The influence of trinucleotide repeats in the androgen receptor gene on androgen-related traits and diseases.

CONTEXT: Trinucleotide repeats in the androgen receptor have been proposed to influence testosterone signaling in men, but the clinical relevance of these trinucleotide repeats remains controversial. OBJECTIVE: To examine how androgen receptor trinucleotide repeat lengths affect androgen-related traits and disease risks and whether they influence the clinical importance of circulating testosterone levels. METHODS: We quantified CAG and GGC repeat lengths in the androgen receptor (AR) gene of European-ancestry male participants in UK Biobank from whole-genome and whole-exome sequence data using ExpansionHunter, and tested associations with androgen-related traits and diseases. We also examined whether the associations between testosterone levels and these outcomes were affected by adjustment for the repeat lengths. RESULTS: We successfully quantified the repeat lengths from whole-genome and/or whole-exome sequence data in 181,217 males. Both repeat lengths were shown to be positively associated with circulating total testosterone level and bone mineral density, whereas CAG repeat length was negatively associated with male-pattern baldness, but their effects were relatively small and were not associated with most of the other outcomes. Circulating total testosterone level was associated with various outcomes, but this relationship was not affected by adjustment for the repeat lengths. CONCLUSION: In this large-scale study, we found that longer CAG and GGC repeats in the AR gene influence androgen resistance, elevate circulating testosterone level via a feedback loop and play a role in some androgen-targeted tissues. Generally, however, circulating testosterone level is a more important determinant of androgen action in males than repeat lengths.

Metabolomic pattern associated with physical sequelae in patients presenting with respiratory symptoms validates the aestivation concept in dehydrated patients.

BACKGROUND: Hypertonic dehydration is associated with muscle wasting and synthesis of organic osmolytes. We recently showed a metabolic shift to amino acid production and urea cycle activation in COVID-19, consistent with the aestivation response. The aim of the present investigation was to validate the metabolic shift and development of long-term physical outcome in the non-COVID cohort of the Biobanque Québécoise de la COVID-19 (BQC19). METHODS: We included 824 patients from BQC19, where of 571 patients had data of dehydration in the form of estimated osmolality (eOSM = 2Na+2K+glucose+urea), and 284 patients had metabolome data and long-term follow-up. We correlated the degree of dehydration to mortality, invasive mechanical ventilation, acute kidney injury, and long-term symptoms. RESULTS: As found in the COVID cohort, higher eOSM correlated with higher proportion of urea and glucose of total eOSM and an enrichment of amino acids compared to other metabolites. Sex stratified analysis indicated that women may show a weaker aestivation response. More severe dehydration was associated with mortality, invasive mechanical ventilation, and acute kidney injury during the acute illness. Importantly, more severe dehydration was associated with physical long-term symptoms but not mental long-term symptoms after adjustment for age, sex, and disease severity. CONCLUSIONS: Patients with water deficit in the form of increased eOSM tend to have more severe disease and experience more physical symptoms after an acute episode of care. This is associated with amino acid and urea production indicating dehydration induced muscle wasting.

Large-scale circulating proteome association study (CPAS) meta-analysis identifies circulating proteins and pathways predicting incident hip fractures.

Hip fractures are associated with significant disability, high cost, and mortality. However, the exact biological mechanisms underlying susceptibility to hip fractures remain incompletely understood. In an exploratory search of the underlying biology as reflected through the circulating proteome, we performed a comprehensive Circulating Proteome Association Study (CPAS) meta-analysis for incident hip fractures. Analyses included 6430 subjects from two prospective cohort studies (Cardiovascular Health Study and Trøndelag Health Study) with circulating proteomics data (aptamer-based 5 K SomaScan version 4.0 assay; 4979 aptamers). Associations between circulating protein levels and incident hip fractures were estimated for each cohort using age and sex-adjusted Cox regression models. Participants experienced 643 incident hip fractures. Compared with the individual studies, inverse-variance weighted meta-analyses yielded more statistically significant associations, identifying 23 aptamers associated with incident hip fractures (conservative Bonferroni correction 0.05/4979, P < 1.0 × 10-5). The aptamers most strongly associated with hip fracture risk corresponded to two proteins of the growth hormone/insulin growth factor system (GHR and IGFBP2), as well as GDF15 and EGFR. High levels of several inflammation-related proteins (CD14, CXCL12, MMP12, ITIH3) were also associated with increased hip fracture risk. Ingenuity pathway analysis identified reduced LXR/RXR activation and increased acute phase response signaling to be overrepresented among those proteins associated with increased hip fracture risk. These analyses identified several circulating proteins and pathways consistently associated with incident hip fractures. These findings underscore the usefulness of the meta-analytic approach for comprehensive CPAS in a similar manner as has previously been observed for large-scale human genetic studies. Future studies should investigate the underlying biology of these potential novel drug targets.

