Takayuki Morisaki

The paper identifies a genetic factor influencing BCAA metabolism independent of traditional risk factors and links it to T2D and CAD.

This study identifies genetic associations with metabolic biomarkers across ancestries and shows their predictive value for disease risk.

This study uses a computational framework to analyze genetic data from 77,539 genomes, identifying 21 novel genetic associations with 269 rare diseases.

The paper uses deep learning and genome-wide association studies to identify genetic loci associated with right heart structure and function in 40,000 individuals.

This paper introduces PolyPred and PolyPred+ to improve cross-population polygenic risk score accuracy by addressing LD, MAF, and causal effect size differences using fine-mapping and non-European training data.

The paper uses supervised learning to fine-map cis-eQTLs, identifying 20,913 additional putative causal eQTLs and 310 candidate genes.

The paper identifies sex-differential participation bias in genetic studies by performing GWAS on sex in 3.3 million individuals, finding 158 loci associated with sex and highlighting traits like BMI that may influence participation.