Rajiv McCoy, Johns Hopkins University

Rajiv McCoy is an Associate Professor in the Department of Biology, with a secondary appointment in the Department of Genetic Medicine, at Johns Hopkins University. He received his PhD in Biology from Stanford University and completed postdoctoral training at the University of Washington and Princeton University before joining Johns Hopkins in 2018. His lab applies computational and statistical approaches to large-scale human genetic datasets. Research accomplishments include contributing to analysis of the first complete human genome, development of functional genomic resources for globally diverse populations, and discovery and characterization of common genetic variants associated with aneuploidy—the leading cause of human pregnancy loss. The McCoy lab is supported by funding from the NIH, NSF, Lalor Foundation, and American Society of Hematology and is actively engaged in teaching and mentoring in computational genetics at both the graduate and undergraduate levels.

Events@Brown Page

"Human genome evolution within and across generations"

Genetic variation at the level of DNA sequence mediates much of the phenotypic diversity that exists in nature, both within and between species, including humans. My lab uses the tools of computational and statistical human genetics to answer questions about how germline and somatic evolution shape 1) genome function and 2) reproduction and development. Our work spans diverse systems but is unified around the goal of developing methods for elucidating the evolutionary forces that impact our genomes across scales of biological organization. Importantly, much of the phenotypic variation in nature traces not to differences in amino acid sequences, but to regulatory variation influencing transcription and splicing of RNA. I will describe my lab’s recent work generating and analyzing a large gene expression dataset from globally diverse human individuals, toward a more complete view of the mechanisms driving gene expression diversity and evolution within our species. In addition to the evolutionary processes operating across generations, my lab is also interested in the recurrent forces of natural selection that shape human development. For example, it is estimated that less than half of all human conceptions survive to birth, primarily due to chromosome mis-segregation during meiosis and mitosis. I will describe our latest work repurposing clinical genetic testing data from in vitro fertilized embryos to understand the genetic basis of variation in human chromosome abnormalities.

CCMB Graduate Student Talks

Speakers TBA

April Wei, Cornell University

April Wei is interested in developing and applying population and evolutionary genetics theory to understand human evolution and health. Her recent research focuses on developing accurate and scalable methods for inferring complex demographic history and for understanding genetic and phenotypic evolution in light of population admixture.

Events@Brown Page

Sumaiya Iqbal, Broad Institute

Sumaiya Iqbal is a senior group leader in the Ladders to Cures (L2C) accelerator at the Broad Institute of MIT and Harvard. She is a computer scientist by training but a life science researcher by determination; she uses data science and machine learning to bridge genetics, proteomics, and therapeutics. She developed the Genomics 2 Proteins portal, a discovery tool for linking genetic screening outputs to protein sequences and structures. The focus of the Iqbal lab is connecting genetic discovery to proteins and mechanisms via several aims: building bioinformatics resources to bridge the gap across complex multi-omics data types, developing methods to unveil the molecular effect of genetic/synthetic mutations on protein structure-function relationships, and building AI-driven innovative tools for small-molecule hit identification using data from DNA-encoded library screening, and other approaches.

Events@Brown Page

Steve Reilly, Yale University

Steve Reilly is a genomicist specializing in human genetics, evolution, and gene-regulation. He is specifically interested in furthering our understanding of non-coding variation, the main cache of human genetic diversity. He develops novel computational + experimental approaches to identify and functionally characterize human variation at scale. These tools include DeepSweep: a machine learning method to identify variants under positive selection, HCR-FlowFISH: a method to directly characterize the functional targets of regulatory elements, and application of the Massively Parallel Reporter Assay (MPRA) to understand the regulatory impact of genomic variation.

Events@Brown Page