Galbase: a comprehensive repository for integrating chicken multi-omics data

Genetic structure, selective characterization and specific molecular identity cards of high-yielding Houdan chickens based on genome-wide SNP

The high-yielding Houdan chicken (GGF) is characterized by high egg production and disease resistance. This study conducted whole genome resequencing of the GGF population and compared it to data from other breeds. Genetic diversity analysis revealed higher observed heterozygosity (Ho), Polymorphism information content (PIC), number of runs of homozygosity (ROH), and inbreeding coefficient (FROH) in GGF. Linkage disequilibrium (LD) decay was slowest in GGF, indicating intensive inbreeding and strong selection. These findings suggest a need for appropriate strategies to enhance genetic diversity conservation in this breed. Population structure analysis demonstrated that GGF was genetically distinct from both the red jungle fowl (RJF) and Chinese indigenous chicken (CIC) populations, highlighting GGF as a unique genetic resource warranting intensive protection and utilization. Selective sweep analysis identified genes under selection in GGF, primarily enriched in signaling pathways related to oocyte meiosis and progesterone-mediated oocyte maturation. Key candidate genes included: CCNE1, SKP1, CDC20, CDK2, ADCY8, RPS6KA6, PPP3CB, PDE3B, HSP90AB1, and AKT3. These findings provide a theoretical foundation for their potential application in poultry breeding. Additionally, this study combined bioinformatics analysis with PCR amplification and Sanger sequencing to identify 4 SNPs that can serve as a molecular identity card (ID) for GGF: SNP1 (Chr2: 136130976), SNP3 (Chr4:11705164), SNP4 (Chr4: 63255588), and SNP5 (Chr24: 3271008). This study provides a scientific basis for effective management and conservation of GGF genetic resources, and establishes a simple, economical, and accurate set of molecular IDs to combat the proliferation of inferior breeds and protect genetic resources.

Tracing the genealogy origin of geographic populations based on genomic variation and deep learning

Assigning a query individual animal or plant to its derived population is a prime task in diverse applications related to organismal genealogy. Such endeavors have conventionally relied on short DNA sequences under a phylogenetic framework. These methods naturally show constraints when the inferred population sources are ambiguously phylogenetically structured, a scenario demanding substantially more informative genetic signals. Recent advances in cost-effective production of whole-genome sequences and artificial intelligence have created an unprecedented opportunity to trace the population origin for essentially any given individual, as long as the genome reference data are comprehensive and standardized. Here, we developed a convolutional neural network method to identify population origins using genomic SNPs. Three empirical datasets (an Asian honeybee, a red fire ant, and a chicken datasets) and two simulated populations are used for the proof of concepts. The performance tests indicate that our method can accurately identify the genealogy origin of query individuals, with success rates ranging from  93 % to 100 %. We further showed that the accuracy of the model can be significantly increased by refining the informative sites through FST filtering. Our method is robust to configurations related to batch sizes and epochs, whereas model learning benefits from the setting of a proper preset learning rate. Moreover, we explained the importance score of key sites for algorithm interpretability and credibility, which has been largely ignored. We anticipate that by coupling genomics and deep learning, our method will see broad potential in conservation and management applications that involve natural resources, invasive pests and weeds, and illegal trades of wildlife products.

RNA sequencing analysis of sexual dimorphism in Japanese quail

Introduction

Japanese quail are of significant economic value, providing protein nutrition to humans through their reproductive activity; however, sexual dimorphism in this species remains relatively unexplored compared with other model species.

Method

A total of 114 RNA sequencing datasets (18 and 96 samples for quail and chicken, respectively) were collected from existing studies to gain a comprehensive understanding of sexual dimorphism in quail. Cross-species integrated analyses were performed with transcriptome data from evolutionarily close chickens to identify sex-biased genes in the embryonic, adult brain, and gonadal tissues.

