Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits.

Hypertension affects more than one billion people worldwide. Here we identify 113 novel loci, reporting a total of 2,103 independent genetic signals (P < 5 × 10-8) from the largest single-stage blood pressure (BP) genome-wide association study to date (n = 1,028,980 European individuals). These associations explain more than 60% of single nucleotide polymorphism-based BP heritability. Comparing top versus bottom deciles of polygenic risk scores (PRSs) reveals clinically meaningful differences in BP (16.9 mmHg systolic BP, 95% CI, 15.5-18.2 mmHg, P = 2.22 × 10-126) and more than a sevenfold higher odds of hypertension risk (odds ratio, 7.33; 95% CI, 5.54-9.70; P = 4.13 × 10-44) in an independent dataset. Adding PRS into hypertension-prediction models increased the area under the receiver operating characteristic curve (AUROC) from 0.791 (95% CI, 0.781-0.801) to 0.826 (95% CI, 0.817-0.836, ∆AUROC, 0.035, P = 1.98 × 10-34). We compare the 2,103 loci results in non-European ancestries and show significant PRS associations in a large African-American sample. Secondary analyses implicate 500 genes previously unreported for BP. Our study highlights the role of increasingly large genomic studies for precision health research.

Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

Endophenotype effect sizes support variant pathogenicity in monogenic disease susceptibility genes.

Accurate and efficient classification of variant pathogenicity is critical for research and clinical care. Using data from three large studies, we demonstrate that population-based associations between rare variants and quantitative endophenotypes for three monogenic diseases (low-density-lipoprotein cholesterol for familial hypercholesterolemia, electrocardiographic QTc interval for long QT syndrome, and glycosylated hemoglobin for maturity-onset diabetes of the young) provide evidence for variant pathogenicity. Effect sizes are associated with pathogenic ClinVar assertions (P < 0.001 for each trait) and discriminate pathogenic from non-pathogenic variants (area under the curve 0.82-0.84 across endophenotypes). An effect size threshold of ≥ 0.5 times the endophenotype standard deviation nominates up to 35% of rare variants of uncertain significance or not in ClinVar in disease susceptibility genes with pathogenic potential. We propose that variant associations with quantitative endophenotypes for monogenic diseases can provide evidence supporting pathogenicity.

Quantifying the Excess Risk of Adverse COVID-19 Outcomes in Unvaccinated Individuals With Diabetes Mellitus, Hypertension, Ischaemic Heart Disease or Myocardial Injury: A Meta-Analysis.

Background: More than 80% of individuals in low and middle-income countries (LMICs) are unvaccinated against coronavirus disease 2019 (COVID-19). In contrast, the greatest burden of cardiovascular disease is seen in LMIC populations. Hypertension (HTN), diabetes mellitus (DM), ischaemic heart disease (IHD) and myocardial injury have been variably associated with adverse COVID-19 outcomes. A systematic comparison of their impact on specific COVID-19 outcomes is lacking. We quantified the impact of DM, HTN, IHD and myocardial injury on six adverse COVID-19 outcomes: death, acute respiratory distress syndrome (ARDS), invasive mechanical ventilation (IMV), admission to intensive care (ITUadm), acute kidney injury (AKI) and severe COVID-19 disease (SCov), in an unvaccinated population. Methodology: We included studies published between 1st December 2019 and 16th July 2020 with extractable data on patients ≥18 years of age with suspected or confirmed SARS-CoV-2 infection. Odds ratios (OR) for the association between DM, HTN, IHD and myocardial injury with each of six COVID-19 outcomes were measured. Results: We included 110 studies comprising 48,809 COVID-19 patients. Myocardial injury had the strongest association for all six adverse COVID-19 outcomes [death: OR 8.85 95% CI (8.08-9.68), ARDS: 5.70 (4.48-7.24), IMV: 3.42 (2.92-4.01), ITUadm: 4.85 (3.94-6.05), AKI: 10.49 (6.55-16.78), SCov: 5.10 (4.26-6.05)]. HTN and DM were also significantly associated with death, ARDS, ITUadm, AKI and SCov. There was substantial heterogeneity in the results, partly explained by differences in age, gender, geographical region and recruitment period. Conclusion: COVID-19 patients with myocardial injury are at substantially greater risk of death, severe disease and other adverse outcomes. Weaker, yet significant associations are present in patients with HTN, DM and IHD. Quantifying these associations is important for risk stratification, resource allocation and urgency in vaccinating these populations. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, registration no: CRD42020201435 and CRD42020201443.

