Articles | Volume 61, issue 1
https://doi.org/10.5194/aab-61-87-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/aab-61-87-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review
| 
13 Feb 2018
Review |
| 13 Feb 2018
| 13 Feb 2018
Invited review: Genetic and genomic mouse models for livestock research
Albrecht Daniel Thaer-Institut für Agrar- und Gartenbauwissenschaften, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
Deike Hesse
Albrecht Daniel Thaer-Institut für Agrar- und Gartenbauwissenschaften, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
Gudrun A. Brockmann
CORRESPONDING AUTHOR
Albrecht Daniel Thaer-Institut für Agrar- und Gartenbauwissenschaften, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
Related subject area
Subject: Quantitative genetics and estimation of breeding value | Animal: Cattle
Expression pattern of ZNF33B in bovine ovaries and the effect of its polymorphism on superovulation traits
The effect of single-nucleotide polymorphisms within heat shock protein beta 1 on beef quantity in Korean native steers
Determination of genetic variation within the DYRK2 gene and its associations with milk traits in cattle
Identification of novel nucleotide sequence variations in an extended region of the bovine leptin gene (LEP) across a variety of cattle breeds from New Zealand and Nigeria
Analysis of lactating cows on commercial Austrian dairy farms: the influence of genotype and body weight on efficiency parameters
Linking first lactation survival to milk yield and components and lactation persistency in Tunisian Holstein cows
Determination of a possible relationship between a single nucleotide polymorphism (SNP) in the promoter region of the SIRT1 gene and production and reproduction traits in the Agerolese cattle breed
(Co)variance components and genetics parameter estimation for linear traits in Holstein cattle in Indonesia: traits related to foot/leg and udder
Body weight prediction using body size measurements in Fleckvieh, Holstein, and Brown Swiss dairy cows in lactation and dry periods
Genetic parameters for longitudinal behavior and health indicator traits generated in automatic milking systems
Q method to map the diversity of stakeholder viewpoints along agricultural innovation systems: a case study on cattle genetic improvement in Niger
Invited review: Reproductive and genomic technologies to optimize breeding strategies for genetic progress in dairy cattle
Individual and combined effects of CAPN1, CAST, LEP and GHR gene polymorphisms on carcass characteristics and meat quality in Holstein bulls
Effects of polymorphisms at LEP, CAST, CAPN1, GHR, FABP4 and DGAT1 genes on fattening performance and carcass traits in Simmental bulls
Genetic analysis of productive life length in Holstein dairy cows using Weibull proportional risk model
Genetic relationship of lactation persistency with milk yield, somatic cell score, reproductive traits, and longevity in Slovak Holstein cattle
AGPAT6 gene EX1_303T > C and EX12_299G > A mutations and associations with economic traits of Chinese Simmental-cross cattle
Estimation of variance components of milk, fat, and protein yields of Tunisian Holstein dairy cattle using Bayesian and REML methods
Genetic parameters of reproductive traits in Tunisian Holsteins
Comparison of breeding values among cows with exceptional longevity and their contemporary herdmates in German Holsteins
Changhong Li, Peijun Xia, Yijuan Ma, Xinyue Zhang, and Yijia Liu
Arch. Anim. Breed., 65, 69–77, https://doi.org/10.5194/aab-65-69-2022, https://doi.org/10.5194/aab-65-69-2022, 2022
Short summary
Short summary
In this study, we first studied the expression of ZNF33B in bovine ovaries and early embryos, and we determined the expression position and quantity of ZNF33B in ovaries and the temporal and spatial specificity of ZNF33B expression in early embryos. Secondly, the G-61G>T mutation of 5' UTR in ZNF33B gene was studied. The frequency of the G genotype was 0.3489 and that of the T genotype was 0.6511. This mutation was significantly correlated with the superovulation traits in cattle.
Jung-Keun Suh, Jae-Sung Lee, Hongsik Kong, Yoonseok Lee, and Hong-Gu Lee
Arch. Anim. Breed., 63, 417–422, https://doi.org/10.5194/aab-63-417-2020, https://doi.org/10.5194/aab-63-417-2020, 2020
Short summary
Short summary
The relationship between HSPB1 expression and muscle growth in beef cattle has previously been reported, but there have been no reports of DNA markers related to meat quantity in Korean native steers. Therefore, the aim of this study was to evaluate the relationship of SNPs within HSPB1 in terms of the carcass traits related to muscle growth in Korean native steers.
Cui Mao, Xing Ju, Haijian Cheng, Xixia Huang, Fugui Jiang, Yuni Yao, Xianyong Lan, and Enliang Song
Arch. Anim. Breed., 63, 315–323, https://doi.org/10.5194/aab-63-315-2020, https://doi.org/10.5194/aab-63-315-2020, 2020
Short summary
Short summary
To speed up the progress of marker-assisted selection (MAS) in cattle breeding, kinesin family member 1A (KIF1A) genes were chosen based on our previous genome-wide association study (GWAS) analysis results. We detected insertion/deletion (InDel) variation in these three candidate genes in 438 individual cattle (Xinjiang Brown cattle and Wagyu × Luxi crossbreed cattle). These findings may aid future analyses of InDel genotypes in cattle breeds and speed up the progress of MAS in cattle breeding.
