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Cellesi M, Correddu F, Manca MG, Serdino J, Gaspa G, Dimauro C, Macciotta NPP. Prediction of Milk Coagulation Properties and Individual Cheese Yield in Sheep Using Partial Least Squares Regression. Animals (Basel) 2019; 9:ani9090663. [PMID: 31500237 PMCID: PMC6770130 DOI: 10.3390/ani9090663] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 09/02/2019] [Accepted: 09/05/2019] [Indexed: 12/31/2022] Open
Abstract
Simple Summary Considered that all sheep milk in Italy is destined for cheese processing, traits describing rennet coagulation aptitude should be among the most important selection goals for dairy breeds. To reduce the costs and logistics related to the large-scale recording of these traits, mid-infrared (MIR) spectroscopy could be conveniently used to generate reliable predictions without any additional cost. The aims of this research were to predict the milk coagulation properties (MCP) and individual cheese yield (ILCY) in sheep by MIR spectrometry using partial least squares regression (PLS), and to compare different data pre-treatment procedures. The prediction results observed in the present study, although moderate, suggest the possibility of adding novel phenotypes (e.g., MCP and ILCY) in breeding schemes for dairy sheep breeds. Mid-infrared spectroscopy coupled with PLS regression could allow the prediction of phenotypes at the population level without additional costs. Abstract The objectives of this study were (i) the prediction of sheep milk coagulation properties (MCP) and individual laboratory cheese yield (ILCY) from mid-infrared (MIR) spectra by using partial least squares (PLS) regression, and (ii) the comparison of different data pre-treatments on prediction accuracy. Individual milk samples of 970 Sarda breed ewes were analyzed for rennet coagulation time (RCT), curd-firming time (k20), and curd firmness (a30) using the Formagraph instrument; ILCY was measured by micro-manufacturing assays. An Furier-transform Infrared (FTIR) milk-analyzer was used for the estimation of the milk gross composition and the recording of MIR spectrum. The dataset (n = 859, after the exclusion of 111 noncoagulating samples) was divided into two sub-datasets: the data of 700 ewes were used to estimate prediction model parameters, and the data of 159 ewes were used to validate the model. Four prediction scenarios were compared in the validation, differing for the use of whole or reduced MIR spectrum and the use of raw or corrected data (locally weighted scatterplot smoothing). PLS prediction statistics were moderate. The use of the reduced MIR spectrum yielded the best results for the considered traits, whereas the data correction improved the prediction ability only when the whole MIR spectrum was used. In conclusion, PLS achieves good accuracy of prediction, in particular for ILCY and RCT, and it may enable increasing the number of traits to be included in breeding programs for dairy sheep without additional costs and logistics.
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Affiliation(s)
- Massimo Cellesi
- Dipartimento di Agraria, Sezione di Scienze Zootecniche, Università degli Studi di Sassari, Viale Italia 39, 07100 Sassari, Italy.
| | - Fabio Correddu
- Dipartimento di Agraria, Sezione di Scienze Zootecniche, Università degli Studi di Sassari, Viale Italia 39, 07100 Sassari, Italy.
| | - Maria Grazia Manca
- Dipartimento di Agraria, Sezione di Scienze Zootecniche, Università degli Studi di Sassari, Viale Italia 39, 07100 Sassari, Italy.
| | - Jessica Serdino
- Dipartimento di Agraria, Sezione di Scienze Zootecniche, Università degli Studi di Sassari, Viale Italia 39, 07100 Sassari, Italy.
| | - Giustino Gaspa
- Dipartimento di Scienze Agrarie Alimentari e Forestali, Università di Torino, 10095 Grugliasco, Italy.
| | - Corrado Dimauro
- Dipartimento di Agraria, Sezione di Scienze Zootecniche, Università degli Studi di Sassari, Viale Italia 39, 07100 Sassari, Italy.
| | - Nicolò Pietro Paolo Macciotta
- Dipartimento di Agraria, Sezione di Scienze Zootecniche, Università degli Studi di Sassari, Viale Italia 39, 07100 Sassari, Italy.
