Current Research
Natural selection is one of the cornerstones of modern biology and remains the primary explanation for adaptive evolution. Population genetic methods have been highly successful in using modern genetic variation data to quantify the amount and type of natural selection in the genome. However, rapid technological advances have led to an explosion in the amount of genetic variation data currently available, and these computational methods to detect selection are ill suited to analyze genome-wide data from large samples of individuals. Moreover, recent studies have generated sequencing data from ancient remains that are thousands of years old and new population genetic approaches have to be developed that make use of this type of data. The overarching theme of my research program is to learn about how natural selection impacts genomes by leveraging a variety of data types, including modern and ancient genomes, and by developing new computational tools.
Detecting adaptive evolution using modern and ancient DNA
The explosion of data sets of modern and ancient DNA in humans and other species will provide new opportunities for asking fundamental questions about evolution in natural populations, e.g. if adaptation to new environments occurs predominantly by selection on pre-existing genetic variation, or selection on new mutations. I am interested in developing statistics for detecting positive selection that are both powerful, as well as robust to confounding factors such as background selection and population structure.
Published work:
Zhen, Y., Huber, C. D., Davies, R. W., Lohmueller, K. E. 2021 ”Greater strength of selection and higher proportion of beneficial amino acid changing mutations in humans compared with mice and Drosophila melanogaster” Genome Research 31 (1):110-120.
Huber, C. D., DeGiorgio, M., Hellmann, I., and Nielsen, R. 2016. “Detecting recent selective sweeps while controlling for mutation rate and background selection.” Molecular Ecology 25 (1): 142–156.
DeGiorgio, M., Huber, C. D., Hubisz, M. J., Hellmann, I., and Nielsen, R. 2016. “SweepFinder2: increased sensitivity, robustness and flexibility.” Bioinformatics 32 (12): 1895–1897.
Huber, C. D. and Lohmueller, K. E. 2016. “Neutral Evolution, Population Genetic Tests of.” In Encyclopedia of Evolutionary Biology, edited by R. M. Kliman, 112–118. Academic Press.
Huber, C. D., Nordborg, M., Hermisson, J., and Hellmann, I. 2014. “Keeping it local: Evidence for positive selection in Swedish Arabidopsis thaliana.” Molecular Biology and Evolution 31 (11):3026–3039.
*Long, Q., *Rabanal, F. A., *Meng, D., *Huber, C. D., *Farlow, A., Platzer, A., Zhang, Q., et al. 2013. “Massive genomic variation and strong selection in Arabidopsis thaliana lines from Sweden.” Nature Genetics 45 (8): 884–890. *Equally contributing first authors.
Comparing deleterious mutations between species and populations
The distribution of fitness effects (DFE) is the distribution of the selection coefficient of a random mutation in the genome, and can be estimated from the Site Frequency Spectrum (SFS). I am interested in quantifying and comparing the DFE between multiple species and sets of genes. I use fitness landscape models to try to understand the possible mechanisms that are responsible for shaping the DFE.
Published work:
Kim, B. Y., Huber, C. D., and Lohmueller, K. E. 2018. “Deleterious variation shapes the genomic landscape of introgression.” PLoS Genetics 14 (10): e1007741.
*Mooney, J., *Huber, C. D., Service, S., Sul, J. H., Marsden, C., D., Zhang, Z., Sabatti, C., et al. 2018. “Understanding the hidden complexity of Latin American population isolates.” American Journal of Human Genetics, no. 103: 1–20. *Equally contributing first authors.
Huber, C. D., Kim, B. Y., Marsden, C. D., and Lohmueller, K. E. 2017. “Determining the factors driving selective effects of new nonsynonymous mutations.” PNAS 114 (12): 4465–4470.
Kim, B. Y., Huber, C. D., and Lohmueller, K. E. 2017. “Inference of the distribution of selection coefficients for new nonsynonymous mutations using large samples.” Genetics 206 (1): 345–361.
Estimating dominance in natural populations
The degree of dominance of genetic variants is a fundamental concept in genetics that is as old as the field itself. However, dominance effects of the majority of new mutations that affect fitness are entirely unknown. I am interested in new approaches for estimating dominance from population genetic variation. In particular, contrasting data from species with different mating systems can increase the ability to discriminate between different modes of dominance.
Published work:
Huber, C. D., Durvasula, A., Hancock, A. M., and Lohmueller, K. E. 2018. “Gene expression drives the evolution of dominance.” Nature Communications 9 (1): 2750.
Functional turnover and the power of comparative genomics
Comparative genomics is based on the principle that functionally important genes or other elements are more conserved (more similar) between species than functionally less important genes or pseudogenes. A number of statistics have been developed to quantify the degree of conservation. I am interested in the power to detect functional elements in realistic models that include variation in population size, functional turnover, and alignment errors. Functional turnover in particular can blur the relation between the functional state in one species, and the conservation level across phylogenetically diverse species. This affects our ability to use conservation statistics to infer the percentage of the genome that is functional, or how strongly mutations in a certain genetic element are selected.
Published work:
Huber, C. D., Kim, B. Y., and Lohmueller, K. E. 2020. "Population genetic models of GERP scores suggest pervasive turnover of constrained sites across mammalian evolution." PLOS Genetics 16 (5): e1008827
Phung, T. N., Huber, C. D., and Lohmueller, K. E. 2016. “Determining the effect of natural selection on linked neutral divergence across species.” PLOS Genetics 12 (8): e1006199.