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Pedigree Analysis

Pedigree Analysis

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Methodology for Accurate Detection of Mitochondrial DNA Methylation

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

Nonparametric method for detecting imprinting effect using all members of general pedigrees with missing data.

Fangyuan Zhang¹, Shili Lin¹

¹Department of Statistics, The Ohio State University, Columbus, OH, USA.

Journal of Human Genetics

|August 15, 2014

Summary

This study introduces a new method, Monte Carlo Pedigree Parental-Asymmetry Test using unaffected offspring (MCPPATu), to detect imprinting effects in complex diseases. MCPPATu improves statistical power by utilizing data from both affected and unaffected offspring, even with missing genetic information.

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Area of Science:

Genetics
Human Disease Research
Statistical Genetics

Background:

Parent-of-origin effects, known as imprinting, influence complex human diseases.
Existing methods like PPAT and MCPPAT for detecting imprinting effects primarily use affected offspring, limiting data utilization.
There is a need for more powerful statistical methods that incorporate all available pedigree data.

Purpose of the Study:

To develop and evaluate a novel statistical test, MCPPATu, for detecting imprinting effects in complex diseases.
To assess the performance of MCPPATu in controlling type I error rates and improving statistical power.
To demonstrate the advantage of including unaffected offspring and handling missing genotypes in imprinting analyses.

Main Methods:

Proposed the Monte Carlo Pedigree Parental-Asymmetry Test using both affected and unaffected offspring (MCPPATu).
Incorporated Monte Carlo sampling to handle pedigrees with missing genotypes.
Conducted simulation studies to evaluate type I error rates and statistical power under various disease scenarios.

Main Results:

MCPPATu effectively controls the empirical type I error rate under the null hypothesis of no imprinting effects.
Utilizing data from unaffected offspring and partially observed genotypes significantly enhances statistical power.
MCPPATu demonstrates considerably greater power than MCPPAT for common diseases, especially with missing data.

Conclusions:

MCPPATu is a powerful and robust statistical tool for detecting imprinting effects in complex human diseases.
The inclusion of unaffected offspring and handling of missing genotypes represent significant advancements in imprinting analysis.
The proposed method offers substantial power improvements, particularly for common diseases, making it valuable for genetic research.