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Influence of single nucleotide polymorphisms in some candidate genes related to the performance traits in Italian beef cattle breeds
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Influence of single nucleotide polymorphisms in some candidate genes related to the performance traits in Italian beef cattle breeds Francesca Maria Sarti , Simone Ceccobelli , Emiliano Lasagna , Piera Di Lorenzo , Fiorella Sbarra , Camillo Pieramati , Andrea Giontella , Francesco Panella PII: DOI: Reference:
S1871-1413(19)31458-1 https://doi.org/10.1016/j.livsci.2019.103834 LIVSCI 103834
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Livestock Science
Received date: Revised date: Accepted date:
4 January 2018 28 August 2019 16 October 2019
Please cite this article as: Francesca Maria Sarti , Simone Ceccobelli , Emiliano Lasagna , Piera Di Lorenzo , Fiorella Sbarra , Camillo Pieramati , Andrea Giontella , Francesco Panella , Influence of single nucleotide polymorphisms in some candidate genes related to the performance traits in Italian beef cattle breeds, Livestock Science (2019), doi: https://doi.org/10.1016/j.livsci.2019.103834
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HIGHLIGHTS The study was carried out on three Italian beef cattle breeds Data from 1196 young bulls tested in the ANABIC genetic station were used 15 SNPs in candidate genes possibly related to the performance traits were analysed This study highlights the potential use of some SNPs in the selection procedures
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Influence of single nucleotide polymorphisms in some candidate genes related to the performance traits in Italian beef cattle breeds Francesca Maria Sarti1, Simone Ceccobelli1, Emiliano Lasagna1, Piera Di Lorenzo1, Fiorella Sbarra2, Camillo Pieramati3, Andrea Giontella3, Francesco Panella1 1
Dipartimento di Scienze Agrarie, Alimentari ed Ambientali, Università degli Studi di Perugia,
Borgo XX giugno 74, 06121, Perugia, Italy; 2
Associazione Nazionale Allevatori Bovini Italiani da Carne, S. Martino in Colle, 06132, Perugia,
Italy; 3
Dipartimento di Medicina Veterinaria, Università degli Studi di Perugia, Via San Costanzo 4,
06126,Perugia, Italy. Corresponding author Prof. Francesca Maria Sarti, Dipartimento di Scienze Agrarie, Alimentari e Ambientali, Università degli Studi di Perugia, Borgo XX giugno, 74, 06121 Perugia (PG), Italy – Tel. +39 075 5857123 – Fax: +39 075 5857122 – Email: [email protected]
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Abstract Because Italy has a long history in beef cattle production, Italian beef cattle breeds such as Chianina, Marchigiana, and Romagnola have always been connected with rural and ethnic traditions, and they became very popular as a result of consumers response to the Bovine Spongiform Encephalopathy (BSE) outbreak and animal welfare in the last years. These three breeds are selected by ANABIC (the National Association of Italian Beef Cattle Breeders), and their selection is focused on choosing young bulls after the performance test. In this study, 15 Single Nucleotide Polymorphisms (SNPs) in 10 candidate genes (growth hormone receptor, growth differentiation factor 8, ghrelin, leptin, myogenic factor 5, insulin-like growth factor 2, leptin receptor, uncoupling protein 2, uncoupling protein 3, and melanocortin 4 receptor) possibly related to the performance traits were analysed by assessing their effects on weights in the three above-mentioned breeds. During their performance test, the weights of 1,196 young bulls were registered and were subsequently used to search for an association with the 15 SNPs; in addition to the registered data, the Best Linear Unbiased Prediction Animal Model (BLUP-AM) indexes computed by ANABIC were considered. Although the genotypic frequencies of several SNPs are rather similar in the breeds, the main differences were found between Romagnola and Chianina. For the first time, the present study highlights the potential application of some SNPs polymorphisms in the selection procedures of the Italian Beef Cattle breeds. The most important SNPs seem to be LEP_1, MYF5, GHR_1 and MC4R; in fact, a general view of the SNPs average allele substitution effect on weights shows the influence of LEP_1 in both Chianina and Marchigiana, MC4R in both Marchigiana and Romagnola, MYF5 and GHR_1 in Marchigiana. The information of these markers could be taken into account in the breeding scheme of the Italian beef cattle breeds to increase the genetic gain for some of their breeding goals. 3
Keywords: Italian beef cattle; animal breeding; growth traits; SNPs; candidate genes.
Introduction Italy has a long history and tradition in beef cattle production, and local beef cattle breeds such as Chianina, Marchigiana, and Romagnola have always been connected with rural and ethnic traditions (Cozzi, 2007; Lasagna et al., 2015). Chianina meat is internationally recognized as a top quality product, and the most famous cut is the Fiorentina steak (Bongiorni et al., 2016); moreover, the Marchigiana and Romagnola are excellent breeds for meat production: the Marchigiana has a mutation in the myostatin gene (MSTN) that originates a double muscle phenotype (Vincenti et al., 2007), and the Romagnola is a very efficient grazing cattle (ANABIC, 2017). After the BSE disease, the market was strongly oriented towards the meat of animals which are traditionally reared at pasture and fattened with grains produced in farms (Nardone, 2003). The actual Herd book census of the three breeds are: Chianina 45,700 heads (847 sires and 1,469 herds), Marchigiana 52,683 heads (855 sires and 2,167 herds) and Romagnola 12,150 heads (295 sires and 369 herds). Their selection is carried out by the National Association of Italian Beef Cattle Breeders (ANABIC), and it is focused on choosing young bulls according to their performance for meat related traits in the genetic station (Sarti et al., 2014). The recent availability of both the genome sequence and a large number of molecular markers underline new opportunities in animal breeding including the use of molecular information in the selection programmes (Meuwissen et al., 2001; Makina et al., 2015). Metabolic pathways genes (candidate genes) influence growth traits, and their polymorphisms are associated with different productive performance. Aiming to identify major genes, several studies analysed polymorphisms of functional and positional candidate genes to assist selection by markers (Paredes-Sánchez et al., 4
