or complex.
  • Model-based approaches can enhance the power of linkage analysis when inheritance models are specified.

Purpose of the Study:

  • To develop and evaluate a likelihood-based linkage analysis method for ASP data.
  • To infer relative risk associated with susceptible genotypes under various inheritance models.
  • To provide a practical alternative to nonparametric ASP tests.

Main Methods:

  • Utilized genetic marker data from affected sibling pairs.
  • Employed likelihood-based linkage analysis under quasi-recessive, quasi-dominant, and general single-locus models.

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  • Derived closed-form maximum likelihood estimators and likelihood ratio statistics.

    • Main Results:

    • Developed a closed-form likelihood ratio test equivalent to Holmans' triangle test under specific conditions.
    • Provided critical values and approximations for the null distribution of test statistics.
    • Demonstrated that the proposed likelihood ratio tests have higher power than nonparametric ASP tests when model assumptions are met and are robust to violations.

    Conclusions:

    • Model-based inferences offer a practical alternative for ASP analysis when the mode of inheritance is partially known.
    • The developed methods can aid in comparing genetic relative risks with environmental risks.
    • This approach enhances the utility of genetic marker data in understanding disease susceptibility.