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Prospective cohort studies have found a relation between sugar-sweetened beverage consumption (SSB; sodas and fruit drinks) and dyslipidemia. There is limited evidence linking SSB consumption to emerging features of dyslipidemia, which can be characterized by variation in lipoprotein particle size, remnant-like particle (RLP), and apolipoprotein concentrations.To examine the association between SSB consumption and plasma lipoprotein cholesterol, apolipoprotein, and lipoprotein particle size concentrations among US adults.We examined participants from the Framingham Offspring Study (FOS) (1987-1995; n = 3047) and the Women's Health Study (1992; n = 26,218). Plasma LDL-C, apolipoprotein (apo) B, HDL-C, apoA1, triglyceride (TG), non-HDL-C, total: HDL-cholesterol ratio, and apoB: apoA1 concentrations were quantified in both cohorts, and apoE, apoC3, RLP-TG, and RLP-cholesterol concentrations in FOS only. Lipoprotein particle sizes were calculated from NMR signals for lipoprotein particle subclass concentrations (triglyceride-rich lipoprotein particles [TRL-P; very large, large, medium, small, and very small], LDL-particles [LDL-P; large, medium, and small], HDL-particles [HDL-P; large, medium, and small]). SSB consumption was estimated from food frequency questionnaire data. We examined the associations between SSB consumption and all lipoprotein and apoprotein measures in linear regression models, adjusting for confounding factors, such as lifestyle, diet, and traditional lipoprotein risk factors.SSB consumption was positively associated with LDL-C, apoB, TG, RLP-TG, RLP-C, non-HDL-C concentrations and total: HDL cholesterol and apoB: apoA1 ratio, and negatively associated with HDL-C and apoA1 concentrations (P-trend ranges from 0.0001 to 0.003). After adjustment for traditional lipoprotein risk factors, SSB consumers had smaller LDL-P and HDL-P sizes, lower concentrations of large LDL-P and medium HDL-P, and higher concentrations of small LDL-P, small HDL-P, and large TRL-P (P-trend ranges from 0.0001 to 0.0009).Higher SSB consumption was associated with multiple emerging features of dyslipidemia that have been linked to higher cardiometabolic risk in US adults.

Background: Carotid atherosclerosis is associated with cognitive impairment and dementia, though there is limited evidence of a direct link between carotid disease and amyloid-β (Aβ) burden. Objective: We studied the association of baseline and progressive carotid intima media thickness (CIMT) with Aβ on 11C-Pittsburgh Compound B (PiB) to determine if those with carotid atherosclerosis would have higher Aβ burden. Methods: We studied 47 participants from the Framingham Offspring cohort with carotid ultrasounds measuring CIMT at their 6th clinic examination (aged 49.5±5.7 years) and an average of 9.6 years later, and PiB imaging measuring Aβ on average 22.1 years post baseline. We used multivariate linear regression analyses to relate baseline, follow-up, mean, and progression of internal carotid artery (ICA) and common carotid artery (CCA) CIMT to Aβ in brain regions associated with Alzheimer’s disease (AD) and related dementias (ADRD), adjusting for age, sex, and other vascular risk factors. Results: Participants with higher mean ICA IMT had more Aβ in the precuneus (beta±standard error [β±SE]: 0.466±0.171 mm, p = 0.01) and the frontal, lateral, and retrosplenial regions (β±SE: 0.392±0.164 mm, p = 0.022) after adjusting for age, sex, vascular risk factors, and medication use. We did not find an association between any CCA IMT measures and Aβ or progression of ICA or CCA IMT and Aβ. Conclusion: Carotid atherosclerosis, as measured by ICA IMT, is associated with increased Aβ burden later in life. These findings support a link between vascular disease and AD/ADRD pathophysiology.

A challenge in standard genetic studies is maintaining good power to detect associations, especially for low prevalent diseases and rare variants. The traditional methods are most powerful when evaluating the association between variants in balanced study designs. Without accounting for family correlation and unbalanced case-control ratio, these analyses could result in inflated type I error. One cost-effective solution to increase statistical power is exploitation of available family history (FH) that contains valuable information about disease heritability. Here, we develop methods to address the aforementioned type I error issues while providing optimal power to analyze aggregates of rare variants by incorporating additional information from FH. With enhanced power in these methods exploiting FH and accounting for relatedness and unbalanced designs, we successfully detect genes with suggestive associations with Alzheimer disease, dementia, and type 2 diabetes by using the exome chip data from the Framingham Heart Study.

Aims To estimate how much information conveyed by self-reported family history of heart disease (FHHD) is already explained by clinical and genetic risk factors. Methods and results Cross-sectional analysis of UK Biobank participants without pre-existing coronary artery disease using a multivariable model with self-reported FHHD as the outcome. Clinical (diabetes, hypertension, smoking, apolipoprotein B-to-apolipoprotein AI ratio, waist-to-hip ratio, high sensitivity C-reactive protein, lipoprotein(a), triglycerides) and genetic risk factors (polygenic risk score for coronary artery disease [PRSCAD], heterozygous familial hypercholesterolemia [HeFH]) were exposures. Models were adjusted for age, sex, and cholesterol-lowering medication use. Multiple logistic regression models were fitted to associate FHHD with risk factors, with continuous variables treated as quintiles. Population attributable risks (PAR) were subsequently calculated from the resultant odds ratios. Among 166 714 individuals, 72 052 (43.2%) participants reported an FHHD. In a multivariable model, genetic risk factors PRSCAD (OR 1.30, CI 1.27–1.33) and HeFH (OR 1.31, 1.11–1.54) were most strongly associated with FHHD. Clinical risk factors followed: hypertension (OR 1.18, CI 1.15–1.21), lipoprotein(a) (OR 1.17, CI 1.14–1.20), apolipoprotein B-to-apolipoprotein AI ratio (OR 1.13, 95% CI 1.10–1.16), and triglycerides (OR 1.07, CI 1.04–1.10). For the PAR analyses: 21.9% (CI 18.19–25.63) of the risk of reporting an FHHD is attributed to clinical factors, 22.2% (CI% 20.44–23.88) is attributed to genetic factors, and 36.0% (CI 33.31–38.68) is attributed to genetic and clinical factors combined. Conclusions A combined model of clinical and genetic risk factors explains only 36% of the likelihood of FHHD, implying additional value in the family history.