Hip fractures are associated with significant disability, high cost, and mortality. However, the exact biological mechanisms underlying susceptibility to hip fractures remain incompletely understood. To increase the understanding of the underlying mechanisms, we performed a meta-analysis of the associations between 4860 circulating proteins and risk of fractures using two large cohorts, including 6430 participants with 643 incident hip fractures. We identified 23 proteins/aptamers associated with incident hip fractures. Two proteins of the growth hormone/insulin growth factor system (GHR and IGFBP2), as well as GDF15 and EGFR were most strongly associated with hip fracture risk. High levels of several inflammation-related proteins were also associated with increased hip fracture risk. Pathway analysis identified reduced LXR/RXR activation and increased acute phase response signaling to be overrepresented among those proteins associated with increased hip fracture risk. Future mechanistic studies should investigate the underlying biology of these novel protein biomarkers which may be potential drug targets.

HLA allele-calling using multi-ancestry whole-exome sequencing from the UK Biobank identifies 129 novel associations in 11 autoimmune diseases.

The human leukocyte antigen (HLA) region on chromosome 6 is strongly associated with many immune-mediated and infection-related diseases. Due to its highly polymorphic nature and complex linkage disequilibrium patterns, traditional genetic association studies of single nucleotide polymorphisms do not perform well in this region. Instead, the field has adopted the assessment of the association of HLA alleles (i.e., entire HLA gene haplotypes) with disease. Often based on genotyping arrays, these association studies impute HLA alleles, decreasing accuracy and thus statistical power for rare alleles and in non-European ancestries. Here, we use whole-exome sequencing (WES) from 454,824 UK Biobank (UKB) participants to directly call HLA alleles using the HLA-HD algorithm. We show this method is more accurate than imputing HLA alleles and harness the improved statistical power to identify 360 associations for 11 auto-immune phenotypes (at least 129 likely novel), leading to better insights into the specific coding polymorphisms that underlie these diseases. We show that HLA alleles with synonymous variants, often overlooked in HLA studies, can significantly influence these phenotypes. Lastly, we show that HLA sequencing may improve polygenic risk scores accuracy across ancestries. These findings allow better characterization of the role of the HLA region in human disease.

An atlas of genetic determinants of forearm fracture.

Osteoporotic fracture is among the most common and costly of diseases. While reasonably heritable, its genetic determinants have remained elusive. Forearm fractures are the most common clinically recognized osteoporotic fractures with a relatively high heritability. To establish an atlas of the genetic determinants of forearm fractures, we performed genome-wide association analyses including 100,026 forearm fracture cases. We identified 43 loci, including 26 new fracture loci. Although most fracture loci associated with bone mineral density, we also identified loci that primarily regulate bone quality parameters. Functional studies of one such locus, at TAC4, revealed that Tac4-/- mice have reduced mechanical bone strength. The strongest forearm fracture signal, at WNT16, displayed remarkable bone-site-specificity with no association with hip fractures. Tall stature and low body mass index were identified as new causal risk factors for fractures. The insights from this atlas may improve fracture prediction and enable therapeutic development to prevent fractures.

Identifying Rare Genetic Determinants for Improved Polygenic Risk Prediction of Bone Mineral Density and Fracture Risk.

