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To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both Φ PSII and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.Tra catfish (Pangasianodon hypophthalmus), also known as striped catfish, is a facultative air-breather that uses its swim bladder as an air-breathing organ (ABO). A related species in the same order (Siluriformes), channel catfish (Ictalurus punctatus), does not possess an ABO and thus cannot breathe in the air. Tra and channel catfish serve as great comparative models for investigating possible genetic underpinnings of aquatic to land transitions, as well as for understanding genes that are crucial for the development of the swim bladder and the function of air-breathing in tra catfish. In this study, hypoxia challenge and microtomy experiments collectively revealed critical time points for the development of the air-breathing function and swim bladder in tra catfish. Seven developmental stages in tra catfish were selected for RNA-seq analysis based on their transition to a stage that could live at 0 ppm oxygen. More than 587 million sequencing clean reads were generated, and a total of 21,448 unique genes were detected. A comparative genomic analysis between channel catfish and tra catfish revealed 76 genes that were present in tra catfish, but absent from channel catfish. In order to further narrow down the list of these candidate genes, gene expression analysis was performed for these tra catfish-specific genes. Fourteen genes were inferred to be important for air-breathing. check details Of these, HRG, GRP, and CX3CL1 were identified to be the most likely genes related to air-breathing ability in tra catfish. This study provides a foundational data resource for functional genomic studies in air-breathing function in tra catfish and sheds light on the adaptation of aquatic organisms to the terrestrial environment.Whole-genome sequence (WGS) data are increasingly being applied into genomic predictions, offering a higher predictive ability by including causal mutations or single-nucleotide polymorphisms (SNPs) putatively in strong linkage disequilibrium with causal mutations affecting the trait. This study aimed to improve the predictive performance of the customized Hanwoo 50 k SNP panel for four carcass traits in commercial Hanwoo population by adding highly predictive variants from sequence data. A total of 16,892 Hanwoo cattle with phenotypes (i.e., backfat thickness, carcass weight, longissimus muscle area, and marbling score), 50 k genotypes, and WGS imputed genotypes were used. We partitioned imputed WGS data according to functional annotation [intergenic (IGR), intron (ITR), regulatory (REG), synonymous (SYN), and non-synonymous (NSY)] to characterize the genomic regions that will deliver higher predictive power for the traits investigated. Animals were assigned into two groups, the discovery set (7324 animals) e regular 50 k SNP panel, respectively. In terms of prediction bias, regression coefficients for all sets of genotypes in all traits were close to 1, indicating an unbiased prediction. The strategy used to select variants based on functional annotation did not show a clear advantage compared to using whole-genome. Nonetheless, such pre-selected SNPs from the IGR region gave the highest improvement in prediction accuracy among genomic regions and the values were close to those obtained using the WGS data for all traits. We concluded that additional gain in prediction accuracy when using pre-selected variants appears to be trait-dependent, and using WGS data remained more accurate compared to using a specific genomic region.Background The dilution of color in rabbits is associated with many different genetic mechanisms that form different color groups. A number of previous studies have revealed potential regulatory mechanisms by which epigenetics regulate pigmentation. However, the genome-wide DNA methylation involved in animal coat color dilution remains unknown. Results We compared genome-wide DNA methylation profiles in Rex rabbit hair follicles in a Chinchilla group (Ch) and a diluted Chinchilla group (DCh) through whole-genome bisulfite sequencing (WGBS). Approximately 3.5% of the cytosine sites were methylated in both groups, of which the CG methylation type was in greatest abundance. In total, we identified 126,405 differentially methylated regions (DMRs) between the two groups, corresponding to 11,459 DMR-associated genes (DMGs). Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that these DMGs were principally involved in developmental pigmentation and Wnt signaling pathways. In addition, two DMRs were randomly selected to verify that the WGBS data were reliable using bisulfite sequencing PCR, and seven DMGs were analyzed to establish the relationship between the level of DNA methylation and mRNA expression using qRT-PCR. Due to the limitation of small sample size, replication of the results with a larger sample size would be important in future studies. Conclusion These findings provide evidence that there is an association between inherited color dilution and DNA methylation alterations in hair follicles, greatly contributing to our understanding of the epigenetic regulation of rabbit pigmentation.The human microbiome consists of a community of microbes in varying abundances and is shown to be associated with many diseases. An important first step in many microbiome studies is to identify possible distinct microbial communities in a given data set and to identify the important bacterial taxa that characterize these communities. The data from typical microbiome studies are high dimensional count data with excessive zeros due to both absence of species (structural zeros) and low sequencing depth or dropout. Although methods have been developed for identifying the microbial communities based on mixture models of counts, these methods do not account for excessive zeros observed in the data and do not differentiate structural from sampling zeros. In this paper, we introduce a zero-inflated Latent Dirichlet Allocation model (zinLDA) for sparse count data observed in microbiome studies. zinLDA builds on the flexible Latent Dirichlet Allocation model and allows for zero inflation in observed counts. We develop an efficient Markov chain Monte Carlo (MCMC) sampling procedure to fit the model.