Population demographic history and population structure for Pakistani Nili-Ravi breeding bulls based on SNP genotyping to identify genomic regions associated with male effects for milk yield and body weight

Islam, Saher; Reddy, Umesh K.; Natarajan, Purushothaman; Abburi, Venkata Lakshmi; Bajwa, Amna Arshad; Imran, Muhammad; Zahoor, Muhammad; Abdullah, Muhammad; Bukhari, Aamir Mehmood; Iqbal, Sajid; Ashraf, Kamran; Nadeem, Asif; Rehman, Habib-Ur; Rashid, Imran; Shehzad, Wasim

doi:10.1371/journal.pone.0242500

Cited by 2 publications

(1 citation statement)

References 133 publications

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“…Campos et al [ 12 ] identified genomic regions associated with growth traits in Hereford and Braford cattle and for this purpose they used different tag SNP selection techniques. Saher et al [ 13 ] identified SNPs to estimate different genetic parameters such as diversity, pairwise population differentiation, linkage disequilibrium (LD) distribution, and for genome-wide association study for milk yield and body weight traits in the Nili-Ravi dairy bulls. Cattle resistance to ticks is known to be under genetic control with a complex biological mechanism within and among breeds.…”

Section: Related Workmentioning

confidence: 99%

Assessing effectiveness of many-objective evolutionary algorithms for selection of tag SNPs

2022

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Background Studies on genome-wide associations help to determine the cause of many genetic diseases. Genome-wide associations typically focus on associations between single-nucleotide polymorphisms (SNPs). Genotyping every SNP in a chromosomal region for identifying genetic variation is computationally very expensive. A representative subset of SNPs, called tag SNPs, can be used to identify genetic variation. Small tag SNPs save the computation time of genotyping platform, however, there could be missing data or genotyping errors in small tag SNPs. This study aims to solve Tag SNPs selection problem using many-objective evolutionary algorithms. Methods Tag SNPs selection can be viewed as an optimization problem with some trade-offs between objectives, e.g. minimizing the number of tag SNPs and maximizing tolerance for missing data. In this study, the tag SNPs selection problem is formulated as a many-objective problem. Nondominated Sorting based Genetic Algorithm (NSGA-III), and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), which are Many-Objective evolutionary algorithms, have been applied and investigated for optimal tag SNPs selection. This study also investigates different initialization methods like greedy and random initialization. optimization. Results The evaluation measures used for comparing results for different algorithms are Hypervolume, Range, SumMin, MinSum, Tolerance rate, and Average Hamming distance. Overall MOEA/D algorithm gives superior results as compared to other algorithms in most cases. NSGA-III outperforms NSGA-II and other compared algorithms on maximum tolerance rate, and SPEA2 outperforms all algorithms on average hamming distance. Conclusion Experimental results show that the performance of our proposed many-objective algorithms is much superior as compared to the results of existing methods. The outcomes show the advantages of greedy initialization over random initialization using NSGA-III, SPEA2, and MOEA/D to solve the tag SNPs selection as many-objective optimization problem.

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Section: Related Workmentioning

confidence: 99%

Assessing effectiveness of many-objective evolutionary algorithms for selection of tag SNPs

2022

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Identification of Buffalo Breeds Using Self-Activated-Based Improved Convolutional Neural Networks

et al. 2022

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The livestock of Pakistan includes different animal breeds utilized for milk farming and exporting worldwide. Buffalo have a high milk production rate, and Pakistan is the third-largest milk-producing country, and its production is increasing over time. Hence, it is essential to recognize the best Buffalo breed for a high milk- and meat yield to meet the world’s demands and breed production. Pakistan has the second-largest number of buffalos among countries worldwide, where the Neli-Ravi breed is the most common. The extensive demand for Neli and Ravi breeds resulted in the new cross-breed “Neli-Ravi” in the 1960s. Identifying and segregating the Neli-Ravi breed from other buffalo breeds is the most crucial concern for Pakistan’s dairy-production centers. Therefore, the automatic detection and classification of buffalo breeds are required. In this research, a computer-vision-based recognition framework is proposed to identify and classify the Neli-Ravi breed from other buffalo breeds. The proposed framework employs self-activated-based improved convolutional neural networks (CNN) combined with self-transfer learning. Moreover, feature maps extracted from CNN are further transferred to obtain rich feature vectors. Different machine learning (Ml) classifiers are adopted to classify the feature vectors. The proposed framework is evaluated on two buffalo breeds, namely, Neli-Ravi and Khundi, and one additional target class contains different buffalo breeds collectively called Mix. The proposed research achieves a maximum of 93% accuracy using SVM and more than 85% accuracy employing recent variants.

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Population demographic history and population structure for Pakistani Nili-Ravi breeding bulls based on SNP genotyping to identify genomic regions associated with male effects for milk yield and body weight

Cited by 2 publications

References 133 publications

Assessing effectiveness of many-objective evolutionary algorithms for selection of tag SNPs

Assessing effectiveness of many-objective evolutionary algorithms for selection of tag SNPs

Identification of Buffalo Breeds Using Self-Activated-Based Improved Convolutional Neural Networks

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