Notice Number: NOT-HL-19-676

Key Dates
Release Date : February 13, 2019 (Rescinded March 16, 2021)

Issued by
National Heart, Lung, and Blood Institute (NHLBI)

Purpose

Background and Purpose:

The symptom-based diagnosis and treatment of heart, lung, blood, and sleep (HLBS) diseases has vastly improved in recent years, yet an understanding of the molecular mechanisms underlying many of these diseases has remained elusive. Furthermore, in most cases the impact of genetic variation on severity of disease and treatment outcomes remains unknown. Therefore, the NHLBI has recently created the Trans Omics for Precision Medicine (TOPMed) program, which aims to utilize high throughput omics to characterize a variety of HLBS diseases. TOPMed is well on its way to collecting whole genome sequence from over 130,000 well-phenotyped individuals and is currently generating high-throughput expression and other omics data (e.g. RNA, DNA methylation, metabolites, and proteins) from many of these individuals to complement whole genome sequence information.

Having produced an unprecedented volume of high-throughput data, TOPMed now seeks to turn its attention to effectively leveraging this resource through novel systems biology analyses to uncover disease pathobiology. Although lower costs and technological improvements in sequencing technology have vastly expanded our ability to generate large volumes of omics data, the ability to analyze such large datasets to extract biologically meaningful insights from them remains challenging. Systems level models incorporating trans-omics analyses will be an important step in uncovering the underlying biological networks, gene-gene and gene-environment interactions influencing disease and treatment outcomes. Thus, advanced analyses that incorporate genotype and phenotype datasets from thousands to tens of thousands of individuals are required to move TOPMed to the next phase of discovery.

Research Objectives:

The trans-omics resource currently being built by the TOPMed program presents a unique set of challenges and opportunities for genomic analysis. Whole genome sequence coupled with expression and other deep clinical and molecular phenotyping data from tens of thousands of individuals promises to hold important information about rare and common genetic variants influencing disease. Several phases of data generation have brought together a collection of cohorts and studies that span a wide variety of HLBS diseases, geographic location, ethnic variation, and population structure. More information about participating studies can be found at https://www.nhlbiwgs.org/.

As this data resource gains maturity, analyses that effectively synthesize very large volumes of information from across many different datatypes remain scarce. NHLBI seeks to fund analyses that utilize existing TOPMed omics data to uncover the molecular mechanisms driving HLBS disease. Investigators are encouraged to utilize a systems approach incorporating computational modeling to bring together high throughput genotype and phenotype datasets. Analyses may incorporate new or existing data generated outside of TOPMed. However, this notice seeks studies that will use existing TOPMed data as the major resource for analysis of HLBS diseases and measures. Furthermore, applications seeking to utilize TOPMed data for use as controls, model testing, or solely for replication will not be prioritized. TOPMed has particular interest in questions about identifying associations between variants and disease phenotypes and analyses using existing functional data to reveal associations and/or make functional inferences. Investigators are encouraged to use the ethnic diversity of TOPMed for discovery and not merely for replication of findings in a single ethnic group.

Grants funded under this initiative will have access to data via dbGaP, including phenotype and genotype data, transcriptomics, methylation, metabolomics, and harmonized phenotypes produced by the TOPMed Data Coordinating Center (DCC) and jointly called sequence data generated by the TOPMed Informatics Research Center (IRC). Further information about the dbGaP data access application process can be found here: https://www.ncbi.nlm.nih.gov/books/NBK482114/#DArequest.would_you_give_me_a_stepbystep. Applicants are also encouraged to utilize emerging NHLBI cloud resources as they become available. This notice seeks to garner analysis of TOPMed data but not new tool development.

Suggested research examples include, but are not limited to:

• Investigation of pleiotropic gene effects and gene expression patterns across several cardiovascular risk factors
• Identification of biomarkers (metabolites, genetic variants and DNA methylation) related to severity of outcomes in sickle cell patients
• Machine learning approaches to search for likely areas of the genome related to hypertension and chronic kidney disease in African Americans
• Network analysis across genetic variation, expression profiling, and clinical data to reveal pathways associated with increased COPD risk
• Spatio-temporal dynamic modeling approaches to integrate environment and geographic information into TOPMed for study of gene-environment interactions
  

Application and Submission Information:

Applications in response to this Notice must be submitted through the NIH Parent Announcement PA-19-056: NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed).

All instructions for the Parent Announcement must be followed.

Submissions should indicate that they are in response to NOT-HL-19-676 in Field 4.b on the SF 424 form.

Inquiries

Please direct all inquiries to:

Rebecca Beer, Ph.D.
Division of Cardiovascular Sciences
National Heart, Lung, and Blood Institute (NHLBI)
Telephone: 301-594-0977
Email: rebecca.beer@nih.gov

Weiniu Gan, Ph.D.
Division of Lung Diseases
National Heart, Lung, and Blood Institute (NHLBI)
Telephone: 301-435-0202
Email: ganw2@mail.nih.gov

Pankaj Qasba, Ph.D.
Division of Blood Diseases and Resources
National Heart, Lung, and Blood Institute (NHLBI)
Telephone: 301-435-0050
Email: qasbap@nhlbi.nih.gov