In this work we consider smooth unbounded worm domains $\mathcal Z_\lambda$ in $\mathbb C^2$ and show that the Bergman projection, densely defined on the Sobolev spaces $H^{s,p}(\mathcal Z_\lambda)$, $p\in(1,\infty)$, $s\ge0$, does not extend to a bounded operator $P_\lambda:H^{s,p}(\mathcal Z_\lambda)\to H^{s,p}(\mathcal Z_\lambda)$ when $s>0$ or $p\neq2$. The same irregularity was known in the case of the non-smooth unbounded worm. This improved result shows that the irregularity of the projection is not a consequence of the irregularity of the boundary but instead of the infinite windings of the worm domain., Comment: Revised version. To appear on the Mediterranean Journal of Mathematics

Psoriasis, an immune-mediated inflammatory disease, affects nearly 125 million people globally. The interleukin (IL)-17A homodimer is a key driver of psoriasis and other autoimmune diseases, including psoriatic arthritis, axial spondyloarthritis, hidradenitis suppurativa, and uveitis. Treatment with monoclonal antibodies (mAbs) against IL-17A provides an improvement in the Psoriasis Area and Severity Index compared to conventional systemic agents. In this study, the Affibody(CIRCLED LATIN CAPITAL LETTER R) technology was used to identify and optimize a novel, small, biological molecule comprising three triple helical affinity domains, izokibep (previously ABY-035), for the inhibition of IL-17A signaling. Preclinical studies show that izokibep, a small 18.6 kDa IL-17 ligand trap comprising two IL-17A-specific Affibody domains and one albumin-binding domain, selectively inhibits human IL-17A in vitro and in vivo with superior potency and efficacy relative to anti-IL-17A mAbs. A Phase 1 first-in-human study was conducted to establish the safety, pharmacokinetics, and preliminary efficacy of izokibep, when administered intravenously and subcutaneously as single doses to healthy subjects, and as single intravenous and multiple subcutaneous doses to patients with psoriasis (NCT02690142; EudraCT No: 2015-004531-13). Izokibep was well tolerated with no meaningful safety concerns identified in healthy volunteers and patients with psoriasis. Rapid efficacy was seen in all psoriasis patients after one dose which further improved in patients receiving multiple doses. A therapeutic decrease in joint pain was also observed in a single patient with concurrent psoriatic arthritis. The study suggests that izokibep has the potential to safely treat IL17A-associated diseases such as psoriasis, psoriatic arthritis, axial spondyloarthritis, hidradenitis suppurativa, and uveitis.

Introduction: Relative to the general population, patients with schizophrenia or schizoaffective disorder have higher rates of suicide attempts and mortality from COVID-19 infection. Therefore, determining whether a history of suicide attempt is associated with COVID-19 in patients with schizophrenia or schizoaffective disorder has implications for COVID-19 vulnerability stratification in this patient population. Methods: We carried out cross-sectional analyses of electronic health records of veterans with a diagnosis of schizophrenia or schizoaffective disorder that received treatment at any United States Veterans Affairs Medical Center between January 1, 2020, and January 31, 2021. We used logistic regression to estimate unadjusted and adjusted (including age, sex, race, marital status, body mass index (BMI), and a medical comorbidity score) odds ratios (ORs) for COVID-19 positivity in suicide attempters relative to nonattempters. Results: A total of 101,032 veterans (mean age 56.67 ± 13.13 years; males 91,715 [90.8%]) were included in the analyses. There were 2,703 (2.7%) suicide attempters and 719 (0.7%) patients were positive for COVID-19. The association between history of suicide attempt and COVID-19 positivity was modified by age and BMI, such that the relationship was only significant in patients younger than 59 years, and in obese (BMI ≥30) patients (adjusted OR 3.42, 95% CI 2.02–5.79 and OR 2.85, 95% CI 1.65–4.94, respectively). Conclusions: Higher rates of COVID-19 in young or obese suicide attempters with a diagnosis of schizophrenia or schizoaffective disorder might be due to the elevated risk for the infection in this subgroup of patients.

In this paper we study Toeplitz and Ces\`aro-type operators on holomorphic function spaces on a homogeneous Siegel domain of Type II. We prove several necessary conditions and sufficient conditions for these operators to be continuous or compact, or to belong to suitable Schatten classes., Comment: 25 pages, no figures

Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry metaanalysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk., United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Heart Lung & Blood Institute (NHLBI) R01HL127564 R01HL142711, Wellcome Trust 201543/B/16/Z 202802/Z/16/Z, European Commission HEALTH-F2-2013-601456 608765 786833, TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation NNF15CC0018486, American Diabetes Association 1-19-ICTS-068, Academy of Finland 312062 Finnish Foundation for Cardiovascular Research, Sigrid Juselius Foundation, Finnish innovation fund Sitra (EW) Finska Lakaresallskapet, American Heart Association 15POST24470131 17POST33650016, University of Bristol NIHR Biomedical Research Centre BRC-1215-2001, MRC & WT 217065/Z/19/Z, UK Research & Innovation (UKRI), Medical Research Council UK (MRC) MC_UU_00011, CRUK Integrative Cancer Epidemiology Programme C18281/A19169, Medical Research Council UK (MRC) MC_UU_00011/1, UK National Institute for Health Research Academic Clinical Fellowship, American Heart Association 18CDA34110116, Miguel Servet contract from the ISCIII Spanish Health Institute CP17/00142, European Social Fund (ESF), Westlake Education Foundation, British Heart Foundation FS/14/66/3129 Z01HG200362 R01HL142302 R01HL105756

