eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis.

Sepsis is a clinical syndrome of life-threatening organ dysfunction caused by a dysregulated response to infection, for which disease heterogeneity is a major obstacle to developing targeted treatments. We have previously identified gene-expression-based patient subgroups (sepsis response signatures [SRS]) informative for outcome and underlying pathophysiology. Here, we aimed to investigate the role of genetic variation in determining the host transcriptomic response and to delineate regulatory networks underlying SRS. Using genotyping and RNA-sequencing data on 638 adult sepsis patients, we report 16,049 independent expression (eQTLs) and 32 co-expression module (modQTLs) quantitative trait loci in this disease context. We identified significant interactions between SRS and genotype for 1,578 SNP-gene pairs and combined transcription factor (TF) binding site information (SNP2TFBS) and predicted regulon activity (DoRothEA) to identify candidate upstream regulators. Overall, these approaches identified putative mechanistic links between host genetic variation, cell subtypes, and the individual transcriptomic response to infection.

Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction.

Restless legs syndrome (RLS) affects up to 10% of older adults. Their healthcare is impeded by delayed diagnosis and insufficient treatment. To advance disease prediction and find new entry points for therapy, we performed meta-analyses of genome-wide association studies in 116,647 individuals with RLS (cases) and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci eightfold to 164, including three on chromosome X. Sex-specific meta-analyses revealed largely overlapping genetic predispositions of the sexes (rg = 0.96). Locus annotation prioritized druggable genes such as glutamate receptors 1 and 4, and Mendelian randomization indicated RLS as a causal risk factor for diabetes. Machine learning approaches combining genetic and nongenetic information performed best in risk prediction (area under the curve (AUC) = 0.82-0.91). In summary, we identified targets for drug development and repurposing, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up and provided evidence that nonlinear interactions are likely relevant to RLS risk prediction.

Global diversity, recurrent evolution, and recent selection on amylase structural haplotypes in humans.

The adoption of agriculture, first documented ~12,000 years ago in the Fertile Crescent, triggered a rapid shift toward starch-rich diets in human populations. Amylase genes facilitate starch digestion and increased salivary amylase copy number has been observed in some modern human populations with high starch intake, though evidence of recent selection is lacking. Here, using 52 long-read diploid assemblies and short read data from ~5,600 contemporary and ancient humans, we resolve the diversity, evolutionary history, and selective impact of structural variation at the amylase locus. We find that amylase genes have higher copy numbers in populations with agricultural subsistence compared to fishing, hunting, and pastoral groups. We identify 28 distinct amylase structural architectures and demonstrate that nearly identical structures have arisen recurrently on different haplotype backgrounds throughout recent human history. AMY1 and AMY2A genes each exhibit multiple duplications/deletions with mutation rates >10,000-fold the SNP mutation rate, whereas AMY2B gene duplications share a single origin. Using a pangenome graph-based approach to infer structural haplotypes across thousands of humans, we identify extensively duplicated haplotypes present at higher frequencies in modern day populations with traditionally agricultural diets. Leveraging 533 ancient human genomes we find that duplication-containing haplotypes (i.e. haplotypes with more amylase gene copies than the ancestral haplotype) have increased in frequency more than seven-fold over the last 12,000 years providing evidence for recent selection in West Eurasians. Together, our study highlights the potential impacts of the agricultural revolution on human genomes and the importance of long-read sequencing in identifying signatures of selection at structurally complex loci.

Inherited polygenic effects on common hematological traits influence clonal selection on JAK2V617F and the development of myeloproliferative neoplasms.

Myeloproliferative neoplasms (MPNs) are chronic cancers characterized by overproduction of mature blood cells. Their causative somatic mutations, for example, JAK2V617F, are common in the population, yet only a minority of carriers develop MPN. Here we show that the inherited polygenic loci that underlie common hematological traits influence JAK2V617F clonal expansion. We identify polygenic risk scores (PGSs) for monocyte count and plateletcrit as new risk factors for JAK2V617F positivity. PGSs for several hematological traits influenced the risk of different MPN subtypes, with low PGSs for two platelet traits also showing protective effects in JAK2V617F carriers, making them two to three times less likely to have essential thrombocythemia than carriers with high PGSs. We observed that extreme hematological PGSs may contribute to an MPN diagnosis in the absence of somatic driver mutations. Our study showcases how polygenic backgrounds underlying common hematological traits influence both clonal selection on somatic mutations and the subsequent phenotype of cancer.

A genome-wide association study of blood cell morphology identifies cellular proteins implicated in disease aetiology.

