Catfish is an important aquaculture species in the USA. Columnaris disease is distributed worldwide, affecting a wide variety of fish species including catfish . It leads to huge economic losses each year to the US catfish industry. Channel catfish in general is highly resistant to the disease, while blue catfish is highly susceptible. Genomic selection is an effective and accurate way to predict the breeding values and thus was expected to improve the prediction veracity of columnaris disease resistance in catfish effectively. In this study, two different methods, elastic net genomic best linear unbiased prediction (ENGBLUP) and genomic best linear unbiased prediction (GBLUP), were used to predict the columnaris disease resistance evaluated by binary survival status. Cross-validation showed that the prediction accuracy of ENGBLUP and GBLUP was 0.7347 and 0.4868, respectively, showing that ENGBLUP had a high prediction accuracy. It was shown that fitting QTL and polygenic effect with different distribution will improve genomic prediction accuracy for binary traits. In this study, an accurate and effective genomic selection method was proposed to predict the columnaris resistance in catfish, and its application should be beneficial to catfish breeding.

中文翻译：

---

Cat鱼抗专栏病的基因组预测。

fish鱼是美国重要的水产养殖物种。柱状病在世界范围内分布，影响到包括cat鱼在内的多种鱼类。每年给美国cat鱼业造成巨大的经济损失。通常，鱼对这种疾病具有高度抵抗力，而蓝cat鱼则高度易感。基因组选择是预测育种值的有效而准确的方法，因此有望有效提高cat鱼抗专性病的预测准确性。在这项研究中，使用了两种不同的方法，即弹性网基因组最佳线性无偏预测（ENGBLUP）和基因组最佳线性无偏预测（GBLUP），来预测由二元生存状态评估的柱菌病抗性。交叉验证表明ENGBLUP和GBLUP的预测准确性为0。7347和0.4868，分别表明ENGBLUP具有较高的预测准确性。结果表明，拟合QTL和具有不同分布的多基因效应将提高二元性状的基因组预测准确性。本研究提出了一种准确有效的基因组选择方法来预测cat鱼的抗柱虫性，其应用应有利于cat鱼的育种。