A comprehensive characterization of regulatory elements in the chicken genome across tissues will have substantial impacts on both fundamental and applied research. Here, we systematically identified and characterized regulatory elements in the chicken genome by integrating 377 genome-wide sequencing datasets from 23 adult tissues. In total, we annotated 1.57 million regulatory elements, representing 15 distinct chromatin states, and predicted about 1.2 million enhancer-gene pairs and 7662 super-enhancers. This functional annotation of the chicken genome should have wide utility on identifying regulatory elements accounting for gene regulation underlying domestication, selection, and complex trait regulation, which we explored. In short, this comprehensive atlas of regulatory elements provides the scientific community with a valuable resource for chicken genetics and genomics.

Uncovering the genetic architecture of economic traits in pigs is important for agricultural breeding. However, high-density haplotype reference panels are unavailable in most agricultural species, limiting accurate genotype imputation in large populations. Moreover, the infinitesimal model of quantitative traits implies that weak association signals tend to be spread across most of the genome, further complicating the genetic analysis. Hence, there is a need to develop new methods for sequencing large cohorts without large reference panels. We describe a Tn5-based highly accurate, cost- and time-efficient, low-coverage sequencing method to obtain 11.3 million whole-genome single-nucleotide polymorphisms in 2,869 Duroc boars at a mean depth of 0.73×. On the basis of these single-nucleotide polymorphisms, a genome-wide association study was performed, resulting in 14 quantitative trait loci (QTLs) for 7 of 21 important agricultural traits in pigs. These QTLs harbour genes, such as ABCD4 for total teat number and HMGA1 for back fat thickness, and provided a starting point for further investigation. The inheritance models of the different traits varied greatly. Most follow the minor-polygene model, but this can be attributed to different reasons, such as the shaping of genetic architecture by artificial selection for this population and sufficiently interconnected minor gene regulatory networks. Genome-wide association study results for 21 important agricultural traits identified 14 QTLs/genes and showed their genetic architectures, providing guidance for genetic improvement harnessing genomic features. The Tn5-based low-coverage sequencing method can be applied to large-scale genome studies for any species without a good reference panel and can be used for agricultural breeding.

The gene numbers and evolutionary rates of birds were assumed to be much lower than those of mammals, which is in sharp contrast to the huge species number and morphological diversity of birds. It is, therefore, necessary to construct a complete avian genome and analyze its evolution. We constructed a chicken pan-genome from 20 de novo assembled genomes with high sequencing depth, and identified 1,335 protein-coding genes and 3,011 long noncoding RNAs not found in GRCg6a. The majority of these novel genes were detected across most individuals of the examined transcriptomes but were seldomly measured in each of the DNA sequencing data regardless of Illumina or PacBio technology. Furthermore, different from previous pan-genome models, most of these novel genes were overrepresented on chromosomal subtelomeric regions and microchromosomes, surrounded by extremely high proportions of tandem repeats, which strongly blocks DNA sequencing. These hidden genes were proved to be shared by all chicken genomes, included many housekeeping genes, and enriched in immune pathways. Comparative genomics revealed the novel genes had 3-fold elevated substitution rates than known ones, updating the knowledge about evolutionary rates in birds. Our study provides a framework for constructing a better chicken genome, which will contribute toward the understanding of avian evolution and the improvement of poultry breeding.

Chicken growth traits are economically important, but the relevant genetic mechanisms have not yet been elucidated. Herein, we performed a genome-wide association study to identify the variants associated with growth traits. In total, 860 chickens from a Gushi-Anka F₂resource population were phenotyped for 68 growth and carcass traits, and 768 samples were genotyped based on the genotyping-by-sequencing (GBS) method. Finally, 734 chickens and 321,314 SNPs remained after quality control and removal of the sex chromosomes, and these data were used to carry out a GWAS analysis. A total of 470 significant single-nucleotide polymorphisms (SNPs) for 43 of the 68 traits were detected and mapped on chromosomes (Chr) 1–6, -9, -10, -16, -18, -23, and -27. Of these, the significant SNPs in Chr1, -4, and -27 were found to be associated with more than 10 traits. Multiple traits shared significant SNPs, indicating that the same mutation in the region might have a large effect on multiple growth or carcass traits. Haplotype analysis revealed that SNPs within the candidate region of Chr1 presented a mosaic pattern. The significant SNPs and pathway enrichment analysis revealed that theMLNR,MED4,CAB39L,LDB2, andIGF2BP1genes could be putative candidate genes for growth and carcass traits. The findings of this study improve our understanding of the genetic mechanisms regulating chicken growth and carcass traits and provide a theoretical basis for chicken breeding programs.

High-density single nucleotide polymorphism (SNP) markers are crucial to improve the resolution and accuracy of genome-wide association study (GWAS) and genomic selection (GS). Numerous approaches, including whole genome sequencing, genome sampling sequencing, and SNP chips are able to discover or genotype markers at different densities and costs. Achieving an optimal balance between sequencing resolution and budgets, especially in large-scale population genetics research, constitutes a major challenge. Here, we performed improved double-enzyme digestion genotyping by sequencing (ddGBS) on chicken. We evaluated eight double-enzyme digestion combinations, and EcoR I- Mse I was chosen as the optimal combination for the chicken genome. We firstly proposed that two parameters, optimal read-count point (ORP) and saturated read-count point (SRP), could be utilized to determine the optimal sequencing volume. A total of 291,772 high-density SNPs from 824 animals were identified. By validation using the SNP chip, we found that the consistency between ddGBS data and the SNP chip is over 99%. The approach that we developed in chickens, which is high-quality, high-density, cost-effective (300 K, $30/sample), and time-saving (within 48 h), will have broad applications in animal breeding programs.