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Interpreting genetics of gene expression: integrative architecture in Bioconductor
1Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Ave., Boston, MA 02115, USA. stvjc@channing.harvard.edu
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 13, 2009
Summary
This study introduces Bioconductor tools for analyzing gene expression genetics across multiple human populations. These methods streamline the complex task of linking genetic variations to gene activity, aiding researchers in expression genetics studies.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Numerous studies investigate genotypic determinants of human gene expression using populations like HapMap.
- The large scale of analyzing transcriptomes against the SNP-genome presents significant analytical challenges.
Purpose of the Study:
- To describe Bioconductor facilities for genetic inference of gene expression.
- To enable efficient, thorough, and auditable analysis across multiple HapMap cohorts.
- To provide tools adaptable for privately generated expression genetics data.
Main Methods:
- Utilizing Bioconductor software infrastructure.
- Applying inference methods to multiple HapMap cohorts simultaneously.
- Adapting tools for diverse expression genetics datasets.
Main Results:
- Demonstration of Bioconductor's utility for complex genetic analyses.
- Facilitation of simultaneous analysis across multiple populations.
- Development of adaptable tools for expression genetics research.
Conclusions:
- Bioconductor provides essential facilities for gene expression genetics inference.
- The described methods enhance the efficiency and auditability of such analyses.
- Tools are versatile for both public (HapMap) and private datasets in expression genetics.
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