Summary Eosinophilic esophagitis (EoE) is diagnosed by symptoms, and at least 15 intraepithelial eosinophils per high power field in an esophageal biopsy. Other pathologic features have not been emphasized. We developed a histology scoring system for esophageal biopsies that evaluates eight features: eosinophil density, basal zone hyperplasia, eosinophil abscesses, eosinophil surface layering, dilated intercellular spaces (DIS), surface epithelial alteration, dyskeratotic epithelial cells, and lamina propria fibrosis. Severity (grade) and extent (stage) of abnormalities were scored using a 4-point scale (0 normal; 3 maximum change). Reliability was demonstrated by strong to moderate agreement among three pathologists who scored biopsies independently (P ≤ 0.008). Several features were often abnormal in 201 biopsies (101 distal, 100 proximal) from 104 subjects (34 untreated, 167 treated). Median grade and stage scores were significantly higher in untreated compared with treated subjects (P ≤ 0.0062). Grade scores for features independent of eosinophil counts were significantly higher in biopsies from untreated compared with treated subjects (basal zone hyperplasia P ≤ 0.024 and DIS P ≤ 0.005), and were strongly correlated (R-square >0.67). Principal components analysis identified three principal components that explained 78.2% of the variation in the features. In logistic regression models, two principal components more closely associated with treatment status than log distal peak eosinophil count (PEC) (R-square 17, area under the curve (AUC) 77.8 vs. R-square 9, AUC 69.8). In summary, the EoE histology scoring system provides a method to objectively assess histologic changes in the esophagus beyond eosinophil number. Importantly, it discriminates treated from untreated patients, uses features commonly found in such biopsies, and is utilizable by pathologists after minimal training. These data provide rationales and a method to evaluate esophageal biopsies for features in addition to PEC.

Background Eosinophilic esophagitis (EE) is an emerging worldwide disease that mimics gastroesophageal reflux disease. Objective Early studies have suggested that esophageal eosinophilia occurs in association with T helper type 2 allergic responses, yet the local and systemic expression of relevant cytokines has not been well characterized. Methods A human inflammatory cytokine and receptor PCR array containing 84 genes followed by PCR validation and multiplex arrays were used to quantify cytokine mRNA in esophageal biopsies and blood levels. Results Esophageal transcripts of numerous chemokines [e.g. CCL1, CCL23, CCL26 (eotaxin-3), CXCL1, and CXCL2], cytokines (e.g. IL13 and ABCF1), and cytokine receptors (e.g. IL5RA) were induced at least 4-fold in individuals with EE. Analysis of esophageal biopsies (n=288) revealed that eotaxin-3 mRNA level alone had 89% sensitivity for distinguishing EE from non-EE individuals. The presence of allergy was associated with significantly increased esophageal expression of IL4 and IL5 mRNA in active EE patients. We identified 8 cytokines (IL-4, IL-13, IL-5, IL-6, IL-12p70, CD40L, IL-1α, and IL-17) whose blood levels retrospectively distinguished 12 non-EE from 13 EE patients with 100% specificity and 100% sensitivity. When applied to a blinded, prospectively recruited group of 36 patients, the cytokine panel scoring system had a 79% positive predictive value, 68% negative predictive value, 61% sensitivity, and 83% specificity for identifying EE. Conclusion Evidence is presented that IL13 and IL5 associate with eosinophil and eotaxin-3 levels, indicating the key role of adaptive Th2 immunity in regulating eotaxin-3-driven esophageal eosinophilia in the absence of a consistent systemic change in cytokines.

Background Eosinophilic esophagitis (EoE) is a chronic antigen-driven allergic inflammatory disease, likely involving the interplay of genetic and environmental factors, yet their respective contributions to heritability are unknown. Objective To quantify risk associated with genes and environment on familial clustering of EoE. Methods Family history was obtained from a hospital-based cohort of 914 EoE probands, (n=2192 first-degree “Nuclear-Family” relatives) and the new international registry of monozygotic and dizygotic twins/triplets (n=63 EoE “Twins” probands). Frequencies, recurrence risk ratios (RRRs), heritability and twin concordance were estimated. Environmental exposures were preliminarily examined. Results Analysis of the Nuclear-Family–based cohort revealed that the rate of EoE, in first-degree relatives of a proband, was 1.8% (unadjusted) and 2.3% (sex-adjusted). RRRs ranged from 10–64, depending on the family relationship, and were higher in brothers (64.0; p=0.04), fathers (42.9; p=0.004) and males (50.7; p<0.001) compared to sisters, mothers and females, respectively. Risk of EoE for other siblings was 2.4%. In the Nuclear-Families, combined gene and common environment heritability (hgc2) was 72.0±2.7% (p<0.001). In the Twins cohort, genetic heritability was 14.5±4.0% (p<0.001), and common family environment contributed 81.0±4% (p<0.001) to phenotypic variance. Proband-wise concordance in MZ co-twins was 57.9±9.5% compared to 36.4±9.3% in DZ (p=0.11). Greater birth-weight difference between twins (p=0.01), breastfeeding (p=0.15) and Fall birth season (p=0.02) were associated with twin discordance in disease status. Conclusions EoE recurrence risk ratios are increased 10–64-fold compared with the general population. EoE in relatives is 1.8–2.4%, depending upon relationship and sex. Nuclear-Family heritability appeared to be high (72.0%). However, Twins cohort analysis revealed a powerful role for common environment (81.0%) compared with additive genetic heritability (14.5%).

