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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
A two-stage classification approach identifies seven susceptibility genes for a simulated complex disease
1Department of Medical and Molecular Genetics, 410 West 10th Street, HS4000, Indiana University, Indianapolis, Indiana 46202-3002, USA. npankrat@iupui.edu
BMC Proceedings
|May 10, 2008
Summary
Genetic linkage analysis identified significant associations on multiple chromosomes, with data transformation improving detection power. Classification methods and genome-wide association analysis further pinpointed susceptibility loci.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Genetic Analysis Workshop 15 provided simulated data with affection status, quantitative traits, and covariates.
- Linkage analysis is a crucial tool for identifying genes associated with diseases or traits.
Purpose of the Study:
- To perform linkage analysis on simulated data without prior model knowledge.
- To investigate the impact of data transformation on linkage detection power.
- To identify susceptibility loci using classification and association analyses.
Main Methods:
- Linkage analysis on the first replicate of the Genetic Analysis Workshop 15 data.
- Data transformation for skewed and kurtotic distributions (IgM, anti-CCP).
- Logistic regression and multifactor dimensionality reduction (MDR) for classification.
- Genome-wide association analysis on misclassified individuals.
Main Results:
- Significant linkage identified on chromosomes 6 (DR locus), 8, 9, 11, and 18.
- Data transformation significantly increased the power to detect linkage, especially for chromosome 11.
- Single-nucleotide polymorphism association identified on chromosomes 11 and 18.
- Logistic regression and MDR performed comparably for classification.
- Two additional susceptibility loci identified through genome-wide association analysis.
Conclusions:
- Careful examination and transformation of phenotypic data are essential prior to genetic analysis.
- A two-stage classification and association approach can effectively identify additional susceptibility loci.
- The study highlights the utility of various statistical methods in genetic research.
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