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Genetic Tools to Improve Reproduction Traits in Dairy Cattle

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Genetic Tools to Improve Reproduction Traits in Dairy Cattle Genetic tools to improve reproduction traits in dairy cattle Aurelien Capitan, Pauline Michot, Aurélia Baur, Romain Saintilan, Chris Hoze, Damien Valour, François Guillaume, D Boichon, Anne Barbat, Didier Boichard, et al. To cite this version: Aurelien Capitan, Pauline Michot, Aurélia Baur, Romain Saintilan, Chris Hoze, et al.. Genetic tools to improve reproduction traits in dairy cattle. Reproduction, Fertility and Development, CSIRO Publishing, 2015, 27 (1), pp.14-21. 10.1071/RD14379. hal-01194016 HAL Id: hal-01194016 https://hal.archives-ouvertes.fr/hal-01194016 Submitted on 28 May 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. CSIRO PUBLISHING Reproduction, Fertility and Development, 2015, 27, 14–21 http://dx.doi.org/10.1071/RD14379 Genetic tools to improve reproduction traits in dairy cattle A. CapitanA,B,F, P. MichotA,B, A. BaurA,B, R. SaintilanA,B, C. Hoze´ A,B, D. ValourA,D, F. GuillaumeC, D. BoichonE, A. BarbatB, D. BoichardB, L. SchiblerA and S. FritzA,B AUNCEIA (Union Nationale des Coope´ratives d’Elevage et d’Inse´mination Animale), 149 rue de Bercy, 75012 Paris, France. BINRA (Institut National de la Recherche Agronomique), UMR1313 Ge´ne´tique Animale et Biologie Inte´grative, Domaine de Vilvert, 78352 Jouy-en-Josas, France. CEVOLUTION, 69 rue de la Motte Bruˆlon, 35706 Rennes, France. DINRA, UMR 1198 Biologie du De´veloppement et Reproduction, Domaine de Vilvert, 78352 Jouy-en-Josas, France. EMIDATEST, Les Nauzes, 81580 Soual, France. FCorresponding author. Email: [email protected] Abstract. Fertility is a major concern in the dairy cattle industry and has been the subject of numerous studies over the past 20 years. Surprisingly, most of these studies focused on rough female phenotypes and, despite their important role in reproductive success, male- and embryo-related traits have been poorly investigated. In recent years, the rapid and important evolution of technologies in genetic research has led to the development of genomic selection. The generalisation of this method in combination with the achievements of the AI industry have led to the constitution of large databases of genotyping and sequencing data, as well as refined phenotypes and pedigree records. These resources offer unprecedented opportunities in terms of fundamental and applied research. Here we present five such examples with a focus on reproduction-related traits: (1) detection of quantitative trait loci (QTL) for male fertility and semen quality traits; (2) detection of QTL for refined phenotypes associated with female fertility; (3) identification of recessive embryonic lethal mutations by depletion of homozygous haplotypes; (4) identification of recessive embryonic lethal mutations by mining whole-genome sequencing data; and (5) the contribution of high-density single nucleotide polymorphism chips, whole-genome sequencing and imputation to increasing the power of QTL detection methods and to the identification of causal variants. Introduction traditional selection for traits with low heritability, such as Fertility is a major concern in the modern dairy cattle industry. health or reproductive performance (Calus et al. 2008). These Over the past 40 years, the calving rate has decreased steadily advantages have contributed to a marked increase in genetic as milk production has increased, making low fertility one of gain per year and the clever use of genomic selection has the main causes of culling and replacement of cows. Whereas recently enabled us to halt the decrease in female fertility in the metabolic demand for milk production negatively impacts dairy cattle. Although these first results are encouraging, the reproductive function (Beam and Butler 1999), an indirect road is still long and more efforts have to be made to recover response to selection has been shown to account for only half of female fertility to an acceptable level. Although reproductive this decrease (Barbat et al. 2010), suggesting the existence of success depends on numerous and complex genetic factors, other factors of an environmental or genetic nature. most of the research has focused surprisingly on female Since 2008, the commercial availability of single nucleotide fertility, particularly on rough phenotypes, such as non-return polymorphism (SNP) chips has accelerated the detection of rates (for a review, see Fortes et al. 2013). Much may be genomic regions associated with production, reproduction, and expected from genetic studies based on refined phenotypes in health traits, and has enabled the development of genomic females, as well as in males and embryos. In addition, the selection (Goddard and Hayes 2007). This novel tool, which accumulation of large genotyping and whole-genome