Replication of Newly Identified Genetic Associations Between Abdominal Aortic Aneurysm and SMYD2, LINC00540, PCIF1/MMP9/ZNF335, and ERG

OBJECTIVE: A recently published genome wide association study of abdominal aortic aneurysms (AAA), based on pooled case control data of European ancestry, identified four new loci for AAA: SMYD2 (top single nucleotide polymorphism [SNP] rs1795061), LINC00540 (rs9316871), PCIF1/MMP9/ZNF335 (rs3827066), and ERG (rs2836411). Of the four, rs1795061 and rs2836411 showed significant heterogeneity across studies and the p value for rs9316871 did not reach the genome wide significance threshold until discovery and replication data were pooled together in that study. The objective of this study was to replicate these newly identified genetic associations for AAA in a US based prospective cohort study, the Atherosclerosis Risk in Communities (ARIC) Study, and a Greece based case control study. METHODS: ARIC identified 408 clinically diagnosed AAAs among 8 962 individuals of European ancestry during a median of 22 years of follow up. The Greek case control study included 341 AAAs of European ancestry recruited in a tertiary referral centre and 292 geographically and ethnically matched controls recruited from the same institution. A Cox proportional hazards model was used to analyse the ARIC data and logistic regression to analyse the Greek data. RESULTS: In ARIC, rs9316871 and rs3827066 were significantly associated with AAA risk (HR [p] was 0.77 [.004] and 1.22 [.03], respectively), rs2836411 was associated at borderline significance (1.13 [.08]), whereas rs1795061 was not associated (p = .55). In the Greek case control study, rs1795061 and rs2836411 were significantly associated with AAA (OR [p] was 1.66 [< .001] and 1.29 [.04], respectively), whereas rs9316871 was not (p = .81). Genotyping of rs3827066 did not succeed. In the meta-analysis of the two studies, the association for rs9316871and rs2836411 was statistically significant and consistent between the two studies: p = .02 and .007, respectively. CONCLUSIONS: Associations between rs9316871and rs2836411 and AAA risk were replicated in the meta-analysis of the two independent cohorts, providing further support for the importance of these loci in the aetiology of AAA

Genetically predicted cortisol levels and risk of venous thromboembolism

Introduction - In observational studies, venous thromboembolism (VTE) has been associated with Cushing’s syndrome and with persistent mental stress, two conditions associated with higher cortisol levels. However, it remains unknown whether high cortisol levels within the usual range are causally associated with VTE risk. We aimed to assess the association between plasma cortisol levels and VTE risk using Mendelian randomization. Methods - Three genetic variants in the SERPINA1/SERPINA6 locus (rs12589136, rs11621961 and rs2749527) were used to proxy plasma cortisol. The associations of the cortisol-associated genetic variants with VTE were acquired from the INVENT (28 907 cases and 157 243 non-cases) and FinnGen (6913 cases and 169 986 non-cases) consortia. Corresponding data for VTE subtypes were available from the FinnGen consortium and UK Biobank. Two-sample Mendelian randomization analyses (inverse-variance weighted method) were performed. Results - Genetic predisposition to higher plasma cortisol levels was associated with a reduced risk of VTE (odds ratio [OR] per one standard deviation increment 0.73, 95% confidence interval [CI] 0.62–0.87, p Conclusions - This study provides evidence that genetically predicted plasma cortisol levels in the high end of the normal range are associated with a decreased risk of VTE and that this association may be mediated by blood pressure. This study has implications for the planning of observational studies of cortisol and VTE, suggesting that blood pressure traits should be measured and accounted for

Stroke genetics informs drug discovery and risk prediction across ancestries

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

Leveraging Summary Statistics and Integrative Analysis for Prediction and Inference in Genome-Wide Association Studies

