Girish N. Nadkarni

This study uses EHR data to characterize FMR1 premutation carriers, identifying common diagnoses before mutation confirmation and highlighting diagnostic delays.

This study compares gene expression in living and postmortem human brain tissue, finding significant differences that challenge the assumption that postmortem data accurately represents living brain gene expression.

This study compares A-to-I RNA editing in postmortem and living human brain tissues, revealing significant differences in editing levels and their association with cellular processes and neurological conditions.

The paper compares gene expression differences between living and postmortem human brain samples using snRNA-seq and develops a model to account for these differences in analysis.

This paper conducts a multi-ancestry time-to-event GWAS to identify genetic variants associated with incident cardiovascular disease in people with type 2 diabetes, finding three novel loci and confirming known CAD variants.

This study identifies 31 plasma proteins and expands genetic loci associated with PCOS, highlighting metabolic and hormonal influences and polygenic risk effects on cardio-metabolic outcomes.

The paper identifies four SNPs in the LILRB3 gene associated with graft failure in African American kidney transplant recipients and links them to immune-related diseases.

This study identifies 135 genomic loci associated with venous thromboembolism risk through cross-ancestry GWAS and provides insights into biological pathways and potential therapeutic targets.

This paper develops a genome-wide polygenic score for chronic kidney disease that incorporates APOL1 risk genotypes and shows consistent performance across four major continental ancestries.

This study evaluates the prognostic value of polygenic risk scores for schizophrenia and finds they do not improve outcome prediction beyond clinical features.

This study identifies robust gene-phenotype associations by analyzing rare loss-of-function variants in exome-wide data linked to electronic health records.

The paper identifies gene- and protein-level features distinguishing gain-of-function and loss-of-function mutations using NLP and machine learning on a new database of pathogenic mutations.

This study identifies 5,552 blood-cell trait-variant associations across 746,667 individuals from 5 global populations, including 71 novel loci and ancestry-specific variants not found in European ancestry populations.

This study identifies 5,106 new genetic variants associated with blood cell traits using large-scale GWAS, highlighting the polygenic and monogenic basis of hematopoiesis.

The paper identifies 7 novel IBD-causing genes in Ashkenazi Jewish IBD patients using exome sequencing and analyzes their overlap with early-onset IBD and other diseases.

This study identifies two stable subtypes of PCOS with distinct genetic associations through phenotypic clustering and GWAS.

This study identifies 210 genetic loci associated with the PR interval through multi-ancestry GWAS, highlighting their role in cardiac conduction and cardiovascular disease.

This study shows that natural language processing applied to electronic health records can accurately identify patients with non-alcoholic fatty liver disease and detect those at risk for disease progression.

This study identifies 93 genetic loci associated with kidney function across four ancestry groups and highlights causal effects on kidney-related diseases.

This study uses unsupervised machine learning on EHR data to identify two subphenotypes of sepsis-associated AKI with differing mortality rates, emphasizing the importance of intra-patient variability.

This study fine-maps type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps, identifying 73 variants with >80% posterior probability of association.

The paper identifies a common collagen disease in Puerto Ricans using identity-by-descent mapping and genomic data from a health system.