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. 2024 Feb 1;103(4):103515. doi: 10.1016/j.psj.2024.103515

Identification of candidate genes affecting the tibia quality in Nonghua duck

Yinjuan Lu ⁎,†,‡, Bin Wei ⁎, Qinglan Yang ⁎,†, Xu Han ⁎,†,‡, Xinxin He ⁎,†,‡, Qiuyu Tao ⁎,†,‡, Shuaixue Jiang ⁎,†,‡, Mengru Xu ⁎,†,‡, Yuan Bai ⁎,†,‡, Tao Zhang ⁎,†,‡, Lili Bai ⁎,†, Jiwei Hu ⁎,†, Hehe Liu ⁎,†,‡, Liang Li ⁎,†,‡,1
PMCID: PMC10875613  PMID: 38350390

Abstract

The skeleton is a vital organ providing structural support in poultry. Weakness in bone structure can lead to deformities, osteoporosis, cage fatigue, and fractures, resulting in economic losses. Research has substantiated that genetic factors play a significant role in influencing bone quality. The discovery of genetic markers associated with bone quality holds paramount importance for enhancing genetic traits related to the skeletal system in poultry. This study analyzed nine phenotypic indicators of tibia quality in 120-day-old ducks. The phenotypic correlation revealed a high correlation among diameter, Perimeter, and weight (0.69–0.78), and a strong correlation was observed between toughness and breaking strength (0.62). Then, we conducted a genome-wide association analysis of the phenotypic indicators to elucidate the genetic basis of tibial quality in Nonghua ducks. Among the 11 candidate genes that were annotated, TAPT1, BST1, and STIM2 were related to the diameter indicator, ZNF652, IGF2BP1, CASK, and GREB1L were associated with the weight and toughness indicators. RFX8, GLP1R, and DNAAF5 were identified for ash, calcium, and phosphorus content, respectively. Finally, KEGG and GO analysis for annotated genes were performed. STIM2 and BST1 were enriched into the Calcium signalling pathway and Niacin and nicotinamide metabolic pathway, which may be key candidate genes affecting bone quality phenotypes. Gene expression analysis of the candidate genes, such as STIM2, BST1, TAPT1, and CASK showed higher expression levels in bones compared to other tissues. The obtained results can contribute to new insights into tibial quality and provide new genetic biomarkers that can be employed in duck breeding.

Key words: tibia, GWAS, candidate genes, duck

INTRODUCTION

Skeletons are the most rigid organs in vertebrates and serve various functions such as providing support and protection for the body, facilitating movement, and storing minerals (Suniaga et al., 2018). Poultry bones are primarily categorized into the skull, sternum, keel, humerus, and tibia. Among these, the tibia stands out as the bone with the most rapid growth throughout the entire body of ducks (Zhang et al., 2019). It also serves as one of the vital organs for duck mobility, body support, and protection. Studying tibia quality parameters holds significant reference value for assessing the overall health of ducks.

The decrease in bone quality led to an increased in fractures and mortality rates (Webster, 2004). Fractures, in turn, potentially puncture muscles and blood vessels, leading to meat contamination and degradation. The compromised meat quality stemming from fractures adversely affects the economic viability of poultry farming (Grajeta, 2003). To assess the risk of fracture in ducks, bone quality parameters such as breaking strength, toughness, calcium, and phosphorus content can be taken into consideration (Rodriguez-Navarro et al., 2018). Extensive studies have shown the significance of tibial quality in poultry and other vertebrates. Ferket et al. (Ferket and Sell, 1989) found that the incidence of tibial diseases in turkeys decreases when protein levels in the diet decrease. A study (el-Maraghi et al., 1965) reported that young mice are prone to osteoporosis when protein levels in the feed are high, and calcium content is low. Hejazi et al (Stránský and Ryšavá, 2009) stated that High protein intake can lead to calcium excretion and bone loss. In addition, calcium deficiency increases the risk of osteoporosis, According to statistics, Part of the deaths of poultry are caused by bone problems (Webster, 2004; Huang et al., 2017). Moreover, research also demonstrated that engaging in suitable exercise during the bone formation phase can stimulate healthy bone growth and enhance bone fracture resistance (Warden et al., 2007). However, the absence of exercise among caged poultry can readily result in osteoporosis (Fleming et al., 2006).

