Kazuyoshi Ishigaki

The paper introduces UNIChro-seq, a method for quantifying chromatin accessibility at target loci, and demonstrates its application in analyzing disease risk alleles and genome-edited variants.

CATaN integrates TF-GRNs and transcriptomes to identify genetic risk components across complex diseases, finding 588 significant CC components enriched for SNP heritability.

The paper identifies a treatment-resistant fibroblast state in RA synovitis that drives chronic inflammation through senescence-associated mechanisms and highlights therapeutic targets beyond cytokine inhibition.

SPP1hi macrophages in RA synovial fibrin deposits promote tissue hyperplasia.

Circulating miR-1285-3p in SLE promotes ABC differentiation by suppressing OXPHOS and relieving IKZF2-mediated restraint.

This study conducts a multi-ancestry GWAS of rheumatoid arthritis using the VA Million Veteran Program, identifying 152 genome-wide significant loci and improving fine-mapping resolution.

The paper identifies novel cell states and molecular profiles in lupus patients using single-cell data, linking them to disease severity and treatment outcomes.

This study identifies HLA-associated CDR3-QTLs that influence T-cell receptor composition in IBD and healthy controls, revealing disease-specific and universal effects on TRB repertoires.

This study identifies granzyme K as a driver of a novel complement activation pathway independent of classical, lectin, or alternative pathways.

The paper introduces CAMBUS, a machine-learning framework to identify functionally important open chromatin regions by measuring sequence sensitivity, and finds these regions are enriched for evolutionary constraint and disease-related variants.

The paper presents AIDA, a diverse single-cell RNA-seq atlas of immune cells, revealing sub-continental diversity impacts on immune cell properties and identifying functional genetic variants.

The paper develops SCENT, a method to create cell-type-specific enhancer-gene maps from single-cell multimodal data, identifying causal variants and genes for diseases.

The paper maps cell-type-specific eQTLs for HLA genes across multiple tissues and identifies dynamic regulation of HLA-DQ genes in immune cells.

The paper identifies a regulatory pathway involving rs7034653, FRA2, and TRAF1 that explains how a non-coding variant increases risk of inflammatory arthritis.

This study identifies eQTLs in psoriatic skin that are modulated by local inflammation, showing that gene regulation is primarily influenced by skin-specific factors rather than systemic variables.

This paper provides a statistical genetics guide for imputing HLA alleles from genotype data and offers best practices for association testing to identify HLA variants driving complex diseases.

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 investigates how HLA alleles influence TCR CDR3 composition and finds strong associations between HLA risk alleles and specific CDR3 amino acid features linked to autoimmune diseases.

The paper deconstructs RA synovium using single-cell data to identify six cell type abundance phenotypes associated with distinct inflammatory states.

This study identifies cell-type-specific transcriptomic signatures associated with SLE disease establishment and exacerbation, highlighting their clinical relevance and potential limitations in capturing genetic risk factors.

The paper analyzes splicing QTLs focusing on coding sequences to identify disease-causal variants associated with GWAS traits through novel integration and selection approaches.

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.

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 identifies state-dependent eQTLs in memory T cells by analyzing single-cell RNA and protein data from Peruvians, showing that gene regulation varies with cell states and disease-associated variants are enriched in these contexts.

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 study identifies 113 genomic loci for SLE in East Asians, including 46 novel loci, and highlights non-immune cell-related biological mechanisms.

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.

The paper develops MENTR, a machine learning framework to predict cell-type-specific ncRNA transcription from genome sequence, identifying causal variants and ncRNAs associated with complex traits.