Low-cost DNA sequencing has provided researchers with abundant genomic data in which to search for the unique footprints left by natural selection. However, a number of non-adaptive forces can obscure these signals, making it important to develop statistical methods that can account for multiple factors that influence genetic variation. My research in this area has focused on the design and application of statistical approaches for identifying regions undergoing balancing selection, which maintains the frequency of alleles in a population, and positive selection, which increases the frequency of beneficial alleles in a population. Specifically, we contributed to a number of advances in this area, including developing the first model-based methods for detecting balancing selection, the first likelihood approach for identifying positive selection while accounting for the confounding effects of negative selection, the first likelihood method for detecting adaptive introgression within a single population, and a computationally-efficient statistic tailored for identifying signals of ancestral positive selection. Our applications of these and other methods to human genomic data have uncovered novel candidates for high- altitude adaptation in Ethiopians and adaptation to European-borne pathogens in Native Americans, as well as for balancing selection via segregation distortion. During the next five years, I propose to develop novel statistical methods that leverage information about how different evolutionary forces shape the spatial distribution of genetic diversity around adaptive sites to identify genomic targets affected by complex modes of natural selection. These methods will be applied to whole-genome sequencing data from primates to answer questions about the role of adaptation in ancient and recent evolutionary history. In particular, our future research will be subdivided into several interrelated goals: designing statistical techniques for identifying positive selection in admixed populations, and using these techniques to identify genomic regions undergoing positive selection in admixed human populations; developing methods for identifying regions that underwent complex ancient balancing selection, and applying these methods to multiple primate species to investigate the prevalence of ancient balancing selection in this lineage; constructing statistics for uncovering adaptive footprints that integrate data from ancient and modern samples, and using these statistics to understand past adaptive history in European human populations; and building novel functional data analysis procedures for classifying modes of selection acting across the genome, and using these procedures to better understand the relative roles of hard sweeps, soft sweeps, adaptive introgression, and recent and ancient balancing selection in human evolutionary history. Advantages of these studies are two-fold, in that they will both yield powerful new approaches for identifying signatures of diverse modes of adaptation from genomic data, as well as elucidate evolutionary forces underlying the acquisition of adaptive phenotypes, such as those involved in disease resistance and pathogen defense.
Natural selection is an important evolutionary force that enables humans to adapt to new environments and fight disease-causing pathogens. However, the unique footprints of natural selection in our genome can be buried beneath those left by other evolutionary forces. Thus, by leveraging information about multiple evolutionary forces, we can identify signatures of natural selection in the human genome, and ultimately determine its role in human adaptation and disease.
| Harris, Alexandre M; Garud, Nandita R; DeGiorgio, Michael (2018) Detection and Classification of Hard and Soft Sweeps from Unphased Genotypes by Multilocus Genotype Identity. Genetics 210:1429-1452 |
