Shefali Setia Verma

Shefali Setia Verma, PhD serves as an Instructor in the Department of Pathology and Laboratory Medicine, holding an entry-level faculty appointment that combines teaching and translational research responsibilities.

Education & Training:
While specific degrees are not detailed in the provided text, her credential as “PhD Instructor” indicates doctoral-level training followed by post-doctoral or fellowship experience in computational biology, genomics, or a related quantitative biomedical field.

Research Interests:
Dr. Verma’s work is anchored at the intersection of genomics, bioinformatics, and women’s health. Core themes include:

• Integration and harmonization of large-scale electronic health record (EHR) data with genomic datasets to uncover disease mechanisms.
• Development and validation of polygenic risk scores (PRS) across diverse ancestries, with special attention to African and other under-represented populations.
• Translational application of multi-omics (genomics, proteomics, imaging-derived phenotypes) to complex diseases such as endometriosis, primary open-angle glaucoma, coronary microvascular disease, and cardiometabolic traits.

Her methodological toolkit spans genome-wide association studies (GWAS), exome sequencing analyses, machine-learning-based risk modelling, and causal inference approaches, all aimed at advancing precision medicine.

Publication Portfolio:
Across 2024–2025 she has authored or co-authored more than thirty peer-reviewed articles. The collective body of work demonstrates a clear trend toward (1) expanding genomic discovery in ancestrally diverse cohorts, (2) integrating imaging or proteomic endophenotypes with germline genetics to sharpen disease prediction, and (3) translating these insights into clinically actionable tools for women’s health conditions such as endometriosis, preeclampsia, and breast cancer.

Scientific Awards & Recognition:
No specific prizes, fellowships, or named lectureships are mentioned in the provided text.

Advising & Mentorship:
No doctoral or master’s students are explicitly listed; however, her collaborative publication record suggests active mentoring and team science within large consortium efforts.

Laboratory & Consortium Affiliations:
While no dedicated laboratory name is supplied, her work is embedded within multi-institutional initiatives such as the Million Veteran Program and Global Biobank Meta-analysis Initiative, indicating access to extensive computational resources and diverse patient cohorts.