Genetic colocalization is an approach for determining whether a genetic variant at a particular locus is shared across multiple phenotypes. Genome-wide association studies (GWAS) have successfully mapped genetic variants associated with thousands of complex traits and diseases. However, a large proportion of GWAS signals fall in non-coding regions of the genome, making functional interpretation a challenge. Colocalization relies on a Bayesian framework that can integrate summary statistics, for example those derived from GWAS and expression quantitative trait loci (eQTL) mapping, to assess whether two or more independent association signals at a region of interest are consistent with a shared causal variant. The results from a colocalization analysis may be used to evaluate putative causal relationships between omics-based molecular measurements and a complex disease, and can generate hypotheses that may be followed up by tailored experiments. In this article, we present an easy and straightforward protocol for conducting a Bayesian test for colocalization of two traits using the 'coloc' package in R with summary-level results derived from GWAS and eQTL studies. We also provide general guidelines that can assist in the interpretation of findings generated from colocalization analyses. © 2022 Wiley Periodicals LLC. Basic Protocol: Performing a genetic colocalization analysis using the 'coloc' package in R and summary-level data Support Protocol: Installing the 'coloc' R package.

Clonal hematopoiesis of indeterminate potential (CHIP) is associated with an increased risk of cardiovascular diseases (CVD), putatively via inflammasome activation. We pursued an inflammatory gene modifier scan for CHIP-associated CVD risk among 424,651 UK Biobank participants. CHIP was identified using whole exome sequencing data of blood DNA and modeled both as a composite and for common drivers (DNMT3A, TET2, ASXL1, andJAK2) separately. We developed predicted gene expression scores for 26 inflammasome-related genes and assessed how they modify CHIP-associated CVD risk. We identifyIL1RAPas a potential key molecule for CHIP-associated CVD risk across genes and increasedAIM2gene expression leading to heightenedJAK2- andASXL1-associated CVD risks. We show that CRISPR-inducedAsxl1mutated murine macrophages have a particularly heightened inflammatory response to AIM2 agonism. Our study provides new evidence to support gene-specific strategies to address CHIP-associated CVD risk.

In this paper we consider the (ray) representations of the group $\mathrm{Aut}$ of biholomorphisms of the Siegel upper half-space $\mathcal U$ defined by $U_s(\varphi) f=(f\circ \varphi^{-1}) (J \varphi^{-1})^{s/2}$, $s\in\mathbb R$, and characterize the semi-Hilbert spaces $H$ of holomorphic functions on $\mathcal U$ satisfying the following assumptions: (a) $H$ is strongly decent; (b) $U_s$ induces a bounded ray representation of the group $\mathrm{Aff}$ of affine automorphisms of $\mathcal U$ in $H$. We use this description to improve the known characterization of the semi-Hilbert spaces of holomorphic functions on $\mathcal U$ satisfying (a) and (b) with $\mathrm{Aff}$ replaced by $\mathrm{Aut}$. In addition, we characterize the mean-periodic holomorphic functions on $\mathcal U$ under the representation $U_0$ of $\mathrm{Aff}$., Comment: 29 pages, no figures

BackgroundPublications suggest immunomodulation co-therapy improves responder rates in uncontrolled/refractory gout patients undergoing pegloticase treatment. The MIRROR open-label trial showed a 6-month pegloticase + methotrexate co-therapy responder rate of 79%, compared to an established 42% pegloticase monotherapy responder rate. Longer-term efficacy/safety data are presented here.MethodsUncontrolled gout patients (serum urate [SU] ≥ 6 mg/dL and SU ≥ 6 mg/dL despite urate-lowering therapy [ULT], ULT intolerance, or functionally-limiting tophi) were included. Patients with immunocompromised status, G6PD deficiency, severe kidney disease, or methotrexate contraindication were excluded. Oral methotrexate (15 mg/week) and folic acid (1 mg/day) were administered 4 weeks before and during pegloticase therapy. Twelve-month responder rate (SU ResultsFourteen patients were included (all male, 49.3 ± 8.7 years, 13.8 ± 7.4-year gout history, pre-therapy SU 9.2 ± 2.5 mg/dL). Three patients were non-responders and discontinued study treatment before 24 weeks, one patient exited the study per protocol at 24 weeks (enrolled prior to treatment extension amendment), and 10 remained in the study through week 52. Of the 10, 8 completed 52 weeks of pegloticase + methotrexate and were 12-month responders. The remaining two discontinued pegloticase + methotrexate at week 24 (met treatment goals) and stayed in the study under observation (allopurinol prescribed at physicians’ discretion); one remained a responder at 12 months. At 52 weeks, change from baseline in SU was − 8.2 ± 4.1 mg/dL (SU 1.1 ± 2.4 mg/dL,n = 10). Gout flares were common early in treatment but progressively decreased while on therapy (weeks 1–12, 13/14 [92.9%]; weeks 36–52, 2/8 [25.0%]). One patient recovered from sepsis (serious AE). Two non-responders developed high ADA titers; fewer patients had trough concentrations (Cmin) below the quantitation limit (BQL), and the medianCminwas higher (1.03 µg/mL vs. BQL) than pegloticase monotherapy trials.ConclusionsPegloticase + methotrexate co-therapy was well-tolerated over 12 months, with sustained SU lowering, progressive gout flare reduction, and no new safety concerns. Antibody/PK findings suggest methotrexate attenuates ADA formation, coincident with higher treatment response rates.Trial registrationClinicalTrials.gov,NCT03635957. Registered on 17 August 2018.

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%., The goals, resources and design of the NHLBI Trans-Omics for Precision Medicine (TOPMed) programme are described, and analyses of rare variants detected in the first 53,831 samples provide insights into mutational processes and recent human evolutionary history.

