Department of Animal Sciences and Agricultural Education, California State University Fresno, Fresno, CA 93740, USA.
Hy-Line International, Dallas Center, IA 50063, USA.
Poult Sci. 2018 Nov 1;97(11):3801-3806. doi: 10.3382/ps/pey273.
Assessing pedigreed broiler lines for ascites resistance in an industry setting is time consuming. Further, the use of sibling selection implies study subjects are not used in the breeding program, and instead, siblings take their place in pedigree systems, which reduces overall genetic accuracy. The purpose of this study is to evaluate the effectiveness of prediction models produced with SNP with the goal of predicting ascites incidence. Ascites is the manifestation of a series of adverse changes in a broiler beginning with hypoxia. Increased blood pressure, accumulation of fluid in the abdominal cavity, and death can result. Ascites results in losses estimated at $100 million/year in the USA. A multi-generational genome wide association study in an unselected line maintained at the University of Arkansas since the 1990s identified chromosomal regions associated with ascites incidence in males when challenged at high altitude. From the identified regions of significance 20 SNP were selected to construct a predictive model (8 SNP on chromosome 11, and 12 SNP on chromosome Z). Ascites phenotype and genotype data were obtained for 295 male and female individuals from the REL line. Five modeling techniques were compared for their ascites predictive ability using a 70/30 split between training and validation. For both males and females, the artificial neural network model was the best fit prediction model due to the large area under the curve value of 0.997 and 0.997, respectively, as well as a low misclassification ratio of 0.027 and 0.037, respectively. Using a parameter decreasing method, the total number of SNP inputs used to construct artificial neural network (ANN) models was reduced. A 13 SNP male ANN model and an 18 SNP female ANN model were constructed with equally high levels of prediction accuracy compared with the 20 SNP input models. The construction of predictive ANN models indicates that we have found the genetic predictors to ascites outcome in male and female broilers from an elite line of the 1990s with a high level of accuracy.
在工业环境中评估品系肉鸡的腹水抗性是一项耗时的工作。此外，采用同胞选择意味着研究对象未被用于育种计划，而是由其同胞代替他们在系谱系统中，这降低了整体遗传准确性。本研究的目的是评估使用 SNP 生成的预测模型的有效性，以期预测腹水发病率。腹水是肉鸡一系列不良变化的表现，始于缺氧。这可能导致血压升高、腹腔积液积聚和死亡。腹水在美国造成的损失估计为每年 1 亿美元。自 20 世纪 90 年代以来，阿肯色大学一直在未选择的品系中进行多世代全基因组关联研究，发现了与高海拔雄性肉鸡腹水发病率相关的染色体区域。从确定的显著区域中选择了 20 个 SNP 来构建预测模型（11 号染色体上 8 个 SNP，Z 染色体上 12 个 SNP）。从 REL 系获得了 295 只雄性和雌性个体的腹水表型和基因型数据。使用 70/30 的训练集和验证集将五种建模技术进行了比较，以评估它们对腹水的预测能力。对于雄性和雌性，人工神经网络模型是最佳拟合预测模型，因为其曲线下面积分别为 0.997 和 0.997，并且误分类率分别为 0.027 和 0.037。使用参数递减方法，构建人工神经网络 (ANN) 模型所需的 SNP 输入总数减少。构建了一个 13 SNP 的雄性 ANN 模型和一个 18 SNP 的雌性 ANN 模型，其预测准确性与 20 SNP 输入模型相当。预测 ANN 模型的构建表明，我们已经找到了 20 世纪 90 年代一个精英品系雄性和雌性肉鸡腹水结果的遗传预测因子，具有很高的准确性。
