Ryan N Gutenkunst

Ryan N Gutenkunst

Associate Department Head, Molecular and Cellular Biology
Associate Professor, Applied BioSciences - GIDP
Associate Professor, Applied Mathematics - GIDP
Associate Professor, Cancer Biology -
Associate Professor, Ecology and Evolutionary Biology
Associate Professor, Genetics - GIDP
Associate Professor, Molecular and Cellular Biology
Associate Professor, Public Health
Associate Professor, Statistics-GIDP
Associate Professor, BIO5 Institute
Member of the Graduate Faculty
Director, Graduate Studies
Primary Department
(520) 626-0569

Work Summary

Work Summary

We learn history from the genomes of humans, tumors, and other species. Our studies reveal how evolution works at the molecular level, offering fundamental insight into how humans and pathogens adapt to challenges.

Research Interest

The Gutenkunst group studies the function and evolution of the complex molecular networks that comprise life. To do so, they integrate computational population genomics, bioinformatics, and molecular evolution. They focus on developing new computational methods to extract biological insight from genomic data and applying those methods to understand population history and natural selection.


Qi, X., An, H., Ragsdale, A. P., Hall, T. E., Gutenkunst, R. N., Pires, J. C., & Barker, M. S. (2017). Genome wide analyses of diverse Brassica rapa cultivars reveal significant genetic structure and corroborate historical record of domestication. Molecular Ecology.
BIO5 Collaborators
Michael S Barker, Ryan N Gutenkunst
Dornhaus, A. R., Gutenkunst, R. N., Wang, X., & Leighton, G. M. (2017). Behavioral caste is associated with distinct gene expression profiles in workers in Temnothorax rugatulus. BMC Genomics.
BIO5 Collaborators
Anna R Dornhaus, Ryan N Gutenkunst
Xin, M. a., Kelley, J. L., Eilertson, K., Musharoff, S., Degenhardt, J. D., Martins, A. L., Vinar, T., Kosiol, C., Siepel, A., Gutenkunst, R. N., & Bustamante, C. D. (2013). Population Genomic Analysis Reveals a Rich Speciation and Demographic History of Orang-utans (Pongo pygmaeus and Pongo abelii). PLoS ONE, 8(10).

PMID: 24194868;PMCID: PMC3806739;Abstract:

To gain insights into evolutionary forces that have shaped the history of Bornean and Sumatran populations of orang-utans, we compare patterns of variation across more than 11 million single nucleotide polymorphisms found by previous mitochondrial and autosomal genome sequencing of 10 wild-caught orang-utans. Our analysis of the mitochondrial data yields a far more ancient split time between the two populations (∼3.4 million years ago) than estimates based on autosomal data (0.4 million years ago), suggesting a complex speciation process with moderate levels of primarily male migration. We find that the distribution of selection coefficients consistent with the observed frequency spectrum of autosomal non-synonymous polymorphisms in orang-utans is similar to the distribution in humans. Our analysis indicates that 35% of genes have evolved under detectable negative selection. Overall, our findings suggest that purifying natural selection, genetic drift, and a complex demographic history are the dominant drivers of genome evolution for the two orang-utan populations. © 2013 Ma et al.

Waterfall, J. J., Casey, F. P., Gutenkunst, R. N., Brown, K. S., Myers, C. R., Brouwer, P. W., Elser, V., & Sethna, J. P. (2006). Sloppy-model universality class and the vandermonde matrix. Physical Review Letters, 97(15).

PMID: 17155311;Abstract:

In a variety of contexts, physicists study complex, nonlinear models with many unknown or tunable parameters to explain experimental data. We explain why such systems so often are sloppy: the system behavior depends only on a few "stiff" combinations of the parameters and is unchanged as other "sloppy" parameter combinations vary by orders of magnitude. We observe that the eigenvalue spectra for the sensitivity of sloppy models have a striking, characteristic form with a density of logarithms of eigenvalues which is roughly constant over a large range. We suggest that the common features of sloppy models indicate that they may belong to a common universality class. In particular, we motivate focusing on a Vandermonde ensemble of multiparameter nonlinear models and show in one limit that they exhibit the universal features of sloppy models. © 2006 The American Physical Society.

Coffman, A. J., Hsieh, P. H., Gravel, S., & Gutenkunst, R. N. (2016). Computationally Efficient Composite Likelihood Statistics for Demographic Inference. Molecular biology and evolution, 35, 591.

Many population genetics tools employ composite likelihoods, because fully modeling genomic linkage is challenging. But traditional approaches to estimating parameter uncertainties and performing model selection require full likelihoods, so these tools have relied on computationally expensive maximum-likelihood estimation (MLE) on bootstrapped data. Here, we demonstrate that statistical theory can be applied to adjust composite likelihoods and perform robust computationally efficient statistical inference in two demographic inference tools: ∂a∂i and TRACTS. On both simulated and real data, the adjustments perform comparably to MLE bootstrapping while using orders of magnitude less computational time.