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Updated: Jun 3, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Likelihood ratio testing for admixture models with application to genetic linkage analysis.

Chong-Zhi Di¹, Kung-Yee Liang

¹Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-B500, Seattle, Washington 98109, USA. cdi@fhcrc.org

|March 10, 2011

Summary

This summary is machine-generated.

Likelihood ratio tests (LRT) for admixture models are nonstandard. This study shows LRT statistics converge to a squared Gaussian process, offering insights for genetic linkage analysis.

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

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Published on: November 3, 2010

Area of Science:

Statistics
Genetics
Biostatistics

Background:

Admixture models are two-component mixture models used in genetic linkage analysis.
Testing for homogeneity in these models is nonstandard, with LRT statistics not following a conventional chi-squared distribution.

Purpose of the Study:

To investigate the asymptotic behavior of likelihood ratio tests (LRT) in general admixture models.
To compare LRT with modified LRT and score tests for homogeneity testing.

Main Methods:

Asymptotic analysis of LRT statistics for admixture models.
Comparison of LRT with alternative statistical tests.
Simulation studies and application to a schizophrenia genetic linkage study.

Main Results:

The limiting distribution of LRT statistics in admixture models is shown to be the supremum of a squared Gaussian process.
LRT is an omnibus test, powerful for general alternatives.
Alternative tests may offer higher power for specific alternatives but less power for others.

Conclusions:

The asymptotic distribution of LRT in admixture models is characterized.
Understanding LRT behavior is crucial for accurate genetic linkage analysis under heterogeneity.
Simulation and real-world data analysis validate the theoretical findings.