Adjunct Assistant Professor of Biological Sciences
Research Geneticist, USDA-ARS
PhD, University of Wisconsin
|Office:||203 Curtis Hall|
|Additional:||Website, Twitter, Google-Scholar|
Evolution of plants in response to natural and artificial selection
Research descriptionIn the Beissinger lab, we develop statistical methods and leverage quantitative and population genetic theory to study plant evolution. We strive to better understand how plants have evolved in the past, how phenotypes and genotypes are related in the present, and how these sets of information can be used for improvement in the future. Our primary interests fall into two categories:
1. We are interested in understanding how organisms respond to evolutionary forces such as natural or artificial selection.
2. We are working to achieve an improved understanding of complexities of genotype-phenotype relationships including interactions and non-additivity.
Currently, the primary areas of focus in the lab include research to understand the predominant patterns of selection during maize domestication, the development of new statistical methods to improve genomic prediction based on evidence of past selection, the development of tests to identify quantitative traits that have been under selection, and the creation of 'Epistasis Mapping Populations' to map epistatic regions. We are also working in the area of experimental-evolution using a replicated maize population selected for plant height.
Beissinger, T.M., Morota, G. Medical Subject Heading (MeSH) annotations illuminate maize genetics and evolution (2017) Plant Methods, 13 (1), art. no. 8,
Beissinger, T.M., Wang, L., Crosby, K., Durvasula, A., Hufford, M.B., Ross-Ibarra, J. Recent demography drives changes in linked selection across the maize genome (2016) Nature Plants, 2 (7), art. no. 16084, .
Beissinger, T.M., Gholami, M., Erbe, M., Weigend, S., Weigend, A., De Leon, N., Gianola, D., Simianer, H. Using the variability of linkage disequilibrium between subpopulations to infer sweeps and epistatic selection in a diverse panel of chickens (2016) Heredity, 116 (2), pp. 158-166.
Morota, G., Beissinger, T.M., Peñagaricano, F. MeSH-informed enrichment analysis and MeSH-guided semantic similarity among functional terms and gene products in chicken (2016) G3: Genes, Genomes, Genetics, 6 (8), pp. 2447-2453.
Beissinger, T.M., Rosa, G.J., Kaeppler, S.M., Gianola, D., De Leon, N. Defining window-boundaries for genomic analyses using smoothing spline techniques (2015) Genetics Selection Evolution, 47 (1), art. no. 30.
Lorenz, A.J., Beissinger, T.M., Silva, R.R., de Leon, N. Selection for silage yield and composition did not affect genomic diversity within the Wisconsin Quality Synthetic maize population (2015) G3 (Bethesda, Md.), 5 (4), pp. 541-549.
Foerster, J.M., Beissinger, T., de Leon, N., Kaeppler, S. Large effect QTL explain natural phenotypic variation for the developmental timing of vegetative phase change in maize (Zea mays L.) (2015) Theoretical and Applied Genetics, 128 (3), pp. 529-538.
Haase, N.J., Beissinger, T., Hirsch, C.N., Vaillancourt, B., Deshpande, S., Barry, K., Robin Buell, C., Kaeppler, S.M., de Leon, N. Shared genomic regions between derivatives of a large segregating population of maize identified using bulked segregant analysis sequencing and traditional linkage analysis (2015) G3: Genes, Genomes, Genetics, 5 (8), pp. 1593-1602.
Hirsch, C.N., Flint-Garcia, S.A., Beissinger, T.M., Eichten, S.R., Deshpande, S., Barry, K., McMullen, M.D., Holland, J.B., Buckler, E.S., Springer, N., Buell, C.R., de Leon, N., Kaeppler, S.M. Insights into the effects of long-term artificial selection on seed size in maize (2014) Genetics, 198 (1), pp. 409-421.
Beissinger, T.M., Hirsch, C.N., Vaillancourt, B., Deshpande, S., Barry, K., Robin Buell, C., Kaeppler, S.M., Gianola, D., de Leon, N. A genome-wide scan for evidence of selection in a maize population under long-term artificial selection for ear number (2014) Genetics, 196 (3), pp. 829-840.
Beissinger, T.M., Hirsch, C.N., Sekhon, R.S., Foerster, J.M., Johnson, J.M., Muttoni, G., Vaillancourt, B., Robin Buell, C., Kaeppler, S.M., de Leon, N. Marker density and read depth for genotyping populations using genotyping-by-sequencing (2013) Genetics, 193 (4), pp. 1073-1081.
Wu, X.-L., Sun, C., Beissinger, T.M., Rosa, G.J.M., Weigel, K.A., Gatti, N.D.L., Gianola, D. Parallel Markov chain Monte Carlo - Bridging the gap to high-performance Bayesian computation in animal breeding and genetics (2012) Genetics Selection Evolution, 44 (1), art. no. 29.
Wu, X.-L., Beissinger, T.M., Bauck, S., Woodward, B., Rosa, G.J.M., Weigel, K.A., Gatti, N.L., Gianola, D. A primer on high-throughput computing for genomic selection (2011) Frontiers in Genetics, 2 (FEB), art. no. Article 4.