Genomic selection for meat quality traits based on VIS/NIR spectral information

doi:10.1016/j.jia.2023.09.019

Journal of Integrative Agriculture

2025, Vol. 24

Issue (1): 235-245 DOI: 10.1016/j.jia.2023.09.019

Animal Science · Veterinary Medicine

Advanced Online Publication | Current Issue | Archive | Adv Search

Genomic selection for meat quality traits based on VIS/NIR spectral information

Xi Tang, Lei Xie, Min Yan, Longyun Li, Tianxiong Yao, Siyi Liu, Wenwu Xu, Shijun Xiao, Nengshui Ding, Zhiyan Zhang^#, Lusheng Huang

State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, China

Highlights
● The genomic selection strategy based on visible/near-infrared (VIS/NIR) spectroscopy significantly improves the prediction accuracy of pork quality.
● The structural similarity between spectral data and genomic data provides a theoretical basis for the application of the model.
● The proposed strategy bypasses the need for full individual or genotype data to provide a cost-effective breeding solution.

Abstract
References

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摘要

基因组选择(GS)的原理是通过归纳SNPs的所有多基因效应的总和以此估计育种值(BV)。可见光/近红外光谱(VIS/NIRS)的波长和丰度可以直接反映化学物质的浓度，利用VIS/NIRS预测肉品性状的过程，类似于通过对光谱特征波段的所有“多基因效应”求和来进行基因组选择处理。因此，将VIS/NIRS信息纳入基因组选择过程以建立高效、低成本的育种模型是非常有意义的。本研究测定了来自中国广西的359头杜洛克×长白×约克夏三元杂群体的6项肉质性状，并用高密度SNP芯片对其进行了基因分型。根据目标群体信息的完整性，我们提出了4种不同情况下的育种策略：策略I，目标群体只拥有光谱和基因型数据；策略II，目标种群只有光谱数据；策略Ⅲ，目标种群也只有光谱和基因型数据，但预测过程不同；策略Ⅳ，目标种群只拥有光谱和表型数据。本研究旨在这四种情况下，探究将VIS/NIR光谱信息纳入基因组选择中对GEBV预测准确性的影响。5折交叉验证的结果显示，遗传算法在特征波长预选方面表现出显著的潜力。策略Ⅱ、Ⅲ、Ⅳ在大多数性状上的育种效性优于传统的GS方法，其GEBV预测准确性在六项性状的均值上分别提高了32.2%、40.8%和15.5%。其中，策略II对脂肪（%）的预测准确性甚至比传统GS提高了50.7%。策略I的GEBV预测准确性与传统GS结果基本一致，波动范围小于7%。此外，这4种新策略的育种成本均低于传统的GS方法，其中策略IV的育种成本最低，因为它不需要对目标群体进行基因分型。我们的研究结果表明，基于VIS/NIRS数据的GS方法具有显著的预测潜力，值得进一步研究，可为制定高效、经济的育种策略提供有价值的参考。

Abstract

The principle of genomic selection (GS) entails estimating breeding values (BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy (VIS/NIRS) wavelength and abundance values can directly reflect the concentrations of chemical substances, and the measurement of meat traits by VIS/NIRS is similar to the processing of genomic selection data by summing all ‘polygenic effects’ associated with spectral feature peaks. Therefore, it is meaningful to investigate the incorporation of VIS/NIRS information into GS models to establish an efficient and low-cost breeding model. In this study, we measured 6 meat quality traits in 359 Duroc×Landrace×Yorkshire pigs from Guangxi Zhuang Autonomous Region, China, and genotyped them with high-density SNP chips. According to the completeness of the information for the target population, we proposed 4 breeding strategies applied to different scenarios: I, only spectral and genotypic data exist for the target population; II, only spectral data exist for the target population; III, only spectral and genotypic data but with different prediction processes exist for the target population; and IV, only spectral and phenotypic data exist for the target population. The 4 scenarios were used to evaluate the genomic estimated breeding value (GEBV) accuracy by increasing the VIS/NIR spectral information. In the results of the 5-fold cross-validation, the genetic algorithm showed remarkable potential for preselection of feature wavelengths. The breeding efficiency of Strategies II, III, and IV was superior to that of traditional GS for most traits, and the GEBV prediction accuracy was improved by 32.2, 40.8 and 15.5%, respectively on average. Among them, the prediction accuracy of Strategy II for fat (%) even improved by 50.7% compared to traditional GS. The GEBV prediction accuracy of Strategy I was nearly identical to that of traditional GS, and the fluctuation range was less than 7%. Moreover, the breeding cost of the 4 strategies was lower than that of traditional GS methods, with Strategy IV being the lowest as it did not require genotyping. Our findings demonstrate that GS methods based on VIS/NIRS data have significant predictive potential and are worthy of further research to provide a valuable reference for the development of effective and affordable breeding strategies.

