A Research-Practice Partnership to Develop the R-CITY Multi-Component, Equity-Focused Social-Emotional Learning Intervention.

There is growing interest in the integration of social-emotional learning (SEL) and equity approaches in schools, yet systematic research on how to blend these two frameworks is limited. In this article, we describe the process by which a research-practice partnership (RPP) collaborated to iteratively co-create a multi-component equity-focused SEL preventive intervention in the context of a politically charged landscape related to the 'dual pandemics' of racial injustice and COVID-19 in the early 2020s. We conducted a document review of informal data sources (e.

Updated assessment of occupational safety and health hazards of climate change.

Workers, particularly outdoor workers, are among the populations most disproportionately affected by climate-related hazards. However, scientific research and control actions to comprehensively address these hazards are notably absent. To assess this absence, a seven-category framework was developed in 2009 to characterize the scientific literature published from 1988-2008.

Uterine fibroid polygenic risk score (PRS) associates and predicts risk for uterine fibroid.

Uterine fibroids (UF) are common pelvic tumors in women, heritable, and genome-wide association studies (GWAS) have identified ~ 30 loci associated with increased risk in UF. Using summary statistics from a previously published UF GWAS performed in a non-Hispanic European Ancestry (NHW) female subset from the Electronic Medical Records and Genomics (eMERGE) Network, we constructed a polygenic risk score (PRS) for UF. UF-PRS was developed using PRSice and optimized in the separate clinical population of BioVU.

The power of genetic diversity in genome-wide association studies of lipids.

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels, heart disease remains the leading cause of death worldwide. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease.

Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies.

Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate ("A-by-G" grid).

Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics.

Increasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, pleiotropy analysis using individual-level EHR data has been limited to data from one site. However, it is desirable to integrate EHR data from multiple sites to improve the detection power and generalizability of the results.

Loci identified by a genome-wide association study of carotid artery stenosis in the eMERGE network.

Carotid artery atherosclerotic disease (CAAD) is a risk factor for stroke. We used a genome-wide association (GWAS) approach to discover genetic variants associated with CAAD in participants in the electronic Medical Records and Genomics (eMERGE) Network. We identified adult CAAD cases with unilateral or bilateral carotid artery stenosis and controls without evidence of stenosis from electronic health records at eight eMERGE sites.

Interpretation of machine learning predictions for patient outcomes in electronic health records.

Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions. In this paper we report an empirical study of predictive modeling of seven patient outcomes using three state-of-the-art machine learning methods. Our primary goal is to validate the models by interpreting the importance of predictors in the final models.

A genome-wide association study of polycystic ovary syndrome identified from electronic health records.

Background: Polycystic ovary syndrome is the most common endocrine disorder affecting women of reproductive age. A number of criteria have been developed for clinical diagnosis of polycystic ovary syndrome, with the Rotterdam criteria being the most inclusive. Evidence suggests that polycystic ovary syndrome is significantly heritable, and previous studies have identified genetic variants associated with polycystic ovary syndrome diagnosed using different criteria.

A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies.

Context: As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice.

Objective: Utilizing polygenic risk prediction, we aim to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment.

Design, Patients, And Methods: Leveraging the electronic health records (EHRs) of 124 852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS).

A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans.

We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype.

Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels.

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy.

A -Ethnic Genome-Wide Association Study of Uterine Fibroids.

Uterine fibroids affect up to 77% of women by menopause and account for up to $34 billion in healthcare costs each year. Although fibroid risk is heritable, genetic risk for fibroids is not well understood. We conducted a two-stage case-control meta-analysis of genetic variants in European and African ancestry women with and without fibroids classified by a previously published algorithm requiring pelvic imaging or confirmed diagnosis.

A catalog of genetic loci associated with kidney function from analyses of a million individuals.

Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178).

Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network.

The link between cardiovascular diseases and neurological disorders has been widely observed in the aging population. Disease prevention and treatment rely on understanding the potential genetic nexus of multiple diseases in these categories. In this study, we were interested in detecting pleiotropy, or the phenomenon in which a genetic variant influences more than one phenotype.

A phenome-wide association study to discover pleiotropic effects of , , and .

We conducted an electronic health record (EHR)-based phenome-wide association study (PheWAS) to discover pleiotropic effects of variants in three lipoprotein metabolism genes , , and . Using high-density genotype data, we tested the associations of variants in the three genes with 1232 EHR-derived binary phecodes in 51,700 European-ancestry (EA) individuals and 585 phecodes in 10,276 African-ancestry (AA) individuals; 457 , 730 , and 720 variants were filtered by imputation quality (  > 0.4), minor allele frequency (>1%), linkage disequilibrium (  < 0.