Satoshi Asai

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.

Cross-population GWAS of 220 phenotypes in BioBank Japan, UK Biobank, and FinnGen (n=628,000) identifies 5,343 previously unreported loci and decomposes summary statistics into latent genetic components for disease subtyping.

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

A large-scale GWAS in 212,453 Japanese individuals identified 45 novel susceptibility loci for 22 diseases, highlighting the importance of non-European population studies.