Disease resistance represents a key trait for breeding programs in aquaculture species. Here we re-analysed 2bRAD sequence data from two experimental challenges of gilthead sea bream with Photobacterium damsealae piscicida. Using a high quality reference genome, we carried out variant calling and data imputation with Beagle to obtain a large set of SNPs (80,744). This allowed the identification of eight novel QTLs for resistance to photobacteriosis across different chromosomes and revealed a highly polygenic genetic architecture.

Bayesian regression approaches and machine learning methods (support vector machines and linear bagging) were compared to evaluate relative performance to classify susceptible-resistant individuals. Both data sets showed higher Matthew Correlation Coefficient (MCC) and accuracy values for machine learning methods, particularly linear bagging, with 20–70 % increase in prediction performance. Overall, machine learning methods should be explored in parallel with parametric regression approaches to increase the chances of highly effective genomic prediction.

抗病性是水产养殖物种育种计划的关键特征。在这里，我们重新分析了来自金头鲷的两个实验性挑战，即用淡水金刚细菌进行的2bRAD序列数据。使用高质量的参考基因组，我们用Beagle进行了变异调用和数据插补，以获取大量的SNP（80,744）。这样就可以鉴定出八个新的QTL，这些QTL对不同染色体的光细菌病具有抗性，并揭示了高度多基因的遗传结构。

比较了贝叶斯回归方法和机器学习方法（支持向量机和线性装袋），以评估相对性能以对易感人群进行分类。两种数据集均显示出较高的Matthew Correlation Coefficient（MCC）和机器学习方法（尤其是线性装袋法）的准确性值，预测性能提高了20％至70％。总体而言，应将机器学习方法与参数回归方法并行研究，以增加高效基因组预测的机会。