Blood cells contain functionally important intracellular structures, such as granules, critical to immunity and thrombosis. Quantitative variation in these structures has not been subjected previously to large-scale genetic analysis. We perform genome-wide association studies of 63 flow-cytometry derived cellular phenotypes-including cell-type specific measures of granularity, nucleic acid content and reactivity-in 41,515 participants in the INTERVAL study. We identify 2172 distinct variant-trait associations, including associations near genes coding for proteins in organelles implicated in inflammatory and thrombotic diseases. By integrating with epigenetic data we show that many intracellular structures are likely to be determined in immature precursor cells. By integrating with proteomic data we identify the transcription factor FOG2 as an early regulator of platelet formation and α-granularity. Finally, we show that colocalisation of our associations with disease risk signals can suggest aetiological cell-types-variants in IL2RA and ITGA4 respectively mirror the known effects of daclizumab in multiple sclerosis and vedolizumab in inflammatory bowel disease.

Methods and Insights from Single-Cell Expression Quantitative Trait Loci.

Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.

Building pangenome graphs.

Pangenome graphs can represent all variation between multiple genomes, but existing methods for constructing them are biased due to reference-guided approaches. In response, we have developed PanGenome Graph Builder (PGGB), a reference-free pipeline for constructing unbi-ased pangenome graphs. PGGB uses all-to-all whole-genome alignments and learned graph embeddings to build and iteratively refine a model in which we can identify variation, measure conservation, detect recombination events, and infer phylogenetic relationships.

An atlas of genetic scores to predict multi-omic traits.

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

GWAS of genetic factors affecting white blood cell morphological parameters in Sardinians uncovers influence of chromosome 11 innate immunity gene cluster on eosinophil morphology.

Few genome-wide association studies (GWAS) analyzing genetic regulation of morphological traits of white blood cells have been reported. We carried out a GWAS of 12 morphological traits in 869 individuals from the general population of Sardinia, Italy. These traits, included measures of cell volume, conductivity and light scatter in four white-cell populations (eosinophils, lymphocytes, monocytes, neutrophils). This analysis yielded seven statistically significant signals, four of which were novel (four novel, PRG2, P2RX3, two of CDK6). Five signals were replicated in the independent INTERVAL cohort of 11 822 individuals. The most interesting signal with large effect size on eosinophil scatter (P-value = 8.33 x 10-32, beta = -1.651, se = 0.1351) falls within the innate immunity cluster on chromosome 11, and is located in the PRG2 gene. Computational analyses revealed that a rare, Sardinian-specific PRG2:p.Ser148Pro mutation modifies PRG2 amino acid contacts and protein dynamics in a manner that could possibly explain the changes observed in eosinophil morphology. Our discoveries shed light on genetics of morphological traits. For the first time, we describe such large effect size on eosinophils morphology that is relatively frequent in Sardinian population.

Rare and common genetic determinants of metabolic individuality and their effects on human health.

Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10-11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.

Whole-exome sequencing identifies rare genetic variants associated with human plasma metabolites.

Metabolite levels measured in the human population are endophenotypes for biological processes. We combined sequencing data for 3,924 (whole-exome sequencing, WES, discovery) and 2,805 (whole-genome sequencing, WGS, replication) donors from a prospective cohort of blood donors in England. We used multiple approaches to select and aggregate rare genetic variants (minor allele frequency [MAF] < 0.1%) in protein-coding regions and tested their associations with 995 metabolites measured in plasma by using ultra-high-performance liquid chromatography-tandem mass spectrometry. We identified 40 novel associations implicating rare coding variants (27 genes and 38 metabolites), of which 28 (15 genes and 28 metabolites) were replicated. We developed algorithms to prioritize putative driver variants at each locus and used mediation and Mendelian randomization analyses to test directionality at associations of metabolite and protein levels at the ACY1 locus. Overall, 66% of reported associations implicate gene targets of approved drugs or bioactive drug-like compounds, contributing to drug targets' validating efforts.

Immune disease variants modulate gene expression in regulatory CD4+ T cells.

Identifying cellular functions dysregulated by disease-associated variants could implicate novel pathways for drug targeting or modulation in cell therapies. However, follow-up studies can be challenging if disease-relevant cell types are difficult to sample. Variants associated with immune diseases point toward the role of CD4+ regulatory T cells (Treg cells). We mapped genetic regulation (quantitative trait loci [QTL]) of gene expression and chromatin activity in Treg cells, and we identified 133 colocalizing loci with immune disease variants. Colocalizations of immune disease genome-wide association study (GWAS) variants with expression QTLs (eQTLs) controlling the expression of CD28 and STAT5A, involved in Treg cell activation and interleukin-2 (IL-2) signaling, support the contribution of Treg cells to the pathobiology of immune diseases. Finally, we identified seven known drug targets suitable for drug repurposing and suggested 63 targets with drug tractability evidence among the GWAS signals that colocalized with Treg cell QTLs. Our study is the first in-depth characterization of immune disease variant effects on Treg cell gene expression modulation and dysregulation of Treg cell function.

