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.
Keywords: Binary trait; Catfish; Columnaris disease; Genomic selection; QTL.