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.

Stroke genetics informs drug discovery and risk prediction across ancestries.

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

Inherited myeloproliferative neoplasm risk affects haematopoietic stem cells.

Myeloproliferative neoplasms (MPNs) are blood cancers that are characterized by the excessive production of mature myeloid cells and arise from the acquisition of somatic driver mutations in haematopoietic stem cells (HSCs). Epidemiological studies indicate a substantial heritable component of MPNs that is among the highest known for cancers1. However, only a limited number of genetic risk loci have been identified, and the underlying biological mechanisms that lead to the acquisition of MPNs remain unclear. Here, by conducting a large-scale genome-wide association study (3,797 cases and 1,152,977 controls), we identify 17 MPN risk loci (P < 5.0 × 10-8), 7 of which have not been previously reported. We find that there is a shared genetic architecture between MPN risk and several haematopoietic traits from distinct lineages; that there is an enrichment for MPN risk variants within accessible chromatin of HSCs; and that increased MPN risk is associated with longer telomere length in leukocytes and other clonal haematopoietic states-collectively suggesting that MPN risk is associated with the function and self-renewal of HSCs. We use gene mapping to identify modulators of HSC biology linked to MPN risk, and show through targeted variant-to-function assays that CHEK2 and GFI1B have roles in altering the function of HSCs to confer disease risk. Overall, our results reveal a previously unappreciated mechanism for inherited MPN risk through the modulation of HSC function.

Comparative Effectiveness of BNT162b2 and mRNA-1273 Vaccines in U.S. Veterans.

BACKGROUND: The messenger RNA (mRNA)-based vaccines BNT162b2 and mRNA-1273 are more than 90% effective against coronavirus disease 2019 (Covid-19). However, their comparative effectiveness for a range of outcomes across diverse populations is unknown. METHODS: We emulated a target trial using the electronic health records of U.S. veterans who received a first dose of the BNT162b2 or mRNA-1273 vaccine between January 4 and May 14, 2021, during a period marked by predominance of the SARS-CoV-2 B.1.1.7 (alpha) variant. We matched recipients of each vaccine in a 1:1 ratio according to their risk factors. Outcomes included documented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, symptomatic Covid-19, hospitalization for Covid-19, admission to an intensive care unit (ICU) for Covid-19, and death from Covid-19. We estimated risks using the Kaplan-Meier estimator. To assess the influence of the B.1.617.2 (delta) variant, we emulated a second target trial that involved veterans vaccinated between July 1 and September 20, 2021. RESULTS: Each vaccine group included 219,842 persons. Over 24 weeks of follow-up in a period marked by alpha-variant predominance, the estimated risk of documented infection was 5.75 events per 1000 persons (95% confidence interval [CI], 5.39 to 6.23) in the BNT162b2 group and 4.52 events per 1000 persons (95% CI, 4.17 to 4.84) in the mRNA-1273 group. The excess number of events per 1000 persons for BNT162b2 as compared with mRNA-1273 was 1.23 (95% CI, 0.72 to 1.81) for documented infection, 0.44 (95% CI, 0.25 to 0.70) for symptomatic Covid-19, 0.55 (95% CI, 0.36 to 0.83) for hospitalization for Covid-19, 0.10 (95% CI, 0.00 to 0.26) for ICU admission for Covid-19, and 0.02 (95% CI, -0.06 to 0.12) for death from Covid-19. The corresponding excess risk (BNT162b2 vs. mRNA-1273) of documented infection over 12 weeks of follow-up in a period marked by delta-variant predominance was 6.54 events per 1000 persons (95% CI, -2.58 to 11.82). CONCLUSIONS: The 24-week risk of Covid-19 outcomes was low after vaccination with mRNA-1273 or BNT162b2, although risks were lower with mRNA-1273 than with BNT162b2. This pattern was consistent across periods marked by alpha- and delta-variant predominance. (Funded by the Department of Veterans Affairs and others.).

A multi-population phenome-wide association study of genetically-predicted height in the Million Veteran Program.

BACKGROUND: Height has been associated with many clinical traits but whether such associations are causal versus secondary to confounding remains unclear in many cases. To systematically examine this question, we performed a Mendelian Randomization-Phenome-wide association study (MR-PheWAS) using clinical and genetic data from a national healthcare system biobank. METHODS AND FINDINGS: Analyses were performed using data from the US Veterans Affairs (VA) Million Veteran Program in non-Hispanic White (EA, n = 222,300) and non-Hispanic Black (AA, n = 58,151) adults in the US. We estimated height genetic risk based on 3290 height-associated variants from a recent European-ancestry genome-wide meta-analysis. We compared associations of measured and genetically-predicted height with phenome-wide traits derived from the VA electronic health record, adjusting for age, sex, and genetic principal components. We found 345 clinical traits associated with measured height in EA and an additional 17 in AA. Of these, 127 were associated with genetically-predicted height at phenome-wide significance in EA and 2 in AA. These associations were largely independent from body mass index. We confirmed several previously described MR associations between height and cardiovascular disease traits such as hypertension, hyperlipidemia, coronary heart disease (CHD), and atrial fibrillation, and further uncovered MR associations with venous circulatory disorders and peripheral neuropathy in the presence and absence of diabetes. As a number of traits associated with genetically-predicted height frequently co-occur with CHD, we evaluated effect modification by CHD status of genetically-predicted height associations with risk factors for and complications of CHD. We found modification of effects of MR associations by CHD status for atrial fibrillation/flutter but not for hypertension, hyperlipidemia, or venous circulatory disorders. CONCLUSIONS: We conclude that height may be an unrecognized but biologically plausible risk factor for several common conditions in adults. However, more studies are needed to reliably exclude horizontal pleiotropy as a driving force behind at least some of the MR associations observed in this study.