Monogenic and Polygenic Contributions to QTc Prolongation in the Population.

BACKGROUND: Rare sequence variation in genes underlying cardiac repolarization and common polygenic variation influence QT interval duration. However, current clinical genetic testing of individuals with unexplained QT prolongation is restricted to examination of monogenic rare variants. The recent emergence of large-scale biorepositories with sequence data enables examination of the joint contribution of rare and common variations to the QT interval in the population. METHODS: We performed a genome-wide association study of the QTc in 84 630 UK Biobank participants and created a polygenic risk score (PRS). Among 26 976 participants with whole-genome sequencing and ECG data in the TOPMed (Trans-Omics for Precision Medicine) program, we identified 160 carriers of putative pathogenic rare variants in 10 genes known to be associated with the QT interval. We examined QTc associations with the PRS and with rare variants in TOPMed. RESULTS: Fifty-four independent loci were identified by genome-wide association study in the UK Biobank. Twenty-one loci were novel, of which 12 were replicated in TOPMed. The PRS composed of 1 110 494 common variants was significantly associated with the QTc in TOPMed (ΔQTc/decile of PRS=1.4 ms [95% CI, 1.3 to 1.5]; P=1.1×10-196). Carriers of putative pathogenic rare variants had longer QTc than noncarriers (ΔQTc=10.9 ms [95% CI, 7.4 to 14.4]). Of individuals with QTc>480 ms, 23.7% carried either a monogenic rare variant or had a PRS in the top decile (3.4% monogenic, 21% top decile of PRS). CONCLUSIONS: QTc duration in the population is influenced by both rare variants in genes underlying cardiac repolarization and polygenic risk, with a sizeable contribution from polygenic risk. Comprehensive assessment of the genetic determinants of QTc prolongation includes incorporation of both polygenic and monogenic risk.

Bayesian approaches to fixed effects meta-analysis.

Bayesian methods seem a natural choice for combining sources of evidence in meta-analyses. However, in practice, their sensitivity to the choice of prior distribution is much less attractive, particularly for parameters describing heterogeneity. A recent non-Bayesian approach to fixed-effects meta-analysis provides novel ways to think about estimation of an average effect and the variability around this average. In this paper, we describe the Bayesian analogs of those results, showing how Bayesian inference on fixed-effects parameters-properties of the study populations at hand-is more stable and less sensitive than standard random-effects approaches. As well as these practical insights, our development also clarifies different ways in which prior beliefs like homogeneity and correlation can be reflected in prior distributions. We also carefully distinguish how random-effects models can be used to reflect sampling uncertainty versus their use reflecting a priori exchangeability of study-specific effects, and how subsequent inference depends on which motivation is used. With some important theoretical results, illustrated in an applied meta-analysis example, we show the robustness of the fixed effects even for small numbers of studies.

Nasal airway transcriptome-wide association study of asthma reveals genetically driven mucus pathobiology.

To identify genetic determinants of airway dysfunction, we performed a transcriptome-wide association study for asthma by combining RNA-seq data from the nasal airway epithelium of 681 children, with UK Biobank genetic association data. Our airway analysis identified 95 asthma genes, 58 of which were not identified by transcriptome-wide association analyses using other asthma-relevant tissues. Among these genes were MUC5AC, an airway mucin, and FOXA3, a transcriptional driver of mucus metaplasia. Muco-ciliary epithelial cultures from genotyped donors revealed that the MUC5AC risk variant increases MUC5AC protein secretion and mucus secretory cell frequency. Airway transcriptome-wide association analyses for mucus production and chronic cough also identified MUC5AC. These cis-expression variants were associated with trans effects on expression; the MUC5AC variant was associated with upregulation of non-inflammatory mucus secretory network genes, while the FOXA3 variant was associated with upregulation of type-2 inflammation-induced mucus-metaplasia pathway genes. Our results reveal genetic mechanisms of airway mucus pathobiology.

