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, BIO5 Institute
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
Director, Graduate Studies
Member of the Graduate Faculty
Primary Department
Contact
(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

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.

Publications

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
Nielsen, R., Hubisz, M. J., Hellmann, I., Torgerson, D., Andrés, A. M., Albrechtsen, A., Gutenkunst, R., Adams, M. D., Cargill, M., Boyko, A., Indap, A., Bustamante, C. D., & Clark, A. G. (2009). Darwinian and demographic forces affecting human protein coding genes. Genome Research, 19(5), 838-849.

PMID: 19279335;PMCID: PMC2675972;Abstract:

Past demographic changes can produce distortions in patterns of genetic variation that can mimic the appearance of natural selection unless the demographic effects are explicitly removed. Here we fit a detailed model of human demography that incorporates divergence, migration, admixture, and changes in population size to directly sequenced data from 13,400 protein coding genes from 20 European-American and 19 African-American individuals. Based on this demographic model, we use several new and established statistical methods for identifying genes with extreme patterns of polymorphism likely to be caused by Darwinian selection, providing the first genome-wide analysis of allele frequency distributions in humans based on directly sequenced data. The tests are based on observations of excesses of high frequency-derived alleles, excesses of low frequency-derived alleles, and excesses of differences in allele frequencies between populations. We detect numerous new genes with strong evidence of selection, including a number of genes related to psychiatric and other diseases. We also show that microRNA controlled genes evolve under extremely high constraints and are more likely to undergo negative selection than other genes. Furthermore, we show that genes involved in muscle development have been subject to positive selection during recent human history. In accordance with previous studies, we find evidence for negative selection against mutations in genes associated with Mendelian disease and positive selection acting on genes associated with several complex diseases. © 2009 by Cold Spring Harbor Laboratory Press.

Gutenkunst, R. N., Hernandez, R. D., Williamson, S. H., & Bustamante, C. D. (2009). Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genetics, 5(10).

PMID: 19851460;PMCID: PMC2760211;Abstract:

Demographic models built from genetic data play important roles in illuminating prehistorical events and serving as null models in genome scans for selection. We introduce an inference method based on the joint frequency spectrum of genetic variants within and between populations. For candidate models we numerically compute the expected spectrum using a diffusion approximation to the one-locus, two-allele Wright-Fisher process, involving up to three simultaneous populations. Our approach is a composite likelihood scheme, since linkage between neutral loci alters the variance but not the expectation of the frequency spectrum. We thus use bootstraps incorporating linkage to estimate uncertainties for parameters and significance values for hypothesis tests. Our method can also incorporate selection on single sites, predicting the joint distribution of selected alleles among populations experiencing a bevy of evolutionary forces, including expansions, contractions, migrations, and admixture. We model human expansion out of Africa and the settlement of the New World, using 5 Mb of noncoding DNA resequenced in 68 individuals from 4 populations (YRI, CHB, CEU, and MXL) by the Environmental Genome Project. We infer divergence between West African and Eurasian populations 140 thousand years ago (95% confidence interval: 40-270 kya). This is earlier than other genetic studies, in part because we incorporate migration. We estimate the European (CEU) and East Asian (CHB) divergence time to be 23 kya (95% c.i.: 17-43 kya), long after archeological evidence places modern humans in Europe. Finally, we estimate divergence between East Asians (CHB) and Mexican-Americans (MXL) of 22 kya (95% c.i.: 16.3-26.9 kya), and our analysis yields no evidence for subsequent migration. Furthermore, combining our demographic model with a previously estimated distribution of selective effects among newly arising amino acid mutations accurately predicts the frequency spectrum of nonsynonymous variants across three continental populations (YRI, CHB, CEU).

Smith, A. M., Adler, F. R., Ribeiro, R. M., Gutenkunst, R. N., McAuley, J. L., McCullers, J. A., & Perelson, A. S. (2013). Kinetics of Coinfection with Influenza A Virus and Streptococcus pneumoniae. PLoS Pathogens, 9(3).

PMID: 23555251;PMCID: PMC3605146;Abstract:

Secondary bacterial infections are a leading cause of illness and death during epidemic and pandemic influenza. Experimental studies suggest a lethal synergism between influenza and certain bacteria, particularly Streptococcus pneumoniae, but the precise processes involved are unclear. To address the mechanisms and determine the influences of pathogen dose and strain on disease, we infected groups of mice with either the H1N1 subtype influenza A virus A/Puerto Rico/8/34 (PR8) or a version expressing the 1918 PB1-F2 protein (PR8-PB1-F2(1918)), followed seven days later with one of two S. pneumoniae strains, type 2 D39 or type 3 A66.1. We determined that, following bacterial infection, viral titers initially rebound and then decline slowly. Bacterial titers rapidly rise to high levels and remain elevated. We used a kinetic model to explore the coupled interactions and study the dominant controlling mechanisms. We hypothesize that viral titers rebound in the presence of bacteria due to enhanced viral release from infected cells, and that bacterial titers increase due to alveolar macrophage impairment. Dynamics are affected by initial bacterial dose but not by the expression of the influenza 1918 PB1-F2 protein. Our model provides a framework to investigate pathogen interaction during coinfections and to uncover dynamical differences based on inoculum size and strain. © 2013 Smith et al.