Colocalization analysis of pancreas eQTLs with risk loci from alcoholic and novel non-alcoholic chronic pancreatitis GWAS suggests potential disease causing mechanisms.

BACKGROUND: Previous genome-wide association studies (GWAS) identified genome-wide significant risk loci in chronic pancreatitis and investigated underlying disease causing mechanisms by simple overlaps with expression quantitative trait loci (eQTLs), a procedure which may often result in false positive conclusions. METHODS: We conducted a GWAS in 584 non-alcoholic chronic pancreatitis (NACP) patients and 6040 healthy controls. Next, we applied Bayesian colocalization analysis of identified genome-wide significant risk loci from both, our recently published alcoholic chronic pancreatitis (ACP) and the novel NACP dataset, with pancreas eQTLs from the GTEx V8 European cohort to prioritize candidate causal genes and extracted credible sets of shared causal variants. RESULTS: Variants at the CTRC (p = 1.22 × 10-21) and SPINK1 (p = 6.59 × 10-47) risk loci reached genome-wide significance in NACP. CTRC risk variants colocalized with CTRC eQTLs in ACP (PP4 = 0.99, PP4/PP3 = 95.51) and NACP (PP4 = 0.99, PP4/PP3 = 95.46). For both diseases, the 95% credible set of shared causal variants consisted of rs497078 and rs545634. CLDN2-MORC4 risk variants colocalized with CLDN2 eQTLs in ACP (PP4 = 0.98, PP4/PP3 = 42.20) and NACP (PP4 = 0.67, PP4/PP3 = 7.18), probably driven by the shared causal variant rs12688220. CONCLUSIONS: A shared causal CTRC risk variant might unfold its pathogenic effect in ACP and NACP by reducing CTRC expression, while the CLDN2-MORC4 shared causal variant rs12688220 may modify ACP and NACP risk by increasing CLDN2 expression.

Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for 12 immune-mediated diseases.

The resolution of causal genetic variants informs understanding of disease biology. We used regulatory quantitative trait loci (QTLs) from the BLUEPRINT, GTEx and eQTLGen projects to fine-map putative causal variants for 12 immune-mediated diseases. We identify 340 unique loci that colocalize with high posterior probability (≥98%) with regulatory QTLs and apply Bayesian frameworks to fine-map associations at each locus. We show that fine-mapping credible sets derived from regulatory QTLs are smaller compared to disease summary statistics. Further, they are enriched for more functionally interpretable candidate causal variants and for putatively causal insertion/deletion (INDEL) polymorphisms. Finally, we use massively parallel reporter assays to evaluate candidate causal variants at the ITGA4 locus associated with inflammatory bowel disease. Overall, our findings suggest that fine-mapping applied to disease-colocalizing regulatory QTLs can enhance the discovery of putative causal disease variants and enhance insights into the underlying causal genes and molecular mechanisms.

Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits.

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10 000 distinct trait-specific risk loci. However, at these loci, the underlying causal mechanisms remain incompletely characterized. To elucidate novel biology and better understand causal mechanisms at known loci, we performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399 835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 gene-trait associations for hematological traits distinct from previously reported GWAS variants in European populations. Among the 557 associations, 301 were available for replication in a cohort of 141 286 participants of European ancestry from the Million Veteran Program. Of these 301 associations, 108 replicated at a strict Bonferroni adjusted threshold ($\alpha$= 0.05/301). Using our TWAS results, we systematically assigned 4261 out of 16 900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity in external datasets to assign variants to target genes.

Higher body mass index raises immature platelet count: potential contribution to obesity-related thrombosis.

Higher body mass index (BMI) is a risk factor for thrombosis. Platelets are essential for hemostasis but contribute to thrombosis when activated pathologically. We hypothesized that higher BMI leads to changes in platelet characteristics, thereby increasing thrombotic risk. The effect of BMI on platelet traits (measured by Sysmex) was explored in 33 388 UK blood donors (INTERVAL study). Linear regression showed that higher BMI was positively associated with greater plateletcrit (PCT), platelet count (PLT), immature platelet count (IPC), and side fluorescence (SFL, a measure of mRNA content used to derive IPC). Mendelian randomization (MR), applied to estimate a causal effect with BMI proxied by a genetic risk score, provided causal estimates for a positive effect of BMI on both SFL and IPC, but there was little evidence for a causal effect of BMI on PCT or PLT. Follow-up analyses explored the functional relevance of platelet characteristics in a pre-operative cardiac cohort (COPTIC). Linear regression provided observational evidence for a positive association between IPC and agonist-induced whole blood platelet aggregation. Results indicate that higher BMI raises the number of immature platelets, which is associated with greater whole blood platelet aggregation in a cardiac cohort. Higher IPC could therefore contribute to obesity-related thrombosis.

Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease.

Genetic association studies for blood cell traits, which are key indicators of health and immune function, have identified several hundred associations and defined a complex polygenic architecture. Polygenic scores (PGSs) for blood cell traits have potential clinical utility in disease risk prediction and prevention, but designing PGS remains challenging and the optimal methods are unclear. To address this, we evaluated the relative performance of 6 methods to develop PGS for 26 blood cell traits, including a standard method of pruning and thresholding (P + T) and 5 learning methods: LDpred2, elastic net (EN), Bayesian ridge (BR), multilayer perceptron (MLP) and convolutional neural network (CNN). We evaluated these optimized PGSs on blood cell trait data from UK Biobank and INTERVAL. We find that PGSs designed using common machine learning methods EN and BR show improved prediction of blood cell traits and consistently outperform other methods. Our analyses suggest EN/BR as the top choices for PGS construction, showing improved performance for 25 blood cell traits in the external validation, with correlations with the directly measured traits increasing by 10%-23%. Ten PGSs showed significant statistical interaction with sex, and sex-specific PGS stratification showed that all of them had substantial variation in the trajectories of blood cell traits with age. Genetic correlations between the PGSs for blood cell traits and common human diseases identified well-known as well as new associations. We develop machine learning-optimized PGS for blood cell traits, demonstrate their relationships with sex, age, and disease, and make these publicly available as a resource.

An Expanded Genome-Wide Association Study of Fructosamine Levels Identifies RCN3 as a Replicating Locus and Implicates FCGRT as the Effector Transcript.

Fructosamine is a measure of short-term glycemic control, which has been suggested as a useful complement to glycated hemoglobin (HbA1c) for the diagnosis and monitoring of diabetes. To date, a single genome-wide association study (GWAS) including 8,951 U.S. White and 2,712 U.S. Black individuals without a diabetes diagnosis has been published. Results in Whites and Blacks yielded different association loci, near RCN3 and CNTN5, respectively. In this study, we performed a GWAS on 20,731 European-ancestry blood donors and meta-analyzed our results with previous data from U.S. White participants from the Atherosclerosis Risk in Communities (ARIC) study (Nmeta = 29,685). We identified a novel association near GCK (rs3757840, ßmeta = 0.0062; minor allele frequency [MAF] = 0.49; Pmeta = 3.66 × 10-8) and confirmed the association near RCN3 (rs113886122, ßmeta = 0.0134; MAF = 0.17; Pmeta = 5.71 × 10-18). Colocalization analysis with whole-blood expression quantitative trait loci data suggested FCGRT as the effector transcript at the RCN3 locus. We further showed that fructosamine has low heritability (h2 = 7.7%), has no significant genetic correlation with HbA1c and other glycemic traits in individuals without a diabetes diagnosis (P > 0.05), but has evidence of shared genetic etiology with some anthropometric traits (Bonferroni-corrected P < 0.0012). Our results broaden knowledge of the genetic architecture of fructosamine and prioritize FCGRT for downstream functional studies at the established RCN3 locus.

Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases.

Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1-3. Polygenic scores (PGS) aggregate these into a metric representing an individual's genetic predisposition to disease. PGS have shown promise for early risk prediction4-7 and there is an open question as to whether PGS can also be used to understand disease biology8. Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus.

Mitochondrial DNA variants modulate N-formylmethionine, proteostasis and risk of late-onset human diseases.

Mitochondrial DNA (mtDNA) variants influence the risk of late-onset human diseases, but the reasons for this are poorly understood. Undertaking a hypothesis-free analysis of 5,689 blood-derived biomarkers with mtDNA variants in 16,220 healthy donors, here we show that variants defining mtDNA haplogroups Uk and H4 modulate the level of circulating N-formylmethionine (fMet), which initiates mitochondrial protein translation. In human cytoplasmic hybrid (cybrid) lines, fMet modulated both mitochondrial and cytosolic proteins on multiple levels, through transcription, post-translational modification and proteolysis by an N-degron pathway, abolishing known differences between mtDNA haplogroups. In a further 11,966 individuals, fMet levels contributed to all-cause mortality and the disease risk of several common cardiovascular disorders. Together, these findings indicate that fMet plays a key role in common age-related disease through pleiotropic effects on cell proteostasis.