2015; Mazzucco et al., 2016; Dias et al., 2016). The candidate gene strategy is efficient because it investigates genes that define the expression of interesting traits (Zhu et al., 2007); therefore, it must be highlighted that a small panel of markers could be useful in reducing costs associated with centralized test of animals. In this paper, polymorphisms in several candidate genes related to the performance traits of Chianina, Marchigiana, and Romagnola cattle breeds were analysed for the first time. Materials and Methods Animal and data source Selection programs in the studied breeds (www.anabic.it) are mainly based on performance testing in males. On the basis of their pedigree and morphological traits, young bulls eligible for reproduction are identified at approximately 5 months of age, and they are then transferred to the performance test station. During testing period, weights are registered twice every 21 days (Sbarra et al., 2009). Only the Marchigiana breed sires are genotyped at nucleotide 874 in exon 3 (g.874G>T) of MSTN gene (ANABIC, 2017; Sarti et al., 2014) as this breed can show a doublemuscled shape because of this gene effect (Bellinge et al., 2005). At the end of the trial, three aggregated BLUP-AM indexes are estimated: the Growth Index (GI: 30% average daily gain pretest + 70% average daily gain in test); the Muscle Index (MI: average of linear scores of muscularity traits – width of withers, convexity of shoulder, rump and buttocks, width of back and loin, thigh thickness, and buttocks length); and the Total Index (TI: 50% GI + 50% MI). In addition to these genetic indexes, three morphological aggregate scores are also assessed and estimated according to BLUP-AM procedure: the Dimension Morphological Score (DMS), the Muscle Morphological Score (MMS), and the Total Morphological Score (TMS). DMS and MMS are factors estimated by multivariate analysis on dimension and muscularity traits; TMS is computed adding together DMS and MMS corrected by different emphasis coefficients in each of the three 5
breeds. The BLUP-AM indexes as well as the morphological scores are standardised (mean=100 and standard deviation=10). A total of 1,196 young bulls, born from 2005 to 2012, were considered: 473 Chianina bulls from 117 herds, 419 Marchigiana bulls from 151 herds, and 304 Romagnola bulls from 71 herds; these animals from 6 months of age were reared in the performance station under stringent standardised conditions. It is important to point out that these animals must be considered as the selected subpopulations that spread the genetic progress to the whole breeds. In this study, 9 live weights (from W1 to W9), which were recorded every 21 days until animals were 12 months old as well as the previously mentioned BLUP-AM indexes were analysed (Sarti et al., 2014). DNA preparation and SNPs genotyping Blood samples were collected from each animal with Vacutainer® system in tubes added with EDTA as anticoagulant, and stored at -20°C until analyses were performed. Genomic DNA was extracted using the GenElute Blood Genomic DNA kit (Sigma Aldrich, St. Louis, MO, USA). The genotyping of the investigated SNPs was performed by LGC Genomics (Hoddesdon, Herts, UK) using the KBiosciences Competitive Allele Specific PCR SNP genotyping system (KASPar). To assess the genotyping accuracy, 10% of the samples were genotyped in duplicates. The SNPs According to previous studies on beef traits association (Table S1), 15 SNPs in the following 10 genes were selected on the basis of their biological functions: growth hormone receptor (GHR), growth differentiation factor 8 (GDF8), ghrelin (GHRL), leptin (LEP), myogenic factor 5 (MYF5), insulin-like growth factor 2 (IGF2), leptin receptor (LEPR), uncoupling protein 2 (UCP2), uncoupling protein 3 (UCP3), and melanocortin 4 receptor (MC4R). Further information on the studied SNPs are reported in Table S2. The haplogroups
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Three chromosomes carried more than one SNP; therefore, all the possible haplogroups composed by seven SNPs in three chromosomes (Table S3) were found through Fast PHASE 1.2.0 (Scheet and Stephens, 2006). Only the influence of leptin haplotype, however, was analysed as shown in the average allele substitution section because of the effect of this gene on the weights. Frequencies Allelic and genotypic frequencies were directly computed dividing the number of copies of each allele and genotype in each breed by their respective totals. The Hardy Weinberg equilibrium was verified at P≤0.05 according to Rodriguez et al. (2009). Average allele substitution effect (AAS) The AAS of SNPs and haplogroups for weights were computed by MTDFREML software (Boldman et al., 1995) according to an individual BLUP-AM that used a pedigree of 4,829 animals in Chianina, 3,540 in Marchigiana, and 2,604 in Romagnola. The statistical model considered the linear regression effect of each SNPs (coded as 2, 1, 0 according to the copies of the first nucleotide in alphabetical order) or the haplogroups. The age covariate was also considered. The model for Marchigiana breed included the fixed effect of myostatin gene too. The year of test factor was supposed to have a real effect on the studied traits; however, in a preliminary statistical approach, it was found to be not significant, and thus it was not considered in the final model. The contribution of each SNP to the genetic variance of a trait has been calculated as follows:
where p and q are the allelic frequencies, AAS is the average allelic substitution effect and Var (add) is the estimate of the additive variance (Falconer and Mackay, 1996). Effect on BLUP-AM indexes
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The effect of the studied SNPs on GI, MI, TI, MMS, DMS, and TMS BLUP-AM indexes was studied. Taking into consideration that the indexes are “in itself” estimated breeding values (EBV), the effect of each SNP was computed by a STEPWISE regression (SAS, 2000). Results and discussion The genotypic and allelic frequencies are reported in Table S4. The most represented genotype is the homozygote “AA” in GHRL with a minimum of 75.0% in Chianina, increasing to 89.1% in Marchigiana, and 90.8% in Romagnola. Many SNPs showed rather similar genotypic frequencies in the three breeds: in GHR_2 the homozygote “AA” is the most abundant in Chianina (60.2%), Marchigiana (47.4%), and Romagnola (54.0%), and the homozygote “GG” is less represented (5.4, 10.2, 5.9 respectively); in GDF8_1 the smallest frequencies are observed in “AA” homozygote (5.0, 3.6, 0.4 respectively), and the highest in “TT” (56.8, 68.1, 79.9 respectively). A similar trend is observed also in MYF5, MC4R, UCP3_1, and UCP3_2 where the heterozygote genotype is the most represented. The genotypic frequencies in these candidate genes were estimated by different authors. For example, the prevalence of the genotype “CC” in the LEP_2 SNP was also observed in Baladi cattle by Ghoneim et al. (2016). The same prevalence of the genotype “AG” for the GHR_1 and “AA” for GHR_2 in beef cattle (mostly Angus and Charolais breeds) was observed by Sherman et al. (2008); on the contrary, Zangh et al. (2007) registered different genotypic frequencies in three Chinese cattle breeds (Nanyang, Qinchuan, and Jiaxian) in which MYF5 SNP, “GG” showed the highest frequency. It has to be pointed out that the main differences between breeds are in Romagnola and Chianina (GHR_1, LEP_1, LEP_2, LEPR). The Marchigiana is usually closer to one of the two other breeds; for example, UCP3_1, UCP3_2 with Chianina, and GHRL with Romagnola. This can be easily explained by the history of the Marchigiana breed, which originated by crossbreeding Chianina with Romagnola (Bonadonna, 1976).
