CAREER: Selection Pipeline for Ancient and Modern Genomes: Improving Access to Genomic Methods for Studying Human Adaptations

As modern humans dispersed into new environments around the globe, directional selection played an important role in shaping the genetic variants present across different populations. Many diverse and unique adaptations exist, often playing a large role in human health and culture. Tools that capture genomic patterns of selection are instrumental for determining how directional selection has impacted modern human populations, and rapid advances in sequencing technology have greatly expanded the number of ancient and present-day human genomes for study. However, the number of researchers with the bioinformatic expertise needed to use these tools is limited, and refined demographic models to better inform selection inferences are not widely available for different human populations. This project will develop software infrastructure for studying adaptations in modern humans using ancient and present-day genomic datasets that can be implemented by researchers with low computational expertise. This project will implement the pipeline in Course-based Undergraduate Research Experiences (CUREs) to accelerate detection and characterization of positive selection in genomic regions of interest for worldwide human populations. In addition, this project will provide applied computing training to undergraduate students and professional development related to the selection software for higher education biology faculty, which will help more students across the nation gain genomics-based research skills. In total, this project will support future researchers by training one postdoctoral fellow, two post-baccalaureate students, and ten undergraduate research students, as well as approximately 60 undergraduate students and 30 higher education biology faculty in related data science short courses and genomics workshops. This project will develop software infrastructure establishing a flexible, modularized, and accessible selection pipeline incorporating ancient and present-day human genomic datasets that can be implemented by researchers with limited computational background. A reliable demographic model for East and Southeast Asians will be created and incorporated into this pipeline, and this tool will be used to characterize selection at putatively adapted genes in East and Southeast Asians, which will be beneficial to further research on human health and evolutionary history. To facilitate data science and research skills across the future STEM workforce, this research will be integrated with education in the following ways. First, undergraduate students will conduct original research projects using the selection pipeline through independent summer research or an upper-level biology course. Second, this project will create workshops for higher education faculty focused on the curriculum and implementation of the selection pipeline as a CURE, extending this opportunity to more undergraduates across the nation. Third, this project will develop a one-week applied computing short course for University of Richmond summer research students that will provide computing training to the future STEM workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2543276 | Program: 01003031DB NSF RESEARCH & RELATED ACTIVIT,01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Melinda Yang | Institution: University of Richmond, RICHMOND, VA | Award Amount: $847,878 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2543276 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2543276.html