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University of Virginia School of Medicine scientists have developed a new approach to genetic research that is poised to accelerate the development of treatments for inherited diseases and better tailor treatments to different groups of people.

The new tool developed by the UVA researchers has proved both more efficient and more effective in identifying specific genes responsible for diseases. Identifying these disease-causing genes is a crucial step in developing treatments to block their harmful effects.

UVA’s approach also incorporates data from groups historically underrepresented in genetic research, ensuring that new treatments will benefit people of different races and ethnic backgrounds. This is particularly important for diseases that predominantly affect certain groups, such as how sickle cell anemia predominately strikes people of African heritage.

“Our method improves the detection of genes that are likely to be linked to disease,” said researcher Xiaowei Hu, PhD, part of UVA’s Department of Genome Sciences. “The ultimate benefits of our research to patients are twofold: Our research can aid in finding gene targets for drug discovery and prioritize risk genes for disease prevention.” 

A More Sophisticated Approach to Genetic Research

Genetic research can be like looking for a needle in a haystack. Our genomes are vast, containing roughly 20,000 different protein-producing genes, so identifying a gene or handful of genes that drive a particular disease can be extremely difficult. Scientists often identify many different “candidate” genes and then try to narrow down from there. Complicating this already laborious process is the fact that genes can have subtle variations, or “variants,” that differ from person to person.

UVA’s new approach, however, leverages those subtle variations to scientists’ advantage. The UVA team developed three methods that focus on variants already shown to influence the activity of other genes. Individually, each of these three methods had significant limitations, the scientists found. But when the researchers combined them, they found their “omnibus” approach was almost 24% more accurate than the current gold standard.

UVA’s approach is also notable for incorporating data from people from diverse backgrounds. Genetic research has, historically, focused heavily on people of European ancestry, but the UVA team trained its model on almost 1,300 participants from the Trans-Omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA), which includes participants of self-reported European, African-American, Hispanic and Asian ancestry recruited within the United States. This initiative looked at atherosclerosis, or hardening of the arteries, and it has provided scientists valuable genetic information from a wide array of groups.

“By including diverse groups of participants in genetic research, we increase the likelihood that the knowledge gained from these studies will provide benefits to a broader group of people. Additionally, research has shown genetic studies that include diverse participants yield great power and precision for gene discovery in each of the included groups compared to those that focus on a single subset alone,” said UVA’s Ani Manichaikul, PhD, the project’s senior scientist. “Currently, the available tools for our genetic research are primarily based on individuals of European ancestry. Our approach built on populations with different genetic backgrounds increases statistical power for gene detection, which allows us to detect gene-disease associations that are too weak to see in some smaller non-European ancestry groups. Furthermore, our approach supports identification of disease genes and biology that is shared across ancestries.”

The researchers hope their new tool will ultimately streamline genetic research, helping us understand genetic diseases more quickly and get new treatments to patients faster. (Accelerating the development of new medicines and treatments is the primary mission of UVA’s new Paul and Diane Manning Institute of Biotechnology.)

“While our work has used gene expression from blood, the proposed methods in this work can be extended naturally to other tissues and other types of molecular data sets as well,” Hu noted. “We hope this work can be extended to other disease-relevant tissues to identify causal genes more accurately for disease, as well as to other types of molecular measurements leading to greater clarity in defining of disease mechanisms.” 

“Our long-term hope is that our field expands genomics and genetics resources for populations with different genetic backgrounds,” Manichaikul added. “Genetic research can benefit from these approaches through improved health equity and precision medicine.”

Method Described

The researchers have described their new approach in the American Journal of Human Genetics (AJHG). The article is open access, meaning it is free to read.

The research team consisted of Hu, Daniel S. Araujo, Chachrit Khunsriraksakul, Lida Wang, Quan Sun, Jia Wen, Lingbo Zhou, Lynette Ekunwe, Leslie A. Lange, Ethan M. Lange, Stephen B. Montgomery, Alexander P. Reiner, Francois Aguet, Kristin G. Ardlie, Tuuli Lappalainen, Christopher R. Gignoux, Esteban G. Burchard, Kent D. Taylor, Xiuqing Guo, Jerome I. Rotter, Stephen S. Rich, Elaine Cornell, Peter Durda, Russell P. Tracy, Yongmei Liu, W. Craig Johnson, George P. Papanicolaou, Minoli A. Perera, Michael H. Cho, Dajiang J. Liu, Laura M. Raffield, Yun Li, the TOPMed Multi-Omics Working Group, Heather E. Wheeler, Hae Kyung Im and Manichaikul.

The researchers and their work were supported by the National Institutes of Health, grants R01-HL153248, R01-ES036042, R01AG075884, U01HG011720, R01HL146500, R01HL165061 and R15HG009569.

A list of the authors’ disclosures is included in the paper.

To keep up with the latest medical discoveries from the UVA School of Medicine and the Manning Institute, bookmark the Making of Medicine blog.