Recent evidence suggests that a substantial portion of complex disease risk alleles modify gene expression in a cell-specific manner. To identify candidate causal genes and biological pathways of immune-related complex diseases, we conducted expression quantitative trait loci (eQTL) analysis on five subsets of immune cells (CD4 T cells, CD8 T cells, B cells, natural killer (NK) cells and monocytes) and unfractionated peripheral blood from 105 healthy Japanese volunteers. We developed a three-step analytical pipeline comprising (i) prediction of individual gene expression using our eQTL database and public epigenomic data, (ii) gene-level association analysis and (iii) prediction of cell-specific pathway activity by integrating the direction of eQTL effects. By applying this pipeline to rheumatoid arthritis data sets, we identified candidate causal genes and a cytokine pathway (upregulation of tumor necrosis factor (TNF) in CD4 T cells). Our approach is an efficient way to characterize the polygenic contributions and potential biological mechanisms of complex diseases.

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Trans-ancestry genetic research promises to improve power to detect genetic signals, fine-mapping resolution, and performances of polygenic risk score (PRS). We here present a large-scale genome-wide association study (GWAS) of rheumatoid arthritis (RA) which includes 276,020 samples of five ancestral groups. We conducted a trans-ancestry meta-analysis and identified 124 loci (P < 5 × 10-8), of which 34 were novel. Candidate genes at the novel loci suggested essential roles of the immune system (e.g., TNIP2 and TNFRSF11A) and joint tissues (e.g., WISP1) in RA etiology. Trans-ancestry fine mapping identified putatively causal variants with biological insights (e.g., LEF1). Moreover, PRS based on trans-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between European and East Asian populations. Our study provides multiple insights into the etiology of RA and improves genetic predictability of RA.

Splicing quantitative trait loci (sQTLs) are one of the major causal mechanisms in genome-wide association study (GWAS) loci, but their role in disease pathogenesis is poorly understood. One reason is the complexity of alternative splicing events producing many unknown isoforms. Here, we propose two approaches, namely integration and selection, for this complexity by focusing on protein-structure of isoforms. First, we integrate isoforms with the same coding sequence (CDS) and identify 369-601 integrated-isoform ratio QTLs (i2-rQTLs), which altered protein-structure, in six immune subsets. Second, we select CDS incomplete isoforms annotated in GENCODE and identify 175-337 isoform-ratio QTL (i-rQTL). By comprehensive long-read capture RNA-sequencing among these incomplete isoforms, we reveal 29 full-length isoforms with unannotated CDSs associated with GWAS traits. Furthermore, we show that disease-causal sQTL genes can be identified by evaluating their trans-eQTL effects. Our approaches highlight the understudied role of protein-altering sQTLs and are broadly applicable to other tissues and diseases.