Search

Simple Summary: By integrating prior biological information into genomic selection methods using appropriate models, it is possible to improve prediction accuracy for complex traits. In this context, we conducted a comparative assessment of two genomic prediction models, namely, genomic best linear unbiased prediction and genomic feature best linear unbiased prediction. The accuracy of these models in predicting the growth traits of backfat thickness and loin muscle area was evaluated. Our results revealed that the genomic feature best linear unbiased prediction model can effectively integrate prior information into the model, which is superior to the genomic best linear unbiased prediction model in some cases. These findings provide valuable ideas for enhancing the genomic prediction accuracy of growth traits in pigs. Enhancing the accuracy of genomic prediction is a key goal in genomic selection (GS) research. Integrating prior biological information into GS methods using appropriate models can improve prediction accuracy for complex traits. Genome-wide association study (GWAS) is widely utilized to identify potential candidate loci associated with complex traits in livestock and poultry, offering essential genomic insights. In this study, a GWAS was conducted on 685 Duroc × Landrace × Yorkshire (DLY) pigs to extract significant single-nucleotide polymorphisms (SNPs) as genomic features. We compared two GS models, genomic best linear unbiased prediction (GBLUP) and genomic feature BLUP (GFBLUP), by using imputed whole-genome sequencing (WGS) data on 651 Yorkshire pigs. The results revealed that the GBLUP model achieved prediction accuracies of 0.499 for backfat thickness (BFT) and 0.423 for loin muscle area (LMA). By applying the GFBLUP model with GWAS-based SNP preselection, the average prediction accuracies for BFT and LMA traits reached 0.491 and 0.440, respectively. Specifically, the GFBLUP model displayed a 4.8% enhancement in predicting LMA compared to the GBLUP model. These findings suggest that, in certain scenarios, the GFBLUP model may offer superior genomic prediction accuracy when compared to the GBLUP model, underscoring the potential value of incorporating genomic features to refine GS models. [ABSTRACT FROM AUTHOR]

Summary A high-amylose rice with 64.8% amylose content (AC) was developed by transgenic inhibition of two isoforms of starch branching enzyme (SBE), SBEI and SBEIIb, in an indica rice cultivar. The expression of SBEI and SBEIIb was completely inhibited in the transgenic line, whereas the expression of granule-bound starch synthase was normal. Compared with wild-type rice, drastic reductions in both SBEs in the transgenic rice increased apparent AC in flour from 27.2% to 64.8%, resistant starch (RS) content from 0% to 14.6% and total dietary fibre (TDF) from 6.8% to 15.2%. Elevated AC increased the proportion of long unit chains in amylopectin and increased onset gelatinization temperature and resistance to alkaline digestion; however, kernel weight was decreased. A rat feeding trial indicated that consumption of high-amylose rice decreased body weight gain significantly ( P < 0.01); increased faecal mass, faecal moisture and short-chain fatty acids; and lowered the faecal pH. An acute oral rice tolerance test revealed that the high-amylose rice had a positive effect on lowering the blood glucose response in diabetic Zucker fatty rats. This novel rice with its high AC, RS and TDF offers potential benefits for its use in foods and in industrial applications. [ABSTRACT FROM AUTHOR]