Objective-To estimate the economic burden of vision loss and eye disorders in the United States population younger than 40 years in 2012.Design-Econometric and statistical analysis of survey, commercial claims, and census data. Participants-The United States population younger than 40 years in 2012.Methods-We categorized costs based on consensus guidelines. We estimated medical costs attributable to diagnosed eye-related disorders, undiagnosed vision loss, and medical vision aids using Medical Expenditure Panel Survey and MarketScan data. The prevalence of vision impairment and blindness were estimated using National Health and Nutrition Examination Survey data. We estimated costs from lost productivity using Survey of Income and Program Participation. We estimated costs of informal care, low vision aids, special education, school screening, government spending, and transfer payments based on published estimates and federal budgets. We estimated quality-adjusted life years (QALYs) lost based on published utility values. Main Outcome Measures-Costs and QALYs lost in 2012.Results-The economic burden of vision loss and eye disorders among the United States population younger than 40 years was $27.5 billion in 2012 (95% confidence interval, $21.5-$37.2 billion), including $5.9 billion for children and $21.6 billion for adults 18 to 39 years of age. Direct costs were $14.5 billion, including $7.3 billion in medical costs for diagnosed disorders, Correspondence: John S. Wittenborn, BS, National Opinion Research Center, University of Chicago, 1981 Grace Point Road, Morrisville, NC 27560. wittenborn-john@norc.org; JohnSWittenborn@gmail.com. * A complete listing of the Vision Cost-effectiveness Study Group is available at http://aaojournal.org. * Group members listed online in Appendix 1 (available at http://aaojournal.org). Financial Disclosure(s):The author(s) have no proprietary or commercial interest in any materials discussed in this article. HHS Public Access Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript $4.9 billion in refraction correction, $0.5 billion in medical costs for undiagnosed vision loss, and $1.8 billion in other direct costs. Indirect costs were $13 billion, primarily because of $12.2 billion in productivity losses. In addition, vision loss cost society 215 000 QALYs.Conclusions-We found a substantial burden resulting from vision loss and eye disorders in the United States population younger than 40 years, a population excluded from previous studies.Monetizing quality-of-life losses at $50 000 per QALY would add $10.8 billion in additional costs, indicating a total economic burden of $38.2 billion. Relative to previously reported estimates for the population 40 years of age and older, more than one third of the total cost of vision loss and eye disorders may be incurred by persons younger than 40 years.Disorders of the eye and resulting vision loss impose a significant burden on the United States, both economically and socially. In addition to medical costs, the debili...

The Collaborative Cross (CC) is a mouse genetic reference population whose range of applications includes quantitative trait loci (QTL) mapping. The design of a CC QTL mapping study involves multiple decisions, including which and how many strains to use, and how many replicates per strain to phenotype, all viewed within the context of hypothesized QTL architecture. Until now, these decisions have been informed largely by early power analyses that were based on simulated, hypothetical CC genomes. Now that more than 50 CC strains are available and more than 70 CC genomes have been observed, it is possible to characterize power based on realized CC genomes. We report power analyses from extensive simulations and examine several key considerations: 1) the number of strains and biological replicates, 2) the QTL effect size, 3) the presence of population structure, and 4) the distribution of functionally distinct alleles among the founder strains at the QTL. We also provide general power estimates to aide in the design of future experiments. All analyses were conducted with our R package, SPARCC (Simulated Power Analysis in the Realized Collaborative Cross), developed for performing either large scale power analyses or those tailored to particular CC experiments.

