Forward genetics approaches using animal models are undergoing a period of change, in part in response to the changes that have occurred in human genetics in the past few years. New experimental designs, including the collaborative cross and advanced heterogeneous stock populations, have the potential to yield large populations of animals with a genetic constitution that more accurately reflects the human genetic state with regard to diversity and heterozygosity. In addition, there has been rapid development of inexpensive high- throughput phenotyping capabilities, notably with gene expression microarrays, but metabolite and protein profiling will soon cross thresholds of quality and affordability. These changes necessitate the development of new computational and statistical tools for interpreting data.
Our aims are to develop statistical methods in anticipation of new experimental approaches, to develop and disseminate software and data resources, and to analyze and interpret new and historical data from forward genetics experiments in mice. Our focus will shift from the historical objectives which emphasized gene discovery to new model-based approaches that exploit high dimensional and cumulative data to model systemic responses to genetic and environmental perturbations. The timely development of statistical methods and software will be critical to the success of mouse genetics in the coming years.
Experimental animals provide an important complement to genetic studies in humans and are essential when experiments involve potentially harmful exposure or are impossible to carry out with human subjects. This project will develop statistical and computational tools that are needed to interpret the outcomes of these experiments including newly developed approaches which enable us to work with mouse populations that more accurately reflect the human genetic state. )
|Gatti, D M; Weber, S N; Goodwin, N C et al. (2017) Genetic background influences susceptibility to chemotherapy-induced hematotoxicity. Pharmacogenomics J :|
|Morgan, Andrew P; Gatti, Daniel M; Najarian, Maya L et al. (2017) Structural Variation Shapes the Landscape of Recombination in Mouse. Genetics 206:603-619|
|Srivastava, Anuj; Morgan, Andrew P; Najarian, Maya L et al. (2017) Genomes of the Mouse Collaborative Cross. Genetics 206:537-556|
|Winter, Jean M; Gildea, Derek E; Andreas, Jonathan P et al. (2017) Mapping Complex Traits in a Diversity Outbred F1 Mouse Population Identifies Germline Modifiers of Metastasis in Human Prostate Cancer. Cell Syst 4:31-45.e6|
|Chesler, Elissa J; Gatti, Daniel M; Morgan, Andrew P et al. (2016) Diversity Outbred Mice at 21: Maintaining Allelic Variation in the Face of Selection. G3 (Bethesda) 6:3893-3902|
|Gu, Tongjun; Gatti, Daniel M; Srivastava, Anuj et al. (2016) Genetic Architectures of Quantitative Variation in RNA Editing Pathways. Genetics 202:787-98|
|Tian, Jianan; Keller, Mark P; Broman, Aimee Teo et al. (2016) The Dissection of Expression Quantitative Trait Locus Hotspots. Genetics 202:1563-74|
|Tyler, Anna L; Donahue, Leah Rae; Churchill, Gary A et al. (2016) Weak Epistasis Generally Stabilizes Phenotypes in a Mouse Intercross. PLoS Genet 12:e1005805|
|Chick, Joel M; Munger, Steven C; Simecek, Petr et al. (2016) Defining the consequences of genetic variation on a proteome-wide scale. Nature 534:500-5|
|Shreif, Zeina Z; Gatti, Daniel M; Periwal, Vipul (2016) Block network mapping approach to quantitative trait locus analysis. BMC Bioinformatics 17:544|
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