NEW YORK (GenomeWeb News) – The National Heart, Lung, and Blood Institute seeks to fund new research projects that use and apply a wide range of 'omics data to the study of the genetic interactions involved in pulmonary disease.
In a new funding announcement, NHLBI said that molecular and genomic data has the potential to illuminate interactions between genes and gene products involved in lung diseases and could be used to develop new ways of treating them.
To tap into that potential, NHLBI plans to fund four-year projects to study this genetic interactivity by conducting integrative analysis of data from genomic, transcriptomic, proteomic, metabolomic, and phenotypic studies of pulmonary diseases, including data that highlights the differences between normal and diseased states and is derived from human cells.
The researchers also may propose a second phase of study beyond the data analysis project that involves testing the connectivity links that were identified during the analysis phase.
NHLBI said that the "explosion" of data from genome-wide association studies and functional genomics research have generated large amounts of data that is now made widely available through several major resources, such as the database of Genotypes and Phenotypes, the HuGE Navigator, GWAS Central, GAWS DB, the Gene Expression Omnibus (GEO) repository, and the ArrayExpress Archive. The aim of this program is to put that data to work to understand lung diseases.
Scientists applying for the funds may propose a range of research projects, including, but not limited to, integrative genomics approaches to identify new therapeutic targets for controlling the progression of lung diseases; identifying early-clinical molecular signatures that reflect the effects of cigarette smoking on the lung and risk of COPD; elucidating molecular signatures to define an index of severity in sleep apnea; integrating gene expression data with clinical phenotypes to identify gene targets and pathways for defining idiopathic pulmonary fibrosis; integrating metabolomic and proteomic measures with genomic data to enhance understanding of acute lung injury; and integrating imaging and 'omics data to define factors involved in regional heterogeneity of lung disease.