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Empirical Bayes analysis of single nucleotide polymorphisms
Holger Schwender1, Katja Ickstadt
1Collaborative Research Center 475, Faculty of Statistics, Dortmund University of Technology, 44221 Dortmund, Germany. holger.schw@gmx.de
BMC Bioinformatics
|March 8, 2008
Summary
This study extends empirical Bayes analysis for high-dimensional categorical SNP data, enabling association studies beyond binary responses. The modified approach can identify important SNP interactions and genotypes.
Area of Science:
- Genomics
- Statistical Genetics
Background:
- Whole-genome studies aim to identify single nucleotide polymorphisms (SNPs) associated with covariates like cancer type.
- High-dimensional SNP data present a multiple testing challenge.
- Empirical Bayes analysis of microarrays is established for continuous gene expression data with binary responses.
Purpose of the Study:
- To adapt empirical Bayes analysis for high-dimensional categorical SNP data.
- To extend the application beyond binary responses.
- To enable the testing of SNP interactions and quantification of their importance.
Main Methods:
- Modification of the empirical Bayes analysis of microarrays.
- Application to high-dimensional categorical SNP data.
- Development of a generalized empirical Bayes method.
Main Results:
- The modified empirical Bayes analysis successfully analyzes categorical SNP data with non-binary responses.
- The approach is applicable to continuous gene expression data as well.
- The R package siggenes (version 1.10.0+) implements these methods.
Conclusions:
- Empirical Bayes analysis is versatile for both continuous gene expression and categorical SNP data.
- The method facilitates the testing of SNP interactions in association studies.
- Posterior probabilities quantify the importance of identified SNP interactions and genotypes.
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01:05Single Nucleotide Polymorphisms-SNPs
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Overview