Manolis Kellis
Manolis Kellis is a professor of computer science at MIT, a member of the Broad
Institute of MIT and Harvard, a principal investigator of the Computer Science and
Artificial Intelligence Lab at MIT, and head of the MIT Computational Biology
Group (compbio.mit.edu). His research includes disease circuitry, genetics,
genomics, epigenomics, coding genes, non-coding RNAs, regulatory genomics,
and comparative genomics, applied to Alzheimer's Disease, Obesity,
Schizophrenia, Cardiac Disorders, Cancer, and Immune Disorders, and multiple
other disorders. He has helped lead 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 NIH Director’s Transformative Research
Award, the Boston Patent Law Association award, 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 280 journal publications cited more
than 148,000 times. He has obtained more than 20 multi-year grants from the NIH, and his trainees hold
faculty positions at Stanford, Harvard, CMU, McGill, Johns Hopkins, UCLA, and other top universities. 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: compbio.mit.edu
From genomics to therapeutics: Single-cell dissection and manipulation of disease circuitry.
Disease-associated variants lie primarily in non-coding regions, increasing the urgency of understanding
how gene-regulatory circuitry impacts human disease. To address this challenge, we generate comparative
genomics, epigenomic, and transcriptional maps, spanning 823 human tissues, 1500 individuals, and 20
million single cells. 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 map genetic
effects into epigenomic, transcriptional, and function changes at single-cell resolution, to recognize cell-
type-specific disease-associated somatic mutations indicative of mosaicism, and to recognize multi-tissue
single-cell effects. We expand these methods to electronic health records to recognize multi-phenotype
effects of genetics, environment, and disease, combining clinical notes, lab tests, and diverse data
modalities despite missing data. We integrate large cohorts to factorize phenotype-genotype correlations to
reveal distinct biological contributors of complex diseases and traits, to partition disease complexity, and to
stratify patients for pathway-matched treatments. Lastly, we develop massively-parallel, programmable and
modular technologies for manipulating these pathways by high-throughput reporter assays, genome
editing, and gene targeting in human cells and mice, to propose new therapeutic hypotheses in
Alzheimer’s, ALS, obesity, cardiac disease, schizophrenia, aging, and cancer. These results provide a
roadmap for translating genetic findings into mechanistic insights and ultimately new therapeutic avenues
for complex disease and cancer.
Take me back to speakers!
