Interpreting genetics of gene expression: integrative architecture in Bioconductor.
Several influential studies of genotypic determinants of gene expression in humans have now been published based on various populations including HapMap cohorts. The magnitude of the analytic task (transcriptome vs. SNP-genome) is a hindrance to dissemination of efficient, thorough, and auditable inference methods for this project. We describe the structure and use of Bioconductor facilities for inference in genetics of gene expression, with simultaneous application to multiple HapMap cohorts. Tools distributed for this purpose are readily adapted for the structure and analysis of privately-generated data in expression genetics.
Carey
V J
VJ
Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Ave., Boston, MA 02115, USA. stvjc@channing.harvard.edu
Gentleman
R
R
D001699:Biometry; D002352:Carrier Proteins / Q000235:genetics; D015331:Cohort Studies; D030541:Databases, Genetic; D051858:Forkhead Transcription Factors / Q000235:genetics; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D005828:Genetics, Population; D006684:HLA-DR Antigens / Q000235:genetics; D059811:HLA-DRB1 Chains; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D050436:Regulatory Elements, Transcriptional; D012984:Software; D014579:Urotensins / Q000235:genetics
D002352:Carrier Proteins; C486413:FOXF2 protein, human; D051858:Forkhead Transcription Factors; D006684:HLA-DR Antigens; D059811:HLA-DRB1 Chains; D014579:Urotensins; C110203:copine; C029677:urotensin II