Genetic and clinical determinants of abdominal aortic diameter: genome-wide association studies, exome array data and Mendelian randomization study.

Progressive dilation of the infrarenal aortic diameter is a consequence of the ageing process and is considered the main determinant of abdominal aortic aneurysm (AAA). We aimed to investigate the genetic and clinical determinants of abdominal aortic diameter (AAD). We conducted a meta-analysis of genome-wide association studies in 10 cohorts (n = 13 542) imputed to the 1000 Genome Project reference panel including 12 815 subjects in the discovery phase and 727 subjects [Partners Biobank cohort 1 (PBIO)] as replication. Maximum anterior-posterior diameter of the infrarenal aorta was used as AAD. We also included exome array data (n = 14 480) from seven epidemiologic studies. Single-variant and gene-based associations were done using SeqMeta package. A Mendelian randomization analysis was applied to investigate the causal effect of a number of clinical risk factors on AAD. In genome-wide association study (GWAS) on AAD, rs74448815 in the intronic region of LDLRAD4 reached genome-wide significance (beta = -0.02, SE = 0.004, P-value = 2.10 × 10-8). The association replicated in the PBIO1 cohort (P-value = 8.19 × 10-4). In exome-array single-variant analysis (P-value threshold = 9 × 10-7), the lowest P-value was found for rs239259 located in SLC22A20 (beta = 0.007, P-value = 1.2 × 10-5). In the gene-based analysis (P-value threshold = 1.85 × 10-6), PCSK5 showed an association with AAD (P-value = 8.03 × 10-7). Furthermore, in Mendelian randomization analyses, we found evidence for genetic association of pulse pressure (beta = -0.003, P-value = 0.02), triglycerides (beta = -0.16, P-value = 0.008) and height (beta = 0.03, P-value < 0.0001), known risk factors for AAA, consistent with a causal association with AAD. Our findings point to new biology as well as highlighting gene regions in mechanisms that have previously been implicated in the genetics of other vascular diseases.

Rare coding variants in RCN3 are associated with blood pressure.

BACKGROUND: While large genome-wide association studies have identified nearly one thousand loci associated with variation in blood pressure, rare variant identification is still a challenge. In family-based cohorts, genome-wide linkage scans have been successful in identifying rare genetic variants for blood pressure. This study aims to identify low frequency and rare genetic variants within previously reported linkage regions on chromosomes 1 and 19 in African American families from the Trans-Omics for Precision Medicine (TOPMed) program. Genetic association analyses weighted by linkage evidence were completed with whole genome sequencing data within and across TOPMed ancestral groups consisting of 60,388 individuals of European, African, East Asian, Hispanic, and Samoan ancestries. RESULTS: Associations of low frequency and rare variants in RCN3 and multiple other genes were observed for blood pressure traits in TOPMed samples. The association of low frequency and rare coding variants in RCN3 was further replicated in UK Biobank samples (N = 403,522), and reached genome-wide significance for diastolic blood pressure (p = 2.01 × 10- 7). CONCLUSIONS: Low frequency and rare variants in RCN3 contributes blood pressure variation. This study demonstrates that focusing association analyses in linkage regions greatly reduces multiple-testing burden and improves power to identify novel rare variants associated with blood pressure traits.

