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1. In silico identification of variations in microRNAs with a potential impact on dairy traits using whole ruminant genome SNP datasets.

Author

Bourdon C, Boussaha M, Bardou P, Sanchez MP, Le Guillou S, Tribout T, Larroque H, Boichard D, Rupp R, Le Provost F, and Tosser-Klopp G

Subjects
Animals, Computational Biology methods, Genome-Wide Association Study methods, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Genetic Variation, Genome, Genomics methods, MicroRNAs genetics, Quantitative Trait Loci, Quantitative Trait, Heritable, Ruminants genetics
Abstract

MicroRNAs are small noncoding RNAs that have important roles in the lactation process and milk biosynthesis. Some polymorphisms have been studied in various livestock species from the perspective of pathology or production traits. To target variants that could be the causal variants of dairy traits, genetic variants of microRNAs expressed in the mammary gland or present in milk and localized in dairy quantitative trait loci (QTLs) were investigated in bovine, caprine, and ovine species. In this study, a total of 59,124 (out of 28 millions), 13,427 (out of 87 millions), and 4761 (out of 38 millions) genetic variants in microRNAs expressed in the mammary gland or present in milk were identified in bovine, caprine, and ovine species, respectively. A total of 4679 of these detected bovine genetic variants are located in dairy QTLs. In caprine species, 127 genetic variants are localized in dairy QTLs. In ovine species, no genetic variant was identified in dairy QTLs. This study leads to the detection of microRNA genetic variants of interest in the context of dairy production, taking advantage of whole genome data to identify microRNA genetic variants expressed in the mammary gland and localized in dairy QTLs., (© 2021. The Author(s).)

Published
2021
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2. Using sequence variants of a QTL region improves the accuracy of genomic evaluation in French Saanen goats.

Author

Talouarn E, Teissier M, Bardou P, Larroque H, Clément V, Palhière I, Tosser-Klopp G, Rupp R, and Robert-Granié C

Subjects
Animals, Chromosome Mapping veterinary, Genotype, Linkage Disequilibrium, Milk metabolism, Models, Genetic, Oligonucleotide Array Sequence Analysis veterinary, Phenotype, Polymorphism, Single Nucleotide, Genetic Variation, Genomics methods, Goats genetics, Quantitative Trait Loci
Abstract

The enhanced availability of sequence data in livestock provides an opportunity for more accurate predictions in routine genomic evaluations. Such evaluations would therefore no longer rely only on the linkage disequilibrium between a chip marker and the causal mutation. The objective of this study was to assess the usefulness of sequence data in Saanen goats (n = 33) to better capture a quantitative trait locus (QTL) on chromosome 19 (CHI19) and improve the accuracy of predictions for 3 milk production traits, 5 type traits, and somatic cell scores. All 1,207 50K genotypes were imputed to the sequence level. Four scenarios, each using a subset of CHI19 imputed variants, were then tested. Sequence-derived information included all CHI19 variants (529,576), all variants in the QTL region (22,269), 178 variants selected in the QTL region and added to an updated chip, or 178 randomly selected variants on CHI19. Two genomic evaluation models were applied: single-step genomic BLUP and weighted single-step genomic BLUP. All scenarios were compared with single-step genomic BLUP using 50K genotypes. Best overall results were obtained using single-step genomic BLUP on 50K genotypes completed with all variants in the QTL region of chromosome 19 (6.2% average increase in accuracy for 9 traits) with the highest accuracy gain for fat yield (17.9%), significant increases for milk (13.7%) and protein yields (12.5%), and type traits associated with CHI19. Despite its association with the QTL region of chromosome 19, the somatic cell score showed decreased accuracy in every alternative scenario. Using all CHI19 variants led to an overall decrease of 4.8% in prediction accuracy. The updated chip was efficient and improved genomic evaluations by 3.1 to 6.4% on average, depending on the scenario. Indeed, information from only a few carefully selected variants increased accuracies for traits of interest when used in a single-step genomic BLUP model. In conclusion, using QTL region variants imputed from sequence data in single-step genomic evaluations represents a promising perspective for such evaluations in dairy goats. Furthermore, using only a limited number of selected variants in QTL regions, as available on SNP chip updates, significantly increases the accuracy for QTL-associated traits without deteriorating the evaluation accuracy for other traits. The latter approach is interesting, as it avoids time-consuming imputation and data formatting processes and provides reliable genotypes., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

Published
2021
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3. Genome wide association analysis on semen volume and milk yield using different strategies of imputation to whole genome sequence in French dairy goats.