We study several connected problems of holomorphic function spaces on homogeneous Siegel domains. The main object of our study concerns weighted mixed norm Bergman spaces on homogeneous Siegel domains of type II. These problems include: sampling, atomic decomposition, duality, boundary values, boundedness of the Bergman projectors. Our analysis include the Hardy spaces, and suitable generalizations of the classical Bloch and Dirichlet spaces. One of the main novelties in this work is the generality of the domains under consideration, that is, homogeneous Siegel domains, extending many results from the more particular cases of the upper half-plane, Siegel domains of tube type over irreducible cones, or symmetric, irreducible Siegel domains of type II., Comment: 135 pages + Corrigendum

ObjectiveTo determine the joint role of ideal cardiovascular health (CVH) and genetic risk on risk of dementia.MethodsWe categorized CVH on the basis of the American Heart Association Ideal CVH Index and genetic risk through a genetic risk score (GRS) of common genetic variants and the APOE ε4 genotype in 1,211 Framingham Heart Study (FHS) offspring cohort participants. We used multivariable Cox proportional hazards regression models to examine the association between CVH, genetic risk, and incident all-cause dementia with up to 10 years of follow-up (mean 8.4 years, 96 incident dementia cases), adjusting for age, sex, and education.ResultsWe observed that a high GRS (>80th percentile) was associated with a 2.6-fold risk of dementia (95% confidence interval [CI] of hazard ratio [HR] 1.23–5.29; p = 0.012) compared with having a low GRS (APOE ε4 allele was associated with a 2.3-fold risk of dementia compared with not carrying an APOE ε4 allele (95% CI of HR 1.49–3.53; p = 0.0002), and having a favorable CVH showed a 0.45-fold lower risk of dementia (95% CI of HR 0.20–1.01; p = 0.0527) compared to having an unfavorable CVH when all 3 components were included in the model. We did not observe an interaction between CVH and GRS (p = 0.99) or APOE ε4 (p = 0.16).ConclusionsWe observed that both genetic risk and CVH contribute additively to dementia risk.

Population stratification may cause an inflated type-I error and spurious association when assessing the association between genetic variations with an outcome. Many genetic association studies are now using exonic variants, which captures only 1% of the genome, however population stratification adjustments have not been evaluated in the context of exonic variants. We compare the performance of two established approaches: principal components analysis (PCA) and mixed effects models and assess the utility of genome-wide (GW) and exonic variants, by simulation and using a dataset from the Framingham Heart Study. Our results illustrate that although the PCs and genetic relationship matrices (GRM) computed by GW and exonic markers are different, the type-I error rate of association tests for common variants with additive effect appear to be properly controlled in the presence of population stratification. In addition, by considering single nucleotide variants (SNVs) that have different levels of counfounding by population stratification, we also compare the power across multiple association approaches to account for population stratification such as PC-based corrections and mixed effects models. We find that while these 2 methods achieve a similar power for SNVs that have a low or medium level of confounding by population stratification, mixed effects model can reach a higher power for SNVs highly confounded by population stratification.

Objective This systematic literature review determined whether there is clinical utility for dual-energy computed tomography (DECT) to inform on prognosis for patients with gout. With DECT, individualized treatment plans could be developed based on the patient's unique urate burden, with DECT being used as a clinical outcome measure in gout management. Methods To evaluate DECT as a reliable, valid, and sensitive prognostic instrument, a librarian-assisted search was undertaken in PubMed and Embase for articles on gout and DECT informing on reliability; content, construct, and criterion validity; sensitivity to change; and minimum clinically important changes. Results This systematic literature review showed that DECT has high intra- and interrater reliability. Tophus burden correlates with functional loss to show content validity. DECT volume is positively correlated with death, cardiovascular risk factors, and the risk for future gout flares. DECT has excellent sensitivity to change with effective urate-lowering therapies. Conclusion DECT is a promising prognostic tool based on its high reliability, sensitivity to change, and emerging validity. Additional large, well-designed, prospective cohort studies are needed to fully evaluate its prognostic utility. This systematic review suggests that DECT very likely has additional prognostic information beyond clinical tophi assessment alone.

To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis-eQTL variant-gene transcript (eGene) pairs at p − 8 (2,855,111 unique cis-eQTL variants and 15,982 unique eGenes) and 1,469,754 trans-eQTL variant-eGene pairs at p trans-eQTL variants and 7,233 unique eGenes). In addition, 442,379 cis-eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis-eGenes are enriched for immune functions (FDR cis-eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.

BackgroundExpression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression and may reveal molecular mechanisms of disease. The present study describes an eQTM resource of CpG-transcript pairs.MethodsDNA methylation was measured in blood samples from 1,045 Framingham Heart Study (FHS) participants using the Illumina 450K BeadChip and in 1,070 FHS participants using the Illumina EPIC array. Blood gene expression data were collected from all 2,115 participants using RNA sequencing (RNA-seq). The association between DNA methylation and gene expression was quantified for all cis (i.e., within 1Mb) and trans (>1Mb) CpG-transcript pairs. Significant results (pcis and trans) were subsequently tested for enrichment of biological pathways and of clinical traits.ResultsWe identified 70,047 significant cis CpG-transcript pairs where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1,208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1,191 clinical traits (enrichment FDR ≤ 0.05). Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with these cardiometabolic traits.ConclusionsWe developed a robust and powerful resource of eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease.

Human genetic studies support an inverse causal relationship between leukocyte telomere length (LTL) and coronary artery disease (CAD), but directionally mixed effects for LTL and diverse malignancies. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by expansion of hematopoietic cells bearing leukemogenic mutations, predisposes both hematologic malignancy and CAD. TERT (which encodes telomerase reverse transcriptase) is the most significantly associated germline locus for CHIP in genome-wide association studies. Here, we investigated the relationship between CHIP, LTL, and CAD in the Trans-Omics for Precision Medicine (TOPMed) program ( n = 63,302) and UK Biobank ( n = 47,080). Bidirectional Mendelian randomization studies were consistent with longer genetically imputed LTL increasing propensity to develop CHIP, but CHIP then, in turn, hastens to shorten measured LTL (mLTL). We also demonstrated evidence of modest mediation between CHIP and CAD by mLTL. Our data promote an understanding of potential causal relationships across CHIP and LTL toward prevention of CAD.

