Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations.

Most loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 × 10-9, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.

The Polygenic and Monogenic Basis of Blood Traits and Diseases.

Blood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation.

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

The power of genetic diversity in genome-wide association studies of lipids.

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice.

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids.

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

Genetic architecture of microRNA expression and its link to complex diseases in the Japanese population.

Understanding the genetic effects on non-coding RNA (ncRNA) expression facilitates functional characterization of disease-associated genetic loci. Among several classes of ncRNAs, microRNAs (miRNAs) are key post-transcriptional gene regulators. Despite its biological importance, previous studies on the genetic architecture of miRNA expression focused mostly on the European individuals, underrepresented in other populations. Here, we mapped miRNA expression quantitative trait loci (miRNA-eQTL) for 343 miRNAs in 141 Japanese using small RNA sequencing and whole-genome sequencing, identifying 1275 cis-miRNA-eQTL variants for 40 miRNAs (false discovery rate < 0.2). Of these, 25 miRNAs having eQTL were unreported in the European studies, including 5 miRNAs with their lead variant monomorphic in the European populations, which demonstrates the value of miRNA-eQTL analysis in diverse ancestral populations. MiRNAs with eQTL effect showed allele-specific expression (ASE; e.g. miR-146a-3p), and ASE analysis further detected cis-regulatory variants not captured by the conventional miRNA-eQTL mapping (e.g. miR-933). We identified a copy number variation associated with miRNA expression (e.g. miR-570-3p, P = 7.2 × 10-6), which contributes to a more comprehensive landscape of miRNA-eQTLs. To elucidate a post-transcriptional modification in miRNAs, we created a catalog of miRNA-editing sites, including 10 canonical and 6 non-canonical sites. Finally, by integrating the miRNA-eQTLs and Japanese genome-wide association studies of 25 complex traits (mean n = 192 833), we conducted a transcriptome-wide association study, identifying miR-1908-5p as a potential mediator for adult height, colorectal cancer and type 2 diabetes (P < 9.1 × 10-5). Our study broadens the population diversity in ncRNA-eQTL studies and contributes to functional annotation of disease-associated loci found in non-European populations.

Genome-Wide Natural Selection Signatures Are Linked to Genetic Risk of Modern Phenotypes in the Japanese Population.

Elucidation of natural selection signatures and relationships with phenotype spectra is important to understand adaptive evolution of modern humans. Here, we conducted a genome-wide scan of selection signatures of the Japanese population by estimating locus-specific time to the most recent common ancestor using the ascertained sequentially Markovian coalescent (ASMC), from the biobank-based large-scale genome-wide association study data of 170,882 subjects. We identified 29 genetic loci with selection signatures satisfying the genome-wide significance. The signatures were most evident at the alcohol dehydrogenase (ADH) gene cluster locus at 4q23 (PASMC = 2.2 × 10-36), followed by relatively strong selection at the FAM96A (15q22), MYOF (10q23), 13q21, GRIA2 (4q32), and ASAP2 (2p25) loci (PASMC < 1.0 × 10-10). The additional analysis interrogating extended haplotypes (integrated haplotype score) showed robust concordance of the detected signatures, contributing to fine-mapping of the genes, and provided allelic directional insights into selection pressure (e.g., positive selection for ADH1B-Arg48His and HLA-DPB1*04:01). The phenome-wide selection enrichment analysis with the trait-associated variants identified a variety of the modern human phenotypes involved in the adaptation of Japanese. We observed population-specific evidence of enrichment with the alcohol-related phenotypes, anthropometric and biochemical clinical measurements, and immune-related diseases, differently from the findings in Europeans using the UK Biobank resource. Our study demonstrated population-specific features of the selection signatures in Japanese, highlighting a value of the natural selection study using the nation-wide biobank-scale genome and phenotype data.

GREP: genome for REPositioning drugs.

SUMMARY: Making use of accumulated genetic knowledge for clinical practice is our next goal in human genetics. Here we introduce GREP (Genome for REPositioning drugs), a standalone python software to quantify an enrichment of the user-defined set of genes in the target of clinical indication categories and to capture potentially repositionable drugs targeting the gene set. We show that genes identified by the large-scale genome-wide association studies were robustly enriched in the approved drugs to treat the trait of interest. This enrichment analysis was also highly applicable to other sets of biological genes such as those identified by gene expression studies and genes somatically mutated in cancers. This software should accelerate investigators to reposition drugs to other indications with the guidance of known genomics. AVAILABILITY AND IMPLEMENTATION: GREP is available at https://github.com/saorisakaue/GREP as a python source code. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integration of genetics and miRNA-target gene network identified disease biology implicated in tissue specificity.

MicroRNAs (miRNAs) modulate the post-transcriptional regulation of target genes and are related to biology of complex human traits, but genetic landscape of miRNAs remains largely unknown. Given the strikingly tissue-specific miRNA expression profiles, we here expand a previous method to quantitatively evaluate enrichment of genome-wide association study (GWAS) signals on miRNA-target gene networks (MIGWAS) to further estimate tissue-specific enrichment. Our approach integrates tissue-specific expression profiles of miRNAs (∼1800 miRNAs in 179 cells) with GWAS to test whether polygenic signals enrich in miRNA-target gene networks and whether they fall within specific tissues. We applied MIGWAS to 49 GWASs (nTotal = 3 520 246), and successfully identified biologically relevant tissues. Further, MIGWAS could point miRNAs as candidate biomarkers of the trait. As an illustrative example, we performed differentially expressed miRNA analysis between rheumatoid arthritis (RA) patients and healthy controls (n = 63). We identified novel biomarker miRNAs (e.g. hsa-miR-762) by integrating differentially expressed miRNAs with MIGWAS results for RA, as well as novel associated loci with significant genetic risk (rs56656810 at MIR762 at 16q11; n = 91 482, P = 3.6 × 10-8). Our result highlighted that miRNA-target gene network contributes to human disease genetics in a cell type-specific manner, which could yield an efficient screening of miRNAs as promising biomarkers.

