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      Rapid Discovery of De Novo Deleterious Mutations in Cattle Enhances the Value of Livestock as Model Species

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      1 , 2 , 3 , 4 , 5 , 1 , 6 , 1 , 1 , 2 , 4 , 1 , 6 , 1 , 6 , 1 , 7 , 1 , 8 , 1 , 1 , 6 , 1 , 6 , 1 , 2 , 9 , 10 , 1 , 1 , 1 , 6 , 11 , 11 , 12 , 1 , 6 , 1 , 13 , 14 , 15 , 16 , 4 , 17 , 18 , 19 , 20 , 5 , 21 , 22 , 23 , 24 , 7 , 6 , 1 , 25 , 26 , 25 , 27 , 4 , 1 , 9 , 5 , 1 , 6 ,
      Scientific Reports
      Nature Publishing Group UK

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          Abstract

          In humans, the clinical and molecular characterization of sporadic syndromes is often hindered by the small number of patients and the difficulty in developing animal models for severe dominant conditions. Here we show that the availability of large data sets of whole-genome sequences, high-density SNP chip genotypes and extensive recording of phenotype offers an unprecedented opportunity to quickly dissect the genetic architecture of severe dominant conditions in livestock. We report on the identification of seven dominant de novo mutations in CHD7, COL1A1, COL2A1, COPA, and MITF and exploit the structure of cattle populations to describe their clinical consequences and map modifier loci. Moreover, we demonstrate that the emergence of recessive genetic defects can be monitored by detecting de novo deleterious mutations in the genome of bulls used for artificial insemination. These results demonstrate the attractiveness of cattle as a model species in the post genomic era, particularly to confirm the genetic aetiology of isolated clinical case reports in humans.

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          Most cited references45

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          A new approach for efficient genotype imputation using information from relatives

          Background Genotype imputation can help reduce genotyping costs particularly for implementation of genomic selection. In applications entailing large populations, recovering the genotypes of untyped loci using information from reference individuals that were genotyped with a higher density panel is computationally challenging. Popular imputation methods are based upon the Hidden Markov model and have computational constraints due to an intensive sampling process. A fast, deterministic approach, which makes use of both family and population information, is presented here. All individuals are related and, therefore, share haplotypes which may differ in length and frequency based on their relationships. The method starts with family imputation if pedigree information is available, and then exploits close relationships by searching for long haplotype matches in the reference group using overlapping sliding windows. The search continues as the window size is shrunk in each chromosome sweep in order to capture more distant relationships. Results The proposed method gave higher or similar imputation accuracy than Beagle and Impute2 in cattle data sets when all available information was used. When close relatives of target individuals were present in the reference group, the method resulted in higher accuracy compared to the other two methods even when the pedigree was not used. Rare variants were also imputed with higher accuracy. Finally, computing requirements were considerably lower than those of Beagle and Impute2. The presented method took 28 minutes to impute from 6 k to 50 k genotypes for 2,000 individuals with a reference size of 64,429 individuals. Conclusions The proposed method efficiently makes use of information from close and distant relatives for accurate genotype imputation. In addition to its high imputation accuracy, the method is fast, owing to its deterministic nature and, therefore, it can easily be used in large data sets where the use of other methods is impractical.
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            Mutations at the mouse microphthalmia locus are associated with defects in a gene encoding a novel basic-helix-loop-helix-zipper protein.

            Mice with mutations at the microphthalmia (mi) locus have some or all of the following defects: loss of pigmentation, reduced eye size, failure of secondary bone resorption, reduced numbers of mast cells, and early onset of deafness. Using a transgenic insertional mutation at this locus, we have identified a gene whose expression is disrupted in transgenic animals. This gene encodes a novel member of the basic-helix-loop-helix-leucine zipper (bHLH-ZIP) protein family of transcription factors, is altered in mice carrying two independent mi alleles (mi and miws), and is expressed in the developing eye, ear, and skin, all anatomical sites affected by mi. The multiple spontaneous and induced mutations available at mi provide a unique biological resource for studying the role of a bHLH-ZIP protein in mammalian development.
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              Coatomer is essential for retrieval of dilysine-tagged proteins to the endoplasmic reticulum.

              Dilysine motifs in cytoplasmic domains of transmembrane proteins are signals for their continuous retrieval from the Golgi back to the endoplasmic reticulum (ER). We describe a system to assess retrieval to the ER in yeast cells making use of a dilysine-tagged Ste2 protein. Whereas retrieval was unaffected in most sec mutants tested (sec7, sec12, sec13, sec16, sec17, sec18, sec19, sec22, and sec23), a defect in retrieval was observed in previously characterized coatomer mutants (sec21-1, sec27-1), as well as in newly isolated retrieval mutants (sec21-2, ret1-1). RET1 was cloned by complementation and found to encode the alpha subunit of coatomer. While temperature-sensitive for growth, the newly isolated coatomer mutants exhibited a very modest defect in secretion at the nonpermissive temperature. Coatomer from beta'-COP (sec27-1) and alpha-COP (ret1-1) mutants, but not from gamma-COP (sec21) mutants, had lost the ability to bind dilysine motifs in vitro. Together, these results suggest that coatomer plays an essential role in retrograde Golgi-to-ER transport and retrieval of dilysine-tagged proteins back to the ER.
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                Author and article information

