In this application, we propose a highly ambitious yet realistically attainable goal: to align existing expertise at UNC-CH into a center of excellence in order to develop as a resource and demonstrate the utility of the murine Collaborative Cross (CC) to delineate genetic and environmental determinants of complex phenotypes drawn from psychiatry, the most intractable set of problems in all of biomedicine. We propose a particularly challenging definition of success - we will identify high probability etiological models (which can be realistically complex) and then prove the predictive capacity of these models by generating novel strains of mice bred to be at either very low or very high risk of the phenotype. Once validated, these high confidence models can then be tested in subsequent human studies. The data collected at the UNC center would be a valuable resource to the wider scientific community and could be used to interrogate any number of biological problems. The development of sophisticated, user-interactive databases to access the large, complex datasets collected represents a key component of the project. Accomplishing this overarching goal requires an exceptional diversity of scientific expertise - psychiatry, human genetics, mouse phenotyping, mouse genetics, statistical genetics, computational biology, and systems biology. Experts in all of these disciplines were deeply involved in the preparation of this application and are committed to the projects described here. Moreover, successful integration of these diverse fields is non-trivial;however, we can document that all scientists on this application have a history of extensive interactions over the past five years, know now how to work together and have a working knowledge of their colleagues'expertise. UNC-Chapel Hill has a shown an intense commitment to promoting inter-disciplinary genomics research and is one of the most collegial biomedical research institutions in the US which provides a fertile backdrop for """"""""Science 2.0"""""""" projects such as that proposed here.
Psychiatric disorders are a paradox-the associated morbidity, mortality, and societal costs are enormous and yet, despite over a century of scientific study, there are few hard facts about the etiology of the core diseases. Although GWAS meta-analyses are in progress, early results suggest that strong and replicable findings are elusive. Our proposal provides an alternative model approach to complement the study of fundamental psychiatric phenotypes.
|Kim, Y; Giusti-Rodriguez, P; Crowley, J J et al. (2017) Comparative genomic evidence for the involvement of schizophrenia risk genes in antipsychotic effects. Mol Psychiatry :|
|Srivastava, Anuj; Morgan, Andrew P; Najarian, Maya L et al. (2017) Genomes of the Mouse Collaborative Cross. Genetics 206:537-556|
|Morgan, Andrew P; Fu, Chen-Ping; Kao, Chia-Yu et al. (2015) The Mouse Universal Genotyping Array: From Substrains to Subspecies. G3 (Bethesda) 6:263-79|
|Crowley, James J; Zhabotynsky, Vasyl; Sun, Wei et al. (2015) Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance. Nat Genet 47:353-60|
|Sun, Wei; Liu, Yufeng; Crowley, James J et al. (2015) IsoDOT Detects Differential RNA-isoform Expression/Usage with respect to a Categorical or Continuous Covariate with High Sensitivity and Specificity. J Am Stat Assoc 110:975-986|
|Rutledge, Holly; Baran-Gale, Jeanette; de Villena, Fernando Pardo-Manuel et al. (2015) Identification of microRNAs associated with allergic airway disease using a genetically diverse mouse population. BMC Genomics 16:633|
|Morgan, Andrew P; Welsh, Catherine E (2015) Informatics resources for the Collaborative Cross and related mouse populations. Mamm Genome 26:521-39|
|Phillippi, J; Xie, Y; Miller, D R et al. (2014) Using the emerging Collaborative Cross to probe the immune system. Genes Immun 15:38-46|
|Crowley, James J; Kim, Yunjung; Lenarcic, Alan B et al. (2014) Genetics of adverse reactions to haloperidol in a mouse diallel: a drug-placebo experiment and Bayesian causal analysis. Genetics 196:321-47|
|Zou, Fei; Sun, Wei; Crowley, James J et al. (2014) A novel statistical approach for jointly analyzing RNA-Seq data from F1 reciprocal crosses and inbred lines. Genetics 197:389-99|
Showing the most recent 10 out of 23 publications