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The allelic frequencies (p and q) are rarely strongly different (Table S4). In some SNPs, one allele increases to 90% or more (GDF8_1 in Romagnola, GHRL in Marchigiana and Romagnola, LEP_2 and LEP_3 in Chianina); because UCP_2 was detected to be monomorphic in Chianina and Marchigiana, it was not taken into consideration in these two breeds. In general, these frequencies were rather similar in the breeds; however, in GHR_1, “A” allele was the most common in Chianina and Marchigiana (51%, 62%) and less common in Romagnola. Smaller differences among breeds were also observed in LEP_1, LEPR, UCP3_1 and UCP3_2. All the allelic frequencies were in HW equilibrium, except for LEP_3 in Chianina. The means and the standard deviations of the weights estimated on the three breeds are reported in Table S5; the value of the W1 was 326.9 kg and the last weight (W9) reached up to 552.2 kg. The variability along the weights was homogeneous, as the variability coefficients were always close to 11%. The significant AAS in the weights are reported in Table 1. The most important SNPs seem to be LEP_1 and MC4R. In LEP_1, the “C” allele has a significant negative effect in all Chianina weights. As expected, the absolute value grows from the first (-8.61 kg) to the ninth (-14.00 kg) weight. The same allele has a significant effect in Marchigiana, but some differences are to be pointed out; the computed values are significant starting from weight 4 and their magnitude is around an half compared to the Chianina ones. Lisa et al. (2013) investigated the effect of the same SNP (LEP_1) in the LEP gene in the Piemontese beef cattle breed: they registered a negative effect of the allele “C” for all the studied traits with the exception of bone thickness. The LEP gene has been associated with feed consumption and energy balance both in humans and mice; therefore, it could be effectively used in beef production as a genetic marker. MC4R always has significant effects in Romagnola with a positive increasing trend from weight 1 (8.20 kg) to weight 9 (12.20 kg). MC4R is also significant in Marchigiana starting from weight 3; the magnitude of the values observed in the two breeds is rather similar, mainly in the last weights 9
(from weight 6). The “C” allele was found to have a negative effect in other breeds, such as in Piemontese cattle breed (Ribeca et al., 2014) and in Qinchuan cattle (Liu et al., 2010). Other significant SNPs in Marchigiana are MYF5 (in 6 weights), LEP_1 (in the last 6 weights), and GHR_1 (in 6 weights). In Romagnola, UCP3_2 (in three weights) and GDF8_1 (in only one weight) are significant. It seems that the effect of the alleles is detected as a function of the physiological age and mainly of the growth-related traits for the genes studied. According to the discussion above on weights, a general view of the SNPs AAS highlights a real effect of LEP_1 in Chianina and Marchigiana, MC4R in Marchigiana and Romagnola, MYF5 and GHR1 in Marchigiana. These polymorphisms could have a relevant practical impact on the preselection of the best calves that are candidates for the genetic centre because improvement in feed efficiency could contribute to reduce the feed costs as well as the environmental impact of cattle breeding. Taking into account the associations between genetic polymorphisms and growth traits that are highlighted in this study, we suggest including this molecular information in a markers assisted selection scheme. Although estimating the heritability of weights was not a goal of the present work, it is important to check the contribution of the SNPs with statistically significant effect on the additive variance of the trait. In the three breeds, the h² estimates ranged between 0.56 for trait W1 and 0.40 for trait W8. For all the weights, the contribution of the significant SNPs was rather low; in fact, the average percentages of additive variance explained by the SNPs were as follows: LEP_1 in Chianina 5.66% (3.51% W1 - 6.54% W6) and in Marchigiana 4.34% (2.14% W8 - 5.61% W5); MC4R in Marchigiana 3.14% (1.86% W4 - 4.76% W5) and in Romagnola 2.56% (1.49% W2 - 4.10% W4); finally, MYF5 3.69% (2.28% W8 - 5.98% W5) and GHR1 2.64% (1.88% W5 – 4.02% W7) only in Marchigiana. The SNPs effects on the BLUP-AM indexes are shown by the significances reported in Table 2. It is possible to highlight a rather strong effect of MC4R in Chianina (MI, TI, MMS, TMS); on the other 10
hand, GHR_2 (MI, TI, MMS, DMS), LEP_1 (MI, TI, MMS, TMS), LEP_2 (GI, MI, TI, DMS), MYF5 (GI, MMS, DMS, TMS), LEPR (GI, TI, MMS, DMS), and UCP3_1 (GI, TI, MMS) were mainly significant in Marchigiana and Romagnola. Other SNPs are often significant in one or few indexes and in one or two breeds, without a clear trend of differences exploitable across all the three breeds. Therefore it is quite difficult to hypothesize a real practical effect in a same gene-assisted program shared by these breeds, that are also subjected to the same selection scheme based on the performance test. A general view of Table 2 shows that usually TI is significant when at least one of the other two indexes (GI, MI) is significant, but the same correspondence is not always observed between TMS and its components (MMS and DMS). As mentioned above, because leptins are one of the most interesting SNPs, the frequencies of haplotypes LEP_1-LEP_2-LEP_3 (BTA 4 chromosome) are reported: the “TCG” was the most recurring haplotype (71.56%-Chianina; 53.46%-Marchigiana; 37.50-Romagnola); the haplotype effect was observed to be not significant in any weight and breed. The present study highlights, for the first time, that the use of some SNPs polymorphisms in the selection practice of the Italian beef cattle breeds could be rather useful. Our results suggest that some polymorphisms in the studied candidate genes could influence economically important traits and could be useful in a marker-assisted selection scheme to improve beef productive traits. The practical use of the genetic polymorphisms could give the opportunity to carry out a pre-selection of the candidate calves on the base of their SNPs genotype, to decide which animals should enter a performance testing program, increasing in this way the selection intensity. Besides, also this genetic information could be considered in the choose of the dams of sire. A validation study could be useful to verify the genotype of LEP_1, MYF5, and MC4R SNPs in the candidates for the performance test. If the validation shall confirm the results obtained in this study, the information of these markers could be taken in account in the breeding scheme of the Italian beef cattle breeds to increase the genetic gain for some of their breeding goals. 11
Conflict of interest The authors declare that there are no conflicts of interest. Acknowledgements A special thanks to Associazione Nazionale Allevatori Bovini Italiani da Carne (ANABIC) for making the experimental data available. The research was financially supported by INNOVAGEN project. The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest in the subject matter or materials discussed in this manuscript. The authors wish to thank the two anonymous referees for their valuable comments to the manuscript and their constructive suggestions. References A.N.A.B.I.C. home page. http://anabic.it (accessed 29 June 2017) Bellinge, R.H.S., Liberles, D.A., Laschi, S.P.A., O’Brien, P.A., Tay, G.H., 2005. Myostatin and its implication on animal breeding: a review. Anim. Genet. 36, 1-6. Boldman, K.G., Kriese, L.A., Van Vleck, L.D., Van Tassell, C.P., Kachman, S.D., 1995. A manual for use of MTDFREML. A set of programs to obtain estimates of variances and covariances. U.S. Department of Agriculture, Agricultural Research Service. Bonadonna, T., 1976. Etnologia zootecnica. UTET, Torino. Bongiorni, S., Gruber, C.E.M., Bueno, S., Chillemi, G., Ferrè, F., Failla, S., Moioli, B., Valentini, A., 2016. Transcriptomic investigation of meat tenderness in two Italian cattle breeds. Anim. Genet. 47, 273–287. Cozzi, G., 2007. Present situation and future challenges of beef cattle production in Italy and the role of the research. Ital. J. Anim. Sci. 6, 389-396. Dias, V.A.D., Curi, R.A., Pereira, G.L., Malheiros, J.M., Espigolan, R., Albuquerque, L.G.D., Chardulo, L.A.L., Oliveira, H.N.D., 2016. Frequencies of candidate genes and associations with carcass and meat traits in Nellore and crossbred cattle. Pesqui. Agropecu. Bras. 51, 169-176. 12
Falconer, D.S., Mackay, T., 1996. Introduction to quantitative genetics. Longman Group Ltd, Edinburgh Gate, Harlow Essex, England. Ghoneim, M.A., Ogaly, H.A., Gouda, E.M., El-Behairy, A.M., 2016. Prediction of desirable genotype patterns in Baladi beef cattle and water buffalo by identification of new leptin gene SNPs. Livest. Sci. 194, 51-56. Lasagna, E., Ceccobelli, S., Di Lorenzo, P., Albera, A., Filippini, F., Sarti, F.M., Panella, F., Di Stasio, L., 2015. Comparison of four Italian beef cattle breeds by means of functional genes. Ital. J. Anim. Sci. 14, 3465, 86-89. Lisa, C., Albera, A., Carnier, P., Di Stasio, L., 2013. Variability in candidate genes revealed associations with meat traits in the Piemontese cattle breed. Ital. J. Anim. Sci., 12:e46, 280-285. Liu, H., Tian, W., Zan, L., Wang, H., Cui, H., 2010. Mutations of MC4R gene and its association with economic traits in Qinchuan cattle. Mol. Biol. Rep. 37, 535-540. Makina, S.O., Muchadeyi, F.C., Marle-Köster, E., Taylor, J.F., Makgahlela, M.L., Maiwashe, A., 2015. Genome-wide scan for selection signatures in six cattle breeds in South Africa. Genet. Selec. Evol. 47, 92. Mazzucco, J.P., Goszczynski, D.E., Ripoli, M.V., Melucci, L.M., Pardo, A.M., Colatto, E., Rogberg-Muñoz, A., Mezzadra, C.A., Depetris, G.J., Giovambattista, G., Villarreal, E.L., 2016. Growth, carcass and meat quality traits in beef from Angus, Hereford and cross-breed grazing steers, and their association with SNPs in genes related to fat deposition metabolism. Meat Sci. 114, 121-129. Meuwissen, T.H.E., Hayes, B.J., Goddard, M.E., 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 157, 1819-1829. Nardone, A. 2003. Impact of BSE on livestock production system. Vet. Res. Commun. 27, 39-52.
