We are seeking a highly qualified and motivated Biostatistician with a strong background in statistics and computational biology to study the genetic and biological causes of complex eye diseases, including glaucoma, age-related macular degeneration, and diabetic retinopathy, and detect genetic factors for differential drug response. The candidate will work under the supervision of Dr. Ayellet Segrè and Dr. Janey Wiggs in the Ocular Genomic Institute and Department of Ophthalmology at Massachusetts Eye and Ear (MEE), whom are affiliated with the Medical and Population Genetics Program at the Broad Institute of Harvard and MIT (oculargenomics.meei.harvard.edu/labs/). MEE is a teaching hospital of Harvard Medical School and is an international leader for treatment and research in both Ophthalmology and Otolaryngology. Dr. Segrè develops and applies new statistical and computational methods that integrate functional genomics (e.g., eQTLs, single cell RNA-seq data) data with large-scale human association and sequencing studies to identify causal regulatory mechanisms, genes and pathways that lead to common eye diseases, with the ultimate goal of proposing new preventative and therapeutic targets for eye disease. Dr. Wiggs, a glaucoma specialist at MEE, studies the genetic basis of common and rare forms of glaucoma, and leads the largest genome-wide association study (GWAS) meta-analyses for primary open angle glaucoma, with national and international collaborators. To learn more about the Segrè lab please visit: www.asegrelab.org/
The successful candidate will have a strong background in statistics, statistical genetics, computational genomics, bioinformatics, or a related quantitative field, and strong programming skills, and should be excited to contribute to advancing the science and medicine of eye disease. Research experience with large-scale genomic data desired. Research projects will involve statistical analyses of GWAS, whole genome and whole exome sequencing data, and large-scale genotype and phenotype data from the UK Biobank, and integration with functional genomic data, to identify novel genetic risk factors and biological processes associated with complex eye diseases.
• Apply and develop pipelines for preprocessing, quality control, imputation, and phasing of genotype data (array, whole exome, and whole genome-sequencing), using available and custom-built tools.
• Pharmacogenetic study of differential response to diabetic retinopathy treatments.
• Perform genetic association analyses at the variant, regulatory element, gene, and gene set levels in large cohorts (e.g., UK Biobank) to identify common and rare variants, genes, and pathways associated with glaucoma and other common eye diseases. Build polygenic risk scores.
• Develop and apply new statistical and computational methods that integrate functional genomic and single cell transcriptomic data with genome-wide association and sequencing studies to gain biological insights into the causal mechanisms of eye disease.
• Organize all scripts in a publicly available repository (e.g., github) with clear documentation.
• Critically review, analyze, and communicate results to our team and collaborators.
• Work on both collaborative and independent projects and write up work for publications.
• M.Sc. or Ph.D. in statistical genetics, (bio)statistics, computational genomics, bioinformatics, mathematics, computer science, or a related quantitative discipline required. If MSc level, several years of experience preferred
• Strong programming skills and in-depth experience with several programming languages required, e.g., Python, R, Matlab, C++.
• Experience with Unix/Linux environments required, including shell scripting.
• Research experience with statistical analyses of large-scale data required; experience in the field of genomics and bioinformatics highly desired.
• Demonstrate critical thinking, rigorous work, and ability to meet deadlines.
• Strong personal skills, and excellent organization and verbal and written communication skills.
• Ability to work effectively both independently and collaboratively in a fast-paced, academic environment and evolving field.
DESIRED EXPERIENCE AND SKILLS:
• Research experience in statistical genetics, genomics, or next-generation sequencing desired.
• Background in regression models, machine learning, statistical genetics, or computational genomics a plus.
• A strong motivation to contribute to the development of statistical methods for genomic research of eye diseases.
If interested, please send your CV, a cover letter describing your previous research experience and future research interests, and contact information for 3 references to Dr. Ayellet Segrè: ayellet_segre [at] meei [dot] harvard [dot] edu.