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. This work was supported by a grant from the Swedish Research Council (2016-06830) and grants from the National Heart, Lung, and Blood Institute (NHLBI): R01HL142711 and R01HL127564. Please refer to the supplemental information for the full acknowledgements. Sí

Purpose We aimed to discover loci associated with triglyceride (TG) levels in the context of type 2 diabetes (T2D). We conducted a genome-wide association study (GWAS) in 424,120 genotyped participants of the UK Biobank (UKB) with T2D status and TG levels. Methods We stratified the cohort based on T2D status and conducted association analyses of TG levels for genetic variants with minor allele count (MAC) at least 20 in each stratum. Effect differences of genetic variants by T2D status were determined by Cochran’s Q-test and we validated the significantly associated variants in the Mass General Brigham Biobank (MGBB). Results Among 21,176 T2D and 402,944 non-T2D samples from UKB, stratified GWAS identified 19 and 315 genomic risk loci significantly associated with TG levels, respectively. Only chr6p21.32 exhibited genome-wide significant heterogeneity (I2 = 98.4%; pheterogeneity = 2.1x10-15), with log(TG) effect estimates of -0.066 (95%CI: -0.082, -0.050) and 0.002 (95%CI: -0.002, 0.006) for T2D and non-T2D, respectively. The lead variant rs9274619:A (allele frequency 0.095) is located 2Kb upstream of the HLA-DQB1 gene, between HLA-DQB1 and HLA-DQA2 genes. We replicated this finding among 25,137 participants (6,951 T2D cases) of MGBB (pheterogeneity = 9.5x10-3). Phenome-wide interaction association analyses showed that the lead variant was strongly associated with a concomitant diagnosis of type 1 diabetes (T1D) as well as diabetes-associated complications. Conclusion In conclusion, we identified an intergenic variant near HLA-DQB1/DQA2 significantly associates with decreased triglycerides only among those with T2D and highlights an immune overlap with T1D.

ImportanceProtein-truncating variants (PTVs) in apolipoprotein B (APOB) and proprotein convertase subtilisin/kexin type 9 (PCSK9) are associated with significantly lower low-density lipoprotein (LDL) cholesterol concentrations. The association of these PTVs with coronary heart disease (CHD) warrants further characterization in large, multiracial prospective cohort studies.ObjectiveTo evaluate the association of PTVs in APOB and PCSK9 with LDL cholesterol concentrations and CHD risk.Design, Setting, and ParticipantsThis studied included participants from 5 National Heart, Lung, and Blood Institute (NHLBI) studies and the UK Biobank. NHLBI study participants aged 5 to 84 years were recruited between 1971 and 2002 across the US and underwent whole-genome sequencing. UK Biobank participants aged 40 to 69 years were recruited between 2006 and 2010 in the UK and underwent whole-exome sequencing. Data were analyzed from June 2021 to October 2022.ExposuresPTVs in APOB and PCSK9.Main Outcomes and MeasuresEstimated untreated LDL cholesterol levels and CHD.ResultsAmong 19 073 NHLBI participants (10 598 [55.6%] female; mean [SD] age, 52 [17] years), 139 (0.7%) carried an APOB or PCSK9 PTV, which was associated with 49 mg/dL (95% CI, 43-56) lower estimated untreated LDL cholesterol level. Over a median (IQR) follow-up of 21.5 (13.9-29.4) years, incident CHD was observed in 12 of 139 carriers (8.6%) vs 3029 of 18 934 noncarriers (16.0%), corresponding to an adjusted hazard ratio of 0.51 (95% CI, 0.28-0.89; P = .02). Among 190 464 UK Biobank participants (104 831 [55.0%] female; mean [SD] age, 57 [8] years), 662 (0.4%) carried a PTV, which was associated with 45 mg/dL (95% CI, 42-47) lower estimated untreated LDL cholesterol level. Estimated CHD risk by age 75 years was 3.7% (95% CI, 2.0-5.3) in carriers vs 7.0% (95% CI, 6.9-7.2) in noncarriers, corresponding to an adjusted hazard ratio of 0.51 (95% CI, 0.32-0.81; P = .004).Conclusions and RelevanceAmong 209 537 individuals in this study, 0.4% carried an APOB or PCSK9 PTV that was associated with less exposure to LDL cholesterol and a 49% lower risk of CHD.

Mendelian randomization is a framework that uses measured variation in genes for assessing and estimating the causal effect of an exposure on an outcome. Multivariable Mendelian randomization is an extension that can assess the causal effect of multiple exposures on an outcome, and can be advantageous when considering a set (1) of potentially correlated candidate risk factors in evaluating the causal effect of each on a health outcome, accounting for measured pleiotropy. This can be seen, for example, in determining the causal effects of lipids and cholesterol on type 2 diabetes risk, where the correlated risk factors share genetic predictors. Similar to univariate Mendelian randomization, multivariable Mendelian randomization can be conducted using two-sample summary-level data where the gene-exposure and gene-outcome associations are derived from separate samples from the same underlying population. Here, we present a protocol for conducting a two-sample multivariable Mendelian randomization study using the 'MVMR' package in R and summary-level genetic data. We also provide a protocol for searching and obtaining instruments using available data sources in the 'MRInstruments' R package. Finally, we provide general guidelines and discuss the utility of performing a multivariable Mendelian randomization analysis for simultaneously assessing causality of multiple exposures. © 2021 Wiley Periodicals LLC. Basic Protocol: Performing a two-sample multivariable Mendelian randomization analysis using the 'MVMR' package in R and summarized genetic data Support Protocol 1: Installing the 'MVMR' R package Support Protocol 2: Obtaining instruments from the 'MRInstruments' R package.