Ishaku L. Haruna, Sibusiso A. Hadebe, Oyekunle J. Oladosu, Ghassan Mahmoud, Huitong Zhou, and Jon G. H. Hickford
Arch. Anim. Breed., 63, 241–248, https://doi.org/10.5194/aab-63-241-2020, https://doi.org/10.5194/aab-63-241-2020, 2020
Maria Ledinek, Leonhard Gruber, Franz Steininger, Birgit Fuerst-Waltl, Karl Zottl, Martin Royer, Kurt Krimberger, Martin Mayerhofer, and Christa Egger-Danner
Arch. Anim. Breed., 62, 491–500, https://doi.org/10.5194/aab-62-491-2019, https://doi.org/10.5194/aab-62-491-2019, 2019
Short summary
Short summary
The aim of this study was to evaluate the influence of body weight on the efficiency of dairy cows. Data were derived from 161 commercial Austrian dairy farms. An optimum body weight range for efficiency does exist as the relationship of milk yield and body weight is nonlinear. Specialized dairy breeds seem to respond more intensively to body weight range than dual-purpose breeds. Cows with medium weights are the most efficient. A further increase in dairy cows’ body weights should be avoided.
Marwa Grayaa, Sylvie Vanderick, Boulbaba Rekik, Abderrahman Ben Gara, Christian Hanzen, Siwar Grayaa, Rodrigo Reis Mota, Hedi Hammami, and Nicolas Gengler
Arch. Anim. Breed., 62, 153–160, https://doi.org/10.5194/aab-62-153-2019, https://doi.org/10.5194/aab-62-153-2019, 2019
Short summary
Short summary
The lactation curve and lactation survival are important in dairy cattle breeding. Their simultaneous improvement results in economic benefits. The genetic relationship between the lactation survival and the lactation curve shape traits of milk yield and fat and protein percentages using information from of 25 981 primiparous Tunisian Holsteins was investigated. Cows that had higher persistencies for fat and protein percentages were more likely not to survive.
Maria Selvaggi, Claudia Carbonara, Francesca Ciotola, Sara Albarella, Giulio Aiudi, Vincenzo Tufarelli, and Cataldo Dario
Arch. Anim. Breed., 62, 107–112, https://doi.org/10.5194/aab-62-107-2019, https://doi.org/10.5194/aab-62-107-2019, 2019
Short summary
Short summary
The aims of the study were to estimate allele and genotype frequencies at g.-274C > G locus in the promoter region of the SIRT1 gene and to investigate, for the first time, the relationship among different genotypes and milk and reproduction traits in the Agerolese cattle breed. The investigated population was found to be polymorphic at the investigated locus. Concerning milk production performance, significant differences between genotypes were found.
Agus Susanto, Suyadi, Veronica Margareta Ani Nurgiartiningsih, and Luqman Hakim
Arch. Anim. Breed., 61, 491–496, https://doi.org/10.5194/aab-61-491-2018, https://doi.org/10.5194/aab-61-491-2018, 2018
Short summary
Short summary
Improved milk yield in dairy cows results in worsening health and reproduction. Some linear traits are genetically associated with health and reproduction and can be used to assist selection programs. The National Breeding Centre for Dairy and Forage of Indonesia has not included linear traits in the breeding program. This study estimates genetic parameters of linear traits. The traits under study were found to be heritable and can be included in the breeding program along with milk yield.
Leonhard Gruber, Maria Ledinek, Franz Steininger, Birgit Fuerst-Waltl, Karl Zottl, Martin Royer, Kurt Krimberger, Martin Mayerhofer, and Christa Egger-Danner
Arch. Anim. Breed., 61, 413–424, https://doi.org/10.5194/aab-61-413-2018, https://doi.org/10.5194/aab-61-413-2018, 2018
Short summary
Short summary
The objective of this study was to predict dairy cows' body weight from body size measurements. Body weight is an important trait for both management and breeding. Data were derived from 167 commercial Austrian dairy farms. To ensure high prediction accuracy, the use of a combination of both heart girth and belly girth is recommended if the use of scales is impossible. The large and heterogeneous data set supported a valid prediction.
Laura Viviana Santos, Kerstin Brügemann, Asja Ebinghaus, and Sven König
Arch. Anim. Breed., 61, 161–171, https://doi.org/10.5194/aab-61-161-2018, https://doi.org/10.5194/aab-61-161-2018, 2018
Short summary
Short summary
The impact of the milking technique on the individual animal and the reaction of the animal on the technique were investigated. With the use of objectively recorded data from automatic milking systems (AMSs), auxiliary traits that reflect animal behavior in the milking system were defined, free from subjective impressions of classifiers. There is an apparent possibility to breed cows for AMS systems based on AMS data, though it is imperative to have further validation based on larger datasets.
Seyni Siddo, Nassim Moula, Issa Hamadou, Moumouni Issa, Salissou Issa, Marichatou Hamani, Pascal Leroy, and Nicolas Antoine-Moussiaux
Arch. Anim. Breed., 61, 143–151, https://doi.org/10.5194/aab-61-143-2018, https://doi.org/10.5194/aab-61-143-2018, 2018
Short summary
Short summary
The complex balance between innovation and conservation regarding animal genetic resources makes it difficult to find mutually accepted improvement pathways between breeders, government agencies, and research and education institutions. This study maps stakeholder viewpoints on cattle genetic improvement in Niger using the Q method, which appears effective in identifying the concerns of stakeholders on complex agricultural innovation topics.
Allison Fleming, Emhimad A. Abdalla, Christian Maltecca, and Christine F. Baes
Arch. Anim. Breed., 61, 43–57, https://doi.org/10.5194/aab-61-43-2018, https://doi.org/10.5194/aab-61-43-2018, 2018
Short summary
Short summary
Dairy cattle breeders have exploited technological advances in regard to reproduction and genomics. The implementation of such technologies in routine breeding programs has permitted genetic gains in traditional milk production traits as well as, more recently, in low heritability traits like health and fertility. Here we review a number of technologies that have helped shape dairy breeding programs in the past and present, along with those potentially forthcoming.
