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Each human, when born, has slightly different DNA sequences, which make each of us unique. The variations in DNA sequences are called genetic variants. The primary aim of genome-wide association study (GWAS) is to detect associations between genetic variants and human phenotypes. Since GWAS focuses on germ-line variants, there is no reverse causation. Therefore, GWAS is one of the few tools that can assess the causality of human diseases. In the past 10 years, many large-scale GWAS have been conducted. Although the primary outputs of GWAS are just a series of statistics, its downstream analyses provided many insights beyond simple associations: the causal mechanisms for autoimmune diseases and shared etiology between diseases. Moreover, GWAS downstream analyses generated scores potentially helpful in predicting clinical outcomes of each patient. This review focuses on GWAS for autoimmune diseases and introduces significant achievements of its downstream analyses. We also provide future directions that potentially overcome current limitations. We restrict our discussion to common autoimmune diseases (e.g., rheumatoid arthritis) since rare Mendelian diseases possess distinct genetic etiologies and are not tested by GWAS.

Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan ( n  = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen ( n total  = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics. Genome-wide analyses in BioBank Japan, UK Biobank and FinnGen identify ~5,000 new loci associated with 220 human traits. Statistical decomposition of matrices of phenome-wide summary statistics further highlights variants underpinning diseases across populations.

Human height is a representative phenotype to elucidate genetic architecture. However, the majority of large studies have been performed in European population. To investigate the rare and low-frequency variants associated with height, we construct a reference panel ( N  = 3,541) for genotype imputation by integrating the whole-genome sequence data from 1,037 Japanese with that of the 1000 Genomes Project, and perform a genome-wide association study in 191,787 Japanese. We report 573 height-associated variants, including 22 rare and 42 low-frequency variants. These 64 variants explain 1.7% of the phenotypic variance. Furthermore, a gene-based analysis identifies two genes with multiple height-increasing rare and low-frequency nonsynonymous variants ( SLC27A3 and CYP26B1 ; P SKAT-O  < 2.5 × 10 −6 ). Our analysis shows a general tendency of the effect sizes of rare variants towards increasing height, which is contrary to findings among Europeans, suggesting that height-associated rare variants are under different selection pressure in Japanese and European populations. Thousands of genetic loci are known to associate with human height, but these are mainly based on studies in European ancestry populations. Here, Akiyama et al. construct a genotype reference panel for the Japanese population followed by GWAS and report 573 height associated variants in 191,787 Japanese.

Poor trans-ancestry portability of polygenic risk scores is a consequence of Eurocentric genetic studies and limited knowledge of shared causal variants. Leveraging regulatory annotations may improve portability by prioritizing functional over tagging variants. We constructed a resource of 707 cell-type-specific IMPACT regulatory annotations by aggregating 5,345 epigenetic datasets to predict binding patterns of 142 transcription factors across 245 cell types. We then partitioned the common SNP heritability of 111 genome-wide association study summary statistics of European (average n  ≈ 189,000) and East Asian (average n  ≈ 157,000) origin. IMPACT annotations captured consistent SNP heritability between populations, suggesting prioritization of shared functional variants. Variant prioritization using IMPACT resulted in increased trans-ancestry portability of polygenic risk scores from Europeans to East Asians across all 21 phenotypes analyzed (49.9% mean relative increase in R 2 ). Our study identifies a crucial role for functional annotations such as IMPACT to improve the trans-ancestry portability of genetic data. A resource of cell-type-specific IMPACT regulatory annotations improves the trans-ancestry portability of polygenic risk scores by prioritizing variants enriched for trait heritability.

The extent to which the biology of oncogenesis and ageing are shaped by factors that distinguish human populations is unknown. Haematopoietic clones with acquired mutations become common with advancing age and can lead to blood cancers 1 – 10 . Here we describe shared and population-specific patterns of genomic mutations and clonal selection in haematopoietic cells on the basis of 33,250 autosomal mosaic chromosomal alterations that we detected in 179,417 Japanese participants in the BioBank Japan cohort and compared with analogous data from the UK Biobank. In this long-lived Japanese population, mosaic chromosomal alterations were detected in more than 35.0% (s.e.m., 1.4%) of individuals older than 90 years, which suggests that such clones trend towards inevitability with advancing age. Japanese and European individuals exhibited key differences in the genomic locations of mutations in their respective haematopoietic clones; these differences predicted the relative rates of chronic lymphocytic leukaemia (which is more common among European individuals) and T cell leukaemia (which is more common among Japanese individuals) in these populations. Three different mutational precursors of chronic lymphocytic leukaemia (including trisomy 12, loss of chromosomes 13q and 13q, and copy-neutral loss of heterozygosity) were between two and six times less common among Japanese individuals, which suggests that the Japanese and European populations differ in selective pressures on clones long before the development of clinically apparent chronic lymphocytic leukaemia. Japanese and British populations also exhibited very different rates of clones that arose from B and T cell lineages, which predicted the relative rates of B and T cell cancers in these populations. We identified six previously undescribed loci at which inherited variants predispose to mosaic chromosomal alterations that duplicate or remove the inherited risk alleles, including large-effect rare variants at NBN , MRE11 and CTU2 (odds ratio, 28–91). We suggest that selective pressures on clones are modulated by factors that are specific to human populations. Further genomic characterization of clonal selection and cancer in populations from around the world is therefore warranted. Population-specific patterns of genomic mutations and selection of haematopoietic clones in Japanese and European participants predict the divergent rates of chronic lymphocytic leukaemia and T cell leukaemia in these populations.

