Position Description

We are seeking a highly motivated and analytically skilled Research Associate to support a translational genomics project focused on the genetic determinants of kidney disease in individuals with type 1 diabetes (T1D). The primary focus of this position is to analyze the association between GLP1R gene variants and renal outcomes—such as diabetic nephropathy, glomerular filtration rate (eGFR), and albuminuria—using large-scale biobank and clinical datasets. This is a remote, part-time position (20 hours/week) for a 6-month term, with the goal of generating preliminary data to support future NIH grant applications.

The selected candidate will work closely with our research team and collaborate with the Alagpulinsa Laboratory at Yale University, which brings complementary expertise in statistical genetics and diabetes research. This position offers a unique opportunity to contribute to a growing interdisciplinary effort aimed at advancing mechanistic understanding and therapeutic discovery in diabetic kidney disease.  

Key Responsibilities:

• Conduct genotype-phenotype analyses using population-scale datasets (e.g., UK Biobank, FinnGen, BioMe) to identify associations between GLP1R variation and kidney disease outcomes in T1D.
• Primary outcomes of interest include:
• Diabetic nephropathy
• Estimated glomerular filtration rate (eGFR)
• Albumin-to-creatinine ratio (ACR) and other measures of renal function
• Secondary outcomes may include:
• Cardiovascular disease (e.g., myocardial infarction, stroke)
• Glycemic control (e.g., HbA1c, insulin dose requirements)
• Apply appropriate statistical models (linear, logistic, Cox) accounting for covariates, ancestry, and data structure.
• Generate reproducible code, summary reports, and publication-ready tables and figures.
• Collaborate with the PI through regular remote meetings to review findings and revise analysis strategies.

Minimum Qualifications:

• Master’s degree (or equivalent experience) in biostatistics, statistical genetics, bioinformatics, epidemiology, or a related quantitative discipline.
• Experience analyzing human genetic data, with demonstrated proficiency in statistical software (e.g., R, Python, PLINK, SAIGE).
• Familiarity with chronic kidney disease phenotypes and population-based studies.
• Excellent organizational, communication, and documentation skills.
• Ability to work independently and manage remote responsibilities effectively.

Preferred Qualifications:

• Prior experience working with large-scale biobank datasets (e.g., UK Biobank, the All of US Research Program).
• Knowledge of GLP-1 receptor biology or type 1 diabetes pathophysiology.

Experience in phenotype curation and working with harmonized clinical data.