David C. Christiani

The paper identifies lung cancer susceptibility copy number variants (CNVs) and shows their potential role in genomic instability through association analyses and functional validation.

This study identifies inherited genetic risk factors in young-onset lung cancer, finding higher burdens of rare pathogenic variants in DDR and cilia-related genes and elevated polygenic risk scores.

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.

This study evaluates how imputation from large-scale sequencing data improves the power of rare variant association studies, finding that TOPMed imputation increases detected variants and genes compared to whole-genome sequencing.

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 integrates multi-omics QTL and GWAS data to identify 123 novel biomarkers for lung cancer and reveals cross-omics regulatory pathways.

The paper identifies genetic links between impaired pulmonary function and increased lung cancer risk, showing causal relationships through Mendelian randomization and highlighting immune-related pathways.

This paper introduces a deep learning model to improve GWAS by detecting complex genetic effects, revealing potential causal pathways in disease development.

This study quantifies genetic correlations between six solid cancers and other diseases using GWAS data, finding shared heritability and functional enrichment in regulatory regions.

This study identifies shared genetic architectures between asthma and allergic diseases using genome-wide data from 112,000 individuals.