Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulata.

Genetic improvement for quality traits, especially color and meat yield, has been limited in aquaculture because the assessment of these traits requires that the animals be slaughtered first. Genotyping technologies do, however, provide an opportunity to improve the selection efficiency for these traits. The main purpose of this study is to assess the potential for using genomic information to improve meat yield (soft tissue weight and condition index), body shape (cup and fan ratios), color (shell and mantle), and whole weight traits at harvest in the Portuguese oyster, Crassostrea angulata. The study consisted of 647 oysters: 188 oysters from 57 full-sib families from the first generation and 459 oysters from 33 full-sib families from the second generation. The number per family ranged from two to eight oysters for the first and 12-15 oysters for the second generation. After quality control, a set of 13,048 markers were analyzed to estimate the genetic parameters (heritability and genetic correlation) and predictive accuracy of the genomic selection for these traits. The multi-locus mixed model analysis indicated high estimates of heritability for meat yield traits: 0.43 for soft tissue weight and 0.77 for condition index. The estimated genomic heritabilities were 0.45 for whole weight, 0.24 for cup ratio, and 0.33 for fan ratio and ranged from 0.14 to 0.54 for color traits. The genetic correlations among whole weight, meat yield, and body shape traits were favorably positive, suggesting that the selection for whole weight would have beneficial effects on meat yield and body shape traits. Of paramount importance is the fact that the genomic prediction showed moderate to high accuracy for the traits studied (0.38-0.92). Therefore, there are good prospects to improve whole weight, meat yield, body shape, and color traits using genomic information. A multi-trait selection program using the genomic information can boost the genetic gain and minimize inbreeding in the long-term for this population.

Prediction Accuracies of Genomic Selection for Nine Commercially Important Traits in the Portuguese Oyster (Crassostrea angulata) Using DArT-Seq Technology.

Genomic selection has been widely used in terrestrial animals but has had limited application in aquaculture due to relatively high genotyping costs. Genomic information has an important role in improving the prediction accuracy of breeding values, especially for traits that are difficult or expensive to measure. The purposes of this study were to (i) further evaluate the use of genomic information to improve prediction accuracies of breeding values from, (ii) compare different prediction methods (BayesA, BayesCπ and GBLUP) on prediction accuracies in our field data, and (iii) investigate the effects of different SNP marker densities on prediction accuracies of traits in the Portuguese oyster (Crassostrea angulata). The traits studied are all of economic importance and included morphometric traits (shell length, shell width, shell depth, shell weight), edibility traits (tenderness, taste, moisture content), and disease traits (Polydora sp. and Marteilioides chungmuensis). A total of 18,849 single nucleotide polymorphisms were obtained from genotyping by sequencing and used to estimate genetic parameters (heritability and genetic correlation) and the prediction accuracy of genomic selection for these traits. Multi-locus mixed model analysis indicated high estimates of heritability for edibility traits; 0.44 for moisture content, 0.59 for taste, and 0.72 for tenderness. The morphometric traits, shell length, shell width, shell depth and shell weight had estimated genomic heritabilities ranging from 0.28 to 0.55. The genomic heritabilities were relatively low for the disease related traits: Polydora sp. prevalence (0.11) and M. chungmuensis (0.10). Genomic correlations between whole weight and other morphometric traits were from moderate to high and positive (0.58-0.90). However, unfavourably positive genomic correlations were observed between whole weight and the disease traits (0.35-0.37). The genomic best linear unbiased prediction method (GBLUP) showed slightly higher accuracy for the traits studied (0.240-0.794) compared with both BayesA and BayesCπ methods but these differences were not significant. In addition, there is a large potential for using low-density SNP markers for genomic selection in this population at a number of 3000 SNPs. Therefore, there is the prospect to improve morphometric, edibility and disease related traits using genomic information in this species.

Effect of chemical factors on integrated fungal fermentation of sugarcane bagasse for ethanol production by a white-rot fungus, Phlebia sp. MG-60.

Bioethanol production through integrated fungal fermentation (IFF), involving a unified process for biological delignification with consolidated biological processing by the white-rot fungus Phlebia sp. MG-60, was applied to sugarcane bagasse. Initial moisture content of the bagasse was found to affect biological delignification by MG-60, and 75% moisture content was suitable for selective lignin degradation and subsequent ethanol production. Additives, such as basal media, organic compounds, or minerals, also affected biological delignification of bagasse by MG-60. Basal medium addition improved both delignification and ethanol production. Some inorganic chemical factors, such as Fe(2+), Mn(2+), or Cu(2+), reduced bagasse carbohydrate degradation by MG-60 during delignifying incubations and resulted in increased ethanol production. The present results indicated that suitable culture conditions could significantly improve IFF efficiency.