About the Webinar
The usage of Genome-Wide Association Studies (GWAS) continues to grow in the discovery and development of disease models; however, they are often restricted to interrogation within predefined European ancestry populations. With the continued development of scalable and cost-effective technologies like low-pass whole genome sequencing (LP-WGS), however, this flaw has been addressed by enabling inexpensive genome-wide variant data generation without pre-defined population limitations.
In this webinar, Dr. Louie will first review the science behind LP-WGS technology, which uses computation methods, such as imputation, and discuss advantages and considerations of LP-WGS when compared to traditional genotyping arrays and other whole genome or targeted approaches. Jeremy Li will then review the main results from Gencove’s recently-published paper in Genome Research comparing the performance of LP-WGS to that of commonly-used genotyping arrays in the context of GWAS and polygenic risk scores. Iris Kalka will conclude the webinar by discussing her current work at the Weizmann Institute of Science constructing a cohort consisting of 10,000 healthy individuals, all of whom are deeply phenotyped and genotyped using LP-WGS, in the hopes to unravel novel early disease markers with the potential of becoming drug targets.
About the Presenters
Elizabeth Louie, Ph.D.
Supervisor, Technical Applications
Azenta Life Sciences
Dr. Elizabeth Louie is the supervisor of the technical applications team and provides consultation to genomics solutions' customers, including next generation sequencing, gene and DNA synthesis, and any custom molecular biology needs. Dr. Louie received her Ph.D. in molecular and cellular pharmacology from Stony Brook University.
Jeremy Li
Head of Data Science
Gencove
Jeremy Li is the head of data science at Gencove working on solving problems in genomics with low-pass sequencing data. Prior to joining Gencove, he worked on polygenic screening in the context of IVF at another early-stage genetics start-up. He earned his B.S. in physics and a B.A. in chemistry from the University of Washington.
Iris Kalka
Ph.D. Student
Eran Segal's lab, Weizmann Institute of Science
Iris Kalka is a Ph.D. student studying computer science at the Weizmann Institute of Science and is proud to be a part of Eran Segal’s group. Her research focuses on the integration of healthcare and biology with machine learning.