Results

Our findings indicate that the expression patterns of genes involved in sex-determination mechanisms during embryonic development, as well as those of most sex-biased genes in the adult brain and gonads, are identical between quails and chickens. Similar to most birds with a ZW sex determination system, quails lacked global dosage compensation for the Z chromosome, resulting in directional outcomes that supported the hypothesis that sex is determined by the individual dosage of Z-chromosomal genes, including long non-coding RNAs located in the male hypermethylated region. Furthermore, genes, such as WNT4 and VIP, reversed their sex-biased patterns at different points in embryonic development and/or in different adult tissues, suggesting a potential hurdle in breeding and transgenic experiments involving avian sex-related traits.

Discussion

The findings of this study are expected to enhance our understanding of sexual dimorphism in birds and subsequently facilitate insights into the field of breeding and transgenesis of sex-related traits that economically benefit humans.

The Omics Revolution in Understanding Chicken Reproduction: A Comprehensive Review

Omics approaches have significantly contributed to our understanding of several aspects of chicken reproduction. This review paper gives an overview of the use of omics technologies such as genomics, transcriptomics, proteomics, and metabolomics to elucidate the mechanisms of chicken reproduction. Genomics has transformed the study of chicken reproduction by allowing the examination of the full genetic makeup of chickens, resulting in the discovery of genes associated with reproductive features and disorders. Transcriptomics has provided insights into the gene expression patterns and regulatory mechanisms involved in reproductive processes, allowing for a better knowledge of developmental stages and hormone regulation. Furthermore, proteomics has made it easier to identify and quantify the proteins involved in reproductive physiology to better understand the molecular mechanisms driving fertility, embryonic development, and egg quality. Metabolomics has emerged as a useful technique for understanding the metabolic pathways and biomarkers linked to reproductive performance, providing vital insights for enhancing breeding tactics and reproductive health. The integration of omics data has resulted in the identification of critical molecular pathways and biomarkers linked with chicken reproductive features, providing the opportunity for targeted genetic selection and improved reproductive management approaches. Furthermore, omics technologies have helped to create biomarkers for fertility and embryonic viability, providing the poultry sector with tools for effective breeding and reproductive health management. Finally, omics technologies have greatly improved our understanding of chicken reproduction by revealing the molecular complexities that underpin reproductive processes.

DeepOCR: A multi-species deep-learning framework for accurate identification of open chromatin regions in livestock

A wealth of experimental evidence has suggested that open chromatin regions (OCRs) are involved in many critical biological activities, such as DNA replication, enhancer activity, and gene transcription. Accurately identifying OCRs in livestock species can provide critical insights into the distribution and characteristics of OCRs for disease treatment in livestock, thereby improving animal welfare. However, most current machine-learning methods for OCR prediction were originally designed for a limited number of model organisms, such as humans and some model organisms, and thus their performance on non-model organisms, specifically livestock, is often unsatisfactory. To bridge this gap, we propose DeepOCR, a lightweight depth-separable residual network model for predicting OCRs in livestock, including chicken, cattle, and sheep. DeepOCR integrates a single convolution layer and two improved residue structure blocks to extract and learn important features from the input DNA sequences. A fully connected layer was also employed to further process the extracted features and improve the robustness of the entire network. Our benchmarking experiments demonstrated superior prediction performance of DeepOCR compared to state-of-the-art approaches on testing datasets of the three species. The source code of DeepOCR is freely available for academic purposes at https://github.com/jasonzhao371/DeepOCR/. We anticipate DeepOCR servers as a practical and reliable computational tool for OCR-related studies in livestock species.

Convergent genomic signatures associated with vertebrate viviparity

BACKGROUND

Viviparity-live birth-is a complex and innovative mode of reproduction that has evolved repeatedly across the vertebrate Tree of Life. Viviparous species exhibit remarkable levels of reproductive diversity, both in the amount of care provided by the parent during gestation, and the ways in which that care is delivered. The genetic basis of viviparity has garnered increasing interest over recent years; however, such studies are often undertaken on small evolutionary timelines, and thus are not able to address changes occurring on a broader scale. Using whole genome data, we investigated the molecular basis of this innovation across the diversity of vertebrates to answer a long held question in evolutionary biology: is the evolution of convergent traits driven by convergent genomic changes?