Clinical and biomarker modifiers of vitamin D treatment response: the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Different 25-hydroxyvitamin D [25(OH)D] thresholds for treatment with vitamin D supplementation have been suggested and are derived almost exclusively from observational studies. Whether other characteristics, including race/ethnicity, BMI, and estimated glomerular filtration rate (eGFR), should also influence the threshold for treatment is unknown. OBJECTIVES: The aim was to identify clinical and biomarker characteristics that modify the response to vitamin D supplementation. METHODS: A total of 666 older adults in the Multi-Ethnic Study of Atherosclerosis (MESA) were randomly assigned to 16 wk of oral vitamin D3 (2000 IU/d; n = 499) or placebo (n = 167). Primary outcomes were changes in serum parathyroid hormone (PTH) and 1,25-dihydroxyvitamin D [1,25(OH)2D] concentrations from baseline to 16 wk. RESULTS: Among 666 participants randomly assigned (mean age: 72 y; 53% female; 66% racial/ethnic minority), 611 (92%) completed the study. The mean (SD) change in PTH was -3 (16) pg/mL with vitamin D3 compared with 2 (18) pg/mL with placebo (estimated mean difference: -5; 95% CI: -8, -2 pg/mL). Within the vitamin D3 group, lower baseline 25-hydroxyvitamin D [25(OH)D] was associated with a larger decline in PTH in a nonlinear fashion. With baseline 25(OH)D ≥30 ng/mL as the reference, 25(OH)D <20 ng/mL was associated with a larger decline in PTH with vitamin D3 supplementation (-10; 95% CI: -15, -6 pg/mL), whereas 25(OH)D of 20-30 ng/mL was not (-2; 95% CI: -6, 1 pg/mL). A segmented threshold model identified a baseline 25(OH)D concentration of 21 (95% CI: 13, 31) ng/mL as an inflection point for difference in change in PTH. Race/ethnicity, BMI, and eGFR did not modify vitamin D treatment response. There was no significant change in 1,25(OH)2D in either treatment group. CONCLUSIONS: Of characteristics most commonly associated with vitamin D metabolism, only baseline 25(OH)D <20 ng/mL modified the PTH response to vitamin D supplementation, providing support from a clinical trial to use this threshold to define insufficiency. This trial was registered at clinicaltrials.gov as NCT02925195.

BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion.

Whole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.

Variant-specific inflation factors for assessing population stratification at the phenotypic variance level.

In modern Whole Genome Sequencing (WGS) epidemiological studies, participant-level data from multiple studies are often pooled and results are obtained from a single analysis. We consider the impact of differential phenotype variances by study, which we term 'variance stratification'. Unaccounted for, variance stratification can lead to both decreased statistical power, and increased false positives rates, depending on how allele frequencies, sample sizes, and phenotypic variances vary across the studies that are pooled. We develop a procedure to compute variant-specific inflation factors, and show how it can be used for diagnosis of genetic association analyses on pooled individual level data from multiple studies. We describe a WGS-appropriate analysis approach, implemented in freely-available software, which allows study-specific variances and thereby improves performance in practice. We illustrate the variance stratification problem, its solutions, and the proposed diagnostic procedure, in simulations and in data from the Trans-Omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), used in association tests for hemoglobin concentrations and BMI.

The Multi-Ethnic Study of Atherosclerosis individual response to vitamin D trial: Building a randomized clinical trial into an observational cohort study.

The INdividual response to VITamin D (INVITe) trial was a randomized, placebo-controlled, parallel group trial of vitamin D3 supplementation (2000 IU daily) designed to determine clinical and genetic characteristics that modify the response to vitamin D supplementation. To enhance internal and external validity and reduce cost, the INVITe trial was nested within the Multi-Ethnic Study of Atherosclerosis (MESA), an ongoing prospective observational cohort study. The INVITe trial enrolled a community-based population of 666 racially and ethnically diverse participants from January 2017 to April 2019. This represents 30% of 2210 MESA participants approached for screening, and 96% of those found to be eligible. Barriers to enrollment included delayed initiation of the trial relative to scheduled MESA study visits, a lower number of available MESA participants than expected, and a high prevalence (18%) of high-dose vitamin D supplementation (>1000 IU daily, an exclusion criterion). The final study visit was attended by 611 participants (92%), and median adherence was 98%. Our experience suggests that integration of a randomized trial into an existing observational cohort study may leverage strengths of the source population and enhance enrollment, retention, and adherence, although with limited enrollment capacity. The INVITe trial will use rigorously-collected data to advance understanding of individual determinants of vitamin D response.

