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ObjectiveGenome-wide association studies (GWAS) have identified >100 risk loci for systemic lupus erythematosus (SLE), but the disease genes at most loci remain unclear, hampering translation of these genetic discoveries. We aimed to prioritise genes underlying the 110 SLE loci that were identified in the latest East Asian GWAS meta-analysis.MethodsWe built gene expression predictive models in blood B cells, CD4+and CD8+T cells, monocytes, natural killer cells and peripheral blood cells of 105 Japanese individuals. We performed a transcriptome-wide association study (TWAS) using data from the latest genome-wide association meta-analysis of 208 370 East Asians and searched for candidate genes using TWAS and three data-driven computational approaches.ResultsTWAS identified 171 genes for SLE (p–5); 114 (66.7%) showed significance only in a single cell type; 127 (74.3%) were in SLE GWAS loci. TWAS identified a strong association betweenCD83and SLE (p–8). Meta-analysis of genetic associations in the existing 208 370 East Asian and additional 1498 cases and 3330 controls found a novel single-variant association at rs72836542 (OR=1.11, p=4.5×10–9) aroundCD83. For the 110 SLE loci, we identified 276 gene candidates, including 104 genes at recently-identified SLE novel loci. We demonstrated in vitro that putative causal variant rs61759532 exhibited an allele-specific regulatory effect onACAP1, and that presence of the SLE risk allele decreasedACAP1expression.ConclusionsCell-level TWAS in six types of immune cells complemented SLE gene discovery and guided the identification of novel genetic associations. The gene findings shed biological insights into SLE genetic associations.

ObjectiveSystemic lupus erythematosus (SLE), an autoimmune disorder, has been associated with nearly 100 susceptibility loci. Nevertheless, these loci only partially explain SLE heritability and their putative causal variants are rarely prioritised, which make challenging to elucidate disease biology. To detect new SLE loci and causal variants, we performed the largest genome-wide meta-analysis for SLE in East Asian populations.MethodsWe newly genotyped 10 029 SLE cases and 180 167 controls and subsequently meta-analysed them jointly with 3348 SLE cases and 14 826 controls from published studies in East Asians. We further applied a Bayesian statistical approach to localise the putative causal variants for SLE associations.ResultsWe identified 113 genetic regions including 46 novel loci at genome-wide significance (p−8). Conditional analysis detected 233 association signals within these loci, which suggest widespread allelic heterogeneity. We detected genome-wide associations at six new missense variants. Bayesian statistical fine-mapping analysis prioritised the putative causal variants to a small set of variants (95% credible set size ≤10) for 28 association signals. We identified 110 putative causal variants with posterior probabilities ≥0.1 for 57 SLE loci, among which we prioritised 10 most likely putative causal variants (posterior probability ≥0.8). Linkage disequilibrium score regression detected genetic correlations for SLE with albumin/globulin ratio (rg=−0.242) and non-albumin protein (rg=0.238).ConclusionThis study reiterates the power of large-scale genome-wide meta-analysis for novel genetic discovery. These findings shed light on genetic and biological understandings of SLE.

Human leukocyte antigen (HLA) gene variants in the major histocompatibility complex (MHC) region are associated with numerous complex human diseases and quantitative traits. Previous phenome-wide association studies (PheWAS) for this region demonstrated that HLA association patterns to the phenome have both population-specific and population-shared components. We performed MHC PheWAS in the Korean population by analyzing associations between phenotypes and genetic variants in the MHC region using the Korea Biobank Array project data samples from the Korean Genome and Epidemiology Study cohorts. Using this single-population dataset, we curated and analyzed 82 phenotypes for 125 673 Korean individuals after imputing HLA using CookHLA, a recently developed imputation framework. More than one-third of these phenotypes showed significant associations, confirming 56 known associations and discovering 13 novel association signals that were not reported previously. In addition, we analyzed heritability explained by the variants in the MHC region and genetic correlations among phenotypes based on the MHC variants.

We assembled an ancestrally diverse collection of genome-wide association studies of type 2 diabetes (T2D) in 180,834 cases and 1,159,055 controls (48.9% non-European descent). We identified 277 loci at genome-wide significance (p-8), including 237 attaining a more stringent trans-ancestry threshold (p-9), which were delineated to 338 distinct association signals. Trans-ancestry meta-regression offered substantial enhancements to fine-mapping, with 58.6% of associations more precisely localised due to population diversity, and 54.4% of signals resolved to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying foundations for functional investigations. Trans-ancestry genetic risk scores enhanced transferability across diverse populations, providing a step towards more effective clinical translation to improve global health.

Systemic lupus erythematosus (SLE), an autoimmune disorder, has been associated with nearly 100 susceptibility loci1-8. Nevertheless, these loci only partially explain SLE heritability and provide limited biological insight. We report the largest study of SLE in East Asians (13,377 cases and 194,993 controls), identifying 233 association signals within 113 (46 novel) genetic loci. We detect six new lead missense variants and prioritize ten most likely putative causal variants, one of which we demonstrate exhibits allele-specific regulatory effect on ACAP1 in vitro. We suggest 677 effector genes with potential for drug repurposing, and provide evidence that two distinct association signals at a single locus act on different genes (NCF2 and SMG7). We demonstrate that SLE-risk variants overlap with cell-specific active regulatory elements, notably EBNA2-mediated super-enhancers in Epstein-Barr Virus-transformed B cells, and implicate the role for non-immune cells in SLE biology. These findings shed light on genetic and biological understandings of SLE.