A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program.

The study aims to determine the shared genetic architecture between COVID-19 severity with existing medical conditions using electronic health record (EHR) data. We conducted a Phenome-Wide Association Study (PheWAS) of genetic variants associated with critical illness (n = 35) or hospitalization (n = 42) due to severe COVID-19 using genome-wide association summary data from the Host Genetics Initiative. PheWAS analysis was performed using genotype-phenotype data from the Veterans Affairs Million Veteran Program (MVP). Phenotypes were defined by International Classification of Diseases (ICD) codes mapped to clinically relevant groups using published PheWAS methods. Among 658,582 Veterans, variants associated with severe COVID-19 were tested for association across 1,559 phenotypes. Variants at the ABO locus (rs495828, rs505922) associated with the largest number of phenotypes (nrs495828 = 53 and nrs505922 = 59); strongest association with venous embolism, odds ratio (ORrs495828 1.33 (p = 1.32 x 10-199), and thrombosis ORrs505922 1.33, p = 2.2 x10-265. Among 67 respiratory conditions tested, 11 had significant associations including MUC5B locus (rs35705950) with increased risk of idiopathic fibrosing alveolitis OR 2.83, p = 4.12 × 10-191; CRHR1 (rs61667602) associated with reduced risk of pulmonary fibrosis, OR 0.84, p = 2.26× 10-12. The TYK2 locus (rs11085727) associated with reduced risk for autoimmune conditions, e.g., psoriasis OR 0.88, p = 6.48 x10-23, lupus OR 0.84, p = 3.97 x 10-06. PheWAS stratified by ancestry demonstrated differences in genotype-phenotype associations. LMNA (rs581342) associated with neutropenia OR 1.29 p = 4.1 x 10-13 among Veterans of African and Hispanic ancestry but not European. Overall, we observed a shared genetic architecture between COVID-19 severity and conditions related to underlying risk factors for severe and poor COVID-19 outcomes. Differing associations between genotype-phenotype across ancestries may inform heterogenous outcomes observed with COVID-19. Divergent associations between risk for severe COVID-19 with autoimmune inflammatory conditions both respiratory and non-respiratory highlights the shared pathways and fine balance of immune host response and autoimmunity and caution required when considering treatment targets.

Multiancestry Genome-Wide Association Study of Aortic Stenosis Identifies Multiple Novel Loci in the Million Veteran Program.

BACKGROUND: Calcific aortic stenosis (CAS) is the most common valvular heart disease in older adults and has no effective preventive therapies. Genome-wide association studies (GWAS) can identify genes influencing disease and may help prioritize therapeutic targets for CAS. METHODS: We performed a GWAS and gene association study of 14 451 patients with CAS and 398 544 controls in the Million Veteran Program. Replication was performed in the Million Veteran Program, Penn Medicine Biobank, Mass General Brigham Biobank, BioVU, and BioMe, totaling 12 889 cases and 348 094 controls. Causal genes were prioritized from genome-wide significant variants using polygenic priority score gene localization, expression quantitative trait locus colocalization, and nearest gene methods. CAS genetic architecture was compared with that of atherosclerotic cardiovascular disease. Causal inference for cardiometabolic biomarkers in CAS was performed using Mendelian randomization and genome-wide significant loci were characterized further through phenome-wide association study. RESULTS: We identified 23 genome-wide significant lead variants in our GWAS representing 17 unique genomic regions. Of the 23 lead variants, 14 were significant in replication, representing 11 unique genomic regions. Five replicated genomic regions were previously known risk loci for CAS (PALMD, TEX41, IL6, LPA, FADS) and 6 were novel (CEP85L, FTO, SLMAP, CELSR2, MECOM, CDAN1). Two novel lead variants were associated in non-White individuals (P<0.05): rs12740374 (CELSR2) in Black and Hispanic individuals and rs1522387 (SLMAP) in Black individuals. Of the 14 replicated lead variants, only 2 (rs10455872 [LPA], rs12740374 [CELSR2]) were also significant in atherosclerotic cardiovascular disease GWAS. In Mendelian randomization, lipoprotein(a) and low-density lipoprotein cholesterol were both associated with CAS, but the association between low-density lipoprotein cholesterol and CAS was attenuated when adjusting for lipoprotein(a). Phenome-wide association study highlighted varying degrees of pleiotropy, including between CAS and obesity at the FTO locus. However, the FTO locus remained associated with CAS after adjusting for body mass index and maintained a significant independent effect on CAS in mediation analysis. CONCLUSIONS: We performed a multiancestry GWAS in CAS and identified 6 novel genomic regions in the disease. Secondary analyses highlighted the roles of lipid metabolism, inflammation, cellular senescence, and adiposity in the pathobiology of CAS and clarified the shared and differential genetic architectures of CAS with atherosclerotic cardiovascular diseases.