To understand the genetics of type 2 diabetes in people of Japanese ancestry, we conducted A meta-analysis of four genome-wide association studies (GWAS; 36,614 cases and 155,150 controls of Japanese ancestry). We identified 88 type 2 diabetes–associated loci ( P  < 5.0 × 10 −8 ) with 115 independent signals ( P  < 5.0 × 10 −6 ), of which 28 loci with 30 signals were novel. Twenty-eight missense variants were in linkage disequilibrium ( r 2  > 0.6) with the lead variants. Among the 28 missense variants, three previously unreported variants had distinct minor allele frequency (MAF) spectra between people of Japanese and European ancestry (MAF JPN  > 0.05 versus MAF EUR  < 0.01), including missense variants in genes related to pancreatic acinar cells ( GP2 ) and insulin secretion ( GLP1R ). Transethnic comparisons of the molecular pathways identified from the GWAS results highlight both ethnically shared and heterogeneous effects of a series of pathways on type 2 diabetes (for example, monogenic diabetes and beta cells). Genome-wide association analyses identify 28 new susceptibility loci for type 2 diabetes in the Japanese population. Transethnic comparisons highlight the key role of beta cell dysfunction in type 2 diabetes across different ancestry groups.

To perform detailed fine-mapping of the major-histocompatibility-complex region, we conducted next-generation sequencing (NGS)-based typing of the 33 human leukocyte antigen (HLA) genes in 1,120 individuals of Japanese ancestry, providing a high-resolution allele catalog and linkage-disequilibrium structure of both classical and nonclassical HLA genes. Together with population-specific deep-whole-genome-sequencing data ( n  = 1,276), we conducted NGS-based HLA, single-nucleotide-variant and indel imputation of large-scale genome-wide-association-study data from 166,190 Japanese individuals. A phenome-wide association study assessing 106 clinical phenotypes identified abundant, significant genotype–phenotype associations across 52 phenotypes. Fine-mapping highlighted multiple association patterns conferring independent risks from classical HLA genes. Region-wide heritability estimates and genetic-correlation network analysis elucidated the polygenic architecture shared across the phenotypes. Sequencing of the MHC region in the Japanese population provides insight into population-specific allelic and structural variability. These data enable discovery and fine-mapping of genotype–phenotype associations across 52 phenotypes.

Understanding natural selection is crucial to unveiling evolution of modern humans. Here, we report natural selection signatures in the Japanese population using 2234 high-depth whole-genome sequence (WGS) data (25.9×). Using rare singletons, we identify signals of very recent selection for the past 2000–3000 years in multiple loci (ADH cluster, MHC region, BRAP-ALDH2 , SERHL2 ). In large-scale genome-wide association study (GWAS) dataset ( n  = 171,176), variants with selection signatures show enrichment in heterogeneity of derived allele frequency spectra among the geographic regions of Japan, highlighted by two major regional clusters (Hondo and Ryukyu). While the selection signatures do not show enrichment in archaic hominin-derived genome sequences, they overlap with the SNPs associated with the modern human traits. The strongest overlaps are observed for the alcohol or nutrition metabolism-related traits. Our study illustrates the value of high-depth WGS to understand evolution and their relationship with disease risk. Recent natural selection left signals in human genomes. Here, Okada et al. generate high-depth whole-genome sequence (WGS) data (25.9×) from 2,234 Japanese people of the BioBank Japan Project (BBJ), and identify signals of recent natural selection which overlap variants associated with human traits.

Common variants strongly influence the risk of human autoimmunity. Two categories of variants contribute substantially to the risk: (i) coding variants of HLA genes and (ii) non-coding variants at the non-HLA loci. We recently developed a novel analytic pipeline of T cell receptor (TCR) repertoire to understand how HLA coding variants influence the risk. We identified that the risk variants increase the frequency of auto-reactive T cells. In addition, to understand how non-coding variants contribute to the risk, the researchers conducted integrative analyses using expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) and demonstrated that the risk non-coding variants dysregulate specific genes’ expression and splicing. These studies provided novel insight into the immunological consequences of two major genetic risks, and we will introduce these research achievements in detail in this review.

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 (i 2 -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. Splicing QTL (sQTL), genetic variants regulating alternative splicing, can be biologically important, but complex to detect and interpret. Here, the authors identify sQTL by focusing on protein coding sequences, as an alternative to junction-based approaches.