Yoichiro Kamatani

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.

The paper sequenced 3,135 Japanese individuals to identify genetic variants and construct a reference haplotype, highlighting the need for diverse population inclusion in genetic catalogs.

The paper develops and validates multi-ancestry polygenic risk scores for LUAD in East Asian never-smokers, showing improved risk prediction compared to East Asian-only models.

ERICH-GENE collects and analyzes genomic and neuroimaging data from over 10,000 multi-ethnic ICH cases to identify genetic risk factors and trans-ethnic variation in ICH.

This study develops a polygenic risk score (metaGRS) for predicting recurrent stroke in the Japanese population using data from the BioBank Japan cohort, finding it associates with increased recurrence risk, particularly in individuals without hypertension.

This study identifies 128 BP-associated loci in Chinese adults through GWAS and shows causal links between BP traits and CVD using Mendelian randomisation.

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 identifies 67 genetic loci associated with head size, finding strong overlaps with cancer pathways and genes, suggesting shared genetic mechanisms between head growth and cancer risk.

The paper evaluates genotype imputation accuracy in multi-ancestry reference panels and finds that the Rsq metric overestimates dosage r2 due to systematic bias in imputation quality.

This study identifies genetic loci and causal traits associated with ossification of the posterior longitudinal ligament of the spine using GWAS and Mendelian randomization analyses.

This paper improves genetic scores for adiposity traits in East Asian populations by using trans-ancestry meta-analysis, increasing variance explained compared to European-derived scores.

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 develops a tool to genotype mobile element variants (MEVs) using short-read data and identifies population-specific MEs linked to disease and gene regulation.

The paper develops DeepSpaCE, a deep learning model that predicts spatial transcriptomics profiles to improve histological interpretation of breast cancer tissue sections without additional experiments.

This study conducts a trans-ancestry GWAS of rheumatoid arthritis, identifying 124 loci, 34 novel, and shows improved polygenic risk score performance across populations.

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.

This paper identifies new genetic loci for atrial fibrillation through a trans-ancestry meta-analysis and develops a polygenic risk score linked to cardioembolic risk.

Fine-mapping 148 complex traits in three biobanks with combined n = 811,261 yields 4,518 causal variant-trait pairs, 285 of them repeating in more than one population.

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

A coronary artery disease GWAS of 181,522 cases among 1,165,690 participants yields 241 associations (54 new); an eight-predictor consensus nominates 185 candidate causal genes, 94 with ≥3 predictors.

This paper introduces MAMA, a method for multi-ancestry GWAS meta-analysis, which identifies novel genetic associations and improves power across populations.

DEEP*HLA improves HLA allelic imputation for low-frequency alleles, enabling trans-ethnic MHC fine-mapping of type 1 diabetes with high accuracy.

This study combines multiple GWAS of ageing traits to identify genetic components associated with human ageing and finds LPA and VCAM1 as potential drug targets.

Tractor improves GWAS power and resolution by including admixed individuals through local ancestry-aware analysis.

The paper investigates the association between age-related mosaic chromosomal alterations and infection risk in 767,891 individuals without blood cancer, finding increased infection susceptibility linked to mCAs.

This study identifies 5,552 blood-cell trait-variant associations across 746,667 individuals from 5 global populations, including 71 novel loci and ancestry-specific variants not found in European ancestry populations.

This study identifies 5,106 new genetic variants associated with blood cell traits using large-scale GWAS, highlighting the polygenic and monogenic basis of hematopoiesis.

The paper analyzes HHV-6 integration in human genomes, identifies co-evolution with chr22q, and reports evidence of in vivo excision and reactivation.