Dean Billheimer

Dean Billheimer

Professor, Public Health
Director, Statistical Consulting
Professor, Statistics-GIDP
Professor, BIO5 Institute
Member of the General Faculty
Member of the Graduate Faculty
Primary Department
Contact
(520) 626-9902

Work Summary

My research develops new clinical trial and experimental study designs to allow 'learning from data' more efficiently. My research also develops new analysis methods to understand latent structure in data. This allows better understanding of disease processes, better targeting of existing treatments, and development of more effective new treatments. Finally, I am developing new statistical methods based on prediction of future events.

Research Interest

Dean Billheimer, PhD, works with the Arizona Statistics Consulting Laboratory (StatLab) to partner with scientists and physicians to advance discovery and understanding. The 'Stat Lab' provides statistical expertise, personnel and computing resources to facilitate study design and conduct, data acquisition protocols, data analysis, and the preparation of grants and manuscripts. Dr. Billheimer also works to adapt and develop new statistical methods to address emerging problems in science and medicine. Dr. Billheimer facilitates discovery translation and economic development by consulting with public and private organizations external to the University of Arizona. Keywords: Biostatistics, Bioinformatics, Study Design, Bayesian Analysis

Publications

Gomez-Rubio, P., Gomez-Rubio, P., Klimentidis, Y., Klimentidis, Y., Cantu-Soto, E., Cantu-Soto, E., Meza-Montenegro, M., Meza-Montenegro, M., Billheimer, D., Billheimer, D., Lu, Z., Lu, Z., Chen, Z., Chen, Z., Klimecki, W., & Klimecki, W. (2012). Indigenous American Ancestry is Associated with Arsenic Methylation Efficiency in an Admixed Population of Northwest Mexico. J Toxicol Environ Health A, 75(1), 36-49.
BIO5 Collaborators
Dean Billheimer, Walter Klimecki
Jacquemin, J., Ammiraju, J. S., Haberer, G., Billheimer, D. D., Yu, Y., Liu, L. C., Rivera, L. F., Mayer, K., Chen, M., & Wing, R. A. (2013). Fifteen Million Years of Evolution in the Oryza Genus Shows Extensive Gene Family Expansion. Molecular plant.

In analyzing gene families in the whole-genome sequences available for O. sativa (AA), O. glaberrima (AA), and O. brachyantha (FF), we observed large size expansions in the AA genomes compared to FF genomes for the super-families F-box and NB-ARC, and five additional families: the Aspartic proteases, BTB/POZ proteins (BTB), Glutaredoxins, Trypsin α-amylase inhibitor proteins, and Zf-Dof proteins. Their evolutionary dynamic was investigated to understand how and why such important size variations are observed between these closely related species. We show that expansions resulted from both amplification, largely by tandem duplications, and contraction by gene losses. For the F-box and NB-ARC gene families, the genes conserved in all species were under strong purifying selection while expanded orthologous genes were under more relaxed purifying selection. In F-box, NB-ARC, and BTB, the expanded groups were enriched in genes with little evidence of expression, in comparison with conserved groups. We also detected 87 loci under positive selection in the expanded groups. These results show that most of the duplicated copies in the expanded groups evolve neutrally after duplication because of functional redundancy but a fraction of these genes were preserved following neofunctionalization. Hence, the lineage-specific expansions observed between Oryza species were partly driven by directional selection.

Hanusch, K., Janssen, C. H., Billheimer, D., Jenkins, I., Spurgeon, E., Lowry, C. A., & Raison, C. L. (2013). Whole-body hyperthermia for the treatment of major depression: Associations with thermoregulatory cooling. American Journal of Psychiatry, 170(7), 802-804.
Meyrick, B. O., Friedman, D. B., Billheimer, D. D., Cogan, J. D., Prince, M. A., A., J., & Loyd, J. E. (2008). Proteomics of transformed lymphocytes from a family with familial pulmonary arterial hypertension. American Journal of Respiratory and Critical Care Medicine, 177(1), 99-107.

PMID: 17932379;PMCID: PMC2176118;Abstract:

Rationale: Not all family members with BMPR2 mutations develop pulmonary arterial hypertension (PAH), implying that additional modifier genes or proteins are necessary for full expression of the disease. Objectives: To determine whether protein expression is altered in patients with familial PAH (FPAH) compared with obligate carriers and nondiseased control subjects. Methods: Protein extractsfrom transformed blood lymphocytes from four patients with FPAH, three obligate carriers, and three marriedin control subjects from one family with a known BMPR2 mutation (exon 3 T354G) were labeled with either Cy3 or Cy5. Cy3/5 pairs were separated by standard two-dimensional differential gel electrophoresis using a Cy2-labeled internal standard of all patient samples. Log volume ratios were analyzed using a linear mixed effects model. Proteins were identified by matrix-assisted laser desorption ionization, time-of-flight mass spectrometry (MALDI-TOF MS) and tandem TOF/TOF MS/MS. Measurements and Main Results: Hierarchical clustering, heat-map, and principal components analysis revealed marked changes in protein expression in patients with FPAH when compared with obligate carriers. Significant changes were apparent in expression of 16 proteins (P0.05) when affected patients were compared with obligates: nine showed a significant increase and seven showed a significant reduction. Conclusions: A series of novel proteins with altered expression were found that could distinguish affected patients from obligate carriers and married-in controls in a single family with a BMPR2 mutation. These differences provide new information highlighting proteins that may be involved in the mechanism(s) that differentiates those individuals with a BMPR2 mutation who develop FPAH from those who do not.

Paulovich, A. G., Billheimer, D., Ham, A. L., Vega-Montoto, L., Rudnick, P. A., Tabb, D. L., Wang, P., Blackman, R. K., Bunk, D. M., Cardasis, H. L., Clauser, K. R., Kinsinger, C. R., Schilling, B., Tegeler, T. J., Variyath, A. M., Wang, M., Whiteaker, J. R., Zimmerman, L. J., Fenyo, D., , Carr, S. A., et al. (2010). Interlaboratory study characterizing a yeast performance standard for benchmarking LC-MS platform performance. Molecular and Cellular Proteomics, 9(2), 242-254.

PMID: 19858499;PMCID: PMC2830837;Abstract:

Optimal performance of LC-MS/MS platforms is critical to generating high quality proteomics data. Although individual laboratories have developed quality control samples, there is no widely available performance standard of biological complexity (and associated reference data sets) for benchmarking of platform performance for analysis of complex biological proteomes across different laboratories in the community. Individual preparations of the yeast Saccharomyces cerevisiae proteome have been used extensively by laboratories in the proteomics community to characterize LC-MS platform performance. The yeast proteome is uniquely attractive as a performance standard because it is the most extensively characterized complex biological proteome and the only one associated with several large scale studies estimating the abundance of all detectable proteins. In this study, we describe a standard operating protocol for large scale production of the yeast performance standard and offer aliquots to the community through the National Institute of Standards and Technology where the yeast proteome is under development as a certified reference material to meet the long term needs of the community. Using a series of metrics that characterize LC-MS performance, we provide a reference data set demonstrating typical performance of commonly used ion trap instrument platforms in expert laboratories; the results provide a basis for laboratories to benchmark their own performance, to improve upon current methods, and to evaluate new technologies. Additionally, we demonstrate how the yeast reference, spiked with human proteins, can be used to benchmark the power of proteomics platforms for detection of differentially expressed proteins at different levels of concentration in a complex matrix, thereby providing a metric to evaluate and minimize preanalytical and analytical variation in comparative proteomics experiments.