Genetic factors play a crucial role in bone quality traits, and studies on twins and their families have estimated the heritability of bone mineral density (BMD) to range from 0.46 to 0.92 (Mullin et al., 2016). In the field of livestock and poultry, the size of the tibia, both in terms of length and width, directly influences meat production performance. Interestingly, the heritability of chicken tibia length has been determined to be 0.59±0.04 (Karsenty and Wagner, 2002). Additionally, the fracture strength of bones is a significant indicator of bone health, and it is also related to the reproductive performance of livestock and poultry. The heritability of bone fracture strength is estimated to be 0.58 (Shim et al., 2012). Therefore, enhancing bone strength can serve as a long-term strategy to mitigate the issue of osteoporosis in livestock and poultry.

Modern poultry production focuses on breeding fast and large meat birds and high-yield eggs, but bone strength has not been used as a breeding indicator. As a result of multiple generations of genetic selection for traits such as high egg and meat yields, the bone quality of cultivated poultry varieties has been negatively affected. Extensive research has demonstrated that genetic factors primarily determine bone quality traits. In this regard, genome-wide association studies (GWAS) (Estrada et al., 2012; Zheng et al., 2015; Mullin et al., 2017) have been very successful in identifying genetic variants related to bone parameters.

The present study was conducted on 120-day-old Nonghua ducks, which are fast-growing broiler ducks, weighing up to about 3.2 kg at the 8 wk. Due to the fast growth and development, the tolerance of the tibia is required to be high. The quality of tibia was studied by determining the length, diameter, perimeter, weight, breaking strength, toughness, ash, calcium and phosphorus content of the tibia. Furthermore, the Genome-wide association study for these traits results was performed to find candidate genes related to bone quality. Combining the results of GWAS analysis, we aimed to identify key metabolic pathways that regulate bone homeostasis, providing a research basis for revealing key factors affecting bone homeostasis and expanding our approach to genetic improvement of bone quality traits.

MATERIALS AND METHODS

Experimental Animals

The Animal Ethics and Welfare Committee of Sichuan Agricultural University (DKY20170913) has reviewed and approved the following animal management and sampling plan. In total, 400 one-day-old ducklings (200 males and 200 females) were provided by the waterfowl breeding farm at Sichuan Agricultural University, Sichuan, China. The selected ducklings were raised and managed using the conventional duck breeding method. The ducklings were fed freely during brooding and ensured a continuous supply of light for 24 h. After 14 d of brooding, the ducklings were transferred to ground-level pens with the same temperature, ventilation, and access to ample water supply kept inside the shelter. Male and female mixed feeding and following routine immunization procedures. Nutrient levels of Nonghua duck in different periods (Table S1). Blood was collected from 358 experimental ducks at the age of 9 wk, and we randomly selected 250 Nonghua ducks (1/2 males and females) of both tibia were slaughtered and collected. The muscles were completely excised with a scalpel, and the muscles and connective tissues attached to the tibiae were carefully removed with a scalpel and used in subsequent experiments.

Tibial Phenotype Measurement

Measurement of Bone Basic Traits

After the experimental duck flock's neck was bled and slaughtered, the right leg tibia was collected, and muscles and connective tissue attached to the tibia were removed. The weight of the tibia was then measured using an electronic balance. The cleaned tibia was placed horizontally on the laboratory workbench, the length of the bone was measured using a ruler, the midpoint of the bone was marked, and the data was recorded. Cotton thread was wrapped 5 times around the midpoint of the bone, we then measured the length of the thread and divided it by 5 to obtain the bone Perimeter value. Furthermore, a vernier caliper was clamped to the midpoint of the tibia. The midpoint diameter was then measured in a unified direction at the same location, and the data was recorded (Fig. S1).

Measurement of Bone Mechanical Traits

All the tibias were placed in the same arrangement on the support platform of the texture analyzer, with the surface facing upwards, and the 3-point fracture method was used to detect the breaking strength and toughness of the left tibia. When measuring the fracture strength, the distance between the two support points of the tibia was 5 cm. The load was perpendicular to the bone and applied at a displacement rate of 2 mm/min to the midpoint of the bone until the bone broke. The data measured were converted into a load-displacement curve. The load force measured during bone breaking was the breaking strength, and the toughness was calculated based on the slope of the linear (elastic) part of the load-displacement curve.

