Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed

Ghotbaldini, Hamidreza; Mohammadabadi, Mohammadreza; Nezamabadi‐pour, Hossein; Babenko, Olena; Bushtruk, Maryna; Tkachenko, Serhii

doi:10.4025/actascianimsci.v41i1.45282

Cited by 43 publications

(17 citation statements)

References 20 publications

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“…Around 44.9% of the world’s sheep populations are live in Asian continent [ 6 ], and Iran has the largest sheep population of 52 million in the Middle East. More than 27 sheep ecotypes have been recognized in Iran which are vary in different factors, including their genetic potential for milk, meat and wool production traits [ 7 , 8 ]. These breeds are conventionally named in accordance with their geographical origin, also thay have categorized according to productive performance and morphological features [ 9 – 12 ].…”

Section: Introductionmentioning

confidence: 99%

Signature selection analysis reveals candidate genes associated with production traits in Iranian sheep breeds

et al. 2021

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Background Sheep were among the first animals to be domesticated. They are raised all over the world and produce a major scale of animal-based protein for human consumption and play an important role in agricultural economy. Iran is one of the important locations for sheep genetic resources in the world. Here, we compared the Illumina Ovine SNP50 BeadChip data of three Iranian local breeds (Moghani, Afshari and Gezel), as a population that does not undergone artificial breeding programs as yet, and five other sheep breeds namely East Friesian white, East Friesian brown, Lacaune, DorsetHorn and Texel to detect genetic mechanisms underlying economical traits and daptation to harsh environments in sheep. Results To identify genomic regions that have been targeted by positive selection, we used fixation index (Fst) and nucleotide diversity (Pi) statistics. Further analysis indicated candidate genes involved in different important traits such as; wool production included crimp of wool (PTPN3, NBEA and KRTAP20–2 genes), fiber diameter (PIK3R4 gene), hair follicle development (LHX2 gene), the growth and development of fiber (COL17A1 gene)), adaptation to hot arid environments (CORIN gene), adaptive in deficit water status (CPQ gene), heat stress (PLCB4, FAM107B, NBEA, PIK3C2B and USP43 genes) in sheep. Conclusions We detected several candidate genes related to wool production traits and adaptation to hot arid environments in sheep that can be applicable for inbreeding goals. Our findings not only include the results of previous researches, but also identify a number of novel candidate genes related to studied traits. However, more works will be essential to acknowledge phenotype- genotype relationships of the identified genes in our study.

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Section: Introductionmentioning

confidence: 99%

Signature selection analysis reveals candidate genes associated with production traits in Iranian sheep breeds

et al. 2021

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“…The ANN model had an insignificant lack of fit tests, which means the model satisfactorily predicted Yield Traits and Fertility Traits prediction variables. A high r 2 is indicative that the variation was accounted for and that the data fitted the proposed model satisfactorily (EHRET et al, 2015;GHOTBALDINI et al, 2019;NAYERI et al, 2019).…”

Section: Ann Modelmentioning

confidence: 87%

“…A multi-layer perceptron model (MLP), which consisted of three layers (input, hidden, and output) were used for modeling an artificial neural network model (ANN) for prediction of Yield Traits (Fat%, Protein%, Cheese Merit, Fluid Merit, LIV) and Fertility Traits prediction variables (SCE, HCR, CCR, Daughter Stillbirth, Sire Stillbirth, and GL). In the known literature, the ANN model was proven as quite capable of approximating nonlinear functions (GÖRGÜLÜ, 2011;SHAHINFAR et al, 2012;YUN et al, 2013;EHRET et al, 2015;KLEIJNEN, 2018;GHOTBALDINI et al, 2019;NAYERI et al, 2019). Before the calculation, both input and output data were normalized to improve the behavior of the ANN.…”

Section: Ann Modelingmentioning

confidence: 99%

Mathematical modeling for genomic selection in Serbian dairy cattle

Beskorovajní¹,

Jovanovic²,

Pezo

et al. 2021

Genetika

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This manuscript has come as a result of an efficient breeding program in Serbian cattle populations for some economically important traits. Genomic selection in the last two decades has been the main challenge in animal breeding programs and genetics. Many SNP markers are used in statistical analysis in predicting the accuracy of breeding values for young animals without their performance. The new breeding tendency in the selection of young animals allows their genetic progress with reducing cost. In this study, 92 Holstein cows from various regions in Serbia were analyzed based on SNP molecular markers. Within this investigation, an empirical model was developed for the prediction of Yield Traits and Fertility Traits variables, according to Key traits data for dairy cattle. The developed model gave a reasonable fit to the data and successfully predicted Yield Traits (such as Fat and Protein Percent, Cheese Merit, Fluid Merit, and Cow Livability) and Fertility Traits variables (such as Sire Calving Ease, Heifer Conception Rate, Cow Conception Rate, Daughter Stillbirth, Sire Stillbirth, and Gestation Length). A total of 92 dairy cattle data were used to build a prediction model for the prediction of Yield Traits and Fertility Traits variables. The artificial neural network model, based on the Broyden- Fletcher-Goldfarb-Shanno iterative algorithm, showed good prediction capabilities (the r2 values during the training cycle for the before mentioned output variables were in the range between 0.444 and 0.989).

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“…In animal production, livestock body weight (BW) is a very important and widely used feature that has a significant impact on feed consumption ( Putnam et al, 1964 ), breeding potential ( Buckley et al, 2003 ; Ghotbaldini et al, 2019 ), social behavior ( Bouissou, 1972 ; Hong et al, 2017 ), energy balance ( Thorup et al, 2012 ), and overall farm management ( Halachmi et al, 2019 ). It may be used indirectly in the assessment of health and welfare status ( Dikmen et al, 2012 ), and in the determination of time-to-market for animals ( Mc Hugh et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

Wang

Shadpour

Chan

et al. 2021

Journal of Animal Science

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Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reached the point of slaughter represent a burden for the feedlot. There are currently two main approaches (direct and indirect) to measure the BW in livestock. Direct approaches include partial-weight or full-weight industrial scales placed in designated locations on large farms that measure passively or dynamically the weight of livestock. While these devices are very accurate, their acquisition, intended purpose and operation size, repeated calibration and maintenance costs associated with their placement in high-temperature variability, and corrosive environments are significant and beyond the affordability and sustainability limits of small and medium size farms and even of commercial operators. As a more affordable alternative to direct weighing approaches, indirect approaches have been developed based on observed or inferred relationships between biometric and morphometric measurements of livestock and their BW. Initial indirect approaches involved manual measurements of animals using measuring tapes and tubes and the use of regression equations able to correlate such measurements with BW. While such approaches have good BW prediction accuracies, they are time consuming, require trained and skilled farm laborers, and can be stressful for both animals and handlers especially when repeated daily. With the concomitant advancement of contactless electro-optical sensors (e.g., 2D, 3D, infrared cameras), computer vision (CV) technologies, and artificial intelligence fields such as machine learning (ML) and deep learning (DL), 2D and 3D images have started to be used as biometric and morphometric proxies for BW estimations. This manuscript provides a review of CV-based and ML/DL-based BW prediction methods and discusses their strengths, weaknesses, and industry applicability potential.

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Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed

Cited by 43 publications

References 20 publications

Signature selection analysis reveals candidate genes associated with production traits in Iranian sheep breeds

Signature selection analysis reveals candidate genes associated with production traits in Iranian sheep breeds

Mathematical modeling for genomic selection in Serbian dairy cattle

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

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