Francisco M. De La Vega, Consulting Professor (Genetics)
francisco.delavega at stanford dot edu
The availability of human genome sequences is enabling studies in disease and evolution not possible before, and could lead to a revolution in personalized genomics. Challenges include the massive amounts of data, the complex relationships among the types of relevant data, and the need to make the data accessible from different perspectives. The variation in the DNA sequence among the billions of separate copies of extant human genomes can occasionally be of medical significance because it can alter disease susceptibility and reactions to drugs and pathogens. Aspects of the effective collection, representation, and use of high throughput genomics data in the elucidation of the etiology of common disease have been a major theme of Dr. De La Vega's work. He is currently interested in: the application of ultra-high throughput sequencing technologies in genetic epidemiology and population studies, aiming to identify the role of rare and structural variants in complex diseases; the study of genetic variation of populations of mixed ancestry and personal genome sequence analysis and annotation; and the development of ancestry deconvolution methods, panels of ancestry informative markers, and annotations of genetic variants of medical significance and their prevalence based on ethnic groups and ancestral origin.
Snehit Prabhu, Senior Research Scientist
snehit at stanford dot edu
Snehit is a computer scientist and statistician by training with an abiding interest in quantitative and clinical genetics. As part of the Clinical Genomics (ClinGen) initiative, he works on several projects that are trying to bring rigor, standardization, and reproducibility to the burgeoning field of next-generation genetic testing. His areas of research include statistical methodologies for genetic tests, interpretation and actionability of test results, data sharing modalities, and lastly, regulatory and privacy frameworks surrounding all aspects of such testing. He received his PhD at Columbia University under the supervision of Itsik Pe’er, where he worked on a variety of problems like cost-efficient large scale sequencing, gene-gene interaction mapping and statistical inference techniques.