Author

Talouarn E, Bardou P, Palhière I, Oget C, Clément V, Tosser-Klopp G, Rupp R, and Robert-Granié C

Subjects
Algorithms, Animals, Genetic Linkage, Genotype, Male, Models, Genetic, Quantitative Trait Loci, Whole Genome Sequencing, Genome, Genome-Wide Association Study, Genomics methods, Goats genetics, Milk, Phenotype, Semen
Abstract

Background: Goats were domesticated 10,500 years ago to supply humans with useful resources. Since then, specialized breeds that are adapted to their local environment have been developed and display specific genetic profiles. The VarGoats project is a 1000 genomes resequencing program designed to cover the genetic diversity of the Capra genus. In this study, our main objective was to assess the use of sequence data to detect genomic regions associated with traits of interest in French Alpine and Saanen breeds., Results: Direct imputation from the GoatSNP50 BeadChip genotypes to sequence level was investigated in these breeds using FImpute and different reference panels: within-breed, all Capra hircus sequenced individuals, European goats and French mainland goats. The best results were obtained with the French goat panel with allele and genotype concordance rates reaching 0.86 and 0.75 in the Alpine and 0.86 and 0.73 in the Saanen breed respectively. Mean correlations tended to be low in both breeds due to the high proportion of variants with low frequencies. For association analysis, imputation was performed using FImpute for 1129 French Alpine and Saanen males using within-breed and French panels on 23,338,436 filtered variants. The association results of both imputation scenarios were then compared. In Saanen goats, a large region on chromosome 19 was significantly linked to semen volume and milk yield in both scenarios. Significant variants for milk yield were annotated for 91 genes on chromosome 19 in Saanen goats. For semen volume, the annotated genes include YBOX2 which is related to azoospermia or oligospermia in other species. New signals for milk yield were detected on chromosome 2 in Alpine goats and on chromosome 5 in Saanen goats when using a multi-breed panel., Conclusion: Even with very small reference populations, an acceptable imputation quality can be achieved in French dairy goats. GWAS on imputed sequences confirmed the existence of QTLs and identified new regions of interest in dairy goats. Adding identified candidates to a genotyping array and sequencing more individuals might corroborate the involvement of identified regions while removing potential imputation errors.

Published
2020
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4. The BioMart community portal: an innovative alternative to large, centralized data repositories.

Author

Smedley, Damian, Haider, Syed, Durinck, Steffen, Pandini, Luca, Provero, Paolo, Allen, James, Arnaiz, Olivier, Awedh, Mohammad Hamza, Baldock, Richard, Barbiera, Giulia, Bardou, Philippe, Beck, Tim, Blake, Andrew, Bonierbale, Merideth, Brookes, Anthony J, Bucci, Gabriele, Buetti, Iwan, Burge, Sarah, Cabau, Cédric, Carlson, Joseph W, Chelala, Claude, Chrysostomou, Charalambos, Cittaro, Davide, Collin, Olivier, Cordova, Raul, Cutts, Rosalind J, Dassi, Erik, Di Genova, Alex, Djari, Anis, Esposito, Anthony, Estrella, Heather, Eyras, Eduardo, Fernandez-Banet, Julio, Forbes, Simon, Free, Robert C, Fujisawa, Takatomo, Gadaleta, Emanuela, Garcia-Manteiga, Jose M, Goodstein, David, Gray, Kristian, Guerra-Assunção, José Afonso, Haggarty, Bernard, Han, Dong-Jin, Han, Byung Woo, Harris, Todd, Harshbarger, Jayson, Hastings, Robert K, Hayes, Richard D, Hoede, Claire, Hu, Shen, Hu, Zhi-Liang, Hutchins, Lucie, Kan, Zhengyan, Kawaji, Hideya, Keliet, Aminah, Kerhornou, Arnaud, Kim, Sunghoon, Kinsella, Rhoda, Klopp, Christophe, Kong, Lei, Lawson, Daniel, Lazarevic, Dejan, Lee, Ji-Hyun, Letellier, Thomas, Li, Chuan-Yun, Lio, Pietro, Liu, Chu-Jun, Luo, Jie, Maass, Alejandro, Mariette, Jerome, Maurel, Thomas, Merella, Stefania, Mohamed, Azza Mostafa, Moreews, Francois, Nabihoudine, Ibounyamine, Ndegwa, Nelson, Noirot, Céline, Perez-Llamas, Cristian, Primig, Michael, Quattrone, Alessandro, Quesneville, Hadi, Rambaldi, Davide, Reecy, James, Riba, Michela, Rosanoff, Steven, Saddiq, Amna Ali, Salas, Elisa, Sallou, Olivier, Shepherd, Rebecca, Simon, Reinhard, Sperling, Linda, Spooner, William, Staines, Daniel M, Steinbach, Delphine, Stone, Kevin, Stupka, Elia, Teague, Jon W, Dayem Ullah, Abu Z, Wang, Jun, and Ware, Doreen