Sena Ardicli, Hale Samli, Deniz Dincel, Bahadir Soyudal, and Faruk Balci
Arch. Anim. Breed., 60, 303–313, https://doi.org/10.5194/aab-60-303-2017, https://doi.org/10.5194/aab-60-303-2017, 2017
Short summary
Short summary
The impact of polymorphisms of the calpain 1 (CAPN1), calpastatin (CAST), leptin (LEP) and growth hormone receptor (GHR) on carcass characteristics and meat quality traits in 400 purebred Holstein bulls was examined using polymerase chain reaction and restriction fragment length polymorphism method. In the current study, the CAPN1, CAST and GHR genotypes confirmed significant associations with important traits in adequate numbers of animals.
Sena Ardicli, Deniz Dincel, Hale Samli, and Faruk Balci
Arch. Anim. Breed., 60, 61–70, https://doi.org/10.5194/aab-60-61-2017, https://doi.org/10.5194/aab-60-61-2017, 2017
Short summary
Short summary
The present study was based on the association of polymorphisms at LEP, CAST, CAPN1, GHR, FABP4 and DGAT1 genes with fattening performance and carcass traits in Simmental bulls. Results indicated that final weight, fattening period, total weight gain and average daily gain differentiated the CAST and CAPN1 marker genotypes. Moreover, a novel effect of the LEP A80V on carcass weight and dressing percentage was observed. The results could be indicative for future studies on beef production.
Hamed Amirpour Najafabadi, Saeid Ansari Mahyari, Mohammad Ali Edriss, and Eva Strapakova
Arch. Anim. Breed., 59, 387–393, https://doi.org/10.5194/aab-59-387-2016, https://doi.org/10.5194/aab-59-387-2016, 2016
Short summary
Short summary
This study aimed to use survival analysis and examine the factors affecting functional herd life (FHL) of cows in Isfahan using the Weibull proportional risk model in Isfahan to estimate of breeding values for functional herd life. The SC, milk production, age at first calving and milk period showed significant effects on LPL (P < 0.001) according to the likelihood ratio test. Although there were some fluctuations in genetic trends, an overall increase was observed which leads to a longer FHL
Eva Strapáková, Juraj Candrák, and Peter Strapák
Arch. Anim. Breed., 59, 329–335, https://doi.org/10.5194/aab-59-329-2016, https://doi.org/10.5194/aab-59-329-2016, 2016
Short summary
Short summary
The advantages of increasing lactation persistency in dairy cattle are indisputable on the practical herd level. More persistent lactation brings a longer high-production period. In this study estimate the breeding values of lactation persistency, test day of milk yield, somatic cell score, reproductive traits (calving interval, days open), and longevity in Slovak Holstein dairy cattle were estimated and the relationship between persistency of lactation and other selected trait was analyzed.
Xiaojuan Long, Xibi Fang, Ping Jiang, Hang Xiao, Haibin Yu, Mengjiao Zhou, Yunzhi Pan, Chunyan Lu, Zhihui Zhao, and Runjun Yang
Arch. Anim. Breed., 59, 301–307, https://doi.org/10.5194/aab-59-301-2016, https://doi.org/10.5194/aab-59-301-2016, 2016
Short summary
Short summary
AGPAT6 is a key candidate gene in the lipid metabolism pathway. We detected two single nucleotide polymorphisms (SNPs) in bovine AGPAT6 exons, and we analyzed 33 traits associated with meat quality and the carcass of Chinese Simmental cattle. SNP1 was significantly associated with back fat thickness and fat coverage rate. SNP2 was significantly associated with fat coverage rate and marbling score. Thus, the SNPs of AGPAT6 may be genetic factors influencing carcass composition of beef cattle.
Hafedh Ben Zaabza, Abderrahmen Ben Gara, Hedi Hammami, Mohamed Amine Ferchichi, and Boulbaba Rekik
Arch. Anim. Breed., 59, 243–248, https://doi.org/10.5194/aab-59-243-2016, https://doi.org/10.5194/aab-59-243-2016, 2016
Short summary
Short summary
A Bayesian and REML analyses were used on Tunisian dairy cattle data. Genetic parameters for 305-day milk, fat, and protein yields were estimated. Heritability estimates by using Bayesian method ranged from 0.187 to 0.273, and slightly higher than the corresponding REML. Large genetic correlations (> 0.88) among milk, fat, and protein yields were found for the two methods. This study suggests that results from both methods were reasonably similar to suggest both methods can be used.
Hafedh Ben Zaabza, Abderrahmen Ben Gara, Hedi Hammami, Borni Jemmali, Mohamed Amine Ferchichi, and Boulbaba Rekik
Arch. Anim. Breed., 59, 209–213, https://doi.org/10.5194/aab-59-209-2016, https://doi.org/10.5194/aab-59-209-2016, 2016
Short summary
Short summary
Genetic parameters for 11 456 Tunisian Holstein cows were estimated for the following five reproductive traits: calving interval (CI), calving to first service interval, calving to conception interval (CCI), first service to conception interval and number of services per conception. Low heritabilities were estimated for these traits, and genetic correlation estimates between them were moderately high. The CCI–CI genetic correlation was 0.85, indicating that they are the same trait genetically.
