Connecting gene mutations, rare genetic diseases

Clinical exome sequencing has revolutionized genetic testing for children with inherited disorders, and Baylor College of Medicine researchers have led efforts to apply these DNA methods in the clinic. Nevertheless, in more than two-thirds of cases, the underlying genetic changes in children who undergo sequencing are unknown. Researchers everywhere are looking to new methods to analyze exome sequencing data to look for new associations between specific genes and those rare genetic diseases – called Mendelian disorders. Investigators at the Human Genome Sequencing Center have developed new approaches for large-scale analysis of Mendelian disorders, published today in the American Journal of Human Genetics.

The investigators used an Apache Hadoop data lake, a data management platform, to aggregate the exome sequencing data from approximately 19,000 individuals from different sources. Using information from previously solved disease cases, they established methods to rapidly select candidates for Mendelian disease. They found 154 candidate disease-associating genes, which previously had no known association between mutation and rare genetic disease, according to Adam Hansen, lead author of the study and graduate student in molecular and human genetics at Baylor. 

“We found at least five people for each of these 154 genes that have very rare genetic mutations that we suspect might be causing disease,” Hansen said. “This shows the power of big data approaches toward accelerating the rate of discovery of associations between genes and rare diseases.”

“These computational methods solve the dual problems of large-scale data management and careful management of data access permission.” said Dr. Richard Gibbs, study author and professor of molecular and human genetics and director of the Human Genome Sequencing Center at Baylor. “They are perfect for outward display of data from the Baylor College of Medicine programs.”