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Gautason E, Sahana G, Guldbrandtsen B, Berg P. Impact of kinship matrices on genetic gain and inbreeding with optimum contribution selection in a genomic dairy cattle breeding program. Genet Sel Evol 2023; 55:48. [PMID: 37460999 DOI: 10.1186/s12711-023-00826-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 07/07/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Genomic selection has increased genetic gain in dairy cattle, but in some cases it has resulted in higher inbreeding rates. Therefore, there is need for research on efficient management of inbreeding in genomically-selected dairy cattle populations, especially for local breeds with a small population size. Optimum contribution selection (OCS) minimizes the increase in average kinship while it maximizes genetic gain. However, there is no consensus on how to construct the kinship matrix used for OCS and whether it should be based on pedigree or genomic information. VanRaden's method 1 (VR1) is a genomic relationship matrix in which centered genotype scores are scaled with the sum of 2p(1-p) where p is the reference allele frequency at each locus, and VanRaden's method 2 (VR2) scales each locus with 2p(1-p), thereby giving greater weight to loci with a low minor allele frequency. We compared the effects of nine kinship matrices on genetic gain, kinship, inbreeding, genetic diversity, and minor allele frequency when applying OCS in a simulated small dairy cattle population. We used VR1 and VR2, each using base animals, all genotyped animals, and the current generation of animals to compute reference allele frequencies. We also set the reference allele frequencies to 0.5 for VR1 and the pedigree-based relationship matrix. We constrained OCS to select a fixed number of sires per generation for all scenarios. Efficiency of the different matrices were compared by calculating the rate of genetic gain for a given rate of increase in average kinship. RESULTS We found that: (i) genomic relationships were more efficient than pedigree-based relationships at managing inbreeding, (ii) reference allele frequencies computed from base animals were more efficient compared to reference allele frequencies computed from recent animals, and (iii) VR1 was slightly more efficient than VR2, but the difference was not statistically significant. CONCLUSIONS Using genomic relationships for OCS realizes more genetic gain for a given amount of kinship and inbreeding than using pedigree relationships when the number of sires is fixed. For a small genomic dairy cattle breeding program, we recommend that the implementation of OCS uses VR1 with reference allele frequencies estimated either from base animals or old genotyped animals.
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Affiliation(s)
- Egill Gautason
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark.
- Faculty of Agricultural Sciences, Agricultural University of Iceland, 311, Borgarbyggð, Iceland.
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark
| | - Bernt Guldbrandtsen
- Department of Veterinary and Animal Sciences, University of Copenhagen, 1870, Frederiksberg C, Denmark
| | - Peer Berg
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark
- Faculty of Life Sciences, Norwegian University of Life Sciences, 1430, Ås, Norway
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2
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Fitzwilliams T, Wolff‐Sneedorff JL, Fredholm M, Karlskov‐Mortensen P, Guldbrandtsen B, Bruun CS. Evaluation of the value of genetic testing for cystinuria in the Danish population of English bulldogs. Anim Genet 2023. [DOI: 10.1111/age.13321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 02/21/2023] [Accepted: 03/13/2023] [Indexed: 03/29/2023]
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Nyman S, Johansson AM, Palucci V, Schönherz AA, Guldbrandtsen B, Hinrichs D, de Koning DJ. Inbreeding and pedigree analysis of the European red dairy cattle. Genet Sel Evol 2022; 54:70. [PMID: 36274137 PMCID: PMC9590155 DOI: 10.1186/s12711-022-00761-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 10/12/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Red dairy cattle breeds have an important role in the European dairy sector because of their functional characteristics and good health. Extensive pedigree information is available for these breeds and provides a unique opportunity to examine their population structure, such as effective population size, depth of the pedigree, and effective number of founders and ancestors, and inbreeding levels. Animals with the highest genetic contributions were identified. Pedigree data included 9,073,403 animals that were born between 1900 and 2019 from Denmark, Finland, Germany, Latvia, Lithuania, the Netherlands, Norway, Poland, and Sweden, and covered 32 breeds. The numerically largest breeds were Red Dairy Cattle and Meuse-Rhine-Yssel. RESULTS The deepest average complete generation equivalent (9.39) was found for Red Dairy Cattle in 2017. Mean pedigree completeness ranged from 0.6 for Finncattle to 7.51 for Red Dairy Cattle. An effective population size of 166 animals was estimated for the total pedigree and ranged from 35 (Rotes Höhenvieh) to 226 (Red Dairy Cattle). Average generation intervals were between 5 and 7 years. The mean inbreeding coefficient for animals born between 1960 and 2018 was 1.5%, with the highest inbreeding coefficients observed for Traditional Angler (4.2%) and Rotes Höhenvieh (4.1%). The most influential animal was a Dutch Meuse-Rhine-Yssel bull born in 1960. The mean inbreeding level for animals born between 2016 and 2018 was 2% and highest for the Meuse-Rhine-Yssel (4.64%) and Rotes Hohenvieh breeds (3.80%). CONCLUSIONS We provide the first detailed analysis of the genetic diversity and inbreeding levels of the European red dairy cattle breeds. Rotes Höhenvieh and Traditional Angler have high inbreeding levels and are either close to or below the minimal recommended effective population size, thus it is necessary to implement tools to monitor the selection process in order to control inbreeding in these breeds. Red Dairy Cattle, Vorderwälder, Swedish Polled and Hinterwälder hold more genetic diversity. Regarding the Meuse-Rhine-Yssel breed, given its decreased population size, increased inbreeding and low effective population size, we recommend implementation of a breeding program to prevent further loss in its genetic diversity.
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Affiliation(s)
- Sofia Nyman
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Anna M. Johansson
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Valentina Palucci
- Interbull Centre, Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | | | - Bernt Guldbrandtsen
- Department of Animal Science, Aarhus University, Tjele, Denmark
- Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Dirk Hinrichs
- Department of Animal Breeding, University of Kassel, Witzenhausen, Germany
| | - Dirk-Jan de Koning
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
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Hauser M, Signer-Hasler H, Küttel L, Capitan A, Guldbrandtsen B, Hinrichs D, Flury C, Seefried FR, Drögemüller C. Identification of two new recessive MC1R alleles in red-coloured Evolèner cattle and other breeds. Anim Genet 2022; 53:427-435. [PMID: 35451516 PMCID: PMC9373916 DOI: 10.1111/age.13206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/01/2022] [Accepted: 04/09/2022] [Indexed: 11/27/2022]
Abstract
Sequence variations in the melanocortin-1 receptor (MC1R) gene are associated with melanism in different animal species. Six functionally relevant alleles have been described in cattle to date. In a hypothesis-free approach we performed a genome-wide allelic association study with black, red and wild-coloured cattle of three Alpine cattle breeds (Eringer, Evolèner and Valdostana), revealing a single significant association signal close to the MC1R gene. We searched for candidate causative variants by sequencing the entire coding sequence and identified two novel protein-changing variants. We propose designating the mutant alleles at MC1R:c.424C>T as ev1 and at MC1R:c.263G>A as ev2 . Both affect conserved amino acid residues in functionally important transmembrane domains (p.Arg142Cys and p.Ser88Asn). Both alleles segregate predominantly in the Swiss Evolèner breed. They occur in other European cattle breeds such as Abondance and Rotes Höhenvieh as well. We observed almost perfect association between the MC1R genotypes and the coat colour phenotype in a cohort of 513 black, red and wild-coloured cattle. Animals carrying two copies of MC1R loss-of-function alleles or that were compound heterozygous for e, ev1 , or ev2 have a red to dark red (chestnut-like red) coat colour. These findings expand the spectrum of causal MC1R variants causing recessive red in cattle.
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Affiliation(s)
- Miriam Hauser
- Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Heidi Signer-Hasler
- School of Agricultural, Forest and Food Sciences (HAFL), Bern University of Applied Sciences, Zollikofen, Switzerland
| | - Luzia Küttel
- Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Aurélien Capitan
- ALLICE, Paris, France.,INRAE, AgroParisTech, GABI, Université Paris-Saclay, Jouy-en-Josas, France
| | - Bernt Guldbrandtsen
- Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg C, Denmark
| | - Dirk Hinrichs
- Department of Animal Breeding, Faculty of Organic Agricultural Sciences, University of Kassel, Witzenhausen, Germany
| | - Christine Flury
- School of Agricultural, Forest and Food Sciences (HAFL), Bern University of Applied Sciences, Zollikofen, Switzerland
| | | | - Cord Drögemüller
- Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland
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Meyer I, Forkman B, Fredholm M, Glanville C, Guldbrandtsen B, Ruiz Izaguirre E, Palmer C, Sandøe P. Pampered pets or poor bastards? The welfare of dogs kept as companion animals. Appl Anim Behav Sci 2022. [DOI: 10.1016/j.applanim.2022.105640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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6
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Gautason E, Sahana G, Su G, Benjamínsson BH, Jóhannesson G, Guldbrandtsen B. Corrigendum to: Short communication: investigation of the feasibility of genomic selection in Icelandic cattle. J Anim Sci 2022; 100:6533618. [PMID: 35188963 DOI: 10.1093/jas/skac016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Egill Gautason
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Baldur Helgi Benjamínsson
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark.,Farmers' Association of Iceland, 107 Reykjavík, Iceland
| | | | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark.,Department of Animal Sciences, University of Bonn, 53115 Bonn, Germany
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7
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Mesbah-Uddin M, Guldbrandtsen B, Capitan A, Lund MS, Boichard D, Sahana G. Genome-wide association study with imputed whole-genome sequence variants including large deletions for female fertility in 3 Nordic dairy cattle breeds. J Dairy Sci 2021; 105:1298-1313. [PMID: 34955274 DOI: 10.3168/jds.2021-20655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 09/22/2021] [Indexed: 11/19/2022]
Abstract
Fertility is an economically important trait in livestock. Poor fertility in dairy cattle can be due to loss-of-function variants affecting any essential gene that causes early embryonic mortality in homozygotes. To identify fertility-associated quantitative trait loci, we performed single-marker association analyses for 8 fertility traits in Holstein, Jersey, and Nordic Red Dairy cattle using imputed whole-genome sequence variants including SNPs, indels, and large deletion. We then performed stepwise selection of independent markers from GWAS loci using conditional and joint association analyses. From single-marker analyses for fertility traits, we reported genome-wide significant associations of 30,384 SNPs, 178 indels, and 3 deletions in Holstein; 23,481 SNPs, 189 indels, and 13 deletions in Nordic Red; and 17 SNPs in Jersey cattle. Conditional and joint association analyses identified 37 and 23 independent associations in Holstein and Nordic Red Dairy cattle, respectively. Fertility-associated GWAS loci were enriched for developmental and cellular processes (Gene Ontology enrichment, false discovery rate < 0.05). For these quantitative trait loci regions (top marker and 500 kb of surrounding regions), we proposed several candidate genes with functional annotations corresponding to embryonic lethality and various fertility-related phenotypes in mouse and cattle. The inclusion of these top markers in future releases of the custom SNP chip used for genomic evaluations will enable their validation in independent populations and improve the accuracy of genomic predictions.
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Affiliation(s)
- Md Mesbah-Uddin
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark; Génétique Animale et Biologie Intégrative (GABI), Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (INRAE), AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Aurélien Capitan
- Génétique Animale et Biologie Intégrative (GABI), Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (INRAE), AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France; Allice, 75595 Paris, France
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Didier Boichard
- Génétique Animale et Biologie Intégrative (GABI), Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (INRAE), AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark.
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8
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Hartfield M, Poulsen NA, Guldbrandtsen B, Bataillon T. Using singleton densities to detect recent selection in Bos taurus. Evol Lett 2021; 5:595-606. [PMID: 34917399 PMCID: PMC8645200 DOI: 10.1002/evl3.263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 10/07/2021] [Accepted: 10/08/2021] [Indexed: 11/05/2022] Open
Abstract
Many quantitative traits are subject to polygenic selection, where several genomic regions undergo small, simultaneous changes in allele frequency that collectively alter a phenotype. The widespread availability of genome data, along with novel statistical techniques, has made it easier to detect these changes. We apply one such method, the "Singleton Density Score" (SDS), to the Holstein breed of Bos taurus to detect recent selection (arising up to around 740 years ago). We identify several genes as candidates for targets of recent selection, including some relating to cell regulation, catabolic processes, neural-cell adhesion and immunity. We do not find strong evidence that three traits that are important to humans-milk protein content, milk fat content, and stature-have been subject to directional selection. Simulations demonstrate that because B. taurus recently experienced a population bottleneck, singletons are depleted so the power of SDS methods is reduced. These results inform on which genes underlie recent genetic change in B. taurus, while providing information on how polygenic selection can be best investigated in future studies.