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Paredes-Sánchez, F.A., Sifuentes-Rincón, A.M., Cabrera, A.S., Pérez, C.A.G., Bracamonte, G.M.P., Morales, P.A., 2015. Associations of SNPs located at candidate genes to bovine growth traits, prioritized with an interaction networks construction approach. BMC Genet. 16, 91. Ribeca, C., Bonfatti, V., Cecchinato, A., Albera, A., Gallo, L., Carnier, P., 2014. Effect of polymorphisms in candidate genes on carcass and meat quality traits in double muscled Piemontese cattle. Meat Sci. 96, 1376-1383. Rodriguez, S., Gaunt, T.R., Day, I.N.M., 2009. Hardy-Weinberg equilibrium testing of biological ascertainment for mendelian randomization studies. Am. J. Epidemiol. 169, 505-514. Sarti, F.M., Lasagna, E., Ceccobelli, S., Di Lorenzo, P., Filippini, F., Sbarra, F., Giontella, A., Panella, F., 2014. Influence of single nucleotide polymorphisms in the myostatin and myogenic factor 5 muscle growth-related genes on the performance traits of Marchigiana beef cattle. J. Anim. Sci. 92, 3804-3810. SAS. 2000. SAS online doc – Version 8 SAS Institute Inc. Cary (NC). Sbarra, F., Mantovani, R., Bittante, G., 2009. Heritability of performance test traits in Chianina, Marchigiana and Romagnola breeds. Ital. J. Anim. Sci. 8, 107-109. Scheet, P., Stephens, M., 2006. A fast and flexible statistical model for large-scale population genotype data: Applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet. 78, 629–644. Sherman, E.L., Nkrumah, J.D., Murdoch, B.M., Li, C., Wang, Z., Fu, A., Moore, S.S., 2008. Polymorphisms and haplotypes in the bovine neuropeptide Y, growth hormone receptor, ghrelin, insulin-like growth factor 2, and uncoupling proteins 2 and 3 genes and their associations with measures of growth, performance, feed efficiency, and carcass merit in beef cattle. J. Anim. Sci. 86, 1-16. Vincenti, F., Failla, S., Gigli, S., Lasagna, E., Landi, V., Mangione, A., Berti, C., Sarti, F.M., 2007. The Hypertrophic Marchigiana: physical and biochemical parameters for meat quality evaluation. 14
Ital. J. Anim. Sci. 6, 491-493. Zhang, R.F., Chen, H., Lei, C.Z., Zhang, C.L., Lan, X.Y., Zhang, Y.D., Zhang, H.J., Bao, B., Niu, H., Wang, X.Z., 2007. Association between polymorphisms of MSTN and MYF5 genes and growth traits in three Chinese cattle breeds. Asian-Aust. J. Anim. Sci. 20, 1798-1804. Zhu, M., Zhao, S., 2007. Candidate gene identification approach: progress and challenges. Int. J. Biol. Sci. 3, 420-427. Table 1. Average allele substitution (AAS±SE) of the significant effects (Kg) in the weights
C
SNP
WEIGHTS W1
W2
W3
W4
W5
W6
W7
W8
W9
-8.61
-8.65
-10.15
-11.09
-11.86
-13.03
-13.01
-13.09
-14.00
±3.20
±3.53
±3.82
±3.98
±4.06
±4.15
±4.33
±4.55
±4.62
1.20
2.35
4.16
6.74
8.07
8.73
9.86
10.83
11.20
±2.35
±2.92
±4.49
±4.72
±5.11
±5.41
±5.76
±6.08
±6.29
-0.12
-0.24
-1.39
-4.78
-5.12
-6.61
-7.04
-8.25
-10.01
±0.45
±0.61
±2.05
±2.05
±3.15
±3.81
±3.91
±4.10
±5.28
2.86
3.56
4.06
4.96
5.87
6.97
8.15
9.15
10.15
±2.99
±3.11
±3.35
±3.49
±3.61
±3.89
±3.84
±4.64
±5.04
2.31
3.16
5.20
7.88
8.21
9.15
11.02
11.3
11.8
±2.29
±3.05
±2.68
±3.91
±4.05
±4.63
±5.15
±5.88
±5.47
8.59
11.11
10.00
1.81
1.44
0.47
0.34
0.69
0.70
±5.35
±5.75
±6.23
±6.42
±6.75
±7.03
±7.19
±7.27
±7.42
-1.85
-2.36
-3.49
1.01
0.34
1.21
1.65
2.02
2.52
±0.61
±0.73
±1.07
±5.03
±5.33
±5.75
±5.85
±5.87
±5.97
8.20
8.49
10.39
12.91
11.07
11.33
10.98
10.36
12.20
±4.01
±4.31
±4.66
±4.95
±5.22
±5.47
±5.58
±5.62
±5.73
LEP_1
GHR_1
M
LEP_1
MYF5
MC4R
GDF8_1
R
UCP3_2
MC4R C: Chianina; M: Marchigiana; R: Romagnola In bold significant values (P≤0.05)
Table 2. Significance of the SNPs effect in the BLUP Animal Model indexes in the three studied breeds. 15
SNP
INDEXES TI
MMS
DMS
M
R
M-R
GDF8_1
R
M
GHRL
R
M-R
R
R
GHR_1
GI
MI
C
M
GHR_2
M
IGF_2
M
M
LEP_1
M
M
M
M
LEP_2
C-R
LEP_3
M
MYF5
R
C
M
M
LEPR
C-R
R
M
R
UCP_2
R
R
UCP3_1
R-M
M-R
UCP3_2
R
MC4R
TMS
M
M M
M C
R-M
R M
C
C
M
C
Chianina: C; Marchigiana: M; Romagnola: R GI: Growth index; MI: Muscle index; TI: Total index; MMS: Muscle Morphological Score; DMS: Dimension Morphological Score; TMS: Total Morphological Score.
