Genome-Wide Association Study for Small Intestine Length in Qiandongnan Xiaoxiang Chickens

Genome-Wide Association Study for Small Intestine Length in Qiandongnan Xiaoxiang Chickens | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-Wide Association Study for Small Intestine Length in Qiandongnan Xiaoxiang Chickens Tao Xiong, Xiaoling Yang, Haiqun Meng, Hongfa Zhang, Yongxian Yang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9213239/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Small intestine length (SIL) is closely associated with nutrient absorption efficiency in farm animals. To elucidate the genetic basis of SIL in chickens, we measured SIL in 194 Qiandongnan Xiaoxiang chickens at 70 days of age. Using whole-genome resequencing data, we performed a genome-wide association study (GWAS) to identify key genes and pathways underlying SIL variation. Our results showed that SIL exhibited moderate heritability ( h ² = 0.41). Furthermore, correlation analysis revealed a highly significant positive correlation between SIL and 70-day body weight as well as average daily gain (ADG) ( P < 0.01). Using both FarmCPU and GLM models, GWAS detected 11 significant SNPs or InDel associated with SIL ( P < 5×10⁻⁶). These loci, which were distributed on chromosomes 1, 4, and 8, explained 0.13%-0.14% of the phenotypic variance in SIL. Gene annotation further indicated that these loci were located within three genes: IL1RAPL1 , PBX1 , and USP35 . Collectively, this study preliminarily dissects the genetic architecture of SIL in chickens and provides valuable candidate genes for improving growth performance through targeted molecular breeding. Qiandongnan Xiaoxiang chicken Small intestine length Genome-wide association study Candidate gene Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Qiandongnan Xiaoxiang chicken is an indigenous breed distributed in Qiandongnan Prefecture, Guizhou Province, China, which is famous for its tender meat and distinctive flavor. Nevertheless, the slow growth rate and long reproductive cycle of this breed have severely limited its industrial production and large-scale popularization [ 1 ]. As a key site for nutrient absorption in the digestive tract [ 2 ], small intestinal length (SIL) plays a critical role in regulating growth performance in farm animals [ 3 ]. Variations in SIL lead to differences in intestinal absorptive surface area, digestive enzyme activities, and intestinal microbiota composition, which in turn influence growth performance and feed conversion efficiency in poultry [ 4 – 6 ]. Although the relationship between SIL and growth performance is complex, a positive association between longer small intestine and higher weight gain has been widely reported in previous studies [ 7 – 10 ]. Therefore, deciphering the genetic basis underlying the SIL trait is of great theoretical and practical importance for developing marker-assisted selection programs to breed chickens with favorable SIL phenotypes, thereby enhancing growth rate and feed utilization efficiency in chickens. Previous studies have identified several genomic regions and candidate genes associated with SIL in livestock and poultry via GWAS. In pigs, multiple quantitative trait loci (QTLs) and candidate genes (e.g., SOX6 , APOA4 , SIDT2 , TAGLN , and TMPRSS13 ) have been found to be associated with SIL, and this trait has been shown to be significantly correlated with body weight [ 9 , 11 ]. In chicken studies, specific QTLs influencing different intestinal segments (particularly on chromosomes GGA11 and GGA14) and SNPs have also been identified, among which genes such as GNB1L and G0S2 are regarded as closely associated with SIL [ 12 – 14 ]. Studies using the candidate gene approach have further confirmed that genes such as INS are associated with intestinal length [ 15 ]. Beyond genetic factors, other factors have also been found to significantly affect the SIL of poultry, such as gender, species, and nutrition. In general, the intestinal length of female ducks is longer than that of male ducks [ 16 ]. Fast-growing broilers have longer intestinal tracts than laying hens or wild ducks [ 8 , 10 ]. In addition, nutritional intervention effectively regulates intestinal development. For instance, previous studies have shown that inactivated Bacillus subtilis (a probiotic), metabolites of Lactobacillus plantarum (a postbiotic), and their combinations consistently promote intestinal elongation and improve growth performance in chickens [ 17 , 18 ]. As a vital organ for nutrient absorption and a critical determinant of growth performance in chickens, the small intestine remains poorly characterized in terms of its genetic basis. Consequently, the genetic architecture underlying SIL in chickens is still largely unexplored. To fill this knowledge gap, we performed a genome-wide association study in a total of 194 Qiandongnan Xiaoxiang chickens to identify genetic variants associated with SIL. Our results are expected to supply candidate genes for marker-assisted selection, thereby facilitating the genetic improvement of growth traits in indigenous chicken breeds. Materials and Methods 2.1 Experimental Animals and Phenotypes The experimental animals used in this study were Qiandongnan Xiaoxiang chickens, which were reared in the Animal Breeding Room of the College of Animal Science, Guizhou University, from April to September 2024. A total of 194 chickens were raised, including 108 roosters and 86 hens, with consistent incubation conditions, feed, feeding methods, and management practices. The specific rearing regime was as follows: chickens were housed in the same room from 1 to 4 weeks of age and transferred to another room from 5 to 10 weeks of age. The stocking density was 16 birds per cage for 0–4 weeks of age and 8 birds per cage for 5–10 weeks of age, with no separation by sex. The daily light duration was 16 hours, and the ambient temperature ranged from 15°C to 35°C. Chickens were fed commercial compound feed (purchased from Guiyang Hengchen Feed Co., Ltd.) at 9:00 AM and 5:00 PM daily. In brief, broiler starter diet 510 was provided from 1 to 5 weeks of age; a transitional diet consisting of a mixture of 510 and 511 feeds was provided during the 6th week; and broiler grower diet 511 was administered from 7 to 10 weeks of age to ensure consistent feeding standards across all individuals. Feed and water were available ad libitum throughout the entire experimental period. All chickens were raised until 10 weeks of age. Prior to slaughter, birds were subjected to an 8-hour fasting period. After fasting, each chicken was individually weighed to record pre-slaughter live body weight, and blood samples were collected from the wing vein for subsequent DNA extraction. Birds were humanely euthanized by cervical dislocation, followed by immediate dissection. Cervical dislocation induces immediate unconsciousness and insensibility to pain, making it a rapid, humane method for poultry euthanasia that complies with national and institutional ethical standards for experimental animal sacrifice. The entire small intestine was identified and completely excised from the abdominal cavity, extending from the duodenal origin (post-pyloric region of the stomach) to the ileocecal junction (ileocecal ligament). The intestinal lumen was gently squeezed longitudinally and rinsed with tepid physiological saline to thoroughly remove residual chyme, mucus, and fecal contents, with care taken not to damage the intestinal wall. The emptied and gently relaxed small intestine was then laid flat on a clean surface. Total length measurement was conducted using a soft ruler, with the starting point at the origin of the duodenal bulb (adjacent to the pyloric sphincter) and the endpoint at the junction between the ileal terminus and the cecum (ileocecal ligament). The SIL was measured accurately and recorded. 2.2 Genome-Wide Association Study 2.2.1 Extraction of Genomic DNA All samples used for DNA extraction were derived from anticoagulated blood collected from the wing vein of 10-week-old experimental chickens. The experimental procedure was as follows: Approximately 2 mL of blood was collected using a disposable syringe and stored at − 80°C until further use. A total of 200µL of anticoagulated whole blood was used for DNA extraction with a universal genomic DNA isolation kit. The extraction protocol was performed strictly according to the instructions of the manufacturer: 15µL of Proteinase K solution, 200µL of lysis buffer, and 20µL of RNase A solution (10 mg/mL) were added sequentially, followed by vortexing to ensure thorough mixing. The DNA concentration was determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). DNA samples that met quality requirements were stored at 4°C for subsequent experiments. 2.2.2 DNA Quality Control Concentration and purity of the genomic DNA were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Samples with an OD₂₆₀/OD₂₈₀ ratio of 1.8–2.0 and OD₂₆₀/OD₂₃₀ ratio ≥ 2.0 were selected for whole-genome resequencing 2.2.3 Sequencing Data Preprocessing Sequencing libraries were constructed using the TruSeq Nano DNA Library Prep Kit (Illumina, San Diego, CA, USA). Briefly, approximately 2µg of genomic DNA from each individual was randomly sheared into short fragments and purified. The 3' ends of the fragmented DNA were repaired, an A-tail was added, and the fragments were ligated with paired-end Y-shaped adapters. PCR enrichment was then performed for library amplification, and the concentration was quantified using a Qubit 4 Fluorometer. Paired-end high-throughput sequencing was conducted on the Illumina NovaSeq Xplus™ platform, with an average whole-genome sequencing depth of approximately 10×. Clean reads from all individuals were aligned to the Gallus gallus reference genome using BWA-MEME software. SNPs were called from the aligned sequence data following the Best Practices pipeline implemented in GATK software [ 19 ]. A total of 21.18 million SNPs were identified in the chicken population, with an average genotype call rate of 95.35%. Functional annotation of the identified SNPs was performed using SNPEff software [ 20 ] combined with gene annotation information from the reference genome, generating complete and systematic functional annotation results. 2.2.4 Heritability Estimation Prior to genome-wide association study (GWAS), a genomic relationship matrix (GRM) was constructed for the studied chicken population using Genome-wide Complex Trait Analysis (GCTA) software. Based on the preprocessed phenotypic data for small intestine length, heritability estimates were obtained using the available genotypic data and the constructed genomic relationship matrix. Specifically, the restricted maximum likelihood (REML) module implemented in GCTA was used to estimate the heritability of small intestine length. 2.2.5 Genome-wide association study Three representative statistical models were employed to perform genome-wide association analysis, namely the general linear model (GLM), mixed linear model (MLM), and Fixed and Random Model Circulating Probability Unification (FarmCPU) model. Each model was used to account for confounding factors such as population stratification and genetic relatedness at different levels, thereby improving the reliability and robustness of association results. (1) Association analysis based on the general linear model (GLM) was conducted by incorporating population structure information, including the Q-matrix and principal components (PCs), as covariates. y = Xα + Zβ + e where y is the vector of phenotypic values; X is the incidence matrix for fixed effects, and α is the vector of fixed effect estimates; Z is the incidence matrix for SNPs, and β is the vector of SNP effects; e is the vector of random residuals assumed to follow a normal distribution e ∼ (0, δ e 2 ​). (2) The mixed linear model (MLM) was applied by treating population genetic structure as a fixed effect and individual genetic relatedness as a random effect, thereby correcting for confounding caused by population stratification and kinship. y = Xα + Zβ + Wµ + e In contrast to the GLM, the MLM introduces an additional random effect term with a design matrix W, where µ represents the vector of random individual genetic effects. (3) The FarmCPU method was used to improve computational efficiency by transforming the kinship matrix (random effect) into a set of associated SNP matrices (S matrix / QTN matrix) fitted as fixed effects. This model effectively reduces both false-positive and false-negative rates in association mapping. 2.2.6 Population Stratification Analysis Population stratification may induce false-positive associations in GWAS and distort analytical results. To evaluate the potential influence of population stratification in the present study, quantile‑quantile (Q‑Q) plots were constructed to examine the stratification level of the target trait. The degree of stratification was determined by comparing deviations between the observed SNP association distribution and the expected null distribution for non‑associated loci. All Q‑Q plot analyses and visualizations were performed using the R software environment. 2.2.7 Candidate Region Gene Screening and GO/KEGG Functional Enrichment Analysis Following the identification of trait-associated SNPs or InDel, 100 kb flanking regions centered on these significant loci were extracted as candidate genomic regions for the target trait. Genes located within these regions were regarded as potential candidate genes underlying phenotypic variation. Gene Ontology (GO) enrichment analysis was performed to categorize candidate genes into three functional groups: cellular component, molecular function, and biological process. Furthermore, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted to explore coordinated biological functions among these genes, as genes often act in concert to exert biological effects. Pathway-based annotation enabled further in-depth interpretation of the biological functions of candidate genes. Results 3.1 Experimental Animals and Phenotype of Small Intestinal Length Statistical analysis revealed that the average SIL of the chickens in this study was 92.38 cm, with a coefficient of variation (CV) of 11.8% and a variation range of 46.1-118.2cm (Table 1 ). The phenotypic variation range and coefficient of variation collectively demonstrated that the small intestine length trait exhibited abundant genetic variation among chickens of the same age. A skewness value of -0.54 and a kurtosis value of 1.83 indicated that the data followed an approximately normal distribution, which is a typical characteristic of quantitative traits (Fig. 1 A). Heritability estimation for SIL yielded a value of 0.41, indicating that this trait possessed moderate heritability. Correlation analysis revealed a significant positive correlation between 70-day body weight and intestinal length ( r = 0.41, P = 3.8e-9, Fig. 1 B). Likewise, average daily gain (ADG) from hatching to 70 days of age was significantly positively correlated with SIL ( r = 0.40, P = 5.3e-9, Fig. 1 C). These results suggest that small intestinal length is associated with growth rate in chickens. Table 1 Phenotypic distribution and genetic parameters of small intestine length (SIL) in Qiandongnan Xiaoxiang chickens Indices Parameters Mean ± SD (cm) 92.38 ± 10.97 Range (cm) 46.1-118.2 CV (%) 11.88 Skewness -0.54 Kurtosis 1.83 Heritability ( h 2 ) 0.41 3.2 Population Stratification Evaluation and GWAS for Small Intestine Length Three representative statistical models, namely GLM, MLM, and FarmCPU, were employed to conduct GWAS in the present study. Visualization analysis of Q-Q plots based on these models demonstrated high consistency between theoretical and observed P-value distributions across all individuals. The population stratification inflation factors (λ values) were 0.976 (FarmCPU), 0.976 (GLM), and 0.981 (MLM) (Fig. 2 D, 2 E, 2 F), respectively, all of which were markedly lower than the critical threshold for population stratification (λ > 1.05). These findings indicated an absence of significant population stratification in the study population, thereby ensuring the reliability and robustness of the GWAS results. In the GWAS analysis, a total of 11 significant SNPs exceeding the genome-wide significance threshold ( P < 5×10⁻⁶) were identified using the FarmCPU and GLM models. These SNPs showed obvious clustering across chromosomes: 4 on chromosome 1 (chr1), 5 on chromosome 4 (chr4), and 2 on chromosome 8 (chr8) (Fig. 2 A-B). Notably, no genome-wide significant variants were detected under the MLM model (Fig. 2 C), which may be attributed to the overly conservative correction of population structure and polygenic effects in this model. 3.3 Identification of SNPs and Candidate Genes for Small Intestine Length Data analysis demonstrated that the 4 significant SNPs or InDel identified on chr1 exhibited negative genetic effects on chicken SIL (Table 2 ), among which rs735185319 (chr1) showed the strongest negative regulatory effect on SIL. In contrast, the significant SNPs on chr4 and chr8 displayed positive genetic effects, with rs80639551 on chr4 exerting the most pronounced positive regulatory effect on SIL. Specifically, the 5 significant SNPs on chr4 and the 2 on chr8 contributed 0.13%-0.14% of the phenotypic variance for SIL, suggesting that these genetic variants play key roles in the genetic architecture of SIL. Furthermore, three candidate genes, namely IL1RPAP1 , USP35 , and PBX1 , were annotated within the 100-kb genomic regions flanking the 11 significant SNPs. Table 2 Summary of SNP loci significantly associated with small intestine length in Qiandongnan Xiaoxiang chickens. Chrom Pos (bp) SNP or InDel Alleles MAF Effect SE P-value Genes PVE chr1 116495592 rs31729699 T/A 0.062 -10.355 1.95 3.04E-07 IL1RPAPL1 — chr1 117484439 rs735185319 A/G — -13.927 2.57 1.87E-07 — — chr1 165085462 rs14911716 A/T 0.170 -6.732 1.30 6.21E-07 — — chr1 192690841 chr1: 192690841 GGTT/G — -10.421 2.02 6.61E-07 USP35 — chr4 59680883 rs739376375 A/C 0.273 5.314 1.03 6.58E-07 — 0.134 chr4 59688062 rs731324443 C/T 0.175 6.924 1.29 2.25E-07 — 0.146 chr4 59688137 rs80608829 C/T 0.170 6.979 1.32 3.34E-07 — 0.142 chr4 59688142 rs736580889 T/C 0.170 6.979 1.32 3.34E-07 — 0.142 chr4 59688161 rs80639551 A/T 0.162 7.116 1.33 2.51E-07 — 0.145 chr8 5265620 rs14636789 G/A 0.278 5.586 1.07 5.09E-07 PBX1 0.136 chr8 5265720 rs1059441214 C/A 0.198 6.240 1.18 3.29E-07 PBX1 0.142 3.4 Identification of Favorable Genotypes in Key Genes for Small Intestine Length Genotype analysis was performed on the four key SNPs or InDel (rs31729699, rs14636789, rs1059441214 and chr1:192690841) annotated to the corresponding genes (Table 3 ). The results showed that individuals with the AA genotype at the rs31729699 locus in the IL1RAPL1 gene had significantly longer SIL than those with the AC genotype (Fig. 3 A). For the variant locus in the USP35 , individuals carrying the GG genotype exhibited significantly longer SIL compared to those with the GGTT genotype (Fig. 3 B). Regarding the two variant loci (rs14636789 and rs1059441214) in the PBX1 gene, individuals with the GG genotype at rs14636789 had longer SIL than those with the AA genotype, and individuals with the CC genotype at rs1059441214 had longer SIL than those with the AA genotype (Fig. 3 C-D). Table 3 SNP Genotypes of Genes Associated with Small Intestine Length Chrom Pos (bp) SNP or InDel Gene Genotype Allele Frequency SIL (cm) 116495592 rs31729699 IL1RPAPL1 AA 88.66% 93.58 ± 10.07 chr1 AT 10.31% 85.15 ± 10.81 TT 1.03% 61.35 ± 21.57 192690841 chr1: 192690841 USP35 GG 85.57% 93.62 ± 9.83 chr1 G/GGTT 13.92% 84.26 ± 13.95 GGTT/GGTT 0.52% 106.6 5265620 rs14636789 PBX1 AA 53.09% 89.72 ± 11.11 chr8 AG 38.66% 94.24 ± 10.1 GG 8.25% 100.59 ± 8.2 5265720 rs1059441214 PBX1 AA 66.49% 89.96 ± 11.08 chr8 AC 26.80% 96.5 ± 9.32 CC 6.70% 100 ± 7.57 3.5 GO and KEGG Functional Enrichment Analysis To explore the biological functions of candidate genes and their potential regulatory roles in the formation mechanism of chicken SIL phenotype, as well as their associations with other genes, GO and KEGG enrichment analyses were performed on the candidate genes annotated within the 100-kb flanking regions of the significant SNPs or InDel. The results of GO functional enrichment showed that the main molecular functions (MF) of these candidate genes included interleukin-1 receptor activity and transcriptional corepressor binding, which may be involved in regulating intestinal tissue development and cell differentiation to affect SIL. The involved biological processes (BP) primarily encompassed the regulation of mitotic cell cycle G2/M phase transition, positive regulation of cell cycle G2/M phase transition, and adrenal gland development (Fig. 4 B). The pathways enriched in KEGG analysis included cortisol synthesis and secretion, Cushing's syndrome, and transcriptional mis-regulation in cancer (Fig. 4 A). Notably, cortisol synthesis and secretion pathway is closely associated with the regulation of nutrient absorption and intestinal tissue development, which may indirectly regulate the formation of SIL phenotype by affecting intestinal mucosal function and cell growth. Discussion The small intestine is a vital organ responsible for nutrient absorption in chickens. Previous studies have consistently demonstrated that SIL is closely correlated with growth performance in chickens. Importantly, SIL exhibits moderate heritability, indicating that it is subject to regulation by host genetic factors. However, the genetic architecture underlying SIL variation in chickens remains largely elusive. Accordingly, the present study conducted a GWAS to identify key genes associated with SIL in a Chinese indigenous chicken breed. Our results revealed a significant positive correlation between SIL and body weight. Through combined GWAS and candidate gene analysis, we identified three key genes ( IL1RAPL1 , USP35 , and PBX1 ) that were significantly associated with individual variations in SIL. These findings suggest that SIL is a crucial determinant of body weight and highlight potential genetic factors regulating SIL. Collectively, this study advances our understanding of the key genes involved in intestinal development in chickens and provides a solid foundation for dissecting the host genetics-SIL-growth axis. In the present study, body weight at 70 days of age and ADG from 1 to 70 days of age were both significantly positively correlated with SIL. This observation indicates that intestinal development and intestinal length are key determinants driving postnatal growth performance in chickens, which is in line with the findings of Kimiaeitalab et al., who reported that broiler chickens with enhanced growth performance exhibit greater intestinal length compared to slow-growing breeds [ 21 ]. As the primary organ responsible for nutrient absorption, the small intestine is typically modeled as a cylinder with absorptive surface area calculated by the formula S = 2πrd [ 10 ]. Consistently, a longer small intestine provides a larger absorptive surface area, which represents one of the essential prerequisites for the efficient absorption of key nutrients, including carbohydrates, amino acids, and proteins [ 22 – 24 ]. Additionally, a longer small intestine is associated with prolonged digesta transit time [ 25 ]. Extended digesta transit time can improve nutrient digestion efficiency and modulate the secretion of satiety and hunger signals [ 26 , 27 ]. Such regulatory effects may enhance feed intake and energy assimilation [ 28 ] and consequently promote body weight gain. Conversely, a shortened small intestine or one suffering from pathological damage exhibits diminished effective absorptive surface area, resulting in malabsorption syndrome that subsequently compromises growth and development [ 29 ]. The heritability of SIL estimated in this study ( h² = 0.41) falls into the moderate range (0.2 < h² < 0.5), which is highly consistent with previous reports on the heritability of intestinal traits in poultry ( h² = 0.30–0.37) [ 30 , 31 ].This moderate heritability range has also been validated in other species; for instance, the estimated heritability of SIL in Yorkshire pigs is 0.25 [ 11 ]. Furthermore, different segments of the chicken small intestine (e.g., duodenum: h² = 0.36; jejunum: h² = 0.43; ileum: h² = 0.49)and their relative lengths ( h² = 0.28 ± 0.06) also generally exhibit moderate to high heritability [ 12 ]. Despite certain variations among breeds or species, the heritability of SIL and its respective segments is generally at or above the moderate level [ 32 ]. A moderate heritability indicates that although SIL is influenced by environmental factors [ 33 ], its basic length is still subject to considerable genetic regulation. This provides a solid theoretical foundation and feasibility for improving SIL trait through genetic selection [ 34 ]. Key genes governing SIL in chickens, namely IL1RAPL1 , USP35 , and PBX1 , were identified through GWAS analysis. Among these genes, IL1RAPL1 is a member of the Toll/IL-1 receptor family, while IL1RAPL1 is primarily associated with synaptogenesis and X-linked intellectual disability [ 35 ], as a member of the deubiquitinating enzyme family, USP35 is involved in multiple cancer-related processes [ 36 ]. To date, no studies have reported direct links between these two genes and chicken intestinal length development. Nevertheless, IL1RAPL1 has been associated with high‑body‑weight chicken lines [ 37 ]. This gene displays clear pleiotropic effects [ 38 ], and has been proposed as a candidate selection marker for enhancing feed efficiency [ 39 ]. Given that feed efficiency is tightly linked to appetite regulation mediated by the olfactory system, research in Holstein cattle has shown substantial overlap between feed efficiency‑related genes and olfactory genes [ 40 ]. Moreover, studies in zebrafish have established the critical role of IL1RAPL1 in presynaptic differentiation of olfactory sensory neurons [ 41 ]. Collectively, these observations suggest that IL1RAPL1 may modulate appetite and feeding behavior via the olfactory pathway, thereby enhancing feed utilization efficiency and consequently promoting body weight gain in chickens. PBX1 is a nuclear-localized protein and a member of the TALE homeodomain PBC transcription factor family [ 42 ]. Accordingly, PBX1 may serve as a crucial candidate gene underlying variation in chicken small intestine length (SIL). Existing evidence indicates that PBX1 can physically interact with Cdx-2, a core intestinal transcription factor, and markedly augment the transcriptional activation of the proglucagon gene promoter by Cdx-2 [ 43 ]. The downstream peptide product of this gene, glucagon-like peptide-2 (GLP-2), is a potent intestinal growth-promoting hormone [ 44 ] that drives intestinal development by enhancing crypt cell proliferation and suppressing cellular apoptosis [ 45 ]. Furthermore, KEGG enrichment analysis uncovered significant enrichment of the “cortisol synthesis and secretion” pathway linked to PBX1 . Notably, normal adrenal cortex development relies on the transcription factor PBX1 , the deficiency of which leads to multiple organ hypoplasia in mice [ 46 ]. Studies have reported a strong significant association between the PBX1 gene and body weight in Rwandan chickens [ 47 ]. As a pivotal transcriptional regulator, PBX1 broadly governs the development of nearly all organs and tissues during embryogenesis [ 48 ]. In addition, he homeodomain of PBX1 has been demonstrated to be indispensable for the proper activation of fibroblast growth factor (FGF) signaling [ 49 ], and FGF2 , a key member of the FGF family, exerts critical regulatory effects on chicken growth and development [ 50 ]. Although this study offers novel insights into the genetic architecture underlying SIL in chickens, it possesses several limitations. First, the GWAS population comprises only 194 individuals. Although this sample size is statistically sufficient for preliminary screening, its limited scale restricts the detection of minor-effect QTLs and may elevate the probability of false-negative results. Validation using a larger independent cohort is therefore required to verify the robustness of the significant SNPs and their contributions to phenotypic variance. Second, the biological functions of the identified candidate genes remain largely speculative. Further in vivo functional investigations, such as gene knockout mediated by gene-editing techniques in chicken embryos or cell lines, are essential to establish causal relationships between these genes and chicken intestinal development. Conclusion This study demonstrated a significant positive phenotypic correlation between SIL and body weight in chickens, with the estimated heritability of SIL being 0.41. In addition, a total of 11 significant SNP loci associated with SIL were detected, from which three critical candidate genes— IL1RAPL1 , PBX1 , and USP35 —were prioritized. Functional enrichment analysis further revealed that pathways related to cortisol synthesis and secretion, as well as IL‑1 receptor activity, serve as key regulatory modules underlying SIL variation. Collectively, these results advance our understanding of the genetic basis of intestinal development and offer valuable genetic resources for improving growth performance in chickens. Declarations ETHICS STATEMENT All animal works in this study were conducted in accordance with the guidelines for the care and use of experimental animals established by the Ministry and Rural Affairs of the People’s Republic of China. The Animal Care and Use Committee at Guizhou University (Guiyang, China) approved the project of this animal experiment (No.: EAE-GZU-2022-T050). CONSENT FOR PUBLICATION Not applicable. DATA AVAILABILITY The whole-genome resequencing data used and described in this study have been stored in the CNGB Sequence Database (CNSA) of the China National Gene Bank (CNGBdb) (https://db.cngb.org/), accession number CNP0008107. DECLARATION OF COMPETING INTEREST The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. FUNDING This work was supported by the National Natural Science Foundation of China (32260829, 32560876), Guizhou Provincial Science and Technology Project (QKH-ZC2022-key34)，and Guizhou Ecological Broiler Advantage Characteristic Industrial Cluster Project (Qiancainong [2024]80). CREDIT AUTHORSHIP CONTRIBUTION STATEMENT Tao Xiong: Investigation, Formal analysis, and Writing-review & editing, Writing-original draft; Xiaoling Yang: Formal analysis, visualization; Haiqun Meng: Investigation; Hongfa Zhang: Investigation; Yongxian Yang: Data Curation; Xiaoxia Long: Data Curation; Liqi Wang: Supervision; Zhong Wang: Writing-review & editing, Funding acquisition, and Conceptualization. ACKNOWLEDGEMENT This work was financially supported by the National Natural Science Foundation of China (32260829, 32560876). The authors wish to thank the College of Animal Science at Guizhou University for providing the experimental platform and technical support. We are also grateful to the editors and reviewers of BMC Genomics for their constructive comments and suggestions on this manuscript. References Wang L, Zhang F, Li H, Yang S, Chen X, Long S, Yang S, Yang Y, Wang Z. Metabolic and inflammatory linkage of the chicken cecal microbiome to growth performance. FRONT MICROBIOL. 2023;14:12. Spiller RC. Intestinal absorptive function. Gut. 1994;35(1 Suppl):S5–9. Jensen EA, Young JA, Kuhn J, Onusko M, Busken J, List EO, Kopchick JJ, Berryman DE. Growth hormone alters gross anatomy and morphology of the small and large intestines in age- and sex-dependent manners. PITUITARY. 2022;25(1):116–30. Akhoundzadeh MH, Mahdavi AH, Sedghi M, Shahsavan M. 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Poult SCI. 2023;102(11):103031. Xie XF, Wang ZY, Zhong ZQ, Pan DY, Hou GY, Xiao Q. Genome-wide scans for selection signatures in indigenous chickens reveal candidate genes associated with local adaptation. ANIMAL. 2024;18(5):101151. Chacko Kaitholil SR, Mooney MH, Aubry A, Rezwan F, Shirali M. Insights into the influence of diet and genetics on feed efficiency and meat production in sheep. ANIM GENET. 2024;55(1):20–46. Zhou Y, Connor EE, Wiggans GR, Lu Y, Tempelman RJ, Schroeder SG, Chen H, Liu GE. Genome-wide copy number variant analysis reveals variants associated with 10 diverse production traits in Holstein cattle. BMC Genomics. 2018;19:9. Yoshida T, Mishina M. Zebrafish orthologue of mental retardation protein IL1RAPL1 regulates presynaptic differentiation. MOL CELL NEUROSCI. 2008;39(2):218–28. Laurent A, Bihan R, Deschamps S, Guerrier D, Dupe V, Omilli F, Burel A, Pellerin I. Identification of a new type of PBX1 partner that contains zinc finger motifs and inhibits the binding of HOXA9-PBX1 to DNA. MECH DEVELOP. 2007;124(5):364–76. Liu T, Branch DR, Jin T. Pbx1 is a co-factor for Cdx-2 in regulating proglucagon gene expression in pancreatic A cells. MOL CELL ENDOCRINOL. 2006;249(1–2):140–9. Sandoval DA, D'Alessio DA. Physiology of proglucagon peptides: role of glucagon and GLP-1 in health and disease. PHYSIOL REV. 2015;95(2):513–48. Fesler Z, Mitova E, Brubaker PL. GLP-2, EGF, and the Intestinal Epithelial IGF-1 Receptor Interactions in the Regulation of Crypt Cell Proliferation. ENDOCRINOLOGY 2020, 161(4). Kim SK, Selleri L, Lee JS, Zhang AY, Gu X, Jacobs Y, Cleary ML. Pbx1 inactivation disrupts pancreas development and in Ipf1-deficient mice promotes diabetes mellitus. NAT GENET. 2002;30(4):430–5. Habimana R, Ngeno K, Okeno TO, Hirwa CDA, Keambou Tiambo C, Yao NK. Genome-Wide Association Study of Growth Performance and Immune Response to Newcastle Disease Virus of Indigenous Chicken in Rwanda. FRONT GENET. 2021;12:723980. Liu M, Xing Y, Tan J, Chen X, Xue Y, Qu L, Ma J, Jin X. Comprehensive summary: the role of PBX1 in development and cancers. FRONT CELL DEV BIOL. 2024;12:18. McWhirter JR, Goulding M, Weiner JA, Chun J, Murre C. A novel fibroblast growth factor gene expressed in the developing nervous system is a downstream target of the chimeric homeodomain oncoprotein E2A-Pbx1. DEVELOPMENT. 1997;124(17):3221–32. Xue Q, Zhang G, Li T, Ling J, Zhang X, Wang J. Transcriptomic profile of leg muscle during early growth in chicken. PLoS ONE. 2017;12(3):e0173824. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 14 May, 2026 Reviews received at journal 09 May, 2026 Reviewers agreed at journal 09 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Editor invited by journal 30 Mar, 2026 Submission checks completed at journal 28 Mar, 2026 First submitted to journal 28 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9213239","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623561925,"identity":"32a3d017-041e-4216-a9c8-30bed39db757","order_by":0,"name":"Tao Xiong","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Xiong","suffix":""},{"id":623561926,"identity":"ac507fb4-515c-4e95-a6ff-95d2d038663c","order_by":1,"name":"Xiaoling Yang","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoling","middleName":"","lastName":"Yang","suffix":""},{"id":623561927,"identity":"c301f5ca-398a-49b2-8737-cb1ff57c65c0","order_by":2,"name":"Haiqun Meng","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Haiqun","middleName":"","lastName":"Meng","suffix":""},{"id":623561928,"identity":"b20e2279-509d-4fc9-9a17-09fd372af9ed","order_by":3,"name":"Hongfa Zhang","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Hongfa","middleName":"","lastName":"Zhang","suffix":""},{"id":623561929,"identity":"4541317f-6e39-4fc2-a3de-01713a5b4c8c","order_by":4,"name":"Yongxian Yang","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Yongxian","middleName":"","lastName":"Yang","suffix":""},{"id":623561930,"identity":"cf1b367a-1fbf-4b8f-b60b-a6c307ba5714","order_by":5,"name":"Xiongxia Long","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiongxia","middleName":"","lastName":"Long","suffix":""},{"id":623561931,"identity":"750fd0c4-b50e-45bc-85cd-7c847a378543","order_by":6,"name":"Liqi Wang","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Liqi","middleName":"","lastName":"Wang","suffix":""},{"id":623561932,"identity":"a27c7876-ba8b-485e-91bc-7d97c6f8d98e","order_by":7,"name":"Zhong Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYNCCChsIzUO8ljNppGphbDtMghb5GcnPHn45c95u/owExgdv2xjkzQlaMCPN3Fim4nZy44wEZsO5bQyGOxsIaGGWTjCTljhzO5lZIoFNmreNIcHgAAEtbNLp36Ql284ls0kksP8mSguPdI6Z5Me2A3Y8QFuYidIiIf+mTJrhTHKCBM/DZsk55yQMNxDSIt9zfJvkjwo7e/n25IMf3pTZyBO0BQSYgdGR2MDA2ACylQj1QMD4g4HBnjilo2AUjIJRMCIBACE1OxklBTnrAAAAAElFTkSuQmCC","orcid":"","institution":"Guizhou University","correspondingAuthor":true,"prefix":"","firstName":"Zhong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-24 14:26:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9213239/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9213239/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107090020,"identity":"82db16d5-10ab-46c0-a07f-fb788598a3c5","added_by":"auto","created_at":"2026-04-16 15:43:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":177603,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic distribution and correlation analysis of small intestine length and body weight in Qiandongnan Xiaoxiang chickens. (A) Frequency distribution of small intestine length in 70-day-old chickens; (B)Correlation between small intestine length and body weight at 70 days of age; (C) Correlation between small intestine length and average daily gain from 1 to 70 days of age.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9213239/v1/cba69c113a2fcfc392bcba14.png"},{"id":107484327,"identity":"bf71953f-d9dc-4371-8d72-4950edfb9fd9","added_by":"auto","created_at":"2026-04-22 02:31:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":585677,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManhattan and Q-Q plots from GWAS for small intestine length in Qiandongnan Xiaoxiang chickens.\u003c/strong\u003e \u003cstrong\u003e(A-C)\u003c/strong\u003e Manhattan plots based on FarmCPU, GLM, and MLM models, respectively; \u003cstrong\u003e(D-F)\u003c/strong\u003eCorresponding Q-Q plots for each model.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9213239/v1/1704cf4aa342bc2b1d310a8b.png"},{"id":107090022,"identity":"4c99d239-fe18-4c80-a267-6e67804ee9e0","added_by":"auto","created_at":"2026-04-16 15:43:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":237691,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox plots of small intestine length (SIL) among different genotypes in key genes of Qiandongnan Xiaoxiang chickens.\u003c/strong\u003e (A) rs31729699 locus in \u003cem\u003eIL1RPAPL1\u003c/em\u003e; (B) Indel locus in \u003cem\u003eUSP35\u003c/em\u003e; (C) rs14636789 locus in \u003cem\u003ePBX1\u003c/em\u003e; (D) rs1059441214 locus in \u003cem\u003ePBX1\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9213239/v1/6eba134927c3cc2d57efba53.png"},{"id":107090023,"identity":"de76c702-14cd-488e-9b13-67864b0f24a3","added_by":"auto","created_at":"2026-04-16 15:43:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109667,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG enrichment plots for genes associated with small intestine length in chickens. (A) KEGG pathway enrichment; (B) GO functional enrichment.\u003c/p\u003e","description":"","filename":"floatimage41.png","url":"https://assets-eu.researchsquare.com/files/rs-9213239/v1/4805fbb9d6729d2ae4ba747c.png"},{"id":108005778,"identity":"3bb3ca10-43a6-429f-99f1-f924730966a4","added_by":"auto","created_at":"2026-04-28 12:48:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1482868,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9213239/v1/d80e3162-62d4-4aa1-8ae3-a72f7538428d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-Wide Association Study for Small Intestine Length in Qiandongnan Xiaoxiang Chickens","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eQiandongnan Xiaoxiang chicken is an indigenous breed distributed in Qiandongnan Prefecture, Guizhou Province, China, which is famous for its tender meat and distinctive flavor. Nevertheless, the slow growth rate and long reproductive cycle of this breed have severely limited its industrial production and large-scale popularization [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As a key site for nutrient absorption in the digestive tract [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], small intestinal length (SIL) plays a critical role in regulating growth performance in farm animals [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Variations in SIL lead to differences in intestinal absorptive surface area, digestive enzyme activities, and intestinal microbiota composition, which in turn influence growth performance and feed conversion efficiency in poultry [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although the relationship between SIL and growth performance is complex, a positive association between longer small intestine and higher weight gain has been widely reported in previous studies [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, deciphering the genetic basis underlying the SIL trait is of great theoretical and practical importance for developing marker-assisted selection programs to breed chickens with favorable SIL phenotypes, thereby enhancing growth rate and feed utilization efficiency in chickens.\u003c/p\u003e \u003cp\u003ePrevious studies have identified several genomic regions and candidate genes associated with SIL in livestock and poultry via GWAS. In pigs, multiple quantitative trait loci (QTLs) and candidate genes (e.g., \u003cem\u003eSOX6\u003c/em\u003e, \u003cem\u003eAPOA4\u003c/em\u003e, \u003cem\u003eSIDT2\u003c/em\u003e, \u003cem\u003eTAGLN\u003c/em\u003e, and \u003cem\u003eTMPRSS13\u003c/em\u003e) have been found to be associated with SIL, and this trait has been shown to be significantly correlated with body weight [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In chicken studies, specific QTLs influencing different intestinal segments (particularly on chromosomes GGA11 and GGA14) and SNPs have also been identified, among which genes such as GNB1L and G0S2 are regarded as closely associated with SIL [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Studies using the candidate gene approach have further confirmed that genes such as \u003cem\u003eINS\u003c/em\u003e are associated with intestinal length [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Beyond genetic factors, other factors have also been found to significantly affect the SIL of poultry, such as gender, species, and nutrition. In general, the intestinal length of female ducks is longer than that of male ducks [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Fast-growing broilers have longer intestinal tracts than laying hens or wild ducks [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, nutritional intervention effectively regulates intestinal development. For instance, previous studies have shown that inactivated Bacillus subtilis (a probiotic), metabolites of Lactobacillus plantarum (a postbiotic), and their combinations consistently promote intestinal elongation and improve growth performance in chickens [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs a vital organ for nutrient absorption and a critical determinant of growth performance in chickens, the small intestine remains poorly characterized in terms of its genetic basis. Consequently, the genetic architecture underlying SIL in chickens is still largely unexplored. To fill this knowledge gap, we performed a genome-wide association study in a total of 194 Qiandongnan Xiaoxiang chickens to identify genetic variants associated with SIL. Our results are expected to supply candidate genes for marker-assisted selection, thereby facilitating the genetic improvement of growth traits in indigenous chicken breeds.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental Animals and Phenotypes\u003c/h2\u003e \u003cp\u003eThe experimental animals used in this study were Qiandongnan Xiaoxiang chickens, which were reared in the Animal Breeding Room of the College of Animal Science, Guizhou University, from April to September 2024. A total of 194 chickens were raised, including 108 roosters and 86 hens, with consistent incubation conditions, feed, feeding methods, and management practices. The specific rearing regime was as follows: chickens were housed in the same room from 1 to 4 weeks of age and transferred to another room from 5 to 10 weeks of age. The stocking density was 16 birds per cage for 0\u0026ndash;4 weeks of age and 8 birds per cage for 5\u0026ndash;10 weeks of age, with no separation by sex. The daily light duration was 16 hours, and the ambient temperature ranged from 15\u0026deg;C to 35\u0026deg;C. Chickens were fed commercial compound feed (purchased from Guiyang Hengchen Feed Co., Ltd.) at 9:00 AM and 5:00 PM daily. In brief, broiler starter diet 510 was provided from 1 to 5 weeks of age; a transitional diet consisting of a mixture of 510 and 511 feeds was provided during the 6th week; and broiler grower diet 511 was administered from 7 to 10 weeks of age to ensure consistent feeding standards across all individuals. Feed and water were available ad libitum throughout the entire experimental period.\u003c/p\u003e \u003cp\u003eAll chickens were raised until 10 weeks of age. Prior to slaughter, birds were subjected to an 8-hour fasting period. After fasting, each chicken was individually weighed to record pre-slaughter live body weight, and blood samples were collected from the wing vein for subsequent DNA extraction. Birds were humanely euthanized by cervical dislocation, followed by immediate dissection. Cervical dislocation induces immediate unconsciousness and insensibility to pain, making it a rapid, humane method for poultry euthanasia that complies with national and institutional ethical standards for experimental animal sacrifice. The entire small intestine was identified and completely excised from the abdominal cavity, extending from the duodenal origin (post-pyloric region of the stomach) to the ileocecal junction (ileocecal ligament). The intestinal lumen was gently squeezed longitudinally and rinsed with tepid physiological saline to thoroughly remove residual chyme, mucus, and fecal contents, with care taken not to damage the intestinal wall. The emptied and gently relaxed small intestine was then laid flat on a clean surface. Total length measurement was conducted using a soft ruler, with the starting point at the origin of the duodenal bulb (adjacent to the pyloric sphincter) and the endpoint at the junction between the ileal terminus and the cecum (ileocecal ligament). The SIL was measured accurately and recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Genome-Wide Association Study\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Extraction of Genomic DNA\u003c/h2\u003e \u003cp\u003eAll samples used for DNA extraction were derived from anticoagulated blood collected from the wing vein of 10-week-old experimental chickens. The experimental procedure was as follows: Approximately 2 mL of blood was collected using a disposable syringe and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until further use. A total of 200\u0026micro;L of anticoagulated whole blood was used for DNA extraction with a universal genomic DNA isolation kit. The extraction protocol was performed strictly according to the instructions of the manufacturer: 15\u0026micro;L of Proteinase K solution, 200\u0026micro;L of lysis buffer, and 20\u0026micro;L of RNase A solution (10 mg/mL) were added sequentially, followed by vortexing to ensure thorough mixing. The DNA concentration was determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). DNA samples that met quality requirements were stored at 4\u0026deg;C for subsequent experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 DNA Quality Control\u003c/h2\u003e \u003cp\u003eConcentration and purity of the genomic DNA were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Samples with an OD₂₆₀/OD₂₈₀ ratio of 1.8\u0026ndash;2.0 and OD₂₆₀/OD₂₃₀ ratio\u0026thinsp;\u0026ge;\u0026thinsp;2.0 were selected for whole-genome resequencing\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Sequencing Data Preprocessing\u003c/h2\u003e \u003cp\u003eSequencing libraries were constructed using the TruSeq Nano DNA Library Prep Kit (Illumina, San Diego, CA, USA). Briefly, approximately 2\u0026micro;g of genomic DNA from each individual was randomly sheared into short fragments and purified. The 3' ends of the fragmented DNA were repaired, an A-tail was added, and the fragments were ligated with paired-end Y-shaped adapters. PCR enrichment was then performed for library amplification, and the concentration was quantified using a Qubit 4 Fluorometer. Paired-end high-throughput sequencing was conducted on the Illumina NovaSeq Xplus\u0026trade; platform, with an average whole-genome sequencing depth of approximately 10\u0026times;. Clean reads from all individuals were aligned to the Gallus gallus reference genome using BWA-MEME software. SNPs were called from the aligned sequence data following the Best Practices pipeline implemented in GATK software [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A total of 21.18\u0026nbsp;million SNPs were identified in the chicken population, with an average genotype call rate of 95.35%. Functional annotation of the identified SNPs was performed using SNPEff software [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] combined with gene annotation information from the reference genome, generating complete and systematic functional annotation results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Heritability Estimation\u003c/h2\u003e \u003cp\u003ePrior to genome-wide association study (GWAS), a genomic relationship matrix (GRM) was constructed for the studied chicken population using Genome-wide Complex Trait Analysis (GCTA) software. Based on the preprocessed phenotypic data for small intestine length, heritability estimates were obtained using the available genotypic data and the constructed genomic relationship matrix. Specifically, the restricted maximum likelihood (REML) module implemented in GCTA was used to estimate the heritability of small intestine length.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Genome-wide association study\u003c/h2\u003e \u003cp\u003eThree representative statistical models were employed to perform genome-wide association analysis, namely the general linear model (GLM), mixed linear model (MLM), and Fixed and Random Model Circulating Probability Unification (FarmCPU) model. Each model was used to account for confounding factors such as population stratification and genetic relatedness at different levels, thereby improving the reliability and robustness of association results.\u003c/p\u003e \u003cp\u003e(1) Association analysis based on the general linear model (GLM) was conducted by incorporating population structure information, including the Q-matrix and principal components (PCs), as covariates.\u003c/p\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;\u003cem\u003eXα\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eZβ\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003ee\u003c/em\u003e\u003c/p\u003e \u003cp\u003ewhere y is the vector of phenotypic values; X is the incidence matrix for fixed effects, and α is the vector of fixed effect estimates; Z is the incidence matrix for SNPs, and β is the vector of SNP effects; e is the vector of random residuals assumed to follow a normal distribution \u003cem\u003ee\u003c/em\u003e\u0026sim; (0, \u003cem\u003eδ\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e​).\u003c/p\u003e \u003cp\u003e(2) The mixed linear model (MLM) was applied by treating population genetic structure as a fixed effect and individual genetic relatedness as a random effect, thereby correcting for confounding caused by population stratification and kinship.\u003c/p\u003e \u003cp\u003ey =\u003cem\u003eXα\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eZβ\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eW\u0026micro;\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003ee\u003c/em\u003e\u003c/p\u003e \u003cp\u003eIn contrast to the GLM, the MLM introduces an additional random effect term with a design matrix W, where \u0026micro; represents the vector of random individual genetic effects.\u003c/p\u003e \u003cp\u003e(3) The FarmCPU method was used to improve computational efficiency by transforming the kinship matrix (random effect) into a set of associated SNP matrices (S matrix / QTN matrix) fitted as fixed effects. This model effectively reduces both false-positive and false-negative rates in association mapping.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6 Population Stratification Analysis\u003c/h2\u003e \u003cp\u003ePopulation stratification may induce false-positive associations in GWAS and distort analytical results. To evaluate the potential influence of population stratification in the present study, quantile‑quantile (Q‑Q) plots were constructed to examine the stratification level of the target trait. The degree of stratification was determined by comparing deviations between the observed SNP association distribution and the expected null distribution for non‑associated loci. All Q‑Q plot analyses and visualizations were performed using the R software environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.7 Candidate Region Gene Screening and GO/KEGG Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eFollowing the identification of trait-associated SNPs or InDel, 100 kb flanking regions centered on these significant loci were extracted as candidate genomic regions for the target trait. Genes located within these regions were regarded as potential candidate genes underlying phenotypic variation. Gene Ontology (GO) enrichment analysis was performed to categorize candidate genes into three functional groups: cellular component, molecular function, and biological process. Furthermore, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted to explore coordinated biological functions among these genes, as genes often act in concert to exert biological effects. Pathway-based annotation enabled further in-depth interpretation of the biological functions of candidate genes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Experimental Animals and Phenotype of Small Intestinal Length\u003c/h2\u003e \u003cp\u003eStatistical analysis revealed that the average SIL of the chickens in this study was 92.38 cm, with a coefficient of variation (CV) of 11.8% and a variation range of 46.1-118.2cm (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The phenotypic variation range and coefficient of variation collectively demonstrated that the small intestine length trait exhibited abundant genetic variation among chickens of the same age. A skewness value of -0.54 and a kurtosis value of 1.83 indicated that the data followed an approximately normal distribution, which is a typical characteristic of quantitative traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eHeritability estimation for SIL yielded a value of 0.41, indicating that this trait possessed moderate heritability. Correlation analysis revealed a significant positive correlation between 70-day body weight and intestinal length (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.8e-9, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Likewise, average daily gain (ADG) from hatching to 70 days of age was significantly positively correlated with SIL (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.3e-9, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). These results suggest that small intestinal length is associated with growth rate in chickens.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhenotypic distribution and genetic parameters of small intestine length (SIL) in Qiandongnan Xiaoxiang chickens\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.38\u0026thinsp;\u0026plusmn;\u0026thinsp;10.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.1-118.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeritability (\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Population Stratification Evaluation and GWAS for Small Intestine Length\u003c/h2\u003e \u003cp\u003eThree representative statistical models, namely GLM, MLM, and FarmCPU, were employed to conduct GWAS in the present study. Visualization analysis of Q-Q plots based on these models demonstrated high consistency between theoretical and observed P-value distributions across all individuals. The population stratification inflation factors (λ values) were 0.976 (FarmCPU), 0.976 (GLM), and 0.981 (MLM) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF), respectively, all of which were markedly lower than the critical threshold for population stratification (λ\u0026thinsp;\u0026gt;\u0026thinsp;1.05). These findings indicated an absence of significant population stratification in the study population, thereby ensuring the reliability and robustness of the GWAS results.\u003c/p\u003e \u003cp\u003eIn the GWAS analysis, a total of 11 significant SNPs exceeding the genome-wide significance threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10⁻⁶) were identified using the FarmCPU and GLM models. These SNPs showed obvious clustering across chromosomes: 4 on chromosome 1 (chr1), 5 on chromosome 4 (chr4), and 2 on chromosome 8 (chr8) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). Notably, no genome-wide significant variants were detected under the MLM model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), which may be attributed to the overly conservative correction of population structure and polygenic effects in this model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identification of SNPs and Candidate Genes for Small Intestine Length\u003c/h2\u003e \u003cp\u003eData analysis demonstrated that the 4 significant SNPs or InDel identified on chr1 exhibited negative genetic effects on chicken SIL (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), among which rs735185319 (chr1) showed the strongest negative regulatory effect on SIL. In contrast, the significant SNPs on chr4 and chr8 displayed positive genetic effects, with rs80639551 on chr4 exerting the most pronounced positive regulatory effect on SIL. Specifically, the 5 significant SNPs on chr4 and the 2 on chr8 contributed 0.13%-0.14% of the phenotypic variance for SIL, suggesting that these genetic variants play key roles in the genetic architecture of SIL. Furthermore, three candidate genes, namely \u003cem\u003eIL1RPAP1\u003c/em\u003e, \u003cem\u003eUSP35\u003c/em\u003e, and \u003cem\u003ePBX1\u003c/em\u003e, were annotated within the 100-kb genomic regions flanking the 11 significant SNPs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of SNP loci significantly associated with small intestine length in Qiandongnan Xiaoxiang chickens.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChrom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePos (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP or InDel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlleles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116495592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers31729699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-10.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.04E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eIL1RPAPL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117484439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers735185319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-13.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.87E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e165085462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers14911716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.21E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e192690841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr1: 192690841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGGTT/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-10.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.61E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eUSP35\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59680883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers739376375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.58E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59688062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers731324443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.25E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59688137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers80608829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.34E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59688142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers736580889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.34E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59688161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers80639551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.51E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5265620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers14636789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.09E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ePBX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5265720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1059441214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.29E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ePBX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Identification of Favorable Genotypes in Key Genes for Small Intestine Length\u003c/h2\u003e \u003cp\u003eGenotype analysis was performed on the four key SNPs or InDel (rs31729699, rs14636789, rs1059441214 and chr1:192690841) annotated to the corresponding genes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results showed that individuals with the AA genotype at the rs31729699 locus in the \u003cem\u003eIL1RAPL1\u003c/em\u003e gene had significantly longer SIL than those with the AC genotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). For the variant locus in the \u003cem\u003eUSP35\u003c/em\u003e, individuals carrying the GG genotype exhibited significantly longer SIL compared to those with the GGTT genotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Regarding the two variant loci (rs14636789 and rs1059441214) in the \u003cem\u003ePBX1\u003c/em\u003e gene, individuals with the GG genotype at rs14636789 had longer SIL than those with the AA genotype, and individuals with the CC genotype at rs1059441214 had longer SIL than those with the AA genotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSNP Genotypes of Genes Associated with Small Intestine Length\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChrom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePos (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP or InDel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAllele Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSIL (cm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e116495592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ers31729699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eIL1RPAPL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93.58\u0026thinsp;\u0026plusmn;\u0026thinsp;10.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e85.15\u0026thinsp;\u0026plusmn;\u0026thinsp;10.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.35\u0026thinsp;\u0026plusmn;\u0026thinsp;21.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e192690841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003echr1: 192690841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eUSP35\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG/GGTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84.26\u0026thinsp;\u0026plusmn;\u0026thinsp;13.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGGTT/GGTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e106.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5265620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ers14636789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ePBX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89.72\u0026thinsp;\u0026plusmn;\u0026thinsp;11.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94.24\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.59\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5265720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ers1059441214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ePBX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89.96\u0026thinsp;\u0026plusmn;\u0026thinsp;11.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u0026thinsp;\u0026plusmn;\u0026thinsp;7.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 GO and KEGG Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eTo explore the biological functions of candidate genes and their potential regulatory roles in the formation mechanism of chicken SIL phenotype, as well as their associations with other genes, GO and KEGG enrichment analyses were performed on the candidate genes annotated within the 100-kb flanking regions of the significant SNPs or InDel. The results of GO functional enrichment showed that the main molecular functions (MF) of these candidate genes included interleukin-1 receptor activity and transcriptional corepressor binding, which may be involved in regulating intestinal tissue development and cell differentiation to affect SIL. The involved biological processes (BP) primarily encompassed the regulation of mitotic cell cycle G2/M phase transition, positive regulation of cell cycle G2/M phase transition, and adrenal gland development (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The pathways enriched in KEGG analysis included cortisol synthesis and secretion, Cushing's syndrome, and transcriptional mis-regulation in cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Notably, cortisol synthesis and secretion pathway is closely associated with the regulation of nutrient absorption and intestinal tissue development, which may indirectly regulate the formation of SIL phenotype by affecting intestinal mucosal function and cell growth.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe small intestine is a vital organ responsible for nutrient absorption in chickens. Previous studies have consistently demonstrated that SIL is closely correlated with growth performance in chickens. Importantly, SIL exhibits moderate heritability, indicating that it is subject to regulation by host genetic factors. However, the genetic architecture underlying SIL variation in chickens remains largely elusive. Accordingly, the present study conducted a GWAS to identify key genes associated with SIL in a Chinese indigenous chicken breed. Our results revealed a significant positive correlation between SIL and body weight. Through combined GWAS and candidate gene analysis, we identified three key genes (\u003cem\u003eIL1RAPL1\u003c/em\u003e, \u003cem\u003eUSP35\u003c/em\u003e, and \u003cem\u003ePBX1\u003c/em\u003e) that were significantly associated with individual variations in SIL. These findings suggest that SIL is a crucial determinant of body weight and highlight potential genetic factors regulating SIL. Collectively, this study advances our understanding of the key genes involved in intestinal development in chickens and provides a solid foundation for dissecting the host genetics-SIL-growth axis.\u003c/p\u003e \u003cp\u003eIn the present study, body weight at 70 days of age and ADG from 1 to 70 days of age were both significantly positively correlated with SIL. This observation indicates that intestinal development and intestinal length are key determinants driving postnatal growth performance in chickens, which is in line with the findings of Kimiaeitalab et al., who reported that broiler chickens with enhanced growth performance exhibit greater intestinal length compared to slow-growing breeds [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As the primary organ responsible for nutrient absorption, the small intestine is typically modeled as a cylinder with absorptive surface area calculated by the formula S\u0026thinsp;=\u0026thinsp;2πrd [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consistently, a longer small intestine provides a larger absorptive surface area, which represents one of the essential prerequisites for the efficient absorption of key nutrients, including carbohydrates, amino acids, and proteins [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, a longer small intestine is associated with prolonged digesta transit time [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Extended digesta transit time can improve nutrient digestion efficiency and modulate the secretion of satiety and hunger signals [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Such regulatory effects may enhance feed intake and energy assimilation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and consequently promote body weight gain. Conversely, a shortened small intestine or one suffering from pathological damage exhibits diminished effective absorptive surface area, resulting in malabsorption syndrome that subsequently compromises growth and development [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe heritability of SIL estimated in this study (\u003cem\u003eh\u0026sup2;\u003c/em\u003e = 0.41) falls into the moderate range (0.2\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eh\u0026sup2;\u003c/em\u003e \u0026lt; 0.5), which is highly consistent with previous reports on the heritability of intestinal traits in poultry (\u003cem\u003eh\u0026sup2;\u003c/em\u003e = 0.30\u0026ndash;0.37) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].This moderate heritability range has also been validated in other species; for instance, the estimated heritability of SIL in Yorkshire pigs is 0.25 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, different segments of the chicken small intestine (e.g., duodenum: \u003cem\u003eh\u0026sup2;\u003c/em\u003e = 0.36; jejunum: \u003cem\u003eh\u0026sup2;\u003c/em\u003e = 0.43; ileum: \u003cem\u003eh\u0026sup2;\u003c/em\u003e = 0.49)and their relative lengths (\u003cem\u003eh\u0026sup2;\u003c/em\u003e = 0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06) also generally exhibit moderate to high heritability [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Despite certain variations among breeds or species, the heritability of SIL and its respective segments is generally at or above the moderate level [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A moderate heritability indicates that although SIL is influenced by environmental factors [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], its basic length is still subject to considerable genetic regulation. This provides a solid theoretical foundation and feasibility for improving SIL trait through genetic selection [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKey genes governing SIL in chickens, namely \u003cem\u003eIL1RAPL1\u003c/em\u003e, \u003cem\u003eUSP35\u003c/em\u003e, and \u003cem\u003ePBX1\u003c/em\u003e, were identified through GWAS analysis. Among these genes, \u003cem\u003eIL1RAPL1\u003c/em\u003e is a member of the Toll/IL-1 receptor family, while \u003cem\u003eIL1RAPL1\u003c/em\u003e is primarily associated with synaptogenesis and X-linked intellectual disability [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], as a member of the deubiquitinating enzyme family, \u003cem\u003eUSP35\u003c/em\u003e is involved in multiple cancer-related processes [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. To date, no studies have reported direct links between these two genes and chicken intestinal length development. Nevertheless, \u003cem\u003eIL1RAPL1\u003c/em\u003e has been associated with high‑body‑weight chicken lines [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This gene displays clear pleiotropic effects [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and has been proposed as a candidate selection marker for enhancing feed efficiency [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Given that feed efficiency is tightly linked to appetite regulation mediated by the olfactory system, research in Holstein cattle has shown substantial overlap between feed efficiency‑related genes and olfactory genes [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Moreover, studies in zebrafish have established the critical role of \u003cem\u003eIL1RAPL1\u003c/em\u003e in presynaptic differentiation of olfactory sensory neurons [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Collectively, these observations suggest that \u003cem\u003eIL1RAPL1\u003c/em\u003e may modulate appetite and feeding behavior via the olfactory pathway, thereby enhancing feed utilization efficiency and consequently promoting body weight gain in chickens.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePBX1\u003c/em\u003e is a nuclear-localized protein and a member of the TALE homeodomain PBC transcription factor family [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Accordingly, \u003cem\u003ePBX1\u003c/em\u003e may serve as a crucial candidate gene underlying variation in chicken small intestine length (SIL). Existing evidence indicates that PBX1 can physically interact with Cdx-2, a core intestinal transcription factor, and markedly augment the transcriptional activation of the proglucagon gene promoter by Cdx-2 [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The downstream peptide product of this gene, glucagon-like peptide-2 (GLP-2), is a potent intestinal growth-promoting hormone [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] that drives intestinal development by enhancing crypt cell proliferation and suppressing cellular apoptosis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Furthermore, KEGG enrichment analysis uncovered significant enrichment of the \u0026ldquo;cortisol synthesis and secretion\u0026rdquo; pathway linked to \u003cem\u003ePBX1\u003c/em\u003e. Notably, normal adrenal cortex development relies on the transcription factor \u003cem\u003ePBX1\u003c/em\u003e, the deficiency of which leads to multiple organ hypoplasia in mice [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Studies have reported a strong significant association between the \u003cem\u003ePBX1\u003c/em\u003e gene and body weight in Rwandan chickens [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. As a pivotal transcriptional regulator, PBX1 broadly governs the development of nearly all organs and tissues during embryogenesis [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In addition, he homeodomain of PBX1 has been demonstrated to be indispensable for the proper activation of fibroblast growth factor (FGF) signaling [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], and \u003cem\u003eFGF2\u003c/em\u003e, a key member of the FGF family, exerts critical regulatory effects on chicken growth and development [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough this study offers novel insights into the genetic architecture underlying SIL in chickens, it possesses several limitations. First, the GWAS population comprises only 194 individuals. Although this sample size is statistically sufficient for preliminary screening, its limited scale restricts the detection of minor-effect QTLs and may elevate the probability of false-negative results. Validation using a larger independent cohort is therefore required to verify the robustness of the significant SNPs and their contributions to phenotypic variance. Second, the biological functions of the identified candidate genes remain largely speculative. Further in vivo functional investigations, such as gene knockout mediated by gene-editing techniques in chicken embryos or cell lines, are essential to establish causal relationships between these genes and chicken intestinal development.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated a significant positive phenotypic correlation between SIL and body weight in chickens, with the estimated heritability of SIL being 0.41. In addition, a total of 11 significant SNP loci associated with SIL were detected, from which three critical candidate genes\u0026mdash;\u003cem\u003eIL1RAPL1\u003c/em\u003e, \u003cem\u003ePBX1\u003c/em\u003e, and \u003cem\u003eUSP35\u003c/em\u003e\u0026mdash;were prioritized. Functional enrichment analysis further revealed that pathways related to cortisol synthesis and secretion, as well as IL‑1 receptor activity, serve as key regulatory modules underlying SIL variation. Collectively, these results advance our understanding of the genetic basis of intestinal development and offer valuable genetic resources for improving growth performance in chickens.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal works in this study were conducted in accordance with the guidelines for the care and use of experimental animals established by the Ministry and Rural Affairs of the\u0026nbsp;People\u0026rsquo;s Republic of China. The Animal Care and Use Committee at Guizhou University (Guiyang, China) approved the project of this animal experiment (No.: EAE-GZU-2022-T050).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe whole-genome resequencing data used and described in this study have been stored in the CNGB Sequence Database (CNSA) of the China National Gene Bank (CNGBdb) (https://db.cngb.org/), accession number CNP0008107.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECLARATION OF COMPETING INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (32260829, 32560876), Guizhou Provincial Science and Technology Project (QKH-ZC2022-key34)，and Guizhou Ecological Broiler Advantage Characteristic Industrial Cluster Project (Qiancainong [2024]80).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCREDIT AUTHORSHIP CONTRIBUTION STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTao Xiong: Investigation, Formal analysis, and Writing-review \u0026amp; editing, Writing-original draft; Xiaoling Yang: Formal analysis, visualization; Haiqun Meng: Investigation; Hongfa Zhang: Investigation; Yongxian Yang: Data Curation; Xiaoxia Long: Data Curation; Liqi Wang: Supervision; Zhong Wang: Writing-review \u0026amp; editing, Funding acquisition, and Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the National Natural Science Foundation of China (32260829, 32560876). The authors wish to thank the College of Animal Science at Guizhou University for providing the experimental platform and technical support. We are also grateful to the editors and reviewers of BMC Genomics for their constructive comments and suggestions on this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang L, Zhang F, Li H, Yang S, Chen X, Long S, Yang S, Yang Y, Wang Z. Metabolic and inflammatory linkage of the chicken cecal microbiome to growth performance. FRONT MICROBIOL. 2023;14:12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpiller RC. Intestinal absorptive function. Gut. 1994;35(1 Suppl):S5\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJensen EA, Young JA, Kuhn J, Onusko M, Busken J, List EO, Kopchick JJ, Berryman DE. Growth hormone alters gross anatomy and morphology of the small and large intestines in age- and sex-dependent manners. 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PLoS ONE. 2017;12(3):e0173824.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Qiandongnan Xiaoxiang chicken, Small intestine length, Genome-wide association study, Candidate gene","lastPublishedDoi":"10.21203/rs.3.rs-9213239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9213239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmall intestine length (SIL) is closely associated with nutrient absorption efficiency in farm animals. To elucidate the genetic basis of SIL in chickens, we measured SIL in 194 Qiandongnan Xiaoxiang chickens at 70 days of age. Using whole-genome resequencing data, we performed a genome-wide association study (GWAS) to identify key genes and pathways underlying SIL variation. Our results showed that SIL exhibited moderate heritability (\u003cem\u003eh\u003c/em\u003e\u0026sup2; = 0.41). Furthermore, correlation analysis revealed a highly significant positive correlation between SIL and 70-day body weight as well as average daily gain (ADG) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Using both FarmCPU and GLM models, GWAS detected 11 significant SNPs or InDel associated with SIL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10⁻⁶). These loci, which were distributed on chromosomes 1, 4, and 8, explained 0.13%-0.14% of the phenotypic variance in SIL. Gene annotation further indicated that these loci were located within three genes: \u003cem\u003eIL1RAPL1\u003c/em\u003e, \u003cem\u003ePBX1\u003c/em\u003e, and \u003cem\u003eUSP35\u003c/em\u003e. Collectively, this study preliminarily dissects the genetic architecture of SIL in chickens and provides valuable candidate genes for improving growth performance through targeted molecular breeding.\u003c/p\u003e","manuscriptTitle":"Genome-Wide Association Study for Small Intestine Length in Qiandongnan Xiaoxiang Chickens","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 15:42:55","doi":"10.21203/rs.3.rs-9213239/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-14T10:06:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-09T07:37:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205120336720757787378588178771413195382","date":"2026-05-09T05:08:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95675665882738695700380324731748133666","date":"2026-05-08T09:09:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89416800510446884877659190917598161941","date":"2026-05-07T10:17:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301841397132600913451006338577273564036","date":"2026-05-07T08:25:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T07:37:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331358187919411617933069112826015357742","date":"2026-04-16T02:09:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8329329813205015626777616554030124122","date":"2026-04-13T16:12:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T05:52:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-09T05:51:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-30T10:45:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-28T09:00:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2026-03-28T08:51:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7c54f37a-8efc-4735-a88d-1d1a7a4461ef","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-14T10:06:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-09T07:37:45+00:00","index":97,"fulltext":""},{"type":"reviewerAgreed","content":"205120336720757787378588178771413195382","date":"2026-05-09T05:08:34+00:00","index":95,"fulltext":""},{"type":"reviewerAgreed","content":"95675665882738695700380324731748133666","date":"2026-05-08T09:09:07+00:00","index":94,"fulltext":""},{"type":"reviewerAgreed","content":"89416800510446884877659190917598161941","date":"2026-05-07T10:17:06+00:00","index":92,"fulltext":""},{"type":"reviewerAgreed","content":"301841397132600913451006338577273564036","date":"2026-05-07T08:25:45+00:00","index":91,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T10:10:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 15:42:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9213239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9213239","identity":"rs-9213239","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}