Analysis of large versus small dogs reveals three genes on the canine X chromosome associated with body weight, muscling and back fat thickness

International audienceDomestic dog breeds display significant diversity in both body mass and skeletal size, resulting from intensive selective pressure during the formation and maintenance of modern breeds. While previous studies focused on the identification of alleles that contribute to small skeletal size, little is known about the underlying genetics controlling large size. We first performed a genome-wide association study (GWAS) using the Illumina Canine HD 170,000 single nucleotide polymorphism (SNP) array which compared 165 large-breed dogs from 19 breeds (defined as having a Standard Breed Weight (SBW) >41 kg [90 lb]) to 690 dogs from 69 small breeds (SBW ≤41 kg). We identified two loci on the canine X chromosome that were strongly associated with large body size at 82–84 megabases (Mb) and 101–104 Mb. Analyses of whole genome sequencing (WGS) data from 163 dogs revealed two indels in the Insulin Receptor Substrate 4 (IRS4) gene at 82.2 Mb and two additional mutations, one SNP and one deletion of a single codon, in Immunoglobulin Superfamily member 1 gene (IGSF1) at 102.3 Mb. IRS4 and IGSF1 are members of the GH/IGF1 and thyroid pathways whose roles include determination of body size. We also found one highly associated SNP in the 5’UTR of Acyl-CoA Synthetase Long-chain family member 4 (ACSL4) at 82.9 Mb, a gene which controls the traits of muscling and back fat thickness. We show by analysis of sequencing data from 26 wolves and 959 dogs representing 102 domestic dog breeds that skeletal size and body mass in large dog breeds are strongly associated with variants within IRS4, ACSL4 and IGSF1

Thin Structures Segmentation Using Anisotropic Neighborhoods

International audienceBayesian and probabilistic models are widely used in image processing to handle noise due to various alteration phenomena. To benefit from the spatial information in a tractable way, Markov Random Fields (MRF) are often assumed with isotropic neighborhoods, that is however at the detrimental of the preservation of thin structures. In this study, we aim at relaxing this assumption on stationarity and isotropy of the neighborhood shape in order to get a prior probability term that is relevant not only within the homogeneous areas but also close to object borders and within thin structures. To tackle the issue of neighborhood shape estimation, we propose to base on tensor voting, that allows for the estimation of structure direction and saliency at various scales. We propose three main ways to derive anisotropic neighborhoods, namely shape-based, target-based and cardinal-based neighborhood. Then, having defined the neighborhood field, we introduce an energy minimized using graph cuts, and illustrate the benefits of our approach against the use of isotropic neighborhoods in the applicative context of crack detection. First results on such a challenging problem are very encouraging

Brain-inspired robust delineation operator

In this paper we present a novel filter, based on the existing COSFIRE filter, for the delineation of patterns of interest. It includes a mechanism of push-pull inhibition that improves robustness to noise in terms of spurious texture. Push-pull inhibition is a phenomenon that is observed in neurons in area V1 of the visual cortex, which suppresses the response of certain simple cells for stimuli of preferred orientation but of non-preferred contrast. This type of inhibition allows for sharper detection of the patterns of interest and improves the quality of delineation especially in images with spurious texture. We performed experiments on images from different applications, namely the detection of rose stems for automatic gardening, the delineation of cracks in pavements and road surfaces, and the segmentation of blood vessels in retinal images. Push-pull inhibition helped to improve results considerably in all applications.Comment: Accepted at Brain-driven Computer Vision workshop at ECCV 201