Lung pathology in select preCC mice.

<p>(A). OR63f51—normal parenchyma. (B). OR181f61—airway debris and cuffing and edema surrounding the associated vasculature. (C) OR220f57—denuded airway blocked with debris. (D) OR380f64 –perivascular cuffing including eosinophilia. (E) OR941f69 –alveolitis including hyaline membrane formation, arrows point to hyaline membranes. (F) OR5030f128 –normal airway and associated vasculature.</p

Phenotypic relationships.

<p>Heat map of the relationships between lung pathology, titer and weight loss in the preCC. Yellow indicates positive correlation and blue indicates negative correlation.</p

PreCC and founder strain phenotypes.

<p>(A) Weight loss is shown as percent of starting weight at day four post infection, individual preCC mice are shown in open diamonds and mean values for founders are shown in color. All CAST/EiJ mice died or were humanely euthanized before day four post infection. (B) Log transformed lung titer at day four post infection in individual preCC mice and founders, dashed line indicates the limit of detection at 100 PFU per lung. Individual preCC mice are shown in open diamonds, mean values for the founders are color coded by strain. (C) Lung titer vs. weight loss at day four post infection. Individual preCC mice are indicated in open diamonds, mean values for the founder strains are shown in color. The dashed line indicates the limit of detection at 100 PFU per lung.</p

Transcriptional analysis.

<p>(A) A heat map showing a comparison of functionally enriched biological pathways between <i>Trim55</i>^<i>-/-</i> and C57BL/6J mice at days two and four post infection based on RNA expression levels in the lung. Three mice used per condition, per timepoint. Experiment performed once. Relative expression of all DE genes (log2 FC of 1 or greater and FDR < .05, Trim55^-/- vs C57BL/6J) involved in granulocyte adhesion and diapedesis at day two (B) or four (C) post infection.</p

SARS QTL details.

<p>The four SARS-CoV susceptibility QTLs with genome region, LOD score, significance and percent of phenotypic variation.</p

Genotypes as a compressed R data object (*.rds)

Genotypes matrix (markers x samples) as an R data object for use with the `argyle` package (https://github.com/andrewparkermorgan/argyle). The object itself is just a matrix, with marker and sample metadata as attributes. For details see the `argyle` manuscript: Morgan AP (2016) argyle: an R package for analysis of Illumina genotyping arrays. G3 6: 281-286. http://www.g3journal.org/content/6/2/28

Genotypes in PLINK binary format (*.bim; 2 of 3)

Genotypes for reference samples in PLINK binary format. The PLINK format entails three separate files: *.bed (binary-encoded genotypes), *.bim (marker map) and *.fam (sample metadata)

Genotypes and hybridization intensities for 522 reference samples.

This file is an Rdata file containing a single object, 'geno'. That object contains genotypes and hybridization intensities for 522 samples used in this paper, of which 500 passed quality controls and were used for most of the analyses. See the README file for further information

Genotypes matrix (markers x samples)

Genotypes matrix (markers x samples) as comma-separated values. First six headers are chromosome, marker name, genetic position (cM), physical position (bp, in build mm10/GRCm38 of the mouse reference genome), reference allele ("A1"), alternate allele ("A2"). Remaining column headers are sample names

Modeling Host Genetic Regulation of Influenza Pathogenesis in the Collaborative Cross

<div><p>Genetic variation contributes to host responses and outcomes following infection by influenza A virus or other viral infections. Yet narrow windows of disease symptoms and confounding environmental factors have made it difficult to identify polymorphic genes that contribute to differential disease outcomes in human populations. Therefore, to control for these confounding environmental variables in a system that models the levels of genetic diversity found in outbred populations such as humans, we used incipient lines of the highly genetically diverse Collaborative Cross (CC) recombinant inbred (RI) panel (the pre-CC population) to study how genetic variation impacts influenza associated disease across a genetically diverse population. A wide range of variation in influenza disease related phenotypes including virus replication, virus-induced inflammation, and weight loss was observed. Many of the disease associated phenotypes were correlated, with viral replication and virus-induced inflammation being predictors of virus-induced weight loss. Despite these correlations, pre-CC mice with unique and novel disease phenotype combinations were observed. We also identified sets of transcripts (modules) that were correlated with aspects of disease. In order to identify how host genetic polymorphisms contribute to the observed variation in disease, we conducted quantitative trait loci (QTL) mapping. We identified several QTL contributing to specific aspects of the host response including virus-induced weight loss, titer, pulmonary edema, neutrophil recruitment to the airways, and transcriptional expression. Existing whole-genome sequence data was applied to identify high priority candidate genes within QTL regions. A key host response QTL was located at the site of the known anti-influenza <i>Mx1</i> gene. We sequenced the coding regions of <i>Mx1</i> in the eight CC founder strains, and identified a novel <i>Mx1</i> allele that showed reduced ability to inhibit viral replication, while maintaining protection from weight loss.</p> </div