16
Influence of single nucleotide polymorphisms in some candidate genes related to the performance traits in Italian beef cattle breeds Francesca Maria Sarti , Simone Ceccobelli , Emiliano Lasagna , Piera Di Lorenzo , Fiorella Sbarra , Camillo Pieramati , Andrea Giontella , Francesco Panella PII: DOI: Reference:
S1871-1413(19)31458-1 https://doi.org/10.1016/j.livsci.2019.103834 LIVSCI 103834
To appear in:
Livestock Science
Received date: Revised date: Accepted date:
4 January 2018 28 August 2019 16 October 2019
Please cite this article as: Francesca Maria Sarti , Simone Ceccobelli , Emiliano Lasagna , Piera Di Lorenzo , Fiorella Sbarra , Camillo Pieramati , Andrea Giontella , Francesco Panella , Influence of single nucleotide polymorphisms in some candidate genes related to the performance traits in Italian beef cattle breeds, Livestock Science (2019), doi: https://doi.org/10.1016/j.livsci.2019.103834
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
HIGHLIGHTS The study was carried out on three Italian beef cattle breeds Data from 1196 young bulls tested in the ANABIC genetic station were used 15 SNPs in candidate genes possibly related to the performance traits were analysed This study highlights the potential use of some SNPs in the selection procedures
1
Influence of single nucleotide polymorphisms in some candidate genes related to the performance traits in Italian beef cattle breeds Francesca Maria Sarti1, Simone Ceccobelli1, Emiliano Lasagna1, Piera Di Lorenzo1, Fiorella Sbarra2, Camillo Pieramati3, Andrea Giontella3, Francesco Panella1 1
Dipartimento di Scienze Agrarie, Alimentari ed Ambientali, Università degli Studi di Perugia,
Borgo XX giugno 74, 06121, Perugia, Italy; 2
Associazione Nazionale Allevatori Bovini Italiani da Carne, S. Martino in Colle, 06132, Perugia,
Italy; 3
Dipartimento di Medicina Veterinaria, Università degli Studi di Perugia, Via San Costanzo 4,
06126,Perugia, Italy. Corresponding author Prof. Francesca Maria Sarti, Dipartimento di Scienze Agrarie, Alimentari e Ambientali, Università degli Studi di Perugia, Borgo XX giugno, 74, 06121 Perugia (PG), Italy – Tel. +39 075 5857123 – Fax: +39 075 5857122 – Email: [email protected]
2
Abstract Because Italy has a long history in beef cattle production, Italian beef cattle breeds such as Chianina, Marchigiana, and Romagnola have always been connected with rural and ethnic traditions, and they became very popular as a result of consumers response to the Bovine Spongiform Encephalopathy (BSE) outbreak and animal welfare in the last years. These three breeds are selected by ANABIC (the National Association of Italian Beef Cattle Breeders), and their selection is focused on choosing young bulls after the performance test. In this study, 15 Single Nucleotide Polymorphisms (SNPs) in 10 candidate genes (growth hormone receptor, growth differentiation factor 8, ghrelin, leptin, myogenic factor 5, insulin-like growth factor 2, leptin receptor, uncoupling protein 2, uncoupling protein 3, and melanocortin 4 receptor) possibly related to the performance traits were analysed by assessing their effects on weights in the three above-mentioned breeds. During their performance test, the weights of 1,196 young bulls were registered and were subsequently used to search for an association with the 15 SNPs; in addition to the registered data, the Best Linear Unbiased Prediction Animal Model (BLUP-AM) indexes computed by ANABIC were considered. Although the genotypic frequencies of several SNPs are rather similar in the breeds, the main differences were found between Romagnola and Chianina. For the first time, the present study highlights the potential application of some SNPs polymorphisms in the selection procedures of the Italian Beef Cattle breeds. The most important SNPs seem to be LEP_1, MYF5, GHR_1 and MC4R; in fact, a general view of the SNPs average allele substitution effect on weights shows the influence of LEP_1 in both Chianina and Marchigiana, MC4R in both Marchigiana and Romagnola, MYF5 and GHR_1 in Marchigiana. The information of these markers could be taken into account in the breeding scheme of the Italian beef cattle breeds to increase the genetic gain for some of their breeding goals. 3
Keywords: Italian beef cattle; animal breeding; growth traits; SNPs; candidate genes.
Introduction Italy has a long history and tradition in beef cattle production, and local beef cattle breeds such as Chianina, Marchigiana, and Romagnola have always been connected with rural and ethnic traditions (Cozzi, 2007; Lasagna et al., 2015). Chianina meat is internationally recognized as a top quality product, and the most famous cut is the Fiorentina steak (Bongiorni et al., 2016); moreover, the Marchigiana and Romagnola are excellent breeds for meat production: the Marchigiana has a mutation in the myostatin gene (MSTN) that originates a double muscle phenotype (Vincenti et al., 2007), and the Romagnola is a very efficient grazing cattle (ANABIC, 2017). After the BSE disease, the market was strongly oriented towards the meat of animals which are traditionally reared at pasture and fattened with grains produced in farms (Nardone, 2003). The actual Herd book census of the three breeds are: Chianina 45,700 heads (847 sires and 1,469 herds), Marchigiana 52,683 heads (855 sires and 2,167 herds) and Romagnola 12,150 heads (295 sires and 369 herds). Their selection is carried out by the National Association of Italian Beef Cattle Breeders (ANABIC), and it is focused on choosing young bulls according to their performance for meat related traits in the genetic station (Sarti et al., 2014). The recent availability of both the genome sequence and a large number of molecular markers underline new opportunities in animal breeding including the use of molecular information in the selection programmes (Meuwissen et al., 2001; Makina et al., 2015). Metabolic pathways genes (candidate genes) influence growth traits, and their polymorphisms are associated with different productive performance. Aiming to identify major genes, several studies analysed polymorphisms of functional and positional candidate genes to assist selection by markers (Paredes-Sánchez et al., 4
