Trans-Omics for Precision Medicine | TOPMed
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What is TOPMed?
Trans-Omics for Precision Medicine (TOPMed), is a program of the National Heart, Lung and Blood Institute (NHLBI), a part of the National Institutes of Health, which aims to improve scientific understanding of the fundamental biological processes that underlie heart, lung, blood, and sleep (HLBS) disorders and advance precision medicine in ways that lead to disease treatments tailored to individuals’ unique genes and environments.
TOPMed supports these scientific advances through the integration of whole-genome sequencing (WGS) and other omics data (e.g., metabolic profiles, epigenomics, protein and RNA expression patterns) with molecular, behavioral, imaging, environmental, and clinical data from pre-existing parent studies that have large samples of human subjects with rich phenotypic characterization and environmental exposure data. TOPMed also collects environmental and behavioral data, such as dietary habits, physical activity, and socioeconomic factors, to provide a more comprehensive understanding of the factors that contribute to these disorders.
TOPMed Artificial Intelligence Initiative (TOPMed-AI)
NHLBI’s TOPMed program aims to leverage the power of artificial intelligence (AI) and machine learning (ML) to accelerate the understanding of HLBS disorders. By utilizing the vast genomic data resources available through TOPMed and the computing infrastructure of BioData Catalyst (BDC), researchers will be able to develop advanced AI methods to analyze complex data and identify patterns that may lead to new insights and potential innovations for precision medicine. The initiative will bring together AI/ML and other multidisciplinary experts to collaborate on innovative approaches to analyze and interpret TOPMed data. The coordination center (AI-CC) at Westat serves as the central hub for coordinating research projects.
Initial use-cases for the TOPMed-AI initiative include:
- Women’s health across the lifespan, starting with a focus on mid-life/menopause transition.
- Imaging of lung disease. Radiogenomics focusing on chest CT data and including other imaging data as the program evolves.
Explore the Data
The TOPMed program provides data resources for researchers studying heart, lung, blood, and sleep disorders. These data resources include various types of genomic and other data, such as whole-genome sequencing, whole-exome sequencing, RNA sequencing, epigenetic data, metabolomic data, and proteomic data. Researchers who wish to access TOPMed data, including electronic health records, medical imaging data, and other patient health information, must get approval through the Database of Genotypes and Phenotypes (dbGaP). Once approval is granted, researchers can access the data from NHLBI BioData Catalyst® (BDC) or dbGaP.
BioData Catalyst (BDC)
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Cloud-based computing ecosystem
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Features secure workspaces, tools, applications, and workflows
- Hosts data and supports collaboration
The TOPMed program uses BioData Catalyst (BDC) as a resource to facilitate research efforts. BDC is a cloud-based ecosystem where researchers can access NHLBI datasets, including TOPMed data, and leverage innovative data analysis tools, applications, and workflows to accelerate their research efforts. Additionally, BDC allows researchers to bring their own data, collaborate, and share their findings with other researchers in the community, ultimately driving discovery and scientific advancement in precision medicine.
TOPMed Data Freeze 9
- Variant discovery was initially made on approximately 206,000 samples.
- 781 million single nucleotide variants were identified.
- 62 million short insertion/deletion variants were identified and passed variant quality control (QC).
Note: These variant counts are slightly smaller than the corresponding numbers in data freeze 9 due to omitting sites that show no variation in TOPMed samples. More information about WGS methods can be found by selecting a freeze listed on the Methods page.
OMICS Data Processing & Sources
TOPMed Omics data processing is being performed by several sequencing centers. The program requires that omics data be submitted to dbGaP with thorough documentation of bio-sampling, laboratory methods, and sample provenance. Visit the Methods webpage and scroll to the Standards, Pipelines and Flowcharts for Data Processing section to find available documented omics pipelines specific to omics type and phase. Below is a summary of the approved data sources for each study/cohort name categorized by data type.
| Short Name Sort descending | Study/Cohort Name | Populations | dbGaP ID | WGS | RNA-seq | Methylation | Metabolomics | Proteomics |
|---|---|---|---|---|---|---|---|---|
| AA_CAC | African American Coronary Artery Calcification project | African American families | phs002194 | 1,159 | ||||
| AFGen | Identification of Common Genetic Variants for Atrial Fibrillation and PR Interval - Atrial Fibrillation Genetics Consortium | European ancestry | AFGen dbGaP IDs | 12,742 | ||||
| ARIC+VTE | Venous Thromboembolism project | African American (20%) | phs001211 phs001402 phs000993 | 10,531 | 6,111 approved | 16,524 approved | 16,524 approved | |
| ATGC | Asthma Translational Genomics Collaborative | African American, Mexican, and Puerto Rican individuals | ATGC dbGaP IDs | 16,494 | 9,290 approved | |||
| Africa6K | Integrative Genomic Studies of Heart and Blood Related Traits in Africans | African ancestry | phs002194 | 6,392 | 2,934 approved | |||
| Amish | Genetics of Cardiometabolic Health in the Amish | Old Order Amish large extended pedigrees | phs000956 | 1,120 | ||||
| BAGS | Barbados Asthma Genetics Study | Barbados families of African descent with >40% of asthmatic members | phs001143 | 1,085 | ||||
| BCC-PREG | The Boston-Colombia Collaborative for Adverse Pregnancy Outcomes | White, Black, Asian, Hispanic, White Hispanic, AfroCarribean, Ameridians, Mixed | Please see this TOPMed Project's Parent Studies. | 14,615 | ||||
| BioMe | Mount Sinai BioMe Biobank | phs001644 | 11,626 | |||||
| Boston-Brazil_SCD | Boston-Brazil Collaborative Study of Sickle Cell Disease | Brazilian | phs001599 | 415 | ||||
| 196,938 | 64,412 | 81,033 | 82,534 | 34,014 |
Published Papers That Used TOPMed Data
Published papers utilize TOPMed data to address topics related to heart, lung, blood, and sleep disorders.
| Title | Journal Name | Publication Date Sort ascending | PMID |
|---|---|---|---|
| Metagenomic Study of the MESA: Detection of Gemella Morbillorum and Association With Coronary Heart Disease | J Am Heart Assoc. | 39344648 | |
| Genetic variants associated with white blood cell count amongst individuals with sickle cell disease | British Journal of Hematology | 39279196 | |
| A genome-wide association study of alloimmunization in the TOPMed OMG-SCD cohort identifies a locus on chromosome 12 | Transfusion | 38966903 | |
| Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium | Human Molecular Genetics | 38747556 | |
| Metabolite signatures associated with microRNA miR-143-3p serve as drivers of poor lung function trajectories in childhood asthma | eBioMedicine | 38458111 |
Resources for the Scientific Community
TOPMed data are being made available to the scientific community as a series of “data freezes”:
- genotypes and phenotypes via dbGaP
- read alignments via the Sequence Read Archive (SRA)
- variant summary information via the Bravo variant server
- single nucleotide polymorphisms (dbSNP)
TOPMed WGS data are contained in study-specific accessions with names containing “NHLBI TOPMed,” while most phenotypic data are in parent study accessions. The TOPMed accessions can be identified by searching the dbGaP website for “TOPMed.” More information about the available data and how to access it can be found on the Data Access page.