Protein Identification for Stroke Progression via Mendelian Randomization in Million Veteran Program and UK Biobank.

BACKGROUND: Individuals who have experienced a stroke, or transient ischemic attack, face a heightened risk of future cardiovascular events. Identification of genetic and molecular risk factors for subsequent cardiovascular outcomes may identify effective therapeutic targets to improve prognosis after an incident stroke. METHODS: We performed genome-wide association studies for subsequent major adverse cardiovascular events (MACE; ncases=51 929; ncontrols=39 980) and subsequent arterial ischemic stroke (AIS; ncases=45 120; ncontrols=46 789) after the first incident stroke within the Million Veteran Program and UK Biobank. We then used genetic variants associated with proteins (protein quantitative trait loci) to determine the effect of 1463 plasma protein abundances on subsequent MACE using Mendelian randomization. RESULTS: Two variants were significantly associated with subsequent cardiovascular events: rs76472767 near gene RNF220 (odds ratio, 0.75 [95% CI, 0.64-0.85]; P=3.69×10-8) with subsequent AIS and rs13294166 near gene LINC01492 (odds ratio, 1.52 [95% CI, 1.37-1.67]; P=3.77×10-8) with subsequent MACE. Using Mendelian randomization, we identified 2 proteins with an effect on subsequent MACE after a stroke: CCL27 ([C-C motif chemokine 27], effect odds ratio, 0.77 [95% CI, 0.66-0.88]; adjusted P=0.05) and TNFRSF14 ([tumor necrosis factor receptor superfamily member 14], effect odds ratio, 1.42 [95% CI, 1.24-1.60]; adjusted P=0.006). These proteins are not associated with incident AIS and are implicated to have a role in inflammation. CONCLUSIONS: We found evidence that 2 proteins with little effect on incident stroke appear to influence subsequent MACE after incident AIS. These associations suggest that inflammation is a contributing factor to subsequent MACE outcomes after incident AIS and highlights potential novel targets.

Multimodal representation learning for predicting molecule-disease relations.

MOTIVATION: Predicting molecule-disease indications and side effects is important for drug development and pharmacovigilance. Comprehensively mining molecule-molecule, molecule-disease and disease-disease semantic dependencies can potentially improve prediction performance. METHODS: We introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular-disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients. Specifically, M2REMAP first learns a multimodal molecule representation that synthesizes chemical property and clinical semantic information by mapping molecule chemicals via a deep neural network onto the clinical semantic embedding space shared by drugs, diseases and other common clinical concepts. To infer molecule-disease relations, M2REMAP combines multimodal molecule representation and disease semantic embedding to jointly infer indications and side effects. RESULTS: We extensively evaluate M2REMAP on molecule indications, side effects and interactions. Results show that incorporating EHR embeddings improves performance significantly, for example, attaining an improvement over the baseline models by 23.6% in PRC-AUC on indications and 23.9% on side effects. Further, M2REMAP overcomes the limitation of existing methods and effectively predicts drugs for novel diseases and emerging pathogens. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/celehs/M2REMAP, and prediction results are provided at https://shiny.parse-health.org/drugs-diseases-dev/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Estimating the Effect of Bariatric Surgery on Cardiovascular Events Using Observational Data?

BACKGROUND: Observational studies have reported strongly protective effects of bariatric surgery on cardiovascular disease, but with oversimplified definitions of the intervention, eligibility criteria, and follow-up, which deviate from those in a randomized trial. We describe an attempt to estimate the effect of bariatric surgery on cardiovascular disease without introducing these sources of bias, which may not be entirely possible with existing observational data. METHODS: We propose two target trials among persons with diabetes: (1) bariatric operation (vs. no operation) among individuals who have undergone preoperative preparation (lifestyle modifications and screening) and (2) preoperative preparation and a bariatric operation (vs. neither preoperative nor operative component). We emulated both target trials using observational data of US veterans. RESULTS: Comparing bariatric surgery with no surgery (target trial #1; 8,087 individuals), the 7-year cardiovascular risk was 18.0% (95% CI = 6.9, 32.7) in the surgery group and 18.9% (95% CI = 17.7, 20.1) in the no-surgery group (risk difference -0.9, 95% CI = -12.0, 14.0). Comparing preoperative components plus surgery vs. neither (target trial #2; 10,065 individuals), the 7-year cardiovascular risk was 17.4% (95% CI = 13.6, 22.0) in the surgery group and 18.8% (95% CI = 17.8, 19.9) in the no-surgery group (risk difference -1.4, 95% CI = -5.1, 3.2). Body mass index and hemoglobin A1c were reduced with bariatric interventions in both emulations. CONCLUSIONS: Within limitations of available observational data, our estimates do not provide evidence that bariatric surgery reduces cardiovascular disease and support equipoise for a randomized trial of bariatric surgery for cardiovascular disease prevention.