DOI: 10.5061/dryad.0vt4b8h85
title: Canine genome-wide association study identifies DENND1B as an obesity gene in dogs and humans

description: Obesity is a far-reaching, heritable disease, but its genetic basis is
 incompletely understood. Canine population history facilitates trait
 mapping. We performed the first successful canine genome-wide association
 study for body condition score, a measure of obesity, in 241 Labrador
 retrievers, with a polygenic score replicated in 350 more. Using an
 innovative cross-species approach, we showed new canine obesity genes are
 also associated with rare and common forms of obesity in humans. The lead
 association in dogs was with rs24430444 within the gene DENN domain
 containing 1B (DENND1B). Each copy of the alternate allele was associated
 with ~7% greater body fat per allele. We demonstrate a novel role for this
 gene in regulating signaling and trafficking of melanocortin 4 receptor, a
 critical controller of energy homeostasis. Thus, canine genetics
 identified novel obesity genes and mechanisms relevant to both dogs and
 humans.
description: To summarise, we studied Labrador retriever dogs kept as pets or
 working assistance dogs. Only adult dogs (age 1-10 years, mean 6 years)
 were included, free of known or suspected systemic illness and not being
 treated with medications likely to affect obesity status. Body fat mass
 was assessed using a well-validated measure of adiposity, Body Condition
 Score (BCS) which uses a combination of haptic and visual cues to assign
 dogs to BCS categories 1-9 according to standardized descriptors. DNA was
 extracted from blood or saliva samples and direct genotyping was performed
 on the CanineHD Genotyping BeadChip (Illumina) array and data were then
 imputed to 9.4 million single nucleotide polymorphisms (SNPs). For the
 GWAS, we retained SNPs called with 70% confidence and which were called in
 &gt;95% of dogs with an allele frequency &gt; 5% and a Hardy
 Weinberg equilibrium test p &gt;0.001%. There were 4.5 million SNPs
 included in the GWAS. We performed a GWAS for BCS in 241 Labrador
 retriever dogs applying a linear mixed effects model (GCTA MLMA-LOCO).
 Regression modeling was used to identify factors significantly affecting
 BCS in the population which were then included as covariates for the GWAS.
 These included sex, neuter status, and sex: neuter status interaction
 term. We constructed a PRS comprising 16 SNPs weighted for GWAS effect
 size on BCS using the ‘clumping and thresholding’ technique and applied
 the PRS to determine its utility as a predictor of BCS in Labradors and
 other breeds (FCR, pugs, golden retrievers).  This data
 could not have been collected, nor research conducted without several
 collaborative efforts and. We are indebted to the owners who volunteered
 for the study and to the dogs who took part, the Northern England
 Flatcoated Retriever Association and Kennel Club for helping with
 recruitment, Guide Dogs UK for sample contributions, Nai-Cheih Liu for
 gathering phenotypes in pugs. We also used data from
 the Golden Retriever Lifetime Study in this research, data for which is
 available via their 'Data Commons' website. 
description: # Canine genome-wide association study identifies DENND1B as an obesity
 gene in dogs and humans
 [https://doi.org/10.5061/dryad.0vt4b8h85](https://doi.org/10.5061/dryad.0vt4b8h85) ## Description of the data and file structure This README file explains the input data used for project outlined in manuscript: Canine genome-wide association study identifies DENND1B as an obesity gene in dogs and humans (Science - ads2145).  Each data file is described below: **labrador_GWAS_imputed_genetic_data.zip** * Genetic data for the population of 241 Labrador retrievers used in the primary BCS GWAS for the study * The data is in plink binary format (.bim, .bed, .fam) * This dataset is from analysis in only pet dogs * This data is imputed, utilising a pipeline which imputes genetic array data up to whole genome level * The pipeline of this data is available here: [https://github.com/GOdogs-Project/Imputation/releases/tag/version-0.1](https://github.com/GOdogs-Project/Imputation/releases/tag/version-0.1) and described in the Supplementary Materials and Methods which is provided alongside the manuscript.  **labrador_GWASresults_gcta-MLMA-loco-LDM_imputed.loco.mlma** * Results from the genome wide mixed linear model association analysis used on the GWAS for BCS in 241 Labrador retrievers * GCTA mlma-loco is the statistical algorithm used * Explanation of this output format (including detail on each column) can be found on the GCTA website here: [https://yanglab.westlake.edu.cn/software/gcta/](https://yanglab.westlake.edu.cn/software/gcta/) * Importantly the p values provided have not been adjusted for lambda and hence we recommend this if being used for further analyses **labrador_replicationset_PRSgenotypes.txt** * Genotypes for 16 SNPs used in the canine polygenic risk score (PRS) - in the replication set of 350 Labrador retrievers * Format of the genotypes is generated using plink * This dataset includes both pet dogs and assistance dogs **pug_PRSgenotypes.txt** * Genotypes for 16 SNPs used in the canine polygenic risk score (PRS) - in the replication set of pugs * This dataset includes only pet dogs * Format of the genotypes is generated using plink **pug_phenotypes.txt** * Phenotypes for the PRS replication set of pugs * This dataset includes only pet dogs **labrador_owner_control.txt** * Owner control 'scores' for all Labrador retrievers used in the study. * Owner control scores are calculated using the Dog Obesity Risk and Appetite (DORA) questionnaire * Full details are provided in the Supplementary Materials and Methods along with the original DORA paper (E. Raffan, S. P. Smith, S. O’Rahilly, J. Wardle, Development, factor structure and application of the Dog Obesity Risk and Appetite (DORA) questionnaire. PeerJ 3, e1278 (2015)) **labrador_phenotypes.txt** * Phenotypes for the Labrador retrievers used In the study, including both the 241 used in the GWAS and the 350 Labradors used a PRS replication set * This dataset includes both pet dogs and assistance dogs **FCR_PRSgenotypes.txt** * Genotypes for 16 SNPs used in the canine polygenic risk score (PRS) - in the replication set of flat-coated retrievers (FCR) * This dataset includes only pet dogs **FCR_phenotypes.txt** * Phenotypes for the PRS replication set of flat-coated retrievers * This dataset includes only pet dogs ### Files and variables #### File: FCR\_phenotypes.txt ##### Variables * DogID_anon: anonymised dog ID * Age_sample_returned: Age of dog in years * Professional_Weight: Weight of dog in kg * Professional_BCS: Body Condition Score of dogs according to the Purina 9 point BCS scale * F_CombinedGreedFactors: Dog food motivation 'score' calculated using the Dog Obesity Risk and Appetite (DORA) questionnaire. Full details are provided in the Supplementary Materials and Methods along with the original DORA paper (E. Raffan, S. P. Smith, S. O’Rahilly, J. Wardle, Development, factor structure and application of the Dog Obesity Risk and Appetite (DORA) questionnaire. PeerJ 3, e1278 (2015)) * sex: Coded as 0/1 where 0 = female and 1 = male * neuter: coded as 0/1 where 0 = entire/not neutered and 1 = neutered #### File: FCR\_PRSgenotypes.txt ##### Variables * DogID_anon: anonymised dog ID * All other columns are in format: chr:base-pair-position_allele * Where 0/1/2 indicates the count for that allele #### File: labrador\_phenotypes.txt ##### Variables * DogID_anon: anonymised dog ID * sex: Coded as 0/1 where 0 = female and 1 = male * neuter: coded as 0/1 where 0 = entire/not neutered and 1 = neutered * Age_sample returned: Age of dog in years * Professional_Weight: Weight of dog in kg * Professional_BCS: Body Condition Score of dogs according to the Purina 9 point BCS scale * FM_score: Dog food motivation 'score' calculated using the Dog Obesity Risk and Appetite (DORA) questionnaire. Full details are provided in the Supplementary Materials and Methods along with the original DORA paper (E. Raffan, S. P. Smith, S. O’Rahilly, J. Wardle, Development, factor structure and application of the Dog Obesity Risk and Appetite (DORA) questionnaire. PeerJ 3, e1278 (2015)) #### File: labrador\_owner\_control.txt ##### Variables * DogID_anon: anonymised dog ID * owner_control: Owner control 'scores' per Labrador retriever are calculated using the Dog Obesity Risk and Appetite (DORA) questionnaire Full details are provided in the original DORA paper (E. Raffan, S. P. Smith, S. O’Rahilly, J. Wardle, Development, factor structure and application of the Dog Obesity Risk and Appetite (DORA) questionnaire. PeerJ 3, e1278 (2015)) #### File: pug\_phenotypes.txt **Description:**  ##### Variables * DogID_anon: anonymised dog ID * Sex: Coded as 0/1 where 0 = female and 1 = male * Neutered: coded as 0/1 where 0 = entire/not neutered and 1 = neutered * Age: Age of dog in years * Weight: Weight of dog in kg * BCS: Body Condition Score of dogs according to the Purina 9 point BCS scale #### File: pug\_PRSgenotypes.txt **Description:**  ##### Variables * DogID_anon: anonymised dog ID * All other columns are in format: chr:base-pair-position_allele * Where 0/1/2 indicates the count for that allele #### File: labrador\_replicationset\_PRSgenotypes.txt **Description:**  ##### Variables * DogID_anon: anonymised dog ID * All other columns are in format: chr:base-pair-position_allele * Where 0/1/2 indicates the count for that allele #### File: labrador\_GWASresults\_gcta-MLMA-loco-LDM\_imputed.loco.mlma **Description:** Results from the genome wide mixed linear model association analysis used on the GWAS for BCS in 241 Labrador retrievers * GCTA mlma-loco is the statistical algorithm used * Explanation of this output format (including detail on each column) can be found on the GCTA website here: [https://yanglab.westlake.edu.cn/software/gcta/](https://yanglab.westlake.edu.cn/software/gcta/) * Importantly the p values provided have not been adjusted for lambda and hence we recommend this if being used for further analyses #### File: labrador\_GWAS\_imputed\_genetic\_data.zip **Description:**  Genetic data for the population of 241 Labrador retrievers used in the primary BCS GWAS for the study * The data is in plink binary format (.bim, .bed, .fam) * Plink file format is explained here: [https://www.cog-genomics.org/plink/1.9/formats](https://www.cog-genomics.org/plink/1.9/formats) * This data is imputed, utilising a pipeline which imputes genetic array data up to whole genome level * The pipeline of this data is available here: [https://github.com/GOdogs-Project/Imputation/releases/tag/version-0.1](https://github.com/GOdogs-Project/Imputation/releases/tag/version-0.1) and described in the Supplementary Materials and Methods which is provided alongside the manuscript. "NA" is used to denote, for example, missing clinical data. For example, some dogs have a body condition score but no food motivation score. ## Code/software All data is on the CanFam3.1 reference assembly (GCA_000002285.2) IMPUTATION: The in-house pipeline which was made up of three wrapper scripts: generating an imputation reference panel, preparing the genotypic data (to be used for the GWAS) and the imputation process itself can be accessed via GitHub ([https://github.com/GOdogs-Project/Imputation/releases/tag/version-0.1](https://github.com/GOdogs-Project/Imputation/releases/tag/version-0.1)). Thepipeline was developed to run on the University of Cambridge High Performance Computing (HPC) system, with a SLURM workload manager, so is not directly transferable but all the programs used within the pipeline are freely available to download and adapt. Software used: * PLINK v.1.9. * SHAPEIT v2.r904 * IMPUTE2 v2 * BEDtools v.2.20.1 * BCFtools v.1.9 GWAS: Genome-wide Efficient Mixed Model Analysis (GEMMA) software v0.98.1 (69) was used to generate a relatedness matrix which was transformed to a distance matrix using R v.4.2.2. GWAS and heritability analysis performed using Genome-wide Complex Trait Analysis (GCTA) v.1.93.2. Access information The final imputation panel represented 676 individuals of 91 breeds, including 31 Labrador retrievers (BioProject accession PRJNA648123 and PRJNA726547). The panel was enriched with whole genome sequence from 7 Labradors extracted from the European Nucleotide Archive (ENA [https://www.ebi.ac.uk/ena/](https://www.ebi.ac.uk/ena/) accession codes SRR7120183, SRR13340562, SRR13340566, SRR13340565, SRR13340564, SRR13340563, SRR13340570) and 5 from in-house WGS data (BioSample IDs SAMEA115942716, SAMEA115942718, SAMEA115942720, SAMEA115942723, SAMEA115942717, SAMEA115942719, SAMEA115942721, SAMEA115942722). ## Access information Other publicly accessible locations of the data: * Data for new canine WGS data used in the imputation panel are provided via ENA accession codes ([https://www.ebi.ac.uk/ena/](https://www.ebi.ac.uk/ena/)) detailed on pg. 4 of the Supplementary materials and methods and below. * The final imputation panel represented 676 individuals of 91 breeds, including 31 Labrador retrievers (BioProject accession PRJNA648123 and PRJNA726547). The panel was enriched with whole genome sequence from 7 Labradors extracted from the European Nucleotide Archive accession codes SRR7120183, SRR13340562, SRR13340566, SRR13340565, SRR13340564, SRR13340563, SRR13340570) and 5 from in-house WGS data (BioSample IDs SAMEA115942716, SAMEA115942718, SAMEA115942720, SAMEA115942723, SAMEA115942717, SAMEA115942719, SAMEA115942721, SAMEA115942722). Data was derived from the following sources: * Data obtained from the Golden Retriever Lifetime Study (GRLS) can be accessed via their data Commons website: [https://datacommons.morrisanimalfoundation.org/](https://datacommons.morrisanimalfoundation.org/). * Several of the human data are reused from existing studies, all of which are referenced throughout the main and supplementary text files. Data for human analyses (G2G, UKBB, rare exome association, UKB, SCOOP, gnomAD) are publicly available to download directly or through creation of an account. Data can be accessed through the original study sources listed below. * Human WHR and BMI GWAS data GIANT consortium: [https://portals.broadinstitute.org/collaboration/giant/index.php?title=GIANT_consortium_data_files&oldid=1066](https://portals.broadinstitute.org/collaboration/giant/index.php?title=GIANT_consortium_data_files&oldid=1066) * Human HDL and triglyceride GWAS via the Global Lipids Genetics Consortium (GLGC) - [https://www.lipidgenetics.org/#data-downloads-title](https://www.lipidgenetics.org/#data-downloads-title) * Human UKBB data: [https://ams.ukbiobank.ac.uk/ams/](https://ams.ukbiobank.ac.uk/ams/) * Human SCOOP case data: European Genome-Phenome Archive (EGA; [https://ega-archive.org](https://ega-archive.org); Study ID: EGAS00001000124; Dataset ID: EGAD00001000432) * gnomAD webportal dataset:  [https://gnomad.broadinstitute.org/downloads](https://gnomad.broadinstitute.org/downloads) * Mouse HypoMap data: [https://www.nature.com/articles/s42255-022-00657-y](https://www.nature.com/articles/s42255-022-00657-y) If you have any questions, please email the corresponding author of the paper Dr Eleanor Raffan [er311@cam.ac.uk](mailto:er311@cam.ac.uk)