K. Abfalter, W. Brade, and O. Distl
Arch. Anim. Breed., 59, 71–77, https://doi.org/10.5194/aab-59-71-2016, https://doi.org/10.5194/aab-59-71-2016, 2016
Short summary
Short summary
Estimated breeding values (EBVs), relative breeding values (RBVs), and daughter yield deviations of Holstein cows with exceptional longevity (more than nine lactations completed) and the RBVs of their sires were analyzed. Exceptional cows had on average significantly lower EBVs for yield traits but significantly higher RBVs for somatic cell score (RZS) and functional longevity (RZN). Correlations among the proportion of exceptional cows per sire and RZN, RZS as well as fitness were positive.
Cited articles
Agarwal, R., Rout, P. K., and Singh, S. K.: Leptin: A biomolecule for enhancing
livestock productivity, Indian J. Biotechnol., 8, 169–176, 2009.
Al-Asmakh, M. and Zadjali, F.: Use of germ-free animal models in microbiota-related
research, J. Microbiol. Biotechnol., 25, 1583–1588, https://doi.org/10.4014/jmb.1501.01039, 2015.
Antoine-Moussiaux, N., Magez, S., and Desmecht, D.: Contributions of
experimental mouse models to the understanding of African trypanosomiasis,
Trends Parasitol., 24, 411–418, https://doi.org/10.1016/j.pt.2008.05.010, 2008.
Arends, D., Prins, P., Jansen, R. C., and Broman, K. W.: R/qtl: high-throughput
multiple QTL mapping, Bioinformatics, 26, 2990–2992, https://doi.org/10.1093/bioinformatics/btq565, 2010.
Arends, D., Heise, S., Karst, S., Trost, J., and Brockmann, G. A.: Fine mapping
a major obesity locus (jObes1) using a Berlin Fat Mouse × B6N advanced
intercross population, Int. J. Obes., 40, 1784–1788, https://doi.org/10.1038/ijo.2016.150, 2016.
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J.
M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., Harris, M. A.,
Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J. C.,
Richardson, J. E., Ringwald, M., Rubin, G. M., and Sherlock, G.: Gene ontology:
tool for the unification of biology. The Gene Ontology Consortium, Nat. Genet.,
25, 25–29, https://doi.org/10.1038/75556, 2000.
Bailey, D. W.: Recombinant-inbred strains: An Aid to Finding Identity, Linkage,
and Function of Histocompatibility and Other Genes, Transplantation, 11, 325–327,
https://doi.org/10.1097/00007890-197103000-00013, 1971.
Baron, T. G. M., Biacabe, A. G., Bencsik, A., and Langeveld, J. P. M.: Transmission
of new bovine prion to mice, Emerg. Infect. Dis., 12, 1125–1128, https://doi.org/10.3201/eid1207.060107, 2006.
Baron, T. G. M., Bencsik, A., Biacabe, A. G., Morignat, E., and Bessen, R. A.:
Phenotypic similarity of transmissible mink encephalopathy in cattle and L-type
bovine spongiform encephalopathy in a mouse model, Emerg. Infect. Dis., 13,
1887–1894, https://doi.org/10.3201/eid13112.070635, 2007.
Blom-Potar, M. C., Chamond, N., Cosson, A., Jouvion, G., Droin-Bergère, S.,
Huerre, M., and Minoprio, P.: Trypanosoma vivax infections: Pushing ahead with
mouse models for the study of nagana. ii. immunobiological dysfunctions, PLoS
Negl. Trop. Dis., 4, e793, https://doi.org/10.1371/journal.pntd.0000793, 2010.
Brockmann, G. A.: Inbreeding and corssbreeding, in: The Mouse in Animal Genetics
and Breeding Research, Imperial College Press and distributed by World Scientific
Publishing Co., Imperial College Press, Covent Garden, London, UK, 57–83, 2005.
Brockmann, G. A. and Bevova, M. R.: Using mouse models to dissect the genetics
of obesity, Trends Genet., 18, 367–376, https://doi.org/10.1016/S0168-9525(02)02703-8, 2002.
Broman, K. W., Wu, H., Sen, S., and Churchill, G. A.: R/qtl: QTL mapping in
experimental crosses, Bioinformatics, 19, 889–890, https://doi.org/10.1093/bioinformatics/btg112, 2003.
Brown, S. D. M. and Moore, M. W.: The International Mouse Phenotyping Consortium:
Past and future perspectives on mouse phenotyping, Mamm. Genome, 23, 632–640,
https://doi.org/10.1007/s00335-012-9427-x, 2012.
Bryant, C. D., Zhang, N. N., Sokoloff, G., Fanselow, M. S., Ennes, H. S., Palmer,
A. A., and McRoberts, J. A.: Behavioral differences among C57BL/6 substrains:
Implications for transgenic and knockout studies, J. Neurogenet., 22, 315–331,
https://doi.org/10.1080/01677060802357388, 2008.
Charleston, B., Bankowski, B. M., Gubbins, S., Chase-Topping, M. E., Schley,
D., Howey, R., Barnett, P. V, Gibson, D., Juleff, N. D., and Woolhouse, M. E. J.:
Relationship between clinical signs and transmission of an infectious disease
and the implications for control, Science, 332, 726–729, https://doi.org/10.1126/science.1199884, 2011.
Chen, X., Yang, J., Ji, Y., Okoth, E., Liu, B., Li, X., Yin, H., and Zhu, Q.:
Recombinant Newcastle disease virus expressing African swine fever virus
protein 72 is safe and immunogenic in mice, Virol. Sin., 31, 150–159,
https://doi.org/10.1007/s12250-015-3692-2, 2016.