sequ- consists of estimating the genetic merit of an animal based on its encing (WGS) datasets is markedly increasing the power of genotype for a set of genetic markers, has revolutionised animal quantitative trait loci (QTL) detection methods and the mining breeding by allowing the identification of elite breeding stock as of such databases now enables the detection of recessive early as their birth. Genomic selection is also more accurate than embryonic lethal mutations. Journal compilation Ó IETS 2015 www.publish.csiro.au/journals/rfd Genetic tools and reproduction in dairy cattle Reproduction, Fertility and Development 15 Table 1. Chromosomal regions (quantitative trait loci) associated with male fertility or semen quality traits in dairy cattle breeds ERCR, estimated relative conception rate; SCR, sire conception rate Trait Breed Chromosomes References ERCR Holstein 1, 4, 9, 21, 27 C. Michot, R. Saintilan, S. Moureaux and S. Fritz, unpubl. data ERCR Normande 1, 2, 5, 8, 10, 15, 17, 19, 22, 23, 25 C. Michot, R. Saintilan, S. Moureaux and S. Fritz, unpubl. data ERCR Montbe´liarde 2, 3, 4, 7, 9, 13, 19, 29 C. Michot, R. Saintilan, S. Moureaux and S. Fritz, unpubl. data SCR Holstein 2, 5, 18, 25, 29 Pen˜agaricano et al. (2012) SCR Holstein 7 Lan et al. (2013) ERCR Holstein 1, 17, 19 Khatib et al. (2010) Scrotal circumference At 12 months Holstein 2, 4, 14, 18, 19 C. Hoze´, C. Ponsart and S. Fritz, unpubl. data At 14 months Holstein 2, 4, 15, 18, 19 C. Hoze´, C. Ponsart and S. Fritz, unpubl. data Idiopathic subfertility Fleckvieh 19 Pausch et al. (2014) Non-compensatory fertility Holstein 3, 4, 5, 6, 8, 10, 12, 13, 14, 15, 17, 19, 22 Blaschek et al. (2011) % Living spermatozoa after thawing Holstein 23, 27 Druet et al. (2009) % Living spermatozoa after thawing Holstein 5, 7, 8, 10, 17, 23 C. Michot, R. Saintilan, S. Moureaux and S. Fritz, unpubl. data Motility Holstein 7, 11, 27 Druet et al. (2009) Motility Holstein 4, 10, 11, 13, 27 C. Michot, R. Saintilan, S. Moureaux and S. Fritz, unpubl. data Sperm volume Holstein 15, 22 Druet et al. (2009) Sperm volume Holstein 3, 14, 15, 17, 22, 23, 28 C. Michot, R. Saintilan, S. Moureaux and S. Fritz, unpubl. data Sperm concentration Holstein 1, 3, 9, 11, 13, 21, 28 C. Michot, R. Saintilan, S. Moureaux and S. Fritz, unpubl. data No. spermatozoa Holstein 3, 12, 21, 28 C. Michot, R. Saintilan, S. Moureaux and S. Fritz, unpubl. data Here we present an overview of the achievements and 5A (STAT5A) genes (Khatib et al. 2010) and with SCR in the perspectives of bovine genetics with a focus on reproduction- prophet of Pit 1 (PROP1) gene (Lan et al. 2013). Interestingly, related traits. the latter is also associated with milk production, providing molecular evidence for the antagonistic relationship between Detection of QTL for male fertility and semen quality traits milk production and fertility. The identification of QTL for Despite the importance of the male contribution to repro- scrotal circumference at 12 and 14 months, which is highly ductive success, a surprisingly low number of studies has correlated with sexual maturity, will allow for the selection for focused on male fertility traits (Fortes et al. 2013). Yet, as for precocity and help breeding schemes to reduce generation the female, several parameters that can be used as phenotypes intervals (C. Hoze´, C. Ponsart and S. Fritz, unpubl. data). In for genome-wide association studies (GWAS) are routinely addition, the recent report on idiopathic male subfertility in computed to evaluate male fertility, such as the non-return Fleckvieh caused by a loss-of-function mutation in a nonde- rate (NRR), estimated relative conception rate (ERCR), sire script transmembrane protein 95 (TMEM95) essential for ferti- conception rate (SCR) and male reproductive ability (MRA), lisation (Pausch et al. 2014) exemplifies the important which are adjusted for environmental and female genetic contribution that such studies can have to the annotation of the effects. Moreover, several parameters are routinely collected by function of unknown genes. Finally, the identification of the AI industry to assess semen quality and production. Thus, QTL (Druet et al. 2009) and candidate mutations (C. Michot, numerous fine phenotypes have been accumulated for male R. Saintilan, S. Moureaux S. and Fritz, unpubl. data) for semen gametes, including scrotal circumference and compensable quality, sperm volume and survival after thawing and five (i.e. concentration, motility and morphology) and non-com- morphology traits also represent a unique opportunity to dissect pensable spermatozoid traits that lead to subfertility (i.e. nuclear the molecular basis of the global phenotypes analysed so far. vacuoles, defective chromatin structure). The main results obtained regarding male fertility and semen Detection of QTL for refined phenotypes associated quality traits are summarised in Table 1.
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