2015; Mazzucco et al., 2016; Dias et al., 2016). The candidate gene strategy is efficient because it investigates genes that define the expression of interesting traits (Zhu et al., 2007); therefore, it must be highlighted that a small panel of markers could be useful in reducing costs associated with centralized test of animals. In this paper, polymorphisms in several candidate genes related to the performance traits of Chianina, Marchigiana, and Romagnola cattle breeds were analysed for the first time. Materials and Methods Animal and data source Selection programs in the studied breeds (www.anabic.it) are mainly based on performance testing in males. On the basis of their pedigree and morphological traits, young bulls eligible for reproduction are identified at approximately 5 months of age, and they are then transferred to the performance test station. During testing period, weights are registered twice every 21 days (Sbarra et al., 2009). Only the Marchigiana breed sires are genotyped at nucleotide 874 in exon 3 (g.874G>T) of MSTN gene (ANABIC, 2017; Sarti et al., 2014) as this breed can show a doublemuscled shape because of this gene effect (Bellinge et al., 2005). At the end of the trial, three aggregated BLUP-AM indexes are estimated: the Growth Index (GI: 30% average daily gain pretest + 70% average daily gain in test); the Muscle Index (MI: average of linear scores of muscularity traits – width of withers, convexity of shoulder, rump and buttocks, width of back and loin, thigh thickness, and buttocks length); and the Total Index (TI: 50% GI + 50% MI). In addition to these genetic indexes, three morphological aggregate scores are also assessed and estimated according to BLUP-AM procedure: the Dimension Morphological Score (DMS), the Muscle Morphological Score (MMS), and the Total Morphological Score (TMS). DMS and MMS are factors estimated by multivariate analysis on dimension and muscularity traits; TMS is computed adding together DMS and MMS corrected by different emphasis coefficients in each of the three 5
breeds. The BLUP-AM indexes as well as the morphological scores are standardised (mean=100 and standard deviation=10). A total of 1,196 young bulls, born from 2005 to 2012, were considered: 473 Chianina bulls from 117 herds, 419 Marchigiana bulls from 151 herds, and 304 Romagnola bulls from 71 herds; these animals from 6 months of age were reared in the performance station under stringent standardised conditions. It is important to point out that these animals must be considered as the selected subpopulations that spread the genetic progress to the whole breeds. In this study, 9 live weights (from W1 to W9), which were recorded every 21 days until animals were 12 months old as well as the previously mentioned BLUP-AM indexes were analysed (Sarti et al., 2014). DNA preparation and SNPs genotyping Blood samples were collected from each animal with Vacutainer® system in tubes added with EDTA as anticoagulant, and stored at -20°C until analyses were performed. Genomic DNA was extracted using the GenElute Blood Genomic DNA kit (Sigma Aldrich, St. Louis, MO, USA). The genotyping of the investigated SNPs was performed by LGC Genomics (Hoddesdon, Herts, UK) using the KBiosciences Competitive Allele Specific PCR SNP genotyping system (KASPar). To assess the genotyping accuracy, 10% of the samples were genotyped in duplicates. The SNPs According to previous studies on beef traits association (Table S1), 15 SNPs in the following 10 genes were selected on the basis of their biological functions: growth hormone receptor (GHR), growth differentiation factor 8 (GDF8), ghrelin (GHRL), leptin (LEP), myogenic factor 5 (MYF5), insulin-like growth factor 2 (IGF2), leptin receptor (LEPR), uncoupling protein 2 (UCP2), uncoupling protein 3 (UCP3), and melanocortin 4 receptor (MC4R). Further information on the studied SNPs are reported in Table S2. The haplogroups
6
Three chromosomes carried more than one SNP; therefore, all the possible haplogroups composed by seven SNPs in three chromosomes (Table S3) were found through Fast PHASE 1.2.0 (Scheet and Stephens, 2006). Only the influence of leptin haplotype, however, was analysed as shown in the average allele substitution section because of the effect of this gene on the weights. Frequencies Allelic and genotypic frequencies were directly computed dividing the number of copies of each allele and genotype in each breed by their respective totals. The Hardy Weinberg equilibrium was verified at P≤0.05 according to Rodriguez et al. (2009). Average allele substitution effect (AAS) The AAS of SNPs and haplogroups for weights were computed by MTDFREML software (Boldman et al., 1995) according to an individual BLUP-AM that used a pedigree of 4,829 animals in Chianina, 3,540 in Marchigiana, and 2,604 in Romagnola. The statistical model considered the linear regression effect of each SNPs (coded as 2, 1, 0 according to the copies of the first nucleotide in alphabetical order) or the haplogroups. The age covariate was also considered. The model for Marchigiana breed included the fixed effect of myostatin gene too. The year of test factor was supposed to have a real effect on the studied traits; however, in a preliminary statistical approach, it was found to be not significant, and thus it was not considered in the final model. The contribution of each SNP to the genetic variance of a trait has been calculated as follows:
where p and q are the allelic frequencies, AAS is the average allelic substitution effect and Var (add) is the estimate of the additive variance (Falconer and Mackay, 1996). Effect on BLUP-AM indexes
7
The effect of the studied SNPs on GI, MI, TI, MMS, DMS, and TMS BLUP-AM indexes was studied. Taking into consideration that the indexes are “in itself” estimated breeding values (EBV), the effect of each SNP was computed by a STEPWISE regression (SAS, 2000). Results and discussion The genotypic and allelic frequencies are reported in Table S4. The most represented genotype is the homozygote “AA” in GHRL with a minimum of 75.0% in Chianina, increasing to 89.1% in Marchigiana, and 90.8% in Romagnola. Many SNPs showed rather similar genotypic frequencies in the three breeds: in GHR_2 the homozygote “AA” is the most abundant in Chianina (60.2%), Marchigiana (47.4%), and Romagnola (54.0%), and the homozygote “GG” is less represented (5.4, 10.2, 5.9 respectively); in GDF8_1 the smallest frequencies are observed in “AA” homozygote (5.0, 3.6, 0.4 respectively), and the highest in “TT” (56.8, 68.1, 79.9 respectively). A similar trend is observed also in MYF5, MC4R, UCP3_1, and UCP3_2 where the heterozygote genotype is the most represented. The genotypic frequencies in these candidate genes were estimated by different authors. For example, the prevalence of the genotype “CC” in the LEP_2 SNP was also observed in Baladi cattle by Ghoneim et al. (2016). The same prevalence of the genotype “AG” for the GHR_1 and “AA” for GHR_2 in beef cattle (mostly Angus and Charolais breeds) was observed by Sherman et al. (2008); on the contrary, Zangh et al. (2007) registered different genotypic frequencies in three Chinese cattle breeds (Nanyang, Qinchuan, and Jiaxian) in which MYF5 SNP, “GG” showed the highest frequency. It has to be pointed out that the main differences between breeds are in Romagnola and Chianina (GHR_1, LEP_1, LEP_2, LEPR). The Marchigiana is usually closer to one of the two other breeds; for example, UCP3_1, UCP3_2 with Chianina, and GHRL with Romagnola. This can be easily explained by the history of the Marchigiana breed, which originated by crossbreeding Chianina with Romagnola (Bonadonna, 1976).
