Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms.

Assessments of genomic prediction accuracies using artificial intelligent (AI) algorithms (i.e., machine and deep learning methods) are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e., pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a nonlinear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e., ML-KAML) and deep learning (i.e., DL-MLP and DL-CNN) together with the four common methods (PBLUP, GBLUP, ssGBLUP, and BayesR) were conducted for two main disease resistance traits (i.e., survival status coded as 0 and 1 and survival time, i.e., days that the animals were still alive after the challenge test) in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 single nucleotide polymorphism (SNPs). The results using 6,470 SNPs after quality control showed that machine learning methods outperformed PBLUP, GBLUP, and ssGBLUP, with the increases in the prediction accuracies for both traits by 9.1-15.4%. However, the prediction accuracies obtained from machine learning methods were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 5.3-19.2% in all the methods and data used. On the other hand, there were insignificant decreases (0.3-5.6%) in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs (P < 0.00001, 318-400 SNPs for survival status and 1,362-1,589 SNPs for survival time) were somewhat lower (0.3-15.6%) than those obtained from the whole set of 6,470 SNPs. In most of our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that although there are prospects for the application of genomic selection to increase disease resistance to E. ictaluri in striped catfish breeding programs, further evaluation of these methods should be made in independent families/populations when more data are accumulated in future generations to avoid possible biases in the genetic parameters estimates and prediction accuracies for the disease-resistant traits studied in this population of striped catfish P. hypophthalmus.

Selection for improved white spot syndrome virus resistance increased larval survival and growth rate of Pacific Whiteleg shrimp, Liptopenaeus vannamei.

Outbreaks of contagious diseases, including White spot syndrome virus (WSSV), occur more frequently due to environment changes and as commercial shrimp production becomes intensified. The over-arching aim of this study was to examine new traits to improve disease resistance of Whiteleg shrimp, Liptopenaeus vannamei, to WSSV. Specifically, we made a compressive evaluation of the breeding population to determine a suitable selection criterion for improved WSSV resistance. To achieve this objective, we analysed five traits (viral titre, WSSV resistance, larval survival, body weight and standard length) recorded for 120,000 individual shrimps that were offspring of 228 sires and 300 dams produced over two generations of selection in 2017 and 2018. Our restricted maximum likelihood mixed model analysis showed that there is additive genetic variation in viral copy number (or viral titre, viral load) with the heritability that equals 0.18 ±â€¯0.02. Viral titre displayed a moderate and negative genetic correlation with WSSV resistance (rg = -0.55). These results suggest that viral titre can be used as a selection criterion to improve WSSV resistance, but selection for decreased viral titre (i.e., increased resistance) may not capture all genetic expression in WSSV resistance. In addition to the estimation of population genetic parameters, we evaluated direct response to selection for increased WSSV resistance, which was measured as differences in estimated breeding values between the high and low resistant lines. The direct genetic gain achieved for WSSV resistance averaged 12.9% after one generation of selection in this Whiteleg shrimp population. The selection program also resulted in positive impacts on growth and larval survival by 7% and 17%, respectively. There is abundant genetic variation in WSSV resistance (h2 = 0.19-0.27), suggesting that the tested Whiteleg shrimp population will continue to respond to future selection. Collectively, the results obtained in our study provide important information to assist the design and implementation of genetic improvement programs for disease traits in aquaculture species, including L. vannamei.

Genetic Variation in Disease Resistance Against White Spot Syndrome Virus (WSSV) in Liptopenaeus vannamei.

White Spot Syndrome Virus (WSSV) is the most damaging pathogen in terms of production and economic losses for the shrimp sector world-wide. Estimation of heritability for WSSV resistance was made in this study to obtain necessary parameter inputs for broadening the breeding objectives of an ongoing selective breeding programme for Whiteleg shrimp (Liptopenaeus vannamei) that has focussed exclusively on improving growth performance since 2014. The present study involved a disease challenge test experiment using a total of 15,000 shrimps from 150 full- and half-sib families (100 individuals per family). Survival rates were recorded at six different experimental periods: 1-3 days (S1), 1-5 days (S2), 1-7 days (S3), 1-9 days (S4), 1-12 days (S5), and 1-15 days (S6) and were used as measures of WSSV resistance. There was significant variation in WSSV resistance among families studied. Quantitative-real time PCR (qPCR) analysis showed that the amount of viral titer (viral load) was significantly lower in high than low resistance families. Analyses of heritability were carried out using linear mixed model (LMM) and threshold logistic generalized model (TLGM). Both linear and threshold models used showed that the heritability (h2) for WSSV resistance was moderate in the early infection phases (S1-S4), whilst a low h2 value was observed for survival after 12 and 15 days of the challenge test (S5 and S6). The transformed heritabilities for WSSV resistance ranged from 1 to 31% which were somewhat lower than those estimated on the liability scale. Genetic correlations between survival rates measured over six different days post-infection were high and positive (0.82-0.99). The phenotypic correlations ranged from 0.31 ± 0.01 to 0.97 ± 0.01. The genetic correlations between body weights and WSSV resistance were negative. Our results on the heritability and genetic correlations show that improvement of WSSV resistance can be achieved through selective breeding in this population of Whiteleg shrimp.