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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Link functions in multi-locus genetic models: implications for testing, prediction, and interpretation.

David Clayton1

  • 1Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge, Institute for Medical Research, Cambridge University, United Kingdom. dc208@cam.ac.uk

Genetic Epidemiology
|April 18, 2012
PubMed
Summary
This summary is machine-generated.

Generalized linear models (GLM) are widely used for complex diseases. This study reviews link function implications and proposes a new genetic association test for improved analysis.

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Area of Science:

  • Biostatistics
  • Statistical Genetics
  • Epidemiology

Background:

  • Complex diseases involve multiple genetic and environmental risk factors.
  • Generalized linear models (GLM) have dominated statistical analysis of joint risk factors for over 40 years.
  • Traditional genetic models for dichotomous traits use a liability threshold model, equivalent to GLM with probit link, while epidemiology often prefers logistic regression (GLM with logit link).

Purpose of the Study:

  • To review the implications of link function choice in GLMs for complex disease analysis.
  • To highlight how link function choice affects association testing, disease prediction from genotype, and epistasis definition.
  • To propose a novel association test for genetic analysis.

Main Methods:

  • Review of statistical methodologies, specifically Generalized Linear Models (GLM) with different link functions (probit vs. logit).
  • Analysis of the impact of link function choice on statistical test statistics, heritability-based prediction, and epistasis interpretation.
  • Development and proposal of a new statistical test for genetic association studies.

Main Results:

  • The choice of link function (probit vs. logit) in GLMs has significant implications for association test statistics, especially with strong risk factors.
  • Link function choice influences the ability to predict disease risk from genotype based on heritability.
  • Different link functions lead to varied definitions and interpretations of epistasis (gene-gene interactions).

Conclusions:

  • The selection of link functions in statistical models for complex diseases is not merely a mathematical convenience but has profound biological and statistical consequences.
  • A new association test is proposed to address limitations associated with traditional link function choices.
  • This work emphasizes the importance of carefully considering link function properties in genetic epidemiology and biostatistics.