The National Institutes of Health (NIH) is channeling $50.3 million over the next five years into a new consortium dedicated to advancing the generation and analysis of multi-omics data for human health research. As part of this team, Washington University in St. Louis is establishing and will lead a central production center that functions as a hub for multi-omics analyses for materials from consortium members at each of six disease study sites identified by the NIH.

Gary Patti, the Michael and Tana Powell Professor of Chemistry in Arts & Sciences and a professor of medicine and of genetics at the School of Medicine, and Ting Wang, head of the Department of Genetics at the School of Medicine and the Sanford C. and Karen P. Loewentheil Distinguished Professor of Medicine, are principal investigators with the new consortium. Patti, who recently founded a multi-omics company called Panome Bio, is an innovator in multi-omics research, and Wang is involved in multiple other NIH consortia, such as the Human Pangenome Reference Consortium.

Collaborators will take advantage of cutting-edge resources at Washington University, such as the Center for Proteomics, Metabolomics, and Isotope Tracing on the Danforth Campus. The university estimates the value of its portion of the new grant at $19.2 million.

“Most human diseases have complex origins that are influenced by a combination of genetic and environmental components such as diet, physical activity, exposure to pollutants and social determinants,” Patti said. “We are working to understand how disease develops in a person by studying the flow of molecular information at multiple levels of ‘omics’ in parallel.

“This consortium model eliminates one important barrier to progress by making it easier to collect multi-omics data from the same sets of samples and developing collaborative teams with the appropriate expertise to harmonize and integrate the results,” he said.

A more holistic view
Multi-omics incorporates several “omics” data types, including genomics, epigenomics, transcriptomics, proteomics and metabolomics. Each of these data types reveals distinct information about different aspects of a biological system, and leveraging all these data types at once is becoming increasingly possible with advances in high-throughput technologies and data science.

The integration of multiple types of data from an individual participant’s biological sample can provide a more holistic view of the molecular factors and cellular processes involved in human health and disease, including untangling genetic and non-genetic factors in health and disease. This approach offers great promise in areas such as defining disease subtypes, identifying biomarkers and discovering drug targets.

“With each additional layer of omics information comes more clues about biology,” Wang said. “I’m really excited about the work we will do in this consortium to integrate genomics, proteomics and metabolomics data. This is the future of systems biology.”

“Beyond gaining insights into individual diseases, the primary goal of this consortium is to develop scalable and generalizable multi-omics research strategies as well as methods to analyze these large and complex datasets,” said Joannella Morales, a National Human Genome Research Institute (NHGRI) program director involved in leading the consortium. “We expect these strategies will ultimately be adopted by other research groups, ensuring the consortium’s work will have broad and long-lasting impacts for clinical research.”

Funding for the consortium will support work at six disease study sites, which will examine conditions such as fatty liver diseases, hepatocellular carcinoma, asthma, chronic kidney disease and preeclampsia, among others.

The sites will enroll research participants, at least 75% of whom will be from ancestral backgrounds underrepresented in genomics research. The sites also will collect data on participants’ environments and social determinants of health to be used in conjunction with the multi-omics data. Combining the multi-omic and environmental data can offer an even more comprehensive view of the factors that contribute to disease risk and outcomes.