University of Minnesota Ph.D. dissertation. July 2020. Major: Biostatistics. Advisor: Wei Pan. 1 computer file (PDF); xi, 145 pages.Genome-wide association studies (GWASs) have attained substantial success in parsing the genetic etiology of complex traits. GWAS analyses have identified many genetic variants associated with various traits, and polygenic risk scores estimated from GWASs have been used to effectively predict certain clinical phenotypes. Despite these accomplishments, GWASs suffer from some pervasive issues with power and interpretability. To address these issues, we develop powerful and novel approaches for prediction and inference on genetic and genomic data. Our approaches focus on two key elements. First is the incorporation of additional sources of genetic and genomic data. A typical GWAS characterizes the genetic basis of a trait in terms of associations between the trait and a set of single nucleotide polymorphisms (SNPs). This approach can often be underpowered and difficult to understand biologically. We can often increase power and interpretability by effectively incorporating other sources of genetic and genomic data into the single SNP analysis structure. Second is the development of methods that are widely applicable in the context of summary statistics. Many published GWAS analyses do not provide so-called individual level genetic and genomic data, and instead provide only summary statistic information. Given this, we want our methods to be able to be flexible in the context of summary statistics without the need for individual level information. We first develop a novel approach to integrating somatic and germline information from tumors to identify genes associated with lung cancer risk. We leverage this approach to discover potentially novel genes associated with lung cancer. We then investigate the problem of estimating powerful and parsimonious models for polygenic risk scores in the context of summary statistics. We develop a set of novel methods for model estimation, model selection, and the assessment of model performance, and demonstrate their beneficial properties in extensive simulation and in application to GWASs of lung cancer, blood lipid levels, and height. Lastly, we integrate our methods for polygenic risk score estimation into a two sample two-stage least squares analysis framework to identify potentially novel endophenotypes associated with increased risk of Alzheimer's disease. We demonstrate via simulation and real data application that our approach is powerful and effective

Penalized regression and model selection methods for polygenic scores on summary statistics.

Polygenic scores quantify the genetic risk associated with a given phenotype and are widely used to predict the risk of complex diseases. There has been recent interest in developing methods to construct polygenic risk scores using summary statistic data. We propose a method to construct polygenic risk scores via penalized regression using summary statistic data and publicly available reference data. Our method bears similarity to existing method LassoSum, extending their framework to the Truncated Lasso Penalty (TLP) and the elastic net. We show via simulation and real data application that the TLP improves predictive accuracy as compared to the LASSO while imposing additional sparsity where appropriate. To facilitate model selection in the absence of validation data, we propose methods for estimating model fitting criteria AIC and BIC. These methods approximate the AIC and BIC in the case where we have a polygenic risk score estimated on summary statistic data and no validation data. Additionally, we propose the so-called quasi-correlation metric, which quantifies the predictive accuracy of a polygenic risk score applied to out-of-sample data for which we have only summary statistic information. In total, these methods facilitate estimation and model selection of polygenic risk scores on summary statistic data, and the application of these polygenic risk scores to out-of-sample data for which we have only summary statistic information. We demonstrate the utility of these methods by applying them to GWA studies of lipids, height, and lung cancer

Comparing Anesthesia and Surgery Controlled Time for Primary Total Knee and Hip Arthroplasty Between an Academic Medical Center and a Community Hospital: Retrospective Cohort Study

