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.

Differential gene expression in the airway epithelium of patients with asthma controls has been reported in several studies. However, there is no consensus on which genes are reproducibly affected in asthma. We sought to identify a consensus list of differentially expressed genes (DEGs) using a meta-analysis approach.We identified eight studies with data that met defined inclusion criteria. These studies comprised 355 cases and 193 controls and involved sampling either bronchial or nasal epithelium. We conductedstudy-level analyses, followed by a meta-analysis. Likewise, we applied a meta-analysis framework to the results of study-level pathway enrichment.We identified 1273 DEGs, 431 of which had not been identified in previous studies. 450 DEGs exhibited large effect sizes and were robust to study population differences in age, sex, race/ethnicity, medication use, smoking status and exacerbations. The magnitude of differential expression of these 450 genes was highly similar in bronchial and nasal airway epithelia. Meta-analysis of pathway enrichment revealed a number of consistently dysregulated biological pathways, including putative transcriptional and post-transcriptional regulators.In total, we identified a set of genes that is consistently dysregulated in asthma, that links to known and novel biological pathways, and that will inform asthma subtype identification.

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.

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.

The complex role of neutrophils in modulating the inflammatory response is increasingly appreciated. Our studies profiled the expression of mRNAs and microRNAs (miRs) in lung neutrophils in mice during S. pneumoniae pneumonia and performed in depth in silico analyses. Lung neutrophils were isolated 24 hours after intratracheal instillation of PBS or S. pneumoniae, and differentially expressed (DE) mRNAs and miRs were identified. Lung neutrophils from mice with S. pneumoniae pneumonia contained 4127 DE mRNAs, 36% of which were upregulated at least 2-fold. During pneumonia, lung neutrophils increase expression of pattern recognition receptors, receptors for inflammatory mediators, transcription factors including NF-κB and AP-1, Nrf2 targets, cytokines, chemokines and other inflammatory mediators. Interestingly, neutrophils responded to Type I interferons, whereas they both produced and responded to Type II interferon. Expression of regulators of the inflammatory and immune response was verified at the mRNA and protein level. Of approximately 1100 miRs queried, 31 increased and 67 decreased more than 2-fold in neutrophils from S. pneumoniae pneumonia. Network analyses of potential DE miR-target DE mRNA interactions revealed candidate key regulatory miRs. Thus, S. pneumoniae modulates mRNA and miR expression by lung neutrophils, increasing their ability to respond and facilitating host defense.

Exposure to ambient ozone (O3) pollution causes airway inflammation, epithelial injury, and decreased lung function. Long-term exposure is associated with increased mortality and exacerbations of respiratory conditions. While the adverse health effects of O3 exposure have been thoroughly described, less is known about the molecular processes that drive these outcomes. The aim of this study was to describe the cellular and molecular alterations observed in murine airways after exposure to either 1 or 2 ppm O3. After exposing adult, female C57BL/6J mice to filtered air, 1 or 2 ppm O3 for 3 hours, we assessed hallmark responses including airway inflammatory cell counts, epithelial permeability, cytokine secretion, and morphological alterations of the large airways. Further, we performed RNA-seq to profile gene expression in two critical tissues involved in O3 responses: conducting airways (CA) and airway macrophages (AM). We observed a concentration-dependent increase in airway inflammation and injury, and a large number of genes were differentially expressed in both target tissues at both concentrations of O3. Genes that were differentially expressed in CA were generally associated with barrier function, detoxification processes, and cellular proliferation. The differentially expressed genes in AM were associated with innate immune signaling, cytokine production, and extracellular matrix remodeling. 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 two important O3-responsive tissues. These profiles provide broad mechanistic insight into pulmonary O3 toxicity, and reveal a variety of targets for refined followup studies.

Multiparental populations (MPPs) are experimental populations in which the genome of every individual is a mosaic of known founder haplotypes. These populations are useful for detecting quantitative trait loci (QTL) because tests of association can leverage inferred founder haplotype descent. It is difficult, however, to determine how haplotypes at a locus group into distinct functional alleles, termed the allelic series. The allelic series is important because it provides information about the number of causal variants at a QTL and their combined effects. In this study, we introduce a fully-Bayesian model selection framework for inferring the allelic series. This framework accounts for sources of uncertainty found in typical MPPs, including the number and composition of functional alleles. Our prior distribution for the allelic series is based on the Chinese restaurant process, a relative of the Dirichlet process, and we leverage its connection to the coalescent to introduce additional prior information about haplotype relatedness via a phylogenetic tree. We evaluate our approach via simulation and apply it to QTL from two MPPs: the Collaborative Cross (CC) and the Drosophila Synthetic Population Resource (DSPR). We find that, although posterior inference of the exact allelic series is often uncertain, we are able to distinguish biallelic QTL from more complex multiallelic cases. Additionally, our allele-based approach improves haplotype effect estimation when the true number of functional alleles is small. Our method, Tree-Based Inference of Multiallelism via Bayesian Regression (TIMBR), provides new insight into the genetic architecture of QTL in MPPs.

Multiparental populations (MPPs) are experimental populations in which the genome of every individual is a mosaic of known founder haplotypes. These populations are useful for detecting quantitative trait loci (QTL) because tests of association can leverage inferred founder haplotype descent. It is difficult, however, to determine how haplotypes at a locus group into distinct functional alleles, termed the allelic series. The allelic series is important because it provides information about the number of causal variants at a QTL and their combined effects. In this study, we introduce a fully-Bayesian model selection framework for inferring the allelic series. This framework accounts for sources of uncertainty found in typical MPPs, including the number and composition of functional alleles. Our prior distribution for the allelic series is based on the Chinese restaurant process, a relative of the Dirichlet process, and we leverage its connection to the coalescent to introduce additional prior information about haplotype relatedness via a phylogenetic tree. We evaluate our approach via simulation and apply it to real QTL from two MPPs: the Collaborative Cross (CC) and the Drosophila Genetic Reference Population (DSPR). We find that, although posterior inference of the exact allelic series is often uncertain, we are able to distinguish biallelic QTL from more complex multiallelic cases. Additionally, our allele-based approach improves haplotype effect estimation when the true number of functional alleles is small. Our method, Tree-Based Inference of Multiallelism via Bayesian Regression (TIMBR), provides new insight into the genetic architecture of QTL in MPPs.