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

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Efficient Software for Multi-marker, Region-Based Analysis of GWAS Data.

Jaleal S Sanjak¹, Anthony D Long², Kevin R Thornton²

¹Department of Ecology and Evolutionary Biology, University of California Irvine, California 92697 Center for Complex Biological Systems, University of California Irvine, California 92697 jsanjak@uci.edu.

G3 (Bethesda, Md.)

|February 12, 2016

Summary

This summary is machine-generated.

New analytical methods using the rank truncated product (RTP) approach can uncover complex disease associations in genome-wide association studies (GWAS) data, even without rare variant coverage. These RTP-based methods improve the identification of disease-related genes.

Keywords:

GWAS gene-based rare variants

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Published on: December 10, 2012

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Genome-wide association studies (GWAS) identify single variants for complex diseases, but much heritable risk remains unexplained.
Existing GWAS data often lack coverage for rare variants, which are increasingly recognized as important contributors to complex disease.
Current analytical tools are often limited by specific population genetic models and challenges in detecting rare variants of subtle effect.

Purpose of the Study:

To develop and validate an analytical procedure for identifying complex disease associations using existing genome-wide association study data.
To demonstrate the utility of rank truncated product (RTP) based methods, particularly in scenarios with limited rare variant coverage.
To provide efficient software for applying RTP-based methods to maximize the insights from GWAS data.

Main Methods:

Developed a rank truncated product (RTP) based procedure, filtered to mitigate the effects of linkage disequilibrium.
Applied the RTP procedure to the Wellcome Trust Case Control Consortium (WTCCC) dataset.
Empirically demonstrated the power of RTP methods in detecting associated genes, even without comprehensive rare variant data.

Main Results:

Uncovered previously unidentified genetic associations with complex diseases within the WTCCC dataset.
Observed that some identified associations were replicated in larger, independent studies.
Showcased the capability of RTP-based methods to detect associated genes even when rare variant coverage is not significant.

Conclusions:

RTP-based methods offer a powerful approach to enhance the discovery of genetic associations for complex diseases from existing GWAS data.
These methods are recommended for application to all available GWAS datasets to maximize their analytical utility.
The developed efficient software facilitates the widespread adoption of RTP-based analyses in genetic research.