Chua, S. C., Chung, W. K., Wu-Peng, X. S., Zhang, Y., Liu, S. M., Tartaglia, L.,
and Leibel, R. L.: Phenotypes of mouse diabetes and rat fatty due to mutations
in the OB (leptin) receptor, Science, 271, 994–996, https://doi.org/10.1126/science.271.5251.994, 1996.
Clavel, T., Lagkouvardos, I., Blaut, M., and Stecher, B.: The mouse gut microbiome
revisited: From complex diversity to model ecosystems, Int. J. Med. Microbiol.,
306, 316–327, https://doi.org/10.1016/j.ijmm.2016.03.002, 2016.
Clop, A., Marcq, F., Takeda, H., Pirottin, D., Tordoir, X., Bibé, B.,
Bouix, J., Caiment, F., Elsen, J. M., Eychenne, F., Larzul, C., Laville, E.,
Meish, F., Milenkovic, D., Tobin, J., Charlier, C., and Georges, M.: A mutation
creating a potential illegitimate microRNA target site in the myostatin gene
affects muscularity in sheep, Nat. Genet., 38, 813–818, https://doi.org/10.1038/ng1810, 2006.
Complex Trait Consortium: The Collaborative Cross, a community resource for the
genetic analysis of complex traits, Nat. Genet., 36, 1133–1137, 2004.
Cordes, S. P.: N-ethyl-N-nitrosourea mutagenesis: boarding the mouse mutant
express, Microbiol. Mol. Biol. Rev., 69, 426–439, https://doi.org/10.1128/MMBR.69.3.426-439.2005, 2005.
Darvasi, A. and Soller, M.: Advanced intercross lines, an experimental population
for fine genetic mapping, Genetics, 141, 1199–1207, 1995.
DeVuyst, E. A., Bullinger, J. R., Bauer, M. L., Berg, P. T., and Larson, D. M.:
An economic analysis of genetic information: Leptin genotyping in fed cattle,
J. Agric. Resour. Econ., 32, 291–305, 2007.
Dogan, A., Lasch, P., Neuschl, C., Millrose, M. K., Alberts, R., Schughart, K.,
Naumann, D., and Brockmann, G. A.: ATR-FTIR spectroscopy reveals genomic loci
regulating the tissue response in high fat diet fed BXD recombinant inbred mouse
strains, BMC Genomics, 14, 386, https://doi.org/10.1186/1471-2164-14-386, 2013.
Elsik, C. G., Tellam, R. L., and Worley, K. C.: The Genome Sequence of Taurine
Cattle: A window to ruminant biology and evolution, Science, 324, 522–528,
https://doi.org/10.1126/science.1169588, 2009.
Esvelt, K. M., Mali, P., Braff, J. L., Moosburner, M., Yaung, S. J., and Church,
G. M.: Orthogonal Cas9 proteins for RNA-guided gene regulation and editing, Nat.
Methods, 10, 1116–1123, https://doi.org/10.1038/nmeth.2681, 2013.
Evsikov, A. V., Dolan, M. E., Genrich, M. P., Patek, E., and Bult, C. J.:
MouseCyc: A curated biochemical pathways database for the laboratory mouse,
Genome Biol., 10, R84, https://doi.org/10.1186/gb-2009-10-8-r84, 2009.
Finger, J. H., Smith, C. M., Hayamizu, T. F., McCright, I. J., Xu, J., Law, M.,
Shaw, D. R., Baldarelli, R. M., Beal, J. S., Blodgett, O., Campbell, J. W.,
Corbani, L. E., Lewis, J. R., Forthofer, K. L., Frost, P. J., Giannatto, S. C.,
Hutchins, L. N., Miers, D. B., Motenko, H., Stone, K. R., Eppig, J. T., Kadin,
J. A., Richardson, J. E., and Ringwald, M.: The mouse Gene Expression Database (GXD):
2017 update, Nucl. Acids Res., 45, D730–D736, https://doi.org/10.1093/nar/gkw1073, 2017.
Flowers, S. A. and Ellingrod, V. L.: The Microbiome in Mental Health: Potential
Contribution of Gut Microbiota in Disease and Pharmacotherapy Management,
Pharmacotherapy, 35, 910–916, https://doi.org/10.1002/phar.1640, 2015.
Foote, S. J., Iraqi, F., and Kemp, S. J.: Controlling malaria and African
trypanosomiasis: The role of the mouse, Brief. Funct. Genom. Proteom., 4,
214–224, https://doi.org/10.1093/bfgp/4.3.214, 2005.
Fürbass, R., Winter, A., Fries, R., and Kühn, C.: Alleles of the bovine
DGAT1 variable number of tandem repeat associated with a milk fat QTL at
chromosome 14 can stimulate gene expression, Physiol. Genomics, 25, 116–120,
https://doi.org/10.1152/physiolgenomics.00145.2005, 2006.
Gatti, D. M., Svenson, K. L., Shabalin, A., Wu, L.-Y., Valdar, W., Simecek, P.,
Goodwin, N., Cheng, R., Pomp, D., Palmer, A., Chesler, E. J., Broman, K. W.,
and Churchill, G. A.: Quantitative Trait Locus Mapping Methods for Diversity
Outbred Mice, G3 Genes Genom. Genet., 4, 1623–1633, https://doi.org/10.1534/g3.114.013748, 2014.
Gene Ontology Consortium: Expansion of the gene ontology knowledgebase and
resources: The gene ontology consortium, Nucl. Acids Res., 45, D331–D338,
https://doi.org/10.1093/nar/gkw1108, 2017.