Subjects
Humans, Neoplasms, Proteomics, Genomics, Internet, Database Management Systems, Developmental Biology, Environmental Sciences, Biological Sciences, Information and Computing Sciences
Abstract

The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

Published
2015

5. VarGoats project : a dataset of 1159 whole-genome sequences to dissect Capra hircus global diversity

Author

Denoyelle, L., Talouarn, E., Bardou, P., Colli, L., Alberti, A., Danchin, C., Del Corvo, M., Engelen, S., Orvain, C., Palhiere, I., Rupp, R., Sarry, J., Salavati, M., Amills, M., Clark, E., Crepaldi, P., Faraut, T., Masiga, C. W., Pompanon, F., Rosen, B. D., Stella, A., Van Tassell, C. P., Tosser-Klopp, G., Kijas, J., Guldbrandtsen, B., Kantanen, J., Duby, D., Martin, P., Duclos, D., Allain, D., Arquet, R., Mandonnet, N., Naves, M., Carolan, S., Foran, M., Crisa, A., Marletta, D., Ottino, M., Randi, E., Benjelloun, B., Lenstra, H., Moaeen-ud-Din, M., Reecy, J., Goyache, F., Alvarez, I., Capote, J., Jordana, J., Pons, A., Martinez, A., Molina, A., Rosen, B., Drogemuller, C., Luikart, G., Mruttu, H. A., Gondwe, T., Sikosana, J., Taela, M. D. G., Nash, O., Agence Nationale de la Recherche (France), Région Occitanie / Pyrénées-Méditerranée, Ministère de l’Enseignement supérieur et de la Recherche (France), Biotechnology and Biological Sciences Research Council (UK), Bill & Melinda Gates Foundation, Department for International Development (UK), Center for Tropical Studies and Conservation (US), University of Edinburgh, Scottish Government's Rural and Environment Science and Analytical Services, Laboratoire d'Ecologie Alpine (LECA ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-École nationale supérieure agronomique de Toulouse (ENSAT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Système d'Information des GENomes des Animaux d'Elevage (SIGENAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Università cattolica del Sacro Cuore [Piacenza e Cremona] (Unicatt), Génomique métabolique (UMR 8030), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Institut de l'élevage (IDELE), Università cattolica del Sacro Cuore [Milano] (Unicatt), The Roslin Institute, Biotechnology and Biological Sciences Research Council (BBSRC), Centre for Tropical Livestock Genetics and Health [Edimburgh] (CTLGH), Centre for Research in Agricultural Genomics (CRAG), Università degli Studi di Milano = University of Milan (UNIMI), Tropical Institute of Development Innovations (TRIDI), USDA Agricultural Research Service [Beltsville, Maryland], USDA-ARS : Agricultural Research Service, Consiglio Nazionale delle Ricerche [Milano] (CNR), CSIRO Agriculture and Food (CSIRO), Unité de Recherches Zootechniques (URZ), APIS-GENE, Occitanie region, Ministere de l'Enseignement superieur, de la Recherche et de l'Innovation, United States Agency for International Development (USAID), CGIAR:OPP1127286, ACTIVEGOAT & CAPRISNP projects, UK Research & Innovation (UKRI) : BBS/OS/GC/000012F, ANR-10-INBS-0009,France-Génomique,Organisation et montée en puissance d'une Infrastructure Nationale de Génomique(2010), ANR-11-INBS-0003,CRB-Anim,Réseau de Centres de Ressources Biologiques pour les animaux domestiques(2011), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE), University of Milan, and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE)

Subjects
[SDV.BIO]Life Sciences [q-bio]/Biotechnology, [SDV]Life Sciences [q-bio], Short Communication, Single-nucleotide polymorphism, 610 Medicine & health, Biology, QH426-470, Genome, SF1-1100, Domestication, Animals, Domestication, Genetic Variation, Genomics, Goats, Genome, Genome-Wide Association Study, 03 medical and health sciences, Capra hircus, Genetics, Animals, Indel, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Genetic association, 2. Zero hunger, 0303 health sciences, Genetic diversity, [SDV.BA.MVSA]Life Sciences [q-bio]/Animal biology/Veterinary medicine and animal Health, Settore AGR/17 - ZOOTECNICA GENERALE E MIGLIORAMENTO GENETICO, 630 Agriculture, Goats, 0402 animal and dairy science, Genetic Variation, 04 agricultural and veterinary sciences, General Medicine, Genomics, 15. Life on land, 040201 dairy & animal science, Animal culture, Evolutionary biology, 570 Life sciences, biology, 590 Animals (Zoology), Animal Science and Zoology, Reference genome, Genome-Wide Association Study
Abstract

