Chicken chromatin accessibility atlas accelerates epigenetic annotation of birds and gene fine-mapping associated with growth traits

Abstract

The development of epigenetic maps, such as the ENCODE project in humans, provides resources for gene regulation studies and a reference for research of disease-related regulatory elements. However, epigenetic information, such as a bird-specific chromatin accessibility atlas, is currently lacking for the thousands of bird species currently described. The major genomic difference between birds and mammals is their shorter introns and intergenic distances, which seriously hinders the use of humans and mice as a reference for studying the function of important regulatory regions in birds. In this study, using chicken as a model bird species, we systematically compiled a chicken chromatin accessibility atlas using 53 Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) samples across 11 tissues. An average of 50 796 open chromatin regions were identified per sample, cumulatively accounting for 20.36% of the chicken genome. Tissue specificity was largely reflected by differences in intergenic and intronic peaks, with specific functional regulation achieved by two mechanisms: recruitment of several sequence-specific transcription factors and direct regulation of adjacent functional genes. By integrating data from genome-wide association studies, our results suggest that chicken body weight is driven by different regulatory variants active in growth-relevant tissues. We propose CAB39L (active in the duodenum), RCBTB1 (muscle and liver), and novel long non-coding RNA ENSGALG00000053256 (bone) as candidate genes regulating chicken body weight. Overall, this study demonstrates the value of epigenetic data in fine-mapping functional variants and provides a compendium of resources for further research on the epigenetics and evolution of birds and mammals.

Complex genetic architecture of the chicken Growth1 QTL region

“…One of the most strongly associated variants we observed (chr1:170835310) ( Table 3 ) was located 62 kb upstream of the long non-coding RNA ENSGALG00000053256, which was recently identified as one of the top candidate genes for controlling growth traits in chicken by intersections of ATAC-sequencing peaks with growth GWAS data [ 49 ]. Notably, several of the most strongly significant variants observed in our study clustered to introns of ENOX1 , whose gene product is involved in plasma membrane transport pathways, but to our knowledge, this gene has not previously been linked directly with growth traits.…”

“…P-value and are presented in S4 Table . None of the SNP markers showing most strongly associated with body weight were predicted to change coding parts of genes (Tables 3 and S4), and were clustered in introns of or intergenic to the genes Ecto-NADPH Oxidase Disulfide-Thiol Exchanger 1 (ENOX1), ENSGALG00000050514, ENSGALG00000052226 ENSGALG00000053256. Interestingly, ENSGALG00000053256, a novel long non-coding RNA has previously been implicated as a candidate gene for regulating chicken body weight [49]. Several strongly associated SNP variants were predicted to cause amino acid substitutions in genes (Tables 3 and S5).…”

Genetic architecture of body weight, carcass, and internal organs traits of Ghanaian local chickens

“…The genes in these genomic regions include ligand dependent nuclear receptor corepressor like (LCORL) and non-SMC condensin I complex subunit G (NCAPG), which play an important role in arginine metabolism and are linked with growth in animals (Wu et al, 2009;Tetens et al, 2013;Tiensuu et al, 2019); leucine aminopeptidase 3 (LAP3) and LIM domain binding 2 (LDB2) genes which have an influence on growth traits of chicken (Gu et al, 2011). SNPs in karyopherin subunit alpha 3 (KPNA3) and RCBTB1 genes are also associated with growth in chicken (Wang et al, 2022;Zhu et al, 2023). Calcium binding protein 39 like (CAB39L) which is on chromosome 1 plays an important role in the regulation of food intake by activating AMP-activated protein kinase through the process of phosphorylation (Proszkowiec et al, 2006) and regulates body weight in chicken (Li et al, 2021;Zhang et al, 2021;Zhu et al, 2023).…”

The ChickenGTEx atlas: the genetic regulation of multi-tissue and single-cell transcriptome signatures in chickens

“…DeepSEA is a deep learning model initially trained for predicting variant effects in humans 37 , while in this study, it was trained by utilizing 310 epigenomic profiles generated by the chicken FAANG consortium 18 and by Zhu et al 124 (Table S9). According to sequencing types and histone marks, we categorized all the 310 epigenomic profiles into seven groups, including ATAC, CTCF, DNaseSeq, H3K27ac, H3K23me3, H3K4me1 and H3K4me3, which were then used as input for the model training using the Selene, a PyTorch-based package 125 .…”