Osteoporosis and fractures severely impact the elderly population. Polygenic risk scores for bone mineral density have demonstrated potential clinical utility. However, the value of rare genetic determinants in risk prediction has not been assessed. With whole-exome sequencing data from 436,824 UK Biobank participants, we assigned White British ancestry individuals into a training data set (n = 317,434) and a test data set (n = 74,825). In the training data set, we developed a common variant-based polygenic risk score for heel ultrasound speed of sound (SOS). Next, we performed burden testing to identify genes harboring rare determinants of bone mineral density, targeting influential rare variants with predicted high deleteriousness. We constructed a genetic risk score, called ggSOS, to incorporate influential rare variants in significant gene burden masks into the common variant-based polygenic risk score. We assessed the predictive performance of ggSOS in the White British test data set, as well as in populations of non-White British European (n = 18,885), African (n = 7165), East Asian (n = 2236), South Asian (n = 9829), and other admixed (n = 1481) ancestries. Twelve genes in pivotal regulatory pathways of bone homeostasis harbored influential rare variants associated with SOS (p < 5.5 × 10-7 ), including AHNAK, BMP5, CYP19A1, FAM20A, FBXW5, KDM5B, KREMEN1, LGR4, LRP5, SMAD6, SOST, and WNT1. Among 4013 (5.4%) individuals in the test data set carrying these variants, a one standard deviation decrease in ggSOS was associated with 1.35-fold (95% confidence interval [CI] 1.16-1.57) increased hazard of major osteoporotic fracture. However, compared with a common variant-based polygenic risk score (C-index = 0.641), ggSOS had only marginally improved prediction accuracy in identifying at-risk individuals (C-index = 0.644), with overlapping confidence intervals. Similarly, ggSOS did not demonstrate substantially improved predictive performance in non-European ancestry populations. In summary, modeling the effects of rare genetic determinants may assist polygenic prediction of fracture risk among carriers of influential rare variants. Nonetheless, improved clinical utility is not guaranteed for population-level risk screening. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Alternative splicing in lung influences COVID-19 severity and respiratory diseases.

Alternative splicing generates functional diversity in isoforms, impacting immune response to infection. Here, we evaluate the causal role of alternative splicing in COVID-19 severity and susceptibility by applying two-sample Mendelian randomization to cis-splicing quantitative trait loci and the results from COVID-19 Host Genetics Initiative. We identify that alternative splicing in lung, rather than total expression of OAS1, ATP11A, DPP9 and NPNT, is associated with COVID-19 severity. MUC1 and PMF1 splicing is associated with COVID-19 susceptibility. Colocalization analyses support a shared genetic mechanism between COVID-19 severity with idiopathic pulmonary fibrosis at the ATP11A and DPP9 loci, and with chronic obstructive lung diseases at the NPNT locus. Last, we show that ATP11A, DPP9, NPNT, and MUC1 are highly expressed in lung alveolar epithelial cells, both in COVID-19 uninfected and infected samples. These findings clarify the importance of alternative splicing in lung for COVID-19 and respiratory diseases, providing isoform-based targets for drug discovery.

From target discovery to clinical drug development with human genetics.

The substantial investments in human genetics and genomics made over the past three decades were anticipated to result in many innovative therapies. Here we investigate the extent to which these expectations have been met, excluding cancer treatments. In our search, we identified 40 germline genetic observations that led directly to new targets and subsequently to novel approved therapies for 36 rare and 4 common conditions. The median time between genetic target discovery and drug approval was 25 years. Most of the genetically driven therapies for rare diseases compensate for disease-causing loss-of-function mutations. The therapies approved for common conditions are all inhibitors designed to pharmacologically mimic the natural, disease-protective effects of rare loss-of-function variants. Large biobank-based genetic studies have the power to identify and validate a large number of new drug targets. Genetics can also assist in the clinical development phase of drugs-for example, by selecting individuals who are most likely to respond to investigational therapies. This approach to drug development requires investments into large, diverse cohorts of deeply phenotyped individuals with appropriate consent for genetically assisted trials. A robust framework that facilitates responsible, sustainable benefit sharing will be required to capture the full potential of human genetics and genomics and bring effective and safe innovative therapies to patients quickly.

Leveraging osteoclast genetic regulatory data to identify genes with a role in osteoarthritis.