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Cesarani A, Gaspa G, Correddu F, Cellesi M, Dimauro C, Macciotta N. Genomic selection of milk fatty acid composition in Sarda dairy sheep: Effect of different phenotypes and relationship matrices on heritability and breeding value accuracy. J Dairy Sci 2019; 102:3189-3203. [DOI: 10.3168/jds.2018-15333] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 12/13/2018] [Indexed: 01/21/2023]
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Dimauro C, Manca E, Rossoni A, Santus E, Cellesi M, Gaspa G. 0323 Use of multivariate statistical analyses to preselect SNP markers for GWAS on residual feed intake in dairy cattle. J Anim Sci 2016. [DOI: 10.2527/jam2016-0323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Sorbolini S, Gaspa G, Steri R, Dimauro C, Cellesi M, Stella A, Marras G, Marsan PA, Valentini A, Macciotta NPP. Use of canonical discriminant analysis to study signatures of selection in cattle. Genet Sel Evol 2016; 48:58. [PMID: 27521154 PMCID: PMC4983034 DOI: 10.1186/s12711-016-0236-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 08/01/2016] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Cattle include a large number of breeds that are characterized by marked phenotypic differences and thus constitute a valuable model to study genome evolution in response to processes such as selection and domestication. Detection of "signatures of selection" is a useful approach to study the evolutionary pressures experienced throughout history. In the present study, signatures of selection were investigated in five cattle breeds farmed in Italy using a multivariate approach. METHODS A total of 4094 bulls from five breeds with different production aptitudes (two dairy breeds: Italian Holstein and Italian Brown Swiss; two beef breeds: Piemontese and Marchigiana; and one dual purpose breed: Italian Simmental) were genotyped using the Illumina BovineSNP50 v.1 beadchip. Canonical discriminant analysis was carried out on the matrix of single nucleotide polymorphisms (SNP) genotyping data, separately for each chromosome. Scores for each canonical variable were calculated and then plotted in the canonical space to quantify the distance between breeds. SNPs for which the correlation with the canonical variable was in the 99th percentile for a specific chromosome were considered to be significantly associated with that variable. Results were compared with those obtained using an FST-based approach. RESULTS Based on the results of the canonical discriminant analysis, a large number of signatures of selection were detected, among which several had strong signals in genomic regions that harbour genes known to have an impact on production and morphological bovine traits, including MSTN, LCT, GHR, SCD, NCAPG, KIT, and ASIP. Moreover, new putative candidate genes were identified, such as GCK, B3GALNT1, MGAT1, GALNTL1, PRNP, and PRND. Similar results were obtained with the FST-based approach. CONCLUSIONS The use of canonical discriminant analysis on 50 K SNP genotypes allowed the extraction of new variables that maximize the separation between breeds. This approach is quite straightforward, it can compare more than two groups simultaneously, and relative distances between breeds can be visualized. The genes that were highlighted in the canonical discriminant analysis were in concordance with those obtained using the FST index.
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Affiliation(s)
- Silvia Sorbolini
- Dipartimento di Agraria, Sezione di Scienze Zootecniche, Università degli Studi di Sassari, V. le Italia, 9, 07100, Sassari, Italy
| | - Giustino Gaspa
- Dipartimento di Agraria, Sezione di Scienze Zootecniche, Università degli Studi di Sassari, V. le Italia, 9, 07100, Sassari, Italy
| | - Roberto Steri
- Consiglio per la Ricerca e la Sperimentazione in Agricoltura, via Salaria 31, 00015, Monterotondo, Italy
| | - Corrado Dimauro
- Dipartimento di Agraria, Sezione di Scienze Zootecniche, Università degli Studi di Sassari, V. le Italia, 9, 07100, Sassari, Italy
| | - Massimo Cellesi
- Dipartimento di Agraria, Sezione di Scienze Zootecniche, Università degli Studi di Sassari, V. le Italia, 9, 07100, Sassari, Italy
| | | | | | - Paolo Ajmone Marsan
- Istituto di Zootecnica, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Alessio Valentini
- Dipartimento per l'Innovazione dei Sistemi Biologici Agroalimentari e Forestali DIBAF, Università della Tuscia, Viterbo, Italy
| | - Nicolò Pietro Paolo Macciotta
- Dipartimento di Agraria, Sezione di Scienze Zootecniche, Università degli Studi di Sassari, V. le Italia, 9, 07100, Sassari, Italy.
