Debraj GuhaThakurta

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A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations.(More)
Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has(More)
The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human diseases, but living systems more generally. Here we propose a novel gene network reconstruction algorithm, derived from classic Bayesian network methods,(More)
We previously used high-density expression arrays to interrogate a genetic cross between strains C3H/HeJ and C57BL/6J and observed thousands of differences in gene expression between sexes. We now report analyses of the molecular basis of these sex differences and of the effects of sex on gene expression networks. We analyzed liver gene expression of(More)
PURPOSE The class I phosphatidylinositol 3' kinase (PI3K) plays a major role in proliferation and survival in a wide variety of human cancers. A key factor in successful development of drugs targeting this pathway is likely to be the identification of responsive patient populations with predictive diagnostic biomarkers. This study sought to identify(More)
Identification and annotation of all the functional elements in the genome, including genes and the regulatory sequences, is a fundamental challenge in genomics and computational biology. Since regulatory elements are frequently short and variable, their identification and discovery using computational algorithms is difficult. However, significant advances(More)
Computational and microarray-based experimental approaches were used to generate a comprehensive transcript index for the human genome. Oligonucleotide probes designed from approximately 50,000 known and predicted transcript sequences from the human genome were used to survey transcription from a diverse set of 60 tissues and cell lines using ink-jet(More)
Changes in gene expression are known to be responsible for phenotypic variation and susceptibility to diseases. Identification and annotation of the genomic sequence variants that cause gene expression changes is therefore likely to lead to a better understanding of the cause of disease at the molecular level. In this study we investigate the pattern of(More)
To identify the genes and pathways that underlie cardiovascular and metabolic phenotypes we performed an integrated analysis of a mouse C57BL/6JxA/J F2 (B6AF2) cross by relating genome-wide gene expression data from adipose, kidney, and liver tissues to physiological endpoints measured in the population. We have identified a large number of trait QTLs(More)
Studies have shown that genetic and sex differences strongly influence gene expression in mice. Given the diversity and complexity of transcripts produced by alternative splicing, we sought to use microarrays to establish the extent of variation found in mouse strains and genders. Here, we surveyed the effect of strain and sex on liver gene and exon(More)