Please cite as: Chitre, A. S., Polesskaya, O., Holl, K., Gao, J., Cheng, R., Bimschleger, H., Garcia Martinez, A., George, T., Gileta, A. F., Han, W., Horvath, A., Hughson, A., Ishiwari, K., King, C. P., Lamparelli, A., Versaggi, C. L., Martin, C., St. Pierre, C. L., Tripi, J. A., … Palmer, Abraham A.,Solberg Woods, L. C. (2022). Data from: Genome‐Wide Association Study in 3,173 Outbred Rats Identifies Multiple Loci for Body Weight, Adiposity, and Fasting Glucose. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0Q240F0 Corresponding author: Leah C. Solberg Woods lsolberg@wakehealth.edu Primary associated publication: Chitre, A. S., Polesskaya, O., Holl, K., Gao, J., Cheng, R., Bimschleger, H., Garcia Martinez, A., George, T., Gileta, A. F., Han, W., Horvath, A., Hughson, A., Ishiwari, K., King, C. P., Lamparelli, A., Versaggi, C. L., Martin, C., St. Pierre, C. L., Tripi, J. A., … Solberg Woods, L. C. (2020). Genome‐Wide Association Study in 3,173 Outbred Rats Identifies Multiple Loci for Body Weight, Adiposity, and Fasting Glucose. Obesity, 28(10), 1964–1973. https://doi.org/10.1002/oby.22927 Description of contents: 1. phenotype_data.zip This zipped folder contains unprocessed and processed phenotype data: Obesity_published_phenotypes_raw_n3173.csv --This file contains the raw phenotypes. Covariates specific to each trait contain the same prefix in the raw phenotype file. For example, bmi_w_tail_age and bmi_w_tail_technician are age and technician initials for the trait bmi_w_tail. Obesity_normalized_phenotypes_n3173.csv --This file contains the residuals (processed phenotype data) used for GWAS. These were produced using the following approach: Each trait within each research site was quantile normalized separately for males and females, this approach is similar to using sex as a covariate. Other relevant covariates (including age and dissector) were identified for each trait, and covariate effects were regressed out if they were significant and explained more than 2% of the variance. (Please note that each technician is specific to each center. Phenotyping center information is in column “center”). Residuals were then quantile normalized again, after which the data for each sex and site was pooled prior to further analysis. trait_ontology.xlsx --This file contains phenotype descriptions and InterLex IDs 2. LD_pruned_genotypes.zip This zipped folder contains the LD pruned genotypes in PLINK binary (LD_pruned_PLINK.zip) and BIMBAM (LD_pruned_0.95.zip) file formats. Prior to GWAS, SNPs in high linkage disequilibrium were removed using PLINK with r2 cut-off 0.95; this produced set of 128,447 SNPs which were used for GWAS, genetic correlations, and heritability estimates. Command used to LD-prune the genotype data in PLINK v1.90 : --indep-pairwise 50 5 0.95. The 50 is the number of variants to look at in a window, the 5 is the number of variants to shift the windows side way each step, and the 0.95 is the r2 threshold for trimming SNPs. BIMBAM file format: The first column is SNP id, the second and third columns are allele types, and the remaining columns are the genotype dosages of different individuals numbered between 0 and 2. PLINK binary format: .bed is the PLINK binary biallelic genotype table. This is the primary representation of genotype calls at biallelic variants. The .bed file is accompanied by .bim (Variant information) and .fam (Sample information) files. 3. Unpruned_genotypes_Oxford_genotype.zip This zipped folder contains unpruned genotype data in Oxford file format. This file has no header line. Each line stores information for a single SNP. The first five fields are: "SNP ID", rsID (treated by PLINK as the main variant ID), Base-pair coordinate, Allele 1 and Allele 2. Each subsequent triplet of values represent the genotype probabilities. The unpruned set of SNPs was used to produce LocusZoom plots 4. grm_gcta.zip This zipped folder contains Genetic Relatedness Matrix (GRM) produced using GCTA. gcta v1.26.0 was used to produce the GRM using the following command: gcta --bfile genotype --autosome-num 20 --autosome --thread-num 1 --make-grm-bin --out obesity_published_grm obesity_published_grm.grm.bin (binary file which contains the lower triangle elements of the GRM). obesity_published_grm.grm.N.bin (binary file which contains the number of SNPs used to calculate the GRM). obesity_published_grm.grm.id (no header line; columns are individual IDs). 5. gwas_results.zip This zipped folder contains the results from the GWAS analysis (.csv files) and the commands used for the analysis (gemma_commands.sh) GWAS analysis employed a linear mixed model using dosages (BIMBAM file format) and genetic relatedness matrices (GRM) to account for the complex family relationships within the HS population and the Leave One Chromosome Out (LOCO) method to avoid proximal contamination. The following column names correspond to data from the GWAS results (.csv files) chr: chromosome numbers rs: snp ids ps: base pair positions on the chromosome n_miss: number of missing individuals for a given snp allele1: minor allele allele0: major allele af: allele frequency beta: beta estimates se:standard errors for beta logl_H1: l_remle: REMLE estimates for lambda l_mle: MLE estimates for lambda p_wald, p_lrt and p_score are the p-values. Technical details: All software versions used to generate used to generate the data in this collection are noted below and in the Methods section of the associated publication. GWAS: GEMMA v0.97 Converting genotype probabilities into dosages: QCTOOL v2 Converting genotype probabilities into Hard-Called Genotypes: PLINK v2.0 LD pruning of the genotype data : PLINK v1.90 License: Creative Commons Attribution 4.0 International Public License (http://creativecommons.org/licenses/by/4.0/)