Presented by Mr. Andrew Sharo, University of California, Berkeley Computational methods are rapidly improving our ability to predict which germline variants cause rare Mendelian disease. The applications are startling. Consider Kathleen Folbigg, who is serving a 30-year prison sentence for the alleged murder of her four children. Years later, scientists have found that her children inherited rare variants that may explain their sudden death. Will variant interpretation eventually exonerate Kathleen? More commonly, clinical geneticists must identify one or two disease-causing variants among millions of neutral variants in the genome of an individual with a rare disease. However, at least half of these cases remain unresolved, even after whole genome sequencing. Structural variants may be the cause of a portion of these unresolved cases. We have developed StrVCTVRE, a random forest method, to prioritize structural variants that overlap exons. StrVCTVRE will allow clinicians to eliminate half of structural variants from consideration with 90% sensitivity. I will also discuss our analysis of cataloged pathogenic variants, those variants that have been identified by clinical laboratories or researchers to cause disease. We consider two popular databases, ClinVar and HGMD. Using population sequencing datasets, we find that pathogenic HGMD variants imply two orders of magnitude more affected individuals than ClinVar. We also find that individuals of African ancestry are five times more likely to be predicted to be affected when HGMD variants are used. Encouragingly, more recent clinical variant interpretation recommendations removed much of the ancestry skew.