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Affiliation(s)
- Matthew Hartfield
- Bioinformatics Research CentreAarhus UniversityAarhusDK‐8000Denmark
- Institute of Evolutionary BiologyUniversity of EdinburghEdinburghEH9 3FLUnited Kingdom
| | | | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and GeneticsAarhus UniversityTjeleDK‐8830Denmark
- Rheinische Friedrich‐Wilhelms‐Universität BonnInstitut für TierwissenschaftenBonnDE‐53115Germany
- Department of Veterinary SciencesCopenhagen UniversityFrederiksberg CDK‐1870Denmark
| | - Thomas Bataillon
- Bioinformatics Research CentreAarhus UniversityAarhusDK‐8000Denmark
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9
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Cubric‐Curik V, Novosel D, Brajkovic V, Rota Stabelli O, Krebs S, Sölkner J, Šalamon D, Ristov S, Berger B, Trivizaki S, Bizelis I, Ferenčaković M, Rothammer S, Kunz E, Simčič M, Dovč P, Bunevski G, Bytyqi H, Marković B, Brka M, Kume K, Stojanović S, Nikolov V, Zinovieva N, Schönherz AA, Guldbrandtsen B, Čačić M, Radović S, Miracle P, Vernesi C, Curik I, Medugorac I. Large‐scale mitogenome sequencing reveals consecutive expansions of domestic taurine cattle and supports sporadic aurochs introgression. Evol Appl 2021; 15:663-678. [PMID: 35505892 PMCID: PMC9046920 DOI: 10.1111/eva.13315] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 10/01/2021] [Accepted: 10/11/2021] [Indexed: 11/29/2022] Open
Affiliation(s)
- Vlatka Cubric‐Curik
- Department of Animal Science University of Zagreb Faculty of Agriculture Zagreb Croatia
| | - Dinko Novosel
- Department of Animal Science University of Zagreb Faculty of Agriculture Zagreb Croatia
- Department of Pathology Croatian Veterinary Institute Zagreb Croatia
| | - Vladimir Brajkovic
- Department of Animal Science University of Zagreb Faculty of Agriculture Zagreb Croatia
| | - Omar Rota Stabelli
- Department of Sustainable Agro‐Ecosystems and Bioresources, Research and Innovation Centre Fondazione Edmund Mach S. Michele all' Adige Italy
| | - Stefan Krebs
- Laboratory for Functional Genome Analysis Gene Center Ludwig Maximilians University Munich Munich Germany
| | - Johann Sölkner
- Division of Livestock Sciences Department of Sustainable Agricultural Systems BOKU‐University of Natural Resources and Life Sciences Vienna Vienna Austria
| | - Dragica Šalamon
- Department of Animal Science University of Zagreb Faculty of Agriculture Zagreb Croatia
| | | | - Beate Berger
- AREC Raumberg‐Gumpenstein Institute of Organic Farming and Biodiversity of Farm Animals Thalheim Austria
| | | | - Iosif Bizelis
- Faculty of Animal Science and Aquaculture Department of Animal Breeding & Husbandry Agricultural University of Athens Athens Greece
| | - Maja Ferenčaković
- Department of Animal Science University of Zagreb Faculty of Agriculture Zagreb Croatia
| | - Sophie Rothammer
- Population Genomics Group Faculty of Veterinary Medicine Department of Veterinary Sciences LMU Munich Munich Germany
| | - Elisabeth Kunz
- Population Genomics Group Faculty of Veterinary Medicine Department of Veterinary Sciences LMU Munich Munich Germany
| | - Mojca Simčič
- Biotechnical Faculty Department of Animal Science University of Ljubljana Ljubljana Slovenia
| | - Peter Dovč
- Biotechnical Faculty Department of Animal Science University of Ljubljana Ljubljana Slovenia
| | - Gojko Bunevski
- Faculty of Agricultural Sciences and Food University Ss. Cyril and Methodius Skopje Macedonia
| | - Hysen Bytyqi
- Faculty of Agriculture and Veterinary Department of Animal Science University of Prishtina Prishtina Kosovo
| | - Božidarka Marković
- Biotechnical Faculty Department of Livestock Science University of Montenegro Podgorica Montenegro
| | - Muhamed Brka
- Faculty of Agriculture and Food Science Institute of Animal Sciences University of Sarajevo Sarajevo Bosnia and Herzegovina
| | | | - Srđan Stojanović
- Ministry of Agriculture, Forestry and Water Management Beograd Serbia
| | - Vasil Nikolov
- Executive Agency for Selection and Reproduction in Animal Breeding Sofia Bulgaria
| | - Natalia Zinovieva
- Center of Biotechnology and Molecular Diagnostics of the L.K. Ernst Institute of Animal Husbandry Moscow Region Russia
| | | | - Bernt Guldbrandtsen
- Department of Animal Sciences Rheinische Friedrich‐Wilhelms‐Universität Bonn Bonn Germany
| | - Mato Čačić
- Croatian Agricultural Agency Zagreb Croatia
| | - Siniša Radović
- Institute for Quaternary Palaeontology and Geology Croatian Academy of Sciences and Arts Zagreb Croatia
| | - Preston Miracle
- Department of Archaeology University of Cambridge Cambridge UK
| | - Cristiano Vernesi
- Department of Sustainable Agro‐Ecosystems and Bioresources, Research and Innovation Centre Fondazione Edmund Mach S. Michele all' Adige Italy
| | - Ino Curik
- Department of Animal Science University of Zagreb Faculty of Agriculture Zagreb Croatia
| | - Ivica Medugorac
- Population Genomics Group Faculty of Veterinary Medicine Department of Veterinary Sciences LMU Munich Munich Germany
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10
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Gautason E, Sahana G, Su G, Benjamínsson BH, Jóhannesson G, Guldbrandtsen B. Erratum to: Short communication: investigation of the feasibility of genomic selection in Icelandic Cattle. J Anim Sci 2021; 99:6357101. [PMID: 34428300 DOI: 10.1093/jas/skab234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Egill Gautason
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Baldur Helgi Benjamínsson
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark.,Farmers Association of Iceland, 107 Reykjavík, Iceland
| | | | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark.,Department of Animal Sciences, University of Bonn, 53115 Bonn, Germany
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11
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Gautason E, Sahana G, Su G, Benjamínsson BH, Jóhannesson G, Guldbrandtsen B. Short communication: investigation of the feasibility of genomic selection in Icelandic Cattle. J Anim Sci 2021; 99:6263455. [PMID: 33942082 DOI: 10.1093/jas/skab139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 04/26/2021] [Indexed: 11/13/2022] Open
Abstract
Icelandic Cattle is a local dairy cattle breed in Iceland. With about 26,000 breeding females, it is by far the largest among the indigenous Nordic cattle breeds. The objective of this study was to investigate the feasibility of genomic selection in Icelandic Cattle. Pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) were compared. Accuracy, bias, and dispersion of estimated breeding values (EBV) for milk yield (MY), fat yield (FY), protein yield (PY), and somatic cell score (SCS) were estimated in a cross validation-based design. Accuracy (r^) was estimated by the correlation between EBV and corrected phenotype in a validation set. The accuracy (r^) of predictions using ssGBLUP increased by 13, 23, 19, and 20 percentage points for MY, FY, PY, and SCS for genotyped animals, compared with PBLUP. The accuracy of nongenotyped animals was not improved for MY and PY, but increased by 0.9 and 3.5 percentage points for FY and SCS. We used the linear regression (LR) method to quantify relative improvements in accuracy, bias (Δ^), and dispersion (b^) of EBV. Using the LR method, the relative improvements in accuracy of validation from PBLUP to ssGBLUP were 43%, 60%, 50%, and 48% for genotyped animals for MY, FY, PY, and SCS. Single-step GBLUP EBV were less underestimated (Δ^), and less overdispersed (b^) than PBLUP EBV for FY and PY. Pedigree-based BLUP EBV were close to unbiased for MY and SCS. Single-step GBLUP underestimated MY EBV but overestimated SCS EBV. Based on the average accuracy of 0.45 for ssGBLUP EBV obtained in this study, selection intensities according to the breeding scheme of Icelandic Cattle, and assuming a generation interval of 2.0 yr for sires of bulls, sires of dams and dams of bulls, genetic gain in Icelandic Cattle could be increased by about 50% relative to the current breeding scheme.
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Affiliation(s)
- Egill Gautason
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | | | | | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark.,Department of Animal Sciences, University of Bonn, 53115 Bonn, Germany
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12
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Gautason E, Schönherz AA, Sahana G, Guldbrandtsen B. Genomic inbreeding and selection signatures in the local dairy breed Icelandic Cattle. Anim Genet 2021; 52:251-262. [PMID: 33829515 DOI: 10.1111/age.13058] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/01/2021] [Indexed: 12/21/2022]
Abstract
Icelandic Cattle is the only dairy cattle breed native to Iceland. It currently numbers ca. 26 000 breeding females. We used 50k genotypes of over 8000 Icelandic Cattle to estimate genomic and pedigree-based inbreeding and to detect selection signatures using the integrated haplotype score. We used 47 Icelandic bulls genotyped with a 770k SNP chip to estimate LD decay for comparison with other Nordic dairy cattle breeds. We detected ROHs on the autosomes and computed ROH-based autosomal inbreeding coefficients. Average inbreeding coefficients according to pedigree and ROHs were 0.0621 and 0.101. Effective population sizes for the years 2009-2017 according to pedigree, ROHs, genomic relationship matrix, excess of homozygosity and individual increase in inbreeding were 81, 65, 60, 58 and 92 respectively. We identified three regions and six candidate genes that showed a signature of selection according to the integrated haplotype score (P < 0.05) on chromosomes 1, 16 and 23. The LD structure of Icelandic Cattle is shaped by a long period of isolation and a small founder population. The estimate of LD at distances closer than 0.3 Mb is lower in Icelandic Cattle than in Danish Jersey, but is higher than in Danish Holstein and Red Nordic Dairy Cattle breeds. Our findings show that inbreeding rates in Icelandic Cattle currently are sustainable according to FAO guidelines, and our results do not indicate severe historical inbreeding.
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Affiliation(s)
- E Gautason
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, 8830, Denmark
| | - A A Schönherz
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, 8830, Denmark.,Department of Animal Science, Aarhus University, Tjele, 8830, Denmark
| | - G Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, 8830, Denmark
| | - B Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, 8830, Denmark.,Department of Animal Sciences, University of Bonn, Endenicher Allee 15, Bonn, 53115, Germany
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13
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Schmidtmann C, Schönherz A, Guldbrandtsen B, Marjanovic J, Calus M, Hinrichs D, Thaller G. Assessing the genetic background and genomic relatedness of red cattle populations originating from Northern Europe. Genet Sel Evol 2021; 53:23. [PMID: 33676402 PMCID: PMC7936461 DOI: 10.1186/s12711-021-00613-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 02/08/2021] [Indexed: 12/20/2022] Open
Abstract
Background Local cattle breeds need special attention, as they are valuable reservoirs of genetic diversity. Appropriate breeding decisions and adequate genomic management of numerically smaller populations are required for their conservation. At this point, the analysis of dense genome-wide marker arrays provides encompassing insights into the genomic constitution of livestock populations. We have analyzed the genetic characterization of ten cattle breeds originating from Germany, The Netherlands and Denmark belonging to the group of red dairy breeds in Northern Europe. The results are intended to provide initial evidence on whether joint genomic breeding strategies of these populations will be successful. Results Traditional Danish Red and Groningen White-Headed were the most genetically differentiated breeds and their populations showed the highest levels of inbreeding. In contrast, close genetic relationships and shared ancestry were observed for the populations of German Red and White Dual-Purpose, Dutch Meuse-Rhine-Yssel, and Dutch Deep Red breeds, reflecting their common histories. A considerable amount of gene flow from Red Holstein to German Angler and to German Red and White Dual-Purpose was revealed, which is consistent with frequent crossbreeding to improve productivity of these local breeds. In Red Holstein, marked genomic signatures of selection were reported on chromosome 18, suggesting directed selection for important breeding goal traits. Furthermore, tests for signatures of selection between Red Holstein, Red and White Dual-Purpose, and Meuse-Rhine-Yssel uncovered signals for all investigated pairs of populations. The corresponding genomic regions, which were putatively under different selection pressures, harboured various genes which are associated with traits such as milk and beef production, mastitis and female fertility. Conclusions This study provides comprehensive knowledge on the genetic constitution and genomic connectedness of divergent red cattle populations in Northern Europe. The results will help to design and optimize breeding strategies. A joint genomic evaluation including some of the breeds studied here seems feasible.
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Affiliation(s)
- Christin Schmidtmann
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, 24098, Kiel, Germany.
| | - Anna Schönherz
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.,Department of Animal Science, Aarhus University, 8830, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.,Department of Animal Sciences, Department of Animal Breeding and Husbandry, University of Bonn, 53115, Bonn, Germany
| | - Jovana Marjanovic
- Animal Breeding and Genomics, Wageningen University and Research, 6700AH, Wageningen, The Netherlands
| | - Mario Calus
- Animal Breeding and Genomics, Wageningen University and Research, 6700AH, Wageningen, The Netherlands
| | - Dirk Hinrichs
- Department of Animal Breeding, University of Kassel, 37213, Witzenhausen, Germany
| | - Georg Thaller
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, 24098, Kiel, Germany
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14
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Villumsen TM, Su G, Guldbrandtsen B, Asp T, Lund MS. Genomic selection in American mink (Neovison vison) using a single-step genomic best linear unbiased prediction model for size and quality traits graded on live mink. J Anim Sci 2021; 99:skab003. [PMID: 33515480 PMCID: PMC7846095 DOI: 10.1093/jas/skab003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 01/07/2021] [Indexed: 11/13/2022] Open
Abstract
Genomic selection relies on single-nucleotide polymorphisms (SNPs), which are often collected using medium-density SNP arrays. In mink, no such array is available; instead, genotyping by sequencing (GBS) can be used to generate marker information. Here, we evaluated the effect of genomic selection for mink using GBS. We compared the estimated breeding values (EBVs) from single-step genomic best linear unbiased prediction (SSGBLUP) models to the EBV from ordinary pedigree-based BLUP models. We analyzed seven size and quality traits from the live grading of brown mink. The phenotype data consisted of ~20,600 records for the seven traits from the mink born between 2013 and 2016. Genotype data included 2,103 mink born between 2010 and 2014, mostly breeding animals. In total, 28,336 SNP markers from 391 scaffolds were available for genomic prediction. The pedigree file included 29,212 mink. The predictive ability was assessed by the correlation (r) between progeny trait deviation (PTD) and EBV, and the regression of PTD on EBV, using 5-fold cross-validation. For each fold, one-fifth of animals born in 2014 formed the validation set. For all traits, the SSGBLUP model resulted in higher accuracies than the BLUP model. The average increase in accuracy was 15% (between 3% for fur clarity and 28% for body weight). For three traits (body weight, silky appearance of the under wool, and guard hair thickness), the difference in r between the two models was significant (P < 0.05). For all traits, the regression slopes of PTD on EBV from SSGBLUP models were closer to 1 than regression slopes from BLUP models, indicating SSGBLUP models resulted in less bias of EBV for selection candidates than the BLUP models. However, the regression coefficients did not differ significantly. In conclusion, the SSGBLUP model is superior to conventional BLUP model in the accurate selection of superior animals, and, thus, it would increase genetic gain in a selective breeding program. In addition, this study shows that GBS data work well in genomic prediction in mink, demonstrating the potential of GBS for genomic selection in livestock species.
