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Start Over Author "Macciotta A." Journal journal of animal science
14 results on '"Macciotta A."'

3. Use of principal component approach to predict direct genomic breeding values for beef traits in Italian Simmental cattle1

Author

Ezequiel L. Nicolazzi, Alessio Valentini, Maria Annunziata Pintus, D. Vicario, Giustino Gaspa, Corrado Dimauro, and Nicolò Pietro Paolo Macciotta

Subjects
education.field_of_study, Variables, business.industry, media_common.quotation_subject, Population, food.cheese_milk_source, Single-nucleotide polymorphism, General Medicine, Phenotypic trait, Biology, Biotechnology, Correlation, food, Statistics, Principal component analysis, Simmental cattle, Genetics, SNP, Animal Science and Zoology, education, business, Food Science, media_common
Abstract

In the current study, principal component (PC) analysis was used to reduce the number of predictors in the estimation of direct genomic breeding values (DGV) for meat traits in a sample of 479 Italian Simmental bulls. Single nucleotide polymorphism marker genotypes were determined with the 54K Illumina beadchip. After edits, 457 bulls and 40,179 SNP were retained. Principal component extraction was performed separately for each chromosome and 2466 new variables able to explain 70% of total variance were obtained. Bulls were divided into reference and validation population. Three scenarios of the ratio reference:validation were tested: 70:30, 80:20, 90:10. Effect of PC scores on polygenic EBV was estimated in the reference population using different models and methods. Traits analyzed were 7 beef traits: daily BW gain, size score, muscularity score, feet and legs score, beef index (economic index), calving ease direct effect, and cow muscularity. Accuracy was calculated as correlation between DGV and polygenic EBV in the validation bulls. Muscularity, feet and legs, and the beef index showed the greatest accuracies; calving ease, the least. In general, accuracies were slightly greater when reference animals were selected at random and the best scenario was 90:10 and no substantial differences in accuracy were found among different methods. Principal component analysis is entirely based on the factorization of the SNP (co)variance matrix and produced a reduced set of variables (6% of the original variables) which may be used for different phenotypic traits. In spite of this huge reduction in the number of independent variables, DGV accuracies resulted similar to those obtained by using the whole set of SNP markers. Accuracies of direct genomic values found in the present work were always greater than those of traditional parental average (PA). Thus, results of the present study may suggest a possible advantage of use of genomic indexes in the preselection of performance test candidates for beef traits. Moreover, the relevant reduction of variable space might allow genomic selection implementation also in small populations.

Published
2013

4. Analysis of ddRAD-seq data provides new insights into the genomic structure and patterns of diversity in Italian donkey populations

Author

Criscione, Andrea, Chessari, Giorgio, Cesarani, Alberto, Ablondi, Michela, Asti, Vittoria, Bigi, Daniele, Bordonaro, Salvatore, Ciampolini, Roberta, Cipolat-Gotet, Claudio, Congiu, Michele, De Palo, Pasquale, Landi, Vincenzo, Macciotta, Nicolò Pietro Paolo, Matassino, Donato, Portolano, Baldassare, Riggio, Silvia, Sabbioni, Alberto, Sardina, Maria Teresa, Senczuk, Gabriele, Tumino, Serena, Vasini, Matteo, Ciani, Elena, and Mastrangelo, Salvatore

