This text files aims to explain the procedure to perform both the data handling and the analyses in the paper: Description of the files: 1. Datasets PhenoAsreml.txt contains the observed values for the phenotypes described in the paper PhenoAsreml_scaled.txt contains the scaled (mean of zero and standard deviation of one) of the same phenotypes. THIS DATASET IS USED FOR BIVARIATE ANALYSES WITH 2x2 STRATA PhenoAsreml_scaledLong.txt contains the same information, organized differently because the dataset is reshaped from wide to long format. PhenoAsreml_scaledLong_3x3strata.txt contains the information for BackFat and BodyWeight, THIS DATASET IS USED FOR BIVARIATE ANALYSES WITH 3x3 STRATA PhenoAsreml_scaledLong_3x2strata.txt contains the information for BackFat and Adiponectin, THIS DATASET IS USED FOR BIVARIATE ANALYSES WITH 3x2 STRATA. It is only an example of dataset that needs to be generated in order to obtain all the results presented in the paper BEDERE_2023_Data_PedigreeHens.txt is the pedigree file (individual/sire/dam) traced back over 5 generations 2. Codes and parameter files BEDERE_2023_RScript_handlingdata_Long.R is an R code to reshape PhenoAsreml_scaled into PhenoAsreml_scaledLong and to subset it to generate PhenoAsreml_scaledLong_3x3strata for instance BEDERE_2023_ASREMLScript_bivariate_2x2strata.as (as well as ...3x2strata.as and ...3x3strata.as) are ASReml parameter files used to state the data, model specification and post-hoc calculation to ASReml Please, note that some variance components have been fixed in some analyses when the algorithm was struggling to converge. BEDERE_2023_RScript_BartlettTest.R is an R code to perform the Bartlett test. 2. Results examples Some output files of ASReml are provided to give an example of results for each type of bivariate analysis. The .asr file is the log of the program, explaining how the program ran The .res file is describing the residuals The .pvc file describes the variance components and provides the genetic parameters with their associated standard errors.

aap9002/UK-Road-Bend-Classification dataset hosted on Hugging Face and contributed by the HF Datasets community

Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes are an established model for testing potential chemical hazards. Inter-individual variability in toxicodynamic sensitivity has also been demonstrated in vitro; however, quantitative characterization of the population-wide variability has not been fully explored. We sought to develop a method to address this gap by combining a population-based iPSC-derived cardiomyocyte model with Bayesian concentration-response modeling. A total of 136 compounds, including 44 pharmaceuticals and 82 environmental chemicals, were tested in iPSC-derived cardiomyocytes from 43 non-diseased humans. Hierarchical Bayesian population concentration-response modeling was conducted for five phenotypes reflecting cardiomyocyte function or viability. Toxicodynamic variability was quantified through the derivation of chemical- and phenotype-specific variability factors (TDVF). Toxicokinetic modeling was used for probabilistic in vitro-to-in vivo extrap...

Aytes, A.*, Mitrofanova, A.*, Lefebvre, C.*, Alvarez, M. J., Castillo-Martin, M., Zheng, T., Eastham, J. A., Gopalan, A., Pienta, K. J., Shen, M. M., Califano, A., and Abate-Shen, C. (2014). Cross-species analysis of genome-wide regulatory networks identifies a synergistic interaction between FOXM1 and CENPF that drives prostate cancer malignancy. Cancer Cell. *Equal contributions.

Additional file 1. R code of StepLMM. The data contains the R function of StepLMM, an example data and users’ guide.

Recent genome-wide association studies (GWASs) of severe malaria have identified several association variants. However, much about the underlying biological functions are yet to be discovered. Here, we systematically predicted plausible candidate genes and pathways from functional analysis of severe malaria resistance GWAS summary statistics (N = 17,000) meta-analysed across 11 populations in malaria endemic regions. We applied positional mapping, expression quantitative trait locus (eQTL), chromatin interaction mapping, and gene-based association analyses to identify candidate severe malaria resistance genes. We further applied rare variant analysis to raw GWAS datasets (N = 11,000) of three malaria endemic populations including Kenya, Malawi, and Gambia and performed various population genetic structures of the identified genes in the three populations and global populations. We performed network and pathway analyses to investigate their shared biological functions. Our functional mapping analysis identified 57 genes located in the known malaria genomic loci, while our gene-based GWAS analysis identified additional 125 genes across the genome. The identified genes were significantly enriched in malaria pathogenic pathways including multiple overlapping pathways in erythrocyte-related functions, blood coagulations, ion channels, adhesion molecules, membrane signalling elements, and neuronal systems. Our population genetic analysis revealed that the minor allele frequencies (MAF) of the single nucleotide polymorphisms (SNPs) residing in the identified genes are generally higher in the three malaria endemic populations compared to global populations. Overall, our results suggest that severe malaria resistance trait is attributed to multiple genes, highlighting the possibility of harnessing new malaria therapeutics that can simultaneously target multiple malaria protective host molecular pathways.

Predicted R-loops specifically detected in 3T3 and E14 were generated by selecting genome bins that were assigned probabilities ≥ 0.8 in one cell line and ≤ 0.2 in the other.