Giblin, L., Butler, S. T., Kearney, B. M., Waters, S. M., Callanan, M. J., and
Berry, D. P.: Association of bovine leptin polymorphisms with energy output and
energy storage traits in progeny tested Holstein-Friesian dairy cattle sires,
BMC Genet., 11, 73, https://doi.org/10.1186/1471-2156-11-73, 2010.
Goddard, M. E., Kemper, K. E., MacLeod, I. M., Chamberlain, A. J., and Hayes,
B. J.: Genetics of complex traits: prediction of phenotype, identification
of causal polymorphisms and genetic architecture, P. Roy. Soc. B, 283,
20160569, https://doi.org/10.1098/rspb.2016.0569, 2016.
Grisart, B., Coppieters, W., Farnir, F., Karim, L., Ford, C., Berzi, P.,
Cambisano, N., Mni, M., Reid, S., Simon, P., Spelman, R., Georges, M., and Snell,
R.: Positional candidate cloning of a QTL in dairy cattle: Identification of a
missense mutation in the bovine DGAT1 gene with major effect on milk yield and
composition, Genome Res., 12, 222–231, https://doi.org/10.1101/gr.224202, 2002.
Grobet, L., Martin, L. J. R., Poncelet, D., Pirottin, D., Brouwers, B., Riquet,
J., Schoeberlein, A., Dunner, S., Ménissier, F., Massabanda, J., Fries, R.,
Hanset, R., and Georges, M.: A deletion in the bovine myostatin gene causes the
double-muscled phenotype in cattle, Nat. Genet., 17, 71–74, https://doi.org/10.1038/ng0997-71, 1997.
Guo, R., Wan, Y., Xu, D., Cui, L., Deng, M., Zhang, G., Jia, R., Zhou, W., Wang,
Z., Deng, K., Huang, M., Wang, F., and Zhang, Y.: Generation and evaluation of
Myostatin knock-out rabbits and goats using CRISPR/Cas9 system, Sci. Rep., 6,
29855, https://doi.org/10.1038/srep29855, 2016.
Gupta, R. M. and Musunuru, K.: Expanding the genetic editing tool kit: ZFNs,
TALENs, and CRISPR-Cas9, J. Clin. Invest., 124, 4154–4161, https://doi.org/10.1172/JCI72992, 2014.
Habiela, M., Seago, J., Perez-Martin, E., Waters, R., Windsor, M., Salguero,
F. J., Wood, J., Charleston, B., and Juleff, N.: Laboratory animal models to
study foot-and-mouth disease: a review with emphasis on natural and vaccine-induced
immunity, J. Gen. Virol., 95, 2329–2345, https://doi.org/10.1099/vir.0.068270-0, 2014.
Hitzemann, R., Malmanger, B., Cooper, S., Coulombe, S., Reed, C., Demarest, K.,
Koyner, J., Cipp, L., Flint, J., Talbot, C., Rademacher, B., Buck, K., and
McCaughran, J.: Multiple cross mapping (MCM) markedly improves the localization
of a QTL for ethanol-induced activation, Genes. Brain. Behav., 1, 214–222,
https://doi.org/10.1034/j.1601-183X.2002.10403.x, 2002.
Humphray, S. J., Scott, C. E., Clark, R., Marron, B., Bender, C., Camm, N.,
Davis, J., Jenks, A., Noon, A., Patel, M., Sehra, H., Yang, F., Rogatcheva, M.
B., Milan, D., Chardon, P., Rohrer, G., Nonneman, D., de Jong, P., Meyers, S. N.,
Archibald, A., Beever, J. E., Schook, L. B., and Rogers, J.: A high utility
integrated map of the pig genome, Genome Biol., 8, R139, https://doi.org/10.1186/gb-2007-8-7-r139, 2007.
Iannuzzi, L., Di Meo, G. P., Perucatti, A., and Incarnato, D.: Comparison of
the human with the sheep genomes by use of human chromosome-specific painting
probes, Mamm. Genome, 10, 719–723, https://doi.org/10.1007/s003359901078, 1999.
Kambadur, R., Sharma, M., Smith, T. P. L., and Bass, J. J.: Mutations in
myostatin (GDF8) in double-muscled Belgian Blue and Piedmontese cattle, Genome
Res., 7, 910–916, https://doi.org/10.1101/gr.7.9.910, 1997.
Kinross, J. M., Darzi, A. W., and Nicholson, J. K.: Gut microbiome-host interactions
in health and disease, Genome Med., 3, 14, https://doi.org/10.1186/gm228, 2011.
Kononoff, P. J., Deobald, H. M., Stewart, E. L., Laycock, A. D., and Marquess,
F. L. S.: The effect of a leptin single nucleotide polymorphism on quality grade,
yield grade, and carcass weight of beef cattle, J. Anim. Sci., 83, 927–932,
https://doi.org/10.2527/2005.834927x, 2005.
Lyon, M. F.: Rules and guidelines for gene nomenclature, in: 2nd Edn., edited
by: Searle, A. G., Oxford University Press, Oxford, 1989.
McPherron, A. C. and Lee, S.-J.: Double muscling in cattle due to mutations in
the myostatin gene, P. Natl. Acad. Sci. USA, 94, 12457–12461, https://doi.org/10.1073/pnas.94.23.12457, 1997.
McPherron, A. C., Lawler, A. M., and Lee, S. J.: Regulation of skeletal muscle
mass in mice by a new TGF-beta superfamily member, Nature, 387, 83–90,
https://doi.org/10.1038/387083a0, 1997.
