Trans-Omics for Precision Medicine (TopMed)

Overview

The Trans-Omics for Precision Medicine (TOPMed) program, sponsored by the National Institutes of Health (NIH) National Heart, Lung and Blood Institute (NHLBI), is part of a broader Precision Medicine Initiative, which aims to provide disease treatments tailored to an individual’s unique genes and environment. TOPMed contributes to this Initiative through the integration of whole-genome sequencing (WGS) and other omics (e.g., metabolic profiles, epigenomics, protein and RNA expression patterns) data with molecular, behavioral, imaging, environmental, and clinical data.

The Human Genome Sequencing Center (HGSC) at Baylor College of Medicine was named a participant in this groundbreaking program in 2017. Previously, the HGSC was awarded funding by NHLBI to sequence whole genomes for TOPMed studies such as sickle cell disease, and venous thromboembolism and has continued to expand this effort in subsequent phases of the program.

To support this trans-omic approach, the HGSC continues its ongoing collaboration with the Alkek Center for Metagenomics and Microbiome Research (CMMR) at Baylor and The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, which aid in executing the methylation and metabolomics tasks.

Whole Genome Sequencing

WGS was performed by the HGSC and other sequencing centers to a median depth of 30X using DNA from blood, PCR-free library construction and Illumina HiSeq X technology. A Support Vector Machine quality filter was trained with known variants and Mendelian-inconsistent variants. More information about WGS methods can be found under Sequencing and Data Processing Methods.

Omics

TOPMed Omics data processing is being performed by several sequencing centers, including the HGSC. The program requires that omics data be submitted to dbGaP, along with thorough documentation of biosampling and laboratory methods, as well as sample provenance.

Data

TOPMed genomic data and pre-existing Parent study phenotypic data are made available to the scientific community in study-specific accessions in the database of Genotypes and Phenotypes (dbGaP) and in the NHLBI BioData Catalyst cloud platform.

For more information about accessing and using the data, visit TOPMed Data Access for the Scientific Community.

Related Publications

Wang Y, Selvaraj MSunitha, Li X, Li Z, Holdcraft JA, Arnett DK, et al.. Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed Whole Genome Sequencing Study. medRxiv. 2023 ;.

Li X, Quick C, Zhou H, Gaynor SM, Liu Y, Chen H, et al.. Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies. Nat Genet. 2023 ;55(1):154-164.

Zhang Y, Liu X, Wiggins KL, Kurniansyah N, Guo X, Rodrigue AL, et al.. Association of Mitochondrial DNA Copy Number With Brain MRI Markers and Cognitive Function: A Meta-analysis of Community-Based Cohorts. Neurology. 2023 ;100(18):e1930-e1943.

Seyerle AA, Laurie CA, Coombes BJ, Jain D, Conomos MP, Brody J, et al.. Whole Genome Analysis of Venous Thromboembolism: the Trans-Omics for Precision Medicine Program. Circ Genom Precis Med. 2023 ;16(2):e003532.

Einson J, Glinos D, Boerwinkle E, Castaldi P, Darbar D, de Andrade M, et al.. Genetic control of mRNA splicing as a potential mechanism for incomplete penetrance of rare coding variants. Genetics. 2023 ;224(4).

Wang Y, Selvaraj MSunitha, Li X, Li Z, Holdcraft JA, Arnett DK, et al.. Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study. Am J Hum Genet. 2023 ;110(10):1704-1717.

Jiang M-Z, Gaynor SM, Li X, Van Buren E, Stilp A, Buth E, et al.. Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium. bioRxiv. 2023 ;.

Li X, Chen H, Selvaraj MSunitha, Van Buren E, Zhou H, Wang Y, et al.. A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies. bioRxiv. 2023 ;.

Wang Z, Choi SWan, Chami N, Boerwinkle E, Fornage M, Redline S, et al.. The Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations. Front Endocrinol (Lausanne). 2022 ;13:863893.