Although the technical and analytic complexity of whole genome sequencing is generally appreciated, best practices for data cleaning and quality control have not been defined. Family based data can be used to guide the standardization of specific quality control metrics in nonfamily based data. Given the low mutation rate, Mendelian inheritance errors are likely as a result of erroneous genotype calls. Thus, our goal was to identify the characteristics that determine Mendelian inheritance errors. To accomplish this, we used chromosome 3 whole genome sequencing family based data from the Genetic Analysis Workshop 18. Mendelian inheritance errors were provided as part of the GAW18 data set. Additionally, for binary variants we calculated Mendelian inheritance errors using PLINK. Based on our analysis, nonbinary single-nucleotide variants have an inherently high number of Mendelian inheritance errors. Furthermore, in binary variants, Mendelian inheritance errors are not randomly distributed. Indeed, we identified 3 Mendelian inheritance error peaks that were enriched with repetitive elements. However, these peaks can be lessened with the inclusion of a single filter from the sequencing file. In summary, we demonstrated that erroneous sequencing calls are nonrandomly distributed across the genome and quality control metrics can dramatically reduce the number of mendelian inheritance errors. Appropriate quality control will allow optimal use of genetic data to realize the full potential of whole genome sequencing.

Genetic studies often collect data on multiple traits. Most genetic association analyses, however, consider traits separately and ignore potential correlation among traits, partially because of difficulties in statistical modeling of multivariate outcomes. When multiple traits are measured in a pedigree longitudinally, additional challenges arise because in addition to correlation between traits, a trait is often correlated with its own measures over time and with measurements of other family members. We developed a Bayesian model for analysis of bivariate quantitative traits measured longitudinally in family genetic studies. For a given trait, family-specific and subject-specific random effects account for correlation among family members and repeated measures, respectively. Correlation between traits is introduced by incorporating multivariate random effects and allowing time-specific trait residuals to correlate as in seemingly unrelated regressions. The proposed model can examine multiple single-nucleotide variations simultaneously, as well as incorporate familyspecific, subject-specific, or time-varying covariates. Bayesian multiplicity technique is used to effectively control false positives. Genetic Analysis Workshop 18 simulated data illustrate the proposed approach's applicability in modeling longitudinal multivariate outcomes in family genetic association studies.

Family based association studies are employed less often than case-control designs in the search for disease-predisposing genes. The optimal statistical genetic approach for complex pedigrees is unclear when evaluating both common and rare variants. We examined the empirical power and type I error rates of 2 common approaches, the measured genotype approach and family-based association testing, through simulations from a set of multigenerational pedigrees. Overall, these results suggest that much larger sample sizes will be required for family-based studies and that power was better using MGA compared to FBAT. Taking into account computational time and potential bias, a 2-step strategy is recommended with FBAT followed by MGA.

Lecture is a frequently employed method of instruction in institutions of higher education, despite evidence that this method may not be effective in achieving student learning (Stains et al., 2018). "Real-world" experiences are needed to bring classroom concepts to life and foster interconnectedness to enhance students' interpersonal competence (Remington-Doucette et al., 2013). Community service-learning (CSL) projects achieve these objectives and extend student learning opportunities in ways that cannot be accomplished solely within a classroom (Bettencort, 2015). Although health services and quality management routinely use community-based internships and team-based learning (TBL) as educational methods, there are few quantitative studies of student perceptions and subject matter content mastery in our discipline that have evaluated outcomes. A prospective, cross-sectional study was conducted to test the hypothesis that CSL, coupled with the TBL employed in Quality Management and Performance Improvement (QM) and health services practice, is an effective intervention to improve student outcomes. The results of this study suggest that CSL, coupled with TBL, facilitates improved QM and teamwork subject matter content knowledge, effort, engagement, and perception of skill acquisition.

Population substructure is a well-known confounder in population-based case-control genetic studies, but its impact in family-based studies is unclear. We performed population substructure analysis using extended families of admixed population to evaluate power and Type I error in an association study framework. Our analysis shows that power was improved by 1.5% after principal components adjustment. Type I error was also reduced by 2.2% after adjusting for family substratification. The presence of population substructure was underscored by discriminant analysis, in which over 92% of individuals were correctly assigned to their actual family using only 100 principal components. This study demonstrates the importance of adjusting for population substructure in family-based studies of admixed populations.