基于可见光/近红外光谱信息对肉质性状进行基因组选择

doi:10.1016/j.jia.2023.09.019

摘要/Abstract

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.

Key words: VIS/NIR , genomic selection , GEBV , machine learning , pig , meat quality

Xi Tang, Lei Xie, Min Yan, Longyun Li, Tianxiong Yao, Siyi Liu, Wenwu Xu, Shijun Xiao, Nengshui Ding, Zhiyan Zhang, Lusheng Huang . 基于可见光/近红外光谱信息对肉质性状进行基因组选择[J]. Journal of Integrative Agriculture, 2025, 24(1): 235-245.

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

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