[Background]: Since their domestication 10,500 years ago, goat populations with distinctive genetic backgrounds have adapted to a broad variety of environments and breeding conditions. The VarGoats project is an international 1000-genome resequencing program designed to understand the consequences of domestication and breeding on the genetic diversity of domestic goats and to elucidate how speciation and hybridization have modeled the genomes of a set of species representative of the genus Capra., [Findings]: A dataset comprising 652 sequenced goats and 507 public goat sequences, including 35 animals representing eight wild species, has been collected worldwide. We identified 74,274,427 single nucleotide polymorphisms (SNPs) and 13,607,850 insertion-deletions (InDels) by aligning these sequences to the latest version of the goat reference genome (ARS1). A Neighbor-joining tree based on Reynolds genetic distances showed that goats from Africa, Asia and Europe tend to group into independent clusters. Because goat breeds from Oceania and Caribbean (Creole) all derive from imported animals, they are distributed along the tree according to their ancestral geographic origin., [Conclusions]: We report on an unprecedented international effort to characterize the genome-wide diversity of domestic goats. This large range of sequenced individuals represents a unique opportunity to ascertain how the demographic and selection processes associated with post-domestication history have shaped the diversity of this species. Data generated for the project will also be extremely useful to identify deleterious mutations and polymorphisms with causal effects on complex traits, and thus will contribute to new knowledge that could be used in genomic prediction and genome-wide association studies., We are grateful to France Génomique “Call for high impact projects” (ANR‐10‐INBS‐09‐08) for selecting our project and providing us the resources to sequence 400 goats. We would like to mention that APIS-GENE funded some WGS sequences through ACTIVEGOAT & CAPRISNP projects. We thank the Occitanie region and the Animal Genetics Division of the French National Institute for Agriculture, Food and Environment (INRAE-GA) for financing the PhD of ET. We thank the Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation for financing LD. We thank André Eggen (Illumina) for providing chips to genotype 192 animals. We thank the Animal Genetics Division of the French National Institute for Agriculture, Food and Environment (INRAE-GA) for funding VarGoats2 grant, which allowed DNA extraction and genotyping of 384 animals and CRB-Anim, Grant Agreement ANR-11-INBS-0003, (https://crb-anim.fr/) for funding French local breeds sampling. We thank the Italian Goat and Sheep Breeders Association (AssoNaPa) for supporting in sampling. Whole-genome sequencing libraries for the African goats were prepared and sequenced by Edinburgh Genomics and funded via Biotechnology and Biological Sciences Research Council research grant (BBS/OS/GC/000012F) ‘Reference genome and population sequencing of African goats’ awarded to The Roslin Institute. USDA-ARS with funding from USAID funded the collection of samples from Uganda, Tanzania, Malawi, Mozambique and Zimbabwe. EC and MS were partially supported by the Bill & Melinda Gates Foundation and with UK aid from the UK Government’s Department for International Development (Grant Agreement OPP1127286) under the auspices of the Centre for Tropical Livestock Genetics and Health (CTLGH), established jointly by the University of Edinburgh, SRUC (Scotland’s Rural College), and the International Livestock Research Institute. The findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies of the Bill & Melinda Gates Foundation nor the UK Government.

Published
2021
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6. Design and characterization of a 52K SNP chip for goats

