Computational annotation of SNPs and rare variants

Rachel Karchin

Genetic variation is critical to our susceptibility to diseases and response to medications. Yet the functional consequences of most genetic variants are unknown. We are working to predict these consequences using computation, by integrating information from molecular modeling and sequence analysis with clinical patient data and functional studies, through collaborations with physicians, genetic counselors, and experimental biologists. We are particularly interested in inherited cancer susceptibilities and gain of function mutations in tumor genomes. Other efforts are focused on the large scale annotation of human genetic variation. To this end, we are developing a genomic-scale software pipeline to annotate human germline variants. The pipeline comprehensively maps SNPs onto human proteins and annotates them with statistical learning methods, based on selected features of sequence and structure.