Bone Composition Measurement

After measuring the breaking strength and toughness of the tibia of the experimental ducks, one side of the tibial tuberosity was boiled in boiling water for 8 min. The muscles and connective tissue attached to the tibia were removed, and the bones were broken using bone scissors. The bones were wrapped in filter paper and soaked in ether for 3 d for degreasing treatment. Afterward, they were taken out and dried in a 105℃ oven for 24 to 36 h until they reached constant weight. Then, the bones were thoroughly crushed with a pulverizer and calcinated in a muffle furnace to obtain samples measuring ash, calcium, and phosphorus content. The measurement method of ash, calcium, and phosphorus content followed Zhang Liying's method (Charuta et al., 2013). In this experiment, EDTA complexometric titration was used to measure calcium content, and the molybdenum yellow colorimetric method was used to measure phosphorus content.

DNA Extraction

Blood samples from 120-day-old Nonghua ducks were obtained from the primary vein of the wing and stored at −20℃ and DNA was extracted using the phenol-chloroform method.

Whole-Genome Resequencing

The quality and quantity of 250 DNA samples were examined using a NanoDrop2000 device and agarose gel electrophoresis. After the examinations, standard procedures were used to generate paired-end libraries for each eligible sample. The average insert size was about 500 bp, and the average read length was 150 bp. All libraries were sequenced on an Illumina HiSeq 2500 platform and stored in FASTQ format. The raw reads were filtered using the NGS QC (v2.3.3) Toolkit with default parameters. All genomic re-sequencing raw data files were submitted to the SRA database(Accession: PRJNA907492 & PRJNA907501).

Variant Discovery and Genotyping

The 150-bp paired-end cleaned reads were mapped to the reference genome (ZJU1.0) with Burrows-Wheeler alignment (BWA aln) using default parameters. We additionally performed local realignment using GATK to enhance the alignments in regions of InDel polymorphisms (McKenna et al., 2010). After mapping, SNP (Single nucleotide polymorphisms) calling was performed using GATK 3.5 exclusively, and the output was further filtered using VCFtools 0.1.15 (Danecek et al., 2011). SNPs were filtered based on the following criteria: 1) SNPs with minor allele frequency > 0.05 and a major allele frequency < 0.99; 2) the maximum missing rate was < 0.1; and 3) SNPs with only 2 alleles. Our team has reported this data on candidate genes for feather color in Nonghua ducks and male mallard ducks. (Ma et al., 2023; Wang et al., 2023).

Genome-wide association analysis

The GWAS an program EMMAX to analyze the phenotypic and genotypic values of bone mass (Kang et al., 2010). The first three principal component values derived from the whole-genome SNP data (PCA feature vectors) were designated as fixed effects to correct for population stratification within the mixed model. Additionally, there were significant differences between males and females in phenotypic data analysis (Table. S2), so gender was used as a fixed covariate in GWAS analysis. This analysis used SNP genotype results and body length phenotype data from all individuals, respectively. The linear model used for individual testing of each SNP is as follows:

y=Xα+Zβ+Wμ+e

In this equation, y represents a vector comprising observed phenotype trait values. Xα represents the fixed effect vector, which includes corrections for population stratification and fixed effects such as candidate SNPs as covariates. Zβ denotes the SNP effect vector, encompassing allelic substitution effects β. Wμ represents random animal effects, with the variance-covariance structure based on the kinship correlation matrix estimated from whole-genome SNP genotypes, e is a vector of random residuals. SNPs with P-values reaching the Bonferroni correction threshold (-log10P = 7) were considered significantly associated, and the analysis results are visualized using Manhattan and Q-Q plots.

LD Analysis

VCFtools was used for retrieving individual genotypes within regions of interest. LD analysis among the most significant SNPs in the candidate region (-Log10P > 7) was conducted using Plink. Gene locus zoom plots are generated using R 3.5.1. Haploview software analyzed the overall LD within the candidate region and the haplotypes of the four selected candidate SNPs.

Functional Annotation

The candidate genes were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using the online tools DAVID (https://david.ncifcrf) and KOBAS (http://kobas.cbi.pku.edu.cn/) software. In this study, genes meeting the selection criteria of log2(Fold) > 2 and P-value < 0.05 were used for analysis.