RESULTS

We reveal convergent changes in protein family sizes, protein-coding regions, introns, and untranslated regions (UTRs) in a number of distantly related viviparous lineages. Specifically, we identify 15 protein families showing evidence of contraction or expansion associated with viviparity. We additionally identify elevated substitution rates in both coding and noncoding sequences in several viviparous lineages. However, we did not find any convergent changes-be it at the nucleotide or protein level-common to all viviparous lineages.

CONCLUSIONS

Our results highlight the value of macroevolutionary comparative genomics in determining the genomic basis of complex evolutionary transitions. While we identify a number of convergent genomic changes that may be associated with the evolution of viviparity in vertebrates, there does not appear to be a convergent molecular signature shared by all viviparous vertebrates. Ultimately, our findings indicate that a complex trait such as viviparity likely evolves with changes occurring in multiple different pathways.

GC-biased gene conversion drives accelerated evolution of ultraconserved elements in mammalian and avian genomes

Ultraconserved elements (UCEs) are the most conserved regions among the genomes of evolutionarily distant species and are thought to play critical biological functions. However, some UCEs rapidly evolved in specific lineages, and whether they contributed to adaptive evolution is still controversial. Here, using an increased number of sequenced genomes with high taxonomic coverage, we identified 2191 mammalian UCEs and 5938 avian UCEs from 95 mammal and 94 bird genomes, respectively. Our results show that these UCEs are functionally constrained and that their adjacent genes are prone to widespread expression with low expression diversity across tissues. Functional enrichment of mammalian and avian UCEs shows different trends indicating that UCEs may contribute to adaptive evolution of taxa. Focusing on lineage-specific accelerated evolution, we discover that the proportion of fast-evolving UCEs in nine mammalian and 10 avian test lineages range from 0.19% to 13.2%. Notably, up to 62.1% of fast-evolving UCEs in test lineages are much more likely to result from GC-biased gene conversion (gBGC). A single cervid-specific gBGC region embracing the uc.359 allele significantly alters the expression of Nova1 and other neural-related genes in the rat brain. Combined with the altered regulatory activity of ancient gBGC-induced fast-evolving UCEs in eutherians, our results provide evidence that synergy between gBGC and selection shaped lineage-specific substitution patterns, even in the most constrained regulatory elements. In summary, our results show that gBGC played an important role in facilitating lineage-specific accelerated evolution of UCEs, and further support the idea that a combination of multiple evolutionary forces shapes adaptive evolution.

Animal-SNPAtlas: a comprehensive SNP database for multiple animals

Single-nucleotide polymorphisms (SNPs) as the most important type of genetic variation are widely used in describing population characteristics and play vital roles in animal genetics and breeding. Large amounts of population genetic variation resources and tools have been developed in human, which provided solid support for human genetic studies. However, compared with human, the development of animal genetic variation databases was relatively slow, which limits the genetic researches in these animals. To fill this gap, we systematically identified ∼ 499 million high-quality SNPs from 4784 samples of 20 types of animals. On that basis, we annotated the functions of SNPs, constructed high-density reference panels and calculated genome-wide linkage disequilibrium (LD) matrixes. We further developed Animal-SNPAtlas, a user-friendly database (http://gong_lab.hzau.edu.cn/Animal_SNPAtlas/) which includes high-quality SNP datasets and several support tools for multiple animals. In Animal-SNPAtlas, users can search the functional annotation of SNPs, perform online genotype imputation, explore and visualize LD information, browse variant information using the genome browser and download SNP datasets for each species. With the massive SNP datasets and useful tools, Animal-SNPAtlas will be an important fundamental resource for the animal genomics, genetics and breeding community.