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Power of affected sibling method tests for linkage

C Tierney¹, B McKnight

¹Division of Biostatistics, Yale School of Medicine, New Haven, Conn. 06510.

|September 1, 1993

Summary

This study compares statistical tests for disease susceptibility gene linkage analysis using affected sibling data. Likelihood ratio tests show slightly higher power but may not justify the increased computational cost.

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Area of Science:

Genetics
Biostatistics
Bioinformatics

Background:

Determining the mode of inheritance for disease susceptibility (DS) genes is crucial for genetic linkage studies.
The affected sibling method is a valuable tool when inheritance patterns are unknown, analyzing allele sharing among siblings.
Existing nonparametric and likelihood ratio tests assess linkage between DS genes and marker genes using affected sibling data.

Purpose of the Study:

To compare the statistical power of various proposed tests for gene linkage analysis.
To evaluate test performance under different genetic models using simulation.

Main Methods:

Simulation studies were employed to compare test statistics.
The analysis focused on the affected sibling method for linkage detection.
Nonparametric and likelihood ratio tests were assessed for their power.

Main Results:

Likelihood ratio tests generally demonstrated slightly greater statistical power compared to nonparametric tests.
The observed increase in power with likelihood ratio tests may not always outweigh the computational demands.
Simulation results provided insights into the performance of different linkage analysis methods.

Conclusions:

The choice of statistical test involves a trade-off between power and computational efficiency.
Likelihood ratio tests offer a marginal power advantage in affected sibling linkage analysis.
Further research may explore optimizing computational aspects of powerful linkage detection methods.

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