Candidate Gene Expression

We utilized transcriptome data from the tibia of Nonghua ducks at 12, 20, and 28 d to identify differences in gene expression at various developmental stages. The data source is consistent with previous experiments conducted by our team and is currently not publicly available. We compared the gene expression levels in the bones of 8-week-old ducks with those in different tissues, such as skin and liver. The bone data were obtained from the transcriptome of Nonghua ducks at the Ya'an Waterfowl Breeding Farm when they were 56 d old. Gene expression data for other tissues were acquired from DuckBase(www.duckbase.org/expression/). We analyzed the expression levels of the candidate genes and created a graph by using R packages.

Statistical Analysis

This study used the SPSS software (version 20.0, Windows, SPSS Inc., Chicago, IL) to test the normality of data distribution. Data that did not conform to a normal distribution were subjected to logarithmic or reciprocal transformations. Differences among groups were analyzed using t-tests and one-way analysis of variance (ANOVA). Sample size, means, standard deviations, coefficients of variation, and maximum, and minimum values of bone quality traits were calculated using Microsoft Excel 2019. Graphs were created using GraphPad Prism (version 8.0.2) and R (version 4.2.3).

RESULTS AND ANALYSIS

Descriptive Statistics of Tibial Basic Traits in Nonghua Duck

The descriptive statistical results for tibial bone quality are shown in Table 1. Notably, tibial toughness exhibits a relatively high coefficient of variation of 51.20%, indicating a substantial level of data dispersion. In contrast, the coefficients of variation for tibial length, diameter, and perimeter are within 10%, suggesting a higher level of data reliability.

Table 1.

Descriptive statistics for duck tibial quality.

Sample number Average value Std CV (%) Max Min
Length (cm) 250 11.09 0.45 4.07 12.4 10.00
Diameter (mm) 250 7.48 0.45 6.03 9.54 6.33
Perimeter (cm) 250 2.35 1.42 6.03 2.99 1.98
Weight (g) 250 8.78 1.20 13.62 11.90 5.70
Breaking strength (N) 238 36863.20 6436.75 17.46 56801.12 19305.50
Toughness 238 34384.80 17604.25 51.20 89411.59 8849.16
Ash content (%) 233 63.06 2.21 3.51 72.78 56.84
Calcium content (%) 233 23.16 1.72 7.45 28.99 17.60
Phosphorus content (%) 233 11.29 1.91 16.96 18.92 4.15
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Correlation Analysis of Tibial Quality Traits in Nonghua Ducks

We conducted a correlation analysis on nine tibial bone quality traits (Figure 1). We found that the length, diameter, Perimeter, weight, and Breaking strength of the tibia exhibit a positive correlation with toughness. Additionally, Breaking strength is positively correlated with ash content. There are varying degrees of positive correlations among the other bone quality traits. Notably, the relationship between tibial Breaking strength and calcium content was not statistically significant.

Figure 1.

Figure 1

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Correlation coefficients of bone quality traits. Diagonal: Frequency distribution histogram of the different phenotypes. The red line is the percentile curve. Above the diagonal: Spearman's correlation coefficients among the different phenotypes. * means P < 0.05, ** means P < 0.01, *** means P < 0.001. Below the diagonal: Scatter plots among the different phenotypes. The red line is the trendline.

GWAS Analysis

Tibial Basic Traits

GWAS analysis was conducted on four basic skeletal characteristics of the tibia, including length, diameter, perimeter, and weight. 41 significant SNPs were detected (to prevent false negative results, the Bonferroni value was adjusted to 7 in this study). In the tibial diameter trait, 38 significant SNPs on chromosome 4 were identified (Figure 2A). Candidate genes including RPGR, STIM2, TBC1D19, LGI2, LOC119716898, TAPT1, PROM1, FGFBP2, LOC119716803, BST1, FBXL5, CC2D2A, C1QTNF7, KIAA0232, CASP7, ZNF652, IGF2BP1, CASK, GREB1L, LOC101805103, RFX8 were annotated within the significant SNPs (Table S3). There were no candidate genes identified in the Tibia length (Figure S2A) and perimeter (Figure S2B) traits. The candidate genes, ZNF652 and IGF2BP1, were annotated within 3 significant SNPs, which were located on chromosome 28 of the tibial weight trait (Figure S2C) (Table S3). Notably, there was a significant signal peak on chromosome 4 for tibial diameter. Further exploration can be conducted using LocusZoom to uncover additional insights.

Figure 2.

Figure 2

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GWAS analysis for diameter. (A) Manhattan plot of whole-genome association for skeletal characteristics traits. (B) Regional maps of 57049361-57945531 and 60413901-61308661 regional loci related to diameter. (C) The candidate genes are contained in the candidate region.

