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Association tests using kernel-based measures of multi-locus genotype similarity between individuals.

Indranil Mukhopadhyay¹, Eleanor Feingold, Daniel E Weeks

¹Human Genetics Unit, Indian Statistical Institute, Kolkata, West Bengal, India.

Genetic Epidemiology

|August 22, 2009

Summary

We developed a new kernel-based association test for jointly analyzing single-nucleotide polymorphisms (SNPs) in genetic studies. This robust, non-parametric test offers higher power for detecting gene-phenotype associations compared to existing methods.

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Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Genetic association studies aim to identify single-nucleotide polymorphisms (SNPs) linked to phenotypes.
Existing methods for joint SNP analysis often rely on specific assumptions or prior knowledge of effect directions.
There is a need for robust, non-parametric tests for comprehensive gene-level association analysis.

Purpose of the Study:

To propose a novel kernel-based association test for the joint analysis of multiple SNPs.
To develop a non-parametric and robust statistical method for genetic association studies.
To offer a flexible test applicable to correlated SNPs within genes or independent markers in biological pathways.

Main Methods:

A kernel-based association test utilizing an analysis of variance (ANOVA) framework.
Comparison of variation between cases and controls against within-group variation.
Construction of a composite statistic by combining kernel-measured marker variations.

Main Results:

The proposed kernel-based test demonstrates higher statistical power than existing methods (Schaid et al., Wessel and Schork).
The test performs effectively across various disease models and assumptions about the number of associated SNPs.
The method is robust and does not require prior knowledge of individual SNP effect directions.

Conclusions:

The new kernel-based association test provides a powerful and flexible tool for genetic association studies.
This non-parametric approach enhances the ability to detect complex genetic associations involving multiple SNPs.
The method is suitable for analyzing genes, pathways, and diverse genetic architectures.