Due to the increased morbidity and societal cost of drug abuse, identification of genetic factors affecting the response to drugs of abuse (DOA) are of particular interest and could provide potential novel targets for therapeutic development . However, a major challenge in biomedical science is determining how genetic differences within a population affect the properties (i.e. phenotypes, traits) of an individual. Using conventional methods, it often requires years of painstaking work to discover and characterize a genetic variant that affects a given phenotypic response. Several years ago, we developed a more efficient method for mapping genes to traits, called haplotype-based computational genetic mapping (HBCGM). because this will aid in identifying at risk populations In an HBCGM experiment, a property of interest is measured in inbred mouse strains; and genetic factors are computationally predicted by identifying the genomic regions where the pattern of genetic variation correlates with the distribution of trait values among the strains. HBCGM analyses are completed much more quickly than conventional genetic analysis methods. However, the methods used for experimental validation of genetic factors have limitations and are time consuming. This project will further develop computational methods that will enable genetic factors affecting many important biomedical traits to be discovered and experimentally characterized. A high-throughput version of HBCGM (HT-HBCGM) will be used to analyze 8,225 publicly available datasets, which measure 213,000 responses in panels of inbred mouse strains. We deploy a novel method that increases genetic discovery power by exploiting the redundancy present in the many datasets that examine similar responses. Novel computational tools that facilitate the integrated analysis of genetic, transcriptional and metabolomic data will also be developed. This includes specialized metabolic networks (for brain and 3 other tissues) for computationally identifying metabolomic changes that correlate with gene expression or genetic differences. To stimulate other investigators to make genetic discoveries, all results and methods from this project will be made fully available to the scientific community. These computational tools will be used to analyze customized `multi-omic' (genetic, transcriptional, and metabolomic) datasets that measure: (i) fifteen responses of inbred strain panels to four DOA (cocaine, methamphetamine, fentanyl, and nicotine); and (ii) corresponding DOA- induced transcriptional and metabolomic changes in brain. Integrated analysis of this data will identify genes/pathways affecting the response to DOA. We then apply a high efficiency method for engineering specific allelic changes into the genome of inbred strains, and the engineered lines are used to experimentally test the effect of an identified genetic factor on the response to a DOA.
Drug addiction is a major and costly public health problem. While genetic discoveries could play an important role in directing the development of new methods for the prevention and treatment of drug addiction, we have very limited information about the genetic variants affecting susceptibility to drug addiction. This project will develop advanced computational methods for analyzing very large datasets of addiction-related responses in mice. Using these methods, we will discover how the responses to four drugs of abuse (morphine, cocaine, methamphetamine and nicotine) are genetically regulated. These results could produce new methods for prevention and treatment of drug addiction.
| Lee, Chang-Min; Cho, Soo Jung; Cho, Won-Kyung et al. (2018) Laminin ?1 is a genetic modifier of TGF-?1-stimulated pulmonary fibrosis. JCI Insight 3: |
| Peltz, Gary (2018) A Flawed Design Produces Flawed Results. J Addict Med 12:252 |
| Peltz, Gary; Südhof, Thomas C (2018) The Neurobiology of Opioid Addiction and the Potential for Prevention Strategies. JAMA 319:2071-2072 |
