As the cost of whole genome sequencing plummets and becomes more attractive for widespread application, utility depends on identifying and understanding the importance of specific genetic variants in disease risk and drug response. While tools do exist for interpretation of variants and for predicting phenotypes of novel variation, few are publicly available and are not integrated with each other. An open source framework for the interpretation of these data is presented in “Sequence to Medical Phenotypes: A Framework for Interpretation of Human Whole Genome DNA Sequence Data,” published in PLoS Genetics on October 8. The Sequence to Medical Phenotypes (STMP) framework integrates existing tools, including PharmGKB, for interpretation of variants related to Mendelian disease and drug response. It is customizable and includes both coding and noncoding variation. Its ability to identify clinically actionable and disease-causing variants has been validated in both large sets of unrelated individuals and in father-mother-child trios. It can also be used for genetic risk predictions and drug response predictions for an individual with exome, targeted resequencing, or whole genome DNA sequence data.
The STMP framework is available on the Ashley lab website: http://ashleylab.stanford.edu/tools/stmp.html.