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Affiliation(s)
- Trine M Villumsen
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Animal Breeding, Department of Animal Science, University of Bonn, Bonn, Germany
| | - Torben Asp
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Mogens S Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
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15
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Cai Z, Sarup P, Ostersen T, Nielsen B, Fredholm M, Karlskov-Mortensen P, Sørensen P, Jensen J, Guldbrandtsen B, Lund MS, Christensen OF, Sahana G. Genomic diversity revealed by whole-genome sequencing in three Danish commercial pig breeds. J Anim Sci 2020; 98:5873883. [PMID: 32687196 DOI: 10.1093/jas/skaa229] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 07/14/2020] [Indexed: 01/04/2023] Open
Abstract
Whole-genome sequencing of 217 animals from three Danish commercial pig breeds (Duroc, Landrace [LL], and Yorkshire [YY]) was performed. Twenty-six million single-nucleotide polymorphisms (SNPs) and 8 million insertions or deletions (indels) were uncovered. Among the SNPs, 493,099 variants were located in coding sequences, and 29,430 were predicted to have a high functional impact such as gain or loss of stop codon. Using the whole-genome sequence dataset as the reference, the imputation accuracy for pigs genotyped with high-density SNP chips was examined. The overall average imputation accuracy for all biallelic variants (SNP and indel) was 0.69, while it was 0.83 for variants with minor allele frequency > 0.1. This study provides whole-genome reference data to impute SNP chip-genotyped animals for further studies to fine map quantitative trait loci as well as improving the prediction accuracy in genomic selection. Signatures of selection were identified both through analyses of fixation and differentiation to reveal selective sweeps that may have had prominent roles during breed development or subsequent divergent selection. However, the fixation indices did not indicate a strong divergence among these three breeds. In LL and YY, the integrated haplotype score identified genomic regions under recent selection. These regions contained genes for olfactory receptors and oxidoreductases. Olfactory receptor genes that might have played a major role in the domestication were previously reported to have been under selection in several species including cattle and swine.
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Affiliation(s)
- Zexi Cai
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
| | - Pernille Sarup
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
| | - Tage Ostersen
- SEGES Danish Pig Research Centre, Copenhagen, Denmark
| | | | - Merete Fredholm
- Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
| | - Just Jensen
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
| | - Ole Fredslund Christensen
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, Tjele, Denmark
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16
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Kotlarz K, Mielczarek M, Suchocki T, Czech B, Guldbrandtsen B, Szyda J. The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines. J Appl Genet 2020; 61:607-616. [PMID: 32996082 PMCID: PMC7652806 DOI: 10.1007/s13353-020-00586-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 09/11/2020] [Accepted: 09/18/2020] [Indexed: 11/18/2022]
Abstract
A downside of next-generation sequencing technology is the high technical error rate. We built a tool, which uses array-based genotype information to classify next-generation sequencing–based SNPs into the correct and the incorrect calls. The deep learning algorithms were implemented via Keras. Several algorithms were tested: (i) the basic, naïve algorithm, (ii) the naïve algorithm modified by pre-imposing different weights on incorrect and correct SNP class in calculating the loss metric and (iii)–(v) the naïve algorithm modified by random re-sampling (with replacement) of the incorrect SNPs to match 30%/60%/100% of the number of correct SNPs. The training data set was composed of data from three bulls and consisted of 2,227,995 correct (97.94%) and 46,920 incorrect SNPs, while the validation data set consisted of data from one bull with 749,506 correct (98.05%) and 14,908 incorrect SNPs. The results showed that for a rare event classification problem, like incorrect SNP detection in NGS data, the most parsimonious naïve model and a model with the weighting of SNP classes provided the best results for the classification of the validation data set. Both classified 19% of truly incorrect SNPs as incorrect and 99% of truly correct SNPs as correct and resulted in the F1 score of 0.21 — the highest among the compared algorithms. We conclude the basic models were less adapted to the specificity of a training data set and thus resulted in better classification of the independent, validation data set, than the other tested models.
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Affiliation(s)
- Krzysztof Kotlarz
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wroclaw, Poland
| | - Magda Mielczarek
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wroclaw, Poland.,Institute of Animal Breeding, Balice, Poland
| | - Tomasz Suchocki
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wroclaw, Poland.,Institute of Animal Breeding, Balice, Poland
| | - Bartosz Czech
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wroclaw, Poland
| | - Bernt Guldbrandtsen
- Animal Breeding Group, Department of Animal Sciences, University of Bonn, Bonn, Germany
| | - Joanna Szyda
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wroclaw, Poland. .,Institute of Animal Breeding, Balice, Poland.
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17
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Liu A, Lund MS, Boichard D, Karaman E, Guldbrandtsen B, Fritz S, Aamand GP, Nielsen US, Sahana G, Wang Y, Su G. Weighted single-step genomic best linear unbiased prediction integrating variants selected from sequencing data by association and bioinformatics analyses. Genet Sel Evol 2020; 52:48. [PMID: 32799816 PMCID: PMC7429790 DOI: 10.1186/s12711-020-00568-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 08/07/2020] [Indexed: 11/30/2022] Open
Abstract
Background Sequencing data enable the detection of causal loci or single nucleotide polymorphisms (SNPs) highly linked to causal loci to improve genomic prediction. However, until now, studies on integrating such SNPs using a single-step genomic best linear unbiased prediction (ssGBLUP) model are scarce. We investigated the integration of sequencing SNPs selected by association (1262 SNPs) and bioinformatics (2359 SNPs) analyses into the currently used 54K-SNP chip, using three ssGBLUP models which make different assumptions on the distribution of SNP effects: a basic ssGBLUP model, a so-called featured ssGBLUP (ssFGBLUP) model that considered selected sequencing SNPs as a feature genetic component, and a weighted ssGBLUP (ssWGBLUP) model in which the genomic relationship matrix was weighted by the SNP variances estimated from a Bayesian whole-genome regression model, with every 1, 30, or 100 adjacent SNPs within a chromosome region sharing the same variance. We used data on milk production and female fertility in Danish Jersey. In total, 15,823 genotyped and 528,981 non-genotyped females born between 1990 and 2013 were used as reference population and 7415 genotyped females and 33,040 non-genotyped females born between 2014 and 2016 were used as validation population. Results With basic ssGBLUP, integrating SNPs selected from sequencing data improved prediction reliabilities for milk and protein yields, but resulted in limited or no improvement for fat yield and female fertility. Model performances depended on the SNP set used. When using ssWGBLUP with the 54K SNPs, reliabilities for milk and protein yields improved by 0.028 for genotyped animals and by 0.006 for non-genotyped animals compared with ssGBLUP. However, with the SNP set that included SNPs selected from sequencing data, no statistically significant difference in prediction reliability was observed between the three ssGBLUP models. Conclusions In summary, when using 54K SNPs, a ssWGBLUP model with a common weight on the SNPs in a given region is a feasible approach for single-trait genetic evaluation. Integrating relevant SNPs selected from sequencing data into the standard SNP chip can improve the reliability of genomic prediction. Based on such SNP data, a basic ssGBLUP model was suggested since no significant improvement was observed from using alternative models such as ssWGBLUP and ssFGBLUP.
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Affiliation(s)
- Aoxing Liu
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Didier Boichard
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Emre Karaman
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Sebastien Fritz
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, 78350, Jouy-en-Josas, France.,ALLICE, 75012, Paris, France
| | | | | | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Yachun Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, MARA; National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, 100193, Beijing, P.R. China
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
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18
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Czech B, Guldbrandtsen B, Szyda J. Patterns of DNA variation between the autosomes, the X chromosome and the Y chromosome in Bos taurus genome. Sci Rep 2020; 10:13641. [PMID: 32788585 PMCID: PMC7423949 DOI: 10.1038/s41598-020-70380-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 07/23/2020] [Indexed: 11/15/2022] Open
Abstract
The new ARS-UCD1.2 assembly of the bovine genome has considerable improvements over the previous assembly and thus more accurate identification of patterns of genetic variation can be achieved with it. We explored differences in genetic variation between autosomes, the X chromosome, and the Y chromosome. In particular, variant densities, annotations, lengths (only for InDels), nucleotide divergence, and Tajima’s D statistics between chromosomes were considered. Whole-genome DNA sequences of 217 individuals representing different cattle breeds were examined. The analysis included the alignment to the new reference genome and variant identification. 23,655,295 SNPs and 3,758,781 InDels were detected. In contrast to autosomes, both sex chromosomes had negative values of Tajima’s D and lower nucleotide divergence. That implies a correlation between nucleotide diversity and recombination rate, which is obviously reduced for sex chromosomes. Moreover, the accumulation of nonsynonymous mutations on the Y chromosome could be associated with loss of recombination. Also, the relatively lower effective population size for sex chromosomes leads to a lower expected density of variants.
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Affiliation(s)
- Bartosz Czech
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wrocław, Poland.
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark.,Department of Animal Sciences, University of Bonn, Endenicher Allee 15, 53115, Bonn, Germany
| | - Joanna Szyda
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wrocław, Poland.,Institute of Animal Breeding, Krakowska 1, 32-083, Balice, Poland
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19
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Cai Z, Dusza M, Guldbrandtsen B, Lund MS, Sahana G. Distinguishing pleiotropy from linked QTL between milk production traits and mastitis resistance in Nordic Holstein cattle. Genet Sel Evol 2020; 52:19. [PMID: 32264818 PMCID: PMC7137482 DOI: 10.1186/s12711-020-00538-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Accepted: 04/01/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Production and health traits are central in cattle breeding. Advances in next-generation sequencing technologies and genotype imputation have increased the resolution of gene mapping based on genome-wide association studies (GWAS). Thus, numerous candidate genes that affect milk yield, milk composition, and mastitis resistance in dairy cattle are reported in the literature. Effect-bearing variants often affect multiple traits. Because the detection of overlapping quantitative trait loci (QTL) regions from single-trait GWAS is too inaccurate and subjective, multi-trait analysis is a better approach to detect pleiotropic effects of variants in candidate genes. However, large sample sizes are required to achieve sufficient power. Multi-trait meta-analysis is one approach to deal with this problem. Thus, we performed two multi-trait meta-analyses, one for three milk production traits (milk yield, protein yield and fat yield), and one for milk yield and mastitis resistance. RESULTS For highly correlated traits, the power to detect pleiotropy was increased by multi-trait meta-analysis compared with the subjective assessment of overlapping of single-trait QTL confidence intervals. Pleiotropic effects of lead single nucleotide polymorphisms (SNPs) that were detected from the multi-trait meta-analysis were confirmed by bivariate association analysis. The previously reported pleiotropic effects of variants within the DGAT1 and MGST1 genes on three milk production traits, and pleiotropic effects of variants in GHR on milk yield and fat yield were confirmed. Furthermore, our results suggested that variants in KCTD16, KCNK18 and ENSBTAG00000023629 had pleiotropic effects on milk production traits. For milk yield and mastitis resistance, we identified possible pleiotropic effects of variants in two genes, GC and DGAT1. CONCLUSIONS Multi-trait meta-analysis improves our ability to detect pleiotropic interactions between milk production traits and identifies variants with pleiotropic effects on milk production traits and mastitis resistance. In particular, this should contribute to better understand the biological mechanisms that underlie the unfavorable genetic correlation between milk yield and mastitis.
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Affiliation(s)
- Zexi Cai
- Faculty of Technical Sciences, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.
| | - Magdalena Dusza
- Department of Animal Sciences, University of Agriculture in Kraków, 30-059, Kraków, Poland
| | - Bernt Guldbrandtsen
- Faculty of Technical Sciences, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Mogens Sandø Lund
- Faculty of Technical Sciences, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Goutam Sahana
- Faculty of Technical Sciences, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
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20
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Wu X, Mesbah-Uddin M, Guldbrandtsen B, Lund MS, Sahana G. Novel haplotypes responsible for prenatal death in Nordic Red and Danish Jersey cattle. J Dairy Sci 2020; 103:4570-4578. [PMID: 32197842 DOI: 10.3168/jds.2019-17831] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 01/27/2020] [Indexed: 01/04/2023]
Abstract
Haplotypes that are common in a population but not observed as homotypes in living animals may harbor lethal alleles that compromise embryo survival. In this study, we searched for homozygous-deficient haplotypes in the genomes of 19,309 Nordic Red Dairy (RDC) and 4,291 Danish Jersey (JER) cattle genotyped using the Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). For statistically significant deficient haplotypes, we evaluated the effect on nonreturn rate in at-risk matings (mating between carrier bull and daughter of carrier sire) versus not-at-risk matings (mating between noncarrier bull and daughter of noncarrier sire). Next, we analyzed whole-genome sequence variants from the 1000 Bull Genomes Project to identify putative causal variants underlying these haplotypes. In RDC, we identified 3 homozygous-deficient regions (HDR) that overlapped with known recessive lethal mutations: a 662-kb deletion on chromosome 12 in RDC [Online Mendelian Inheritance in Animals (OMIA) 001901-9913), a missense mutation in TUBD1, g.11063520T>C, in Braunvieh cattle (OMIA 001939-9913), and a 525-kb deletion on chromosome 23 in RDC (OMIA 001991-9913)]. In addition, we identified 15 novel HDR and their tag haplotypes for the underlying causative variants. The tag haplotype located between 39.2 and 40.3 Mbp on chromosome 18 had a negative effect on nonreturn rate in at-risk mating, confirming embryonic lethality. In Danish Jersey, we identified 12 novel HDR and their tag haplotypes for underlying causative variants. For 3 of these 12 tag haplotypes, insemination records of at-risk mating showed a negative effect on nonreturn rate, confirming the association with early embryonic mortality. Cattle that had both genotype and whole-genome sequence data were analyzed to detect the causative variants underlying each tag haplotype. However, none of the functional variants or deletions showed concordance with carrier status of the novel tag haplotypes. Carrier status of these detected haplotypes can be used to select bulls to reduce the frequencies of lethal alleles in the population and to avoid at-risk matings.
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Affiliation(s)
- Xiaoping Wu
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, 8830 Tjele, Denmark.
| | - Md Mesbah-Uddin
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, 8830 Tjele, Denmark; Animal Genetics and Integrative Biology, UMR 1313 GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, 8830 Tjele, Denmark
| | - Mogens S Lund
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, 8830 Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, 8830 Tjele, Denmark.
