Presented by: 

Gary Peltz, M.D. Ph.D.

Department of Anesthesiology

Stanford University School of Medicine

Haplotype-based computational genetic mapping (HBCGM) has identified the genetic basis for 25 clinically important biomedical responses in mice, which include: the metabolism and toxicity of commonly prescribed drugs; and susceptibility to fungi and viruses, chronic pain, and narcotic drug withdrawal. As one example, genetic variation within the 5-HT₃ receptor was found to be a major susceptibility determinant for narcotic drug withdrawal. 5-HT₃ antagonists (e.g. ondansetron) are widely used to treat nausea and vomiting. Based upon our genetic finding, we demonstrated that ondansetron administration prevented narcotic drug withdrawal symptoms in mice, and then in human subjects. Babies born to mothers taking opiates develop a severe withdrawal syndrome after birth, which is referred to as the Neonatal Abstinence Syndrome (NAS). A clinical trial involving 7 medical centers is now testing whether a brief period o treatment with ondansetron can prevent the development of NAS in at risk neonates.

HBCGM previously analyzed one dataset at a time; but now, a vast amount of mouse phenotypic data is available. Therefore, HBCGM is being used to analyze 8300 publicly available datasets of biomedical responses measured in inbred mouse strain panels. Causative genetic factors affecting susceptibility for a CNS drug toxicity; and for eye, metabolic and infectious diseases were identified using automated methods to analyze the output. One analysis identified a novel genetic effector mechanism; allelic differences within the mitochondrial targeting sequence (MTS) affected the subcellular localization of a protein. Allelic differences within the MTS of many murine and human proteins could affect a wide range of biomedical phenotypes. These initial results indicate that genetic factors affecting biomedical responses could be identified through analysis of very large datasets. They also provide an early indication of how artificial intelligence-like methodology can facilitate genetic discovery, and these methods are now being used to identify genetic factors affecting inbred strain responses to other abused drugs (cocaine and nicotine).

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