Mekada, K., Abe, K., Murakami, A., Nakamura, S., Nakata, H., Moriwaki, K.,
Obata, Y., Yoshiki, A., and Yoshiki, A.: Genetic Differences among C57BL/6
Substrains, Exp. Anim., 58, 141–149, https://doi.org/10.1538/expanim.58.141, 2009.
Nguyen, N. Y. N., Maxwell, M. J., Ooms, L. M., Davies, E. M., Hilton, A. A.,
Collinge, J. E., Hilton, D. J., Kile, B. T., Mitchell, C. A., Hibbs, M. L.,
Jane, S. M., and Curtis, D. J.: An ENU-induced mouse mutant of SHIP1 reveals
a critical role of the stem cell isoform for suppression of macrophage activation,
Blood, 117, 5362–5371, https://doi.org/10.1182/blood-2011-01-331041, 2011.
Nolan, P. M., Hugill, A., and Cox, R. D.: ENU mutagenesis in the mouse:
Application to human genetic disease, Brief. Funct. Genom. Proteom., 1, 278–289,
https://doi.org/10.1093/bfgp/1.3.278, 2002.
Oreper, D., Cai, Y., Tarantino, L. M., de Villena, F. P.-M., and Valdar, W.:
Inbred Strain Variant Database (ISVdb): A Repository for Probabilistically
Informed Sequence Differences Among the Collaborative Cross Strains and Their
Founders, G3 Genes Genom. Genet., 7, 1623–1630, https://doi.org/10.1534/g3.117.041491, 2017.
Rabot, S., Membrez, M., Bruneau, A., Gerard, P., Harach, T., Moser, M., Raymond,
F., Mansourian, R., and Chou, C. J.: Germ-free C57BL/6J mice are resistant to
high-fat-diet-induced insulin resistance and have altered cholesterol metabolism,
FASEB J., 24, 4948–4959, https://doi.org/10.1096/fj.10-164921, 2010.
Revilla, Y., Pena, L., Mampaso, F., Viñuela, E., and Martinez-Alonso, C.:
Swine-reconstituted SCID mice as a model for African swine fever virus infection,
J. Gen. Virol., 75, 1983–1988, https://doi.org/10.1099/0022-1317-75-8-1983, 1994.
Riek, R., Hornemann, S., Wider, G., Billeter, M., Glockshuber, R., and
Wüthrich, K.: NMR structure of the mouse prion protein domain PrP(121–231),
Nature, 382, 180–182, https://doi.org/10.1038/382180a0, 1996.
Rjeibi, M. R., Ben Hamida, T., Dalgatova, Z., Mahjoub, T., Rejeb, A., Dridi, W.,
and Gharbi, M.: First report of surra (Trypanosoma evansi infection) in a
Tunisian dog, Parasite, 22, 3, https://doi.org/10.1051/parasite/2015004, 2015.
Rosshart, S. P., Vassallo, B. G., Angeletti, D., Hutchinson, D. S., Morgan, A.
P., Takeda, K., Hickman, H. D., McCulloch, J. A., Badger, J. H., Ajami, N. J.,
Trinchieri, G., Pardo-Manuel de Villena, F., Yewdell, J. W., and Rehermann, B.:
Wild Mouse Gut Microbiota Promotes Host Fitness and Improves Disease Resistance,
Cell, 171, 1015–1028, https://doi.org/10.1016/j.cell.2017.09.016, 2017.
Schering, L., Hoene, M., Kanzleiter, T., Jähnert, M., Wimmers, K., Klaus,
S., Eckel, J., Weigert, C., Schürmann, A., Maak, S., Jonas, W., and Sell,
H.: Identification of novel putative adipomyokines by a cross-species annotation
of secretomes and expression profiles, Arch. Physiol. Biochem., 24, 1–12,
https://doi.org/10.3109/13813455.2015.1092044, 2015.
Schibler, L., Vaiman, D., Oustry, A., Giraud-Delville, C., and Cribiu, E. P.:
Comparative gene mapping: A fine-scale survey of chromosome rearrangements
between ruminants and humans, Genome Res., 8, 901–915, https://doi.org/10.1101/gr.8.9.901, 1998.
Schmid, M. and Bennewitz, J.: Invited review: Genome-wide association analysis
for quantitative traits in livestock – a selective review of statistical models
and experimental designs, Arch. Anim. Breed., 60, 335–346, https://doi.org/10.5194/aab-60-335-2017, 2017.
Scott, M. R., Safar, J., Telling, G., Nguyen, O., Groth, D., Torchia, M.,
Koehler, R., Tremblay, P., Walther, D., Cohen, F. E., DeArmond, S. J., and
Prusiner, S. B.: Identification of a prion protein epitope modulating transmission
of bovine spongiform encephalopathy prions to transgenic mice, P. Natl. Acad.
Sci. USA, 94, 14279–14284, https://doi.org/10.1073/pnas.94.26.14279, 1997.
Sherry, S. T.: dbSNP: the NCBI database of genetic variation, Nucl. Acids Res.,
29, 308–311, https://doi.org/10.1093/nar/29.1.308, 2001.
Síma, M., Havelková, H., Quan, L., Svobodová, M., Jarošíková,
T., Vojtíšková, J., Stassen, A. P. M., Demant, P., and Lipoldová,
M.: Genetic control of resistance to Trypanosoma brucei brucei infection in mice,
PLoS Negl. Trop. Dis., 5, e1173, https://doi.org/10.1371/journal.pntd.0001173, 2011.
