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Timothy Beissinger

Timothy Beissinger

Adjunct Assistant Professor/Research Geneticist
Division of Plant Sciences/USDA-ARS

E-mail: beissingert at missouri dot edu
Web site: http://beissingerlab.org
Office address: 302 Curtis Hall
Office phone: 573-882-7606

We use genomic data and develop statistical methods to study plant quantitative and population genetics. 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 such as interactions and non-additivity.
Currently, the primary areas of research in the lab include work 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, a search for genomic regions that interact to determine of phenotypes, and the implementation of a long-term selection study for maize plant height.

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Beissinger, T.M., Gholami, M., Erbe, M., Weigend, S., Weigend, A., de Leon, N., Gianola, D., Simianer, H. 2015. Using the variability of linkage disequilibrium between subpopulations to infer sweeps and epistatic selection in a diverse panel of chickens. Heredity. DOI: 10.1038/hdy.2015.81.

Haase, N.J., Beissinger, T.M., Hirsch, C.N., Vaillancourt, B., Deshpande, S., Barry, K., Buell, C.R., Kaeppler, S., de Leon, N. 2015. Genetic Dissection of quantita- tive traits using a bulked segregant analysis (BSA)-sequencing method on a large segregating population of maize. Genes Genomes Genetics. DOI: 10.1534/g3.115.017665.

Beissinger, T.M., Rosa, J.G.M., Kaeppler, S.M., de Leon, N., Gianola, D. 2015. Defining window-boundaries for genomic analyses using smoothing spline techniques. Genetics Selection Evolution. 47(30). DOI: 10.1186/s12711-015-0105-9.

Lorenz, A. J., Beissinger, T.M., Rodrigues, R., de Leon, N. 2015. Selection for silage yield and composition did not affect genomic diversity within the Wisconsin QualitySynthetic maize population. Genes Genomes Genetics. DOI: 10.1534/g3.114.015263.

Foerster, J.M.,Beissinger, T.M., de Leon, N., Kaeppler, S.M. 2015. Large effectQTL explain natural phenotypic variation for the developmental timing of vegetative phase change in maize (Zea mays L.). Theoretical and Applied Genetics. DOI: 10.1007/s00122-014-2451-3.

Hirsch, C.N., Flint-Garcia, S.A., Beissinger, T.M., Eichten, S.R., Deshpande, S., Barry, K., Springer, N.M., Buell, C.R., de Leon, N., Kappler, S.M. 2014. Insights into the effects of long-term artificial selection on seed size in maize. Genetics. 198(1): 409-421.

Beissinger, T.M., Hirsch, C.N., Vaillancourt, B., Deshpande, S., Barry, K., Buell, C. R., Kaeppler, S. M., Gianola, D., de Leon, N. 2014. A genome-wide scan for evidence of selection in a maize population under long-term artificial selection for ear number. Genetics. 196(3): 829-840.

*Beissinger, T.M., Hirsch, C.N., Sekhon, R.S., Foerster, J.M., Johnson, J.M., Muttoni, G., Vaillancourt, B., Buell, C.R., Kaeppler, S.M., de Leon, N. 2013. Marker density and read-depth for genotyping populations using genotyping-by-sequencing. Genetics. 193: 1073-1081. * Selected as a highlighted article by the editorial board.

Wu, X., Chuanyu, S., Beissinger, T.M., Rosa, G., Weigel, K., de Leon, N., Gianola, D. 2012. Parallel Markov chain Monte Carlo - bridging the gap to high performance Bayesian computation in animal breeding and genetics. Genet Sel Evol. 44:29.

Wu, X., Beissinger, T.M., Bauck, S., Woodward, B., Rosa, G., Weigel, K., de Leon, N., Gianola, D. 2011. A primer on high-throughput computing for genomic selection. Frontiers in Genetics. 2, 4.

 

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