New Software Tool Rapidly Finds Links Between Mutations and Disease in Families

pVAAST is a software tool that searches whole-exome and whole-genome sequence data in families to identify genetic variants that directly influence disease risk. pVAAST analyzes the DNA sequences of patients, their relatives, and healthy people in a highly automated fashion to provide probabilistic predictions of the specific genetic variants and genes that are increasing the risk of developing disease. pVAAST combines the existing variant prioritization and case-control association features in VAAST (a probabilistic search tool for identifying damaged genes and their disease-causing variants in personal genome sequences) with a new linkage analysis method specifically designed for sequence data. This model is broadly similar to traditional linkage analysis but is capable of modeling de novo mutations and is more sensitive in scenarios with incomplete penetrance or locus heterogeneity. pVAAST supports dominant, recessive, and de novo inheritance models, and maintains high power across a wide variety of study designs, from monogenic, Mendelian diseases in a single family to highly polygenic, common diseases involving hundreds of families.

The investigators showed that pVAAST outperformed linkage and rare-variant association tests in simulations and identified disease-causing genes from whole-genome sequence data in three human pedigrees with dominant, recessive, and de novo inheritance patterns. The approach was robust to incomplete penetrance and locus heterogeneity and was applicable to a wide variety of genetic traits.

“Linkage analysis and case control association traditionally have been used to find gene mutations,” said senior author Dr. Chad Huff, assistant professor of epidemiology at the MD Anderson Cancer. “Bringing those methods together provides a strong increase in the power to find gene variations that cause disease. We hope that in developing pVAAST, we and other researchers can more rapidly identify genetic variations influencing disease risk by increasing the statistical power of familial genome sequencing.”

Related Links:
University of Utah 
University of Texas MD Anderson Cancer Center