Genetic loci associated with prevalent and incident myocardial infarction and coronary heart disease in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium.

BACKGROUND: Genome-wide association studies have identified multiple genomic loci associated with coronary artery disease, but most are common variants in non-coding regions that provide limited information on causal genes and etiology of the disease. To overcome the limited scope that common variants provide, we focused our investigation on low-frequency and rare sequence variations primarily residing in coding regions of the genome. METHODS AND RESULTS: Using samples of individuals of European ancestry from ten cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, both cross-sectional and prospective analyses were conducted to examine associations between genetic variants and myocardial infarction (MI), coronary heart disease (CHD), and all-cause mortality following these events. For prevalent events, a total of 27,349 participants of European ancestry, including 1831 prevalent MI cases and 2518 prevalent CHD cases were used. For incident cases, a total of 55,736 participants of European ancestry were included (3,031 incident MI cases and 5,425 incident CHD cases). There were 1,860 all-cause deaths among the 3,751 MI and CHD cases from six cohorts that contributed to the analysis of all-cause mortality. Single variant and gene-based analyses were performed separately in each cohort and then meta-analyzed for each outcome. A low-frequency intronic variant (rs988583) in PLCL1 was significantly associated with prevalent MI (OR = 1.80, 95% confidence interval: 1.43, 2.27; P = 7.12 × 10-7). We conducted gene-based burden tests for genes with a cumulative minor allele count (cMAC) ≥ 5 and variants with minor allele frequency (MAF) < 5%. TMPRSS5 and LDLRAD1 were significantly associated with prevalent MI and CHD, respectively, and RC3H2 and ANGPTL4 were significantly associated with incident MI and CHD, respectively. No loci were significantly associated with all-cause mortality following a MI or CHD event. CONCLUSION: This study identified one known locus (ANGPTL4) and four new loci (PLCL1, RC3H2, TMPRSS5, and LDLRAD1) associated with cardiovascular disease risk that warrant further investigation.

Type 2 and interferon inflammation regulate SARS-CoV-2 entry factor expression in the airway epithelium.

Coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2, an emerging virus that utilizes host proteins ACE2 and TMPRSS2 as entry factors. Understanding the factors affecting the pattern and levels of expression of these genes is important for deeper understanding of SARS-CoV-2 tropism and pathogenesis. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci for both ACE2 and TMPRSS2, that vary in frequency across world populations. We find TMPRSS2 is part of a mucus secretory network, highly upregulated by type 2 (T2) inflammation through the action of interleukin-13, and that the interferon response to respiratory viruses highly upregulates ACE2 expression. IL-13 and virus infection mediated effects on ACE2 expression were also observed at the protein level in the airway epithelium. Finally, we define airway responses to common coronavirus infections in children, finding that these infections generate host responses similar to other viral species, including upregulation of IL6 and ACE2. Our results reveal possible mechanisms influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.

Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale.

Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.

Type 2 and interferon inflammation strongly regulate SARS-CoV-2 related gene expression in the airway epithelium.

Coronavirus disease 2019 (COVID-19) outcomes vary from asymptomatic infection to death. This disparity may reflect different airway levels of the SARS-CoV-2 receptor, ACE2, and the spike protein activator, TMPRSS2. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci (eQTL) for both ACE2 and TMPRSS2, that vary in frequency across world populations. Importantly, we find TMPRSS2 is part of a mucus secretory network, highly upregulated by T2 inflammation through the action of interleukin-13, and that interferon response to respiratory viruses highly upregulates ACE2 expression. Finally, we define airway responses to coronavirus infections in children, finding that these infections upregulate IL6 while also stimulating a more pronounced cytotoxic immune response relative to other respiratory viruses. Our results reveal mechanisms likely influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.