UCLA Leverages Genomic Data to Support Health Equity, Precision Medicine
Researchers are analyzing data from the UCLA ATLAS Precision Health Biobank to advance personalized and precision medicine for underrepresented populations.
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By - Researchers analyzing genomic data from the UCLA ATLAS Precision Health Biobank have found a highly diverse patient population within the repository, and they plan to leverage the data to support precision medicine efforts for underrepresented populations.
The patient population researchers are analyzing as part of the ATLAS study mirrors the diversity found in Los Angeles, which the press release states is one of the most ethnically diverse cities in the world. For this reason, UCLA researchers are leveraging data from this population to evaluate disease risk, prevention, and treatment.
“People of European ancestry constitute about 16 percent of the global population, but they account for nearly 80 percent of all genome-wide association study participants, making existing methods to predict disease risk from genetics vastly inaccurate in those of non-European ancestry,” said Bogdan Pasaniuc, PhD, an associate professor at the David Geffen School of Medicine at UCLA specializing in computational medicine, pathology and laboratory medicine, human genetics, and bioinformatics, in the press release.
In addition to genetic ancestry, the study also takes into account self-identified race and identity, which are tied to social constructs and shared values, cultural norms, and behaviors within their subgroups. Self-identified race and ethnicity can provide insight into systemic issues impacting the health of these groups, such as clinical care disparities and care access.
By analyzing genetic ancestry and self-identified race and ethnicity, the researchers hope to explore the interplay between the two as it relates to population health and precision medicine. The researchers have analyzed the genomes of 30,000 patients with ancestries from almost every continent to date.
Because the prevalence of genetic factors that impact disease risk varies across ancestry groups, these data are crucial for studying risk and improving personalized medicine in underrepresented populations.
To obtain information for their study, researchers collect biological samples from consenting UCLA patients. The data are de-identified, coded, and added to the biobank. Self-reported demographics, such as race and ethnicity, are sourced from EHRs, de-identified, and added as well. From there, researchers can access the biobank’s data to support their studies.
The researchers state that as the ATLAS sample size grows, they will be able to continue this research, performing more rigorous genetic and epidemiological studies. Through this research, they aim to better understand the role of genetic ancestry in disease development and use those insights to enhance genomic medicine and health equity for underrepresented populations.
This research is the latest in a string of efforts to bolster population health through personalized and precision medicine.
In June, the University of Maryland launched the My Healthy Maryland Precision Medicine Research initiative, which aims to gather diverse data from state residents to study how genes and lifestyle factors impact an individual's health, focusing on underserved populations who often experience health disparities.
In July, Texas-based Memorial Hermann Health System partnered with population genomics and viral surveillance company Helix to launch a population genomics program to personalize patient care.
