CN114742290A - 一种通过血浆代谢物丰度建模预测白羽肉鸡饲料转化效率的方法 - Google Patents
一种通过血浆代谢物丰度建模预测白羽肉鸡饲料转化效率的方法 Download PDFInfo
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Abstract
本发明公开一种通过血浆代谢物丰度建模预测白羽肉鸡饲料转化效率的方法，用于鉴定出高饲料转化效率肉鸡，本发明通过血浆代谢组学分析找到适合作为肉鸡饲料转化效率的代谢标记，进一步使用多元回归分析方法预测饲料转化效率，进而鉴定出高饲料转化效率的肉鸡。本发明能够高效的区分开高饲料转化效率肉鸡和低饲料转化效率肉鸡，节省育种设备支出，推动家禽遗传改良工作，降低了育种成本。
Description
技术领域
本发明属于家禽遗传育种技术领域，具体涉及一种通过血浆代谢物丰度建模预测白羽肉鸡饲料转化效率的方法。
背景技术
畜禽生产中，饲养高饲料转化效率畜禽有利于降低粮食消耗和节约生产成本。使用采食量自动记录设备来测定个体饲料转化效率，直接选择优秀个体是提高动物饲料转化效率最有效的办法。但采食量自动记录设备价格昂贵使大多数养殖场难以担负。因此，我们需要一种便宜和快速预测白羽肉鸡饲料转化效率的方法，以降低育种成本。新的证据表明，代谢物可用作选择哺乳动物饲料转化效率的生物标志物。然而，代谢物是否可用作选择家禽饲料转化效率的生物标志物还未知。
发明内容
为解决上述问题，本发明提供了一种通过血浆代谢物丰度建模预测白羽肉鸡饲料转化效率的方法，用于鉴定出高饲料转化效率肉鸡，降低育种成本。
本发明所采用的技术方案如下：一种通过血浆代谢物丰度建模预测白羽肉鸡饲料转化效率的方法，步骤如下：
(1)检测待分析的肉鸡的空腹血浆7-酮基胆固醇丰度，血浆ε-(γ-谷氨酰)-赖氨酸丰度，血浆5'-单磷酸腺苷丰度，血浆肾上腺酸丰度，血浆2-氧代己二酸丰度；
(2)根据步骤(1)测得的代谢物丰度建立预测回归模型预测肉鸡饲料转化效率评价指标，指标包括饲料转化率和剩余采食量，模型公式如下：饲料转化率预测值＝2.525+4.905×10-5X1+8.970×10-5X2+1.740×10-4X3–3.010×10-4X4；剩余采食量预测值＝–1.679×102+4.848×10-2X1+1.006×10-1X2+1.613×10-1X3–2.655×10-1X4+1.002×10-1X5，其中，X1为血浆7-酮基胆固醇丰度，X2为血浆ε-(γ-谷氨酰)-赖氨酸丰度，X3为血浆5'-单磷酸腺苷丰度，X4为血浆肾上腺酸丰度，X5为血浆2-氧代己二酸丰度；
(3)当采用饲料转化率鉴定高饲料转化效率肉鸡时，选择饲料转化率预测值低的个体；或当采用剩余采食量鉴定高饲料转化效率肉鸡时，选择剩余采食量预测值低的个体；最后鉴别出高饲料转化效率肉鸡。
本发明的有益效果及优点如下：本发明能够高效的区分开高饲料转化效率肉鸡和低饲料转化效率肉鸡，节省育种设备支出，推动家禽遗传改良工作，降低了育种成本。
具体实施方式
下面结合具体实施例对本发明做进一步说明，但本发明不受实施例的限制。
实施例1
鉴定白羽肉鸡饲料转化效率的血浆代谢物标记
一、实验材料
以东北农业大学肉鸡高、低腹脂双向选择品系第二十三世代441只鸡为实验群体，其中包括低脂系鸡289只和高脂系鸡152只。
二、测定饲料转化率与剩余采食量和检测血浆代谢组
记录每只肉鸡29日龄至49日龄采食量FI，记录四周龄体重BW4和七周龄体重BW7，计算饲料转化率FCR和剩余采食量RFI，计算公式为：
其中b0，b1，b2为偏回归系数。
肉鸡48日龄晚间22：00断料断水，49日龄早晨7：00采血，离心获得血浆；在北京诺禾致源科技股份有限公司完成代谢组学检测；使用人类代谢组学数据库进行注释，正离子模式下检测到284种血浆代谢物，负离子模式下检测到272种血浆代谢物。
三、统计分析方法
使用偏最小二乘判别分析鉴定品系间差异表达代谢物，差异阈值设为变量重要性分析值大于1，显著性小于0.05。使用ASReml(4.0)软件包估计性状遗传参数，包括代谢物与饲料转化率和剩余采食量的遗传相关系数和表型相关系数，及代谢物遗传力。估计遗传参数用到的数学模型如下：Y＝Xb+Za+e，其中Y表示血浆代谢物丰度或饲料转化率和剩余采食量；b表示固定效应向量，包括群体均值、性别效应和品系效应；a表示随机加性遗传效应向量；e表示随机残差向量，X和Z为b和a的关联矩阵。假设随机效应a和e均服从均值为0的正态分布，a的方差为Var(a)＝Ag，其中A表示系谱记录中的个体分子亲缘关系矩阵，g为加性遗传方差；e的方差为Var(e)＝Ir，其中I表示单位矩阵，r为残差方差。使用R语言中的逐步回归函数完成逐步回归分析。
四、鉴定适合作为肉鸡饲料转化效率的代谢标记