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Cellesi M, Dimauro C, Sorbolini S, Nicolazzi EL, Gaspa G, Ajmone-Marsan P, Macciotta NPP. Maximum difference analysis: a new empirical method for genome-wide association studies. Italian Journal of Animal Science 2016. [DOI: 10.1080/1828051x.2016.1216336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Massimo Cellesi
- Dipartimento di Agraria, University of Sassari, Sassari, Italy
| | - Corrado Dimauro
- Dipartimento di Agraria, University of Sassari, Sassari, Italy
| | | | | | - Giustino Gaspa
- Dipartimento di Agraria, University of Sassari, Sassari, Italy
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Sorbolini S, Bongiorni S, Cellesi M, Gaspa G, Dimauro C, Valentini A, Macciotta N. Genome wide association study on beef production traits in Marchigiana cattle breed. J Anim Breed Genet 2016; 134:43-48. [DOI: 10.1111/jbg.12227] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Accepted: 05/24/2016] [Indexed: 12/12/2022]
Affiliation(s)
- S. Sorbolini
- Dipartimento di Agraria; Sezione di Scienze Zootecniche; Università degli Studi di Sassari; Sassari Italy
| | - S. Bongiorni
- Dipartimento per l'Innovazione nei Sistemi Biologici Agroalimentari e Forestali DIBAF; Università della Tuscia; Viterbo Italy
| | - M. Cellesi
- Dipartimento di Agraria; Sezione di Scienze Zootecniche; Università degli Studi di Sassari; Sassari Italy
| | - G. Gaspa
- Dipartimento di Agraria; Sezione di Scienze Zootecniche; Università degli Studi di Sassari; Sassari Italy
| | - C. Dimauro
- Dipartimento di Agraria; Sezione di Scienze Zootecniche; Università degli Studi di Sassari; Sassari Italy
| | - A. Valentini
- Dipartimento per l'Innovazione nei Sistemi Biologici Agroalimentari e Forestali DIBAF; Università della Tuscia; Viterbo Italy
| | - N.P.P. Macciotta
- Dipartimento di Agraria; Sezione di Scienze Zootecniche; Università degli Studi di Sassari; Sassari Italy
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Macciotta N, Gaspa G, Bomba L, Vicario D, Dimauro C, Cellesi M, Ajmone-Marsan P. Genome-wide association analysis in Italian Simmental cows for lactation curve traits using a low-density (7K) SNP panel. J Dairy Sci 2015; 98:8175-85. [DOI: 10.3168/jds.2015-9500] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2015] [Accepted: 06/22/2015] [Indexed: 01/15/2023]
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Sorbolini S, Marras G, Gaspa G, Dimauro C, Cellesi M, Valentini A, Macciotta NP. Detection of selection signatures in Piemontese and Marchigiana cattle, two breeds with similar production aptitudes but different selection histories. Genet Sel Evol 2015; 47:52. [PMID: 26100250 PMCID: PMC4476081 DOI: 10.1186/s12711-015-0128-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2014] [Accepted: 05/20/2015] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Domestication and selection are processes that alter the pattern of within- and between-population genetic variability. They can be investigated at the genomic level by tracing the so-called selection signatures. Recently, sequence polymorphisms at the genome-wide level have been investigated in a wide range of animals. A common approach to detect selection signatures is to compare breeds that have been selected for different breeding goals (i.e. dairy and beef cattle). However, genetic variations in different breeds with similar production aptitudes and similar phenotypes can be related to differences in their selection history. METHODS In this study, we investigated selection signatures between two Italian beef cattle breeds, Piemontese and Marchigiana, using genotyping data that was obtained with the Illumina BovineSNP50 BeadChip. The comparison was based on the fixation index (Fst), combined with a locally weighted scatterplot smoothing (LOWESS) regression and a control chart approach. In addition, analyses of Fst were carried out to confirm candidate genes. In particular, data were processed using the varLD method, which compares the regional variation of linkage disequilibrium between populations. RESULTS Genome scans confirmed the presence of selective sweeps in the genomic regions that harbour candidate genes that are known to affect productive traits in cattle such as DGAT1, ABCG2, CAPN3, MSTN and FTO. In addition, several new putative candidate genes (for example ALAS1, ABCB8, ACADS and SOD1) were detected. CONCLUSIONS This study provided evidence on the different selection histories of two cattle breeds and the usefulness of genomic scans to detect selective sweeps even in cattle breeds that are bred for similar production aptitudes.
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Affiliation(s)
- Silvia Sorbolini
- Dipartimento di Agraria, Sezione di Scienze Zootecniche Università degli Studi di Sassari, 07100, Sassari, Italy.
| | - Gabriele Marras
- Dipartimento di Agraria, Sezione di Scienze Zootecniche Università degli Studi di Sassari, 07100, Sassari, Italy.
| | - Giustino Gaspa
- Dipartimento di Agraria, Sezione di Scienze Zootecniche Università degli Studi di Sassari, 07100, Sassari, Italy.
| | - Corrado Dimauro
- Dipartimento di Agraria, Sezione di Scienze Zootecniche Università degli Studi di Sassari, 07100, Sassari, Italy.
| | - Massimo Cellesi
- Dipartimento di Agraria, Sezione di Scienze Zootecniche Università degli Studi di Sassari, 07100, Sassari, Italy.
| | - Alessio Valentini
- Dipartimento per l'Innovazione dei Sistemi Biologici Agroalimentari e Forestali DIBAF, Università della Tuscia, Viterbo, Italy.
| | - Nicolò Pp Macciotta
- Dipartimento di Agraria, Sezione di Scienze Zootecniche Università degli Studi di Sassari, 07100, Sassari, Italy.
