Over the past decade we have witnessed transformative technological and analytical advances which can query biological systems in a global manner, typically across a select axis in the molecular spectrum. The comprehensive study of genes, gene marks, transcripts, proteins, small molecules, and their interactions have generated enormous data sets that reflect a specific metabolic, developmental or tissue state. Increasingly this activity has been feasible in the organism most relevant to disease, the human: when coupled with the rich clinical and phenotyping information available in observational cohorts, clinical trials or the electronic medical record, tremendous inferences can be made to derive pathophysiological insight. This type of activity has produced a trove of information relevant to type 2 diabetes, obesity and related metabolic traits. There is an urgent need to attract investigators experienced in mathematics, statistics, computational biology and bioinformatics to the analysis and interpretations of such data, and to equip investigators who have an interest in these phenotypes with the tools and skills required to extract valuable knowledge. In this proposal, we have assembled a team of three dozen accomplished investigators across the Harvard system who have cutting-edge capabilities in each of the pertinent skill sets, and whose track record supports a declared interest in metabolic disease. This training grant will leverage their complementary expertise by funding eight selected trainees, providing them with dedicated instruction, and pairing them with faculty mentors who can provide rigorous training in a multidisciplinary setting pertinent to diseases of interest to the NIDDK.
Technological and analytical advances have generated enormous data sets with information relevant to type 2 diabetes, obesity and related metabolic traits. There is an urgent need to attract investigators experienced in mathematics, statistics, computational biology and bioinformatics to the analysis and interpretations of such data, and to equip investigators with an interest in these phenotypes with the tools and skills required to extract valuable knowledge. This training grant will leverage complementary expertise across several Harvard-affiliated institutions by pairing selected trainees with faculty mentors who can provide rigorous training in a multidisciplinary setting pertinent to this disease area.
| Reshef, Yakir A; Finucane, Hilary K; Kelley, David R et al. (2018) Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk. Nat Genet 50:1483-1493 |
| Hormozdiari, Farhad; Gazal, Steven; van de Geijn, Bryce et al. (2018) Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat Genet 50:1041-1047 |
