Breed identification of meat using machine learning and breed tag SNPs

Xu, Zhiting; Diao, Shuqi; Teng, Jinyan; Chen, Zitao; Feng, Xueyan; Cai, Xiaotian; Yuan, Xiaolong; Zhang, Hao; Li, Jiaqi; Zhang, Zhe

doi:10.1016/j.foodcont.2021.107971

Cited by 13 publications

(11 citation statements)

References 38 publications

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“…It is well-established that DNA-based inheritance enables the transmission of selected phenotypes across generations either without changes in the DNA sequence through epigenetic inheritance [31] or through functional mutations involving changes in only one base pair (single nucleotide polymorphisms-SNP). Through next-generation sequencing, SNP are valuable for detecting genetic variability and genomic prediction in sheep breeding programs [32], developing breed-specific DNA markers for breed identification [33,34], animal productivity [35], parentage assignment [36,37], forensics [38], and prediction of meat quality traits [39][40][41].…”

Section: Discussionmentioning

confidence: 99%

Next Generation Sequencing of Single Nucleotide Polymorphic DNA-Markers in Selecting for Intramuscular Fat, Fat Melting Point, Omega-3 Long-Chain Polyunsaturated Fatty Acids and Meat Eating Quality in Tattykeel Australian White MARGRA Lamb

Pewan

Otto

Huerlimann

et al. 2021

Foods

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Meat quality data can only be obtained after slaughter when selection decisions about the live animal are already too late. Carcass estimated breeding values present major precision problems due to low accuracy, and by the time an informed decision on the genetic merit for meat quality is made, the animal is already dead. We report for the first time, a targeted next-generation sequencing (NGS) of single nucleotide polymorphisms (SNP) of lipid metabolism genes in Tattykeel Australian White (TAW) sheep of the MARGRA lamb brand, utilizing an innovative and minimally invasive muscle biopsy sampling technique for directly quantifying the genetic worth of live lambs for health-beneficial omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA), intramuscular fat (IMF), and fat melting point (FMP). NGS of stearoyl-CoA desaturase (SCD), fatty acid binding protein-4 (FABP4), and fatty acid synthase (FASN) genes identified functional SNP with unique DNA marker signatures for TAW genetics. The SCD g.23881050T>C locus was significantly associated with IMF, C22:6n-3, and C22:5n-3; FASN g.12323864A>G locus with FMP, C18:3n-3, C18:1n-9, C18:0, C16:0, MUFA, and FABP4 g.62829478A>T locus with IMF. These add new knowledge, precision, and reliability in directly making early and informed decisions on live sheep selection and breeding for health-beneficial n-3 LC-PUFA, FMP, IMF and superior meat-eating quality at the farmgate level. The findings provide evidence that significant associations exist between SNP of lipid metabolism genes and n-3 LC-PUFA, IMF, and FMP, thus underpinning potential marker-assisted selection for meat-eating quality traits in TAW lambs.

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Section: Discussionmentioning

confidence: 99%

Next Generation Sequencing of Single Nucleotide Polymorphic DNA-Markers in Selecting for Intramuscular Fat, Fat Melting Point, Omega-3 Long-Chain Polyunsaturated Fatty Acids and Meat Eating Quality in Tattykeel Australian White MARGRA Lamb

Pewan

Otto

Huerlimann

et al. 2021

Foods

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“…We applied machine learning algorithms to identify SNP marker combinations for Yeonsan Ogye classification through GWAS and LD pruning. Machine learning has been used to select SNP markers for various livestock species [29][30][31][32]. Moreover, applying feature selection to GWAS results can reduce dimensionality and overfitting errors when identifying markers, resulting in more accurate predictions [33].…”

Section: Discussionmentioning

confidence: 99%

Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach

Cho¹,

Cho²,

Kim³

et al. 2022

J Anim Sci Technol

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Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken ( Gallus gallus domesticus ) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

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“…Reference [11] implemented an effective breed identification system using genetic markers single nucleotide polymorphisms (SNPs) genotyped from pigmeat products. Six machine learning methods were trained to make this identification task.…”

Section: Related Workmentioning

confidence: 99%

Toward an Intelligent System for Taurine Cattle Recognition

Bembamba¹,

Ouédraogo²,

Soudré³

et al. 2022

JILSA

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Unlike zebus, taurine cattle have the natural ability to resist trypanosomosis, a parasitic disease endemic to the humid areas of West Africa. However, repeated crossbreeding between zebus and taurine cattle is jeopardizing the genetic heritage of the Taurines and their ability to resist trypanosomosis. To strengthen protection and conservation efforts, it is essential to accurately distinguish purebred taurines from crossbreds. In this study, five Machine Learning models were built using morphological data collected from 1968 cattle. These models were trained to determine whether a given individual is purebred taurine or not. The classifiers yielded promising results. The random forest model and RBF Kernel SVM performed the best with up to 86% and 85% accuracy respectively. Moreover, the study of the correlation coefficients and the feature importance scores allowed us to define the most discriminating morphological traits.

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Breed identification of meat using machine learning and breed tag SNPs

Cited by 13 publications

References 38 publications

Next Generation Sequencing of Single Nucleotide Polymorphic DNA-Markers in Selecting for Intramuscular Fat, Fat Melting Point, Omega-3 Long-Chain Polyunsaturated Fatty Acids and Meat Eating Quality in Tattykeel Australian White MARGRA Lamb

Next Generation Sequencing of Single Nucleotide Polymorphic DNA-Markers in Selecting for Intramuscular Fat, Fat Melting Point, Omega-3 Long-Chain Polyunsaturated Fatty Acids and Meat Eating Quality in Tattykeel Australian White MARGRA Lamb

Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach

Toward an Intelligent System for Taurine Cattle Recognition

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