Scientists have developed a new database of human gene expression information from various regions worldwide to tackle the historical focus of most human genetics research on individuals of European descent. This bias has the potential to limit the accuracy of scientific predictions for individuals from other backgrounds.
Historically, human genetics research has primarily concentrated on individuals of European descent, leading to a bias that could impact the precision of scientific predictions for non-European populations.
A team of researchers from Johns Hopkins University has compiled a new database of human gene expression data from diverse populations around the globe. By including more individuals from underrepresented populations, scientists can now gain more precise insights into the genetic factors that contribute to human diversity, including characteristics such as height, hormone levels, and disease susceptibility.
This study enhances the comprehension of gene expression in populations across Latin America, South and East Asia, and other regions where data has been limited in previous research.
Published in Nature, these findings have the potential to enhance future studies on human diversity and evolution.
“We now have a global perspective on how gene expression influences diversity worldwide, which is the most comprehensive view to date for populations that have been minimally represented in prior studies,” stated senior author Rajiv McCoy, a geneticist at Johns Hopkins. “We seek to better understand how DNA variation relates to traits, a topic that previous genetic studies have addressed with a persistent bias towards excluding non-European populations.”
While genetic research often focuses on DNA distinctions, this study delved into the examination of “gene expression” – the process through which genes in DNA are transcribed into RNA molecules. RNA acts as a template for assembling amino acids into proteins that provide structure and carry out various functions within cells. Genetic mutations can alter gene expression, affecting the quantity of RNA produced by genes or the structure of the RNA itself. These mutations and their impact on gene expression can significantly influence the development of traits and diseases.
To pinpoint mutations that alter gene expression, researchers analyzed RNA in cells from 731 individuals who had participated in the 1000 Genomes Project, an international collaboration that had previously characterized the DNA sequences of the same individuals.
“We not only possess their genome sequences, as previously published, but we now also have data on their gene expression. By merging these datasets, we can grasp the fundamental genetic sources of differences in gene expression among individuals,” McCoy explained. “This fundamentally contributes to the disparities between individuals, despite being 99.9% identical at the DNA level.”
Although the 731 individuals represent 26 distinct groups across five continents, the study revealed that gene expression profiles are often shared among these groups, a phenomenon also observed in DNA variation patterns. Most discrepancies in gene expression were observed within populations rather than between them.
“Our diversity distribution is more intricate than these geographical, political, or social classifications,” McCoy remarked.
The diversity within the cohort enabled researchers to detect potential connections between mutations and specific traits and health risks, even pinpointing mutations found in specific population subsets that had not been previously examined.
“Our study demonstrates that with a more diverse cohort, we can pinpoint mutations that might be driving these changes in gene expression, impacting variations and influencing traits or disease susceptibility,” added McCoy.
These findings could lead to improved personalized therapies, according to lead author Dylan Taylor, a biology doctoral candidate at Johns Hopkins.
“To predict gene expression accurately across diverse populations for personalized medicine, we need more diverse datasets. Using results from studies that solely include individuals of European descent to predict expression in underrepresented populations like South Asians may not produce reliable outcomes,” Taylor emphasized.
Although advancements have been made, significant gaps remain. The 1000 Genomes dataset does not encompass several groups from regions such as the Middle East, Australia, and the Pacific Islands, and has limited samples from the Americas and Africa.
“The field is progressing towards incorporating diverse individuals into human genetic studies,” Taylor highlighted. “Our research serves as a model for other scientists, demonstrating the feasibility and value of including diverse populations.”
Other authors from Johns Hopkins involved in the study include Surya B. Chhetri, a postdoctoral fellow in biomedical engineering; Michael G. Tassia, Arjun Biddanda, and Stephanie M. Yan, postdoctoral fellows in biology; Genevieve Wojcik, an assistant professor in epidemiology; and Alexis Battle, a professor of biomedical engineering.
The study received support from various NIH grants, including R35GM133747, F31HG012900, F31HG012495, R35GM139580, and OT2OD034190.