满足如下四个标准的代谢物为适合作为肉鸡饲料转化效率的代谢标记：①血浆代谢物丰度在高和低饲料转化效率肉鸡间具有差异；②这些差异代谢物丰度与饲料转化率或剩余采食量具有较高的遗传相关系数；③当代谢物丰度与饲料转化率或剩余采食量具有正遗传相关性，则代谢物丰度在高饲料转化效率肉鸡中较低；当代谢物丰度与饲料转化率或剩余采食量具有负遗传相关性，则代谢物丰度在高饲料转化效率肉鸡中较高；④血浆代谢物丰度具有中等以上程度遗传力。分析结果显示(见表1)，正离子模式下的5种和负离子模式下的9种代谢物满足四个标准。这14种代谢物中的4种暂无文献报道，而其他10种代谢物的生理功能被报道与饲料转化效率具有相互作用。因此我们将其排除在外，认为剩余10种代谢物适合作为选择饲料转化效率的生物标记，包括7-酮基胆固醇(7-ketocholesterol)、二甲基砜(dimethyl sulfone)、ε-(γ-谷氨酰)-赖氨酸(epsilon-(gamma-glutamyl)-lysine)、γ-谷氨酰酪氨酸(gamma-glutamyltyrosine)、2-氧代己二酸(2-oxoadipicacid)、L-高精氨酸(L-homoarginine)、睾酮(testosterone)、5'-单磷酸腺苷(adenosine5'-monophosphate)、肾上腺酸(adrenic acid)、骨化三醇(calcitriol)。
表1适合作为选择饲料转化效率的代谢标记基本信息
实施例2
通过血浆代谢物丰度预测白羽肉鸡饲料转化效率模型
本实施例建立预测白羽肉鸡饲料转化效率模型。将实施例1中鉴定的10种饲料转化效率的生物标记作为自变量，将饲料转化率和剩余采食量作为因变量，从实施例1中的441只肉鸡中随机取431只肉鸡数据用于建立模型。通过逐步回归筛选出最佳回归模型，保留模型中显著的自变量(P<0.01)，获得最佳模型如下：
饲料转化率预测值＝2.525+4.905×10-5X1+8.970×10-5X2+1.740×10-4X3–3.010×10-4X4
剩余采食量预测值＝–1.679×102+4.848×10-2X1+1.006×10-1X2+1.613×10-1X3–2.655×10-1X4+1.002×10-1X5
其中，X1为血浆7-酮基胆固醇丰度，X2为血浆ε-(γ-谷氨酰)-赖氨酸丰度，X3为血浆5'-单磷酸腺苷丰度，X4为血浆肾上腺酸丰度，X5为血浆2-氧代己二酸丰度。
实施例3
实施例2中预测模型的应用及效果
通过实施例2获得一种通过血浆代谢物丰度预测白羽肉鸡饲料转化效率的模型，本实施例要鉴定实施例2数据中剩余10只鸡的饲料转化效率。获得结果如下：使用饲料转化率鉴定高饲料转化效率个体排名为190>154>251>431>224>441>422>78>43>50，真实排名为190>251>154>224>441>43>431>422>78>50，准确性为0.85；使用剩余采食量鉴定高饲料转化效率个体排名为381>359>281>353>349>169>216>253>29>27，真实排名为381>349>216>359>281>169>353>253>29>27，准确性为0.75。因此本发明能够较好的区分开高饲料转化效率肉鸡和低饲料转化效率肉鸡。
虽然本发明已以较佳的实施例公开如上，但其并非用以限定本发明，任何熟悉此技术的人，在不脱离本发明的精神和范围内，都可以做各种改动和修饰，因此本发明的保护范围应该以权利要求书所界定的为准。
Claims (1)
1.一种通过血浆代谢物丰度建模预测白羽肉鸡饲料转化效率的方法，其特征在于，方法步骤如下：
1)检测待分析的肉鸡的空腹血浆7-酮基胆固醇丰度，血浆ε-(γ-谷氨酰)-赖氨酸丰度，血浆5'-单磷酸腺苷丰度，血浆肾上腺酸丰度，血浆2-氧代己二酸丰度；
2)根据步骤1)测得的代谢物丰度建立预测回归模型预测肉鸡饲料转化效率评价指标，指标包括饲料转化率和剩余采食量，模型公式如下：饲料转化率预测值＝2.525+4.905×10-5X1+8.970×10-5X2+1.740×10-4X3–3.010×10-4X4；剩余采食量预测值＝–1.679×102+4.848×10-2X1+1.006×10-1X2+1.613×10-1X3–2.655×10-1X4+1.002×10-1X5，其中，X1为血浆7-酮基胆固醇丰度，X2为血浆ε-(γ-谷氨酰)-赖氨酸丰度，X3为血浆5'-单磷酸腺苷丰度，X4为血浆肾上腺酸丰度，X5为血浆2-氧代己二酸丰度；
3)当采用饲料转化率鉴定高饲料转化效率肉鸡时，选择饲料转化率预测值低的个体；或当采用剩余采食量鉴定高饲料转化效率肉鸡时，选择剩余采食量预测值低的个体；最后鉴别出高饲料转化效率肉鸡。
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