There has been a growing interest in the role of the subchondral bone and its resident osteoclasts in the progression of osteoarthritis (OA). A recent genome-wide association study (GWAS) identified 100 independent association signals for OA traits. Most of these signals are led by noncoding variants, suggesting that genetic regulatory effects may drive many of the associations. We have generated a unique human osteoclast-like cell-specific expression quantitative trait locus (eQTL) resource for studying the genetics of bone disease. Considering the potential role of osteoclasts in the pathogenesis of OA, we performed an integrative analysis of this dataset with the recently published OA GWAS results. Summary data-based Mendelian randomization (SMR) and colocalization analyses identified 38 genes with a potential role in OA, including some that have been implicated in Mendelian diseases with joint/skeletal abnormalities, such as BICRA, EIF6, CHST3, and FBN2. Several OA GWAS signals demonstrated colocalization with more than one eQTL peak, including at 19q13.32 (hip OA with BCAM, PRKD2, and BICRA eQTL). We also identified a number of eQTL signals colocalizing with more than one OA trait, including FAM53A, GCAT, HMGN1, MGAT4A, RRP7BP, and TRIOBP. An SMR analysis identified 3 loci with evidence of pleiotropic effects on OA-risk and gene expression: LINC01481, CPNE1, and EIF6. Both CPNE1 and EIF6 are located at 20q11.22, a locus harboring 2 other strong OA candidate genes, GDF5 and UQCC1, suggesting the presence of an OA-risk gene cluster. In summary, we have used our osteoclast-specific eQTL dataset to identify genes potentially involved with the pathogenesis of OA.

Converging evidence from exome sequencing and common variants implicates target genes for osteoporosis.

In this study, we leveraged the combined evidence of rare coding variants and common alleles to identify therapeutic targets for osteoporosis. We undertook a large-scale multiancestry exome-wide association study for estimated bone mineral density, which showed that the burden of rare coding alleles in 19 genes was associated with estimated bone mineral density (P < 3.6 × 10-7). These genes were highly enriched for a set of known causal genes for osteoporosis (65-fold; P = 2.5 × 10-5). Exome-wide significant genes had 96-fold increased odds of being the top ranked effector gene at a given GWAS locus (P = 1.8 × 10-10). By integrating proteomics Mendelian randomization evidence, we prioritized CD109 (cluster of differentiation 109) as a gene for which heterozygous loss of function is associated with higher bone density. CRISPR-Cas9 editing of CD109 in SaOS-2 osteoblast-like cell lines showed that partial CD109 knockdown led to increased mineralization. This study demonstrates that the convergence of common and rare variants, proteomics and CRISPR can highlight new bone biology to guide therapeutic development.

Colocalization of expression transcripts with COVID-19 outcomes is rare across cell states, cell types and organs.

Identifying causal genes at GWAS loci can help pinpoint targets for therapeutic interventions. Expression studies can disentangle such loci but signals from expression quantitative trait loci (eQTLs) often fail to colocalize-which means that the genetic control of measured expression is not shared with the genetic control of disease risk. This may be because gene expression is measured in the wrong cell type, physiological state, or organ. We tested whether Mendelian randomization (MR) could identify genes at loci influencing COVID-19 outcomes and whether the colocalization of genetic control of expression and COVID-19 outcomes was influenced by cell type, cell stimulation, and organ. We conducted MR of cis-eQTLs from single cell (scRNA-seq) and bulk RNA sequencing. We then tested variables that could influence colocalization, including cell type, cell stimulation, RNA sequencing modality, organ, symptoms of COVID-19, and SARS-CoV-2 status among individuals with symptoms of COVID-19. The outcomes used to test colocalization were COVID-19 severity and susceptibility as assessed in the Host Genetics Initiative release 7. Most transcripts identified using MR did not colocalize when tested across cell types, cell state and in different organs. Most that did colocalize likely represented false positives due to linkage disequilibrium. In general, colocalization was highly variable and at times inconsistent for the same transcript across cell type, cell stimulation and organ. While we identified factors that influenced colocalization for select transcripts, identifying 33 that mediate COVID-19 outcomes, our study suggests that colocalization of expression with COVID-19 outcomes is partially due to noisy signals even after following quality control and sensitivity testing. These findings illustrate the present difficulty of linking expression transcripts to disease outcomes and the need for skepticism when observing eQTL MR results, even accounting for cell types, stimulation state and different organs.

