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Updated: Feb 5, 2026

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Genetic association analysis with pedigrees: Direct inference using the composite likelihood ratio.

Zeynep Baskurt1, Lisa J Strug1,2

  • 1Program in Genetics and Genome Biology and The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada.

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|September 18, 2018
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Summary

Composite likelihoods (CL) offer a valid statistical inference method when the full likelihood function is computationally challenging. This approach reliably identifies genetic variants associated with reading disability in large pedigrees.

Keywords:
correlated binary datafamily datareading disorderstatistical evidencethe evidential paradigm

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Area of Science:

  • Statistics
  • Genetics
  • Bioinformatics

Background:

  • The evidential paradigm (EP) uses likelihood functions for statistical evidence, but computation can be difficult.
  • Composite likelihood (CL) provides an alternative for intractable likelihood functions, especially in complex genetic studies.

Purpose of the Study:

  • To demonstrate that composite likelihoods (CL) possess key properties of the evidential paradigm (EP) for reliable statistical inference.
  • To validate the CL approach in genetic association studies for identifying variants linked to reading disability (RD).

Main Methods:

  • The study theoretically established that CLs share two large-sample properties with the EP.
  • Simulations and a real-world genetic association analysis of reading disability in rolandic epilepsy pedigrees were conducted.

Main Results:

  • CLs were shown to support true parameter values over false ones and bound the probability of favoring false values.
  • The CL approach successfully identified variants associated with reading disability in the studied pedigrees.

Conclusions:

  • Composite likelihoods provide a statistically valid and reliable method for inference, particularly when direct likelihood computation is infeasible.
  • The CL method offers complementary information and more intuitive solutions for multiple hypothesis testing compared to generalized estimating equations in genetic association studies.