Keywords: VIS/NIR genomic selection GEBV machine learning pig meat quality

Received: 24 March 2023 Accepted: 30 June 2023

Fund: This study was supported by the National Natural Science Foundation of China (32160782 and 32060737).

About author: Xi Tang, E-mail: tangxi1997@foxmail.com; #Correspondence Zhiyan Zhang, Tel: +86-791-83813080, E-mail: bioducklily@hotmail.com

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Xi Tang, Lei Xie, Min Yan, Longyun Li, Tianxiong Yao, Siyi Liu, Wenwu Xu, Shijun Xiao, Nengshui Ding, Zhiyan Zhang, Lusheng Huang . 2025. Genomic selection for meat quality traits based on VIS/NIR spectral information. Journal of Integrative Agriculture, 24(1): 235-245.

Akanno E C, Schenkel F S, Sargolzaei M, Friendship R M, Robinson J A. 2014. Persistency of accuracy of genomic breeding values for different simulated pig breeding programs in developing countries. Journal of Animal Breeding and Genetics, 131, 367–378.

Anderson S. 2007. Determination of fat, moisture, and protein in meat and meat products by using the FOSS FoodScan Near-Infrared Spectrophotometer with FOSS Artificial Neural Network Calibration Model and Associated Database: collaborative study. Journal of AOAC International, 90, 1073–1083.

Beć K B, Grabska J, Huck C W. 2020. Near-infrared spectroscopy in bio-applications. Molecules , 25, 2948.

Behmann J, Acebron K, Emin D, Bennertz S, Matsubara S, Thomas S, Bohnenkamp D, Kuska M T, Jussila J, Salo H, Mahlein A K, Rascher U. 2018. Specim IQ: evaluation of a new, miniaturized handheld hyperspectral camera and its application for plant phenotyping and disease detection. Sensors, 18, 441.

Bittante G, Savoia S, Cecchinato A, Pegolo S, Albera A. 2021. Phenotypic and genetic variation of ultraviolet-visible-infrared spectral wavelengths of bovine meat. Scientific Reports, 11, 13946.

Bostian M L, Fish D L, Webb N B, Arey J J. 1985. Automated methods for determination of fat and moisture in meat and poultry products: collaborative study. Journal-Association of Official Analytical Chemists, 68, 876–880.

Cecchinato A, De Marchi M, Penasa M, Casellas J, Schiavon S, Bittante G. 2012. Genetic analysis of beef fatty acid composition predicted by near-infrared spectroscopy. Journal of Animal Science, 90, 429–438.

Dagnachew B S, Kohler A, Adnøy T. 2013. Genetic and environmental information in goat milk Fourier transform infrared spectra. Journal of Dairy Science, 96, 3973–3985.

Gao G, Gao N, Li S, Kuang W, Zhu L, Jiang W, Yu W, Guo J, Li Z, Yang C, Zhao Y. 2021. Genome-wide association study of meat quality traits in a three-way crossbred commercial pig population. Frontiers in Genetics, 12, 614087.

Goddard M E, Hayes B J. 2007. Genomic selection. Journal of Animal Breeding and Genetics, 124, 323–330.

Granleese T, Clark S A, van der Werf J H J. 2019. Genotyping strategies of selection candidates in livestock breeding programmes. Journal of Animal Breeding and Genetics, 136, 91–101.

Hayes B, Goddard M. 2010. Genome-wide association and genomic selection in animal breeding. Genome, 53, 876–883.