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Macciotta N, Dimauro C, Null D, Gaspa G, Cellesi M, Cole J. Dissection of genomic correlation matrices of US Holsteins using multivariate factor analysis. J Anim Breed Genet 2014; 132:9-20. [DOI: 10.1111/jbg.12113] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2014] [Accepted: 07/03/2014] [Indexed: 11/28/2022]
Affiliation(s)
- N.P.P. Macciotta
- Dipartimento di Agraria; Sezione Scienze Zootecniche; Università di Sassari; Sassari Italy
| | - C. Dimauro
- Dipartimento di Agraria; Sezione Scienze Zootecniche; Università di Sassari; Sassari Italy
| | - D.J. Null
- Animal Genomics and Improvement Laboratory; Agricultural Research Service; USDA; Beltsville Maryland USA
| | - G. Gaspa
- Dipartimento di Agraria; Sezione Scienze Zootecniche; Università di Sassari; Sassari Italy
| | - M. Cellesi
- Dipartimento di Agraria; Sezione Scienze Zootecniche; Università di Sassari; Sassari Italy
| | - J.B. Cole
- Animal Genomics and Improvement Laboratory; Agricultural Research Service; USDA; Beltsville Maryland USA
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Dimauro C, Cellesi M, Steri R, Gaspa G, Sorbolini S, Stella A, Macciotta NPP. Use of the canonical discriminant analysis to select SNP markers for bovine breed assignment and traceability purposes. Anim Genet 2013; 44:377-82. [DOI: 10.1111/age.12021] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/13/2012] [Indexed: 11/26/2022]
Affiliation(s)
- C. Dimauro
- Dipartimento di Agraria; Università di Sassari; Via De Nicola 9; 07100; Sassari; Italy
| | - M. Cellesi
- Dipartimento di Agraria; Università di Sassari; Via De Nicola 9; 07100; Sassari; Italy
| | - R. Steri
- Dipartimento di Agraria; Università di Sassari; Via De Nicola 9; 07100; Sassari; Italy
| | - G. Gaspa
- Dipartimento di Agraria; Università di Sassari; Via De Nicola 9; 07100; Sassari; Italy
| | - S. Sorbolini
- Dipartimento di Agraria; Università di Sassari; Via De Nicola 9; 07100; Sassari; Italy
| | - A. Stella
- Istituto di biologia e biotecnologia agraria CNR; -20133; Milano; Italy
| | - N. P. P. Macciotta
- Dipartimento di Agraria; Università di Sassari; Via De Nicola 9; 07100; Sassari; Italy
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Dimauro C, Cellesi M, Pintus MA, Macciotta NPP. The impact of the rank of marker variance-covariance matrix in principal component evaluation for genomic selection applications. J Anim Breed Genet 2011; 128:440-5. [PMID: 22059577 DOI: 10.1111/j.1439-0388.2011.00957.x] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In genomic selection (GS) programmes, direct genomic values (DGV) are evaluated using information provided by high-density SNP chip. Being DGV accuracy strictly dependent on SNP density, it is likely that an increase in the number of markers per chip will result in severe computational consequences. Aim of present work was to test the effectiveness of principal component analysis (PCA) carried out by chromosome in reducing the marker dimensionality for GS purposes. A simulated data set of 5700 individuals with an equal number of SNP distributed over six chromosomes was used. PCs were extracted both genome-wide (ALL) and separately by chromosome (CHR) and used to predict DGVs. In the ALL scenario, the SNP variance-covariance matrix (S) was singular, positive semi-definite and contained null information which introduces 'spuriousness' in the derived results. On the contrary, the S matrix for each chromosome (CHR scenario) had a full rank. Obtained DGV accuracies were always better for CHR than ALL. Moreover, in the latter scenario, DGV accuracies became soon unsettled as the number of animals decreases, whereas in CHR, they remain stable till 900-1000 individuals. In real applications where a 54k SNP chip is used, the largest number of markers per chromosome is approximately 2500. Thus, a number of around 3000 genotyped animals could lead to reliable results when the original SNP variables are replaced by a reduced number of PCs.
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Affiliation(s)
- C Dimauro
- Dipartimento di Scienze Zootecniche, Università di Sassari, via De Nicola, Sassari, Italy.
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