Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets.

Circulating proteins have important functions in inflammation and a broad range of diseases. To identify genetic influences on inflammation-related proteins, we conducted a genome-wide protein quantitative trait locus (pQTL) study of 91 plasma proteins measured using the Olink Target platform in 14,824 participants. We identified 180 pQTLs (59 cis, 121 trans). Integration of pQTL data with eQTL and disease genome-wide association studies provided insight into pathogenesis, implicating lymphotoxin-α in multiple sclerosis. Using Mendelian randomization (MR) to assess causality in disease etiology, we identified both shared and distinct effects of specific proteins across immune-mediated diseases, including directionally discordant effects of CD40 on risk of rheumatoid arthritis versus multiple sclerosis and inflammatory bowel disease. MR implicated CXCL5 in the etiology of ulcerative colitis (UC) and we show elevated gut CXCL5 transcript expression in patients with UC. These results identify targets of existing drugs and provide a powerful resource to facilitate future drug target prioritization.

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.

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.

Misexpression of inactive genes in whole blood is associated with nearby rare structural variants.

Gene misexpression is the aberrant transcription of a gene in a context where it is usually inactive. Despite its known pathological consequences in specific rare diseases, we have a limited understanding of its wider prevalence and mechanisms in humans. To address this, we analyzed gene misexpression in 4,568 whole-blood bulk RNA sequencing samples from INTERVAL study blood donors. We found that while individual misexpression events occur rarely, in aggregate they were found in almost all samples and a third of inactive protein-coding genes. Using 2,821 paired whole-genome and RNA sequencing samples, we identified that misexpression events are enriched in cis for rare structural variants. We established putative mechanisms through which a subset of SVs lead to gene misexpression, including transcriptional readthrough, transcript fusions, and gene inversion. Overall, we develop misexpression as a type of transcriptomic outlier analysis and extend our understanding of the variety of mechanisms by which genetic variants can influence gene expression.

Improving reporting standards for polygenic scores in risk prediction studies.

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

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.

Rare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes.

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

A signature of platelet reactivity in CBC scattergrams reveals genetic predictors of thrombotic disease risk.

Genetic studies of platelet reactivity (PR) phenotypes may identify novel antiplatelet drug targets. However, such studies have been limited by small sample sizes (n < 5000) because of the complexity of measuring PR. We trained a model to predict PR from complete blood count (CBC) scattergrams. A genome-wide association study of this phenotype in 29 806 blood donors identified 21 distinct associations implicating 20 genes, of which 6 have been identified previously. The effect size estimates were significantly correlated with estimates from a study of flow cytometry-measured PR and a study of a phenotype of in vitro thrombus formation. A genetic score of PR built from the 21 variants was associated with the incidence rates of myocardial infarction and pulmonary embolism. Mendelian randomization analyses showed that PR was causally associated with the risks of coronary artery disease, stroke, and venous thromboembolism. Our approach provides a blueprint for using phenotype imputation to study the determinants of hard-to-measure but biologically important hematological traits.

Neurocognitive trajectory and proteomic signature of inherited risk for Alzheimer's disease.

For Alzheimer's disease-a leading cause of dementia and global morbidity-improved identification of presymptomatic high-risk individuals and identification of new circulating biomarkers are key public health needs. Here, we tested the hypothesis that a polygenic predictor of risk for Alzheimer's disease would identify a subset of the population with increased risk of clinically diagnosed dementia, subclinical neurocognitive dysfunction, and a differing circulating proteomic profile. Using summary association statistics from a recent genome-wide association study, we first developed a polygenic predictor of Alzheimer's disease comprised of 7.1 million common DNA variants. We noted a 7.3-fold (95% CI 4.8 to 11.0; p < 0.001) gradient in risk across deciles of the score among 288,289 middle-aged participants of the UK Biobank study. In cross-sectional analyses stratified by age, minimal differences in risk of Alzheimer's disease and performance on a digit recall test were present according to polygenic score decile at age 50 years, but significant gradients emerged by age 65. Similarly, among 30,541 participants of the Mass General Brigham Biobank, we again noted no significant differences in Alzheimer's disease diagnosis at younger ages across deciles of the score, but for those over 65 years we noted an odds ratio of 2.0 (95% CI 1.3 to 3.2; p = 0.002) in the top versus bottom decile of the polygenic score. To understand the proteomic signature of inherited risk, we performed aptamer-based profiling in 636 blood donors (mean age 43 years) with very high or low polygenic scores. In addition to the well-known apolipoprotein E biomarker, this analysis identified 27 additional proteins, several of which have known roles related to disease pathogenesis. Differences in protein concentrations were consistent even among the youngest subset of blood donors (mean age 33 years). Of these 28 proteins, 7 of the 8 proteins with concentrations available were similarly associated with the polygenic score in participants of the Multi-Ethnic Study of Atherosclerosis. These data highlight the potential for a DNA-based score to identify high-risk individuals during the prolonged presymptomatic phase of Alzheimer's disease and to enable biomarker discovery based on profiling of young individuals in the extremes of the score distribution.

