DOI: 10.20350/digitalcsic/15081
title: Dataset related to a study to identify genomic regions in susceptibility to Schistosoma mansoni infection in a murine backcross
title: Identification of genomic regions implicated in susceptibility to "Schistosoma mansoni" infection in a murine genetic model (backcross)

description: This dataset was intended to describe schistosomiasis severity in a backcross cohort and to study the genetic linkage analysis with parasitological, pathological and immunological variables.
description: [Description of methods used for collection/generation of data] F1BX mice were infected with 150 ± 5 S. mansoni cercariae each mouse and nine weeks post-infection were euthanized. We considered 20 variables: granulomas; affected liver surface (mm2/cm2); the number of adult male and female worms; eggs per gram of liver and small intestine; eggs in liver and small intestine per female; CD4, CD8, CD45, CD220 in blood or spleen; IgG, IgG1, IgG2a, IgM antibodies. Multivariate models (cluster and principal component analyses and K-means) identified four levels of infection intensity in the cohort. The genetic regions associated with severity were assessed by massive genotyping and genetic linkage analysis using 961 informative SNPs.
description: [Methods for processing the data] Mean and standard error in each variable, Kolmogorov-Smirnov test. ANOVA and Tukey’ test or Student t-test. The Pearson correlation coefficient (r) and Student t-test for the statistical significance. Multivariant models considering sex influence in worm recovery, eggs in the liver and intestine, fecundity, granulomas and the affected liver surface. All the variables were standardized to 0 mean and standard deviation to 1. Cluster analysis, dendrogram, Principal components analysis and conglomerates by k-means were used to generate clusters. Median proportions were performed. SIMFIT statistical package for Windows version 7.3.7 were used Massive genotyping and geneticlinkage analysis using 961 informative SNPs: The genetic distance based on the recombination frequencies between markers in the F1BX cohort was compared with http://cgd.jax.org/mousemapconverter using maximum-likelihood mapping with HM algorithm. The Haldane function was used with a step size of 2.5 cM and a genotyping error of 0.001. We used the LOD-score to calculate the statistical significance of the linkage of the QTLs found. LOD-score higher than 1.4 suggested linkage. The Ensembl bioinformatics tool (https://www.ensembl.org/index.html) was used to identify syntenic regions between mouse and human.
description: Here we present the dataset used in our study entitled "Identification of genomic regions implicated in susceptibility to Schistosoma mansoni infection in a murine genetic model (backcross)". Thus, we crossed the C57BL/6 mouse strain with the CBA one and then the F1B6CBA females (C57 x CBA) were backcrossed with CBA males generating the F1BX cohort of the study. The study consists of the identification of genetic markers of schistosomiasis. High infection levels and severe liver fibrosis in schistosomiasis appear only in a few highly susceptible infected people. Schistosomiasis could be a complex trait disease and it could be possible to identify genetic markers associated with severity. This study uses a genetically heterogeneous back-cross cohort with genetically unique mice. A backcross (F1BX) mouse cohort was generated after two stages; firstly, we crossed a mouse strain (CBA/2J) susceptible to schistosomiasis with a resistant one (C57BL/6J) to generate the F1B6CBA mice; secondly, the F1BX mice were generated by backcrossing. F1B6CBA female mice with CBA/2J males. F1BX mice were infected with 150 ± 5 S. mansoni cercariae each mouse and nine weeks post-infection were euthanized. We considered 20 variables: granulomas; affected liver surface (mm2/cm2); the number of adult male and female worms; eggs per gram of liver and small intestine; eggs in liver and small intestine per female; CD4, CD8, CD45, CD220 in blood or spleen; IgG, IgG1, IgG2a, IgM antibodies. Multivariate models (cluster and principal component analyses and K-means) identified four levels of infection intensity in the cohort. The genetic regions associated with severity were assessed by massive genotyping and genetic linkage analysis using 961 informative SNPs.