Tuesday, June 22, 2021
Cold Thermogenesis

6.047/6.878 Lecture 15 – eQTLs expression Quantitative Trait Loci (Fall 2020)

6.047/6.878/HST.507 Fall 2020 Prof. Manolis Kellis
Computational Biology: Genomes, Networks, Evolution, Health
Machine Learning in Genomics: Dissecting the circuitry of Human Disease
Latest slides and course today: http://compbio.mit.edu/6047
Fall 2020 slides and materials: http://stellar.mit.edu/S/course/6/fa20/6.047/materials.html

0:00 Review of Lecture 14 (GWAS)
6:44 Lecture 15 overview
7:47 Definition of eQTLs and causality
14:08 Why focus on non-coding variants
19:50 Methylation QTLs in Alzheimer’s brain
23:55 Preprocessing and covariate correction
28:05 Chromatin states and methylation variation
32:40 meQTL power, distance/allele-frequency distributions
42:30 Chromatin states of meQTLs and predictive power
45:10 GWAS, MWAS, meQTLs, and Imputed MWAS (iMWAS)
50:00 cis/trans eQTL conceptual frameworks
53:43 eQTL expression-to-genotype regression
56:30 Preprocessing, normalization, feature selection
58:00 Variance impact on discovery power
1:00:00 MAF, radius, demography, pop, technical covars
1:04:50 Expanded eQTL regression framework with PCs/covars
1:10:40 Insights on eQTL function and biology
1:14:00 Allele-specific analyses and aseQTLs
1:19:45 TWAS using summary statistics
1:22:35 Bayesian linear regression for eQTL modeling
1:26:33 Lecture summary


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