ObjectiveGenome-wide association studies (GWAS) in rheumatoid arthritis (RA) have discovered over 100 RA loci, explaining patient-relevant RA pathogenesis but showing a large fraction of missing heritability. As a continuous effort, we conducted GWAS in a large Korean RA case–control population.MethodsWe newly generated genome-wide variant data in two independent Korean cohorts comprising 4068 RA cases and 36 487 controls, followed by a whole-genome imputation and a meta-analysis of the disease association results in the two cohorts. By integrating publicly available omics data with the GWAS results, a series of bioinformatic analyses were conducted to prioritise the RA-risk genes in RA loci and to dissect biological mechanisms underlying disease associations.ResultsWe identified six new RA-risk loci (SLAMF6, CXCL13, SWAP70, NFKBIA, ZFP36L1 and LINC00158) with pmeta−8 and consistent disease effect sizes in the two cohorts. A total of 122 genes were prioritised from the 6 novel and 13 replicated RA loci based on physical distance, regulatory variants and chromatin interaction. Bioinformatics analyses highlighted potentially RA-relevant tissues (including immune tissues, lung and small intestine) with tissue-specific expression of RA-associated genes and suggested the immune-related gene sets (such as CD40 pathway, IL-21-mediated pathway and citrullination) and the risk-allele sharing with other diseases.ConclusionThis study identified six new RA-associated loci that contributed to better understanding of the genetic aetiology and biology in RA.

SUMMARYMeta-analyses of genome-wide association studies (GWAS) have identified >240 loci associated with type 2 diabetes (T2D), however most loci have been identified in analyses of European-ancestry individuals. To examine T2D risk in East Asian individuals, we meta-analyzed GWAS data in 77,418 cases and 356,122 controls. In the main analysis, we identified 298 distinct association signals at 178 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 56 loci newly implicated in T2D predisposition. Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes. New associations include signals in/near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect muscle and adipose differentiation. At another locus, eQTLs at two overlapping T2D signals act through two genes, NKX6-3 and ANK1, in different tissues. Association studies in diverse populations identify additional loci and elucidate disease genes, biology, and pathways.Type 2 diabetes (T2D) is a common metabolic disease primarily caused by insufficient insulin production and/or secretion by the pancreatic β cells and insulin resistance in peripheral tissues1. Most genetic loci associated with T2D have been identified in populations of European (EUR) ancestry, including a recent meta-analysis of genome-wide association studies (GWAS) of nearly 900,000 individuals of European ancestry that identified >240 loci influencing the risk of T2D2. Differences in allele frequency between ancestries affect the power to detect associations within a population, particularly among variants rare or monomorphic in one population but more frequent in another3,4. Although smaller than studies in European populations, a recent T2D meta-analysis in almost 200,000 Japanese individuals identified 28 additional loci4. The relative contributions of different pathways to the pathophysiology of T2D may also differ between ancestry groups. For example, in East Asian (EAS) populations, T2D prevalence is greater than in European populations among people of similar body mass index (BMI) or waist circumference5. We performed the largest meta-analysis of East Asian individuals to identify new genetic associations and provide insight into T2D pathogenesis.

We introduce the design and implementation of a new array, the Korea Biobank Array (referred to as KoreanChip), optimized for the Korean population and demonstrate findings from GWAS of blood biochemical traits. KoreanChip comprised >833,000 markers including >247,000 rare-frequency or functional variants estimated from >2,500 sequencing data in Koreans. Of the 833 K markers, 208 K functional markers were directly genotyped. Particularly, >89 K markers were presented in East Asians. KoreanChip achieved higher imputation performance owing to the excellent genomic coverage of 95.38% for common and 73.65% for low-frequency variants. From GWAS (Genome-wide association study) using 6,949 individuals, 28 associations were successfully recapitulated. Moreover, 9 missense variants were newly identified, of which we identified new associations between a common population-specific missense variant, rs671 (p.Glu457Lys) of ALDH2, and two traits including aspartate aminotransferase (P = 5.20 × 10−13) and alanine aminotransferase (P = 4.98 × 10−8). Furthermore, two novel missense variants of GPT with rare frequency in East Asians but extreme rarity in other populations were associated with alanine aminotransferase (rs200088103; p.Arg133Trp, P = 2.02 × 10−9 and rs748547625; p.Arg143Cys, P = 1.41 × 10−6). These variants were successfully replicated in 6,000 individuals (P = 5.30 × 10−8 and P = 1.24 × 10−6). GWAS results suggest the promising utility of KoreanChip with a substantial number of damaging variants to identify new population-specific disease-associated rare/functional variants.

Lipid levels in blood are widely used to diagnose and monitor chronic diseases. It is essential to identify the genetic traits involved in lipid metabolism for understanding chronic diseases. However, the influence of genetic traits varies depending on race, sex, age, and ethnicity. Therefore, research focusing on populations of individual countries is required, and the results can be used as a basis for comparison of results of other studies at the cross-racial and cross-country levels. In the present study, we selected lipid-related variants and evaluated their effects on lipid-related diseases in more than 14,000 subjects of three cohorts using the Illumina Human Exome Beadchip. A genome-wide association study was conducted using EPACTs after adjusting for age, sex, and recruitment area. A genome-wide significance cutoff was defined as p