May 28, 2014
(NYSE: SAP) today announced a strategic collaboration with Stanford School of Medicine to research the contribution of genetics, environmental exposures, behavior and other factors to disease susceptibility using the power of the SAP HANA® platform. The company also announced its participation in the Global Alliance for Genomics and Health, a council of industry leaders working toward accelerating worldwide efforts to responsibly share and analyze large amounts of genomic and clinical data.
"SAP HANA has the potential to drive breakthrough innovation in genomics research and the healthcare industry overall," said
, senior vice president, SAP HANA Platform for Healthcare and SAP HANA Native Development, SAP AG. "It is a big step for SAP to closely collaborate with the Stanford School of Medicine and join the Global Alliance for Genomics and Health to ultimately enable a better understanding of genetic underpinnings of health and disease."
Genomic Research Collaboration With Stanford School of Medicine
SAP's collaboration with Stanford School of Medicine aims to achieve a better understanding of global human genome variation and its implications in disease, particularly cardiovascular disease. Led by
Carlos D. Bustamante
, professor and director of the Stanford Center for Computational, Human and Evolutionary Genomics;
Euan A. Ashley
, associate professor of Medicine and Genetics; and
Atul J. Butte
, associate professor of Pediatrics and Genetics, the collaboration aims to advance genomics research in the clinical environment, ultimately leading to improved healthcare and personalized medicine.
Researchers have already leveraged SAP HANA to corroborate the results of a study that discovered that the genetic risk of Type II Diabetes varies between populations. The study looked at 12 genetic variants previously associated with Type II Diabetes across 49 individuals. With SAP HANA, researchers in Dr. Butte's lab were able to simultaneously query all 125 genetic variants previously associated with Type II Diabetes across 629 individuals. Using traditional methods, this analysis on this amount of data would have taken an unreasonable amount of time.