Dr. Patsopoulos the past few years has been leading the genetics of multiple sclerosis. He has analyzed the raw genetic data of more than 100,000 individuals, modelling TBs of data to unravel the genetic architecture of multiple sclerosis. He has been applying and developing advanced statistical models to enable analysis of large-scale data sets with millions of genetic positions and analyzed subjects.
Leveraging our hands-on experience with large-scale genetic data sets and the exhaustive number of analyses one can perform we have designed a framework for fine-mapping. Modern genetics studies involve millions of analyzed positions in the genome, most of which are linked together. Fine-mapping is the application of algorithms to identify statistically independent positions in the genome that contribute to disease susceptibility. We have developed Effect Fine Mapping (EFM), a framework that not only identifies independent positions but further quantifies the probability of any linked genetic variants to be the one truly associated with the disease. This empowers the translational studies of genetic associations by providing highly-accurate lists of disease associated genetic variants. EFM can analyze millions of genetic variants and millions of subjects in any production machine, and is optimized for multi-threaded CPUs, like the Xeon Phis.
List of peer-reviewed publications: http://patslab.bwh.harvard.edu/publications-full-list
Software support by IPCC: http://patslab.bwh.harvard.edu/efm
Software supported by IPCC (git): https://bitbucket.org/patslab/efm