8
The allelic frequencies (p and q) are rarely strongly different (Table S4). In some SNPs, one allele increases to 90% or more (GDF8_1 in Romagnola, GHRL in Marchigiana and Romagnola, LEP_2 and LEP_3 in Chianina); because UCP_2 was detected to be monomorphic in Chianina and Marchigiana, it was not taken into consideration in these two breeds. In general, these frequencies were rather similar in the breeds; however, in GHR_1, “A” allele was the most common in Chianina and Marchigiana (51%, 62%) and less common in Romagnola. Smaller differences among breeds were also observed in LEP_1, LEPR, UCP3_1 and UCP3_2. All the allelic frequencies were in HW equilibrium, except for LEP_3 in Chianina. The means and the standard deviations of the weights estimated on the three breeds are reported in Table S5; the value of the W1 was 326.9 kg and the last weight (W9) reached up to 552.2 kg. The variability along the weights was homogeneous, as the variability coefficients were always close to 11%. The significant AAS in the weights are reported in Table 1. The most important SNPs seem to be LEP_1 and MC4R. In LEP_1, the “C” allele has a significant negative effect in all Chianina weights. As expected, the absolute value grows from the first (-8.61 kg) to the ninth (-14.00 kg) weight. The same allele has a significant effect in Marchigiana, but some differences are to be pointed out; the computed values are significant starting from weight 4 and their magnitude is around an half compared to the Chianina ones. Lisa et al. (2013) investigated the effect of the same SNP (LEP_1) in the LEP gene in the Piemontese beef cattle breed: they registered a negative effect of the allele “C” for all the studied traits with the exception of bone thickness. The LEP gene has been associated with feed consumption and energy balance both in humans and mice; therefore, it could be effectively used in beef production as a genetic marker. MC4R always has significant effects in Romagnola with a positive increasing trend from weight 1 (8.20 kg) to weight 9 (12.20 kg). MC4R is also significant in Marchigiana starting from weight 3; the magnitude of the values observed in the two breeds is rather similar, mainly in the last weights 9
(from weight 6). The “C” allele was found to have a negative effect in other breeds, such as in Piemontese cattle breed (Ribeca et al., 2014) and in Qinchuan cattle (Liu et al., 2010). Other significant SNPs in Marchigiana are MYF5 (in 6 weights), LEP_1 (in the last 6 weights), and GHR_1 (in 6 weights). In Romagnola, UCP3_2 (in three weights) and GDF8_1 (in only one weight) are significant. It seems that the effect of the alleles is detected as a function of the physiological age and mainly of the growth-related traits for the genes studied. According to the discussion above on weights, a general view of the SNPs AAS highlights a real effect of LEP_1 in Chianina and Marchigiana, MC4R in Marchigiana and Romagnola, MYF5 and GHR1 in Marchigiana. These polymorphisms could have a relevant practical impact on the preselection of the best calves that are candidates for the genetic centre because improvement in feed efficiency could contribute to reduce the feed costs as well as the environmental impact of cattle breeding. Taking into account the associations between genetic polymorphisms and growth traits that are highlighted in this study, we suggest including this molecular information in a markers assisted selection scheme. Although estimating the heritability of weights was not a goal of the present work, it is important to check the contribution of the SNPs with statistically significant effect on the additive variance of the trait. In the three breeds, the h² estimates ranged between 0.56 for trait W1 and 0.40 for trait W8. For all the weights, the contribution of the significant SNPs was rather low; in fact, the average percentages of additive variance explained by the SNPs were as follows: LEP_1 in Chianina 5.66% (3.51% W1 - 6.54% W6) and in Marchigiana 4.34% (2.14% W8 - 5.61% W5); MC4R in Marchigiana 3.14% (1.86% W4 - 4.76% W5) and in Romagnola 2.56% (1.49% W2 - 4.10% W4); finally, MYF5 3.69% (2.28% W8 - 5.98% W5) and GHR1 2.64% (1.88% W5 – 4.02% W7) only in Marchigiana. The SNPs effects on the BLUP-AM indexes are shown by the significances reported in Table 2. It is possible to highlight a rather strong effect of MC4R in Chianina (MI, TI, MMS, TMS); on the other 10
hand, GHR_2 (MI, TI, MMS, DMS), LEP_1 (MI, TI, MMS, TMS), LEP_2 (GI, MI, TI, DMS), MYF5 (GI, MMS, DMS, TMS), LEPR (GI, TI, MMS, DMS), and UCP3_1 (GI, TI, MMS) were mainly significant in Marchigiana and Romagnola. Other SNPs are often significant in one or few indexes and in one or two breeds, without a clear trend of differences exploitable across all the three breeds. Therefore it is quite difficult to hypothesize a real practical effect in a same gene-assisted program shared by these breeds, that are also subjected to the same selection scheme based on the performance test. A general view of Table 2 shows that usually TI is significant when at least one of the other two indexes (GI, MI) is significant, but the same correspondence is not always observed between TMS and its components (MMS and DMS). As mentioned above, because leptins are one of the most interesting SNPs, the frequencies of haplotypes LEP_1-LEP_2-LEP_3 (BTA 4 chromosome) are reported: the “TCG” was the most recurring haplotype (71.56%-Chianina; 53.46%-Marchigiana; 37.50-Romagnola); the haplotype effect was observed to be not significant in any weight and breed. The present study highlights, for the first time, that the use of some SNPs polymorphisms in the selection practice of the Italian beef cattle breeds could be rather useful. Our results suggest that some polymorphisms in the studied candidate genes could influence economically important traits and could be useful in a marker-assisted selection scheme to improve beef productive traits. The practical use of the genetic polymorphisms could give the opportunity to carry out a pre-selection of the candidate calves on the base of their SNPs genotype, to decide which animals should enter a performance testing program, increasing in this way the selection intensity. Besides, also this genetic information could be considered in the choose of the dams of sire. A validation study could be useful to verify the genotype of LEP_1, MYF5, and MC4R SNPs in the candidates for the performance test. If the validation shall confirm the results obtained in this study, the information of these markers could be taken in account in the breeding scheme of the Italian beef cattle breeds to increase the genetic gain for some of their breeding goals. 11
Conflict of interest The authors declare that there are no conflicts of interest. Acknowledgements A special thanks to Associazione Nazionale Allevatori Bovini Italiani da Carne (ANABIC) for making the experimental data available. The research was financially supported by INNOVAGEN project. The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest in the subject matter or materials discussed in this manuscript. The authors wish to thank the two anonymous referees for their valuable comments to the manuscript and their constructive suggestions. References A.N.A.B.I.C. home page. http://anabic.it (accessed 29 June 2017) Bellinge, R.H.S., Liberles, D.A., Laschi, S.P.A., O’Brien, P.A., Tay, G.H., 2005. Myostatin and its implication on animal breeding: a review. Anim. Genet. 36, 1-6. Boldman, K.G., Kriese, L.A., Van Vleck, L.D., Van Tassell, C.P., Kachman, S.D., 1995. A manual for use of MTDFREML. A set of programs to obtain estimates of variances and covariances. U.S. Department of Agriculture, Agricultural Research Service. Bonadonna, T., 1976. Etnologia zootecnica. UTET, Torino. Bongiorni, S., Gruber, C.E.M., Bueno, S., Chillemi, G., Ferrè, F., Failla, S., Moioli, B., Valentini, A., 2016. Transcriptomic investigation of meat tenderness in two Italian cattle breeds. Anim. Genet. 47, 273–287. Cozzi, G., 2007. Present situation and future challenges of beef cattle production in Italy and the role of the research. Ital. J. Anim. Sci. 6, 389-396. Dias, V.A.D., Curi, R.A., Pereira, G.L., Malheiros, J.M., Espigolan, R., Albuquerque, L.G.D., Chardulo, L.A.L., Oliveira, H.N.D., 2016. Frequencies of candidate genes and associations with carcass and meat traits in Nellore and crossbred cattle. Pesqui. Agropecu. Bras. 51, 169-176. 12
Falconer, D.S., Mackay, T., 1996. Introduction to quantitative genetics. Longman Group Ltd, Edinburgh Gate, Harlow Essex, England. Ghoneim, M.A., Ogaly, H.A., Gouda, E.M., El-Behairy, A.M., 2016. Prediction of desirable genotype patterns in Baladi beef cattle and water buffalo by identification of new leptin gene SNPs. Livest. Sci. 194, 51-56. Lasagna, E., Ceccobelli, S., Di Lorenzo, P., Albera, A., Filippini, F., Sarti, F.M., Panella, F., Di Stasio, L., 2015. Comparison of four Italian beef cattle breeds by means of functional genes. Ital. J. Anim. Sci. 14, 3465, 86-89. Lisa, C., Albera, A., Carnier, P., Di Stasio, L., 2013. Variability in candidate genes revealed associations with meat traits in the Piemontese cattle breed. Ital. J. Anim. Sci., 12:e46, 280-285. Liu, H., Tian, W., Zan, L., Wang, H., Cui, H., 2010. Mutations of MC4R gene and its association with economic traits in Qinchuan cattle. Mol. Biol. Rep. 37, 535-540. Makina, S.O., Muchadeyi, F.C., Marle-Köster, E., Taylor, J.F., Makgahlela, M.L., Maiwashe, A., 2015. Genome-wide scan for selection signatures in six cattle breeds in South Africa. Genet. Selec. Evol. 47, 92. Mazzucco, J.P., Goszczynski, D.E., Ripoli, M.V., Melucci, L.M., Pardo, A.M., Colatto, E., Rogberg-Muñoz, A., Mezzadra, C.A., Depetris, G.J., Giovambattista, G., Villarreal, E.L., 2016. Growth, carcass and meat quality traits in beef from Angus, Hereford and cross-breed grazing steers, and their association with SNPs in genes related to fat deposition metabolism. Meat Sci. 114, 121-129. Meuwissen, T.H.E., Hayes, B.J., Goddard, M.E., 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 157, 1819-1829. Nardone, A. 2003. Impact of BSE on livestock production system. Vet. Res. Commun. 27, 39-52.
