UCLA Health will receive a $ 4.8 million grant from the National Institutes of Health to develop methods that will improve genetic risk estimates – polygenic risk scores – for specific diseases in people from diverse populations and ancestry mixed.
NIH funding comes from grants provided by the National Human Genome Research Institute (NHGRI) and the National Cancer Institute (NCI) to establish a multi-center research consortium that will pool genomic information from existing and new data sets. Researchers will then develop and evaluate methods for calculating polygenic risk scores (PRS) with an emphasis on studying people from different backgrounds. Each research center can include several collaborating institutions.
At UCLA, the funds will be used to establish the PRS Center for Admixed Populations and Health Equity (CAPE), a collaboration between the Department of Computational Medicine and the Institute for Precision Health (IPH) within the David Geffen School of Medicine and UCLA Health. The center will leverage UCLA’s ATLAS community health initiative, a large collection of blood, saliva and tissue samples from patients being analyzed to help UCLA researchers and clinicians develop and provide the best possible care.
Of the newly funded centers, UCLA is the only one to focus not only on polygenic risk scores for various populations, but specifically for people of “mixed ancestry.” This fits a cornerstone of UCLA IPH’s mission, which is to facilitate the implementation of precision health and genomic medicine for all individuals, including those who are generally under-represented in most. biomedical research.
“Over 30% of people living in the United States identify as having mixed ancestry, generally defined as people with recent ancestry from two or more continental sources, such as African Americans and / or Latinxes,” said Dr. Bogdan Pasaniuc, associate professor at UCLA’s David Geffen School of Medicine, specializing in computational medicine, pathology and laboratory medicine, human genetics and bioinformatics. His research focuses on statistical and computational methods to understand risk factors for common diseases, with particular emphasis on the study of mixed populations.
Researchers and clinicians calculate polygenic risk scores by comparing genomic data from people with and without a particular disease. Bioinformatics analysis is used to identify groups of genomic variants that are found more frequently in people with the disease, and then statistical calculations are used to estimate how a person’s variants affect their risk of getting the disease. .
Although researchers have been able in recent years to calculate risk scores for many conditions, and even to tailor management and medical interventions accordingly, existing large-scale genomic datasets are heavily biased in favor of individuals of European descent and are not effective when used in various populations. In fact, leaders in genomic medicine, healthcare, and research concluded in 2019 that current risk scores have “the potential to worsen outcomes or widen health disparities across groups. under-represented if these groups are not included in the research ”.
People with recent ancestors of different continental origins have “mosaic genomes with segments of different continental ancestry in each region of the genome,” Pasaniuc said. “Due to the lack of diversity in existing genomic studies, existing polygenic risk scores perform poorly in individuals of mixed genetic ancestry, especially for individuals of largely non-European genetic ancestry. between individuals of all ancestry. The diversity of genetic ancestry within mixed genomes poses unique challenges that cannot be addressed by existing paradigms.
The UCLA center – which will include collaborators from the Colorado Center for Personalized Medicine at the University of Colorado and the Institute for Genomic Health at the Mount Sinai Health System in New York City – will focus on integrating genetic diversity. in the PRS to obtain an accurate and fair genetic prediction. for any individual, whatever their ancestry. Researchers will analyze the genomes of more than 200,000 mixed individuals.
The research will build on and benefit from the UCLA ATLAS Community Health Initiative and the UCLA ATLAS Precision Health Biobank, which collects biological samples from as many people as possible, encodes the samples, removes any information from personal identification and provide samples to approved investigators. who are looking for new ways to prevent, detect and treat health problems.
Our investment in the UCLA Institute for Precision Health ATLAS project was not only to build an infrastructure for the treatment of our UCLA Health patients using precision medicine approaches, but also to create an infrastructure that would enable our ability to pursue new research projects and innovative collaborations. with other organizations to improve health care practices nationally and globally. Our participation in this cutting-edge consortium is a major step in this direction as it leverages the diversity of our patients, alongside our goal of reducing disparities. “
Dr Daniel Geschwind, Senior Associate Dean of UCLA Health and Associate Vice Chancellor for Precision Health
The consortium will leverage NHGRI’s Genomic Data Analysis, Visualization, and Computing (AnVIL) lab space, a cloud-based resource, to meet its computational analysis and storage needs in one. shared environment. In addition to developing new approaches and new datasets, researchers from participating institutions will work to increase transparency and standardize practices in how researchers develop and validate polygenic risk scores.
“At UCLA Health, we are investing and committed to advancing personalized health care for everyone in all of our communities,” said Dr. Kelsey C. Martin, Dean of the David Geffen School of Medicine at UCLA. “Through this grant, the efforts of the NIH, and the contributions of other institutions in the consortium, we look forward to ensuring that the promise of precision medicine is shared fairly, regardless of an individual’s ancestry.”