Targeting hepatitis B vaccine escape using immunogenetics in Bangladeshi infants.

Hepatitis B virus (HBV) vaccine escape mutants (VEM) are increasingly described, threatening progress in control of this virus worldwide. Here we studied the relationship between host genetic variation, vaccine immunogenicity and viral sequences implicating VEM emergence. In a cohort of 1,096 Bangladeshi children, we identified human leukocyte antigen (HLA) variants associated with response vaccine antigens. Using an HLA imputation panel with 9,448 south Asian individuals DPB1*04:01 was associated with higher HBV antibody responses (p=4.5×10-30). The underlying mechanism is a result of higher affinity binding of HBV surface antigen epitopes to DPB1*04:01 dimers. This is likely a result of evolutionary pressure at the HBV surface antigen 'a-determinant' segment incurring VEM specific to HBV. Prioritizing pre-S isoform HBV vaccines may tackle the rise of HBV vaccine evasion.

Integrating genetics and metabolomics from multi-ethnic and multi-fluid data reveals putative mechanisms for age-related macular degeneration.

Age-related macular degeneration (AMD) is a leading cause of blindness in older adults. Investigating shared genetic components between metabolites and AMD can enhance our understanding of its pathogenesis. We conduct metabolite genome-wide association studies (mGWASs) using multi-ethnic genetic and metabolomic data from up to 28,000 participants. With bidirectional Mendelian randomization analysis involving 16,144 advanced AMD cases and 17,832 controls, we identify 108 putatively causal relationships between plasma metabolites and advanced AMD. These metabolites are enriched in glycerophospholipid metabolism, lysophospholipid, triradylcglycerol, and long chain polyunsaturated fatty acid pathways. Bayesian genetic colocalization analysis and a customized metabolome-wide association approach prioritize putative causal AMD-associated metabolites. We find limited evidence linking urine metabolites to AMD risk. Our study emphasizes the contribution of plasma metabolites, particularly lipid-related pathways and genes, to AMD risk and uncovers numerous putative causal associations between metabolites and AMD risk.

Dose-dependent Association of Alcohol Consumption With Obesity and Type 2 Diabetes: Mendelian Randomization Analyses.

CONTEXT: Effects of modest alcohol consumption remain controversial. Mendelian randomization (MR) can help to mitigate biases due to confounding and reverse causation in observational studies, and evaluate the potential causal role of alcohol consumption. OBJECTIVE: This work aimed to evaluate dose-dependent effect of alcohol consumption on obesity and type 2 diabetes. METHODS: Assessing 408 540 participants of European ancestry in the UK Biobank, we first tested the association between self-reported alcohol intake frequency and 10 anthropometric measurements, obesity, and type 2 diabetes. We then conducted MR analyses both in the overall population and in subpopulations stratified by alcohol intake frequency. RESULTS: Among individuals having more than 14 drinks per week, a 1-drink-per-week increase in genetically predicted alcohol intake frequency was associated with a 0.36-kg increase in fat mass (SD = 0.03 kg), a 1.08-fold increased odds of obesity (95% CI, 1.06-1.10), and a 1.10-fold increased odds of type 2 diabetes (95% CI, 1.06-1.13). These associations were stronger in women than in men. Furthermore, no evidence was found supporting the association between genetically increased alcohol intake frequency and improved health outcomes among individuals having 7 or fewer drinks per week, as MR estimates largely overlapped with the null. These results withstood multiple sensitivity analyses assessing the validity of MR assumptions. CONCLUSION: As opposed to observational associations, MR results suggest there may not be protective effects of modest alcohol consumption on obesity traits and type 2 diabetes. Heavy alcohol consumption could lead to increased measures of obesity as well as increased risk of type 2 diabetes.

Predicting ExWAS findings from GWAS data: a shorter path to causal genes.