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Paredes-Sánchez, F.A., Sifuentes-Rincón, A.M., Cabrera, A.S., Pérez, C.A.G., Bracamonte, G.M.P., Morales, P.A., 2015. Associations of SNPs located at candidate genes to bovine growth traits, prioritized with an interaction networks construction approach. BMC Genet. 16, 91. Ribeca, C., Bonfatti, V., Cecchinato, A., Albera, A., Gallo, L., Carnier, P., 2014. Effect of polymorphisms in candidate genes on carcass and meat quality traits in double muscled Piemontese cattle. Meat Sci. 96, 1376-1383. Rodriguez, S., Gaunt, T.R., Day, I.N.M., 2009. Hardy-Weinberg equilibrium testing of biological ascertainment for mendelian randomization studies. Am. J. Epidemiol. 169, 505-514. Sarti, F.M., Lasagna, E., Ceccobelli, S., Di Lorenzo, P., Filippini, F., Sbarra, F., Giontella, A., Panella, F., 2014. Influence of single nucleotide polymorphisms in the myostatin and myogenic factor 5 muscle growth-related genes on the performance traits of Marchigiana beef cattle. J. Anim. Sci. 92, 3804-3810. SAS. 2000. SAS online doc – Version 8 SAS Institute Inc. Cary (NC). Sbarra, F., Mantovani, R., Bittante, G., 2009. Heritability of performance test traits in Chianina, Marchigiana and Romagnola breeds. Ital. J. Anim. Sci. 8, 107-109. Scheet, P., Stephens, M., 2006. A fast and flexible statistical model for large-scale population genotype data: Applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet. 78, 629–644. Sherman, E.L., Nkrumah, J.D., Murdoch, B.M., Li, C., Wang, Z., Fu, A., Moore, S.S., 2008. Polymorphisms and haplotypes in the bovine neuropeptide Y, growth hormone receptor, ghrelin, insulin-like growth factor 2, and uncoupling proteins 2 and 3 genes and their associations with measures of growth, performance, feed efficiency, and carcass merit in beef cattle. J. Anim. Sci. 86, 1-16. Vincenti, F., Failla, S., Gigli, S., Lasagna, E., Landi, V., Mangione, A., Berti, C., Sarti, F.M., 2007. The Hypertrophic Marchigiana: physical and biochemical parameters for meat quality evaluation. 14
Ital. J. Anim. Sci. 6, 491-493. Zhang, R.F., Chen, H., Lei, C.Z., Zhang, C.L., Lan, X.Y., Zhang, Y.D., Zhang, H.J., Bao, B., Niu, H., Wang, X.Z., 2007. Association between polymorphisms of MSTN and MYF5 genes and growth traits in three Chinese cattle breeds. Asian-Aust. J. Anim. Sci. 20, 1798-1804. Zhu, M., Zhao, S., 2007. Candidate gene identification approach: progress and challenges. Int. J. Biol. Sci. 3, 420-427. Table 1. Average allele substitution (AAS±SE) of the significant effects (Kg) in the weights
C
SNP
WEIGHTS W1
W2
W3
W4
W5
W6
W7
W8
W9
-8.61
-8.65
-10.15
-11.09
-11.86
-13.03
-13.01
-13.09
-14.00
±3.20
±3.53
±3.82
±3.98
±4.06
±4.15
±4.33
±4.55
±4.62
1.20
2.35
4.16
6.74
8.07
8.73
9.86
10.83
11.20
±2.35
±2.92
±4.49
±4.72
±5.11
±5.41
±5.76
±6.08
±6.29
-0.12
-0.24
-1.39
-4.78
-5.12
-6.61
-7.04
-8.25
-10.01
±0.45
±0.61
±2.05
±2.05
±3.15
±3.81
±3.91
±4.10
±5.28
2.86
3.56
4.06
4.96
5.87
6.97
8.15
9.15
10.15
±2.99
±3.11
±3.35
±3.49
±3.61
±3.89
±3.84
±4.64
±5.04
2.31
3.16
5.20
7.88
8.21
9.15
11.02
11.3
11.8
±2.29
±3.05
±2.68
±3.91
±4.05
±4.63
±5.15
±5.88
±5.47
8.59
11.11
10.00
1.81
1.44
0.47
0.34
0.69
0.70
±5.35
±5.75
±6.23
±6.42
±6.75
±7.03
±7.19
±7.27
±7.42
-1.85
-2.36
-3.49
1.01
0.34
1.21
1.65
2.02
2.52
±0.61
±0.73
±1.07
±5.03
±5.33
±5.75
±5.85
±5.87
±5.97
8.20
8.49
10.39
12.91
11.07
11.33
10.98
10.36
12.20
±4.01
±4.31
±4.66
±4.95
±5.22
±5.47
±5.58
±5.62
±5.73
LEP_1
GHR_1
M
LEP_1
MYF5
MC4R
GDF8_1
R
UCP3_2
MC4R C: Chianina; M: Marchigiana; R: Romagnola In bold significant values (P≤0.05)
Table 2. Significance of the SNPs effect in the BLUP Animal Model indexes in the three studied breeds. 15
SNP
INDEXES TI
MMS
DMS
M
R
M-R
GDF8_1
R
M
GHRL
R
M-R
R
R
GHR_1
GI
MI
C
M
GHR_2
M
IGF_2
M
M
LEP_1
M
M
M
M
LEP_2
C-R
LEP_3
M
MYF5
R
C
M
M
LEPR
C-R
R
M
R
UCP_2
R
R
UCP3_1
R-M
M-R
UCP3_2
R
MC4R
TMS
M
M M
M C
R-M
R M
C
C
M
C
Chianina: C; Marchigiana: M; Romagnola: R GI: Growth index; MI: Muscle index; TI: Total index; MMS: Muscle Morphological Score; DMS: Dimension Morphological Score; TMS: Total Morphological Score.
16