The Collaborative Cross (CC) is a mouse genetic reference population whose range of applications includes quantitative trait loci (QTL) mapping. The design of a CC QTL mapping study involves multiple decisions, including which and how many strains to use, and how many replicates per strain to phenotype, all viewed within the context of hypothesized QTL architecture. Until now, these decisions have been informed largely by early power analyses that were based on simulated, hypothetical CC genomes. Now that more than 50 CC strains are available and more than 70 CC genomes have been observed, it is possible to characterize power based on realized CC genomes. We report power analyses based on extensive simulations and examine several key considerations: 1) the number of strains and biological replicates, 2) the QTL effect size, 3) the presence of population structure, and 4) the distribution of functionally distinct alleles among the founder strains at the QTL.We also provide general power estimates to aide in the design of future experiments. All analyses were conducted with our R package, SPARCC (Simulated Power Analysis in the Realized Collaborative Cross), developed for performing either large scale power analyses or those tailored to particular CC experiments. KEYWORDS recombinant inbred lines, haplotype association, allelic series, multiparental population, MPP, quantitative trait, complex trait 32 et al. 2014) 33 Nonetheless, QTL mapping power depends in part on the 34 number of strains available, and the number strains available 35 in the CC is, and will remain, far less than the 1,000 proposed 36 in Churchill et al. (2004): At the time of this work, mice were 37 QTL mapping power in Collaborative Cross 1 available for 59 CC strains from the UNC Systems Genetics Core, 38 with a subset from these 59 and an additional 11 expected to be 39 offered through the Jackson Laboratory (JAX), a total of 70 CC 40 strains potentially. 41 A reduction in strain numbers as a function of allelic incom-42 patibilities between subspecies (Shorter et al. 2017) was expected, 43 and winnowed the number of resulting CC strains down to 50-44 70. Although smaller than originally intended, this population 45 size reflects the biological and financial realities of maintaining a 46 sustainable mammalian genome reference population. [Whereas 47 129 3. Evaluation of QTL detection accuracy, power and false pos-130itive rate (FPR). 131These are described in detail below, after a description of the 132 genomic data that serves as the basis for the simulations.

Genetic studies often seek to establish a causal chain of events originating from genetic variation through to molecular and clinical phenotypes. When multiple phenotypes share a common genetic association, one phenotype may act as an intermediate for the genetic effects on the other. Alternatively, the phenotypes may be causally unrelated but share genetic loci. Mediation analysis represents a class of causal inference approaches used to determine which of these scenarios is most plausible. We have developed a general approach to mediation analysis based on Bayesian model selection and have implemented it in an R package, bmediatR. Bayesian model selection provides a flexible framework that can be tailored to different analyses. Our approach can incorporate prior information about the likelihood of models and the strength of causal effects. It can also accommodate multiple genetic variants or multi-state haplotypes. Our approach reports posterior probabilities that can be useful in interpreting uncertainty among competing models. We compared bmediatR with other popular methods, including the Sobel test, Mendelian randomization, and Bayesian network analysis using simulated data. We found that bmediatR performed as well or better than these alternatives in most scenarios. We applied bmediatR to transcriptome and proteome data from Diversity Outbred (DO) mice, a multi-parent population, and demonstrate the power of mediation with multi-state haplotypes. We also applied bmediatR to data from human cell lines to identify transcripts that are mediated through or are expressed independently from local chromatin accessibility. We demonstrate that Bayesian model selection provides a powerful and versatile approach to identify causal relationships in genetic studies using model organism or human data.

Ambient ozone (O3) exposure has serious consequences on respiratory health, including airway inflammation and injury. Decades of research have yielded thorough descriptions of these outcomes; however, less is known about the molecular processes that drive them. The aim of this study was to further describe the cellular and molecular responses to O3 exposure in murine airways, with a particular focus on transcriptional responses in 2 critical pulmonary tissue compartments: conducting airways (CA) and airway macrophages (AM). After exposing adult, female C57BL/6J mice to filtered air, 1 or 2 ppm O3, we assessed hallmark responses including airway inflammation (cell counts and cytokine secretion) and injury (epithelial permeability), followed by gene expression profiling of CA and AM by RNA-seq. As expected, we observed concentration-dependent increases in airway inflammation and injury. Conducting airways and AM both exhibited changes in gene expression to both 1 and 2 ppm O3 that were largely compartment-specific. In CA, genes associated with epithelial barrier function, detoxification processes, and cellular proliferation were altered, while O3 affected genes involved in innate immune signaling, cytokine production, and extracellular matrix remodeling in AM. Further, CA and AM also exhibited notable differences in concentration–response expression patterns for large numbers of genes. Overall, our study has described transcriptional responses to acute O3 exposure, revealing both shared and unique gene expression patterns across multiple concentrations of O3 and in 2 important O3-responsive tissues. These profiles provide broad mechanistic insight into pulmonary O3 toxicity, and reveal a variety of targets for focused follow-up studies.