Tuuli Lappalainen, PhD and Olga Troyanskaya, PhD; Function: Genomic Innovation & Precision Medicine; CPMI Conference, April 5, 2022

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Tuuli Lappalainen, PhD (Start of video - Minute 40:30)

Professor, KTH Royal Institute of Technology; Director, Genomics Platform and the National Genomics Infrastructure, SciLifeLab, Sweden; Associate Faculty Member, New York Genome Center

Tuuli Lappalainen, PhD, is a professor at KTH Royal Institute of Technology; the director of the Genomics Platform and the National Genomics Infrastructure of SciLifeLab, Sweden; and an associate faculty member at the New York Genome Center. Dr. Lappalainen received her PhD in genetics from the University of Helsinki, followed by postdoctoral research at the University of Geneva and Stanford University. Her research focuses on functional genetic variation in human populations and its contribution to human traits and diseases, which her lab studies using both computational and experimental approaches. She has pioneered in integrating large-scale genome and transcriptome sequencing data to understand how genetic variation affects gene expression, which gives insight to biological mechanisms underlying genetic disease risk. She has contributed to many of the most important international research consortia in human genetics, including the 1000 Genomes Project, the Geuvadis Consortium, the GTEx Project, MoTrPAC, and TOPMed. She is a principal investigator of numerous NIH grants and a recipient of the Leena Peltonen Prize for Excellence in Human Genetics and the Harold and Golden Lamport Award in Excellence in Basic Research.

Functional Variation in the Human Genome: Lessons from the Transcriptome


Detailed characterization of molecular and cellular effects of genetic variants is essential for understanding biological processes that underlie genetic associations to disease. A particularly scalable approach has been linking genetic variants to effects in the transcriptome that is amenable for scalable measurements in human populations and in experimental settings, including at the single cell level. Our multi-omic analysis in human cohorts in the TOPMed project has identified genetic and environmental effects on molecular variation together with their complex interplay with clinical phenotypes. Furthermore, in this talk I will discuss how CRISPRi silencing of regulatory elements followed by single-cell analysis provides novel insights of mechanisms of genetic associations to complex traits. Altogether, these diverse approaches for integration genome and transcriptome data uncover functional genetic architecture of human traits, and they enhance our understanding of both basic biology and precision medicine applications.

Olga Troyanskaya, PhD (Minute 40:30 - End of video)

Professor, Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science, Princeton University; Deputy Director for Genomics, Flatiron Institute, Simons Foundation

Olga Troyanskaya is a professor in the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science at Princeton University and deputy director for genomics at the Flatiron Institute of the Simons Foundation. Her group is focused on developing machine learning methods in genomics and precision medicine. Dr. Troyanskaya received her PhD from Stanford University. She is a fellow of the International Society for Computational Biology and of the ACM and has been honored as one of the top young technology innovators by the MIT Technology Review; she is a recipient of the Sloan Research Fellowship, the NSF CAREER Award, the Howard Wentz Faculty Award, and the Blavatnik Finalist Award. She is also the 2011 recipient of the Overton Prize from the International Society for Computational Biology and the 2014 recipient of the Ira Herskowitz Award from the Genetic Society of America.

Enabling Precision Medicine: Decoding the Human Genome with Deep Learning Models


A key challenge in medicine and biology is to develop a complete understanding of the genomic architecture of disease. Yet the increasingly wide availability of “omics” and clinical data, including whole genome sequencing, has far outpaced our ability to interpret these sequences. Challenges include interpreting the 98 percent of the genome that is noncoding to identify variants that are functional and may lead to disease, detangling genomic signals regulating celllineage- specific gene expression, and mapping the resulting genetic circuits and networks in disease-relevant cell-types to specific phenotypes and clinical outcomes. I will discuss methods that address these challenges and highlight their applications to cancer and mental health disorders.