Hill W G, Mackay T F. 2004. D. S. Falconer and introduction to quantitative genetics. Genetics, 167, 1529–1536.

Huck C W, Ozaki Y, Huck-Pezzei V A. 2016. Critical review upon the role and potential of fluorescence and near-infrared imaging and absorption spectroscopy in cancer related cells, serum, saliva, urine and tissue analysis. Current Medicinal Chemistry, 23, 3052–3077.

Kirkpatrick M, Meyer K. 2004. Direct estimation of genetic principal components: simplified analysis of complex phenotypes. Genetics, 168, 2295–2306.

Meuwissen T. 2007. Genomic selection: marker assisted selection on a genome wide scale. Journal of Animal Breeding and Genetics, 124, 321–322.

Nakaya A, Isobe S N. 2012. Will genomic selection be a practical method for plant breeding? Annals of Botany, 110, 1303–1316.

Pasquini C. 2018. Near infrared spectroscopy: A mature analytical technique with new perspectives-A review. Analytica Chimica Acta, 1026, 8–36.

Prieto N, Pawluczyk O, Dugan M E R, Aalhus J L. 2017. A review of the principles and applications of near-infrared spectroscopy to characterize meat, fat, and meat products. Applied Spectroscopy, 71, 1403–1426.

Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M A, Bender D, Maller J, Sklar P, de Bakker P I, Daly M J, Sham P C. 2007. PLINK: A tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81, 559–575.

Rovere G, de Los Campos G, Tempelman R J, Vazquez A I, Miglior F, Schenkel F, Cecchinato A, Bittante G, Toledo-Alvarado H, Fleming A. 2019. A landscape of the heritability of Fourier-transform infrared spectral wavelengths of milk samples by parity and lactation stage in Holstein cows. Journal of Dairy Science, 102, 1354–1363.

Rutten M J, Bovenhuis H, van Arendonk J A. 2010. The effect of the number of observations used for Fourier transform infrared model calibration for bovine milk fat composition on the estimated genetic parameters of the predicted data. Journal of Dairy Science, 93, 4872–4882.

Savoia S, Albera A, Brugiapaglia A, Di Stasio L, Ferragina A, Cecchinato A, Bittante G. 2020. Prediction of meat quality traits in the abattoir using portable and hand-held near-infrared spectrometers. Meat Science, 161, 108017.

Tang X, Rao L, Xie L, Yan M, Chen Z, Liu S, Chen L, Xiao S, Ding N, Zhang Z, Huang L. 2023. Quantification and visualization of meat quality traits in pork using hyperspectral imaging. Meat Science, 196, 109052.

Tang X, Xie L, Liu S, Chen Z, Rao L, Chen L, Li L, Xiao S, Zhang Z, Huang L. 2022. Extensive evaluation of prediction performance for 15 pork quality traits using large scale VIS/NIRS data. Meat Science, 192, 108902.

Wang Q, Hulzebosch A, Bovenhuis H. 2016. Genetic and environmental variation in bovine milk infrared spectra. Journal of Dairy Science, 99, 6793–6803.

Willson H E, Oliveira H R D, Schinckel A P, Grossi D, Brito L F. 2020. Estimation of genetic parameters for pork quality, novel carcass, primal-cut and growth traits in duroc pigs. Animals, 10, 779.

Won S, Jung J, Park E, Kim H. 2018. Identification of genes related to intramuscular fat content of pigs using genome-wide association study. Asian-Australasian Journal of Animal Sciences, 31, 157–162.

Xie L, Qin J, Rao L, Tang X, Cui D, Chen L, Xu W, Xiao S, Zhang Z, Huang L. 2021. Accurate prediction and genome-wide association analysis of digital intramuscular fat content in longissimus muscle of pigs. Animal Genetics, 52, 633–644.

Yang A Q, Chen B, Ran M L, Yang G M, Zeng C. 2020. The application of genomic selection in pig cross breeding. Yi Chuan, 42, 145–152.

Yin L, Ma Y, Xing T, Zhu M, Yu M, Li X, Liu X, Zhao S. 2019. The progress and prospect of genomic selection models. Journal of Animal Husbandry and Veterinary Medicine, 50, 233–242.

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