Gregory Allan Wray

About

Gregory Allan Wray is a Professor of Biology at Duke University, with appointments also in Evolutionary Anthropology and Cell Biology. His research focuses on the evolution of genes and genomes, aiming to understand the origins of biological diversity. He studies changes in gene expression using empirical and computational approaches, spanning scales from single nucleotides to entire genomes. His work includes understanding the functional consequences and fitness components of specific genetic variants within regulatory sequences of genes related to ecologically relevant traits. Additionally, he develops molecular and analytical methods to detect changes in gene function across genomes, including statistical frameworks for detecting natural selection on regulatory elements and empirical approaches to identify functional variation in transcriptional regulation. His research investigates functional variation within gene networks in wild populations and natural perturbations, primarily focusing on model systems such as sea urchins and primates, including humans.

Research topics

• Biology
• Genetics
• Computer Science
• Neuroscience
• Machine Learning
• Medicine
• Virology
• Demography
• Immunology
• Zoology

Selected publications

• An early cell shape transition drives evolutionary expansion of the human forebrain

  Cell · 2021 · 261 citations

  The human brain has undergone rapid expansion since humans diverged from other great apes, but the mechanism of this human-specific enlargement is still unknown. Here, we use cerebral organoids derived from human, gorilla, and chimpanzee cells to study developmental mechanisms driving evolutionary brain expansion. We find that neuroepithelial differentiation is a protracted process in apes, involving a previously unrecognized transition state characterized by a change in cell shape. Furthermore,…

  DOI

• Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders

  Nature Communications · 2021 · 107 citations

  Cellular heterogeneity in the human brain obscures the identification of robust cellular regulatory networks, which is necessary to understand the function of non-coding elements and the impact of non-coding genetic variation. Here we integrate genome-wide chromosome conformation data from purified neurons and glia with transcriptomic and enhancer profiles, to characterize the gene regulatory landscape of two major cell classes in the human brain. We then leverage cell-type-specific regulatory l…

  PublisherOA PDFDOI

• The hard clam genome reveals massive expansion and diversification of inhibitors of apoptosis in Bivalvia

  BMC Biology · 2021 · 92 citations

  BACKGROUND: Inhibitors of apoptosis (IAPs) are critical regulators of programmed cell death that are essential for development, oncogenesis, and immune and stress responses. However, available knowledge regarding IAP is largely biased toward humans and model species, while the distribution, function, and evolutionary novelties of this gene family remain poorly understood in many taxa, including Mollusca, the second most speciose phylum of Metazoa. RESULTS: Here, we present a chromosome-level gen…

  DOI

• The epidemiology of Plasmodium vivax among adults in the Democratic Republic of the Congo

  Nature Communications · 2021 · 44 citations

  Reports of P. vivax infections among Duffy-negative hosts have accumulated throughout sub-Saharan Africa. Despite this growing body of evidence, no nationally representative epidemiological surveys of P. vivax in sub-Saharan Africa have been performed. To overcome this gap in knowledge, we screened over 17,000 adults in the Democratic Republic of the Congo (DRC) for P. vivax using samples from the 2013-2014 Demographic Health Survey. Overall, we found a 2.97% (95% CI: 2.28%, 3.65%) prevalence of…

  DOI

• Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

  Human Genetics · 2021 · 39 citations

  The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regress…

  DOI

Recent grants

• Evolutionary Rewiring of a Developmental Gene Regulatory Network

  NSF · $500k · 2015–2018

• Collaborative Research: Assembling the Echinoderm Tree of Life

  NSF · $174k · 2011–2015

• Collaborative Research: Genetic Bases for the Evolution of Human Diet

  NSF · $1.6M · 2008–2014

Frequent coauthors

• Sudhir Kumar

  Malaviya National Institute of Technology Jaipur

  164 shared

• Pamela S. Soltis

  Florida Museum of Natural History

  164 shared

• Chris Henze

  Ames Research Center

  100 shared

• Michael J. Sanderson

  Georgia State University

  100 shared

• James S. Farris

  Gothenburg Botanic Garden

  100 shared

• Victor A. Albert

  University at Buffalo, State University of New York

  100 shared

• David M. Hillis

  The University of Texas at Austin

  100 shared

• Billie J. Swalla

  University of Washington

  85 shared

Education

• Ph.D., Biology

  Duke University

  1987

• B.S.

  College of William and Mary

  1981

Awards & honors

• Embryonic Cell Recognition: Specificity Determinants Researc…
• Roles for uniquely human enhancers in brain development and…

Similar researchers at Duke University

• Susan C. Albertsh-110
• Xinnian Dongh-103
• Rytas J. Vilgalysh-93