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21
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Guldbrandtsen B, Nielsen VH, Schönherz AA. Genomic assessment of suitability of pigs for inclusion in the Pied Danish Pig conservation program – A case study. ACTA AGR SCAND A-AN 2020. [DOI: 10.1080/09064702.2020.1732455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Vivi H. Nielsen
- Danish Center for Agriculture, Aarhus University, Tjele, Denmark
| | - Anna A. Schönherz
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
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22
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Gautason E, Schönherz AA, Sahana G, Guldbrandtsen B. Relationship of Icelandic cattle with Northern and Western European cattle breeds, admixture and population structure. ACTA AGR SCAND A-AN 2019. [DOI: 10.1080/09064702.2019.1699951] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Egill Gautason
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Anna A. Schönherz
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
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23
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Mesbah-Uddin M, Guldbrandtsen B, Lund MS, Boichard D, Sahana G. Joint imputation of whole-genome sequence variants and large chromosomal deletions in cattle. J Dairy Sci 2019; 102:11193-11206. [PMID: 31606212 DOI: 10.3168/jds.2019-16946] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 08/25/2019] [Indexed: 11/19/2022]
Abstract
Genotype imputation, often focused on SNP and small insertions and deletions (indels; size ≤50 bp), is a crucial step for association mapping and estimation of genomic breeding values. Here, we present strategies to impute genotypes for large chromosomal deletions (size >50 bp), along with SNP and indels in cattle. The pipelines include a strategy for extending the whole-genome sequence reference panel for large deletions, a 2-step genotype refinement approach using Beagle4 and SHAPEIT2 software, and finally, joint imputation of SNP, indels, and large deletions to the existing SNP array-typed population using Minimac3 software. Using these pipelines we achieved an imputation accuracy of the squared Pearson correlation (r2) > 0.6 at minor allele frequencies as low as 0.7% for SNP and indels, and 0.2% for large deletions. This highlights the potential of our approach to build a haplotype reference panel and impute different classes of sequence variants across a wide allele frequency spectrum with high accuracy.
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Affiliation(s)
- Md Mesbah-Uddin
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark; GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France.
| | - Bernt Guldbrandtsen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Mogens Sandø Lund
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Didier Boichard
- GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Goutam Sahana
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark.
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Wu X, Mesbah-Uddin M, Guldbrandtsen B, Lund MS, Sahana G. Haplotypes responsible for early embryonic lethality detected in Nordic Holsteins. J Dairy Sci 2019; 102:11116-11123. [PMID: 31548059 DOI: 10.3168/jds.2019-16651] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 08/09/2019] [Indexed: 12/13/2022]
Abstract
Widespread use of a limited number of elite sires in dairy cattle breeding increases the risk of some deleterious allelic variants spreading in the population. Genomic data are being used to detect relatively common (frequency >1%) haplotypes that never occur in the homozygous state in live animals. Such haplotypes likely include recessive lethal or semilethal alleles. The aim of this study was to detect such haplotypes in the Nordic Holstein population and to identify causal genetic factors underlying these haplotypes. Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA) genotypes for 26,312 Nordic Holstein animals were phased to construct haplotypes. Haplotypes that are common in the population but never observed as homozygous were identified. Two such haplotypes overlapped with previously identified recessive lethal mutations in Holsteins-namely, structural maintenance of chromosomes 2 (HH3) and brachyspina. In addition, we identified 9 novel putative recessive lethal-carrying haplotypes, with 26 to 36 homozygous individuals expected among the genotyped animals but only 0 to 3 homozygotes observed. For 2 out of 9 homozygous-deficient haplotypes, insemination records of at-risk mating (carrier bull with daughter of carrier sire) showed reduced insemination success compared with not-at-risk mating (noncarrier bull with daughter of noncarrier sire), supporting early embryonic mortality. To detect the causative variant underlying each homozygous-deficient haplotype, data from the 1000 Bull Genome Project were used. However, no variants or deletions identified in the chromosome regions covered by the haplotypes showed concordance with haplotype carrier status. The carrier status of detected haplotypes could be used to select bulls to reduce the frequency of the latent lethal mutations in the population. If desired, at-risk matings could be avoided.
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Affiliation(s)
- Xiaoping Wu
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark.
| | - Md Mesbah-Uddin
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark; Animal Genetics and Integrative Biology, UMR 1313 GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark
| | - Mogens S Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark
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Larsen FT, Bed'Hom B, Guldbrandtsen B, Dalgaard TS. Identification and tissue-expression profiling of novel chicken c-type lectin-like domain containing proteins as potential targets for carbohydrate-based vaccine strategies. Mol Immunol 2019; 114:216-225. [PMID: 31386978 DOI: 10.1016/j.molimm.2019.07.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 07/25/2019] [Indexed: 12/14/2022]
Abstract
C-type lectin-like domain containing proteins (CTLDcps) mainly bind carbohydrate-based ligands, but also other ligands. CTLDcps are involved in several biological processes including cell adhesion, cell-cell interactions, and pathogen recognition. Pathogen recognition by myeloid cells, e.g. dendritic cells (DCs), can be facilitated through cell surface expressed CTLDcps. Cell surface expressed CTLDcps have been exploited in vaccine designs for specific targeting of human and mouse DCs using antibodies. In recent years, however, DC targeting using carbohydrate-based vaccines has gained interest due to low production cost, limited immunogenicity, and possibility of multivalent adjustment. In chicken, however, only a few CTLDcps have been identified. Identifying and annotating additional chicken CTLDcps (chCTLDcps) is needed to exploit carbohydrate-mediated DC targeting in chicken. Therefore, we searched the chicken GRCg6a assembly for novel chCTLDcps. We identified 28 chCTLDcps of which 10 had previously been described and also experimentally validated. RNA-seq and RT-qPCR confirmed mRNA expression of the remaining 18 identified chCTLDcps. A group of highly related chCTLDcps, moreover, was shown to be avian-specific and comprise novel members mapped to the proposed chicken natural killer gene complex. Two chCTLDcps, chCLEC17AL-A and chCLEC17AL-B, were found to share a recent common ancestor with CLEC17A. Putative mannose or fucose-binding sequence motifs, EPN and WND, were found in the CTLD of chCLEC17AL-A. Both contained intracellular internalisation and signalling sequence motifs. In conclusion, several chCTLDcps were identified and their expression confirmed. Both chCLEC17AL-A and -B showed promise as potential targets in carbohydrate-based chicken vaccine strategies. Determination of DC-specific expression of chCLEC17AL-A and -B, thus, might prove useful in chicken vaccinology.
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Affiliation(s)
- Frederik T Larsen
- Department of Animal Science, Aarhus University, Blichers Allé 20, 8830, Tjele, Denmark
| | - Bertrand Bed'Hom
- GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Bernt Guldbrandtsen
- Department of Molecular Biology and Genetics, Blichers Allé 20, 8830, Tjele, Denmark
| | - Tina S Dalgaard
- Department of Animal Science, Aarhus University, Blichers Allé 20, 8830, Tjele, Denmark.
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Mukherjee S, Cai Z, Mukherjee A, Longkumer I, Mech M, Vupru K, Khate K, Rajkhowa C, Mitra A, Guldbrandtsen B, Lund MS, Sahana G. Whole genome sequence and de novo assembly revealed genomic architecture of Indian Mithun (Bos frontalis). BMC Genomics 2019; 20:617. [PMID: 31357931 PMCID: PMC6664528 DOI: 10.1186/s12864-019-5980-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 07/16/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Mithun (Bos frontalis), also called gayal, is an endangered bovine species, under the tribe bovini with 2n = 58 XX chromosome complements and reared under the tropical rain forests region of India, China, Myanmar, Bhutan and Bangladesh. However, the origin of this species is still disputed and information on its genomic architecture is scanty so far. We trust that availability of its whole genome sequence data and assembly will greatly solve this problem and help to generate many information including phylogenetic status of mithun. Recently, the first genome assembly of gayal, mithun of Chinese origin, was published. However, an improved reference genome assembly would still benefit in understanding genetic variation in mithun populations reared under diverse geographical locations and for building a superior consensus assembly. We, therefore, performed deep sequencing of the genome of an adult female mithun from India, assembled and annotated its genome and performed extensive bioinformatic analyses to produce a superior de novo genome assembly of mithun. RESULTS We generated ≈300 Gigabyte (Gb) raw reads from whole-genome deep sequencing platforms and assembled the sequence data using a hybrid assembly strategy to create a high quality de novo assembly of mithun with 96% recovered as per BUSCO analysis. The final genome assembly has a total length of 3.0 Gb, contains 5,015 scaffolds with an N50 value of 1 Mb. Repeat sequences constitute around 43.66% of the assembly. The genomic alignments between mithun to cattle showed that their genomes, as expected, are highly conserved. Gene annotation identified 28,044 protein-coding genes presented in mithun genome. The gene orthologous groups of mithun showed a high degree of similarity in comparison with other species, while fewer mithun specific coding sequences were found compared to those in cattle. CONCLUSION Here we presented the first de novo draft genome assembly of Indian mithun having better coverage, less fragmented, better annotated, and constitutes a reasonably complete assembly compared to the previously published gayal genome. This comprehensive assembly unravelled the genomic architecture of mithun to a great extent and will provide a reference genome assembly to research community to elucidate the evolutionary history of mithun across its distinct geographical locations.
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Affiliation(s)
- Sabyasachi Mukherjee
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland 797106 India
| | - Zexi Cai
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Anupama Mukherjee
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland 797106 India
- Present address: Dairy Cattle Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana 132001 India
| | - Imsusosang Longkumer
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland 797106 India
| | - Moonmoon Mech
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland 797106 India
| | - Kezhavituo Vupru
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland 797106 India
| | - Kobu Khate
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland 797106 India
| | - Chandan Rajkhowa
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland 797106 India
| | - Abhijit Mitra
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland 797106 India
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
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Schönherz AA, Szekeres BD, Nielsen VH, Guldbrandtsen B. Population structure and genetic characterization of two native Danish sheep breeds. ACTA AGR SCAND A-AN 2019. [DOI: 10.1080/09064702.2019.1639804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- A. A. Schönherz
- Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - B. D. Szekeres
- Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - V. H. Nielsen
- Danish Centre for Food and Agriculture, Aarhus University, Tjele, Denmark
| | - B. Guldbrandtsen
- Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
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Cai Z, Guldbrandtsen B, Lund MS, Sahana G. Weighting sequence variants based on their annotation increases the power of genome-wide association studies in dairy cattle. Genet Sel Evol 2019; 51:20. [PMID: 31077144 PMCID: PMC6511139 DOI: 10.1186/s12711-019-0463-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 05/03/2019] [Indexed: 01/09/2023] Open
Abstract
Background Genome-wide association studies (GWAS) are widely used to identify regions of the genome that harbor genetic determinants of quantitative traits. However, the multiple-testing burden from scanning tens of millions of whole-genome sequence variants reduces the power to identify associated variants, especially if sample size is limited. In addition, factors such as inaccuracy of imputation, complex linkage disequilibrium structures, and multiple closely-located causal variants may result in an identified causative mutation not being the most significant single nucleotide polymorphism in a particular genomic region. Therefore, the use of information from different sources, particularly variant annotations, was proposed to enhance the fine-mapping of causal variants. Here, we tested whether applying significance thresholds based on variant annotation categories increases the power of GWAS compared with a flat Bonferroni multiple-testing correction. Results Whole-genome sequence variants in dairy cattle were categorized according to type and predicted impact. Then, GWAS between markers and 17 quantitative traits were analyzed for enrichment for association of each annotation category. By using annotation categories that were determined with the variants effect predictor software and datasets indicating regions of open chromatin, “low impact” variants were found to be highly enriched. Moreover, when the variants annotated as “modifier” and not located at open chromatin regions were further classified into different types of potential regulatory elements, the high impact variants, moderate impact variants, variants located in the 3′ and 5′ untranslated regions, and variants located in potential non-coding RNA regions exhibited relatively more enrichment. In contrast, a similar study on human GWAS data reported that enrichment of association signals was highest with high impact variants. We observed an increase in power when these variant category-based significance thresholds were applied for GWAS results on stature in Nordic Holstein cattle, as more candidate genes from previous large GWAS meta-analysis for cattle stature were confirmed. Conclusions Use of variant category-based genome-wide significance thresholds can marginally increase the power to detect the candidate genes in cattle. With the continued improvements in annotation of the bovine genome, we anticipate that the growing usefulness of variant category-based significance thresholds will be demonstrated. Electronic supplementary material The online version of this article (10.1186/s12711-019-0463-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zexi Cai
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.
| | - Bernt Guldbrandtsen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Mogens Sandø Lund
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Goutam Sahana
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
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Iso-Touru T, Wurmser C, Venhoranta H, Hiltpold M, Savolainen T, Sironen A, Fischer K, Flisikowski K, Fries R, Vicente-Carrillo A, Alvarez-Rodriguez M, Nagy S, Mutikainen M, Peippo J, Taponen J, Sahana G, Guldbrandtsen B, Simonen H, Rodriguez-Martinez H, Andersson M, Pausch H. A splice donor variant in CCDC189 is associated with asthenospermia in Nordic Red dairy cattle. BMC Genomics 2019; 20:286. [PMID: 30975085 PMCID: PMC6460654 DOI: 10.1186/s12864-019-5628-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 03/20/2019] [Indexed: 01/10/2023] Open
Abstract
Background Cattle populations are highly amenable to the genetic mapping of male reproductive traits because longitudinal data on ejaculate quality and dense microarray-derived genotypes are available for thousands of artificial insemination bulls. Two young Nordic Red bulls delivered sperm with low progressive motility (i.e., asthenospermia) during a semen collection period of more than four months. The bulls were related through a common ancestor on both their paternal and maternal ancestry. Thus, a recessive mode of inheritance of asthenospermia was suspected. Results Both bulls were genotyped at 54,001 SNPs using the Illumina BovineSNP50 Bead chip. A scan for autozygosity revealed that they were identical by descent for a 2.98 Mb segment located on bovine chromosome 25. This haplotype was not found in the homozygous state in 8557 fertile bulls although five homozygous haplotype carriers were expected (P = 0.018). Whole genome-sequencing uncovered that both asthenospermic bulls were homozygous for a mutation that disrupts a canonical 5′ splice donor site of CCDC189 encoding the coiled-coil domain containing protein 189. Transcription analysis showed that the derived allele activates a cryptic splice site resulting in a frameshift and premature termination of translation. The mutated CCDC189 protein is truncated by more than 40%, thus lacking the flagellar C1a complex subunit C1a-32 that is supposed to modulate the physiological movement of the sperm flagella. The mutant allele occurs at a frequency of 2.5% in Nordic Red cattle. Conclusions Our study in cattle uncovered that CCDC189 is required for physiological movement of sperm flagella thus enabling active progression of spermatozoa and fertilization. A direct gene test may be implemented to monitor the asthenospermia-associated allele and prevent the birth of homozygous bulls that are infertile. Our results have been integrated in the Online Mendelian Inheritance in Animals (OMIA) database (https://omia.org/OMIA002167/9913/). Electronic supplementary material The online version of this article (10.1186/s12864-019-5628-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Terhi Iso-Touru
- Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland
| | - Christine Wurmser
- Chair of Animal Breeding, Technische Universität München, 85354, Freising-Weihenstephan, Germany
| | | | - Maya Hiltpold
- Animal Genomics, ETH Zurich, 8001, Zurich, Switzerland
| | | | - Anu Sironen
- Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland
| | - Konrad Fischer
- Chair of Livestock Biotechnology, Technische Universität München, 85354, Freising-Weihenstephan, Germany
| | - Krzysztof Flisikowski
- Chair of Livestock Biotechnology, Technische Universität München, 85354, Freising-Weihenstephan, Germany
| | - Ruedi Fries
- Chair of Animal Breeding, Technische Universität München, 85354, Freising-Weihenstephan, Germany
| | | | - Manuel Alvarez-Rodriguez
- Department of Clinical and Experimental Medicine, Linköping University, 58183, Linköping, Sweden
| | | | - Mervi Mutikainen
- Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland
| | - Jaana Peippo
- Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland
| | | | - Goutam Sahana
- Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | | | | | | | - Hubert Pausch
- Animal Genomics, ETH Zurich, 8001, Zurich, Switzerland.
