Bioinformatician

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Bioinformatician A

Bioinformatician A

Bioinformatician supporting the Levin Lab in the Division of Cardiovascular Medicine. The position focuses on genomic data analysis, bioinformatics pipeline development, and computational analysis of large‑scale biobank datasets to study cardiovascular disease genetics. Primary responsibilities include analyzing common and rare genetic variation from genome‑wide association studies and sequencing data, developing genetic risk models integrating genomic and clinical data, identifying therapeutic targets and causal risk factors through genetic approaches, characterizing interactions between monogenic and polygenic risk, integrating multi‑omics data (genomics, proteomics, transcriptomics), managing electronic health record data extraction and phenotyping, and creating reproducible computational workflows.

The role requires proficiency in genomic data analysis tools, statistical programming (R, Python), high‑performance computing environments, and version control systems. The candidate will collaborate with cardiovascular researchers, clinicians, and statisticians to translate genetic discoveries into clinical applications and contribute to manuscript preparation, presentation of findings, and grant writing.

This position is contingent upon favorable funding.

• Analyze common and rare genetic variation from large‑scale biobanks (eg. VA Million Veteran Program, Penn Medicine Biobank) using genome‑wide association, gene‑based tests, and burden test approaches. Apply quality control procedures and genetic analysis tools (e.g., bcftools, PLINK, SAIGE, GCTA).
• Develop and validate genetic risk models for cardiovascular diseases integrating genomic and clinical data across diverse populations. Methods include polygenic risk scores, rare variant burden scores, and integrative prediction models. Evaluate model performance and clinical utility.
• Identify therapeutic targets and causal risk factors for cardiovascular diseases using genetic approaches. Apply causal inference (e.g., Mendelian randomization) and statistical methods (eg. colocalization, genetic correlation) to assess relationships between molecular traits, risk factors, and disease outcomes.
• Characterize interactions between monogenic and polygenic risk in cardiovascular diseases. Analyze how rare pathogenic variants modify polygenic risk and disease penetrance. Integrate rare and common variant burden in risk prediction models.
• Extract, curate, and analyze electronic health record (EHR) data including ICD codes, laboratory values, medications, and clinical phenotypes. Develop and validate phenotyping algorithms.
• Develop and maintain computational pipelines…