Quantitative Foundations for Human Genetics

Graduate / Professional

This course provides an in-depth, comprehensive foundation in the genetic concepts and statistical methods required to analyze human genetic data. Quantitative frameworks including statistical modeling, inference, and machine learning approaches are taught in the context of human genetics and genomics, with applications spanning population and evolutionary genetics, complex trait modeling, genomic architecture and linkage, genome-wide association studies, and disease risk prediction. Through interactive discussions, primary literature review, and hands-on workshops, students learn to analyze and interpret quantitative, genetic data and to apply computational approaches for statistical analyses. This course is intended for graduate students who plan to use quantitative and statistical methods in their thesis research or who seek deeper knowledge in these areas. Prior experience with basic probability and Python coding is recommended; additional training will be provided to students who have limited background. Permission of the instructor is required. Interested students must contact the instructor to discuss their prior experience and expectations for the course. F 9:30am-11am, MW 1pm-2pm