Stephen D Kachman

Research

My research is focused on the development and application of statistical methodology in the area of statistical genomics. Currently I’m working on methodology on incorporating genomic information, primarily in the form of SNP genotypes, into national beef cattle evaluation (Matthew Spangler, Department of Animal Science).

The statistical methodology development includes extensions based on generalized linear mixed models and Bayesian models. Other projects include genomics of swine reproduction (Daniel Ciobanu, Department of Animal Science, UNL), modeling of the host genetics influence of their gut microbial communities (Andrew Benson, Department of Food Science and Technology, UNL), genetic components of biological responses to stress (Lawrence Harshman, School of Biological Sciences, UNL), and statistical models for the evaluation of teachers and programs (Walter Stroup, Department of Statistics, UNL). I also provide statistical assistance in the design and analysis for both faculty and graduate students at UNL.

• ORCID ID: 0000-0003-0506-513X
• Scopus ID: 6603854801
• Nebraska Center for the Prevention of Obesity Diseases through Dietary Molecules (NPOD)
• Github for NPOD Organizing Data for Analysis Workshophttps://git.unl.edu/kachman/organizing-data-workshop
• Github for NPOD Intro to R Workshop: https://git.unl.edu/kachman/r-workshop-2019
• Github for ASReml Short Course: https://git.unl.edu/asreml/short-course
• Github for my Shiny Apps: https://git.unl.edu/kachman/ShinyApps

Selected Publications:

1. Thallman, R. M., K. J. Hanford, S. D. Kachman, and L. D. Van Vleck. 2004. Sparse inverse of covariance matrix of QTL effects with incomplete marker data. Statistical applications in genetics and molecular biology 3: Article30.
2.  Kachman, S. D., and L. D. Van Vleck. 2007. Technical note: Calculation of standard errors of estimates of genetic parameters with the multiple-trait derivative-free restricted maximal likelihood programs. J Anim Sci 85: 2375-2381.
3. Kachman, S. D.  2008. Incorporation of marker scores into national genetic evaluation Proceedings of the 9th Genetic Prediction Workshop, Beef Improvement Federation. p 92-98, Kansas City, MO.
4. Thallman, R. M., K. J. Hanford, R. L. Quaas, S. D. Kachman, R. J. Tempelman, R. L. Fernando, and E. J. Pollak. 2009. Estimation of the Proportion of Genetic Variation Accounted for by DNA Tests. Proceedings of the 41st Annual research symposium and annual meeting, Beef Improvement Federation: 184-209.
5. Benson, A. K., S. A. Kelly, R. Legge, F. R. Ma, S. J. Low, J. Kim, M. Zhang, P. L. Oh, D. Nehrenberg, K. J. Hua, S. D. Kachman, E. N. Moriyama, J. Walter, D. A. Peterson, and D. Pomp. 2010. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proceedings of the National Academy of Sciences of the United States of America 107: 18933-18938.
6. Harshman, L. G., K. D. Song, J. Casas, A. Schuurmans, E. Kuwano, S. D. Kachman, L. M. Riddiford, and B. D. Hammock. 2010. Bioassays of compounds with potential juvenoid activity on Drosophila melanogaster: juvenile hormone III, bisepoxide juvenile hormone III and methyl farnesoates. J Insect Physiol 56: 1465-1470.
7. Howard, J. T., S. D. Kachman, W. M. Snelling, E. J. Pollak, D. C. Ciobanu, L. A. Kuehn, and M. L. Spangler. 2013. Beef cattle body temperature during climatic stress: a genome-wide association study. International Journal of Biometeorology: 1-8.
8. Leach, R. J., C. G. Chitko-McKown, G. L. Bennett, S. A. Jones, S. D. Kachman, J. W. Keele, K. A. Leymaster, R. M. Thallman, and L. A. Kuehn. 2013. The change in differing leukocyte populations during vaccination to bovine respiratory disease and their correlations with lung scores, health records, and average daily gain. J. Anim. Sci. 91: 3564-3573.
9. Kachman, S. D., M .L. Spangler, G. L. Bennett, K. J. Hanford, L. A. Kuehn, W. M. Snelling, R. M. Thallman, M. Saatchi, D. J. Garrick, R. D. Schnabel, J. F. Taylor, and E. J. Pollak. 2013. Comparison of molecular breeding values based on within- and across-breed training in beef cattle. Genet. Sel. Evol. 45: 30.
10. McKnite, A. M., J. W. Bundy, T. W. Moural, J. K. Tart, T. P. Johnson, E. E. Jobman, S. Y. Barnes, J. K. Qiu, D. A. Peterson, S. P. Harris, M. F. Rothschild, J. A. Galeota, R. K. Johnson, S. D. Kachman and D. C. Ciobanu (2014). Genomic analysis of the differential response to experimental infection with porcine circovirus 2b. Animal Genetics 45(2): 205-214. doi: 10.1111/age.12125.
11. Kachman, S. (2014). Approximation of the Structural Forms of the Variances and Covariances between Molecular and Phenotypic Breeding Values. 10th World Congress on Genetics Applied to Livestock Production, Vancouver.
12. Engle, T. B., E. E. Jobman, T. W. Moural, A. M. McKnite, J. W. Bundy, S. Y. Barnes, E. H. Davis, J. A. Galeota, T. E. Burkey, G. S. Plastow, S. D. Kachman and D. C. Ciobanu (2014). Variation in time and magnitude of immune response and viremia in experimental challenges with Porcine circovirus 2b. BMC Veterinary Research 10(1): 286. doi:10.1186/s12917-014-0286-4.
13. Trenhaile, M. D., J. L. Petersen, S. D. Kachman, R. K. Johnson and D. C. Ciobanu (2016). "Long-term selection for litter size in swine results in shifts in allelic frequency in regions involved in reproductive processes." Anim Genetics 47(5): 534-542 doi: 10.1111/age.12448
14. Lee, J., S. D. Kachman and M. L. Spangler (2017). "The impact of training strategies on the accuracy of genomic predictors in United States Red Angus cattle." Journal of Animal Science 95: 3406-3414. 10.2527/jas2017.1604