Research Identifies Link Between Genetic Makeup and Disease Risk

Using data from the UCLA ATLAS Precision Health Biobank, UCLA Health researchers found differences in diagnoses, hospital use, and genetic disease across various populations, which provided them with a new view ontreatment practices.

According to the Statistic Alatlas, Los Angeles, California, contains a diverse population in terms of ancestry. Although this source indicates that 9.4 percent of the population is unclassified, German, Irish, English, and American are among the most common origins, occupying 4.2 percent, 3.6 percent, 3.0 percent, and 2.9 percent, respectively.

Considering this diversity, UCLA researchers studied the ATLAS Precision Health Biobank. In doing so, they aimed to determine whether disease risks and care access varied across patient populations.

The UCLA ATLAS Precision Health Biobank draws connections between electronic health records and genetic makeup information from biological samples.

While reviewing the large genetic biobank, researchers leveraged information from almost 36,000 enrolled patients. They used a machine-learning algorithm to make connections between groups sharing genetic ancestry. This data allowed them to define 376 population clusters that differed in terms of genetic ancestry.

When comparing patient data across these clusters, researchers noticed dissimilar rates of diagnoses, hospital use, and genetic diseases. Some study results indicated the high level of precision involved in differentiating between ancestry regions.

For example, patients of Mexican and Central American descent varied greatly in terms of health tendencies. After making note of the various subclusters of Mexican patients, researchers noted that only those of the Guatemalan region typically experienced pregnancy complications. Meanwhile, the Central Mexican group was more likely to experience nutritional deficiencies.

Access inconsistencies also emerged from research findings. Those of Iranian Jewish descent, for example, experienced higher rates of adjustment disorder diagnoses, a condition that is rarely accounted for in medical record data. Researchers also found that one medical center provided these diagnoses, which show how social statuses have a high impact on outcomes.

These findings run perpendicular to the typical practices of healthcare systems, which generally rely on self-reported data. They also alert researchers of the importance of further genetic screening.

Along with genetic ancestry, environmental factors are also important to consider when evaluating disease risk. Often, certain populations may face levels of discrimination and disparities, which could have a large impact on care.

“The combination of your genetic risk and your environmental risk are the two most important things in determining whether you get a disease. It’s best for your doctor to have the best understanding of exactly what populations you might be coming from in order to assess things like disease risk or the need for genetic testing,” said lead author Christa Caggiano, a PhD student at UCLA Health, in a press release.

Similar efforts have also aimed to draw connections between genetics and disease risk.

For example, in March 2022,Scripps Research Institute created an app known as MyGeneRank that aimed to predict genetic risk for heart attack. The app did this using information from various sources such as 23andMe.

Using a smartphone, users could upload 23andMe information to the MyGeneRank app. The app then calculated the risk score for heart attack, mainly considering genetic risk factors for coronary artery disease. The app then followed up a year later to discuss the use of lipid-lowering medications among patients.

After reviewing information from app users, researchers found that those who did not take medication before using the app began to participate after receiving their genetic risk score.

“We now have the opportunity to integrate a person’s genetics into their cardiovascular health assessment to help them better understand their individualized risk and empower them to make the necessary modifications – including the addition of statin therapy - to their risk factor optimization plans,” said first author Evan Muse, MD, PhD, a cardiologist and lead for cardiovascular genomics at the Scripps Research Translational Institute, in a press release.