Nathaniel Rothman

This study examines ecDNA in lung cancer, finding it in 18.9% of patients and associating it with worse survival and genomic instability.

This study identifies novel genetic risk loci for pancreatic ductal adenocarcinoma in African ancestry individuals through admixture mapping and GWAS.

The paper presents an isoform-level lung cell atlas using single-cell long-read RNA-sequencing, revealing genetic effects on isoform regulation that improve cell-type classification and identify novel variants associated with lung cancer risk.

The paper constructs a single-cell eQTL dataset from Asian never-smokers, identifying alveolar cell-specific genes linked to lung cancer susceptibility.

The paper identifies functional variants and VNTRs at the 5p15.33 locus using fine-mapping, MPRA, and CRISPRi screens, revealing their role in cancer susceptibility through antagonistic pleiotropy.

This paper develops a deep learning model to predict molecular alterations in never-smoker lung adenocarcinomas from histological images, achieving high accuracy for most features.

LDscore provides a cloud-based, Python 3 web platform for LDSC analysis, improving accessibility and scalability for heritability and genetic correlation studies.

This study identifies three distinct evolutionary trajectories in lung adenocarcinomas using whole-genome sequencing, revealing that some never-smoking tumors follow smoking-like paths driven by KRAS mutations.

The paper identifies three subtypes of NS-LUAD using RNA-seq data and develops a 60-gene signature that predicts survival across subgroups.

This study analyzes 1024 lung adenocarcinomas to identify evolutionary patterns and mutational mechanisms, revealing distinct trajectories linked to smoking, ancestry, and retrotransposition.

The paper identifies cell-type specific lung cancer susceptibility genes by integrating single-cell multi-omics data with GWAS loci.

This study identifies 40 circulating proteins associated with cancer risk using MR and colocalization, highlighting potential therapeutic targets and adverse effects of protein-altering interventions.

This study assesses polygenicity and risk prediction using common variants across 14 cancers, finding varying numbers of susceptibility variants and risk score effectiveness.

This paper improves polygenic risk scores by applying winners curse correction and variable thresholds to GWAS summary data, enhancing prediction accuracy for some diseases.