While statistical methods for analyzing cluster randomized trials with continuous and binary outcomes have been extensively studied and compared, little comparative evidence has been provided for analyzing cluster randomized trials with survival outcomes in the presence of competing risks. Motivated by the Strategies to Reduce Injuries and Develop Confidence in Elders trial, we carried out a simulation study to compare the operating characteristics of several existing population-averaged survival models, including the marginal Cox, marginal Fine and Gray, and marginal multi-state models. For each model, we found that adjusting for the intraclass correlations through the sandwich variance estimator effectively maintained the type I error rate when the number of clusters is large. With no more than 30 clusters, however, the sandwich variance estimator can exhibit notable negative bias, and a permutation test provides better control of type I error inflation. Under the alternative, the power for each model is differentially affected by two types of intraclass correlations—the within-individual and between-individual correlations. Furthermore, the marginal Fine and Gray model occasionally leads to higher power than the marginal Cox model or the marginal multi-state model, especially when the competing event rate is high. Finally, we provide an illustrative analysis of Strategies to Reduce Injuries and Develop Confidence in Elders trial using each analytical strategy considered.

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.

Genetic variants within nearly 1,000 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. To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N=1,654,960) of blood lipids to prioritize putative causal genes for 2,286 lipid associations by combining six gene prediction methods and assigning a confidence score. We assign, most confidently, 118 candidate causal genes and identify potential drug targets including bona-fide (PCSK9) and putative (PNLIP and ARF6) genes. 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. Taken together, our findings provide insights into the mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

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, and JAK2) separately. We developed predicted gene expression scores for 26 inflammasome-related genes and assessed how they modify CHIP-associated CVD risk. We identify IL1RAP as a potential key molecule for CHIP-associated CVD risk across genes and increased AIM2 gene expression leading to heightened JAK2- and ASXL1-associated CVD risks. We show that CRISPR-induced Asxl1 mutated 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.

Clonal hematopoiesis of indeterminate potential (CHIP) is associated with an increased risk of cardiovascular diseases (CVDs), putatively via inflammasome activation. We pursued an inflammatory gene modifier scan for CHIP-associated CVD risk among 424,651 UK Biobank participants. We identified CHIP using whole-exome sequencing data of blood DNA and modeled as a composite, considering all driver genes together, as well as separately for common drivers (DNMT3A, TET2, ASXL1, and JAK2). We developed predicted gene expression scores for 26 inflammasome-related genes and assessed how they modify CHIP-associated CVD risk. We identified IL1RAP as a potential key molecule for CHIP-associated CVD risk across genes and increased AIM2 gene expression leading to heightened JAK2-and ASXL1-associated CVD risk. We show that CRISPR-induced Asxl1-mutated murine macrophages had a particularly heightened inflammatory response to AIM2 agonism, associated with an increased DNA damage response, as well as increased IL-10 secretion, mirroring a CVDprotective effect of IL10 expression in ASXL1 CHIP. Our study supports the role of inflammasomes in CHIP-associated CVD and provides evidence to support gene-specific strategies to address CHIP-associated CVD risk.

Background. Expression 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. Methods. DNA 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 (p<1E-7 for cis and <1E-14 for trans) were subsequently tested for enrichment of biological pathways and of clinical traits. Results. We 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. Conclusions. We 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.

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<5x10-8 (2,855,111 unique cis-eQTL variants and 15,982 unique eGenes) and 1,469,754 trans-eQTL variant-eGene pairs at p<1e-12 (526,056 unique 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 <0.05). The 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 the NCBI Molecular QTL Browser (https://www.ncbi.nlm.nih.gov/) that can be used to advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.

BackgroundSports-related rotator cuff muscle injury is one of the most prevalent pathologies affecting overhead sports athletes. Since the COVID-19 pandemic and its subsequent stay-at-home protocols, physical therapy has transited into a new realm of telehealth. Current evidence regarding examining and managing RTC strain in telehealth physical therapy is minimal.Case presentationA self-referred 14-year-old female Chinese semi-professional tennis player presented with an acute right RTC strain. The mechanism of injury involved forehand strokes with left trunk rotation. No ligamentous or labral damage was observed on Magnetic Resonance Imagining. The individualized care plan included virtual partner-assisted assessment, online instructions on therapeutic exercises, and education with psychosocial considerations.Outcome and follow-upAfter a 6-week intervention, the patient demonstrated complete shoulder range of motion, full muscle strength, complete return-to-practice, 0% Quick DASH disability index, and 6/68 on the Tampa Scale for kinesiophobia.DiscussionThis case report demonstrated that telehealth is an accessible and cost-effective option for youth tennis athletes with RTC strain. This unique case showed a detailed roadmap from examination to discharge of this plan of care. There are also barriers including test and measure validity, and communication difficulties to be considered. Despite the challenges, this case was a good example of telehealth being an effective, repeatable, and cost-efficient option for patients with poor healthcare access.