Search

Given the universality of autopolyploid species in nature, it is crucial to develop genomic selection methods that consider different allele dosages for autopolyploid breeding. However, no method has been developed to deal with autopolyploid data regardless of the ploidy level. In this study, we developed a modified genomic best linear unbiased prediction (GBLUP) model (polyGBLUP) through constructing additive and dominant genomic relationship matrices based on different allele dosages. polyGBLUP could carry out genomic prediction for autopolyploid species regardless of the ploidy level. Through comprehensive simulations and analysis of real data of autotetraploid blueberry and guinea grass and autohexaploid sweet potato , the results showed that polyGBLUP achieved higher prediction accuracy than GBLUP and its superiority was more obvious when the ploidy level of autopolyploids is high. Furthermore, when the dominant effect was added to polyGBLUP (polyGDBLUP), the greater the dominance degree, the more obvious the advantages of polyGDBLUP over the diploid models in terms of prediction accuracy, bias, mean squared error and mean absolute error. For real data, the superiority of polyGBLUP over GBLUP appeared in blueberry and sweet potato populations and a part of the traits in guinea grass population due to the high correlation coefficients between diploid and polyploidy genomic relationship matrices. In addition, polyGDBLUP did not produce higher prediction accuracy than polyGBLUP for most traits of real data as dominant genetic variance was not captured for these traits. Our study will be a significant promising method for genomic prediction of autopolyploid species. [ABSTRACT FROM AUTHOR]

Since its introduction in 2001, genomic selection (GS) has progressed rapidly. As a research and application hot topic, GS has led to a revolution in the field of animal and plant breeding. Thanks to its ability to overcome the shortcomings of traditional breeding methods, GS has garnered increasing attention. Both theoretical and practical breeding studies have revealed the higher accuracy of GS than that of traditional breeding, which can accelerate genetic gain. In recent years, many GS studies have been conducted on aquaculture species, which have shown that GS produces higher prediction accuracy than traditional pedigree‐based method. The present study reviews the principles and processes, preconditions, advantages, analytical methods and factors influencing GS as well as the progress of research in aquaculture into these aspects. Furthermore, future directions of GS in aquaculture are also discussed, which should expand its application to more aquaculture species. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) has great potential to increase genetic gain in aquaculture breeding; however, its implementation is hindered owing to high genotyping cost and the large number of individuals to genotype. This study investigated the efficiency of genomic prediction in four aquaculture species. In total, 749 to 1481 individuals with records for disease resistance and growth traits were genotyped using SNP arrays ranging from 12K to 40K. We compared the prediction accuracies and bias of breeding values obtained from BLUP, genomic BLUP (GBLUP), Bayesian mixture (BayesR), weighted GBLUP (WGBLUP), and genomic feature BLUP (GFBLUP). For GFBLUP, the genomic feature matrix was constructed based on prior information from genome‐wide association studies. Fivefold cross‐validation was performed with 20 replicates. Moreover, to reduce the cost of GS, we reduced the SNP density based on linkage disequilibrium as well as the reference population size. The results showed that the methods with marker information produced more accurate predictions than the pedigree‐based BLUP method. For the genomic model, BayesR performed prediction with a similar or higher accuracy compared to GBLUP. For the four traits, WGBLUP yielded an average of 1.5% higher accuracy than GBLUP. However, the accuracy of genomic prediction decreased by an average of 6.2% for GFBLUP compared to GBLUP. When the density of SNP panels was reduced to 3K, which was sufficient to obtain accuracies similar to those using the whole dataset in the four species, the cost of GS was estimated to be 50% lower than that of genotyping all animals with high‐density panels. In addition, when the reference population size was reduced by 10%, evenly from full‐sib family, the accuracy of genomic prediction was almost unchanged, and the cost reduction was 8% in the four populations. Our results have important implications for translating the benefits of GS to most aquaculture species. [ABSTRACT FROM AUTHOR]

The onset and progression of ovarian cancer (OC) are closely related to dysregulated gene expression. Current treatments for OC are mainly limited to surgery and chemotherapy. However, due to low drug sensitivity, the prognosis OC is exceptionally poor and the recurrence rate remains high. Hence, it is vital to develop new treatment strategies. Gene editing for site-specific genomic modification is a powerful novel tool for the treatment of OC. In this article, current gene editing research for the treatment of OC is reviewed to provide a reference for the clinical application of new approaches to improve treatment outcomes and prognosis. [ABSTRACT FROM AUTHOR]