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Cai Z, Guldbrandtsen B, Lund MS, Sahana G. Prioritizing candidate genes for fertility in dairy cows using gene-based analysis, functional annotation and differential gene expression. BMC Genomics 2019; 20:255. [PMID: 30935378 PMCID: PMC6444876 DOI: 10.1186/s12864-019-5638-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 03/24/2019] [Indexed: 01/27/2023] Open
Abstract
Background An unfavorable genetic correlation between milk production and fertility makes simultaneous improvement of milk production and fertility difficult in cattle breeding. Rapid genetic improvement in milk production traits in dairy cattle has been accompanied by decline in cow fertility. The genetic basis of this correlation remains poorly understood. Expanded reference populations and large sets of sequenced animals make genome-wide association studies (GWAS) with imputed markers possible for large populations and thereby studying genetic architecture of complex traits. Results In this study, we associated 15,551,021 SNPs with female fertility index in 5038 Nordic Holstein cattle. We have identified seven quantitative trait loci (QTL) on six chromosomes in cattle. Along with nearest genes to GWAS hits, we used gene-based analysis and spread of linkage disequilibrium (LD) information to generate a list of potential candidate genes affecting fertility in cattle. Subsequently, we used prior knowledge on gene related to fertility from Gene Ontology terms, Kyoto Encyclopedia of Genes and Genomes pathway analysis, mammalian phenotype database, and public available RNA-seq data to refine the list of candidate genes for fertility. We used variant annotations to investigate candidate mutations within the prioritized candidate genes. Using multiple source of information, we proposed candidate genes with biological relevance underlying each of these seven QTL. On chromosome 1, we have identified ten candidate genes for two QTL. For the rest of chromosomes, we proposed one candidate gene for each QTL. In the candidate genes list, differentially expressed genes from different studies support FRAS1, ITGB5, ADCY5, and SEMA5B as candidate genes for cow fertility. Conclusion The GWAS result not only confirmed previously mapped QTL, but also made new findings. Our findings contributes towards dissecting the genetics for female fertility in cattle. Moreover, this study shows the usefulness of adding independent information to pick candidate genes during post-GWAS analysis.
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Affiliation(s)
- Zexi Cai
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark.
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
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Chen M, Su G, Fu J, Wang A, Liu J, Lund MS, Guldbrandtsen B. Introgression of Chinese haplotypes contributed to the improvement of Danish Duroc pigs. Evol Appl 2019; 12:292-300. [PMID: 30697340 PMCID: PMC6346729 DOI: 10.1111/eva.12716] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Revised: 09/07/2018] [Accepted: 09/12/2018] [Indexed: 11/26/2022] Open
Abstract
The distribution of Asian ancestry in the genome of Danish Duroc pigs was investigated using whole-genome sequencing data from European wild boars, Danish Duroc, Chinese Meishan and Bamaxiang pigs. Asian haplotypes deriving from Meishan and Bamaxiang occur widely across the genome. Signatures of selection on Asian haplotypes are common in the genome, but few of these haplotypes have been fixed. By defining 50-kb windows with more than 50% Chinese ancestry, which did not exhibit extreme genetic differentiation between Meishan and Bamaxiang as candidate regions, the enrichment of quantitative trait loci in candidate regions supports that Asian haplotypes under selection play an important role in contributing genetic variation underlying production, reproduction, meat and carcass, and exterior traits. Gene annotation of regions with the highest proportion of Chinese ancestry revealed genes of biological interest, such as NR6A1. Further haplotype clustering analysis suggested that a haplotype of Chinese origin around the NR6A1 gene was introduced to Europe and then underwent a selective sweep in European pigs. Besides, functional genes in candidate regions, such as AHR and PGRMC2, associated with fertility, and SAL1, associated with meat quality, were identified. Our results demonstrate the contribution of Asian haplotypes to the genomes of European pigs. Findings herein facilitate further genomic studies such as genomewide association study and genomic prediction by providing ancestry information of variants.
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Affiliation(s)
- Minhui Chen
- Department of Molecular Biology and Genetics, Centre for Quantitative Genetics and GenomicsAarhus UniversityTjeleDenmark
- Department of Animal Genetics, Breeding and ReproductionChina Agricultural UniversityBeijingChina
| | - Guosheng Su
- Department of Molecular Biology and Genetics, Centre for Quantitative Genetics and GenomicsAarhus UniversityTjeleDenmark
| | - Jinluan Fu
- Department of Animal Genetics, Breeding and ReproductionChina Agricultural UniversityBeijingChina
| | - Aiguo Wang
- Department of Animal Genetics, Breeding and ReproductionChina Agricultural UniversityBeijingChina
| | - Jian‐Feng Liu
- Department of Animal Genetics, Breeding and ReproductionChina Agricultural UniversityBeijingChina
| | - Mogens S. Lund
- Department of Molecular Biology and Genetics, Centre for Quantitative Genetics and GenomicsAarhus UniversityTjeleDenmark
| | - Bernt Guldbrandtsen
- Department of Molecular Biology and Genetics, Centre for Quantitative Genetics and GenomicsAarhus UniversityTjeleDenmark
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Cai Z, Guldbrandtsen B, Lund MS, Sahana G. Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle. BMC Genet 2019; 20:15. [PMID: 30696404 PMCID: PMC6350337 DOI: 10.1186/s12863-019-0717-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 01/18/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. However, moving from the associations to identify the causal variants and reveal underlying mechanisms have proven complicated. In dairy cattle populations, we face a challenge due to long-range linkage disequilibrium (LD) arising from close familial relationships in the studied individuals. Long range LD makes it difficult to distinguish if one or multiple quantitative trait loci (QTL) are segregating in a genomic region showing association with a phenotype. We had two objectives in this study: 1) to distinguish between multiple QTL segregating in a genomic region, and 2) use of external information to prioritize candidate genes for a QTL along with the candidate variants. RESULTS We observed fixing the lead SNP as a covariate can help to distinguish additional close association signal(s). Thereafter, using the mammalian phenotype database, we successfully found candidate genes, in concordance with previous studies, demonstrating the power of this strategy. Secondly, we used variant annotation information to search for causative variants in our candidate genes. The variant information successfully identified known causal mutations and showed the potential to pinpoint the causative mutation(s) which are located in coding regions. CONCLUSIONS Our approach can distinguish multiple QTL segregating on the same chromosome in a single analysis without manual input. Moreover, utilizing information from the mammalian phenotype database and variant effect predictor as post-GWAS analysis could benefit in candidate genes and causative mutations finding in cattle. Our study not only identified additional candidate genes for milk traits, but also can serve as a routine method for GWAS in dairy cattle.
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Affiliation(s)
- Zexi Cai
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark.
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
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Cai Z, Guldbrandtsen B, Lund MS, Sahana G. Retraction Note: Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle. BMC Genet 2018; 19:111. [PMID: 30537928 PMCID: PMC6288910 DOI: 10.1186/s12863-018-0698-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Zexi Cai
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark.
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
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Zhang Q, Sahana G, Su G, Guldbrandtsen B, Lund MS, Calus MPL. Impact of rare and low-frequency sequence variants on reliability of genomic prediction in dairy cattle. Genet Sel Evol 2018; 50:62. [PMID: 30458700 PMCID: PMC6247626 DOI: 10.1186/s12711-018-0432-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 11/14/2018] [Indexed: 11/05/2022] Open
Abstract
Background Availability of whole-genome sequence data for a large number of cattle and efficient imputation methodologies open a new opportunity to include rare and low-frequency variants (RLFV) in genomic prediction in dairy cattle. The objective of this study was to examine the impact of including RLFV that are within genes and selected from whole-genome sequence variants, on the reliability of genomic prediction for fertility, health and longevity in dairy cattle. Results All genic RLFV with a minor allele frequency lower than 0.05 were extracted from imputed sequence data and subsets were created using different strategies. These subsets were subsequently combined with Illumina 50 k single nucleotide polymorphism (SNP) data and used for genomic prediction. Reliability of prediction obtained by using 50 k SNP data alone was used as reference value and absolute changes in reliabilities are referred to as changes in percentage points. Adding a component that included either all the genic or a subset of selected RLFV into the model in addition to the 50 k component changed the reliability of predictions by − 2.2 to 1.1%, i.e. hardly no change in reliability of prediction was found, regardless of how the RLFV were selected. In addition to these empirical analyses, a simulation study was performed to evaluate the potential impact of adding RLFV in the model on the reliability of prediction. Three sets of causal RLFV (containing 21,468, 1348 and 235 RLFV) that were randomly selected from different numbers of genes were generated and accounted for 10% additional genetic variance of the estimated variance explained by the 50 k SNPs. When genic RLFV based on mapping results were included in the prediction model, reliabilities improved by up to 4.0% and when the causal RLFV were included they improved by up to 6.8%. Conclusions Using selected RLFV from whole-genome sequence data had only a small impact on the empirical reliability of genomic prediction in dairy cattle. Our simulations revealed that for sequence data to bring a benefit, the key is to identify causal RLFV. Electronic supplementary material The online version of this article (10.1186/s12711-018-0432-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Qianqian Zhang
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark. .,Wageningen University and Research, Animal Breeding and Genomics, Wageningen, The Netherlands. .,Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Goutam Sahana
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Guosheng Su
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Mogens Sandø Lund
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Mario P L Calus
- Wageningen University and Research, Animal Breeding and Genomics, Wageningen, The Netherlands
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Bertolini F, Servin B, Talenti A, Rochat E, Kim ES, Oget C, Palhière I, Crisà A, Catillo G, Steri R, Amills M, Colli L, Marras G, Milanesi M, Nicolazzi E, Rosen BD, Van Tassell CP, Guldbrandtsen B, Sonstegard TS, Tosser-Klopp G, Stella A, Rothschild MF, Joost S, Crepaldi P. Signatures of selection and environmental adaptation across the goat genome post-domestication. Genet Sel Evol 2018; 50:57. [PMID: 30449276 PMCID: PMC6240954 DOI: 10.1186/s12711-018-0421-y] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 10/15/2018] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Since goat was domesticated 10,000 years ago, many factors have contributed to the differentiation of goat breeds and these are classified mainly into two types: (i) adaptation to different breeding systems and/or purposes and (ii) adaptation to different environments. As a result, approximately 600 goat breeds have developed worldwide; they differ considerably from one another in terms of phenotypic characteristics and are adapted to a wide range of climatic conditions. In this work, we analyzed the AdaptMap goat dataset, which is composed of data from more than 3000 animals collected worldwide and genotyped with the CaprineSNP50 BeadChip. These animals were partitioned into groups based on geographical area, production uses, available records on solid coat color and environmental variables including the sampling geographical coordinates, to investigate the role of natural and/or artificial selection in shaping the genome of goat breeds. RESULTS Several signatures of selection on different chromosomal regions were detected across the different breeds, sub-geographical clusters, phenotypic and climatic groups. These regions contain genes that are involved in important biological processes, such as milk-, meat- or fiber-related production, coat color, glucose pathway, oxidative stress response, size, and circadian clock differences. Our results confirm previous findings in other species on adaptation to extreme environments and human purposes and provide new genes that could explain some of the differences between goat breeds according to their geographical distribution and adaptation to different environments. CONCLUSIONS These analyses of signatures of selection provide a comprehensive first picture of the global domestication process and adaptation of goat breeds and highlight possible genes that may have contributed to the differentiation of this species worldwide.
