Hermann Brenner

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

PRANA adapts polygenic risk scores to non-European populations, improving predictive accuracy by 5%-20% in South Asian, East Asian, and Ashkenazi Jewish populations.

This paper develops a germline polygenic score (PRSagg) to predict prostate cancer aggressiveness and validates it across multiple datasets.

The paper introduces pathway-based polygenic risk scores (pPRS) to detect gene-environment interactions, finding a significant interaction between NSAIDs and the TGF-β/GRHR pathway in colorectal cancer.

This study identifies genetic signals associated with age at menarche, highlights genes involved in pubertal development, and links reproductive timing to later life disease risk.

This study examines differences in polygenic risk score distributions across European populations and shows how country-specific calibration can improve breast cancer risk prediction.

This paper introduces hiPRS, a novel polygenic risk score method that models high-order SNP interactions, demonstrating superior performance in simulation studies and real data applications.

This study improves polygenic risk scores for colorectal cancer by combining Asian and European ancestry data, enhancing risk prediction across diverse racial and ethnic groups.

The paper integrates DNA methylation with clinical risk factors to improve mortality prediction, identifying CpG biomarkers associated with all-cause, cardiovascular, and cancer mortality.

This paper demonstrates swarm learning for training AI models on decentralized histopathology data to predict molecular features in colorectal cancer.

The paper identifies breast cancer risk variants associated with tumor subtypes and shows their differential effects on luminal and non-luminal subtypes.

The paper improves prostate cancer risk stratification across multiple ancestries using a new polygenic hazard score with additional SNPs, showing higher hazard ratios and positive predictive value for clinically significant prostate cancer.

This study benchmarks AI methods for end-to-end computational pathology, showing ResNet and ViT outperform MIL and CLAM in mutation prediction but not in tumor subtyping.

The paper analyzes rare copy number variants (CNVs) in breast cancer cases and controls, identifying associations with known and novel susceptibility loci.

This study identifies DNA methylation sites associated with coffee and tea consumption using EWAS, finding potential links to fatty liver disease through PHGDH expression.

This study identifies 13 new genetic loci specific to proximal or distal colorectal cancer subtypes, revealing partly distinct genetic architectures for these tumor locations.

This study evaluates a polygenic hazard score in multi-ethnic populations and finds it is associated with age at prostate cancer diagnosis, aggressive diagnosis, and death across European, Asian, and African ancestries.

This study improves a polygenic hazard score for prostate cancer by adding 120 SNPs, enhancing hazard ratios but not positive predictive value of PSA testing.

This paper develops a deep learning method to predict genetic alterations from histological images across multiple cancer types.

This paper evaluates how sample size affects polygenic hazard score models for prostate cancer, finding that 20-30k samples are needed for discovery models, but 10k suffice if SNPs are pre-established.

This study identifies 32 new breast cancer susceptibility loci through GWAS, with associations varying by tumor subtype and genetic correlations among subtypes.

This study uses Mendelian randomization to show that higher physical activity levels are associated with lower risks of breast and colorectal cancer.

This study fine-maps 150 breast cancer risk regions to identify 178 high confidence target genes using genomic and functional annotations.

The paper evaluates a polygenic hazard score for prostate cancer screening, showing it improves positive predictive value by adjusting screening age based on genetic risk.

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 integrates a large prostate cancer GWAS with gene expression data to identify 235 genes associated with risk, prioritizing causal genes through Bayesian credible sets.

This paper develops a genetic risk score to personalize prostate cancer screening age based on polygenic hazard scores and validates its predictive power in a separate dataset.

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