Tibial Mechanical Traits

In terms of breaking strength and toughness of bone mechanical properties, only 4 significant SNPs were identified for toughness (Figure S2D), located on chromosomes 1, 2, and 12, respectively, and 3 candidate genes, including CASK, GREB1L, and LOC101805103 (Table S4), were annotated near the SNPs. There was no candidate gene identified in the breaking strength traits (Figure S2E).

Tibial Composition Traits

In the GWAS analysis of tibial bone component traits, including ash (Figure S2F), calcium (Figure S2G), and phosphorus content (Figure S2H), 4 significant SNP loci were identified (Table S5). For tibial bone ash content, a locus on chromosome 1 was annotated to the RFX8 gene. There were 2 SNPs associated with tibial calcium content, both located on chromosome 3, and the GLP1R gene was found. Regarding significant SNPs associated with the tibial phosphorus content, the DNAAF5 gene was identified. Additionally, there was a significant SNP signal peak on chromosome 3 for tibial calcium content. These regions can be further explored using subsequent LocusZoom and LD analysis.

One Candidate Region Distributed on Chromosome 4 is Associated With Tibial Diameter

Through GWAS analysis, significant signal peaks were observed for the tibial diameter trait on chromosome 4. LocusZoom was used to calculate the degree of association between other SNP loci and the most significant SNP locus. Then, the region where SNP loci with R2 values greater than 0.4 were selected as candidate intervals. In the 57049361-57945531 and 60413901-61308661 regions (Figure 2B), a total of 30 candidate genes were identified (Table 2), with 10 candidate genes overlapping with significant SNPs (Figure 2C and Table S6). The highest point fixed effect was observed, and it was found that the SNP sites in the 60413901-61308661 regions have a more direct impact on the diameter.

Table 2.

Genes within candidate regions.

Trait CHR Range Candidate gene
Diameter chr4 57049361-57945531 STIM2,TBC1D19,LOC106017030,CCKAR,RBPJ,SEL1L3,SLC34A2,ZCCHC4,ANAPC4,PI4K2B,SEPSECS,LGI2,LOC113843641,CCDC149,PPARGC1A
60413901-61308661 LDB2,LOC119716756,CPEB2,LOC119716899,LOC119716898,TAPT1,PROM1,FGFBP2,LOC119716803,FGFBP1,BST1,LOC110352211,FBXL5,CC2D2A,C1QTNF7
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Tibial Calcium Content Candidate Region's Linkage Analysis

The tibial calcium content trait showed a signal peak on chromosome 3, with significant SNPs concentrated in the 34433304-35211249 region (Figure 3A). Then, the region where SNP loci with R2 values greater than 0.4 were selected as candidate intervals (Figure 3A). This region has three candidate genes (Table S7), BTBD9, GLO1, and DNAH8. LD analysis was performed (Figure 3B). We speculated that strong LD exists in this region, hence, the LD analysis was performed in the genomic region from 34.4 to 35.2 Mb, but our results indicate a weak correlation of genes within the candidate region.

Figure 3.

Figure 3

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Pathway and expression analysis of candidate genes. (A). Analysis of locus zoom in candidate regions for calcium content traits (B). Candidate region linkage analysis.

Gene Function Enrichment

The top five metabolic pathways in the rankings are Progesterone mediated oocyte metabolism, Oocyte meiosis, Selenocompound metabolism, Calcium signaling pathway, and Nicotinate and nicotinamide metabolism (Table S8). SEPSECS, STIM2, BST1, GLO1, RBPJ, CCKAR, PPARGC1A, PI4K2B, GLP1R, ANAPC4, CASP7, and CPEB2 candidate genes were enriched in these 18 pathways (Figure 4A), which may play an important role in the regulation of bone homeostasis.

Figure 4.

Figure 4

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Pathway and expression analysis of candidate genes. (A) Bubble plot of candidate gene KEGG analysis. The larger the bubble, the more genes are enriched. The darker the color, the lower the P-value. (B) Bar plot of candidate gene GO analysis.

In addition, we conducted GO functional enrichment analysis on the candidate genes (Figure 4B). The GO terms were categorized into molecular function, biological process, and cellular component, with 10 terms in each category. Our analysis revealed that candidate genes BP, MF, and CC are significantly enriched in somatic stem cell population maintenance, messenger RNA 3 '- UTR binding, and Nucleolus, respectively (Table S9).

