What are the responsibilities and job description for the Computational Associate II- Translational Diabetes Genomics In Diverse Populations position at Broad Institute of MIT and Harvard?
Job Description
Apply your computational and mathematical skills to solving the hardest problems in big-data 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 large-scale 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 multi-omics 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. non-European) populations. The analyst will provide analytic support to analyze gene expression data from diabetes relevant tissues from non-European populations, and analyze the world largest and more diverse whole-exome and whole-genome 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 Life-Span in Populations of Diverse Ancestry (https://primedconsortium.org/), 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 large-scale human disease datasets. The candidate will also identify participants eligible for recruitment in recall-by-genotype 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 large-scale 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 cloud-based 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, 347-357 (2024).
2. Smith, K. et al. Multi-ancestry polygenic mechanisms of type 2 diabetes. Nat Med (2024).
3. Li, J.H. et al. Genome-wide association analysis identifies ancestry-specific genetic variation associated with acute response to metformin and glipizide in SUGAR-MGH. Diabetologia (2023).
4. Huerta-Chagoya, A. et al. The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes. Diabetologia 66, 1273-1288 (2023).
5. O'Connor, M.J. et al. Recessive Genome-Wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes. Diabetes 71, 554-565 (2022).
6. Mahajan, A. et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet 54, 560-572 (2022).
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. Guindo-Martinez, M. et al. The impact of non-additive genetic associations on age-related complex diseases. Nat Commun 12, 2436 (2021).
9. Alonso, L. et al. TIGER: The gene expression regulatory variation landscape of human pancreatic islets. Cell Rep 37, 109807 (2021).
10. Mandla, R. et al. Multi-omics characterization of type 2 diabetes associated genetic variation. medRxiv (2024).
Apply your computational and mathematical skills to solving the hardest problems in big-data 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 large-scale 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 multi-omics 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. non-European) populations. The analyst will provide analytic support to analyze gene expression data from diabetes relevant tissues from non-European populations, and analyze the world largest and more diverse whole-exome and whole-genome 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 Life-Span in Populations of Diverse Ancestry (https://primedconsortium.org/), 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 large-scale human disease datasets. The candidate will also identify participants eligible for recruitment in recall-by-genotype 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 large-scale 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 cloud-based 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, 347-357 (2024).
2. Smith, K. et al. Multi-ancestry polygenic mechanisms of type 2 diabetes. Nat Med (2024).
3. Li, J.H. et al. Genome-wide association analysis identifies ancestry-specific genetic variation associated with acute response to metformin and glipizide in SUGAR-MGH. Diabetologia (2023).
4. Huerta-Chagoya, A. et al. The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes. Diabetologia 66, 1273-1288 (2023).
5. O'Connor, M.J. et al. Recessive Genome-Wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes. Diabetes 71, 554-565 (2022).
6. Mahajan, A. et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet 54, 560-572 (2022).
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. Guindo-Martinez, M. et al. The impact of non-additive genetic associations on age-related complex diseases. Nat Commun 12, 2436 (2021).
9. Alonso, L. et al. TIGER: The gene expression regulatory variation landscape of human pancreatic islets. Cell Rep 37, 109807 (2021).
10. Mandla, R. et al. Multi-omics characterization of type 2 diabetes associated genetic variation. medRxiv (2024).
