Pejaver Lab
Principal Investigator: Vikas Rao Pejaver
Institution: Icahn School of Medicine at Mount Sinai (New York, NY)
Primary Subfield: Genetics/Genomics
Recruitment Status: ✅ Accepting PhD Students ✅ Hiring Postdocs
Research Focus
PROJECT SUMMARY Machine learning-based pathogenicity predictors, trained on known disease and non-disease-implicated variants, predict whether a variant is pathogenic or benign. They have become a key component of genetic variant discovery and clinical genetic testing. However, their use in individual-level genetic and genomic disease risk prediction has been limited by their incompatibility with typically reported measures of risk, and terminology associated with clinical decision-making. Variant pathogenicity predictors generally serve as a key interpretation tool for rare variants in the absence of large enough cohorts to achieve statistical power. As the clinical deployment of genetic and genomic risk prediction models becomes more widespread, it is essential that rare variants be readily incorporated into these models and the risk conferred by such variants be correctly accounted for. To address these gaps, the proposed study will develop methods for the systematic integration and calibration of variant pathogenicity predictors into genetic and genomic disease risk prediction and will test the hypothesis that these methods lead to more accurate and clinically interpretable pre…
Active NIH Awards
- 5R01HG013350-03: Systematic integration of variant interpretation tools into genetic and genomic risk prediction (2026)
- 5R01HG013350-02: Systematic integration of variant interpretation tools into genetic and genomic risk prediction (2025)
- 5R00LM012992-05: Integrative data science approaches for rare disease discovery in health records (2024)
- 1R01HG013350-01: Systematic integration of variant interpretation tools into genetic and genomic risk prediction (2024)
- 1R03OD038386-01: Variant impact prediction on Common Fund data sets towards drug repurposing for rare genetic diseases (2024)
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