Computational Associate II- Translational Diabetes Genomics In Diverse Populations
Computational Associate II- Translational Diabetes Genomics In Diverse Populations
Posted on :
15-03-2025
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Job Description
General information
Location
Cambridge MA
Ref #
41882
Job Family
Research
Workplace
Hybrid
Date published
06Mar2025
Time Type
Full time
Description & Requirements
Job Description
Apply your computational and mathematical skills to solving the hardest problems in bigdata genomics and have a wide impact on science and clinical practice including diabetes and related cardiometabolic traits. Join a team mentored by two investigators (Dr. Josep Maria Mercader an Affiliate Faculty at the Broad Institute and Faculty at Massachusetts General Hospital) as an analyst to develop new methodologies to unravel the genetic basis of type 2 diabetes in diverse populations and its application to healthcare analytics and precision medicine.
This position will be as a member of the Dr. Josep M Mercader Affiliate Faculty at the Broad Institute and faculty member at the Diabetes Unit at the Massachusetts General Hospital and Harvard Medical School as part of the Diabetes Research Group at the Broad Institute which generates and analyzes large scale genetic datasets to illuminate the causal pathways of type 2 diabetes and to identify novel therapeutic targets. Our research group aims to understand how genetic variation or their associated molecular defect affects human physiology and diabetes risk. We thus seek to characterize the glycemic hormonal and metabolomic responses to the dietary and pharmacological perturbations according to genotype. The analyst will analyze genetic and electronic health records data from largescale biobanks including the Mass General Brigham Biobank UK Biobank All of Us Research Program data UK Biobank and others. The candidate will participate in the curation of electronic health records datasets perform quality control and genetic association analyses and analyze multiomics data using machine learning techniques for the prediction of health outcomes within these biobanks. A strong component of this research will be to expand the genetics and genomic resources for understudied (i. e. nonEuropean) populations. The analyst will provide analytic support to analyze gene expression data from diabetes relevant tissues from nonEuropean populations and analyze the world largest and more diverse wholeexome and wholegenome sequencing datasets to identify rare variants associated with complex diseases for diverse populations and contribute to the largest international genetic discovery consortia. This research is funded by a U01 NIH grant to develop and Polygenic Risk Scores (PRS) for Diabetes and Complications across the LifeSpan in Populations of Diverse Ancestry (https://primedconsortium/ the American Diabetes Association and an R01 to study the interplay between genetics and the environment in diverse populations and an FNIH grant to study genomic regulatory variation in insulin relevant target tissues from participants of Latin American Ancestry and an R01 to study and interpret variant pathogenicity in monogenic diabetes genes.
The analyst will be primarily based at the Broad Institute and will have a joint affiliation with the Massachusetts General Hospital. The Broad is a research institution affiliated with Massachusetts Institute of Technology and Harvard University that is transforming medicine and human health by building software to organize process and visualize scientific data on an unprecedented scale. The candidate will be part of the Medical and Population Genetics program Metabolism Program and the broader Diabetes Research Group at the Broad Institute. The position will involve close interaction with team members and collaborators across the Diabetes Research Group the Medical and Population Genetics and the Metabolism program at the Broad Institute. At the MGH our group conducts clinical studies to understand the physiological consequences of genetic variants associated with diabetes or other glycemic traits or those individuals at the extreme of polygenic risk scores.
OVERALL RESPONSIBILITIES:
The successful candidate will serve as an expert in the analytical pipelines and tools the generation polygenic scores for various traits the curation of phenotypes within electronic heath records for longitudinal prediction and largescale human disease datasets. The candidate will also identify participants eligible for recruitment in recallbygenotype studies and will analyze a variety of pharmacogenomic and physiological omics data derived from the clinical studies.
DUTIES AND RESPONSIBILITIES
Work closely with experts in performing genetic analyses and analyzing electronic health records. Works closely with research coordinators and clinicians to help design and analyze the resulting clinical data. The candidate must apply proficiency with analysis software diagnose and resolve user issues and provide scientific/technical support. Perform statistical analyses of disease association across largescale datasets. Experience with statistical analysis software with R coding and Unix expertise and familiarity with job scheduling in a cluster and/or cloud computing will be required.
QUALIFICATIONS
B.S with 3 years of related experience or M.A. in bioinformatics biology statistics data science machine learning computer science or a related field or equivalent practical experience with 1 year of related experience
General knowledge of statistical methods for genomic data analysis
Fluency in Unix standard bioinformatics tools (Python R or equivalent)
Interest in leading major publications and furthering their career in the genomics/biomedical space
Experience with cloudbased computational environments
Excellent communication organization and time management skills
Creative organized motivated team player
SELECTED PUBLICATIONS FROM THE TEAM
1. Suzuki K. et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature 627 .
2. Smith K. et al. Multiancestry polygenic mechanisms of type 2 diabetes. Nat Med 2024.
3. Li J.H. et al. Genomewide association analysis identifies ancestryspecific genetic variation associated with acute response to metformin and glipizide in SUGARMGH. Diabetologia 2023.
4. HuertaChagoya A. et al. The power of TOPMed imputation for the discovery of Latinoenriched rare variants associated with type 2 diabetes. Diabetologia 66 12.
5. OConnor M.J. et al. Recessive GenomeWide Metaanalysis Illuminates Genetic Architecture of Type 2 Diabetes. Diabetes 71 .
6. Mahajan A. et al. Multiancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet 54 .
7. Mercader J.M. Ng M.C.Y. Manning A.K. & Rich S.S. Predicting diabetes risk in diverse populations: what next Lancet Diabetes Endocrinol 2021.
8. GuindoMartinez M. et al. The impact of nonadditive genetic associations on agerelated complex diseases. Nat Commun.
9. Alonso L. et al. TIGER: The gene expression regulatory variation landscape of human pancreatic islets. Cell Rep 37 .
10. Mandla R. et al. Multiomics characterization of type 2 diabetes associated genetic variation. medRxiv 2024.
Required Experience:
IC
Employment Type
Full Time
Key Skills
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