Nothing in the genome makes sense except in the light of the transcriptome


We study functional genetic variation in human populations, and the mechanisms how it affects human traits and disease. Our work combines computational analysis of high-throughput sequencing data, population genetics, and experimental work.We focus in particular on studying genetic effects on the transcriptome traits, which has further applications in other traits at the cellular and individual level.

While some of our projects are closely related to individual diseases, our overall goal is to uncover general rules of the genomic sources of human variation, which is applicable to a variety of different diseases.

We are a highly collaborative lab. We work closely together with other labs at the New York Genome Center where we are physically located, and with colleagues at the Department of Systems Biology at Columbia University. Furthermore, we have collaborators in other NYGC partner institutes as well as in other institutes in the U.S. and abroad.


Characterizing variants that affect the transcriptome

Our lab has a strong track record in integration of large-scale genome and transcriptome sequencing data sets to characterize the genetic architecture of variants that affect the transcriptome. These include both common and rare regulatory variants as well as certain types of coding variants. This offers interesting opportunities for interpreting mechanisms of genetic contribution to human disease such as schizophrenia, autism, and rheumatoid arthritis. We are also interested in leveraging on transcriptome sequencing to improve the interpretation of the personal genome. We are active members of the Genotype Tissue Expression (GTEx) project, and use the GTEx data for most of our computational analyses. While genome and transcriptome data from RNA-sequencing are the main data types that we analyze, the approaches are applicable to epigenomic and other cellular data sets.

Mechanisms of modified penetrance of genetic variants

The functional effect of genetic variants is not a binary or a constant phenomenon, but depends on the context of the variant – including the cell type, external environment, and the genomic environment of other variants in the genome and the haplotype. Better understanding of these factors is a major area of interest in the lab, with some of the specific projects including tissue-specificity of cis-regulatory variants in the GTEx data set, immune response eQTLs where genetic variants contribute to inter-individual response to immunological stimuli, and haplotypic epistasis between regulatory and coding variants. Better understanding of the factors that modify the effect of genetic variants is important for characterizing the full spectrum of effects that variants may have, as well as in applications of genetics in precision medicine.

Computational methods development

Studying biology from big and complex data sets requires deep understanding of the properties and biases of the data, and sophisticated methods for extracting biologically meaningful information. To this end, we have developed and published several methods, approaches, and software tools for allele-specific expression and eQTL analysis.

Genome editing approaches to functional variants in human populations

In addition to our computational work, we run a small wetlab where we use CRISPR-Cas genome editing in human cell lines to obtain a deeper understanding of how genetic variants affect the cell. This includes work to discover causal regulatory variants and demonstrate their activity by genome editing assays, and in-depth characterization of the effects and modifiers of specific variants of particular interest.