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. )
|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|
|Winter, Jean M; Gildea, Derek E; Andreas, Jonathan P et al. (2016) Mapping Complex Traits in a Diversity Outbred F1 Mouse Population Identifies Germline Modifiers of Metastasis in Human Prostate Cancer. Cell Syst :|
|Gu, Tongjun; Gatti, Daniel M; Srivastava, Anuj et al. (2016) Genetic Architectures of Quantitative Variation in RNA Editing Pathways. Genetics 202:787-98|
|Dell'Acqua, Matteo; Gatti, Daniel M; Pea, Giorgio et al. (2015) Genetic properties of the MAGIC maize population: a new platform for high definition QTL mapping in Zea mays. Genome Biol 16:167|
|Gatti, Daniel M; Svenson, Karen L; Shabalin, Andrey et al. (2014) Quantitative trait locus mapping methods for diversity outbred mice. G3 (Bethesda) 4:1623-33|
|Smallwood, Tangi L; Gatti, Daniel M; Quizon, Pamela et al. (2014) High-resolution genetic mapping in the diversity outbred mouse population identifies Apobec1 as a candidate gene for atherosclerosis. G3 (Bethesda) 4:2353-63|
|Ram, Ramesh; Mehta, Munish; Balmer, Lois et al. (2014) Rapid identification of major-effect genes using the collaborative cross. Genetics 198:75-86|
|Rutledge, Holly; Aylor, David L; Carpenter, Danielle E et al. (2014) Genetic regulation of Zfp30, CXCL1, and neutrophilic inflammation in murine lung. Genetics 198:735-45|
|Kelada, Samir N P; Carpenter, Danielle E; Aylor, David L et al. (2014) Integrative genetic analysis of allergic inflammation in the murine lung. Am J Respir Cell Mol Biol 51:436-45|
|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|
Showing the most recent 10 out of 33 publications