Abstract

With more than 150 recognized breeds, donkeys assume relevant economic importance, especially in developing countries. Even if the estimated number of heads worldwide is 53M, this species received less attention than other livestock species. Italy has traditionally been considered one of the cradles of European donkey breeding, and despite a considerable loss of biodiversity, today still counts nine autochthonous populations. A total of 220 animals belonging to nine different populations were genotyped using the double-digest restriction site associated DNA (ddRAD) sequencing to investigate the pattern of diversity using a multi-technique approach. A total of 418,602,730 reads were generated and successfully demultiplexed to obtain a medium-density SNP genotypes panel with about 27K markers. The diversity indices showed moderate levels of variability. The genetic distances and relationships, largely agree with the breeding history of the donkey populations under investigation. The results highlighted the separation of populations based on their genetic origin or geographical proximity between breeding areas, showed low to moderate levels of admixture, and indicated a clear genetic difference in some cases. For some breeds, the results also validate the success of proper management conservation plans. Identified runs of homozygosity islands, mapped within genomic regions related to immune response and local adaptation, are consistent with the characteristics of the species known for its rusticity and adaptability. This study is the first exhaustive genome-wide analysis of the diversity of Italian donkey populations. The results emphasized the high informativeness of genome-wide markers retrieved through the ddRAD approach. The findings take on great significance in designing and implementing conservation strategies. Standardized genotype arrays for donkey species would make it possible to combine worldwide datasets to provide further insights into the evolution of the genomic structure and origin of this important genetic resource.This is the first study deciphering the genetic structure of Italian donkey populations using SNP data. The results of this study confirmed the history of the investigated populations in terms of both genetic and geographical origins.Donkeys assume relevant economic importance in several countries worldwide. However, the genetic structure of these populations is less investigated compared to other species. The aim of this study was to investigate the genetic background of nine different Italian donkey populations. A total of 220 animals were genotyped with about 27K markers extracted by the double-digest restriction site associated DNA sequencing. The consistency of the results across different approaches agreed with the demographic history, the origin, and previous results on the nine donkey populations, suggesting that our conclusions are robust. Moreover, the results of the present study highlighted low to moderate levels of admixture and, for some breeds, confirmed the success of proper management conservation plans.

Published
2024
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5. 0357 ADSA®-EAAP speaker exchange presentation: Genetic analysis of multivariate indices of detailed fatty acid profile determined by gas chromatography in bovine milk

Author

Marcello Mele, Nicolò Pietro Paolo Macciotta, Alessio Cecchinato, Giovanni Bittante, Giuseppe Conte, and Stefano Schiavon

Subjects
chemistry.chemical_classification, Bovine milk, Multivariate statistics, Chromatography, chemistry, Genetics, Fatty acid, Animal Science and Zoology, General Medicine, Gas chromatography, Genetic analysis, Food Science
Published
2016

11. Use of principal component approach to predict direct genomic breeding values for beef traits in Italian Simmental cattle

Author

G, Gaspa, M A, Pintus, E L, Nicolazzi, D, Vicario, A, Valentini, C, Dimauro, and N P P, Macciotta

Subjects
Genetic Markers, Male, Dairying, Principal Component Analysis, Genotype, Animals, Cattle, Female, Genomics, Breeding, Selection, Genetic, Polymorphism, Single Nucleotide
Abstract

In the current study, principal component (PC) analysis was used to reduce the number of predictors in the estimation of direct genomic breeding values (DGV) for meat traits in a sample of 479 Italian Simmental bulls. Single nucleotide polymorphism marker genotypes were determined with the 54K Illumina beadchip. After edits, 457 bulls and 40,179 SNP were retained. Principal component extraction was performed separately for each chromosome and 2466 new variables able to explain 70% of total variance were obtained. Bulls were divided into reference and validation population. Three scenarios of the ratio reference:validation were tested: 70:30, 80:20, 90:10. Effect of PC scores on polygenic EBV was estimated in the reference population using different models and methods. Traits analyzed were 7 beef traits: daily BW gain, size score, muscularity score, feet and legs score, beef index (economic index), calving ease direct effect, and cow muscularity. Accuracy was calculated as correlation between DGV and polygenic EBV in the validation bulls. Muscularity, feet and legs, and the beef index showed the greatest accuracies; calving ease, the least. In general, accuracies were slightly greater when reference animals were selected at random and the best scenario was 90:10 and no substantial differences in accuracy were found among different methods. Principal component analysis is entirely based on the factorization of the SNP (co)variance matrix and produced a reduced set of variables (6% of the original variables) which may be used for different phenotypic traits. In spite of this huge reduction in the number of independent variables, DGV accuracies resulted similar to those obtained by using the whole set of SNP markers. Accuracies of direct genomic values found in the present work were always greater than those of traditional parental average (PA). Thus, results of the present study may suggest a possible advantage of use of genomic indexes in the preselection of performance test candidates for beef traits. Moreover, the relevant reduction of variable space might allow genomic selection implementation also in small populations.

