6.881 Computational Personal Genomics
Spring 2018
Instructor: Manolis Kellis
TAs: Yongjin Park, Alvin Houze Shi
Lecture: M4-6 (32-124)
Class Overview:
6.881 - Computational Personal Genomics
Units: 2-0-10 (G)
Prereqs: 6.047 or equivalent, or permission of instructor.
Date: Monday 4-6pm (first meeting: Tuesday February 20, 2018)
Room: 32-124 (MIT Stata Center, 32 Vassar St)
Instructor: Manolis Kellis
Course website: http://compbio.mit.edu/6.881
With the growing availability and lowering costs of genotyping
and personal genome sequencing, the focus has shifted from the
ability to obtain one's genome 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,
including differences in disease predisposition, gene expression,
or response to treatment.
Topics include:
- genetic variation, linkage disequilibrium, imputation, haplotype
phasing
- genetic association, GWAS, common vs. rare variant analysis
- mechanistic dissection of coding and non-coding
associations
- Bayesian fine-mapping and multi-dimensional GWAS
- intermediate phenotypes, expression QTLs, Mendelian
randomization
- systems genetics, heritability partitioning, polygenic risk
prediction
- single-cell profiling analysis of transcription and epigenomic
variation
- cancer genomics, clonality, immunotherapty, recurrence,
immunoprofiling
- experimental manipulations, genome editing, multiplexed
experiments
- ethics: consumer genetics, consent, privacy, screening,
editing
Each week, we will read and discuss seminal papers in each area, download, use, and extend software for interpreting genetic variation, and cover current research directions and open challenges. In addition, students will complete a term project that builds on one or more labs.
Qualifies for theory or AI engineering concentration.
Students can petition to earn 6 EDPs.
For more information, and a list of topics and papers
discussed,
please visit: http://compbio.mit.edu/6.881
Github for the labs: https://github.com/YPARK/6.881
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