Computational Associate I - Diabetes Research Group

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 Dr. Alisa Manning, (a Principal Investigator of multiple NIH-funded projects) as Computational Associate I to support the development of new methodologies to unravel the genetic basis of type 2 diabetes and its application to healthcare analytics and precision medicine.

The Computational Associate I 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, 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.

The Computational Associate I will be funded by a project which aims to discover novel genetic associations with metabolic disease traits in the newly released All of Us cohort and contribute summary statistics to the Common Metabolic Disease Knowledge Portal. They will organize and analyze large complex datasets of human genetic and phenotypic data from the All of Us cohort. They will also be responsible for maintaining the data organization for the All of Us Cohort analysis and data to be submitted to the Common Metabolic Disease Knowledge Portal.

The position will be also funded by a U01 grant to develop and Polygenic Risk Scores (PRS) for Diabetes and Complications across the Life-Span in Populations of Diverse Ancestry (https://primedconsortium.org/). This project will contribute to address the disparities in PRS across ancestries, we have assembled a multi-disciplinary team to aggregate and analyze the largest existing genetic data from more than 1.8 M individuals (35% non-European) with T1D, T2D, GDM and glycemia-related complications and quantitative traits to improve the PRS prediction of diabetes and progression across the lifespan in diverse ancestries.

The Computational Associate I will be primarily based at the Broad Institute but will include an affiliation at 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 the Broad Institute. At the MGH our group performs large-scale genetic association analysis in the Mass General Brigham Biobank and longitudinal cohorts in the NHLBI’s TOPMed Study (https://topmed.nhlbi.nih.gov/).

OVERALL RESPONSIBILITY The successful candidate will have experience in 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.

Principal Duties And Responsibilities

• The candidate must apply proficiency with analysis software, diagnose and resolve user issues, and provide scientific/technical support.
• Work closely with experts in performing genetic analyses and analyzing electronic health records.
• 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.
• Perform statistical analyses of disease association, across large-scale datasets.

Qualifications

• General knowledge of statistical methods for genomic data analysis
• Creative, organized, motivated, team player
• Experience with cloud-based computational environments desirable
• Excellent communication, organization, and time management skills
• B.A.in bioinformatics, biology, statistics, data science, machine learning, computer science, or a related field, or equivalent practical experience
• Fluency in Unix, standard bioinformatics tools (Python, R, or equivalent).

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.

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