In this paper we study Carleson and reverse Carleson measures on holomorphic function spaces on a homogeneous Siegel domain of Type II. We prove several necessary conditions and sufficient conditions in order for a measure $\mu$ to be Carleson and reverse Carleson on mixed-normed weighted Bergman spaces., Comment: 40 pages, no figures

Introduction: Increased triglycerides (TG) frequently accompanies type 2 diabetes (T2D) mellitus and poor glycemic control. The extent to which genetics influences triglycerides differently in individuals with T2D is not well understood. Methods: We conducted a genome-wide association study (GWAS) of log(TG) stratified by T2D using imputed UKB genotypes with minor allele count>20. We used linear regression model with a leave-one-out cross-validation using REGENIE adjusted for PC1-10, age, age 2 , gender, array-type, and race. We assessed the heterogeneity (I 2 ) between the effects of TG in T2D and non-T2D individuals. We sought replication in the Mass General Brigham Biobank (MGBB). Results: Among 21,176 T2D and 402,944 non-T2D samples, stratified GWAS identified 17 and 45 loci significantly associated with TG levels, respectively. Only one loci exhibited significant genome-wide heterogeneity (I 2 =98.4%; p heterogeneity =2x10 -15 ). The most significantly heterogenous variant was an intronic variant of the HLA-DQB1 gene, where the major allele (frequency 91.3%) was associated with increased TGs among those with T2D but not non-T2D (T2D group: beta=0.066, p-value=3.9x10 -15 ; non-T2D group: beta=-0.002, p-value=0.21). Among 25,136 participants (6,951 T2D cases) of MGBB, we replicated this finding (p heterogeneity =0.01). Conclusions: An intronic variant at HLA-DQB1 significantly associates with increased TGs only in those with T2D.

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare variants’ (RVs) associations with complex human traits. Variant set analysis is a powerful approach to study RV association, and a key component of it is constructing RV sets for analysis. However, existing methods have limited ability to define analysis units in the noncoding genome. Furthermore, there is a lack of robust pipelines for comprehensive and scalable noncoding RV association analysis. Here we propose a computationally-efficient noncoding RV association-detection framework that uses STAAR (variant-set test for association using annotation information) to group noncoding variants in gene-centric analysis based on functional categories. We also propose SCANG (scan the genome)-STAAR, which uses dynamic window sizes and incorporates multiple functional annotations, in a non-gene-centric analysis. We furthermore develop STAARpipeline to perform flexible noncoding RV association analysis, including gene-centric analysis as well as fixed-window-based and dynamic-window-based non-gene-centric analysis. We apply STAARpipeline to identify noncoding RV sets associated with four quantitative lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several noncoding RV associations in an additional 9,123 TOPMed samples.

Background: Publications suggest immunomodulation co-therapy improves responder rates in uncontrolled/refractory gout patients undergoing pegloticase treatment. The MIRROR open-label trial showed a 6-month pegloticase+methotrexate co-therapy responder rate of 79%, compared to an established 42% pegloticase monotherapy responder rate. Longer-term efficacy/safety data are presented here.Methods: Uncontrolled gout patients (serum urate [SU]≥6 mg/dL and SU≥6 mg/dL despite urate-lowering therapy [ULT], ULT intolerance, or functionally-limiting tophi) were included. Patients with immunocompromised status, G6PD deficiency, severe kidney disease, or methotrexate contraindication were excluded. Oral methotrexate (15 mg/week) and folic acid (1 mg/day) were administered 4-weeks before and during pegloticase therapy. Twelve-month responder rate (SUResults: Fourteen patients were included (all male, 49.3 ± 8.7 years, 13.8 ± 7.4 year gout history, pre-therapy SU: 9.2 ± 2.5 mg/dL). Three patients were non-responders and discontinued study treatment before 24-weeks, one patient exited the study per-protocol at 24-weeks (enrolled prior to treatment extension amendment), and 10 remained in study through Week 52. Of the 10, 8 completed 52-weeks of pegloticase+methotrexate and were 12-month responders. The remaining two discontinued pegloticase+methotrexate at Week 24 (met treatment goals) and stayed in study under observation (allopurinol prescribed at physicians’ discretion); one remained a responder at 12-months. At 52-weeks, change from baseline in SU was -8.2 ± 4.1 mg/dL (SU: 1.1 ± 2.4 mg/dL, n=10). Gout flares were common early in treatment but progressively decreased while on therapy (Weeks 1-12: 13/14 [92.9%], Weeks 36-52: 2/8 [25.0%]). One patient recovered from sepsis (serious AE). Two non-responders developed high ADA titres; fewer patients had trough concentrations (Cmin) below quantitation limit (BQL) and median Cmin was higher (1.03 mg/mL vs. BQL) than in pegloticase monotherapy trials.Conclusions: Methotrexate+pegloticase co-therapy was well-tolerated over 12-months, with sustained SU lowering, progressive gout flare reduction, and no new safety concerns. Antibody/PK findings suggest methotrexate attenuates ADA formation, coincident with higher treatment response rates.Trial registration: ClinicalTrials.gov: NCT03635957, registered 17 August 2018, https://clinicaltrials.gov/ct2/show/NCT03635957

Plasma lipids are heritable modifiable causal factors for coronary artery disease, the leading cause of death globally. Despite the well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing, partly due to limited sample sizes, ancestral diversity, and interpretation of potential clinical significance. Increasingly larger whole genome sequence datasets with plasma lipids coupled with methodologic advances enable us to more fully catalog the allelic spectrum for lipids. Here, among 66,329 ancestrally diverse (56% non-European ancestry) participants, we associate 428M variants from deep-coverage whole genome sequences with plasma lipids. Approximately 400M of these variants were not studied in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with plasma lipids through analysis of common and rare coding variants. We additionally discover several significantly associated rare non-coding variants largely at Mendelian lipid genes. Notably, we detect rareLDLRintronic variants associated with markedly increased LDL-C, similar to rareLDLRexonic variants. In conclusion, we conducted a systematic whole genome scan for plasma lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