Analyzing human knockouts to validate GPR151 as a therapeutic target for reduction of body mass index.

Novel drug targets for sustained reduction in body mass index (BMI) are needed to curb the epidemic of obesity, which affects 650 million individuals worldwide and is a causal driver of cardiovascular and metabolic disease and mortality. Previous studies reported that the Arg95Ter nonsense variant of GPR151, an orphan G protein-coupled receptor, is associated with reduced BMI and reduced risk of Type 2 Diabetes (T2D). Here, we further investigate GPR151 with the Pakistan Genome Resource (PGR), which is one of the largest exome biobanks of human homozygous loss-of-function carriers (knockouts) in the world. Among PGR participants, we identify eleven GPR151 putative loss-of-function (plof) variants, three of which are present at homozygosity (Arg95Ter, Tyr99Ter, and Phe175LeufsTer7), with a cumulative allele frequency of 2.2%. We confirm these alleles in vitro as loss-of-function. We test if GPR151 plof is associated with BMI, T2D, or other metabolic traits and find that GPR151 deficiency in complete human knockouts is not associated with clinically significant differences in these traits. Relative to Gpr151+/+ mice, Gpr151-/- animals exhibit no difference in body weight on normal chow and higher body weight on a high-fat diet. Together, our findings indicate that GPR151 antagonism is not a compelling therapeutic approach to treatment of obesity.

Dietary amino acids and risk of stroke subtypes: a prospective analysis of 356,000 participants in seven European countries.

PURPOSE: Previously reported associations of protein-rich foods with stroke subtypes have prompted interest in the assessment of individual amino acids. We examined the associations of dietary amino acids with risks of ischaemic and haemorrhagic stroke in the EPIC study. METHODS: We analysed data from 356,142 participants from seven European countries. Dietary intakes of 19 individual amino acids were assessed using validated country-specific dietary questionnaires, calibrated using additional 24-h dietary recalls. Multivariable-adjusted Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of ischaemic and haemorrhagic stroke in relation to the intake of each amino acid. The role of blood pressure as a potential mechanism was assessed in 267,642 (75%) participants. RESULTS: After a median follow-up of 12.9 years, 4295 participants had an ischaemic stroke and 1375 participants had a haemorrhagic stroke. After correction for multiple testing, a higher intake of proline (as a percent of total protein) was associated with a 12% lower risk of ischaemic stroke (HR per 1 SD higher intake 0.88; 95% CI 0.82, 0.94). The association persisted after mutual adjustment for all other amino acids, systolic and diastolic blood pressure. The inverse associations of isoleucine, leucine, valine, phenylalanine, threonine, tryptophan, glutamic acid, serine and tyrosine with ischaemic stroke were each attenuated with adjustment for proline intake. For haemorrhagic stroke, no statistically significant associations were observed in the continuous analyses after correcting for multiple testing. CONCLUSION: Higher proline intake may be associated with a lower risk of ischaemic stroke, independent of other dietary amino acids and blood pressure.

Genomic atlas of the human plasma proteome.

Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with gene expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

Correction: A missense variant in Mitochondrial Amidoxime Reducing Component 1 gene and protection against liver disease.

[This corrects the article DOI: 10.1371/journal.pgen.1008629.].

A nutritional biomarker score of the Mediterranean diet and incident type 2 diabetes: Integrated analysis of data from the MedLey randomised controlled trial and the EPIC-InterAct case-cohort study.

BACKGROUND: Self-reported adherence to the Mediterranean diet has been modestly inversely associated with incidence of type 2 diabetes (T2D) in cohort studies. There is uncertainty about the validity and magnitude of this association due to subjective reporting of diet. The association has not been evaluated using an objectively measured biomarker of the Mediterranean diet. METHODS AND FINDINGS: We derived a biomarker score based on 5 circulating carotenoids and 24 fatty acids that discriminated between the Mediterranean or habitual diet arms of a parallel design, 6-month partial-feeding randomised controlled trial (RCT) conducted between 2013 and 2014, the MedLey trial (128 participants out of 166 randomised). We applied this biomarker score in an observational study, the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study, to assess the association of the score with T2D incidence over an average of 9.7 years of follow-up since the baseline (1991 to 1998). We included 22,202 participants, of whom 9,453 were T2D cases, with relevant biomarkers from an original case-cohort of 27,779 participants sampled from a cohort of 340,234 people. As a secondary measure of the Mediterranean diet, we used a score estimated from dietary-self report. Within the trial, the biomarker score discriminated well between the 2 arms; the cross-validated C-statistic was 0.88 (95% confidence interval (CI) 0.82 to 0.94). The score was inversely associated with incident T2D in EPIC-InterAct: the hazard ratio (HR) per standard deviation of the score was 0.71 (95% CI: 0.65 to 0.77) following adjustment for sociodemographic, lifestyle and medical factors, and adiposity. In comparison, the HR per standard deviation of the self-reported Mediterranean diet was 0.90 (95% CI: 0.86 to 0.95). Assuming the score was causally associated with T2D, higher adherence to the Mediterranean diet in Western European adults by 10 percentiles of the score was estimated to reduce the incidence of T2D by 11% (95% CI: 7% to 14%). The study limitations included potential measurement error in nutritional biomarkers, unclear specificity of the biomarker score to the Mediterranean diet, and possible residual confounding. CONCLUSIONS: These findings suggest that objectively assessed adherence to the Mediterranean diet is associated with lower risk of T2D and that even modestly higher adherence may have the potential to reduce the population burden of T2D meaningfully. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12613000602729 https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=363860.