Author

Tosser-Klopp G., Bardou P., Bouchez O., Cabau C., Crooijmans R., Dong Y., Donnadieu-Tonon C., Eggen A., Heuven H.C.M., Jamli S., Jiken A.J., Klopp C., Lawley C.T., McEwan J., Martin P., Moreno C.R., Mulsant P., Nabihoudine I., Pailhoux E., Palhiere I., Rupp R., Sarry J., Sayre B.L., Tircazes A., Wang J., Wang W., Zhang W., Ajmone P., Amills M., Boitard S., Faraut T., San Cristobal M., Servin B., Chen W., Cheng S., Liu X., Pan S., Song C., Xu X., Ye C., Zhang B., Lv J., Li X., Ren L., Shi P., Yu J., Faruque O., Lenstra H., Poli M.A., Zhao J., Rui S., Zhang Y., Stella A., Valentini A., Zhao S., Ecole Nationale Vétérinaire de Toulouse - ENVT (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Institut National de la Recherche Agronomique - INRA (FRANCE), Laboratoire de Génétique Cellulaire (LGC), Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Animal Breeding and Genomics Centre, Wageningen University and Research Centre [Wageningen] (WUR), Kunming Institute of Zoology, State Key Laboratory of Genetic Resources and Evolution, Chinese Academy of Sciences [Changchun Branch] (CAS), Faculty of Veterinary Medicine, Utrecht University [Utrecht], Strategic Livestock Research Centre, Malaysian Agricultural Research Development Institute, Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National de la Recherche Agronomique (INRA), Invermay Agricultural Center, AgResearch, Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Station d'Amélioration Génétique des Animaux (SAGA), Biologie du Développement et Reproduction (BDR), Department of Biology, Virginia State University, Inner Mongolia Key Laboratory of Animal Genetics, Breeding and Reproduction, Inner Mongolia Agricultural University, ANR-09-GENM-009-03 GENIDOV, ANR CHEST-454, CAPRISNIP programme: UNCEIA, CAPGENES and APIS-GENE French Breeding organizations, Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Wageningen University and Research [Wageningen] (WUR), Biologie du développement et reproduction (BDR), Centre National de la Recherche Scientifique (CNRS)-École nationale vétérinaire d'Alfort (ENVA)-Institut National de la Recherche Agronomique (INRA), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), and Amills i Eras, Marcel

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Gene Identification and Analysis, lcsh:Medicine, Sequence assembly, polledness, Molecular Inversion Probe, Genome, Sequence alignment, generation, single-nucleotide polymorphisms, lcsh:Science, Génétique, Animal Management, Genetics, 0303 health sciences, Multidisciplinary, Goats, Illumina 52K SNP Chip, goat, Agriculture, Genomics, 04 agricultural and veterinary sciences, Tag SNP, SNP genotyping, Grasslands, Research Article, dbSNP, Animal Breeding and Genomics, Biology, Molecular Genetics, 03 medical and health sciences, Genetic Mutation, Biologie animale, Genome-Wide Association Studies, Fokkerij en Genomica, BioNanoTechnology, genome, prp gene, Alleles, 030304 developmental biology, classical scrapie, capra-hircus, lcsh:R, Sequence assembly tools, 0402 animal and dairy science, association, snp, Genome analysis, 040201 dairy & animal science, Sustainable Agriculture, genotyping assay, Genetic Polymorphism, WIAS, lcsh:Q, Veterinary Science, Cattle, Animal Genetics, Population Genetics, Genomic databases, discovery, Reference genome
Abstract

The success of Genome Wide Association Studies in the discovery of sequence variation linked to complex traits in humans has increased interest in high throughput SNP genotyping assays in livestock species. Primary goals are QTL detection and genomic selection. The purpose here was design of a 50-60,000 SNP chip for goats. The success of a moderate density SNP assay depends on reliable bioinformatic SNP detection procedures, the technological success rate of the SNP design, even spacing of SNPs on the genome and selection of Minor Allele Frequencies (MAF) suitable to use in diverse breeds. Through the federation of three SNP discovery projects consolidated as the International Goat Genome Consortium, we have identified approximately twelve million high quality SNP variants in the goat genome stored in a database together with their biological and technical characteristics. These SNPs were identified within and between six breeds (meat, milk and mixed): Alpine, Boer, Creole, Katjang, Saanen and Savanna, comprising a total of 97 animals. Whole genome and Reduced Representation Library sequences were aligned on >10 kb scaffolds of the de novo goat genome assembly. The 60,000 selected SNPs, evenly spaced on the goat genome, were submitted for oligo manufacturing (Illumina, Inc) and published in dbSNP along with flanking sequences and map position on goat assemblies (i.e. scaffolds and pseudo-chromosomes), sheep genome V2 and cattle UMD3.1 assembly. Ten breeds were then used to validate the SNP content and 52,295 loci could be successfully genotyped and used to generate a final cluster file. The combined strategy of using mainly whole genome Next Generation Sequencing and mapping on a contig genome assembly, complemented with Illumina design tools proved to be efficient in producing this GoatSNP50 chip. Advances in use of molecular markers are expected to accelerate goat genomic studies in coming years. Copyright: © 2014 Tosser-Klopp et al.

Published
2014
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