Tuesday, October 19, 2021
Cold Thermogenesis

MIT Deep Learning Genomics – Lecture 17 – Genetics2: Systems Genetics

Lecture 17 – Genetics 2 – Systems Genetics

MIT 6.874 Lecture 17. Spring 2020
Course website: https://mit6874.github.io/
Lecture 17 slides: https://mit6874.github.io/assets/sp2020/slides/L17_SystemsGenetics.pdf

Outline: Deep Learning for Human Genetics and Disease
1. Review: GWAS, fine-mapping, Bayesian variant prioritization
2. Deep Learning for GWAS: calling SNPs, prioritize function
3. eQTLs/Mediation: intermediate molecular phenotypes
4. Linear Mixed Models (LMMs) for GWAS and for eQTLcalling
5. Polygenic Risk Scores (PRS): summing over many variants
6. Heritability: definition(s), missing heritability, partitioning
7. LD SCoreregression (LDSC) for fast heritability partitioning
8. Polygenic/Omnigenicdisease models: core vs. periphery
9. Disease gene networks from GWAS evidence boosting


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One thought on “MIT Deep Learning Genomics – Lecture 17 – Genetics2: Systems Genetics
  1. You have inspired me so much, thank you and thank you to your team. I discovered you on Lex's podcast and I am utterly enthralled. I now have a new source for triggering flow state: listening to you! Awesomeness.

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