6.881  Computational Personal Genomics - Making sense of complete genomes

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, such as genotype phasing and haplotype reconstruction, exploiting linkage for variant imputation, ancestry painting for admixed genomes, predicting likely causal variants using functional or comparative genomics annotations of coding and non-coding elements, relating regulatory variation to gene expression or chromatin state changes, measuring recent evolution and human selection, using systems biology and network information for understanding weak contributions, and the challenge of deciphering complex multi-genic traits such as height, Alzheimer’s, diabetes, or cancer. Students will read and discuss seminal papers in the area, download and use existing tools, write their own software to interpret existing human genomes, and discuss limitations, challenges, and ethical, legal and social implications.