Correction: Lipoprotein signatures of cholesteryl ester transfer protein and HMG-CoA reductase inhibition.

[This corrects the article DOI: 10.1371/journal.pbio.3000572.].

Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity.

A major goal of biomedicine is to understand the function of every gene in the human genome. Loss-of-function mutations can disrupt both copies of a given gene in humans and phenotypic analysis of such 'human knockouts' can provide insight into gene function. Consanguineous unions are more likely to result in offspring carrying homozygous loss-of-function mutations. In Pakistan, consanguinity rates are notably high. Here we sequence the protein-coding regions of 10,503 adult participants in the Pakistan Risk of Myocardial Infarction Study (PROMIS), designed to understand the determinants of cardiometabolic diseases in individuals from South Asia. We identified individuals carrying homozygous predicted loss-of-function (pLoF) mutations, and performed phenotypic analysis involving more than 200 biochemical and disease traits. We enumerated 49,138 rare (<1% minor allele frequency) pLoF mutations. These pLoF mutations are estimated to knock out 1,317 genes, each in at least one participant. Homozygosity for pLoF mutations at PLA2G7 was associated with absent enzymatic activity of soluble lipoprotein-associated phospholipase A2; at CYP2F1, with higher plasma interleukin-8 concentrations; at TREH, with lower concentrations of apoB-containing lipoprotein subfractions; at either A3GALT2 or NRG4, with markedly reduced plasma insulin C-peptide concentrations; and at SLC9A3R1, with mediators of calcium and phosphate signalling. Heterozygous deficiency of APOC3 has been shown to protect against coronary heart disease; we identified APOC3 homozygous pLoF carriers in our cohort. We recruited these human knockouts and challenged them with an oral fat load. Compared with family members lacking the mutation, individuals with APOC3 knocked out displayed marked blunting of the usual post-prandial rise in plasma triglycerides. Overall, these observations provide a roadmap for a 'human knockout project', a systematic effort to understand the phenotypic consequences of complete disruption of genes in humans.

The influence of rare variants in circulating metabolic biomarkers.

Circulating metabolite levels are biomarkers for cardiovascular disease (CVD). Here we studied, association of rare variants and 226 serum lipoproteins, lipids and amino acids in 7,142 (discovery plus follow-up) healthy participants. We leveraged the information from multiple metabolite measurements on the same participants to improve discovery in rare variant association analyses for gene-based and gene-set tests by incorporating correlated metabolites as covariates in the validation stage. Gene-based analysis corrected for the effective number of tests performed, confirmed established associations at APOB, APOC3, PAH, HAL and PCSK (p<1.32x10-7) and identified novel gene-trait associations at a lower stringency threshold with ACSL1, MYCN, FBXO36 and B4GALNT3 (p<2.5x10-6). Regulation of the pyruvate dehydrogenase (PDH) complex was associated for the first time, in gene-set analyses also corrected for effective number of tests, with IDL and LDL parameters, as well as circulating cholesterol (pMETASKAT<2.41x10-6). In conclusion, using an approach that leverages metabolite measurements obtained in the same participants, we identified novel loci and pathways involved in the regulation of these important metabolic biomarkers. As large-scale biobanks continue to amass sequencing and phenotypic information, analytical approaches such as ours will be useful to fully exploit the copious amounts of biological data generated in these efforts.

A missense variant in Mitochondrial Amidoxime Reducing Component 1 gene and protection against liver disease.

Analyzing 12,361 all-cause cirrhosis cases and 790,095 controls from eight cohorts, we identify a common missense variant in the Mitochondrial Amidoxime Reducing Component 1 gene (MARC1 p.A165T) that associates with protection from all-cause cirrhosis (OR 0.91, p = 2.3*10-11). This same variant also associates with lower levels of hepatic fat on computed tomographic imaging and lower odds of physician-diagnosed fatty liver as well as lower blood levels of alanine transaminase (-0.025 SD, 3.7*10-43), alkaline phosphatase (-0.025 SD, 1.2*10-37), total cholesterol (-0.030 SD, p = 1.9*10-36) and LDL cholesterol (-0.027 SD, p = 5.1*10-30) levels. We identified a series of additional MARC1 alleles (low-frequency missense p.M187K and rare protein-truncating p.R200Ter) that also associated with lower cholesterol levels, liver enzyme levels and reduced risk of cirrhosis (0 cirrhosis cases for 238 R200Ter carriers versus 17,046 cases of cirrhosis among 759,027 non-carriers, p = 0.04) suggesting that deficiency of the MARC1 enzyme may lower blood cholesterol levels and protect against cirrhosis.