Genome‐Wide Association Study in 3,173 Outbred Rats Identifies Multiple Loci for Body Weight, Adiposity, and Fasting Glucose

Abstract

Objective Obesity is influenced by genetic and environmental factors. Despite the success of human genome‐wide association studies, the specific genes that confer obesity remain largely unknown. The objective of this study was to use outbred rats to identify the genetic loci underlying obesity and related morphometric and metabolic traits. Methods This study measured obesity‐relevant traits, including body weight, body length, BMI, fasting glucose, and retroperitoneal, epididymal, and parametrial fat pad weight in 3,173 male and female adult N/NIH heterogeneous stock (HS) rats across three institutions, providing data for the largest rat genome‐wide association study to date. Genetic loci were identified using a linear mixed model to account for the complex family relationships of the HS and using covariates to account for differences among the three phenotyping centers. Results This study identified 32 independent loci, several of which contained only a single gene (e.g., Epha5, Nrg1, Klhl14) or obvious candidate genes (e.g., Adcy3, Prlhr). There were strong phenotypic and genetic correlations among obesity‐related traits, and there was extensive pleiotropy at individual loci. Conclusions This study demonstrates the utility of HS rats for investigating the genetics of obesity‐related traits across institutions and identify several candidate genes for future functional testing.

Sept8/SEPTIN8 involvement in cellular structure and kidney damage is identified by genetic mapping and a novel human tubule hypoxic model

“…HS rats were created by interbreeding eight inbred founder strains and maintaining the colony to minimize inbreeding 16 . We have previously used HS rats to fine-map genetic loci and identify underlying causal genes for diabetes 17 – 19 and adiposity 20 , 21 , and have demonstrated phenotypic variation for multiple kidney traits 22 . In the current study, we identified genomic loci for urinary protein excretion (UPE) and intermediary serum biochemistries associated with CKD, leading to Septin 8 (Sept8) as a candidate gene linked with renal injury.…”

The Cocaine and Oxycodone Biobanks, Two Repositories from Genetically Diverse and Behaviorally Characterized Rats for the Study of Addiction

“…Whole-genome sequencing identifies hundreds of thousands of single nucleotide polymorphisms (SNPs) per individual and, following imputation, provides genotypes at millions of SNPs. Combining hundreds of animals’ genetic and genomic data will feed a subsequent GWAS to identify gene variants contributing to vulnerability and resilience to SUD-like behavior ( Chitre et al, 2020 ), which forms the basis to identify specific genetic loci, estimate heritability, investigate genetic correlations and perform phenome-wide association studies (PheWAS) and transcriptome-wide association studies (TWAS). All of which may identify novel gene variants to inform human personalized diagnosis and treatment.…”

“…These are highly recombinant animals, established by crossbreeding eight genetically diverse founder strains ( Hansen and Spuhler, 1984 ; STAR Consortium et al, 2008 ; Baud, 2013 ; Solberg Woods and Palmer, 2019 ), resulting in a diversity that mimics the diversity found in the human population. Together with the behavioral characterization, this allows for genome-wide association studies (GWAS) for a variety of addiction-related quantitative traits ( Valdar et al, 2006 ; Baud, 2013 ; Keele, 2018 ; Chitre et al, 2020 ) by whole-genome sequencing of each individual.…”

Analysis of independent cohorts of outbred CFW mice reveals novel loci for behavioral and physiological traits and identifies factors determining reproducibility

“…Therefore, rodent studies have not lent themselves to meta- or mega-analysis (Wuschke et al 2007; Schmidt et al 2008). However, the more recent transition to genetic association using outbred mice (Parker and Palmer 2011; Flint and Eskin 2012; Chesler 2014) or panels of inbred animals (Ghazalpour et al 2012) provides opportunities for deploying meta- (Kang et al 2014) and mega-analysis (Chitre et al 2020; Zhou et al 2020) to increase power and test reproducibility.…”