Tackling Lack of Diversity in Genetics Research

| July 25, 2019

Researchers at the USC Viterbi Information Sciences Institute managed vast amounts of genetic data from multi-ethnic populations in a groundbreaking new study.

Up until now, large-scale genetic studies have drawn mostly on data from people of European descent. Image/iStock

Genotyping, or determining which genetic variants an individual possesses, has paved the way for understanding how common variations in our DNA can be associated with health conditions like heart disease and diabetes. The hope is to determine your risk of developing a disease and, hopefully, prevent it.

But if you’re not white, the science may fall short.

That’s because, up until now, large-scale genetic studies have drawn mostly on data from people of European descent. A new study, published in Nature, June 19, aims to address this by focusing on genetic variations in multi-ethnic populations.

Researchers at the USC Viterbi Information Sciences Institute (ISI) played a key role in helping scientists manage the huge amount of genetic data analyzed by the group, which included researchers from Stanford University, the University of North Carolina at Chapel Hill and the Fred Hutchinson Cancer Research Center.

Reducing health disparities 

The research study is one of the largest genetic studies analyzing genetic variants, or mutations, in almost 50,000 people of non-European descent, including Hispanic, African American, Asian, Native Hawaiian, Native American and other minority populations.

Studying diverse, multi- ethnic populations is crucial to increase genetic discoveries and reduce health disparities. The research could identify mutations that cause debilitating diseases so that effective treatments can be developed.

For example, the study revealed a genetic mutation linked to blood sugar levels, which occurs in one percent of Hispanics and in six percent of African- Americans, despite being rare in Europeans. In blood test analysis, this mutation could lead physicians to falsely conclude a patient’s glucose levels are under control, increasing the risk of complications caused by type 2 diabetes.

Providing a window into greater genetic diversity 

The ISI team provided critical data management, data dissemination and computational infrastructure for the consortium members.

The team comprised Jose- Luis Ambite, an ISI research team leader and research associate professor in computer science; Lily Fierro, a former ISI data analyst; Ewa Deelman, a computer science research professor and ISI research director; and Karan Vahi and Rajiv Mayani, both ISI computer scientists.

Specifically, Ambite and Fierro developed a large database with more than 600 genetic annotations. These annotations provided scientists additional information on around 50 million locations in the genome, including gene mutations such as single nucleotide polymorphisms (SNPs), DNA insertions and deletions.

Deelman, Vahi and Mayani developed computational workflows to efficiently extrapolate the 2 million genotyped SNPs into 50 million imputed SNPs, providing scientists more data to help determine the links between genetic variation and diseases.

SNPs, the most common type of genetic variation among people, can act as biomarkers, allowing scientists to identify, catalogue, and study small genetic variations among people that could lead to more specialized and effective medical treatments.

“We provided genetic annotations to the biostatisticians to help them with the analysis of the data and the biological interpretation of the findings,” said Ambite.

“This research is crucial to ameliorate health disparities, as it provides a window into greater genetic diversity among multiple populations, beyond what has been found among European-descent populations. We hope this research will provide a better understanding of how genetic variation causes diseases, which will ultimately benefit all populations.”

Share This Story