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Multivariate and multilocus variance components method, based on structural relationships to assess quantitative

M A Province1, T K Rice, I B Borecki

  • 1Division of Biostatistics, Washington University School of Medicine, Box 8067, 660 S. Euclid, St. Louis, MO 63110, USA. mike@wubios.wustl.edu

Genetic Epidemiology
|January 28, 2003
PubMed
Summary

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The SEGPATH framework now offers robust linkage analysis using identity-by-descent (IBD) sharing. This extension enhances genetic linkage detection by accounting for familial resemblance and covariates.

Area of Science:

  • Biostatistics
  • Statistical Genetics
  • Computational Biology

Background:

  • The SEGPATH framework was initially developed for joint path and segregation analysis.
  • Existing methods for linkage analysis often require specific model assumptions or struggle with complex data structures.

Purpose of the Study:

  • To extend the SEGPATH framework for robust, model-free variance-components linkage analysis using identity-by-descent (IBD) sharing.
  • To integrate segregation analysis, path analysis, and linkage analysis within a unified modeling approach.

Main Methods:

  • Extended SEGPATH to incorporate "model-free" robust, variance-components linkage analysis based on IBD sharing.
  • Enabled single-point and multipoint linkage analysis for single or multivariate phenotypes in pedigrees.

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  • Incorporated various covariates, population heterogeneity, repeated measures, and gene-environment interactions.

    • Main Results:

    • The extended SEGPATH models can perform segregation, path, and linkage analyses, or combinations thereof.
    • The framework accommodates complex data with missing values and allows for ascertainment corrections.
    • Demonstrated improved power for linkage detection and protection against spurious inference by accounting for familial resemblance.

    Conclusions:

    • The enhanced SEGPATH provides a flexible and powerful approach for genetic linkage and segregation analysis.
    • Accurate modeling of familial resemblance is crucial for robust linkage detection.
    • The framework's versatility extends to non-genetic hierarchical data analysis.