The duck (Anas platyrhynchos) is not only a vital farm animal but also an excellent model for genetic dissection of economic traits. The integration of multiomics data provides a powerful approach to elucidate the genetic basis of domestication and phenotype variation. Since its inception in 2014, the Duck 1000 Genomes Project has aimed to uncover the genetic foundation of key economic traits in ducks by combining multiomics data including genomic, transcriptomic, and metabolomic from various natural and segregating populations. This paper summarizes the strategies and achievements of the Duck 1000 Genomes Project, highlighting the reference genome assembly, genome evolution analysis, and the identification of genes and causative mutations responsible for key economic traits in ducks. We also discuss perspectives and potential challenges in functional genomic studies that could further accelerate duck molecular breeding.

Obesity has reached epidemic proportions in most parts of the world, and it is estimated that 1 billion people globally are trapped in an obesity pandemic, which has seriously compromised human health. Recently, there has been a flood of research into obesity as well as redox and lipid metabolism; however, our understanding of the pathways and specific molecular mechanisms by which obesity-induced oxidative stress affects female reproductive function remains limited. In this review, we discuss how the obesity pandemic has led to lower female fertility. We focus on multiple facets of obesity-mediated reproductive dysfunction, including follicular atresia, oocyte maturation, embryo implantation, reproductive aging, and discuss therapeutic interventions that have the potential to normalize reproductive function in obese females, such as targeting mitochondrial lipid metabolism and antioxidant pathways.

Spotted sea bass (Lateolabrax maculatus) is a species of significant economic importance in aquaculture. However, genetic degeneration, such as declining growth performance, has severely impeded industry development, necessitating urgent genetic improvement. Here, we conducted a genomewide association study (GWAS) and genomic prediction for growth traits using insertion and deletion (InDel) markers, and systematically compared the results with our previous studies using single nucleotide polymorphism (SNP) markers. A total of 97 significant InDels including a 6 bp insertion in an exon region were identified. It is worth noting that only 5 and 1 candidate genes for DY and TS populations were also detected in previous GWAS using SNPs, and numerous novel genes including c4b, fgf4, and dnajb9 were identified as vital candidate genes. Moreover, several novel growth-related procedures, such as the growth and development of the bone and muscle, were also detected. These findings indicated that InDel-based GWAS can provide valuable complement to SNP-based studies. The comparison of genomic predictive performance for total length trait under different marker selection strategies and genomic selection models indicated that GWAS selection strategy exhibits more stable predictive performance compared to the evenly selection strategy. Additionally, support vector machine model demonstrated better predictive accuracy and efficiency than traditional best linear unbiased prediction and Bayes models. Furthermore, the superior predictive performance using InDel markers compared to SNP markers highlighted the potential of InDels to enhance genomic predictive accuracy and efficiency. Our results carry significant implications for dissecting genetic mechanisms and contributing genetic improvement of growth traits in spotted sea bass through genomic resources.

Milk mid-infrared (MIR) spectra have been shown to provide valuable information on a wide range of traits to be used in dairy cattle breeding programs. Selecting the most informative variables from complex data can improve the prediction accuracy and model robustness and, consequently, the interpretability of MIR spectra. Thus, we aimed to investigate the prediction performance of feature selection methods based on MIR spectra data, using the milk fatty acid (FA) profile as an example to illustrate the evaluated procedure. Data of MIR spectra, milk test-day records, and reference FA concentrations of 155 first-parity Holstein cows were used in the analyses. Four models comprising different explanatory variables and three feature selection methods were evaluated. The results indicated that competitive adaptive reweighted sampling (CARS) method can effectively select the most informative variables from the MIR spectra, resulting in higher prediction accuracies than other variable selection approaches. The model including selected MIR spectra and cow information variables yielded the best FA profile predictions based on partial least square regression. C8:0, C10:0, C14:1, C17:0 isomers, C18:1, C18:1 isomer, medium-chain FA, unsaturation FA, monounsaturated FA, and polyunsaturated FA presented accuracies based on the determination coefficient ranging from 0.66 to 0.85 in internal validation and from 0.65 to 0.84 in external validation. The most related wavenumbers to 35 FAs were found within 1003 to 1145 cm⁻¹. Generally, using CARS and cow information improved predictions of FAs based on MIR spectra in Chinese Holstein dairy cows. Additional validation studies should be conducted as larger datasets become available.

Circular RNA (circRNA), a stable ring-shaped RNA molecule found in eukaryotic cells, plays significant roles in biological regulation, particularly by interfering with transcription factor binding or enhancing gene expression. Using transcriptomic sequencing, we identified differentially expressed circRNAs in bovine muscle at various time points. Specifically, circDYRK1A was discovered and shown to enhance differentiation while suppressing proliferation of adult myoblasts. Rescue experiments further demonstrated that circDYRK1A regulates the KLF5 gene expression by interacting with miR21-5p, thus exerting its influence at the transcriptional level. This study marks the first identification of circDYRK1A in cattle and elucidates its role in bovine myoblast development through the circDYRK1A-miR21-5p-KLF5 regulatory axis. These findings contribute novel insights into molecular breeding of cattle and advance fundamental research on beef cattle breeding and muscle development.

Understanding the genetic factors related to meat drip loss is of great importance for animal breeding and production. In this study, we employed a combination of genome-wide association study (GWAS) mapping and RNA sequencing (RNA-seq) data to effectively identify potentially functional single nucleotide polymorphisms (SNPs) as well as candidate genes associated with drip loss (DL) in Beijing Black pigs. Initially, we conducted a single- and multi-trait GWAS on drip loss traits in 441 Beijing Black pigs at 24 (DL24) and 48 (DL48) hours postmortem using the Illumina pig 50K SNP chip. Five SNPs with annotations for four genes (FGGY, LHFPL6, OSBPL1A, and NMNAT3) were consistently identified in single or multiple trait GWAS results, indicating their potential pleiotropic effects on drip loss. Next, a comprehensive comparative transcriptomic analysis was performed on samples of Beijing Black pigs exhibiting extremely high and low drip loss, resulting in the identification of 21 differentially expressed genes (DGEs) as potential candidates. Additionally, protein–protein interaction (PPI) network analysis revealed reciprocal regulatory relationships between FOXO1, OSBPL1A, DOCK1 (identified from GWAS) and the candidate DGEs obtained from RNA-seq data. Therefore, we propose that these genes may impact drip loss traits through gene interactions. In conclusion, our integrative analysis screened candidate genes that may affect the drip loss traits in Beijing Black pigs, which provides crucial insights into the molecular mechanisms of drip loss and serves as a theoretical reference for improving meat quality in Beijing Black pigs.