Collaborating with the Center for Human Genome Variation at Duke University, Roswell Park Cancer Institute (RPCI) researchers have developed a method to dexterously determine genetic factors that cause disease.
In a recent research study published in The American Journal of Human Genetics, RPCI’s Dr. Qiangian Zhu and fellow researchers have established a computational method called the “preferential linkage disequilibrium” approach to isolate causal variants, the genetic irregularities that suggest the presence of a particular disease.
Dr. Zhu is a biostatician who is also the Assistant Member of the Department of Biostatistics and Bioinformatics and the Director of Statistical Genetics and Genomics Resource at RPCI. Her research interests lie in developing statistically sound and computationally efficient methods to find the causal genetic variants of human diseases and traits utilizing high-throughput genetics and genomics data.
Continuing her postdoctoral research after joining RPCI, Dr. Zhu, along with her research collaborators, used variants recorded from genome-wide association studies (GWASs) that analyze people’s DNA to capture genetic variations associated with a disease. The group of researchers cross-referenced variants with a comprehensive variant catalog generated through robust “next generation” sequencing in order to identify the causal variants.
The study examined the DNA from 479 individuals of European descent. “To test our method, we ran it on five diseases for which the causal variants are known, and in every case we did identify the real causal variant,” said Zhu. The group hopes to have the method applied to GWASs related to diseases that do not have specific causal variants, resulting in advances towards the development of targeted approaches to treating diseases.
Fellow author of the study, David B. Goldstein, Richard and Pat Johnson Distinguished University Professor and Director of the Center for Human Genome Variation at DUMC stated that “This approach helps to intergrade the large body of data available in GWASs with the rapidly accumulating sequence data.”
Learn more about the study: Prioritizing Genetic Variants for Causality on the Basis of Preferential Linkage Disequilibrium