The research team set out to decode the regulatory architecture of subcutaneous fat deposition by comparing two dramatically divergent duck lines: the Pekin duck, a fast-growing commercial breed prized for its thick layer of subcutaneous fat, and the Liancheng duck, a leaner indigenous Chinese breed. Rather than relying on a single type of genomic data, the investigators assembled an unusually rich multi-omics portrait of the animals’ subcutaneous adipose tissue, integrating epigenomic maps of open chromatin generated with ATAC-seq, histone modification profiles produced by CUT&Tag-seq targeting H3K27ac, three-dimensional genome organization captured through chromatin interaction mapping, and transcriptomic profiles of gene expression across the two breeds.

This layered approach allowed the researchers to move beyond the classic limitation of association studies, which can flag a genomic region as relevant but rarely identify the actual causal variant and its target gene. The team catalogued differential open chromatin regions between the fat and lean lines, annotated them as candidate cis-regulatory elements such as enhancers and super-enhancers, and then linked these regulatory elements to distant target genes using chromatin loops and topologically associating domains, the three-dimensional compartments within which enhancers typically operate. The result was a comprehensive catalogue of variant-to-gene interactions, termed IMVGI, that connected noncoding variants to the genes they plausibly regulate.

To separate true regulators from statistical noise, the researchers developed what they describe as a mixed-strategy gene prioritization framework. This combined weighted gene co-expression network analysis, or WGCNA, which groups genes into modules whose coordinated expression tracks the trait of interest, with a Bayesian model that integrates multiple independent lines of evidence and assigns each candidate gene a posterior probability of being genuinely involved in fat deposition. The Bayesian machinery, validated through robustness checks across different prior distributions, distilled the field of candidates down to 112 high-confidence genes—a manageable set from which the team could hunt for the master switches controlling avian adiposity.

Among these candidates, one regulatory element stood out. The team identified a key selected SNP—a single nucleotide change that bears the signature of natural or artificial selection, as assessed by cross-population extended haplotype homozygosity and fixation index analyses—that acts as a remote control for the expression of the ADM gene, which encodes adrenomedullin, a peptide signaling molecule. Critically, this variant does not sit near the ADM promoter. Instead, it lies within a distal enhancer, and the study’s chromatin interaction maps show that this enhancer physically loops across a long genomic distance to contact the ADM locus, delivering regulatory input from afar. Comparisons of chromatin accessibility, enhancer activity and ADM expression between Pekin and Liancheng ducks consistently supported this long-distance regulatory relationship.

The mechanistic detail is where the study becomes particularly striking. Through transcription factor motif scanning of the sequence flanking the variant, the researchers found that the two alleles differ in their ability to recruit SMAD2, a transcription factor best known for its role in the TGF-beta signaling pathway. Allele-specific binding was supported by experiments including electrophoretic mobility shift assays with supershift validation using purified SMAD2 protein, alongside in silico mutagenesis predictions generated with the AlphaGenome model trained on human data and cross-species comparisons in the homologous chicken region. In effect, the selected SNP rewires the enhancer’s protein-binding landscape, changing how strongly SMAD2 can dock onto the DNA and thereby tuning the volume of ADM expression in the fat tissue.

What does ADM actually do once its expression is dialed up? The study presents converging evidence that adrenomedullin functions as a cellular signal driving fat accumulation by promoting the proliferation and differentiation of preadipocytes, the precursor cells that populate subcutaneous adipose tissue and mature into fat-storing adipocytes. Histological staining of fat tissue from the two breeds revealed differences in cell size consistent with divergent adipogenic activity, and ligand-receptor interaction analyses showed that ADM’s receptor components, including CALCRL and RAMP2, are expressed in the relevant tissue compartments, with single-cell resolution data from chicken preadipocytes corroborating the signaling axis. Human phenome-wide association data for the ADM locus further linked the gene to metabolic traits, hinting that the regulatory logic uncovered in ducks may echo in mammalian biology.

The broader significance of the work lies in its demonstration that functional dark matter—the noncoding majority of the genome—can be systematically interrogated when the right biological system and the right analytical toolkit are brought together. The duck’s extreme lipogenic efficiency acted as a magnifying glass, amplifying the phenotypic consequences of regulatory variation so that the causal architecture became visible. The team’s integrative pipeline, spanning epigenomics, 3D genomics, co-expression networks and Bayesian statistics, offers a template that could be applied to other livestock species and, potentially, to the interpretation of human GWAS loci that have long resisted functional annotation. In an independent segregating duck population, variants linked through the IMVGI framework explained a meaningful portion of the variance in subcutaneous fat phenotypes, underscoring the predictive power of the approach.

There are also immediate practical implications for agriculture. Subcutaneous fat percentage, thickness and weight are central economic traits in duck production, influencing carcass value, feed efficiency and consumer preference in markets where duck fat is a prized ingredient. The high-resolution epigenomic map and the prioritized candidate gene list produced by this study constitute what the authors describe as a precision blueprint for genetic selection, offering breeders molecular markers that track the fat-associated alleles identified through selection signature analyses. Marker-assisted or genomic selection programs built on such variants could reshape fat deposition in duck lines with far greater precision than traditional phenotypic selection alone.

For the biomedical community, the study adds a compelling chapter to the growing recognition that distal enhancers, rather than protein-coding mutations, frequently hold the keys to complex metabolic traits. The finding that a single selected SNP can reorchestrate transcription factor recruitment at a remote enhancer and, through that molecular switch, reshape an entire tissue’s fat-storing capacity illustrates the elegance and economy of regulatory evolution. As the authors note, fat deposition represents a global health threat whose genetic architecture remains unresolved; by illuminating how a duck enhancer commands ADM signaling to build fat, the research opens a window onto regulatory mechanisms that may well operate, in variant forms, across the vertebrate lineage—including in humans grappling with the genetics of obesity.

Subject of Research: Regulatory genetics of fat deposition in ducks

Article Title: A selected distal enhancer orchestrates avian fat deposition in coordination with ADM signaling

Article References: Liu, H., Zhang, H., Tang, H., Liu, S., Liu, D., Mou, Q., Wang, Z., Xiao, Y., Zhang, L., Zhang, Y., Yuan, L., Hou, S., & Zhou, Z. (2026). A selected distal enhancer orchestrates avian fat deposition in coordination with ADM signaling. BMC Biology. https://doi.org/10.1186/s12915-026-02748-8

Image Credits: AI Generated

DOI: 10.1186/s12915-026-02748-8

Keywords: fat deposition, duck, enhancer, ADM, SMAD2, epigenomics, ATAC-seq, 3D genome, WGCNA, Bayesian gene prioritization, preadipocyte, GWAS