BackgroundOsteoarthritis is a significant cause of disability, resulting in increased joint replacement surgeries and health care costs. Establishing benchmarks that more accurately predict surgical duration could help to decrease costs, maximize efficiency, and improve patient experience. We compared the anesthesia-controlled time (ACT) and surgery-controlled time (SCT) of primary total knee (TKA) and total hip arthroplasties (THA) between an academic medical center (AMC) and a community hospital (CH) for 2 orthopedic surgeons. ObjectiveThis study aims to validate and compare benchmarking times for ACT and SCT in a single patient population at both an AMC and a CH. MethodsThis retrospective 2-center observational cohort study was conducted at the University of Colorado Hospital (AMC) and UCHealth Broomfield Hospital (CH). Cases with current procedural terminology codes for THA and TKA between January 1, 2019, and December 31, 2020, were assessed. Cases with missing data were excluded. The primary outcomes were ACT and SCT. Primary outcomes were tested for association with covariates of interest. The primary covariate of interest was the location of the procedure (CH vs AMC); secondary covariates of interest included the American Society of Anesthesiologists (ASA) classification and anesthetic type. Linear regression models were used to assess the relationships. ResultsTwo surgeons performed 1256 cases at the AMC and CH. A total of 10 THA cases and 12 TKA cases were excluded due to missing data. After controlling for surgeon, the ACT was greater at the AMC for THA by 3.77 minutes and for TKA by 3.58 minutes (P.05). ConclusionsWe observed lower ACT and SCT at the CH for both TKA and THA after controlling for the surgeon of record and ASA classification. These findings underscore the efficiency advantages of performing primary joint replacements at the CH, showcasing an average reduction of 16 minutes in SCT and 4 minutes in ACT per case. Overall, establishing more accurate benchmarks to improve the prediction of surgical duration for THA and TKA in different perioperative environments can increase the reliability of surgical duration predictions and optimize scheduling. Future studies with study populations at multiple community hospitals and academic medical centers are needed before extrapolating these findings

Satisfaction scores.

GI proceduralist and nursing satisfaction scores. (XLSX)</p

Distribution of clinician satisfaction score, stratified by provider type and sedation type.

Distribution of clinician satisfaction score, stratified by provider type and sedation type.</p

Primary and secondary outcome definitions.

BackgroundOn July 1st, 2021, the University of Colorado Hospital (UCH) implemented new sedation protocols in the luminal gastrointestinal (GI) suite. GI proceduralist supervised, Nurse Administered Sedation with fentanyl, midazolam, and diphenhydramine (NAS) sedation was transitioned to Monitored Anesthesia Care with propofol under physician anesthesiologist supervision (MAC).ObjectiveTo determine if there are statistically significant reductions in Sedation-Start to Scope-In time (SSSI) when using Monitored Anesthesia Care with propofol (MAC) versus Nurse Administered Sedation with fentanyl, midazolam, and diphenhydramine (NAS). Secondary objectives were to determine if statistically significant improvements to other operational times, quality measures, and satisfaction metrics were present.MethodThis study was a retrospective analysis of a natural experiment resultant of a change from NAS to MAC sedation protocols. Outcomes for NAS protocols from 1/1/21–6/30/21 were compared to outcomes of MAC protocols from the dates 8/1/21–10/31/21. Results were analyzed using Quasi-Poisson regression analysis and stratified based on upper GI, lower GI, and combined procedures. Patient demographic data including age, biological sex, comorbidities, and BMI, were adjusted for in the analysis. ASA matching was not performed as nursing sedation does not use ASA classifications. Pre-anesthesia co-morbidities were assessed via evaluation of a strict set of comorbidities abstracted from the electronic medical record. Perioperative operational outcomes include Sedation Start to Scope-In (SSSI), In-Room to Scope-In Time (IRSI), Scope Out to Out of Room (SOOR), Total Case Length (TCL), and Post Anesthesia Care Unit Length of Stay (PACU LOS). Quality outcomes include PACU Administered Medications (PAM), and Clinician Satisfaction Scores (CSS).ResultsA total of 5,582 gastrointestinal (GI) endoscopic cases (upper, lower, and combined endoscopies) were observed. Statistically significant decreases in SSSI of 2.5, 2.1, and 2.2 minutes for upper, lower, and dual GI procedures were observed when using MAC protocols. A statistically significant increase in satisfaction scores of 47.0 and 19.6 points were observed for nurses and proceduralists, respectively, when using MAC.ConclusionMAC protocols for endoscopic GI procedures at UCH led to statistically significant decreases in the time required to complete procedures thus increasing operational efficiency.</div

Distribution of clinical covariates in the study population, stratified by sedation type.

Distribution of clinical covariates in the study population, stratified by sedation type.</p