Published
2012

13. Phenotypic relationships between multivariate measures of lactation curve shape and somatic cell count in Italian Simmental cows.

Author

Macciotta, N. P. P., Vicario, D., and Cappio-Borlino, A.

Subjects
*SOMATIC cells, *CELL morphology, *LACTATION, *LATENT variables, *PRINCIPAL components analysis, *RECEIVER operating characteristic curves
Abstract

Multivariate factor Analysis (MFA) and Principal Component Analysis (PCA) are dimension reduction techniques able to extract from the correlation matrix of milk test day records new latent variables related to lactation curve shape. In this paper, MFA and PCA were applied to a data set of 16,364 lactations of Italian Simmental cows, each with seven TD records for milk yield and Somatic Cell Count (SCC). The aim was to study relationships between lactation curve shape and two measures of SCC: lactational geometric mean of SCC (LGSCC) and lactational means of Somatic Cell Score (LSCS). MFA extracted two latent variables, related with level of production in early lactation (PEL) and lactation persistency (PERS) respectively, whereas PCA yields a leading component related to the average level of production (AVY) and a second component positively related with tests of early lactation and negatively with tests of the second part of lactation (SLOPE). Both for MFA and PCA, latent variables were able to explain more than 80% of the original variance. Correlations between indexes of lactation curve shape obtained with MFA and measures of somatic cells are favourable, i.e near zero or negative as in the case of PERS. Around zero are also correlations of the principal component related with level of production whereas positive and slightly higher (0.18) is the correlation between LSCS and SLOPE, indicating that curves with higher peaks and more pronounced slope in the second part of lactation tend to have higher values of SCC. Results of the present study highlight the ability of both MFA and PCA to extract latent variables related to lactation curve shape that, with the exception of the variable SHAPE, show very low or negative correlations with measures of SCC along the lactation. [ABSTRACT FROM AUTHOR]

Published
2006

14. Use of Principal Components and Factor Analysis to factorize genetic correlation matrices of multivariate phenotypes.

Author

Macciotta, N. P. P., Bacciu, N., Dimauro, C., and Cappio-Borlino, A.

Subjects
*PRINCIPAL components analysis, *GENETIC correlations, *MILK yield, *LATENT variables, *FACTOR analysis, *MULTIVARIATE analysis, *SAMPLING errors
Abstract

Principal Component Analysis (PCA) has been used by several authors to address issues of genetic analysis of multivariate complex phenotypes, i.e. large number of parameters to be estimated, computational constraints and sampling error. PCA is able to describe the maximum amount of original variation with the minimum number of orthogonal new variables. However, it is scarcely flexible and results are not always easy to interpret. In order to cope with these shortcomings, the multivariate Factor Analysis (FA) could be proposed. FA, via the rotation technique, is able to extract latent variables with defined relationships with original variables. In the present work the ability of PCA and FA to factorize genetic correlation matrices (published data) of milk yield, logSCC for dairy cattle and weights at different ages for beef cattle is compared. The PCA yields a first component related to all original variables, able to explain most of the original variance (82%, 50%, 82% for milk yield, logSCC and body weight, respectively). Other principal components highlight the contribution of specific original variables to the total variance in decreasing order. On the other hand, FA extracts common latent factors related to specific groups of variables and that have a clear biological meaning: in the case of milk yield, for example, the fist latent common factor is able to explain 55% of the original variance, it is related to the milk tests of the second part of lactation and can be considered as an index of lactation persistency; the second factor (42% of explained variance) is related with tests of the first part of lactation and can be considered as an index of level of production in early lactation. Similar results are obtained for body weights and, even if less defined, for logSCC. Results of the present work suggest that a combined used of PCA and FA could be usefully for a deep understanding of latent structure of correlations in multivariate complex phenotypes. [ABSTRACT FROM AUTHOR]

Published
2006

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