In this paper we develop a theory of Besov and Triebel–Lizorkin spaces on general noncompact connected Lie groups endowed with a sub-Riemannian structure. Such spaces are defined by means of hypoelliptic sub-Laplacians with drift, and endowed with a measure whose density with respect to a right Haar measure is a continuous positive character of the group. We prove several equivalent characterizations of their norms, we establish comparison results also involving Sobolev spaces of recent introduction, and investigate their complex interpolation and algebra properties.

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

SARS-CoV-2 has caused symptomatic COVID-19 and widespread death across the globe. We sought to determine genetic variants contributing to COVID-19 susceptibility and hospitalization in a large biobank linked to a national United States health system. We identified 19,168 (3.7%) lab-confirmed COVID-19 cases among Million Veteran Program participants between March 1, 2020, and February 2, 2021, including 11,778 Whites, 4,893 Blacks, and 2,497 Hispanics. A multi-population genome-wide association study (GWAS) for COVID-19 outcomes identified four independent genetic variants (rs8176719, rs73062389, rs60870724, and rs73910904) contributing to COVID-19 positivity, including one novel locus found exclusively among Hispanics. We replicated eight of nine previously reported genetic associations at an alpha of 0.05 in at least one population-specific or the multi-population meta-analysis for one of the four MVP COVID-19 outcomes. We used rs8176719 and three additional variants to accurately infer ABO blood types. We found that A, AB, and B blood types were associated with testing positive for COVID-19 compared with O blood type with the highest risk for the A blood group. We did not observe any genome-wide significant associations for COVID-19 severity outcomes among those testing positive. Our study replicates prior GWAS findings associated with testing positive for COVID-19 among mostly White samples and extends findings at three loci to Black and Hispanic individuals. We also report a new locus among Hispanics requiring further investigation. These findings may aid in the identification of novel therapeutic agents to decrease the morbidity and mortality of COVID-19 across all major ancestral populations.

ImportanceAs patients with schizophrenia or schizoaffective disorder have a high risk of suicide, and a history of suicide attempt is a strong predictor of suicide, determining whether history of suicide attempt is associated with COVID-19 in patients with schizophrenia or schizoaffective disorder has implications for suicide prevention in this patient population.ObjectiveTo determine whether a history of suicide attempt is associated with COVID-19 in Veterans with schizophrenia or schizoaffective disorder.DesignCross-sectional analyses of nation-wide electronic health records (EHR).SettingUnited States Veterans Health Administration.ParticipantsVeterans with a diagnosis of schizophrenia or schizoaffective disorder that received treatment at any United States Veterans Affairs Medical Center from January 1, 2020 to January 31, 2021.ExposureHistory of suicide attempt.Main OutcomeAdjusted and unadjusted odds ratios (ORs) for COVID-19 positivity in suicide attempters relative to non-attempters. Adjusted analyses included age, sex, race, marital status, BMI, and a medical comorbidity score.ResultsA total of 101,032 Veterans [mean age 56.67 ± 13.13 years; males 91,715 (90.8%)] were included in the analyses. There were 2,703 (2.7%) suicide attempters and 719 (0.7%) patients were positive for COVID-19. There was effect modification by age and BMI in the association of history of suicide attempt with COVID-19 positivity such that the association was only significant in obese (BMI ≥ 30) patients and patients younger than 59 years respectively. In the entire sample, the unadjusted OR for COVID-19 positivity in attempters was 1.42 (95% CI 0.97 to 2.10) and the adjusted odds ratio was 1.90 (95% CI 1.28 to 2.80). In patients younger than 59 years, and in the obese patients respectively, history of suicide attempt was associated with COVID-positive status in unadjusted analyses [OR 3.53 (95% CI 2.10 to 5.94); OR 2.22 (95% CI 1.29 to 3.81)] and adjusted analyses [OR 3.42 (95% CI 2.02 to 5.79); OR 2.85 (95% CI 1.65 to 4.94)].Conclusions and RelevanceYoung or obese suicide attempters with a diagnosis of schizophrenia or schizoaffective disorders have higher rates of COVID-19 diagnosis; due to possible long-term neuropsychiatric sequelae of infection with SARS-CoV-2, such patients should be monitored closely.

Supplemental Digital Content is available in the text., Background: ChREBP (carbohydrate responsive element binding protein) is a transcription factor that responds to sugar consumption. Sugar-sweetened beverage (SSB) consumption and genetic variants in the CHREBP locus have separately been linked to HDL-C (high-density lipoprotein cholesterol) and triglyceride concentrations. We hypothesized that SSB consumption would modify the association between genetic variants in the CHREBP locus and dyslipidemia. Methods: Data from 11 cohorts from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (N=63 599) and the UK Biobank (N=59 220) were used to quantify associations of SSB consumption, genetic variants, and their interaction on HDL-C and triglyceride concentrations using linear regression models. A total of 1606 single nucleotide polymorphisms within or near CHREBP were considered. SSB consumption was estimated from validated questionnaires, and participants were grouped by their estimated intake. Results: In a meta-analysis, rs71556729 was significantly associated with higher HDL-C concentrations only among the highest SSB consumers (β, 2.12 [95% CI, 1.16–3.07] mg/dL per allele; P

Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a given trait. However, the standard PRS fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). Machine learning algorithms can be used to account for such non-linearities and interactions. We trained and validated polygenic prediction models for five complex phenotypes in a multi-ancestry population: total cholesterol, triglycerides, systolic blood pressure, sleep duration, and height. We used an ensemble method of LASSO for feature selection and gradient boosted trees (XGBoost) for non-linearities and interaction effects. In an independent test set, we found that combining a standard PRS as a feature in the XGBoost model increases the percentage variance explained (PVE) of the prediction model compared to the standard PRS by 25% for sleep duration, 26% for height, 44% for systolic blood pressure, 64% for triglycerides, and 85% for total cholesterol. Machine learning models trained in specific racial/ethnic groups performed similarly in multi-ancestry trained models, despite smaller sample sizes. The predictions of the machine learning models were superior to the standard PRS in each of the racial/ethnic groups in our study. However, among Blacks the PVE was substantially lower than for other groups. For example, the PVE for total cholesterol was 8.1%, 12.9%, and 17.4% for Blacks, Whites, and Hispanics/Latinos, respectively. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.