Candidate Genes Expression Analysis

To further identify candidate genes that affect tibial traits, we conducted expression level analysis on significant candidate genes. In the tibia of 12, 20, and 28-day-old Nonghua ducks, we found that the expression levels of candidate genes in tibial diameter, including STIM2, TAPT1, BST1, and FBXL5, were higher during the development of the tibia. At the same time, the expression levels of the candidate genes, including ZNF652, GREB1L, and RFX8, in the tibial weight and toughness also increased during the development of the tibia. The candidate genes FGFBP2 and CC2D2A in tibial diameter are highly expressed at the primary stage of bone development (Figure 5A). We also analyzed the expression levels of significant candidate genes in multiple tissues such as skin, liver, kidney, and sternum. Among them, STIM2, LGI2, BST1, CASK, DNAAF5, and ZNF652 genes have higher expression levels in the sternum, indicating that these genes also have higher expression levels in bones (Figure 5B).

Figure 5.

Figure 5

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Candidate gene expression. (A) candidate gene expression in tibia of different ages. (B) candidate gene expression in different tissues.

DISCUSSION

The skeleton plays a crucial role in poultry production (Martin-Silverstone et al., 2015). It is not only a determinant of body weight, but also has a direct impact on the athletic ability of the bird (Martin-Silverstone et al., 2015). Good bone structure supports body weight and improves production efficiency (Erasmus, 2017); moderate density and strength ensure normal standing and walking and help to obtain food (Wu et al., 2018). At the same time, healthy bones promote normal movement, energy consumption, and muscle development and reduce the risk of osteoporosis (Nassari et al., 2017). Bone mechanics performance is the most intuitive indicator of bone quality. This experiment measured the tibial phenotype indicators of agricultural ducks, and there were significant individual differences in bone quality and toughness traits. The correlation analysis found that the tibia length had the highest correlation coefficient with body size traits and weight, followed by diameter and tibia circumference. When investigating two morphological measurement indicators that served as indicators of bone density in rat bones, Monteagudo et al. (Monteagudo et al., 1997) found a stronger correlation between length and weight. This finding is in harmony with the results of the current study. Zioupos et al. (Zioupos et al., 2020) observed that as cortical bone toughness declines with age, there is a corresponding decrease in the required critical stress intensity level. Our research has revealed a strong correlation between fracture strength and toughness, it should be Zioupos et al. supporting the findings of our research results. This proves that skeletal quality traits are interrelated (Hart et al., 2017). Hence, when breeding meat ducks, it is essential to consider the influence of various characteristics on bone quality. Special attention should be directed towards selecting traits such as tibia circumference, length, and diameter. This emphasis is crucial for identifying individuals with outstanding tibia performance, contributing valuable insights and references for improving meat duck bone quality, preventing bone diseases in poultry, and promoting healthy poultry breeding practices.

The experimental ducks in this study are the same age and have similar physiological and consistent feeding and management conditions. Therefore, it is inferred that genetic factors may be the main reason for these individual differences. Through the GWAS, genetic variation sites related to bone quality traits were identified, and the auxiliary verification of the expression of candidate genes, STIM2, TAPT1, FGFBP2, and BST1 was implicated in calcium metabolism and bone development, potentially serving as pivotal genes in bone homeostasis.

Stromal interaction molecule 2 (STIM2) is a protein encoded by a gene that is a member of the Matrix Interaction Molecule (STIM) family, which only consists of two members and their homolog STIM1 (Williams et al., 2001). The STIM2 candidate gene has a high expression level in bones and is enriched in the calcium signaling pathway. Studies have shown that overexpression of its homolog STIM1 may help enhance the formation of new blood vessels in fibrotic bone marrow (Dragoni et al., 2014). The STIM protein is responsible for sensing changes in Ca2+ levels stored in the ER cavity. It regulates the release of Ca2+ and activated Ca2+ channels in the plasma membrane (Feske, 2007), indicating that the STIM protein is a sensor for Ca2+ levels in the ER (Park et al., 2020). Some studies reported that overexpression of STIM2 in cultured HEK293 cells increased intracellular Ca2+ levels, while cortical neurons, lacking STIM2, showed decreased Ca2+ levels (Berna-Erro et al., 2009). