Rajiv McCoy
Johns Hopkins University, MD·Genetics
Small differences in DNA from person to person change how genes are switched on, how their messages are edited, and how proteins work. Some of those differences also affect survival and reproduction, the raw material of evolution.
- Fields
Genetics
Ecology & Evolution
Computer Science
- Topics
human genetic variation
population genetics
evolutionary genomics
- Grants
- 1 known grant, through Jun 2029
What they work on
- Measure how gene expression and splicing vary within and between human populations, using RNA-seq data generated from diverse 1000 Genomes Project samples, and link the patterns to demographic history and natural selection.
- Pinpoint DNA variants that directly change gene expression or splicing, using the haplotype structure (variants inherited together) of matched DNA and RNA data from the same individuals.
- Map the diversity of RNA isoforms, the alternative versions of a gene's message, within and between human populations, using single-molecule long-read RNA sequencing of the same samples.
- Build a quantitative genetic model of the fitness tradeoff in meiotic recombination, which helps chromosomes separate correctly but also causes mutations, and test its predictions on preimplantation embryo data.
- Develop and test phylogenetic methods for clonal hematopoiesis, where a few blood stem cells dominate blood production, using simulations and published single-cell sequencing data from blood cells.
More about the research
Using humans as the model, this research builds computational and statistical tools to trace how DNA variation shapes traits in cells and whole organisms. It studies evolution at two levels: across human populations over many generations, and among cells inside a single body as it develops.
Why it matters
The work adds to basic understanding of how genetic variation drives differences in gene activity, reproduction and development. Data from diverse populations could make gene expression findings apply more broadly than results based mostly on European-ancestry samples. Modeling the costs and benefits of recombination could inform understanding of chromosome errors in early embryos. New methods for clonal hematopoiesis could clarify how blood stem cell populations change within a person.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Ryan D. Hernandez
University of California-San Francisco, CA·Genetics
Measure how race, ethnicity and genetic ancestry each contribute to molecular traits and to heart, lung, blood and sleep disorders, using TOPMed whole genome sequencing, RNA-seq gene expression and metabolomic data from over 180,000 people.
Xinjun Zhang
University of Michigan, MI·Genetics
Develop machine learning methods to detect archaic introgression from unknown 'ghost' archaic humans in East Asian genomes.
Caitlin Smukowski Heil
North Carolina State University, NC·Genetics
Build genome-wide recombination maps for multiple Saccharomyces uvarum populations using whole genome sequencing, and compare how double-strand breaks are repaired as crossovers versus non-crossover gene conversions.
The school
Johns Hopkins University: 100 labs on PI Pages, $4.1B a year on research, 622 research doctorates a year.
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