6.881 Computational Personal Genomics: Making sense of complete genomes


Prerequisites: 6.047, or permission of instructor
Units:  2-0-10
Instructor:  Professor Manolis Kellis (manoli@mit.edu)
Schedule: L M4-6, room 32-124

This subject qualifies as an Artificial Intelligence concentration subject.

With the growing availability and lowering costs of genotyping and personal genome sequencing, the focus has shifted from the ability to obtain the sequence to the ability to make sense of the resulting information. This course is aimed at exploring the computational challenges associated with interpreting how sequence differences between individuals lead to phenotypic differences such as gene expression, disease predisposition, or response to treatment. It will cover the computational challenges associated with personal genomics, including genetic and epigenetic association with disease and other complex traits, predicting disease driver mutations with functional and comparative genomics, epigenomic and transcriptional variation as intermediate phenotypes, polygenic risk prediction and heritability partitioning, rare variant association tests and cancer genomics, pharmacogenomics and therapeutic development, recent selection in the human genome, and social and ethical implications of personal genomics. For each topic, we will (1) read, present, and discuss seminal papers including challenges, limitations, and current directions, and (2) gain practical experience analyzing large-scale genomic datasets in a lab setting, by extending provided code and software tools. Students will complete a term project by extending or combining one or more labs, which can develop into a thesis or student-led publications