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William Beavis

Most often cited for his discovery of bias in estimates of genetic effects in statistical analyses (the "Beavis Effect"), Dr. William Beavis developed and applied novel statistical genetic methods at Pioneer Hi-Bred from 1986-1998. He left Pioneer for a one year sabbatical at the National Center for Genome Resources (NCGR) in Santa Fe, New Mexico, but ended up staying from 1998 to 2007. As CSO at NCGR, Bill provided scientific leadership in building a sustainable non-profit Bioinformatics research institute. In the fall semester of 2007, Bill joined the faculty at Iowa State University as the endowed G.F. Sprague Chair and has served ISU as Interim Director of the Plant Sciences Institute from 2009-2014. Bill’s research interests include development of accurate predictive models and optimization of breeding processes for purposes of genetic improvement. Currently, he collaborates with engineers and computer scientists in developing and evaluating predictive and optimization methods from Operations Research to address genetic improvement projects. Specifically, his group is working on development of systematic approaches to optimize Genomic Prediction methods and translation of genetic improvement goals into mathematical objective functions for optimization. Bill’s teaching interests are in assuring that the next generation of biologists will be prepared to address complex problems with quantitative skills, and his administrative interests are in developing entrepreneurial faculty to transform plant biology from a descriptive to a predictive science and plant breeding from an art to an engineering discipline.


Reka Howard's primary research interests are in the application of statistical methodology in plant sciences, quantitative genetics, plant breeding education and curriculum development. For her dissertation research she evaluated 10 parametric and 4 nonparametric statistical methods for prediction purposes in Plant Breeding using simulated data. The results of this project shed light on the conditions under which the nonparametric methods outperform the parametric methods for phenotype prediction. Reka also investigated factor combinations that optimize the performance of the nonparametric methods for phenotype prediction purposes in Plant Breeding. For this purpose, she used Response Surface Methodology. Most recently, she is working on evaluating prediction methods where she is predicting the phenotype for the subsequent generation. Her other major research project involves statistical software development and evaluation in R for molecular data analysis for a structured population of soybeans. The two main components of the research are the use of statistical methods for QTL detection and phenotype prediction. Although her primary area of research is statistical methodology and data analysis in plant sciences, she has additional experience and interest in curriculum development. Reka is involved in in the development of MSc curriculum in Cultivar Development for Africa supported by the Bill and Melinda Gates Foundation. Her role is course content development and review in terms of statistical and quantitative genetics theory. She is also interested in exploring innovative uses of technology in plant breeding education to support learning and teaching, and the influence of different teaching strategies in Plant Breeding across different contexts of learning (for example, classroom versus online learning).

Danielle Dykema is a Master’s student in plant breeding with Dr. Beavis and is working on a genome study of Arabidopsis ecotypes from the 1001 Genomes Project.  She is a graduate of the University of Minnesota, Twin Cities as of 2010.  

John Cameron received a B.S. in botany from the University of Wisconsin-Madison in 2008. After graduating he worked as an analytical chemist before joining the Peace Corps, where he assisted villages in southern Malawi with agricultural development projects. His PhD research is focused on the optimization of breeding strategies.

Vishnu is a PhD student in Bioinformatics and Computational Biology program, working with the group since 2014. For his dissertation project, he is working on simulation methods to evaluate genetic gain for long term selection in plant breeding .

Group Alumni