Research

I am a postdoc in Mark Daly’s lab in the Analytic & Translational Genetics Unit at Massachusetts General Hospital as well as with the Stanley Center for Psychiatric Research at the Broad Institute. I completed my PhD in Genetics and MS in Biomedical Informatics in Carlos Bustamante’s lab at Stanford.

I am interested in using genetic variation to understanding human demographic history and using evolutionary principles as a lens into complex phenotypic diversity. More specifically, I work in the following areas: human evolution and population history, statistical genetics, and regulatory variation.

Human evolution and population history

HGDP RNAseq mapUsing genetic data, I am interested in looking back through time to learn about dynamics historically shaping human history, such as migration, admixture, and population size changes. I am particularly interested in human evolution over recent history (i.e. within the last ~100 generations) during the time-span most relevant for disease variants undergoing negative selection. Given these demographic parameters, I am interested in exploring how phenotypes that were important for human evolution, such as skin pigmentation and height, evolved, and how they changed through the great human diaspora.

Statistical genetics

Genetic risk prediction
I am interested in the applicability of widely used tools and methodologies in statistical genetics to the breadth of human diversity. The vast majority of participants in genetic studies are of European descent, and consequently there are biases in genotyping array technologies, imputation quality, and genetic risk prediction accuracy to Europeans. I am developing methods to increase transferability of GWAS summary statistics and tools.

Regulatory variation

Splicing
While there is a substantial enrichment of disease-associated variants in coding regions of the genome, the majority of associations from genome-wide association studies comes from non-coding regions of the genome. Ever-growing reference databases aid the interpretation of variants especially for mutations in protein coding genes, but considerable challenges remain in interpreting non-coding regions that comprise the bulk of disease associations. Integrative omics enables a better understanding of regulatory variation.