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Postdocs - Therapeutics

Postdoctoral Fellow, Computational Biology

South San Francisco, California, United States

Who We Are

23andMe, the leading consumer genetics and research company, has accumulated a wealth of genotypic and phenotypic information from participants committed to improving human health through advances in genomics. Our Therapeutics team in South San Francisco leverages this data to discover and develop new treatments that can offer significant benefits for patients with serious, unmet medical needs.

This dedicated research and drug development group identifies novel targets using 23andMe's genetic database and performs preclinical research to advance programs towards clinical development. We currently have programs across several therapeutic areas, including but not limited to oncology, immunology, and cardiovascular diseases. More information about our Therapeutics team is available at https://therapeutics.23andme.com.

23andMe has committed to the initiation of a postdoctoral fellowship program that would create opportunities to utilize the 23andMe human genetics platform to conduct cutting-edge science and explore novel biology. Six inaugural fellowships are being offered within the Therapeutics division. The postdoctoral fellow will be expected to focus on innovative, genetics or biological research questions with a goal of presenting their work externally at scientific meetings and publishing in top-ranked peer-reviewed journals.  This opportunity will allow postdoctoral fellows to gain exposure to the biotechnology industry to either pursue a career in industry or bring the industry perspective back to an academic career. Computational Biology fellowships are for a two-year period, with an optional one-year extension.

 

Project Summary

We are looking for a talented postdoctoral fellow to join our team and perform cutting edge research on some of the largest open questions at the forefront of using human genetics for the greater good. 

Gene mapping, that is identifying the gene(s) that’s responsible for the association signal(s) at a GWAS locus, is a key step in genetics-based drug discovery. The human disease genetics field has come to appreciate that the simple GWAS hit + eQTL = drug target model does not adequately capture the complexity of disease biology, but there are no better models to explain the effects of ~80% of GWAS hits. Providing biological context to the ever larger number of GWAS hits by integrating genetics with differential gene expression, pathway analysis and other drug discovery strategies that were successful on their own, is a potentially promising way forward we will explore.

Genetics-based drug discovery makes use of naturally occurring genetic variation. However, biological consequences -- and therefore genetic effects as read out in GWAS --  of available genetic variation are not directly related to what the effect of therapeutically modifying the function of a protein would be. Multiple independent GWAS signals at a growing number of loci, coupled with experimental methods to characterize and computational methods to predict the effects of individual variants, open the possibility of predicting the effects of loss-of-function variants. We will investigate the feasibility of building reliable models for predicting biological effects of therapeutic intervention and their utility in prioritizing drug targets. 

 

What You’ll Do

  • Analyze 23andMe and external GWAS datasets to identify likely risk variants and implicated genes 
  • Download, curate and analyze gene expression and other functional genomics datasets to identify relevant disease genes
  • Develop models to integrate gene expression and functional genomics with GWAS results to prioritize possible drug targets, and predict the effects of variants
  • Present results in Computational Biology and Statistical Genetics group meetings, and participate in scientific exchange in other 23andMe forums
  • Describe research results at conferences, in posters and high-quality journals 
  • Make results, and if applicable any relevant code, publicly available to the human disease genetics community

 

What You’ll Bring

  • Ph.D. in Computational Biology, Bioinformatics, Statistical Genetics, Biostatistics, Computer Science, Statistics or a related quantitative field
  • Strong quantitative skills and a background in data analysis or modeling
  • Hands-on experience with human genetics and genome-wide association studies
  • Experience in analysis of large scale RNA-seq, microarray, or other genomics datasets
  • Demonstrated proficiency in data analysis using R or Python 
  • Strong organizational skills and the ability to independently propose and execute on research ideas
  • Excellent communication skills and the ability to convey complex ideas and results to other scientists

 

Strongly Preferred

  • Expertise in interpretation and fine mapping of genetic association signals
  • Track record of high quality publications

 

Note: This role will be hybrid and based in 23andMe's South San Francisco, California office.

 

Mentor

Dr. Vladimir Vacic’s Bio

Vladimir Vacic is a Research Fellow in the Computational Biology group within 23andMe’s Therapeutics division. His team is focused on developing methods and building tools to advance the understanding of genetics and genomics of human diseases, with the goal of identifying disease risk genes and contributing to target discovery. Dr. Vacic joined 23andMe in September 2015 as the Computational Biology group lead within the Research team, where he managed and contributed to internal, commercial and academic genomics projects, oversaw research sequencing and coordinated research infrastructure work with several Engineering groups. 

Prior to 23andMe, Dr. Vacic was one of the founding scientists at the New York Genome Center, where he led a cancer genomics team and actively contributed to the Center’s growth from a 20- to a 150-person organization. He earned a Ph.D. in Computer Science from the University of California at Riverside, specializing in computational biology, algorithms and machine learning. He trained as a postdoctoral fellow at Columbia University and Cold Spring Harbor Laboratory, specializing in computational genetics of psychiatric and neurodegenerative diseases. Vladimir has co-authored 40 publications and 2 patents.

 

About Us

23andMe, headquartered in Sunnyvale, CA, is a leading consumer genetics and research company. Founded in 2006, the company’s mission is to help people access, understand, and benefit from the human genome. 23andMe has pioneered direct access to genetic information as the only company with multiple FDA authorizations for genetic health risk reports. The company has created the world’s largest crowdsourced platform for genetic research, with 80 percent of its customers electing to participate. The platform also powers the 23andMe Therapeutics group, currently pursuing drug discovery programs rooted in human genetics across a spectrum of disease areas, including oncology, respiratory, and cardiovascular diseases, in addition to other therapeutic areas. More information is available at www.23andMe.com.

At 23andMe, we value a diverse, inclusive workforce and we provide equal employment opportunity for all applicants and employees. All qualified applicants for employment will be considered without regard to an individual’s race, color, sex, gender identity, gender expression, religion, age, national origin or ancestry, citizenship, physical or mental disability, medical condition, family care status, marital status, domestic partner status, sexual orientation, genetic information, military or veteran status, or any other basis protected by federal, state or local laws.  If you are unable to submit your application because of incompatible assistive technology or a disability, please contact us at accommodations-ext@23andme.com. 23andMe will reasonably accommodate qualified individuals with disabilities to the extent required by applicable law.

Please note: 23andMe does not accept agency resumes and we are not responsible for any fees related to unsolicited resumes. Thank you.

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