The development of sequencing technology calls for new powerful methods to detect disease associations and lower the cost of sequencing studies. Family history (FH) contains information on disease status of relatives, adding valuable information about the probands’ health problems and risk of diseases. Incorporating data from FH is a cost-effective way to improve statistical evidence in genetic studies, and moreover, overcomes limitations in study designs with insufficient cases or missing genotype information for association analysis. We proposed family history aggregation unit-based test (FHAT) and optimal FHAT (FHAT-O) to exploit available FH for rare variant association analysis. Moreover, we extended liability threshold model of case-control status and FH (LT-FH) method in aggregated unit-based methods and compared that with FHAT and FHAT-O. The computational efficiency and flexibility of the FHAT and FHAT-O were demonstrated through both simulations and applications. We showed that FHAT, FHAT-O and LT-FH method offer reasonable control of the type I error unless case/control ratio is extremely unbalanced, in which case they result in smaller inflation than that observed with conventional methods excluding FH. We also demonstrated that FHAT and FHAT-O are more powerful than LT-FH method and conventional methods in many scenarios. By applying FHAT and FHAT-O to the analysis of all cause dementia and hypertension using the exome sequencing data from the UK Biobank, we showed that our methods can improve significance for known regions. Furthermore, we replicated the previous associations in all cause dementia and hypertension and detected novel regions through the exome-wide analysis.

A locus containing OAS1/2/3 has been identified as a risk locus for severe COVID-19 among Europeans ancestry individuals, with a protective haplotype of ∼75 kilobases derived from Neanderthals. Here, we show that among several potentially causal variants at this locus, a splice variant of OAS1 occurs in people of African ancestry independently of the Neanderthal haplotype and confers protection against COVID-19 of a magnitude similar to that seen in individuals without African ancestry.

Traditional Hardy–Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in data sets composed of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and to evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence data sets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false-positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently among the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

Human genetic studies support an inverse causal relationship between leukocyte telomere length (LTL) and coronary artery disease (CAD), but directionally mixed effects for LTL and diverse malignancies. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by expansion of hematopoietic cells bearing leukemogenic mutations, predisposes both hematologic malignancy and CAD. TERT (which encodes telomerase reverse transcriptase) is the most significantly associated germline locus for CHIP in genome-wide association studies. Here, we investigated the relationship between CHIP, LTL, and CAD in Trans-Omics for Precision Medicine (TOPMed) program (N=63,302) and UK Biobank (N=48,658). Bidirectional Mendelian randomization studies were consistent with LTL lengthening increasing propensity to develop CHIP, but CHIP then in turn hastening LTL shortening. We also demonstrated evidence of modest mediation between CHIP and CAD by LTL. Our data promote an understanding of potential causal relationships across CHIP and LTL toward prevention of CAD.

In this work we study what we call Siegel--dissipative vector of commuting operators $(A_1,\ldots, A_{d+1})$ on a Hilbert space $\mathcal H$ and we obtain a von Neumann type inequality which involves the Drury--Arveson space $DA$ on the Siegel upper half-space $\mathcal U$. The operator $A_{d+1}$ is allowed to be unbounded and it is the infinitesimal generator of a contraction semigroup $\{e^{-i\tau A_{d+1}}\}_{\tau, Comment: 17 pages. Typos were fixed and the references updated

In this paper we introduce and study Bernstein spaces on a class of quadratic CR manifolds, that we call Siegel CR manifolds. These are spaces of entire functions of exponential type whose restrictions to a given Siegel CR submanifold are $L^p$-integrable with respect to a natural measure. For these spaces, among other results, we prove the Plancherel-P\'olya inequality, a Bernstein inequality and a sufficient condition for a sequence to be sampling., Comment: 22 pages, no figures

We introduce and study a family of spaces of entire functions in one variable that generalise the classical Paley–Wiener and Bernstein spaces. Namely, we consider entire functions of exponential type a whose restriction to the real line belongs to the homogeneous Sobolev space $$\dot{W}^{s,p}$$ W ˙ s , p and we call these spaces fractional Paley–Wiener if $$p=2$$ p = 2 and fractional Bernstein spaces if $$p\in (1,\infty )$$ p ∈ ( 1 , ∞ ) , that we denote by $$PW^s_a$$ P W a s and $${\mathcal {B}}^{s,p}_a$$ B a s , p , respectively. For these spaces we provide a Paley–Wiener type characterization, we remark some facts about the sampling problem in the Hilbert setting and prove generalizations of the classical Bernstein and Plancherel–Pólya inequalities. We conclude by discussing a number of open questions.

In this paper we discuss function spaces on a general noncompact Lie group, namely the scales of Triebel–Lizorkin and Besov spaces, defined in terms of a sub-Laplacian with drift. The sub-Laplacian is written as the (negative) sum of squares of a collection of left-invariant vector fields satisfying Hormander’s condition. These spaces were recently introduced by the authors. In this paper we prove a norm characterization in terms of finite differences, the density of test functions, and related isomorphism properties.

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute’s Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948–2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms.