Mapping the regulatory genetic landscape of complex traits using a chicken advanced intercross line

Zhu, Xiaoning; Li, Chong; Luo, Chenglong; Bai, Zhonghao; Shu, Dingming; Chen, Peng; Ren, Jiangli; Song, Ran; Fang, Lingzhao; Qu, Hao; Wang, Yuzhe; Hu, Xiaoxiang

doi:10.1038/s41467-025-60834-x

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Published: 01 July 2025

Mapping the regulatory genetic landscape of complex traits using a chicken advanced intercross line

Xiaoning Zhu ORCID: orcid.org/0000-0001-5743-879X^1,2,3^na1,
Chong Li¹^na1,
Chenglong Luo⁴,
Zhonghao Bai³,
Dingming Shu^2,4,
Peng Chen⁴,
Jiangli Ren^1,5,
Ran Song^1,5,
Lingzhao Fang ORCID: orcid.org/0000-0003-1103-3679³,
Hao Qu^2,4,
Yuzhe Wang ORCID: orcid.org/0000-0003-3474-7957^1,5,6 &
…
Xiaoxiang Hu ORCID: orcid.org/0000-0001-7045-0283^1,5,6

Nature Communications volume 16, Article number: 5841 (2025) Cite this article

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Abstract

Complex traits exhibit a highly polygenic architecture that complicates gene mapping and molecular characterization. As a model organism for birds, chickens possess high-quality reference panels, functional annotations, and molecular quantitative trait locus maps. However, the genetic mechanisms underlying growth traits have not been systematically analyzed. Here, we develop a 16-generation advanced intercross line of chickens to enhance informative recombination and identify 154 single-gene quantitative trait loci. We use multiple co-localization methods to establish a network landscape of tissue-specific regulatory mutations and functional gene relationships. We leverage gene-clustering and restoration quantitative trait loci within the omnigenic model framework to elucidate the genetic regulation system of growth traits. Cross-species comparisons show the conserved functions of growth-related genes and divergent features of regulatory mechanisms in mammals and birds.

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Introduction

The sample size of global genomic studies and the number of traits examined have increased rapidly^1,2, significantly improving the effectiveness of genome-wide association studies (GWAS) for identifying genetic variants associated with complex human traits. Genetic variations affect biological phenotypes through various molecular mechanisms. Among these, coding variations, which directly affect protein-coding sequences and functions, are the most understood³; however, such variants are always observable at low frequencies owing to purifying selection. In contrast, complex traits are primarily driven by non-coding variants that presumably affect gene regulation^4,5. GWAS signals are significantly enriched in regions with regulatory functions, such as chromatin accessibility regions and DNA methylation regions, as well as gene expression quantitative trait locus (eQTLs)^6,7,8. Integrating GWAS, molQTLs, and epigenetic feature annotation improves our understanding of how genetic variants influence complex traits by modulating molecular phenotypes and aids in establishing the association between single-nucleotide polymorphisms (SNPs) and target genes, thereby facilitating the identification of true causal genes⁹. In humans, the v8 freeze of the Genotype-Tissue Expression GTEx data includes 838 post-mortem individuals and 17,382 RNA-seq data¹⁰, and the ENCODE Encyclopedia contains 1,063,878 human cis-regulatory elements (CREs), offering a powerful resource for elucidating the mechanisms underlying complex traits¹¹.

Chickens are an important source of protein for human beings, and analyzing the genetic mechanisms of growth traits in chickens is crucial for accelerating the genetic breeding of chickens and increasing the economic value of the chicken meat industry. Chickens are also important model organisms for avian studies and are utilized in research of osteoporosis, obesity, metabolic disorders, and other diseases, providing important models for human medicine^12,13. Research on the global chicken reference panel (GCRP), functional annotation (chicken FAANG), and regulatory mapping (chicken GTEx) is advancing^14,15,16,17. Despite the availability of extensive gene expression and epigenomic datasets, only a few variant-gene-phenotype links have been established. These observations are generally interpreted within a framework wherein complex traits are driven by the accumulation of minor effects on tissue-specific genes and regulatory pathways. Additionally, in contrast to humans, who have access to large-scale publicly available GWAS data, only the QTL information is publicly available for chickens¹⁸. The identification of functional loci is challenging because of the extensive QTL intervals influenced by linkage disequilibrium (LD). This challenge is partially alleviated through fine mapping using artificially designed populations, such as advanced intercross lines (AILs)^19,20. AIL populations, the concept of which was first proposed in 1995, can increase recombination and improve the accuracy of QTL localization¹⁹. In recent years, AIL populations have been widely established and applied in several species, and the QTLs of AIL populations have been mapped to narrower chromosomal regions and more QTLs have been identified, which helps identify functional genes and causal mutations²¹. However, these approaches require substantial investments of both time and financial resources.

In this work, we introduced the AIL population that has been maintained for 15 years. The initial F₂ population was established through reciprocal crosses between Huiyang Bearded chicken (HB) and the High-Quality Chicken Line A (HQLA), which have significant phenotypic differences in growth traits (Fig. 1a). Subsequent advanced intercross lines (AIL), spanning from F₃ to F₁₆ were derived through random mating (detailed in Supplementary Note 1). We collected 4671 samples across different generations for genome sequencing, analyzed changes in this population structure over 16 generations, and conducted gene fine mapping for 75 phenotypes (Fig. 1b). The high accumulation of recombination events facilitated the identification of numerous QTLs at the single-gene level. Through four types of colocalization analysis, we elucidated the intriguing genetic architecture underlying QTLs associated with various growth traits. Additionally, we emphasize the foundational role of regulatory variants in the mechanisms underlying bird growth and developmental traits, as well as the contribution of functional genes to the identification of genes associated with human growth traits.

**Fig. 1: Advanced intercross line (AIL) population construction and data analysis pipeline.**

Results

Maintenance of AIL population diversity and rapid accumulation of recombination were assessed through genotyping data

We collected 4671 samples from generations F₀, F₁, F₂, F₈, F₉, F₁₅ and F₁₆ of the AIL population (Supplementary Table 1). The genotypes were obtained using the low coverage sequencing method²², resulting in 8,050,756 SNPs (see “Methods” for details, Supplementary Table 2; Supplementary Fig. 1a, b). The average sequencing depth of these samples was 0.89 ± 0.30. Accuracy assessment of the genotype imputation was conducted using 108 high-depth sequencing samples (Supplementary Data 1), yielding a genotype consistency of 0.99 ± 0.002.

Although the AIL is a reciprocal cross population (high-quality chicken Line A (HQLA) × Huiyang Bearded chicken (HB)), the breeding process of the parent HQLA, which involves initial crossing of two ancestral sources followed by directed selection, imparted a mixed genetic representation from three ancestral sources (Supplementary Fig. 2). Principal component analysis (PCA) indicated that the F₁₆ individuals clustered centrally between the two founders. This population exhibited no population stratification, thereby minimizing the risk of false positive associations (Fig. 2a). To mitigate the bottleneck effect on the genetic diversity of the population, the sample size of each generation was maintained at 1292 ± 407 (minimum: 655 in F₃), while that of half-sib families was kept at 94 ± 10 (minimum: 72 in F₂). Over successive generations, the nucleotide diversity remained stable following the initial cross between the founders (Fig. 2b), and the inbreeding coefficient remained constant since the F₈ generation (Fig. 2c). Additionally, the minor allele frequency spectrum exhibited a similar distribution pattern across generations (Fig. 2d). A mere 0.11% loss of genetic polymorphisms was observed from F₀ to F₁₆. These findings suggest that the genetic drift within this population was minimal during the propagation process, with no evident bottleneck effect. Conversely, LD decay analysis indicated that LD declined rapidly with generational transmission (r²_0.1 = 143 kb in F₁₆ and r²_0.1 = 259 kb in F₂) (Fig. 2e). No change in the frequency of major haplotypes from the F₁ generation and an increase in the number of haplotypes suggest that multiple generations of recombination actively fragmented the genomes of this population’s descendants, which significantly enhanced the gene mapping resolution (Fig. 2f, g). In summary, the population is a quality resource for fine mapping.

**Fig. 2: Genetic evaluation of the AIL population.**

GWAS revealed hundreds of genes responsible for various chicken traits

We collected a series of growth and slaughter traits in the F₂ and F₉ generations²³. In the F₁₆ generation, we expanded our phenotypic records to encompass 75 traits across five categories, including growth and development (GD, n = 36), tissue and carcass phenotypes (TC, n = 23), feed intake and efficiency (FE, n = 9), blood biochemistry (BB, n = 3), and feather characteristics (FC, n = 4). Many of the phenotypic measurements were strongly correlated with each other, suggesting that they may be influenced by pleiotropic genes (Supplementary Fig. 3; Supplementary Data 2 and 3). Additionally, we calculated the SNP heritability for all phenotypes and found that FC exhibited the highest level of heritability, while FE demonstrated the lowest overall heritability. The heritabilities of GD and TC phenotypes were 0.31 ± 0.16 and 0.30 ± 0.13, respectively. These results provided a basis for subsequent phenotypic association analyses (Fig. 3a; Supplementary Data 4).

**Fig. 3: Genome-wide association studies (GWAS) and QTL fine mapping.**

We conducted GWAS on 75 phenotypes among which 43 exhibited significant signals (Fig. 3b; Supplementary Data 2). The heritability of a phenotype and the number of QTLs found for this phenotype were significantly correlated (Pearson’s r = 0.64, p = 4.31 × 10⁻⁶, Fig. 3c). Fine mapping of these 43 phenotypes resulted in a total of 682 QTLs (Supplementary Data 5). QTL comparisons across different generations revealed that the QTL lengths significantly decreased with transmission through generations (Fig. 3d), as exemplified by the Gallus gallus chromosome 27 (GGA27) QTL (Supplementary Fig. 4a, b; Supplementary Data 6). Considering the impact of sample size, we standardized the sample size of F₁₆ to match that of F₂. A comparison of the F16_640 group with all F₁₆ samples revealed no significant difference in QTL length, indicating that increasing the sample size does not influence QTL length. However, a larger sample size facilitated the detection of more QTLs, suggesting that the primary advantage of increasing sample size lies in enhancing the detection of small-effect QTLs rather than altering QTL length. This implies that recombination continues to occur across generations (Supplementary Fig. 4c). The average length of the QTL interval in F₁₆ was 244 ± 343 kb, with 84.2% of the QTL lengths being less than 500 kb (Supplementary Fig. 4d; Supplementary Data 5). Of the 682 QTLs identified, 20.38% were detected in the current Animal QTL database (https://www.animalgenome.org/cgi-bin/QTLdb/GG/index). These QTLs allow us to more comprehensively study growth traits using AILs (Supplementary Fig. 5). By analyzing the relationships between QTLs for different phenotypes, we grouped physically overlapping trait-associated loci, resulting in 396 loci with a total length of 94.7 Mb, covering 9.86% of the genome. Among these, 60.76% were associated with more than one trait, and 25.78% were classified as multi-domain loci (Fig. 3e; Supplementary Data 7).

A total of 624 QTLs contained at least one gene. Of these QTLs, 470 were annotated to at least two genes, and the average number of genes annotated to these QTLs was 9.7 ± 8.9 (Fig. 3f). This includes numerous examples of previously identified causal genes, such as HOXB8, which influences muffs and beards, as well as SOX10, which impacts feather color^24,25 (Fig. 3g, h). Additionally, it encompasses previously reported major QTLs on chromosomes 1, 4, and 27 that are associated with chicken growth traits^23,26,27,28. Among these, 154 were single-gene QTLs (Supplementary Data 8); 64 of these involved 52 genes that have been validated in mice, indicating that we could directly locate these functionally validated genes (Supplementary Data 9).

Integration of GWAS with eQTL and FAANG identified the relationship of regulatory variants, target genes, and phenotypes

This population has achieved significant advancements in gene fine mapping. However, among the 77,519 significant SNPs identified within QTLs, only 0.33% were missense mutations, while 55.55% and 17.17% were located in the intronic and intergenic regions, respectively (Supplementary Fig. 6). This finding implies that regulatory variants play a dominant role in growth traits. Subsequently, we used the molQTL data from chickenGTEx¹⁵ and various functional annotations obtained from ChickenFAANG¹⁴ to elucidate the mechanisms underlying these mutations. Among the five molecular QTL and 15 FAANG annotation regions we analyzed, promoters (TxFlnkHet, TxFlnkWk, TssAHet, TssA, TssBiv, and TxFlnk), enhancers (EnhAHet, EnhAWk, EnhAMe, and EnhA), exQTLs, lncQTLs, and eQTLs could explain more heritability than Qui regions without any function (Fig. 4a; Supplementary Table 3). Enrichment analysis indicated that the GWAS signals were significantly enriched in strong promoters (TssA), strong enhancers (EnhA), and eQTL (Fig. 4b; Supplementary Fig. 7). We utilized four complementary approaches to prioritize causal variants and genes, namely summary-data-based Mendelian randomization (SMR, a method used to investigate causal relationships between gene expression and complex traits using summary-level data), fastENLOC-based colocalization, single-tissue transcriptome-wide association study (SPredixcan), and multi-tissue transcriptome-wide association study (SMultixcan). Of the 611 QTL identified for 40 growth phenotypes, 446 contained significant SNPs that affected the expression of at least one gene; we summarized the results of colocalization of these 446 QTLs and found that 245 QTLs could be interpreted by at least one of the methods (Supplementary Fig. 8). We obtained a total of 431 unique functional genes using four methods (Fig. 4c; Supplementary Data 10).

**Fig. 4: GWAS integration with eQTL and functional annotation regions.**

Alkaline phosphatase (ALP) assay is mainly used to diagnose diseases of the hepatic, biliary, and skeletal systems. ALP in chickens may be related to intestinal function, which in turn affects feed intake^29,30. We identified significant signals affecting ALP on chromosome 24 and a conditional control GWAS (rs316348444) further validated this finding (Fig. 4d; Supplementary Fig. 9a). SMR analysis indicated that rs316348444 influenced the ALP phenotype by modulating ST3GAL4 expression in the small intestine (Fig. 4e). A dual-luciferase reporter assay and quantitative polymerase chain reaction (qPCR) confirmed that this mutation enhanced the activities of the promoter and enhancer of ST3GAL4, thereby affecting its expression in the small intestine tissue (Fig. 4f, g; Supplementary Fig. 9b; Supplementary Tables 4 and 5). Additionally, results from human GWAS databases also indicated that ST3GAL4 is associated with serum ALP levels (Supplementary Fig. 9c). These findings confirm that rs316348444 and ST3GAL4 play crucial roles in regulating ALP.

We classified all loci into three categories, namely no colocalization loci (where no GWAS gene loci are linked to eGenes across 28 tissues), nearest gene loci (where GWAS loci are identified through genes closest to the lead SNP) and not-nearest gene loci (where GWAS loci are identified by genes other than those closest to the lead SNP). eGenes are defined as genes significantly regulated by at least one variant (FDR < 0.05). We found that 81.71 ± 3.65% of the identified loci were not-nearest gene loci (SMR = 77.78%, fastENLOC = 86.55%, SPredixcan = 81.82%, and SMultixcan = 80.69%) (Supplementary Fig. 10). For SMR, 108 loci were identified, of which 84 were not-nearest gene loci and 24 were nearest gene loci (Fig. 4h; Supplementary Table 6). For these 84 loci, we identified functional genes located 535 ± 363 kb away from the lead SNP. For example, for shank length at 6 weeks of age (SL6), the lead SNP was rs314511712, and the gene nearest to it was GIP. We did not identify GIP through SMR but identified IGF2BP1 (muscle), HOXB7 (embryo), and KCNH6 (jejunum), which were located 7.5 kb, 119 kb, and 998 kb from the lead SNP, respectively (Fig. 4i, j). The IGF2BP1 gene has been reported in chicken, duck, and goat as a functional gene influencing growth traits such as body size and body weight^31,32,33. Similarly, we observed the same phenomenon in shank circumference at 8 weeks of age (SC8) and shank circumference at 12 weeks of age (SC12) (Supplementary Figs. 11 and 12). These results suggest that the eQTL facilitated our identification of the possible functional genes and mutations.

Example of complex QTL analysis: genomic neighbor genes function in different tissues and together contribute to body weight

We identified a significant effect QTL interval at the distal end of chromosome 1 associated with GD and TC phenotypes. The average heritability of this QTL was 7.6 ± 3.67%, contributing to 21.22 ± 7.86% of the total heritability (Fig. 5a). This is a substantial effect QTL previously demonstrated in various populations (Supplementary Table 7). This QTL is unique because it retains ~800 kb after 15 generations of recombination (Fig. 5b). Our previous haplotype association analyses revealed multiple haplotypes with different effects on chicken weight, possibly owing to segregating polymorphisms at multiple, tightly linked mutation loci in the region²³. In an omnigenic model, we hypothesized that this genetic process operates through diverse pathways, implying that multiple genes function in different tissues to collectively influence body weight. Utilizing a series of colocalization methods, we identified eight multi-omics relationship pairs, including rs732498434-SETDB2-pituitary, rs735425541-RCBTB1-muscle, rs431853734-ARL11-adipose, rs314814283-KPNA3-retina, rs314099581-TRIM13-spleen, rs315380946-KCNRG-liver, rs316815589-RNASEH2B-muscle, PHF11-muscle (Fig. 5c; Supplementary Figs. 13 and 14). This implies that multiple genes influence body weight in this QTL. They have a neighboring relationship in physical distance, showing a pseudo-main effect GWAS findings.

**Fig. 5: Identification of GGA1 primary QTL functional genes.**

The proximity of physical distance also facilitated the acceleration of artificial selection in the formation of this QTL. We selected 10 high- and low-weight chickens, as well as red jungle fowl, from the GCRP database¹⁷ for outgroup verification. Using the haplotype sharing method³⁴, we identified four Q haplotypes (increased body weight) and one q haplotype (decreased body weight) within this interval (Fig. 5d). Notably, the frequency of the Q haplotype in high-weight chickens was significantly higher than that in low-weight chickens, and vice versa. These Q/q haplotypes were also found in the red jungle fowl. High-weight chickens exhibited an enrichment of standing Q segments to a certain extent, but have not fixed, indicating that there remains potential for further selection in the future.

A discovery caused by the drift of local genetic architecture: NCAPG influences the eviscerated weight

By comparing QTLs across different generations, we identified a QTL on chromosome 4 that affected the eviscerated weight (EW), which was generated in F₁₆ and was not detected in F₂ or F₉ (Fig. 6a–c; Supplementary Fig. 15). We discovered that the minimum allele frequency of significant SNPs in F₁₆ was notably higher than those in F₂ and F₉ (Fig. 6d), thereby enhancing the statistical efficiency of GWAS. The sampling GWAS of F₁₆ remained significant (Supplementary Fig. 15), allowing us to eliminate the impact of sample size on the results across different generations. We attribute this intriguing finding to ongoing recombination, suggesting that this population still holds considerable potential for the identification of extra QTLs.

Fig. 6: NCAPG is a functional gene for the eviscerated weight (EW) phenotype. — **Fig. 6: *NCAPG* is a functional gene for the eviscerated weight (EW) phenotype.**

We confirmed that this was a highly significant QTL in chickens and various species (humans, cattle and sheep), as evidenced by the substantial number of previous studies (Supplementary Table 8). We queried promoter-enhancer annotations and candidate target gene profiles for all significant SNPs within this QTL and found that rs14491022 (4:75,892,495, p = 3.64 × 10⁻⁰⁷) was in the NCAPG enhancer region in the jejunum and duodenum, while rs1058217803 (4:76,102,806, p = 5.22 × 10⁻⁰⁷) was in the LDB2 promoter region in the hypothalamus (Fig. 6f; Supplementary Fig. 16a, b). A dual-luciferase reporter assay indicated that rs14491022 enhanced the enhancer activity of NCAPG, whereas rs1058217803 enhanced the promoter activity of LDB2. We further collected duodenum and hypothalamus tissue samples of different genotypes for RNA-seq. The results demonstrated that NCAPG expression was elevated in the duodenum for rs14491022, while no change was observed in LDB2 expression in the hypothalamus for rs1058217803 (Fig. 6g, h; Supplementary Fig. 16c, d). Additionally, rs14491022 is also associated with other growth traits, such as LTFW, SC12, LW, and BW10 (Supplementary Table 9). We inferred a genetic model that rs14491022 affected NCAPG enhancer activity and subsequently influenced NCAPG gene expression in the gut, which in turn impacts the EW phenotype in chickens.

Evolutionary patterns of growth trait genetic mechanisms in humans (mammals) and chickens (birds)

To investigate the correlation between growth-related genes identified in chickens and humans, we transformed the GWAS significant genes (uniquely, n = 329) associated with five chicken growth traits into their human orthologous counterparts (Supplementary Data 11). On average, 88.75% of these genes were classified as chicken-human orthologous genes, a proportion that was significantly higher than the overall proportion (50.48%) of orthologous genes in the chicken genome. We also collected GWAS summary statistics for 20 human complex traits from the UK Biobank and Yanglab (Supplementary Data 12 and 13). Linkage disequilibrium score regression (LDSC) analysis showed that the functional genes influencing chicken growth phenotypes also affected the corresponding human traits (Fig. 7a). We further examined the human homologs of chicken growth-related functional genes in the Phenome-Wide Association Study (PheWAS) database. Among the 323 genes analyzed (orthologs of chicken growth-related genes), 80.50% influenced heel bone mineral density, body mass index, sitting height, waist hip ratio, and so on. Using weight as a focal point, notably, significant peaks were observed on chromosomes 13, 4, and 17, which corresponded to chicken chromosomes 1, 4, and 27, respectively (Fig. 7b). The analysis of a series of significant genes revealed a broad spectrum of one-cause pleiotropy (Fig. 7c, Supplementary Fig. 17). Meanwhile, we converted 431 functional genes obtained from chickens to their homologous genes in pigs and further queried the PigGTEx database³⁵ to identify 289 genes affected pigs Production_Traits Meat_and_Carcass_Traits and other growth- and development-related phenotypes (Supplementary Fig. 18). These findings indicate that the functional genes influencing growth traits in humans, pigs, and chickens are highly conserved, suggesting that the growth-related functional genes identified in each species may serve as valuable references for other species.

**Fig. 7: Association of chicken growth traits with complex traits in humans.**

In contrast, we focused on the significant SNPs associated with all growth traits according to the SMR results. Following the conversion to the human genome, results were unattainable for 81.50% of the SNPs. Among the remaining 18.50% of the SNPs, the gene annotation results exhibited considerable variations (Fig. 7d). Notably, growth-related genes in chickens possessed shorter introns and intergenic regions than their human counterparts (Fig. 7e). This result indicates that the regulatory mechanisms governing growth-related gene functions have undergone substantial changes throughout the evolutionary transition from birds to mammals.

Discussion

An AIL is an artificially designed population conducive to fine mapping in many species^19,20,36. Overall, similar to the previous QTL interval shortening in the mouse AIL population²¹, the length of the QTL interval in the F₁₆ generation was ~85% shorter than that in F₂, resulting in the identification of 64 single-gene level QTLs, including 52 functional genes previously validated through gene-edited mice. Interestingly, a QTL on chromosome 4 was identified in the F₁₆ generation of this population. This finding is attributed to fluctuations in allele frequency resulting from chromosomal recombination, indicating that this population may also harbor other false negative signals caused by excessive bias in allele frequencies. Our population exhibited higher single-nucleotide and haplotype diversities than the other two chicken AIL populations^37,38, which can be attributed to our practice of utilizing more than 72 half-sib families in each generation. However, while we assert that this group possesses genetic diversity from three ancestral sources due to its history, there is a diversity bottleneck in the AIL, resulting from the use of only two parental lines. The multi-parent advanced generation intercross (MAGIC) remarkably contributes to gene mapping^39,40; however, the establishment of MAGIC populations in animals remains challenging and infrequent. We also attempted to conduct a meta-GWAS; however, the available data sources did not facilitate its completion. Consequently, the AIL used in this study represents the most effective genetic resource for investigating the growth and development of chickens and potentially other avian species.

Numerous studies have demonstrated that human body weight exhibits a typical polygenic genetic architecture characterized by small effects, with no single large effect QTL across genome⁴¹. In contrast, genetic studies of poultry have revealed alterations in the genomic landscape resulting from strong artificial selection. Notably, we identified a significant and unique major QTL associated with growth traits at the distal end of chromosome 1. In a previous F₉ report, we concluded that this QTL emerged from a locally highly polygenic genetic architecture rather than being associated with a single major effect locus^23,42. Our findings from the F₁₆ generation and the Virginia chicken lines studies further substantiate this notion³⁴. These weight-promoting (Q) or weight-reducing (q) haplotypes were derived from standing variants of the red jungle fowl (Fig. 5d). Under normal conditions, each haplotype harboring weight-related mutations maintains a balance that allows adaptation to natural selection. Following intense artificial selection, genetic selectable variations are released through recombination, resulting in the emergence of popular haplotypes. These functional genes are located in close proximity, increasing the possibility of hitchhiking and facilitating rapid accumulation in high-weight chicken breeds. The compact genome of chickens is particularly conducive to artificial selection, contributing to their status as one of the most widespread species in the world. By integrating chicken FAANG and GTEx data^14,15, we discovered that regulatory variants and associated genes were active in distinct target tissues, suggesting that these neighboring genes may not be interconnected within biological pathways. This finding underscores the significant advantages of using eQTL data for linear genomic research. We established multiple relationships among regulatory mutations, genes, target tissues, and phenotypes, providing robust statistical evidence for further functional studies of these genes.

The divergence of birds and mammals can be traced back ~300 million years; however, they exhibit distinctly different evolutionary strategies in their coding and regulatory regions⁴³. Furthermore, chromosome length plays a crucial role in determining overall chromosome architecture in vertebrates. For instance, chickens possess relatively short chromosomes, and their long-range contact frequency is 1.408 times higher than that of humans and mice. This finding suggests that long-range interactions may occur more frequently in chickens. Future studies should explore the impact of long-range contacts on gene regulation and their influence on complex traits via methods such as Hi-C and tensorQTL⁴⁴. Our results demonstrated the anticipated conservation pattern of orthologous protein-coding genes between chickens and humans⁴⁵, with this trend being particularly pronounced for functional genes that influence growth-related traits. This suggests that the functional genes identified in chickens contribute to a comprehensive list of those in avian and serve as a reference for research on genes related to human growth and development. This perspective was further supported by the LDSC results (Fig. 7a). Consequently, chickens can be regarded as alternative model animals for studying human growth and developmental phenotypes, particularly given the relatively convenient access to their samples. Conversely, gene regulation may play a significant role in species evolution⁴⁶. The chicken regulatory region sequences correspond only minimally to those of mammals. Although the number of chicken genes is considered at least comparable to that of humans⁴⁷, shorter non-coding regions result in a denser appearance of chicken genes. Only 30–40% of the regulatory elements, including functional promoters, predicted promoters, CpG islands, and predicted transcription factor binding sites, are conserved between humans and chickens⁴⁵. However, over 99% of the avian-specific highly conserved elements are located in non-coding regions and have been under strong selective constraints⁴⁸. This indicates that the obtained eQTL results represent a paradigm of gene regulation in birds that differs from that in the mammalian model and occupies a crucial position in evolutionary studies. Our findings provide valuable resources for research on the regulatory mechanisms of growth traits across nearly 10,000 bird species.

In summary, we developed a robust AIL population characterized by rich polymorphism and high recombination, which facilitates the fine mapping of genes and the exploration of the complex genetic architecture of QTLs. We identified numerous functional genes associated with growth traits and highlighted their conservation across avian and mammalian species. Multi-omics data are crucial for elucidating the relationship between functional mutations and their corresponding genes as well as for demonstrating the tissue-specific pathways associated with each gene. This resource serves as an excellent systematic foundation for studying chickens and other avian species and provides valuable insights for research on human growth traits.

Methods

Ethics approval

All animals involved in this study were housed and treated in accordance with the guidelines approved by the Animal Welfare Committee of Agrobiotechnology of China Agricultural University (Approval No. SKLAB-2014-06-07). Specifically, all animals were individually housed under controlled conditions (22 ± 2 °C, 50–60% humidity) with exposure to natural daylight and free access to feed and water. During the rearing period, qualified veterinary personnel conducted daily inspections and regularly monitored the chickens’ health and welfare, including assessments of physical appearance, behavior, feed and water intake. Any individuals exhibiting signs of abnormal health were promptly isolated and treated. At the end of the experiment, all chickens were euthanized by cervical dislocation performed by trained personnel. Death was confirmed by the cessation of respiration and heartbeat, as well as the absence of the corneal reflex.

Experimental population and phenotyping

A large distant intercross pedigree was initiated in 2008 from two divergent chicken lines, high-quality chicken Line A (HQLA), a broiler line bred by Guangdong Wiz Agricultural Science and Technology, Co. (Guangzhou, China), and Huiyang Bearded chicken (HB), a native Chinese breed. The body weight of HB chickens at 7 weeks of age was, on average, less than one-third of those of HQLA (p < 2.2 × 10⁻¹⁶). Detailed feeding regimes and F₀ to F₁₆ mating schemes have been described in Fig. 1 and earlier by Sheng and Wang et al.^23,49.

The data of 75 traits were collected or processed in the F₁₆ generation, including growth and development (GD, n = 36), tissue and carcass (TC, n = 23), feed intake and efficiency (FE, n = 9), blood biochemical (BB, n = 3) and feather characteristics (FC, n = 4) (Supplementary Data 2; Supplementary Note 2).

Similarly, we selected 21 phenotypes shared by F₂, F_9, and F₁₆, and conducted a comparative analysis with F₁₆ (Supplementary Data 14; Supplementary Note 3).

DNA extraction and Tn5 library construction

Blood samples were collected from the chickens at 5 weeks of age using the Laboratory-made FTA card. DNA was extracted from the blood samples using magnetic bead technology.

Equal amounts of Tn5ME-A/Tn5MErev and Tn5ME-B/Tn5MErev were incubated at 72 °C for 2 min and then placed on ice immediately for 1 min. Tn5 (Karolinska Institutet 171 77 Stockholm, Sweden) was loaded with the Tn5ME-A+rev and Tn5ME-B+rev in 2× Tn5 dialysis buffer at 25 °C for more than 2 h. All linker oligonucleotides were the same as those described in the previous report⁵⁰.

Tagmentation was conducted at 55 °C for 10 min by mixing 4 μL 5×TAPS-MgCl₂, 2 μL dimethylformamide (DMF) (Sigma Aldrich), 1 μL of the Tn5 pre-diluted to 16.5 ng/μL, 10 ng DNA (may be adjusted based on enzyme activity), and nuclease-free water. The total volume of the reaction was 20 μL. Subsequently, 3.5 μL 0.2% SDS was added, and Tn5 was inactivated for another 10 min at 55 °C.

KAPA HiFi HotStart ReadyMix (Roche) was used for PCR amplification by mixing 1 U HotStart DNA Polymerase, 10 μL 5× KAPA HiFi Fidelity Buffer, 2 μL dNTP Mix (10 mM each), 4 + 4 μL i5/i7 primers (5 μM) and 5.5 μL nuclease-free water. The PCR mix was added to the digestion products. The primers were designed for MGI sequencers, with the reverse primers containing different index adapters to distinguish individual libraries. We employed two index systems, one with 96 tags (P_1-96) and the other with 192 tags (C_1-192) to support future expansion. The PCR program was as follows: 9 min at 72 °C, 30 s at 98 °C, and then 9 cycles of 30 s at 98 °C, 30 s at 63 °C, followed by 3 min at 72 °C. The products were quantified using Qubit Fluorometric Quantitation (Invitrogen). The groups of 96 or 192 indexed samples were then pooled with equal amounts.

PCR products were size-selected and purified using the VAHTS DNA Clean Beads (Vazyme). The 0.8× and 1.3× sample volumes of DNA Clean Beads were used to remove most of the short and long fragments, respectively. The fragment sizes obtained via this method were ~300–900 bp, and the fragment size with the highest proportion was 500 bp. Final library quality (concentration and fragment size distribution) was determined using Qubit 4.0 (Qubit® dsDNA BR Assay Kit) and Qsep100, respectively. Sequencing was performed within 48 h after DNA cyclization and rolling circle amplification. All libraries were sequenced in one or two lanes of the MGISEQ-2000 to generate 2 × 100 bp paired-end reads (Supplementary Fig. 19a).

Sequencing and quality control

This study involved two types of sequencing libraries, namely typical whole-genome sequencing libraries (WGS, high coverage for F₀ and 75 F₁₆ samples, http://farmrefpanel.com/GCRP/#/, Supplementary Data 1) and Tn5-based low coverage sequencing libraries (LCS). For LCS, 4772 qualified libraries were sequenced using MGISEQ-2000 to generate 2 × 100 bp paired-end reads. The sequencing experiments were performed on the MGISEQ-2000 system at the National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing) and State Key Laboratory of Animal Biotech Breeding. BCL files as primary sequencing output were converted into FASTQ files using bcl2fastq2 conversion software (version 2.16.0). The sequencing adapter was masked and trimmed during the conversion process. After the trimming step, the PE reads were subjected to a filtering process: SLIDINGWINDOW:4:15 and MINLEN:75 by Trimmomatic-0.36. The average percentage of the clean reads was 95%. The quality control check report of the filtered reads was generated using FastQC software (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Next, we used a custom script to split each sample index and obtained 4772 samples. Of these, samples with a sequencing depth less than 0.1× of the chicken genome (1.06 Gb) and duplicate samples (IBS > 0.9) were removed. The remaining 4671 samples were retained for subsequent analysis.

Mapping and variant and variant calling

WGS data SNP calling

We analyzed the high-depth sequencing data of 31 F₀ founders, F₁₆ samples, and other local chickens and commercial chickens using GTX (Supplementary Data 15). A commercially available FPGA-based hardware accelerator platform was used to map reads to the GRCg6a reference genome (Ensembl version 104) and variant calling. The alignment process was accelerated through the FPGA implementation of a parallel seed-and-extend approach based on the Smith-Waterman algorithm, whereas the variant calling process was accelerated via the FPGA implementation of GATK HaplotypeCaller (PairHMM)⁵¹. GATK multi-sample best practice⁵² was used to call and genotype SNPs for these samples, and the SNPs were hard filtered with a relatively strict option “QD < 2.0||MQ < 40.0||FS > 60.0||SOR > 3.0||MQRankSum < −12.5||ReadPosRankSum < −8.0||QUAL < 30.”

LCS data SNP calling

For LCS data, the clean reads were mapped to the GRCg6a reference genome (Ensembl version 104) using BWA⁵³. This was followed by read group addition, read pair sorting, marking duplicate reads, and building bam index by the Picard tools (http://broadinstitute.github.io/picard, v2.20.4). The indel realignment and base quality recalibration modules in GATK⁵¹ were applied to realign the reads around indel candidate loci and to recalibrate the base quality. The above steps are implemented in GTX-align, which is an FPGA-based hardware accelerator system (1–2 min/sample from clean FASTQ to BAM) on the high-performance computing platform of the State Key Laboratory of Agrobiotechnology. Variant calling was performed using the BaseVar and hard filtered with EAF ≥ 0.01 and the depth ≥ 1.5 times the interquartile range. The detailed BaseVar algorithm used to call SNP variants and to estimate allele frequency has been previously described⁵⁴. We used STITCH⁵⁵ (v1.1.2) to impute genotype probabilities for all individuals. The key parameter K (number of ancestral haplotypes) was set to 15. Raw SNPs were filtered with an imputation info score > 0.4, call rate > 0.95, and minor allele frequency (MAF) > 0.01. After filtering, 8,050,756 SNPs remained (Supplementary Fig. 19b). We utilized the SNPs from 108 samples, each of which simultaneously had both WGS and LCS data. We converted them to the 0/1/2 format and calculated the concordance between WGS and LCS data, defined as genotypic concordance; a higher concordance indicated a higher imputation accuracy of our LCS data. The SNPEff program⁵⁶ was used to annotate variants.

RNA-seq library construction and sequencing data analysis

The total RNA of the duodenum and hypothalamus were extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s recommendations. To eliminate DNA contamination, all samples were treated with RNase-free DNase. The concentration and purity of the RNA samples were determined with a Nano Photometer spectrophotometer (Implen, CA, USA), and the RNA integrity number (RIN) of each RNA sample was determined using Agilent 4150 RNA (Agilent Technologies, CA, USA). cDNA synthesis was performed using a PrimerScript™ RT Reagent Kit (Takara, Dalian, China) according to the manufacturer’s instructions. Briefly, 1 μg of total RNA was mixed with DNA Eraser, and the mixture was incubated at 42 °C for 2 min on an EasyCycler® 96-well thermocycler (Analytik, Jena, Germany). Subsequently, oligo-d(T) primer, random hexamers, and dNTPs were added, and the mixture was incubated in a thermocycler at 37 °C for 15 min and 85 °C for 5 s.

The concentration and integrity of the were measured and checked for concentration and integrity using Nano Photometer spectrophotometer (IMPLEN, CA, USA) and an Agilent Bioanalyzer 4150 system (Agilent Technologies, CA, USA). The library was constructed using Hieff NGS Ultima Dual-mode mRNA Library Prep Kit for MGI (Yeasen Biotechnology, Shanghai, China) according to the manufacturer’s protocol. After library construction, the library was converted using the MGIEasy Universal DNA Library Preparation Reagent Kit (BGI, Shenzhen, China) for compatibility and sequenced on the DNBSE-QT7 platform (MGI). For quality control, we used fastp⁵⁷ default parameters for filtering (v0.22.0). We aligned the clean reads to the GRCg6a reference genome (Ensembl version 104) using hisat2⁵⁸ (v2.2.1) with parameters of “--dta --new-summary -t -p 48 -x $index -1 $fq1 -2 $fq2.” Featurecounts.R of Rsubread (v2.12.0) was used for quantification, and edgeR (v3.40.0) was used for differential analysis.

Population structure analysis

ADMIXTURE⁵⁹ (v1.3.0) was used for estimating an individual’s ancestry proportions from F₀ and F₁₅ genotype data (the F₁₅ individuals were the paternal progenitors of all F₁₆ samples). PCA was performed using Plink⁶⁰ (v1.90) on the parental HB (n = 15), HQLA (n = 16) and 100 random samples from each of F₂, F₉, and F₁₆. Vcftools⁶¹ (v0.1.17) was used to calculate allele frequency with the parameter “--freq2,” and nucleotide diversity analysis with the parameter “--window-pi 300000.” We used Plink⁶⁰ (v1.90) with the parameters “--het” to calculate the inbreeding coefficient. We used PopLDdecay⁶² (v3.41) with parameters of “-MAF 0.01 -MaxDis” to perform LD decay analysis on different generations and use Plot_MultiPop.pl “-bin1 50 -bin2 1500 -break 500 -maxX 2000 -keepR” to draw. For haplotype diversity analysis, we used Plink⁶⁰ (v1.90) with parameters of “--indep-pairwise 1000 kb 1 0.4” to filter LD on 4671 samples, resulting in 18,325 SNPs. We used five SNPs as windows to perform windowing and calculated each window haplotype type and the main effect haplotype frequency (the sum of the top four main effect haplotype frequencies).

GWAS and fine mapping

The heritability of the 75 phenotypes was calculated using a mixed linear model of GCTA⁶³ (v1.93.3) with parameters “gcta64 --grm test --pheno pheno.txt --reml --out test”, corrected for batch and sex as covariates. We used GCTA⁶³ (v1.93.3) with parameter “--reml-bivar” to calculate the genetic correlation between two phenotypes.

We simultaneously conducted GWAS on 75, 18, and 19 phenotypes of F₁₆, F_2, and F₉, respectively (Supplementary Data 2, 6 and 14). First, we used GCTA⁶³ (v1.93.3) with parameters “--make-grm” and “--grm-sparse” to construct the kinship and sparse matrices. Subsequently, we used a mixed linear model of fastGWA⁶⁴ to perform GWAS.

$${{{\rm{y}}}}={{{{\rm{x}}}}}_{{{{\rm{snp}}}}}{{{{\rm{\beta }}}}}_{{{{\rm{snp}}}}}+{{{{\rm{X}}}}}_{{{{\rm{c}}}}}{{{{\rm{\beta }}}}}_{{{{\rm{c}}}}}+{{{\rm{g}}}}+{{{\rm{e}}}}$$

(1)

where y is an n × 1 vector of phenotypes; x_snp is a vector of mean-centered genotype values for the variant of interest, with its effect size β_snp; X_c is the incidence matrix of fixed covariates (sex and batch) with their corresponding coefficients β_c; g is a vector of the total genetic effects captured by pedigree relatedness; e is a vector of residuals.

For the 75 and 19 phenotypes of the F₁₆ and F₉ generations, respectively, we first used the false discovery rate (FDR) for correction and selected SNPs with FDR < 0.05 to determine the initial QTL. SNPs with a distance greater than 500 kb were considered to belong to 2 different QTL. For the 18 phenotypes of F₂, owing to the high LD between SNPs, SimpleM.R was first used to determine the number of independent SNPs; further, the initial QTL was determined based on Bonferroni 0.05 (p < 1.26e-7). SNPs with a distance greater than 500 kb were considered to belong to two different QTLs. Next, fine mapping was performed based on the LD r² < 0.6 of the SNPs in each initial QTL and the top SNP of the QTL.

The heritability of the QTL was calculated using the GCTA⁶³ bivariate GRM model with parameters “gcta64 --reml-bivar --mgrm multi_grm.txt --pheno test.phen”. We assessed the sharing of our detected QTLs with reported QTLs using the QTLdb database (https://www.animalgenome.org/cgi-bin/QTLdb/GG/index, Retrieved 28 December 2023), and if the QTLs of the two collections had overlapped, we considered the two to be shared.

We used Bedtools⁶⁵ with “bedtools intersect” to annotate all QTLs for the 43 F₁₆ phenotypes. For the QTL where a single gene was identified, we queried its gene function in mice according to the https://genealacart.genecards.org/ database and determined its relationship with growth and their development-related genes and corresponding QTLs.

Integrating eQTL and FAANG with GWAS

We integrated the eQTL data of 28 tissues from the chicken GTEx project and the FAANG annotation of 23 tissues from the chicken FAANG project with 40 F₁₆ growth- and development-related phenotypes with significant GWAS signals to further identify functional genes (Supplementary Table 3). We utilized the GCTA⁶³ bivariate GRM model for the calculation of heritability for each functionally annotated region and molQTL:

$${{{\rm{y}}}}={{{\rm{X}}}}{{{\rm{\beta }}}}+{{{\rm{g}}}}1+{{{\rm{g}}}}2+{{{\rm{e}}}}$$

(2)

where y is the phenotype, Xβ is the fixed effect (sex, batch); g1 is the genetic effect of functional annotation regions or molQTL; g2 is the genetic effect of all SNPs upstream and downstream of the functional region beyond 100 kb; e is a vector of residuals.

For the heritability obtained by “gcta64 --reml-bivar --mgrm multi_grm.txt --pheno test.phen --out test”, considering the different number of SNPs in each interval, we corrected for the number of SNPs and performed a log transformation.

For the growth and development-related phenotypes of the F₁₆ generation that exhibited significant GWAS signals, we utilized all SNPs as a background for calculating enrichment and used a hypergeometric distribution test to calculate P values.

Enrichment factor = (target region significant SNPs/total significant SNPs)/(target region all SNPs/genome-wide total SNPs).

To assess whether eQTLs were significantly enriched among the notable GWAS variants, we used QTLEnrich (v2)¹⁰ to measure the enrichment degree between significant eQTLs and GWAS loci.

We performed single- and multi-tissue transcriptome-wide association studies (TWAS) using SPrediXcan⁶⁶ and SMultiXcan⁶⁷, which are parts of the MetaXcan (v0.6.11) framework. Briefly, we trained Nested Cross-Validated Elastic Net models with protein-coding genes and their corresponding SNPs within a 1 Mb cis-window across all 28 tissues. Predictive models with cross-validated correlation ρ > 0.1 and prediction performance p < 0.05 were selected for further analysis. Using the SPrediXcan tool and the trained models, we predicted gene-trait associations at the single-tissue level, resulting in single-tissue TWAS outcomes. Subsequently, we employed the SMultiXcan tool to integrate the single-tissue predictions and obtained multiple-tissue TWAS results. After applying multiple testing corrections using the FDR approach, gene-trait associations with corrected p < 0.05 were deemed significant.

To investigate the pleiotropic association between molecular phenotypes and complex traits, we performed a Mendelian Randomization analysis using the SMR software⁶⁸ (v1.3.1). This software enables the utilization of summary-level data from GWAS and eQTL studies. To configure the SMR software appropriately, the molQTL data generated by tensorQTL⁶⁹ in this study was first converted into the BESD format using the options “--fastqtl-nominal-format --make-besd.” We then conducted the SMR test and applied multiple testing correction using the FDR approach. Gene-trait pairs with corrected p value < 0.05 were selected and considered significant.

To identify shared genetic variants between GWAS and eQTL, we conducted a colocalization analysis using fastENLOC⁷⁰ (v2.0). First, we fine-mapped the putative causal variants for each eGene using a Bayesian multi-SNP genetic association analysis algorithm known as the deterministic approximation of posteriors (DAP), with the current version being DAP-G⁷¹ (v1.0.0). Using the DAP-G results, we generated a probabilistic annotation of molQTL using the “summarize_dap2enloc.pl” script. Next, we calculated approximate LD blocks with PLINK⁶⁰ v1.9, using the options: “--blocks no-pheno-req --blocks-max-kb 1000 --make-founders”. The posterior inclusion probability (PIP) for GWAS loci was determined for each LD block using TORUS, with the options: “--load_zval -dump_pip”. By integrating GWAS PIP values, we performed the final colocalization analysis using the fastENLOC⁷⁰ tool and obtained the gene variant-level colocalization probability (GRCP). GRCP > 0.1 was set as the significance threshold.

Identification of the major effect Q of GGA1 using outgroup data analysis

For the QTL of BW8 (GGA1:170,522,957–171,337,377), we sliding windowed the QTL according to 20 kb and obtained a total of 34 windows, screened the loci with p < 1 × 10⁻¹⁶ in each window, calculated the frequency of each haplotype, and performed haplotype association analysis.

A haplotype-based association analysis was performed in each window using the model:

$${{{\rm{Y}}}}={{{\rm{X}}}}{{{\rm{\beta }}}}+{{{\rm{Zu}}}}+{{{\rm{e}}}}$$

(3)

where Y is a column vector representing the BW8 of the F₁₆ individuals, and X is the design matrix that includes coding for chickens’ sex. For each specific interval, n haplotypes were constructed from m individuals based on several SNPs. Z is the design matrix (m × n) that contains the haplotype counts for each individual, coded as 0, 1, or 2. β is a vector estimating the fixed effect of sex, u is a column vector estimating the allele substitution effects for each haplotype, and e represents the normally distributed residual.

Similarly, we used low-body-weight chickens (HB, CH (Chahua), DWS (Daweishan), SY (Silkies), ZJ (Tibet)) and high-body-weight chickens (HQLA, CBA (commercial Broiler Line A), CBB (commercial Broiler Line B), KB (Cobb), YXB (White Plymouth Rock) and Red Junglefowl (RJF)) from GCRP (http://farmrefpanel.com/GCRP/#/, Supplementary Data 15) to calculate the frequencies of the corresponding haplotypes. Combined with the haplotype association analysis effect values, we determined q/Q; the frequency of high-body-weight chickens minus the low-body-weight chickens of the haplotype was greater than 0.2, and the haplotype was Q for increasing body weight and vice versa for the q haplotype. Combined with the annotations of the candidate functional genes in each window, a total of one q and four Q were identified.

Cross-species analysis

To evaluate the enrichment of genes linked to human traits and diseases in relation to five chicken growth traits (the functional genes identified exceeded 60), we collected GWAS summary statistics for 20 human complex traits from the UK Biobank and Yanglab (Supplementary Data 12 and 13). We mapped the functional genes in chickens to their corresponding human orthologous genes, including 1-1 orthologous, complex orthologous genes, based on the Ensembl database (v102). Subsequently, we conducted LD score regression analysis⁷² (https://github.com/bulik/ldsc). Heritability enrichment was determined by calculating the proportion of trait heritability attributed to SNPs within the specific annotation relative to the total number of SNPs in that annotation. Additionally, we investigated the impact of homologous human genes, identified from five chicken growth traits, on significant human phenotypes using the PheWAS database (https://atlas.ctglab.nl). Chicken and human SNP conversions were achieved using the UCSC liftover tool (https://genome.ucsc.edu/cgi-bin/hgLiftOver), and SNP annotations were obtained using SNPEff⁵⁶.

Dual-luciferase reporter assay

For the candidate SNPs identified through GGA4 and GGA24 (Supplementary Table 4), we constructed a promoter vector (pGL3-basic luciferase reporter vector) and an enhancer vector (pGL3-enhancer luciferase reporter vector) to verify the transcriptional activity of the candidate SNPs on functional genes. The target sequence was centered on the candidate SNPs and extended 500 bp up and down. It was synthesized and cloned into the promoter or enhancer vector. DF-1 cells (chicken fibroblast cell line) were cultured in a Dulbecco’s modified Eagle medium (Gibco, USA) supplemented with 10% fetal bovine serum (Gibco, USA), 100 IU/mL penicillin, and 100 μg/mL streptomycin (Gibco, USA). Lipofectamine 3000 reagent (Invitrogen, USA) was used for transient transfection following the manufacturer’s protocols. The recombinant plasmid was transfected into the DF-1 cells together with the PRL-TK plasmid (Promega, USA). The DF-1 cells were then cultured in 24-well culture plates (Thermo Scientific, USA) at 37 °C and 5% CO₂ for 48 h. Firefly and Renilla luciferase activities were measured at 48 h post-transfection using a Dual-Luciferase Assay System Kit (Promega, USA) according to the manufacturer’s instructions. Luminescence was detected using a microplate reader (Tecan, Switzerland), and firefly luciferase activities were normalized to Renilla luminescence in each well.

Real-time (RT) PCR experimental verification

Small intestine qRT-PCR was performed by using the SYBR Green Master Mix (Takara, Dalian, China) on Applied BiosystemsTM7300 qRT-PCR system (ABI, CA, USA) according to the manufacturer’s protocol. The data were analyzed by using the 2^−ΔΔCT method. The chicken β-actin gene was used as the reference gene for normalization. The primers used for qRT-PCR are listed in Supplementary Table 5.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The DNA and RNA sequencing data generated in this study have been deposited in the NCBI BioProject database with accession numbers PRJNA1197814 and PRJNA1196443. Other breed WGS data are from http://farmrefpanel.com/GCRP/#/. Chicken GTEx data are available at http://chicken.farmgtex.org. FAANG data are available at https://farm.cse.ucdavis.edu/~zhypan/Chicken_FAANG/. GWAS summary data are available on Zenodo (https://zenodo.org/records/15370428)⁷³. Source data are provided with this paper.

Code availability

The processing codes are publicly available on GitHub (https://github.com/xiaoningZhu0506/AIL_GWAS) and Zenodo (https://zenodo.org/records/15370428)⁷³.

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Acknowledgements

We thank Ning Li, Jian Ji, Xu Liu, Xian Zou, Wei Luo, Chungang Feng, Zheya Sheng, Zhenhui Li, Bolin Cai, Wen Luo, Xiaoli Guo, Jianbo Li, Jingyi He, Xinchun Xu, Hanyu Wu, Chengcheng Zhao, Jiaping Li, Di Lian, Yuzhan Wang, Ruifei Yang, Di Zhu, Xuemin Cao, Haitao Wang, Zhuolin Huang, Ming Li, Juan Wang, Lu Zhang, Jing Tian, Xiaorong Gu, Ying Guo, Jinxiu Li and Dezhi Peng for their valuable contributions during sample collection. We also thank Huaijun Zhou, Zhangyuan Pan, and Dailu Guan for their help in analyzing the data. The analysis was performed on the high-performance computing platform of the State Key Laboratory of Agrobiotechnology and the National Research Facility for Phenotypic and Genotypic Analysis of Model Animals. This study was funded by the Biological Breeding-National Science and Technology Major Project (Grant No. 2023ZD0406805 to X.X.H.), the National Natural Science Foundation of China (Grant No. 32272862 to Y.Z.W.), the National Key R&D Program of China (Grant No. 2024YFF1001500 to Y.Z.W.), the Open project of State Key Laboratory of Animal Biotech Breeding (Grant No. 2025SKLAB6-21 to Y.Z.W.) and the 2115 Talent Development Program of China Agricultural University (Grant to X.X.H.).

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These authors contributed equally: Xiaoning Zhu, Chong Li.

Authors and Affiliations

State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, China
Xiaoning Zhu, Chong Li, Jiangli Ren, Ran Song, Yuzhe Wang & Xiaoxiang Hu
State Key Laboratory of Swine and Poultry Breeding Industry, Guangzhou, China
Xiaoning Zhu, Dingming Shu & Hao Qu
Center for Quantitative Genetics and Genomics (QGG), Aarhus University, Aarhus, Denmark
Xiaoning Zhu, Zhonghao Bai & Lingzhao Fang
Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
Chenglong Luo, Dingming Shu, Peng Chen & Hao Qu
National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, Beijing, China
Jiangli Ren, Ran Song, Yuzhe Wang & Xiaoxiang Hu
Frontiers Science Center for Molecular Design Breeding (MOE), China Agricultural University, Beijing, China
Yuzhe Wang & Xiaoxiang Hu

Authors

Xiaoning Zhu
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Chong Li
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Chenglong Luo
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Zhonghao Bai
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Dingming Shu
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Peng Chen
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Jiangli Ren
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Ran Song
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Lingzhao Fang
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Hao Qu
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Yuzhe Wang
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Xiaoxiang Hu
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Contributions

Y.Z.W., X.X.H., and H.Q. conceived and designed the study. X.N.Z. conducted GWAS, fine mapping, and colocalization analysis. C.L. carried out experimental verification. Z.H.B., J.L.R., and R.S. helped generate and analyze the data. C.L.L., D.M.S., and P.C. assisted in sample collection. X.N.Z. and Y.Z.W. wrote the initial manuscript. Y.Z.W., X.X.H., and L.Z.F. were responsible for manuscript revision. All authors read and approved the final version of the manuscript.

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Correspondence to Hao Qu or Yuzhe Wang.

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Zhu, X., Li, C., Luo, C. et al. Mapping the regulatory genetic landscape of complex traits using a chicken advanced intercross line. Nat Commun 16, 5841 (2025). https://doi.org/10.1038/s41467-025-60834-x

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Received: 21 January 2025
Accepted: 04 June 2025
Published: 01 July 2025
Version of record: 01 July 2025
DOI: https://doi.org/10.1038/s41467-025-60834-x

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