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Affiliation(s)
- Francesca Bertolini
- Department of Animal Science, Iowa State University, Ames, IA 50011 USA
- National Institute of Aquatic Resources, Technical University of Denmark (DTU), 2800 Lyngby, Denmark
| | - Bertrand Servin
- GenPhySE, INRA, Université de Toulouse, INPT, ENVT, 31326 Castanet Tolosan, France
| | - Andrea Talenti
- Dipartimento di Medicina Veterinaria, Università degli Studi di Milano, 20133 Milan, Italy
| | - Estelle Rochat
- Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | | | - Claire Oget
- GenPhySE, INRA, Université de Toulouse, INPT, ENVT, 31326 Castanet Tolosan, France
| | - Isabelle Palhière
- GenPhySE, INRA, Université de Toulouse, INPT, ENVT, 31326 Castanet Tolosan, France
| | - Alessandra Crisà
- Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA) - Research Centre for Animal Production and Acquaculture, 00015 Monterotondo, Roma, Italy
| | - Gennaro Catillo
- Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA) - Research Centre for Animal Production and Acquaculture, 00015 Monterotondo, Roma, Italy
| | - Roberto Steri
- Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA) - Research Centre for Animal Production and Acquaculture, 00015 Monterotondo, Roma, Italy
| | - Marcel Amills
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus Universitat Autonoma de Barcelona, Bellaterra, 08193 Barcelona, Spain
| | - Licia Colli
- DIANA Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Università Cattolica del S. Cuore, 29100 Piacenza, Italy
- BioDNA Centro di Ricerca sulla Biodiversità e sul DNA Antico, Università Cattolica del S. Cuore, 29100 Piacenza, Italy
| | - Gabriele Marras
- Fondazione Parco Tecnologico Padano (PTP), 26900 Lodi, Italy
| | - Marco Milanesi
- DIANA Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Università Cattolica del S. Cuore, 29100 Piacenza, Italy
- Department of Support, Production and Animal Health, School of Veterinary Medicine, São Paulo State University (UNESP), Araçatuba, Brazil
| | | | - Benjamin D. Rosen
- Animal Genomics and Improvement Laboratory, ARS USDA, Beltsville, MD 20705 USA
| | | | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | | | - Gwenola Tosser-Klopp
- GenPhySE, INRA, Université de Toulouse, INPT, ENVT, 31326 Castanet Tolosan, France
| | - Alessandra Stella
- BioDNA Centro di Ricerca sulla Biodiversità e sul DNA Antico, Università Cattolica del S. Cuore, 29100 Piacenza, Italy
| | - Max F. Rothschild
- Department of Animal Science, Iowa State University, Ames, IA 50011 USA
| | - Stéphane Joost
- Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Paola Crepaldi
- Dipartimento di Medicina Veterinaria, Università degli Studi di Milano, 20133 Milan, Italy
| | - the AdaptMap consortium
- Department of Animal Science, Iowa State University, Ames, IA 50011 USA
- National Institute of Aquatic Resources, Technical University of Denmark (DTU), 2800 Lyngby, Denmark
- GenPhySE, INRA, Université de Toulouse, INPT, ENVT, 31326 Castanet Tolosan, France
- Dipartimento di Medicina Veterinaria, Università degli Studi di Milano, 20133 Milan, Italy
- Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- Recombinetics Inc, St Paul, 55104 MN USA
- Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA) - Research Centre for Animal Production and Acquaculture, 00015 Monterotondo, Roma, Italy
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus Universitat Autonoma de Barcelona, Bellaterra, 08193 Barcelona, Spain
- DIANA Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Università Cattolica del S. Cuore, 29100 Piacenza, Italy
- BioDNA Centro di Ricerca sulla Biodiversità e sul DNA Antico, Università Cattolica del S. Cuore, 29100 Piacenza, Italy
- Fondazione Parco Tecnologico Padano (PTP), 26900 Lodi, Italy
- Department of Support, Production and Animal Health, School of Veterinary Medicine, São Paulo State University (UNESP), Araçatuba, Brazil
- Animal Genomics and Improvement Laboratory, ARS USDA, Beltsville, MD 20705 USA
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
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Colli L, Milanesi M, Talenti A, Bertolini F, Chen M, Crisà A, Daly KG, Del Corvo M, Guldbrandtsen B, Lenstra JA, Rosen BD, Vajana E, Catillo G, Joost S, Nicolazzi EL, Rochat E, Rothschild MF, Servin B, Sonstegard TS, Steri R, Van Tassell CP, Ajmone-Marsan P, Crepaldi P, Stella A. Genome-wide SNP profiling of worldwide goat populations reveals strong partitioning of diversity and highlights post-domestication migration routes. Genet Sel Evol 2018; 50:58. [PMID: 30449284 PMCID: PMC6240949 DOI: 10.1186/s12711-018-0422-x] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 10/15/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Goat populations that are characterized within the AdaptMap project cover a large part of the worldwide distribution of this species and provide the opportunity to assess their diversity at a global scale. We analysed genome-wide 50 K single nucleotide polymorphism (SNP) data from 144 populations to describe the global patterns of molecular variation, compare them to those observed in other livestock species, and identify the drivers that led to the current distribution of goats. RESULTS A high degree of genetic variability exists among the goat populations studied. Our results highlight a strong partitioning of molecular diversity between and within continents. Three major gene pools correspond to goats from Europe, Africa and West Asia. Dissection of sub-structures disclosed regional gene pools, which reflect the main post-domestication migration routes. We also identified several exchanges, mainly in African populations, and which often involve admixed and cosmopolitan breeds. Extensive gene flow has taken place within specific areas (e.g., south Europe, Morocco and Mali-Burkina Faso-Nigeria), whereas elsewhere isolation due to geographical barriers (e.g., seas or mountains) or human management has decreased local gene flows. CONCLUSIONS After domestication in the Fertile Crescent in the early Neolithic era (ca. 12,000 YBP), domestic goats that already carried differentiated gene pools spread to Europe, Africa and Asia. The spread of these populations determined the major genomic background of the continental populations, which currently have a more marked subdivision than that observed in other ruminant livestock species. Subsequently, further diversification occurred at the regional level due to geographical and reproductive isolation, which was accompanied by additional migrations and/or importations, the traces of which are still detectable today. The effects of breed formation were clearly detected, particularly in Central and North Europe. Overall, our results highlight a remarkable diversity that occurs at the global scale and is locally partitioned and often affected by introgression from cosmopolitan breeds. These findings support the importance of long-term preservation of goat diversity, and provide a useful framework for investigating adaptive introgression, directing genetic improvement and choosing breeding targets.
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Affiliation(s)
- Licia Colli
- DIANA Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Università Cattolica del S. Cuore, Piacenza, Italy. .,BioDNA Centro di Ricerca sulla Biodiversità e sul DNA Antico, Università Cattolica del S. Cuore, Piacenza, Italy.
| | - Marco Milanesi
- DIANA Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Università Cattolica del S. Cuore, Piacenza, Italy.,School of Veterinary Medicine, Department of Support, Production and Animal Health, São Paulo State University (UNESP), Araçatuba, Brazil
| | - Andrea Talenti
- Dipartimento di Medicina Veterinaria, University of Milan, Milan, Italy
| | - Francesca Bertolini
- Department of Animal Science, Iowa State University, Ames, IA, USA.,National Institute of Aquatic Resources, Technical University of Denmark, DTU, Lyngby, Denmark
| | - Minhui Chen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Århus, Denmark.,Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Alessandra Crisà
- Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria (CREA) - Research Centre for Animal Production and Aquaculture, Monterotondo, Rome, Italy
| | - Kevin Gerard Daly
- Population Genetics Lab, Smurfit Institute of Genetics, Trinity College of Dublin, Dublin, Ireland
| | - Marcello Del Corvo
- DIANA Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Università Cattolica del S. Cuore, Piacenza, Italy
| | - Bernt Guldbrandtsen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Århus, Denmark
| | - Johannes A Lenstra
- Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
| | - Benjamin D Rosen
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, USA
| | - Elia Vajana
- DIANA Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Università Cattolica del S. Cuore, Piacenza, Italy.,Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Gennaro Catillo
- Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria (CREA) - Research Centre for Animal Production and Aquaculture, Monterotondo, Rome, Italy
| | - Stéphane Joost
- Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Estelle Rochat
- Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Max F Rothschild
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Bertrand Servin
- GenPhySE, INRA, Université de Toulouse, INPT, ENVT, 31326, Castanet Tolosan, France
| | | | - Roberto Steri
- Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria (CREA) - Research Centre for Animal Production and Aquaculture, Monterotondo, Rome, Italy
| | - Curtis P Van Tassell
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, USA
| | - Paolo Ajmone-Marsan
- DIANA Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Università Cattolica del S. Cuore, Piacenza, Italy.,BioDNA Centro di Ricerca sulla Biodiversità e sul DNA Antico, Università Cattolica del S. Cuore, Piacenza, Italy
| | - Paola Crepaldi
- Dipartimento di Medicina Veterinaria, University of Milan, Milan, Italy
| | - Alessandra Stella
- Fondazione Parco Tecnologico Padano, Lodi, Italy.,Istituto di Biologia e Biotecnologia Agraria, Consiglio Nazionale delle Ricerche, Milan, Italy
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Cai Z, Villumsen TM, Asp T, Guldbrandtsen B, Sahana G, Lund MS. SNP markers associated with body size and pelt length in American mink (Neovison vison). BMC Genet 2018; 19:103. [PMID: 30419805 PMCID: PMC6233529 DOI: 10.1186/s12863-018-0688-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Accepted: 10/29/2018] [Indexed: 11/10/2022] Open
Abstract
Background Identification of genes underlying production traits is a key aim of the mink research community. Recent availability of genomic tools have opened the possibility for faster genetic progress in mink breeding. Availability of mink genome assembly allows genome-wide association studies in mink. Results In this study, we used genotyping-by-sequencing to obtain single nucleotide polymorphism (SNP) genotypes of 2496 mink. After multiple rounds of filtering, we retained 28,336 high quality SNPs and 2352 individuals for a genome-wide association study (GWAS). We performed the first GWAS for body weight, behavior, along with 10 traits related to fur quality in mink. Conclusions Combining association results with existing functional information of genes and mammalian phenotype databases, we proposed WWC3, MAP2K4, SLC7A1 and USP22 as candidate genes for body weight and pelt length in mink. Electronic supplementary material The online version of this article (10.1186/s12863-018-0688-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zexi Cai
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark.
| | - Trine Michelle Villumsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Torben Asp
- Section of Crop Genetics and Biotechnology, Department of Molecular Biology and Genetics, Aarhus University, 4200, Slagelse, Denmark
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
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Marete AG, Guldbrandtsen B, Lund MS, Fritz S, Sahana G, Boichard D. A Meta-Analysis Including Pre-selected Sequence Variants Associated With Seven Traits in Three French Dairy Cattle Populations. Front Genet 2018; 9:522. [PMID: 30459810 PMCID: PMC6232291 DOI: 10.3389/fgene.2018.00522] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2018] [Accepted: 10/16/2018] [Indexed: 12/17/2022] Open
Abstract
A within-breed genome-wide association study (GWAS) is useful when identifying the QTL that segregates in a breed. However, an across-breed meta-analysis can be used to increase the power of identification and precise localization of QTL that segregate in multiple breeds. Precise localization will allow including QTL information from other breeds in genomic prediction due to the persistence of the linkage phase between the causal variant and the marker. This study aimed to identify and confirm QTL detected in within-breed GWAS through a meta-analysis in three French dairy cattle breeds. A set of sequence variants selected based on their functional annotations were imputed into 50 k genotypes for 46,732 Holstein, 20,096 Montbeliarde, and 11,944 Normande cows to identify QTL for milk production, the success rate at insemination of cows (fertility) and stature. We conducted within-breed GWAS followed by across-breed meta-analysis using a weighted Z-scores model on the GWAS summary data (i.e., P-values, effect direction, and sample size). After Bonferroni correction, the GWAS result identified 21,956 significantly associated SNP (P FWER < 0.05), while meta-analysis result identified 9,604 significant SNP (P FWER < 0.05) associated with the phenotypes. The meta-analysis identified 36 QTL for milk yield, 48 QTL for fat yield and percentage, 29 QTL for protein yield and percentage, 13 QTL for fertility, and 16 QTL for stature. Some of these QTL were not significant in the within-breed GWAS. Some previously identified causal variants were confirmed, e.g., BTA14:1802265 (fat percentage, P = 1.5 × 10-760; protein percentage, P = 7.61 × 10-348) both mapping the DGAT1-K232A mutation and BTA14:25006125 (P = 8.58 × 10-140) mapping PLAG1 gene was confirmed for stature in Montbeliarde. New QTL lead SNP shared between breeds included the intronic variant rs109205829 (NFIB gene), and the intergenic variant rs41592357 (1.38 Mb upstream of the CNTN6 gene and 0.65 Mb downstream of the CNTN4 gene). Rs110425867 (ZFAT gene) was the top variant associated with fertility, and new QTL lead SNP included rs109483390 (0.1 Mb upstream of the TNFAIP3 gene and 0.07 Mb downstream of PERP gene), and rs42412333 (0.45 Mb downstream of the RPL10L gene). An across-breed meta-analysis had greater power to detect QTL as opposed to a within breed GWAS. The QTL detected here can be incorporated in routine genomic predictions.
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Affiliation(s)
- Andrew G Marete
- UMR GABI, INRA, AgroParisTech, Université Paris Saclay, 78350 Jouy en Josas, France.,Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Mogens S Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Sébastien Fritz
- UMR GABI, INRA, AgroParisTech, Université Paris Saclay, 78350 Jouy en Josas, France.,ALLICE, Paris, France
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Didier Boichard
- UMR GABI, INRA, AgroParisTech, Université Paris Saclay, 78350 Jouy en Josas, France
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Difford GF, Plichta DR, Løvendahl P, Lassen J, Noel SJ, Højberg O, Wright ADG, Zhu Z, Kristensen L, Nielsen HB, Guldbrandtsen B, Sahana G. Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows. PLoS Genet 2018; 14:e1007580. [PMID: 30312316 PMCID: PMC6200390 DOI: 10.1371/journal.pgen.1007580] [Citation(s) in RCA: 144] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 10/24/2018] [Accepted: 07/20/2018] [Indexed: 01/23/2023] Open
Abstract
Cattle and other ruminants produce large quantities of methane (~110 million metric tonnes per annum), which is a potent greenhouse gas affecting global climate change. Methane (CH4) is a natural by-product of gastro-enteric microbial fermentation of feedstuffs in the rumen and contributes to 6% of total CH4 emissions from anthropogenic-related sources. The extent to which the host genome and rumen microbiome influence CH4 emission is not yet well known. This study confirms individual variation in CH4 production was influenced by individual host (cow) genotype, as well as the host's rumen microbiome composition. Abundance of a small proportion of bacteria and archaea taxa were influenced to a limited extent by the host's genotype and certain taxa were associated with CH4 emissions. However, the cumulative effect of all bacteria and archaea on CH4 production was 13%, the host genetics (heritability) was 21% and the two are largely independent. This study demonstrates variation in CH4 emission is likely not modulated through cow genetic effects on the rumen microbiome. Therefore, the rumen microbiome and cow genome could be targeted independently, by breeding low methane-emitting cows and in parallel, by investigating possible strategies that target changes in the rumen microbiome to reduce CH4 emissions in the cattle industry.
