Abstract

Detailed characterization of cellular effects of genetic variants is essential for understanding biological processes that underlie genetic associations to disease, as well as basic genome function. One approach to address this challenge is to combine genomic data to a cellular phenotype, such as the transcriptome measured by RNA -sequencing. The comprehensive resources of genome and transcriptome data have allowed us to map genetic effects on the transcriptome across multiple human tissues and conditions. In addition to the traditional eQTL approach, allelic expression analysis has proven to be valuable for understanding regulatory variants and their interactions with coding variants, and selective constraint on regulatory mutations. In this talk, I will discuss our recent work in integrated analysis of genome and transcriptome variation as a powerful approach to understanding genetic variants and genome function.

Tuuli Lappalainen New York Genome Center, New York, USA Department of Systems Biology, Columbia University Medical Center, New York, USA

Tuuli Lappalainen is an Assistant Investigator and Core Member at the New York Genome Center, and an Assistant Professor in the Department of Systems Biology at Columbia University. She got her PhD from University of Helsinki in 2009, and did postdoctoral research with Manolis Dermitzakis at University of Geneva, and Carlos Bustamante at Stanford University. Her research focuses on functional genetic variation in human populations and its contribution to traits and diseases. She has pioneered in integration of large-scale genome, transcriptome and epigenetic data to learn how genetic variation affects gene regulation – the largely unknown factor that underlies the majority of human diversity and disease risk. Her research group tackles these questions by both computational analysis of large data sets and experimental work using genome editing assays. She has contributed to several international research consortia in human genomics, including the 1000 Genomes Project, the Geuvadis project, and the Genotype Tissue Expression (GTEx) Project.