GWAS has identified thousands of loci associated with disease, yet the causal genes within these loci remain largely unknown. Identifying these causal genes would enable deeper understanding of the disease and assist in genetics-based drug development. Exome-wide association studies (ExWAS) are more expensive but can pinpoint causal genes offering high-yield drug targets, yet suffer from a high false-negative rate. Several algorithms have been developed to prioritize genes at GWAS loci, such as the Effector Index (Ei), Locus-2-Gene (L2G), Polygenic Prioritization score (PoPs), and Activity-by-Contact score (ABC) and it is not known if these algorithms can predict ExWAS findings from GWAS data. However, if this were the case, thousands of associated GWAS loci could potentially be resolved to causal genes. Here, we quantified the performance of these algorithms by evaluating their ability to identify ExWAS significant genes for nine traits. We found that Ei, L2G, and PoPs can identify ExWAS significant genes with high areas under the precision recall curve (Ei: 0.52, L2G: 0.37, PoPs: 0.18, ABC: 0.14). Furthermore, we found that for every unit increase in the normalized scores, there was an associated 1.3-4.6-fold increase in the odds of a gene reaching exome-wide significance (Ei: 4.6, L2G: 2.5, PoPs: 2.1, ABC: 1.3). Overall, we found that Ei, L2G, and PoPs can anticipate ExWAS findings from widely available GWAS results. These techniques are therefore promising when well-powered ExWAS data are not readily available and can be used to anticipate ExWAS findings, allowing for prioritization of genes at GWAS loci.

Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection.

Background Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Methods Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). Results We demonstrate that COVID-AKI is associated with increased markers of tubular injury ( NGAL ) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2 , trefoil factor 3 , transmembrane emp24 domain-containing protein 10 , and cystatin-C indicating tubular dysfunction and injury. Conclusions Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Increasing serum iron levels and their role in the risk of infectious diseases: a Mendelian randomization approach.

OBJECTIVES: Increased iron stores have been associated with elevated risks of different infectious diseases, suggesting that iron supplementation may increase the risk of infections. However, these associations may be biased by confounding or reverse causation. This is important, since up to 19% of the population takes iron supplementation. We used Mendelian randomization (MR) to bypass these biases and estimate the causal effect of iron on infections. METHODS: As instrumental variables, we used genetic variants associated with iron biomarkers in two genome-wide association studies (GWASs) of European ancestry participants. For outcomes, we used GWAS results from the UK Biobank, FinnGen, the COVID-19 Host Genetics Initiative or 23andMe, for seven infection phenotypes: 'any infections', combined, COVID-19 hospitalization, candidiasis, pneumonia, sepsis, skin and soft tissue infection (SSTI) and urinary tract infection (UTI). RESULTS: Most of our analyses showed increasing iron (measured by its biomarkers) was associated with only modest changes in the odds of infectious outcomes, with all 95% odds ratios confidence intervals within the 0.88 to 1.26 range. However, for the three predominantly bacterial infections (sepsis, SSTI, UTI), at least one analysis showed a nominally elevated risk with increased iron stores (P <0.05). CONCLUSION: Using MR, we did not observe an increase in risk of most infectious diseases with increases in iron stores. However for bacterial infections, higher iron stores may increase odds of infections. Hence, using genetic variation in iron pathways as a proxy for iron supplementation, iron supplements are likely safe on a population level, but we should continue the current practice of conservative iron supplementation during bacterial infections or in those at high risk of developing them.

Proteome-wide Mendelian randomization implicates nephronectin as an actionable mediator of the effect of obesity on COVID-19 severity.

Obesity is a major risk factor for Coronavirus disease (COVID-19) severity; however, the mechanisms underlying this relationship are not fully understood. As obesity influences the plasma proteome, we sought to identify circulating proteins mediating the effects of obesity on COVID-19 severity in humans. Here, we screened 4,907 plasma proteins to identify proteins influenced by body mass index using Mendelian randomization. This yielded 1,216 proteins, whose effect on COVID-19 severity was assessed, again using Mendelian randomization. We found that an s.d. increase in nephronectin (NPNT) was associated with increased odds of critically ill COVID-19 (OR = 1.71, P = 1.63 × 10-10). The effect was driven by an NPNT splice isoform. Mediation analyses supported NPNT as a mediator. In single-cell RNA-sequencing, NPNT was expressed in alveolar cells and fibroblasts of the lung in individuals who died of COVID-19. Finally, decreasing body fat mass and increasing fat-free mass were found to lower NPNT levels. These findings provide actionable insights into how obesity influences COVID-19 severity.

Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases.

Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets.