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      A Study of 41 Canine Orthologues of Human Genes Involved in Monogenic Obesity Reveals Marker in the ADCY3 for Body Weight in Labrador Retrievers

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      Veterinary Sciences
      MDPI AG

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          Abstract

          Obesity and overweight are common conditions in dogs, but individual susceptibility varies with numerous risk factors, including diet, age, sterilization, and gender. In addition to environmental and biological factors, genetic and epigenetic risk factors can influence predisposition to canine obesity, however, they remain unknown. Labrador Retrievers are one of the breeds that are prone to obesity. The purpose of this study was to analyse 41 canine orthologues of human genes linked to monogenic obesity in humans to identify genes associated with body weight in Labrador Retriever dogs. We analysed 11,520 variants from 50 dogs using a linear mixed model with sex, age, and sterilization as covariates and population structure as a random effect. Estimates obtained from the model were subjected to a maxT permutation procedure to adjust p-values for FWER < 0.05. Only the ADCY3 gene showed statistically significant association: TA>T deletion located at 17:19,222,459 in 1/20 intron (per allele effect of 5.56 kg, SE 0.018, p-value = 5.83 × 10−5, TA/TA: 11 dogs; TA/T: 32 dogs; T/T: 7 dogs). Mutations in the ADCY3 gene have already been associated with obesity in mice and humans, making it a promising marker for canine obesity research. Our results provide further evidence that the genetic makeup of obesity in Labrador Retriever dogs contains genes with large effect sizes.

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            Second-generation PLINK: rising to the challenge of larger and richer datasets

            PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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              The Ensembl Variant Effect Predictor

              The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Veterinary Sciences
                Veterinary Sciences
                MDPI AG
                2306-7381
                June 2023
                June 08 2023
                : 10
                : 6
                : 390
                Article
                10.3390/vetsci10060390
                10301554
                37368776
                554eabd7-eaf6-40e3-b043-83609c89dd7d
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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