Portfolio Analysis of Research Grants in Data Science Funded by the National Heart, Lung, and Blood Institute.

Leveraging emerging opportunities in data science to open new frontiers in heart, lung, blood, and sleep research is one of the major strategic objectives of the National Heart, Lung, and Blood Institute (NHLBI), one of the 27 Institutes/Centers within the National Institutes of Health (NIH). To assess NHLBI's recent funding of research grants in data science and to identify its relative areas of focus within data science, a portfolio analysis from fiscal year 2008 to fiscal year 2017 was performed. In this portfolio analysis, an efficient and reliable methodology was used to identify data science research grants by utilizing several NIH databases and search technologies (iSearch, Query View Reporting system, and IN-SPIRE [Pacific Northwest National Laboratory, Richland, WA]). Six hundred thirty data science-focused extramural research grants supported by NHLBI were identified using keyword searches based primarily on NIH's working definitions of bioinformatics and computational biology. Further analysis characterized the distribution of these grants among the heart, lung, blood, and sleep disease areas as well as the subtypes of data science projects funded by NHLBI. Information was also collected for data science research grants funded by other NIH institutes/centers using the same search and analysis methodology. The funding comparison among different NIH institutes/centers highlighted relative data science areas of emphasis and further identified opportunities for potential data science areas in which NHLBI could foster research advances.

Functional Assays to Screen and Dissect Genomic Hits: Doubling Down on the National Investment in Genomic Research.

The National Institutes of Health have made substantial investments in genomic studies and technologies to identify DNA sequence variants associated with human disease phenotypes. The National Heart, Lung, and Blood Institute has been at the forefront of these commitments to ascertain genetic variation associated with heart, lung, blood, and sleep diseases and related clinical traits. Genome-wide association studies, exome- and genome-sequencing studies, and exome-genotyping studies of the National Heart, Lung, and Blood Institute-funded epidemiological and clinical case-control studies are identifying large numbers of genetic variants associated with heart, lung, blood, and sleep phenotypes. However, investigators face challenges in identification of genomic variants that are functionally disruptive among the myriad of computationally implicated variants. Studies to define mechanisms of genetic disruption encoded by computationally identified genomic variants require reproducible, adaptable, and inexpensive methods to screen candidate variant and gene function. High-throughput strategies will permit a tiered variant discovery and genetic mechanism approach that begins with rapid functional screening of a large number of computationally implicated variants and genes for discovery of those that merit mechanistic investigation. As such, improved variant-to-gene and gene-to-function screens-and adequate support for such studies-are critical to accelerating the translation of genomic findings. In this White Paper, we outline the variety of novel technologies, assays, and model systems that are making such screens faster, cheaper, and more accurate, referencing published work and ongoing work supported by the National Heart, Lung, and Blood Institute's R21/R33 Functional Assays to Screen Genomic Hits program. We discuss priorities that can accelerate the impressive but incomplete progress represented by big data genomic research.