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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.

The diversity in our genome is crucial to understanding the demographic history of worldwide populations. However, we have yet to know whether subtle genetic differences within a population can be disentangled, or whether they have an impact on complex traits. Here we apply dimensionality reduction methods (PCA, t -SNE, PCA- t -SNE, UMAP, and PCA-UMAP) to biobank-derived genomic data of a Japanese population ( n  = 169,719). Dimensionality reduction reveals fine-scale population structure, conspicuously differentiating adjacent insular subpopulations. We further enluciate the demographic landscape of these Japanese subpopulations using population genetics analyses. Finally, we perform phenome-wide polygenic risk score (PRS) analyses on 67 complex traits. Differences in PRS between the deconvoluted subpopulations are not always concordant with those in the observed phenotypes, suggesting that the PRS differences might reflect biases from the uncorrected structure, in a trait-dependent manner. This study suggests that such an uncorrected structure can be a potential pitfall in the clinical application of PRS. Population structure, even subtle differences within seemingly homogenous populations, can have an impact on the accuracy of polygenic prediction. Here, Sakaue et al. use dimensionality reduction methods to reveal fine-scale structure in the Biobank Japan cohort and explore the performance of polygenic risk scores.

While polygenic risk scores (PRSs) are poised to be translated into clinical practice through prediction of inborn health risks 1 , a strategy to utilize genetics to prioritize modifiable risk factors driving heath outcome is warranted 2 . To this end, we investigated the association of the genetic susceptibility to complex traits with human lifespan in collaboration with three worldwide biobanks ( n total  = 675,898; BioBank Japan ( n  = 179,066), UK Biobank ( n  = 361,194) and FinnGen ( n  = 135,638)). In contrast to observational studies, in which discerning the cause-and-effect can be difficult, PRSs could help to identify the driver biomarkers affecting human lifespan. A high systolic blood pressure PRS was trans-ethnically associated with a shorter lifespan (hazard ratio = 1.03[1.02–1.04], P meta  = 3.9 × 10 −13 ) and parental lifespan (hazard ratio = 1.06[1.06–1.07], P  = 2.0 × 10 −86 ). The obesity PRS showed distinct effects on lifespan in Japanese and European individuals ( P heterogeneity  = 9.5 × 10 −8 for BMI). The causal effect of blood pressure and obesity on lifespan was further supported by Mendelian randomization studies. Beyond genotype–phenotype associations, our trans-biobank study offers a new value of PRSs in prioritization of risk factors that could be potential targets of medical treatment to improve population health. Cross-biobank analysis reveals that polygenic risk scores (PRS) for hypertension and obesity are associated with shorter lifespan, serving as a proof-of-principle that PRS could pinpoint causal risk factors that affect long-term health outcomes.

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.

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.

In this Tools of the Trade article, Saori Sakaue describes SCENT, a tool to generate cell-type-specific enhancer–gene maps using single-cell multi-omics data, which can help identify disease-causal, non-coding variants and genes from GWAS-defined loci.

In autoimmune diseases such as rheumatoid arthritis, the immune system attacks the body’s own cells. Developing a precise understanding of the cell states where noncoding autoimmune risk variants impart causal mechanisms is critical to developing curative therapies. Here, to identify noncoding regions with accessible chromatin that associate with cell-state-defining gene expression patterns, we leveraged multimodal single-nucleus RNA and assay for transposase-accessible chromatin (ATAC) sequencing data across 28,674 cells from the inflamed synovial tissue of 12 donors. Specifically, we used a multivariate Poisson model to predict peak accessibility from single-nucleus RNA sequencing principal components. For 14 autoimmune diseases, we discovered that cell-state-dependent (‘dynamic’) chromatin accessibility peaks in immune cell types were enriched for heritability, compared with cell-state-invariant (‘cs-invariant’) peaks. These dynamic peaks marked regulatory elements associated with T peripheral helper, regulatory T, dendritic and STAT1 + CXCL10 + myeloid cell states. We argue that dynamic regulatory elements can help identify precise cell states enriched for disease-critical genetic variation. Analysis of single-nucleus RNA sequencing and single-nucleus assay for transposase-accessible chromatin with sequencing data derived from synovium of patients with rheumatoid arthritis identifies regions with dynamic accessibility that correlate with cell states. Dynamic peaks are more strongly enriched for autoimmune disease heritability.

A previously healthy 36-year-old Japanese woman presented with blood pressure that was difficult to control after an emergency cesarean section. CT revealed a disrupted abdominal aorta, severe stenosis of the renal arteries, and development of numerous collateral arteries. A previously healthy 36-year-old Japanese woman presented with blood pressure that was difficult to control after she had undergone an emergency cesarean section at 29 weeks of gestation because of severe hypertension and fetal growth restriction. During pregnancy, her blood pressure had been more than 180/100 mm Hg, and it continued to be more than 160/90 mm Hg after delivery. The patient began taking 40 mg of nifedipine, and the blood pressure level stabilized at approximately 140/80 mm Hg. Computed tomographic (CT) imaging revealed a completely disrupted abdominal aorta, severe stenosis of the renal arteries bilaterally, and development of numerous . . .

Studying the genetic regulation of protein expression (through protein quantitative trait loci (pQTLs)) offers a deeper understanding of regulatory variants uncharacterized by mRNA expression regulation (expression QTLs (eQTLs)) studies. Here we report cis -eQTL and cis -pQTL statistical fine-mapping from 1,405 genotyped samples with blood mRNA and 2,932 plasma samples of protein expression, as part of the Japan COVID-19 Task Force (JCTF). Fine-mapped eQTLs ( n  = 3,464) were enriched for 932 variants validated with a massively parallel reporter assay. Fine-mapped pQTLs ( n  = 582) were enriched for missense variations on structured and extracellular domains, although the possibility of epitope-binding artifacts remains. Trans -eQTL and trans -pQTL analysis highlighted associations of class I HLA allele variation with KIR genes. We contrast the multi-tissue origin of plasma protein with blood mRNA, contributing to the limited colocalization level, distinct regulatory mechanisms and trait relevance of eQTLs and pQTLs. We report a negative correlation between ABO mRNA and protein expression because of linkage disequilibrium between distinct nearby eQTLs and pQTLs. Statistical fine-mapping of mRNA and protein quantitative trait loci in blood samples from the Japan COVID-19 Task Force sheds light on regulatory mechanisms and disease associations.

Genetic variation in the human leukocyte antigen (HLA) loci is associated with risk of immune-mediated diseases, but the molecular effects of HLA polymorphism are unclear. Here we examined the effects of HLA genetic variation on the expression of 2940 plasma proteins across 45,330 Europeans in the UK Biobank, with replication analyses across multiple ancestry groups. We detected 504 proteins affected by HLA variants (HLA-pQTL), including widespread trans effects by autoimmune disease risk alleles. More than 80% of the HLA-pQTL fine-mapped to amino acid positions in the peptide binding groove. HLA-I and II affected proteins expressed in similar cell types but in different pathways of both adaptive and innate immunity. Finally, we investigated potential HLA-pQTL effects on disease by integrating HLA-pQTL with fine-mapped HLA-disease signals in the UK Biobank. Our data reveal the diverse effects of HLA genetic variation and aid the interpretation of associations between HLA alleles and immune-mediated diseases. Genetic variation in the HLA locus is associated with many traits, including autoimmune diseases. Here, the authors show that HLA genetic variation exerts widespread trans effects on plasma protein expression, aiding interpretation of associations between HLA alleles and immune mediated diseases.