Skinner, H. H.: Propagation of Strains of Foot-and-Mouth Disease Virus in
Unweaned White Mice, P. Roy. Soc. Med., 44, 1041–1044, 1951.
Sloan, Z., Arends, D., Broman, K. W., Centeno, A., Furlotte, N., Nijveen, H.,
Yan, L., Zhou, X., Williams, R. W., and Prins, P.: GeneNetwork: framework for
web-based genetics, J. Open Source Softw., 1, 8–10, https://doi.org/10.21105/joss.00025, 2016.
Smith, S. J., Cases, S., Jensen, D. R., Chen, H. C., Sande, E., Tow, B., Sanan,
D. A., Raber, J., Eckel, R. H., and Farese, R. V.: Obesity resistance and
multiple mechanisms of triglyceride synthesis in mice lacking Dgat, Nat. Genet.,
25, 87–90, https://doi.org/10.1038/75651, 2000.
Svenson, K. L., Von Smith, R., Magnani, P. A., Suetin, H. R., Paigen, B.,
Naggert, J. K., Li, R., Churchill, G. A., and Peters, L. L.: Multiple trait
measurements in 43 inbred mouse strains capture the phenotypic diversity
characteristic of human populations, J. Appl. Physiol., 102, 2369–2378,
https://doi.org/10.1152/japplphysiol.01077.2006, 2007.
Telling, G. C.: Transgenic mouse models and prion strains, Top. Curr. Chem.,
305, 79–100, https://doi.org/10.1007/128_2011_166, 2011.
Thaller, G., Krämer, W., Winter, A., Kaupe, B., Erhardt, G., and Fries, R.:
Effects of DGAT1 variants on milk production traits in German cattle breeds,
J. Anim. Sci., 81, 1911–1918, 2003.
Tulman, E. R., Delhon, G. A., Ku, B. K., and Rock, D. L.: African swine fever
virus, Curr. Top. Microbiol. Immunol., 328, 43–87, https://doi.org/10.1007/978-3-540-68618-7_2, 2009.
Wang, K., Tang, X., Xie, Z., Zou, X., Li, M., Yuan, H., Guo, N., Ouyang, H.,
Jiao, H., and Pang, D.: CRISPR/Cas9-mediated knockout of myostatin in Chinese
indigenous Erhualian pigs, Transgenic Res., 26, 799–805, https://doi.org/10.1007/s11248-017-0044-z, 2017.
Williams, J. L.: Genetic control of meat quality traits, in: Meat Biotechnology,
Springer, New York, NY, 21–60, 2008.
Wilson, D. E. and Reeder, D. M.: Mammal Species of the World. A Taxonomic
and Geographic Reference, in: 3rd Edn., Johns Hopkins University Press, Baltimore,
Maryland, USA, 2005.
Wilson, R., Hart, P., Piccardo, P., Hunter, N., Casalone, C., Baron, T., and
Barron, R. M.: Bovine PrP expression levels in transgenic mice influence
transmission characteristics of atypical bovine spongiform encephalopathy,
J. Gen. Virol., 93, 1132–1140, https://doi.org/10.1099/vir.0.040030-0, 2012.
Wu, C., Huang, H., Juan, H., and Chen, S.: GeneNetwork: an interactive tool
for reconstruction of genetic networks using microarray data, Bioinformatics,
20, 3691–3693, https://doi.org/10.1093/bioinformatics/bth428, 2004.
Yalcin, B., Willis-Owen, S. A. G., Fullerton, J., Meesaq, A., Deacon, R. M.,
Rawlins, J. N. P., Copley, R. R., Morris, A. P., Flint, J., and Mott, R.:
Genetic dissection of a behavioral quantitative trait locus shows that Rgs2
modulates anxiety in mice, Nat. Genet., 36, 1197–1202, https://doi.org/10.1038/ng1450, 2004.
Zerbino, D. R., Achuthan, P., Akanni, W., Amode, M. R., Barrell, D., Bhai, J.,
Billis, K., Cummins, C., Gall, A., Girón, C. G., Gil, L., Gordon, L.,
Haggerty, L., Haskell, E., Hourlier, T., Izuogu, O. G., Janacek, S. H.,
Juettemann, T., To, J. K., Laird, M. R., Lavidas, I., Liu, Z., Loveland, J. E.,
Maurel, T., McLaren, W., Moore, B., Mudge, J., Murphy, D. N., Newman, V., Nuhn,
M., Ogeh, D., Ong, C. K., Parker, A., Patricio, M., Riat, H. S., Schuilenburg,
H., Sheppard, D., Sparrow, H., Taylor, K., Thormann, A., Vullo, A., Walts, B.,
Zadissa, A., Frankish, A., Hunt, S. E., Kostadima, M., Langridge, N., Martin,
F. J., Muffato, M., Perry, E., Ruffier, M., Staines, D. M., Trevanion, S. J.,
Aken, B. L., Cunningham, F., Yates, A., and Flicek, P.: Ensembl 2018, Nucl.
Acids Res., 46, D754–D761, https://doi.org/10.1093/nar/gkx1098, 2017.
Zhang, Y., Proenca, R., Maffei, M., Barone, M., Leopold, L., and Friedman, J.
M.: Positional cloning of the mouse obese gene and its human homologue, Nature,
372, 425–432, https://doi.org/10.1038/372425a0, 1994.
Short summary
In this invited review we discuss the value of mice in improving livestock breeding. This review gives an overview of currently available databases for mice that can be used in livestock genetics research. Furthermore, this paper describes how different mouse models have contributed to livestock research in the past.
In this invited review we discuss the value of mice in improving livestock breeding. This review...