Next-generation sequencing identifies contribution of both class I and II HLA genes on susceptibility of multiple sclerosis in Japanese.

BACKGROUND: The spectrum of classical and non-classical HLA genes related to the risk of multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) in the Japanese population has not been studied in detail. We conducted a case-control analysis of classical and non-classical HLA genes. METHODS: We used next-generation sequencing (NGS)-based HLA genotyping methods for mapping risk for 45 MS patients, 31 NMOSD patients, and 429 healthy controls. We evaluated the association of the HLA variants with the risk of MS and NMOSD using logistic regression analysis and Fisher's exact test. RESULTS: We confirmed that HLA-DRB1*15:01 showed the strongest association with MS (P = 2.1 × 10-5; odds ratio [OR] = 3.44, 95% confidence interval [95% CI] = 1.95-6.07). Stepwise conditional analysis identified HLA-DRB1*04:05, HLA-B*39:01, and HLA-B*15:01 as being associated with independent MS susceptibility (PConditional < 8.3 × 10-4). With respect to amino acid polymorphisms in HLA genes, we found that phenylalanine at HLA-DQß1 position 9 had the strongest effect on MS susceptibility (P = 3.7 × 10-8, OR = 3.48, 95% CI = 2.23-5.43). MS risk at HLA-DQß1 Phe9 was independent of HLA-DRB1*15:01 (PConditional = 1.5 × 10-5, OR = 2.91, 95% CI = 1.79-4.72), while HLA-DRB1*15:01 was just significant when conditioned on HLA-DQß1 Phe9 (PConditional = 0.037). Regarding a case-control analysis for NMOSD, HLA-DQA1*05:03 had a significant association with NMOSD (P = 1.5 × 10-4, OR = 6.96, 95% CI = 2.55-19.0). CONCLUSIONS: We identified HLA variants associated with the risk of MS and NMOSD. Our study contributes to the understanding of the genetic architecture of MS and NMOSD in the Japanese population.

Human genetics contributes to the understanding of disease pathophysiology and drug discovery.

BACKGROUND: Today, sequencing technology has markedly reduced the cost and time needed to read the human genome than ever before. Genome-wide association studies have successfully identified a number of disease risk genes. CONTRIBUTION TO UNDERSTANDING OF DISEASE PATHOPHYSIOLOGY: Recent advancements in genomic technology have substantially furthered our understanding of the pathophysiology of many diseases, such as rheumatoid arthritis. TOWARD DRUG DISCOVERY AND FUTURE DIRECTION: Accumulating genomic information is now expected to accelerate the discovery of novel drugs. Rapidly growing multi-dimensional information in life sciences would make human genetics significantly important in the near future.

Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles.

Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.

Identifying genetic variants that influence the abundance of cell states in single-cell data.

Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype-Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P = 1.96 × 10-11) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

The influence of HLA genetic variation on plasma protein expression.

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.

The chromatin landscape of pathogenic transcriptional cell states in rheumatoid arthritis.

Synovial tissue inflammation is a hallmark of rheumatoid arthritis (RA). Recent work has identified prominent pathogenic cell states in inflamed RA synovial tissue, such as T peripheral helper cells; however, the epigenetic regulation of these states has yet to be defined. Here, we examine genome-wide open chromatin at single-cell resolution in 30 synovial tissue samples, including 12 samples with transcriptional data in multimodal experiments. We identify 24 chromatin classes and predict their associated transcription factors, including a CD8 + GZMK+ class associated with EOMES and a lining fibroblast class associated with AP-1. By integrating with an RA tissue transcriptional atlas, we propose that these chromatin classes represent 'superstates' corresponding to multiple transcriptional cell states. Finally, we demonstrate the utility of this RA tissue chromatin atlas through the associations between disease phenotypes and chromatin class abundance, as well as the nomination of classes mediating the effects of putatively causal RA genetic variants.

Statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance.

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.

Clonal associations between lymphocyte subsets and functional states in rheumatoid arthritis synovium.

Rheumatoid arthritis (RA) is an autoimmune disease involving antigen-specific T and B cells. Here, we perform single-cell RNA and repertoire sequencing on paired synovial tissue and blood samples from 12 seropositive RA patients. We identify clonally expanded CD4 + T cells, including CCL5+ cells and T peripheral helper (Tph) cells, which show a prominent transcriptomic signature of recent activation and effector function. CD8 + T cells show higher oligoclonality than CD4 + T cells, with the largest synovial clones enriched in GZMK+ cells. CD8 + T cells with possibly virus-reactive TCRs are distributed across transcriptomic clusters. In the B cell compartment, NR4A1+ activated B cells, and plasma cells are enriched in the synovium and demonstrate substantial clonal expansion. We identify synovial plasma cells that share BCRs with synovial ABC, memory, and activated B cells. Receptor-ligand analysis predicted IFNG and TNFRSF members as mediators of synovial Tph-B cell interactions. Together, these results reveal clonal relationships between functionally distinct lymphocyte populations that infiltrate the synovium of patients with RA.