Yosuke Tanigawa

GeneSIS improves polygenic score transferability by incorporating gene-by-sex interactions, enhancing prediction accuracy in non-European populations for 32 traits.

This paper develops an extended polygenic risk score (ePRS) framework for type 2 diabetes that incorporates gene-gene and gene-environment interactions, showing improved predictive power over traditional additive models.

EpiATLAS provides a comprehensive collection of human reference epigenomes to advance epigenomic research and understanding of cellular function and disease.

This study uses polygenic scores to identify genetic associations with Alzheimer's disease phenotypes and subtypes across multiple cohorts.

The paper identifies a loss-of-function variant in GFRAL associated with increased alcohol consumption in humans and shows GDF15 reduces alcohol drinking in mice.

sc4D integrates optimal transport with autoencoder embeddings to analyze spatio-temporal single-cell transcriptomics data, revealing glial responses to Alzheimer's disease pathology and predicting therapeutic interventions.

PRISM improves polygenic score transferability across populations by integrating ancestry-aware tissue-specific genomic annotations, achieving a 13.10% performance boost for African ancestry.

This study analyzes 26 years of abstracts from two major human genetics conferences to identify geographic disparities in researcher representation and oral presentation success.

The paper identifies transcriptional changes in upper-layer cortical pyramidal neurons in Alzheimer's disease with psychosis, linking them to network dysfunction and vulnerability.

This study integrates genotype, epigenome, and phenotype data to show immune cells contribute to bipolar disorder pathogenesis, heterogeneity, and therapy through epigenomic and genetic mechanisms.

The paper develops a method to identify genetic predictors of cardiometabolic disease progression using time-to-event data from the UK Biobank, finding ten polygenic hazard scores that significantly predict disease progression.

The paper integrates multi-omic data to identify genetic and microbiome factors associated with severe COVID-19, independent of ancestry and social factors.

This study integrates rare large-effect expression variants into polygenic risk scores, showing improved prediction of complex traits by including these variants in risk assessment.

The paper develops sparse polygenic risk scores for 813 traits using UK Biobank data, showing significant predictive performance and a correlation between variant count and performance.

The paper introduces a Bayesian model comparison approach, MRP, for rare variant association studies using summary statistics, demonstrating improved power and discovery of gene-trait associations across multiple phenotypes.

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 introduces a Bayesian mixture model to cluster rare-variant effects across multiple phenotypes and demonstrates its application using UK Biobank data.

The paper develops efficient solvers for large-scale regularized regressions on genetic data, reducing memory usage and improving computational speed.

This paper introduces a latent factor model for polygenic risk scores that decomposes genetic risk into interpretable components, showing improved interpretability over standard PRS for traits like BMI and myocardial infarction.

This paper introduces a scalable framework for sparse regression on large-scale data, demonstrating improved prediction performance on UK Biobank traits using the BASIL method and snpnet package.

This study identifies sex-specific genetic variants and causal links for testosterone using UK Biobank data and Bayesian modeling.

This study identifies genetic variants in APOE and LPA associated with statin selection and higher intensity statin doses in the UK Biobank.

The paper introduces a group-regularized Cox regression method for survival analysis of rare events using common responses, showing improved prediction performance on UK Biobank data.

This paper develops a high-throughput method to generate host and pathogen genomic data from single nasopharyngeal swabs of COVID-19 patients.

The paper develops a scalable sparse reduced-rank regression method for high-dimensional multitask learning, applied to UK Biobank data to generate polygenic risk scores.

The paper analyzes HLA allele associations with 677 phenotypes in the UK Biobank, identifying 88 replicated and 90 novel associations, and uncovering non-additive and homozygous effects on disease risk.

The paper identifies rare protein-altering variants in ANGPTL7 that lower intraocular pressure and reduce glaucoma risk by analyzing genotyping data from UK Biobank and FinnGen.

This study evaluates if GWAS using digital phenotyping data from UK Biobank improves genetic association detection for diseases.

The paper identifies 138 associations between common protein-altering variants in GPCR genes and various phenotypes using UK Biobank data.

The paper shows that reported deviations from Hardy-Weinberg equilibrium for CCR5-Δ32 in Wei and Nielsen's study were due to poor genotyping of rs62625034.