                Contributors
                aurelien.capitan@jouy.inra.fr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                13 September 2017
                13 September 2017
                2017
                : 7
                : 11466
                Affiliations
                [1 ]ISNI 0000 0004 4910 6535, GRID grid.460789.4, GABI, INRA, AgroParisTech, , Université Paris-Saclay, ; 78350 Jouy-en-Josas, France
                [2 ]ISNI 0000 0004 4910 6535, GRID grid.460789.4, LREG, CEA, , Université Paris-Saclay, ; 78350 Jouy-en-Josas, France
                [3 ]Veterinary School of Lyon (VetAgro Sup), Cattle Pathology Unit, Marcy l’Etoile, France
                [4 ]ISNI 0000000123222966, GRID grid.6936.a, Chair of Animal Breeding, , Technische Universitaet Muenchen, ; Freising-Weihenstephan, Germany
                [5 ]ISNI 0000 0001 0726 5157, GRID grid.5734.5, Institute of Genetics, Vetsuisse Faculty, , University of Bern, Bremgartenstrasse 109a, ; 3001 Bern, Switzerland
                [6 ]ALLICE, Paris, France
                [7 ]ISNI 0000 0001 2199 2457, GRID grid.425193.8, Institut de l’Elevage, ; Paris, France
                [8 ]GRID grid.417961.c, INRA, MIMA2 MET – Equipe Plateformes, ; Jouy-en-Josas, France
                [9 ]Université de Lyon, Veterinary School of Lyon (VetAgro Sup), UPSP 2011-03-101 Interactions Cellules Environnement, Marcy l’Etoile, France
                [10 ]Vereinigte Informationssysteme Tierhaltung w.V., Verden, Germany
                [11 ]GRID grid.435020.0, LABOGENA, ; Jouy-en-Josas, France
                [12 ]Veterinary School of Lyon (VetAgro Sup), Imaging Department, Marcy l’Etoile, France
                [13 ]INRA, UMR703 Animal Pathophysiology and Biotherapy for Muscle and Nervous system Diseases, Atlantic Gene Therapies, Nantes, France
                [14 ]ISNI 0000 0001 2175 3974, GRID grid.418682.1, LUNAM University, Oniris, Nantes-Atlantic National College of Veterinary Medicine, Food science and Engineering, ; Nantes, France
                [15 ]ISNI 0000 0001 2169 1988, GRID grid.414548.8, INRA, Sigenae Bioinformatics Group, UR875, ; 31320 Castanet-Tolosan, France
                [16 ]GenPhySE, Université de Toulouse, INRA, INPT, INP-ENVT, 31320 Castanet Tolosan, France
                [17 ]ISNI 0000000123222966, GRID grid.6936.a, Chair of Animal Biotechnology, , Technische Universität München, ; Freising-Weihenstephan, Germany
                [18 ]ZuchtData GmbH, Vienna, Austria
                [19 ]ISNI 0000 0000 9686 6466, GRID grid.6583.8, Clinic for Ruminants, , University of Veterinary Medicine Vienna, ; Vienna, Austria
                [20 ]Lower Saxony State Office for Consumer Protection and Food Safety, Department of Pathology, Oldenburg, Germany
                [21 ]Landesuntersuchungsanstalt für das Gesundheits- und Veterinärwesen Sachsen, Bahnhofstrasse 60, 04158 Leipzig, Germany
                [22 ]Veterinary Practice Zettlitz, Strasse der Jugend 68, 09306 Zettlitz, Germany
                [23 ]INRA, UE0326, Domaine expérimental du Pin-au-Haras, Exmes, France
                [24 ]UMOTEST, Ceyzeriat, France
                [25 ]ISNI 0000 0004 4907 4051, GRID grid.468062.e, AgriBio, Centre for AgriBioscience, , Biosciences Research, DEDJTR, ; Bundoora, Australia
                [26 ]ISNI 0000 0000 9320 7537, GRID grid.1003.2, Queensland Alliance for Agriculture and Food Innovation, , Centre for Animal Science, The University of Queensland, ; Brisbane, Australia
                [27 ]ISNI 0000 0001 2342 0938, GRID grid.1018.8, School of Applied Systems Biology, La Trobe University, ; Bundoora, Australia
                Author information
                http://orcid.org/0000-0002-3668-8893
                http://orcid.org/0000-0002-3181-6083
                http://orcid.org/0000-0003-0361-2961
                http://orcid.org/0000-0001-7424-0013
                http://orcid.org/0000-0001-9773-522X
                Article
                11523
                10.1038/s41598-017-11523-3
                5597596
                28904385
                ade2f22a-5e4a-4fd7-bc3c-358d7b32a7ea
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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                : 13 October 2016
                : 23 August 2017
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