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Research Associate in Forest Genomics

Apply now Job no: 517541
Work type: Research Faculty
Senior management: Natural Resources
Department: For Resources & Environ Consrv
Location: Other - International
Categories: Research / Scientific, Natural Resources

Job Description

The Holliday lab at Virginia Tech, in collaboration with The American Chestnut Foundation (TACF), is seeking a research associate for two years to study the evolutionary genomics of chestnut blight resistance in Castanea. At the turn of the twentieth century, the introduction of the chestnut blight fungus (Cryphonectria parasitica) killed approximately four billion American chestnuts in the forests of Eastern United States. Asian Castanea species are resistant to chestnut blight whereas the North American and European species are susceptible. We would like to better understand the evolution and genetic networks underlying blight resistance to enable gene editing to improve the blight resistance of American chestnut. The successful candidate will take the lead on the following analyses:
• Estimate phylogenies and divergence times among host (Castanea spp.) and pathogen (Cryphonectria spp.) to test alternative hypotheses about the evolution of blight resistance.
• From whole genome resequence data, detect signatures of positive or balancing selection in blight resistant Asian Castanea species that are absent or reduced in susceptible European and North American congeners.
• Use RNA-seq timecourse data to compare gene expression in the stems of Chinese chestnut, American chestnut, and F1 hybrids of these species. Detect which gene are differentially expressed and determine whether these expression differences are regulated in cis or trans.
• Compare the annotated chromosome scale reference genomes of American chestnut and Chinese chestnut to detect presence/absence variants, copy number variants, and non-synonymous, and potentially deleterious alleles in genes and pathways hypothesized to be important for blight resistance.
• Use machine-learning approaches to integrate data sources and discover candidate genes involved in resistance. Specifically, integrate QTL mapping of resistance in hybrid populations, differential gene expression analyses, signatures of natural selection, and comparative genomic evidence.

Required Qualifications

• M.S. in population genomics, computational biology, or a related field.
• Experience and/or desire to learn bioinformatics, phylogenomics, population genomics, differential gene expression analyses, and machine learning.
• Expertise in R, Python, and Linux scripting and implementation on high performance computing clusters

 

Appointment Type

Restricted

Salary Information

Commensurate with experience

Review Date

October 4, 2021

Additional Information

The successful candidate will be required to have a criminal conviction check as well as documentation of COVID-19 vaccination or receive approval from the university for a vaccination exemption due to a medical condition or sincerely held religious belief.

About Virginia Tech

Dedicated to its motto, Ut Prosim (That I May Serve), Virginia Tech pushes the boundaries of knowledge by taking a hands-on, transdisciplinary approach to preparing scholars to be leaders and problem-solvers. A comprehensive land-grant institution that enhances the quality of life in Virginia and throughout the world, Virginia Tech is an inclusive community dedicated to knowledge, discovery, and creativity. The university offers more than 280 majors to a diverse enrollment of more than 36,000 undergraduate, graduate, and professional students in eight undergraduate colleges, a school of medicine, a veterinary medicine college, Graduate School, and Honors College. The university has a significant presence across Virginia, including the Innovation Campus in Northern Virginia; the Health Sciences and Technology Campus in Roanoke; sites in Newport News and Richmond; and numerous Extension offices and research centers. A leading global research institution, Virginia Tech conducts more than $500 million in research annually.

Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, national origin, political affiliation, race, religion, sexual orientation, or veteran status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants, or on any other basis protected by law.

If you are an individual with a disability and desire an accommodation, please contact Mary Williams at masmith5@vt.edu during regular business hours at least 10 business days prior to the event.

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