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Affiliation(s)
- Gareth Frank Difford
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
- Wageningen University & Research, Animal Breeding & Genomics, AH Wageningen, Netherlands
| | - Damian Rafal Plichta
- Center for Biological Sequence Analysis, Dept. of Systems Biology, Technical University of Denmark, Kongens Lyngby, Denmark
- Clinical-Microbiomics A/S, Copenhagen, Denmark
| | - Peter Løvendahl
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Jan Lassen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
- Viking Genetics, Randers SØ, Denmark
| | | | - Ole Højberg
- Department of Animal Science, Aarhus University, Tjele, Denmark
| | - André-Denis G. Wright
- School of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson, AZ, United States of America
| | - Zhigang Zhu
- Department of Animal Science, Aarhus University, Tjele, Denmark
| | - Lise Kristensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Henrik Bjørn Nielsen
- Center for Biological Sequence Analysis, Dept. of Systems Biology, Technical University of Denmark, Kongens Lyngby, Denmark
- Clinical-Microbiomics A/S, Copenhagen, Denmark
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
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Cai Z, Guldbrandtsen B, Lund MS, Sahana G. Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle. BMC Genomics 2018; 19:656. [PMID: 30189836 PMCID: PMC6127918 DOI: 10.1186/s12864-018-5050-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 08/31/2018] [Indexed: 12/31/2022] Open
Abstract
Background Improving resistance to mastitis, one of the costliest diseases in dairy production, has become an important objective in dairy cattle breeding. However, mastitis resistance is influenced by many genes involved in multiple processes, including the response to infection, inflammation, and post-infection healing. Low genetic heritability, environmental variations, and farm management differences further complicate the identification of links between genetic variants and mastitis resistance. Consequently, studies of the genetics of variation in mastitis resistance in dairy cattle lack agreement about the responsible genes. Results We associated 15,552,968 imputed whole-genome sequencing markers for 5147 Nordic Holstein cattle with mastitis resistance in a genome-wide association study (GWAS). Next, we augmented P-values for markers in genes in the associated regions using Gene Ontology terms, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and mammalian phenotype database. To confirm results of gene-based analyses, we used gene expression data from E. coli-challenged cow udders. We identified 22 independent quantitative trait loci (QTL) that collectively explained 14% of the variance in breeding values for resistance to clinical mastitis (CM). Using association test statistics with multiple pieces of independent information on gene function and differential expression during bacterial infection, we suggested putative causal genes with biological relevance for 12 QTL affecting resistance to CM in dairy cattle. Conclusion Combining information on the nearest positional genes, gene-based analyses, and differential gene expression data from RNA-seq, we identified putative causal genes (candidate genes with biological evidence) in QTL for mastitis resistance in Nordic Holstein cattle. The same strategy can be applied for other traits. Electronic supplementary material The online version of this article (10.1186/s12864-018-5050-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zexi Cai
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark.
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
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Nayee N, Sahana G, Gajjar S, Sudhakar A, Trivedi K, Lund MS, Guldbrandtsen B. Suitability of existing commercial single nucleotide polymorphism chips for genomic studies in Bos indicus cattle breeds and their Bos taurus crosses. J Anim Breed Genet 2018; 135:432-441. [PMID: 30117205 DOI: 10.1111/jbg.12356] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 07/24/2018] [Accepted: 07/25/2018] [Indexed: 01/06/2023]
Abstract
Bos indicus cattle breeds are genetically distinct from Bos taurus breeds. We examined the performance of three SNP arrays, the Illumina BovineHD BeadChip (777k; Illumina Inc.), the Illumina BovineSNP50 BeadChip (50k) and the GeneSeek 70k Indicus chip (75Ki; GeneSeek) in four B. indicus breeds (Gir, Kankrej, Sahiwal and Red Sindhi) and their B. taurus crosses, along with two B. taurus breeds, Holstein and Jersey. More SNPs on both Illumina SNP chips were monomorphic in B. indicus breeds (average 20.3%-29.3% on the 777k chip, 35.5%-45.5% on the 50k chip) than in Holstein (19.7% on the 777k chip, 17.1% on the 50k chip). The proportion of monomorphic SNPs on the 75Ki chip was much lower, 4% (2.8%-7%) in B. indicus breeds, while it was 33.5% in Holstein. With on average 164,357 heterozygous loci in B. indicus breeds, the 777k SNP chip has sufficient heterozygous loci to design a chip customized for B. indicus breeds. Principal component analysis clearly differentiated B. indicus from B. taurus breeds. Differentiation among B. indicus breeds was only achieved by plotting the third and fifth principal components using 777k genotype data. Admixture analysis showed that many B. indicus animals, previously believed to be of pure origin, are in fact had mixed ancestry. The extent of linkage disequilibrium showed comparatively higher effective population sizes in four B. indicus breeds compared to two B. taurus breeds. The results of admixture analyses show that it is important to assess the genomic composition of a bull before using it in a breeding programme.
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Affiliation(s)
- Nilesh Nayee
- National Dairy Development Board, Gujarat, India
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | | | | | | | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
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Mukherjee A, Mukherjee S, Dhakal R, Mech M, Longkumer I, Haque N, Vupru K, Khate K, Jamir IY, Pongen P, Rajkhowa C, Mitra A, Guldbrandtsen B, Sahana G. High-density Genotyping reveals Genomic Characterization, Population Structure and Genetic Diversity of Indian Mithun (Bos frontalis). Sci Rep 2018; 8:10316. [PMID: 29985484 PMCID: PMC6037757 DOI: 10.1038/s41598-018-28718-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Accepted: 06/20/2018] [Indexed: 12/20/2022] Open
Abstract
The current study aimed at genomic characterization and improved understanding of genetic diversity of two Indian mithun populations (both farm, 48 animals and field, 24 animals) using genome wide genotype data generated with Illumina BovineHD BeadChip. Eight additional populations of taurine cattle (Holstein and NDama), indicine cattle (Gir) and other evolutionarily closely related species (Bali cattle, Yak, Bison, Gaur and wild buffalo) were also included in this analysis (N = 137) for comparative purposes. Our results show that the genetic background of mithun populations was uniform with few possible signs of indicine admixture. In general, observed and expected heterozygosities were quite similar in these two populations. We also observed increased frequencies of small-sized runs of homozygosity (ROH) in the farm population compared to field mithuns. On the other hand, longer ROH were more frequent in field mithuns, which suggests recent founder effects and subsequent genetic drift due to close breeding in farmer herds. This represents the first study providing genetic evidence about the population structure and genomic diversity of Indian mithun. The information generated will be utilized for devising suitable breeding and conservation programme for mithun, an endangered bovine species in India.
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Affiliation(s)
- Anupama Mukherjee
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland, 797106, India.,Dairy Cattle Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India
| | - Sabyasachi Mukherjee
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland, 797106, India.
| | - Rajan Dhakal
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Moonmoon Mech
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland, 797106, India
| | - Imsusosang Longkumer
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland, 797106, India
| | - Nazrul Haque
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland, 797106, India
| | - Kezhavituo Vupru
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland, 797106, India
| | - Kobu Khate
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland, 797106, India
| | - I Yanger Jamir
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland, 797106, India
| | - Pursenla Pongen
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland, 797106, India
| | - Chandan Rajkhowa
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland, 797106, India
| | - Abhijit Mitra
- Animal Genetics and Breeding Lab., ICAR-National Research Centre on Mithun, Medziphema, Nagaland, 797106, India
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
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Schönherz AA, Forsberg R, Guldbrandtsen B, Buitenhuis AJ, Einer-Jensen K. Introduction of Viral Hemorrhagic Septicemia Virus into Freshwater Cultured Rainbow Trout Is Followed by Bursts of Adaptive Evolution. J Virol 2018; 92:e00436-18. [PMID: 29643236 PMCID: PMC5974487 DOI: 10.1128/jvi.00436-18] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 03/20/2018] [Indexed: 12/25/2022] Open
Abstract
Viral hemorrhagic septicemia virus (VHSV), a rhabdovirus infecting teleost fish, has repeatedly crossed the boundary from marine fish species to freshwater cultured rainbow trout. These naturally replicated cross-species transmission events permit the study of general and repeatable evolutionary events occurring in connection with viral emergence in a novel host species. The purpose of the present study was to investigate the adaptive molecular evolution of the VHSV glycoprotein, one of the key virus proteins involved in viral emergence, following emergence from marine species into freshwater cultured rainbow trout. A comprehensive phylogenetic reconstruction of the complete coding region of the VHSV glycoprotein was conducted, and adaptive molecular evolution was investigated using a maximum likelihood approach to compare different codon substitution models allowing for heterogeneous substitution rate ratios among amino acid sites. Evidence of positive selection was detected at six amino acid sites of the VHSV glycoprotein, within the signal peptide, the confirmation-dependent major neutralizing epitope, and the intracellular tail. Evidence of positive selection was found exclusively in rainbow trout-adapted virus isolates, and amino acid combinations found at the six sites under positive selection pressure differentiated rainbow trout- from non-rainbow trout-adapted isolates. Furthermore, four adaptive sites revealed signs of recurring identical changes across phylogenetic groups of rainbow trout-adapted isolates, suggesting that repeated VHSV emergence in freshwater cultured rainbow trout was established through convergent routes of evolution that are associated with immune escape.IMPORTANCE This study is the first to demonstrate that VHSV emergence from marine species into freshwater cultured rainbow trout has been accompanied by bursts of adaptive evolution in the VHSV glycoprotein. Furthermore, repeated detection of the same adaptive amino acid sites across phylogenetic groups of rainbow trout-adapted isolates indicates that adaptation to rainbow trout was established through parallel evolution. In addition, signals of convergent evolution toward the maintenance of genetic variation were detected in the conformation-dependent neutralizing epitope or in close proximity to disulfide bonds involved in the structural conformation of the neutralizing epitope, indicating adaptation to immune response-related genetic variation across freshwater cultured rainbow trout.
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Affiliation(s)
- Anna A Schönherz
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | | | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Albert J Buitenhuis
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
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Cai Z, Guldbrandtsen B, Lund MS, Sahana G. Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle. BMC Genet 2018; 19:30. [PMID: 29751743 PMCID: PMC5948690 DOI: 10.1186/s12863-018-0620-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 04/30/2018] [Indexed: 01/13/2023] Open
Abstract
Background Genome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. However, moving from the associations to identifying the causal variants and revealing underlying mechanisms have proven complicated. In dairy cattle populations, we face a challenge due to long-range linkage disequilibrium (LD) arising from close familial relationships in the studied individuals. Long range LD makes it difficult to distinguish if one or multiple quantitative trait loci (QTL) are segregating in a genomic region showing association with a phenotype. We had two objectives in this study: 1) to distinguish between multiple QTL segregating in a genomic region, and 2) use of external information to prioritize candidate genes for a QTL along with the candidate variant. Results We observed fixing the lead SNP as a covariate can help to distinguish additional close association signal(s). Thereafter, using the mammalian phenotype database, we successfully found candidate genes, in concordance with previous studies, demonstrating the power of this strategy. Secondly, we used variant annotation information to search for causative variants in our candidate genes. The variant information successfully identified known causal mutations and showed the potential to pinpoint the causative mutation(s) which are located in coding regions. Conclusions Our approach can distinguish multiple QTL segregating on the same chromosome in a single analysis without manual input. Moreover, utilizing information from the mammalian phenotype database and variant effect predictor as post-GWAS analysis could benefit in candidate genes and causative mutations finding in cattle. Our study not only identified additional candidate genes for milk traits, but also can serve as a routine method for GWAS in dairy cattle. Electronic supplementary material The online version of this article (10.1186/s12863-018-0620-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zexi Cai
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark.
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
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45
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Marete A, Sahana G, Fritz S, Lefebvre R, Barbat A, Lund MS, Guldbrandtsen B, Boichard D. Genome-wide association study for milking speed in French Holstein cows. J Dairy Sci 2018; 101:6205-6219. [PMID: 29705414 DOI: 10.3168/jds.2017-14067] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 03/16/2018] [Indexed: 01/01/2023]
Abstract
Using a combination of data from the BovineSNP50 BeadChip SNP array (Illumina, San Diego, CA) and a EuroGenomics (Amsterdam, the Netherlands) custom single nucleotide polymorphism (SNP) chip with SNP pre-selected from whole genome sequence data, we carried out an association study of milking speed in 32,491 French Holstein dairy cows. Milking speed was measured by a score given by the farmer. Phenotypes were yield deviations as obtained from the French evaluation system. They were analyzed with a linear mixed model for association studies. We identified SNP on 22 chromosomes significantly associated with milking speed. As clinical mastitis and somatic cell score have an unfavorable genetic correlation with milking speed, we tested whether the most significant SNP on these 22 chromosomes associated with milking speed were also associated with clinical mastitis or somatic cell score. Nine hundred seventy-one genome-wide significant SNP were associated with milking speed. Of these, 86 were associated with clinical mastitis and 198 with somatic cell score. The most significant association signals for milking speed were observed on chromosomes 7, 8, 10, 14, and 18. The most significant signal was located on chromosome 14 (ZFAT gene). Eleven novel milking speed quantitative trait loci (QTL) were observed on chromosomes 7, 10, 11, 14, 18, 25, and 26. Twelve candidate SNP for milking speed mapped directly within genes. Of these, 10 were QTL lead SNP, which mapped within the genes HMHA1, POLR2E, GNB5, KLHL29, ZFAT, KCNB2, CEACAM18, CCL24, and LHPP. Limited pleiotropy was observed between milking speed QTL and clinical mastitis.
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Affiliation(s)
- Andrew Marete
- INRA, UMR 1313 GABI, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France; Aarhus University, Center for Quantitative Genetics and Genomics, 8830 Tjele, Denmark.
| | - Goutam Sahana
- Aarhus University, Center for Quantitative Genetics and Genomics, 8830 Tjele, Denmark
| | - Sébastien Fritz
- INRA, UMR 1313 GABI, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France; ALLICE, 75595 Paris, France
| | - Rachel Lefebvre
- INRA, UMR 1313 GABI, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Anne Barbat
- INRA, UMR 1313 GABI, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Mogens Sandø Lund
- Aarhus University, Center for Quantitative Genetics and Genomics, 8830 Tjele, Denmark
| | - Bernt Guldbrandtsen
- Aarhus University, Center for Quantitative Genetics and Genomics, 8830 Tjele, Denmark
| | - Didier Boichard
- INRA, UMR 1313 GABI, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
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Bouwman AC, Daetwyler HD, Chamberlain AJ, Ponce CH, Sargolzaei M, Schenkel FS, Sahana G, Govignon-Gion A, Boitard S, Dolezal M, Pausch H, Brøndum RF, Bowman PJ, Thomsen B, Guldbrandtsen B, Lund MS, Servin B, Garrick DJ, Reecy J, Vilkki J, Bagnato A, Wang M, Hoff JL, Schnabel RD, Taylor JF, Vinkhuyzen AAE, Panitz F, Bendixen C, Holm LE, Gredler B, Hozé C, Boussaha M, Sanchez MP, Rocha D, Capitan A, Tribout T, Barbat A, Croiseau P, Drögemüller C, Jagannathan V, Vander Jagt C, Crowley JJ, Bieber A, Purfield DC, Berry DP, Emmerling R, Götz KU, Frischknecht M, Russ I, Sölkner J, Van Tassell CP, Fries R, Stothard P, Veerkamp RF, Boichard D, Goddard ME, Hayes BJ. Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nat Genet 2018; 50:362-367. [PMID: 29459679 DOI: 10.1038/s41588-018-0056-5] [Citation(s) in RCA: 173] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Accepted: 01/03/2018] [Indexed: 11/09/2022]
Abstract
Stature is affected by many polymorphisms of small effect in humans 1 . In contrast, variation in dogs, even within breeds, has been suggested to be largely due to variants in a small number of genes2,3. Here we use data from cattle to compare the genetic architecture of stature to those in humans and dogs. We conducted a meta-analysis for stature using 58,265 cattle from 17 populations with 25.4 million imputed whole-genome sequence variants. Results showed that the genetic architecture of stature in cattle is similar to that in humans, as the lead variants in 163 significantly associated genomic regions (P < 5 × 10-8) explained at most 13.8% of the phenotypic variance. Most of these variants were noncoding, including variants that were also expression quantitative trait loci (eQTLs) and in ChIP-seq peaks. There was significant overlap in loci for stature with humans and dogs, suggesting that a set of common genes regulates body size in mammals.
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Affiliation(s)
- Aniek C Bouwman
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Wageningen, the Netherlands
| | - Hans D Daetwyler
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, Australia
| | - Amanda J Chamberlain
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
| | - Carla Hurtado Ponce
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia.,Faculty of Land and Food Resources, University of Melbourne, Parkville, Victoria, Australia
| | - Mehdi Sargolzaei
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada.,The Semex Alliance, Guelph, Ontario, Canada
| | - Flavio S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | | | - Simon Boitard
- Section for Molecular Genetics and Systems Biology. Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Marlies Dolezal
- Platform of Bioinformatics and Statistics, University of Veterinary Medicine, Vienna, Austria
| | - Hubert Pausch
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia.,Chair of Animal Breeding, Technische Universität München, Freising-Weihenstephan, Germany.,Animal Genomics, ETH Zurich, Zurich, Switzerland
| | - Rasmus F Brøndum
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Phil J Bowman
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
| | - Bo Thomsen
- Section for Molecular Genetics and Systems Biology. Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Mogens S Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Bertrand Servin
- GenPhySE, Université de Toulouse, INRA, INPT, INP-ENVT, Castanet-Tolosan, France
| | - Dorian J Garrick
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - James Reecy
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Johanna Vilkki
- Green Technology, Natural Resources Institute Finland (Luke), Jokioinen, Finland
| | | | - Min Wang
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, Australia
| | - Jesse L Hoff
- Division of Animal Sciences, University of Missouri, Columbia, MO, USA
| | - Robert D Schnabel
- Division of Animal Sciences, University of Missouri, Columbia, MO, USA
| | - Jeremy F Taylor
- Division of Animal Sciences, University of Missouri, Columbia, MO, USA
| | - Anna A E Vinkhuyzen
- University of Queensland, Institute for Molecular Bioscience, St Lucia, Queensland, Australia.,University of Queensland, Queensland Brain Institute, St Lucia, Queensland, Australia
| | - Frank Panitz
- Section for Molecular Genetics and Systems Biology. Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Christian Bendixen
- Section for Molecular Genetics and Systems Biology. Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Lars-Erik Holm
- Section for Molecular Genetics and Systems Biology. Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | | | - Chris Hozé
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France.,Allice, Paris, France
| | - Mekki Boussaha
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France
| | | | - Dominique Rocha
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France
| | - Aurelien Capitan
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France.,Allice, Paris, France
| | - Thierry Tribout
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France
| | - Anne Barbat
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France
| | - Pascal Croiseau
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France
| | | | | | - Christy Vander Jagt
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
| | | | - Anna Bieber
- Research Institute of Organic Agriculture (FiBL), Frick, Switzerland
| | - Deirdre C Purfield
- Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Ireland
| | - Donagh P Berry
- Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Ireland
| | - Reiner Emmerling
- Institute of Animal Breeding, Bavarian State Research Centre for Agriculture, Poing, Germany
| | - Kay-Uwe Götz
- Institute of Animal Breeding, Bavarian State Research Centre for Agriculture, Poing, Germany
| | | | | | - Johann Sölkner
- University of Natural Resources and Life Sciences, Vienna, Austria
| | - Curtis P Van Tassell
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, US Department of Agriculture, Beltsville, MD, USA
| | - Ruedi Fries
- Chair of Animal Breeding, Technische Universität München, Freising-Weihenstephan, Germany
| | - Paul Stothard
- Department of Agricultural, Food and Nutritional Science/Livestock Gentec, University of Alberta, Edmonton, Alberta, Canada
| | - Roel F Veerkamp
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Wageningen, the Netherlands
| | - Didier Boichard
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France
| | - Mike E Goddard
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia.,Faculty of Land and Food Resources, University of Melbourne, Parkville, Victoria, Australia
| | - Ben J Hayes
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia. .,Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, University of Queensland, St Lucia, Queensland, Australia.
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Jardim JG, Guldbrandtsen B, Lund MS, Sahana G. Association analysis for udder index and milking speed with imputed whole-genome sequence variants in Nordic Holstein cattle. J Dairy Sci 2017; 101:2199-2212. [PMID: 29274975 DOI: 10.3168/jds.2017-12982] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 10/30/2017] [Indexed: 12/26/2022]
Abstract
Genome-wide association testing facilitates the identification of genetic variants associated with complex traits. Mapping genes that promote genetic resistance to mastitis could reduce the cost of antibiotic use and enhance animal welfare and milk production by improving outcomes of breeding for udder health. Using imputed whole-genome sequence variants, we carried out association studies for 2 traits related to udder health, udder index, and milking speed in Nordic Holstein cattle. A total of 4,921 bulls genotyped with the BovineSNP50 BeadChip array were imputed to high-density genotypes (Illumina BovineHD BeadChip, Illumina, San Diego, CA) and, subsequently, to whole-genome sequence variants. An association analysis was carried out using a linear mixed model. Phenotypes used in the association analyses were deregressed breeding values. Multitrait meta-analysis was carried out for these 2 traits. We identified 10 and 8 chromosomes harboring markers that were significantly associated with udder index and milking speed, respectively. Strongest association signals were observed on chromosome 20 for udder index and chromosome 19 for milking speed. Multitrait meta-analysis identified 13 chromosomes harboring associated markers for the combination of udder index and milking speed. The associated region on chromosome 20 overlapped with earlier reported quantitative trait loci for similar traits in other cattle populations. Moreover, this region was located close to the FYB gene, which is involved in platelet activation and controls IL-2 expression; FYB is a strong candidate gene for udder health and worthy of further investigation.
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Affiliation(s)
- Júlia Gazzoni Jardim
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark; Laboratory of Reproduction and Animal Breeding, State University of North Fluminense Darcy Ribeiro, Av. Alberto Lamego, 2000 Parque California, Campos dos Goytacazes, RJ, 28013-602, Brazil
| | - Bernt Guldbrandtsen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Mogens Sandø Lund
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Goutam Sahana
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark.
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Jiménez-Mena B, Tataru P, Brøndum RF, Sahana G, Guldbrandtsen B, Bataillon T. One size fits all? Direct evidence for the heterogeneity of genetic drift throughout the genome. Biol Lett 2017; 12:rsbl.2016.0426. [PMID: 27405384 DOI: 10.1098/rsbl.2016.0426] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Accepted: 06/16/2016] [Indexed: 01/18/2023] Open
Abstract
Effective population size (Ne) is a central parameter in population and conservation genetics. It measures the magnitude of genetic drift, rates of accumulation of inbreeding in a population, and it conditions the efficacy of selection. It is often assumed that a single Ne can account for the evolution of genomes. However, recent work provides indirect evidence for heterogeneity in Ne throughout the genome. We study this by examining genome-wide diversity in the Danish Holstein cattle breed. Using the differences in allele frequencies over a single generation, we directly estimated Ne among autosomes and smaller windows within autosomes. We found statistically significant variation in Ne at both scales. However, no correlation was found between the detected regional variability in Ne, and proxies for the intensity of linked selection (local recombination rate, gene density), or the presence of either past strong selection or current artificial selection on traits of economic value. Our findings call for further caution regarding the wide applicability of the Ne concept for understanding quantitatively processes such as genetic drift and accumulation of consanguinity in both natural and managed populations.
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Affiliation(s)
- Belén Jiménez-Mena
- Bioinformatics Research Center (BiRC), Aarhus University, Aarhus, Denmark INRA, UMR1313 Génétique animale et biologie intégrative, 78350 Jouy-en-Josas, France AgroParisTech, UMR1313 Génétique animale et biologie intégrative, 16 rue Claude Bernard, 75231 Paris 05, France
| | - Paula Tataru
- Bioinformatics Research Center (BiRC), Aarhus University, Aarhus, Denmark
| | - Rasmus F Brøndum
- Department of Haematology, Aalborg University Hospital, 9000 Aalborg, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Thomas Bataillon
- Bioinformatics Research Center (BiRC), Aarhus University, Aarhus, Denmark
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Cai Z, Petersen B, Sahana G, Madsen LB, Larsen K, Thomsen B, Bendixen C, Lund MS, Guldbrandtsen B, Panitz F. The first draft reference genome of the American mink (Neovison vison). Sci Rep 2017; 7:14564. [PMID: 29109430 PMCID: PMC5674041 DOI: 10.1038/s41598-017-15169-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 10/23/2017] [Indexed: 01/28/2023] Open
Abstract
The American mink (Neovison vison) is a semiaquatic species of mustelid native to North America. It's an important animal for the fur industry. Many efforts have been made to locate genes influencing fur quality and color, but this search has been impeded by the lack of a reference genome. Here we present the first draft genome of mink. In our study, two mink individuals were sequenced by Illumina sequencing with 797 Gb sequence generated. Assembly yielded 7,175 scaffolds with an N50 of 6.3 Mb and length of 2.4 Gb including gaps. Repeat sequences constitute around 31% of the genome, which is lower than for dog and cat genomes. The alignments of mink, ferret and dog genomes help to illustrate the chromosomes rearrangement. Gene annotation identified 21,053 protein-coding sequences present in mink genome. The reference genome's structure is consistent with the microsatellite-based genetic map. Mapping of well-studied genes known to be involved in coat quality and coat color, and previously located fur quality QTL provide new knowledge about putative candidate genes for fur traits. The draft genome shows great potential to facilitate genomic research towards improved breeding for high fur quality animals and strengthen our understanding on evolution of Carnivora.
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Affiliation(s)
- Zexi Cai
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark.
| | - Bent Petersen
- DTU Bioinformatics, Department of Bio and Health Informatics, Technical University of Denmark, 2800 Kgs, Lyngby, Denmark
- Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), Faculty of Applied Sciences, AIMST University, Kedah, Malaysia
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark
| | - Lone B Madsen
- Section for Molecular Genetics and Systems Biology, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark
| | - Knud Larsen
- Section for Molecular Genetics and Systems Biology, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark
| | - Bo Thomsen
- Section for Molecular Genetics and Systems Biology, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark
| | - Christian Bendixen
- Section for Molecular Genetics and Systems Biology, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark
| | - Frank Panitz
- Section for Molecular Genetics and Systems Biology, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark
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Chen M, Su G, Fu J, Zhang Q, Wang A, Sandø Lund M, Guldbrandtsen B. Population admixture in Chinese and European Sus scrofa. Sci Rep 2017; 7:13178. [PMID: 29030577 PMCID: PMC5640611 DOI: 10.1038/s41598-017-13127-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 09/18/2017] [Indexed: 11/30/2022] Open
Abstract
Relationships between different populations were investigated using Porcine 60 K data from 1,135 domestic pigs and wild boars across Europe and China. The results indicate that most European breeds have been introgressed with Chinese ancestry, but the extent of introgression varies considerably among breeds. Moreover, the main source of this introgression is pigs from South China, closely related to Bamaxiang and Dongshan pigs. Contributions from East and Central Chinese pig breeds are also detectable. Phylogeny reconstruction places European wild boars among European domestic breeds. Coalescent simulations indicate that this may be the result of gene flow from European wild boars to European domestic pigs. These results will facilitate further genomic studies such as genome-wide association studies, selection signature detection and genomic prediction.
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Affiliation(s)
- Minhui Chen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark.,Department of Animal Genetics, Breeding and Reproduction, China Agricultural University, Beijing, China
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark.
| | - Jinluan Fu
- Department of Animal Genetics, Breeding and Reproduction, China Agricultural University, Beijing, China
| | - Qin Zhang
- Department of Animal Genetics, Breeding and Reproduction, China Agricultural University, Beijing, China
| | - Aiguo Wang
- Department of Animal Genetics, Breeding and Reproduction, China Agricultural University, Beijing, China
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
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