MIT-Harvard CINCS / Hamilton Institute Seminar

Wednesday, December 2, 2020 - 16:00 to 17:00
Passcode: 831381

MIT-Harvard CINCS (Communications Information Networks Circuits and Signals) / Hamilton Institute Seminar

Speaker: Professor Manolis Kellis, MIT

Title: "From genomics to therapeutics: single-cell dissection and manipulation of disease circuitry"

Abstract: Disease-associated nucleotides lie primarily in non-coding regions, increasing the urgency of understanding how gene-regulatory circuitry impacts human disease. To address this challenge, we generate transcriptional and epigenomic maps of 823 human tissues, 1000 of individuals, and 7.5 million cells across patients and controls. We link variants to target genes, upstream regulators, cell types of action, and perturbed pathways, and predict causal genes and regions to provide unbiased views of disease mechanisms, sometimes re-shaping our understanding. We find that Alzheimer’s variants act primarily through immune processes, rather than neuronal processes, and the strongest genetic association with obesity acts via energy storage/dissipation rather than appetite/exercise decisions. We combine single-cell profiles, tissue-level variation, and genetic variation across healthy and diseased individuals to deconvolve bulk profiles into single-cell profiles, to recognize changes in cell type proportion associated with disease and aging, to partition genetic effects into the individual cell types where they act, and to recognize cell-type-specific and disease-associated somatic mutations in exonic regions indicative of mosaicism. We expand these methods to electronic health records to recognize meta-phenotypes associated with combinations of clinical notes, prescriptions, lab tests, and billing codes, to impute missing phenotypes in sparse medical records, and to recognize the molecular pathways underlying complex meta-phenotypes in genotyped individuals by integration of molecular phenotypes imputed in disease-relevant cell types. Lastly, we develop programmable and modular technologies for manipulating these pathways by high-throughput reporter assays, genome editing, and gene targeting in human cells and mice, demonstrating tissue-autonomous therapeutic avenues in Alzheimer’s, obesity, and cancer. These results provide a roadmap for translating genetic findings into mechanistic insights and ultimately new therapeutic avenues for complex disease and cancer.

Bio: Prof. Manolis Kellis is a world-recognized leader in the field of precision medicine, disease genomics, digital health, machine learning, and computational biology. He is a Professor of Computer Science at MIT, an Institute Member of the Broad Institute of MIT and Harvard,a Principal Investigator of the Computer Science and Artificial Intelligence Lab at MIT, and the Director of the MIT Computational Biology Group ( His research spans an unusually broad spectrum of areas, including disease genetics, epigenomics, gene circuitry, non-coding RNAs, regulatory genomics, and comparative genomics. His research has made important discoveries for Alzheimer's Disease, Obesity, Schizophrenia, Cardiac Disorders, Cancer, and Immune Disorders, and has broad implications for the study of all human disease. He has led several large-scale genomics projects, including the Roadmap Epigenomics project, the ENCODE project, the Genotype Tissue-Expression (GTEx) project, and comparative genomics projects in mammals, flies, and yeasts. He received the US Presidential Early Career Award in Science and Engineering (PECASE) by US President Barack Obama, the Mendel Medal for Outstanding Achievements in Science, the NSF CAREER award, the Alfred P. Sloan Fellowship, the Technology Review TR35 recognition, the AIT Niki Award, and the Sprowls award for the best Ph.D. thesis in computer science at MIT. He has authored over 200 journal publications, that are cited more than 100,000 times. He lived in Greece and France before moving to the US, and he studied and conducted research at MIT, the Xerox Palo Alto Research Center, and the Cold Spring Harbor Lab. For more info, see: and