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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)

  • Cloud-based computing ecosystem

  • 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.

Access BioData Catalyst

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.

View TOPMed data freeze 9

Omics Data Releases

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.

Omics Data Releases
TOPMed WGS and Omics Summary of Approved Data
Short Name Study/Cohort PI Populations dbGaP ID WGS RNA-seq Methylation Metabolomics Proteomics
ARIC+VTE Atherosclerosis Risk in Communities Study
Eric Boerwinkle
African American (20%) phs001211 phs001402 phs000993 5206 Released
of 10,531 approved
Subjects: 5202
 0 Released
of 6,111 approved
Subjects: 0
 14989 Released
of 16,524 approved
Subjects: 11153
 0 Released
of 16,524 approved
Subjects: 0
CARDIA Coronary Artery Risk Development in Young Adults Study
Myriam Fornage
African American and Caucasian young adults phs001612 2759 Released
of 3,472 approved
Subjects: 2759
 5090 Released
of 6,000 approved
Subjects: 2773
 6441 Released
of 9,480 approved
Subjects: 2037
 7859 Released
of 9,000 approved
Subjects: 3071
 7710 Released
of 9,000 approved
Subjects: 3066
COPDGene Genetic Epidemiology of COPD
Ed Silverman
African American, non-Hispanic White phs000951 phs000946 10825 Released
of 10,829 approved
Subjects: 10677
 723 Released
of 800 approved
Subjects: 386
 12215 Released
of 11,843 approved
Subjects: 7357
 9141 Released
of 8,353 approved
Subjects: 6607
CRA_CAMP The Genetic Epidemiology of Asthma in Costa Rica and the Childhood Asthma Management Program
Scott Weiss
Hispanic population with asthma prevalence at 24% phs001726 phs000988 6462 Released
of 6,647 approved
Subjects: 5979
 1346 Released
of 3,000 approved
Subjects: 1346
 2843 Released
of 3,000 approved
Subjects: 2672
FHS Framingham Heart Study
Joanne Murabito
Three generation European ancestry pedigrees 4145 Released
of 4,089 approved
Subjects: 4133
 2691 Released
of 5,832 approved
Subjects: 2691
 1808 Released
of 4,099 approved
Subjects: 1804
 0 Released
of 7,117 approved
Subjects: 0
 0 Released
of 6,752 approved
Subjects: 0
GALAII Gene-Environment, Admixture and Latino Asthmatics Study
Elad Ziv
Latino 3674 Released
of 3,674 approved
Subjects: 3666
 2215 Released
of 2,500 approved
Subjects: 2191
MESA Multi-Ethnic Study of Atherosclerosis
Jerome Rotter
Multi-Ethnic populations phs001416 7886 Released
of 7,107 approved
Subjects: 7878
 2940 Released
of 8,903 approved
Subjects: 1347
 2084 Released
of 13,400 approved
Subjects: 976
 13258 Released
of 14,760 approved
Subjects: 3806
 8880 Released
of 16,200 approved
Subjects: 3323
SAGE Study of African Americans, Asthma, Genes and Environment
Esteban Burchard
African American 1951 Released
of 1,951 approved
Subjects: 1949
 917 Released
of 1,000 approved
Subjects: 864
WHI Women's Health Initiative
Charles Kooperberg
Women aged 50-79 years 11035 Released
of 11,310 approved
Subjects: 11027
 2391 Released
of 2,365 approved
Subjects: 2368
 4314 Released
of 4,400 approved
Subjects: 3118
 4399 Released
of 4,400 approved
Subjects: 3219
 0 Released
of 1,000 approved
Total Approved 59,610 33,511 62,746 63,154 32,952

Notes: 

“phs” is a dbGaP study accession number prefix indicating a phenotype study. A study accession number is a unique, stable, and versioned identifier.

AFGen dbGaP IDs: phs001435 , phs001543 , phs001624 , phs001732 , phs001600 , phs001189 , phs001546 , phs001606 , phs001547 , phs001725 , phs001545 , phs000993 , phs001598 , phs001062 , phs001434 , phs001544 , phs001024 , phs001601 , phs001933 , phs000997 , phs001032 , phs001040

ATGC dbGaP IDs: phs001728 , phs001729 , phs001730 , phs001602 , phs001603 , phs001604 , phs001605 , phs000920, phs001542 , phs001661 , phs001727 , phs000921 , phs001467

In the table, you may encounter phs links that redirect to a dbGaP error page. If so, this is because the TOPMed dbGaP study webpages are not available until the study accession is released.

TOPMed is generating a rich resource of multi-omics data that will include approximately 40,000 samples undergoing RNA-sequencing, 37,000 samples from metabolomics profiling, 57,000 samples from DNA methylation, and 4,000 samples from proteomics assaying. These projected totals include all stages of progress, from DNA/RNA that are currently being extracted to those that are undergoing sequencing/profiling or those that have completed the sequencing/profiling pipelines. Therefore, most omics data are in the process of being generated and will be released in the future.

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 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
Association analysis of mitochondrial DNA heteroplasmic variants: Methods and application Mitochondrion 39245194
Whole-genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles Blood 39226462
A genome-wide association study of alloimmunization in the TOPMed OMG-SCD cohort identifies a locus on chromosome 12 Transfusion 38966903
View entire Publications Table here

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.