Manuel Corpas

This paper benchmarks large language models for variant interpretation and calling by evaluating safety, validity, and reproducibility rather than accuracy alone.

This paper shows that trustworthy agentic genome interpretation requires executing validated skills as code rather than relying on models to reason or retrieve information, ensuring deterministic and auditable results.

This paper introduces Genomebook, a system that encodes genetic inheritance in LLM agents, showing trait changes over generations under selective pressures.

The Peruvian Genome Project collects and analyzes genomic data from native and mestizo populations in Peru to address underrepresentation in global genome databases and uncover genetic adaptations to local environments.

This study uses low-coverage whole genome sequencing and imputation to analyze genetic variants in severe COVID-19 patients, showing high imputation accuracy and insights into clinical phenotypes.

This study identifies altitude-associated immune gene expression differences in Peruvian Andean populations, revealing distinct transcriptomic profiles linked to high-altitude adaptation.

The paper calculates polygenic risk scores for a family using whole genome sequencing and 1000 Genomes Project data to demonstrate individual-level PRS implementation.

PGP-UK explores citizen science in genomics by recruiting participants, sharing data, and developing tools to advance personalized medicine.

The paper curates a public domain genome dataset of 2,280 individuals from 23andMe under CC0 license for research use.

The paper outlines 10 rules for sharing human genomic data to maximize research value while protecting participant privacy.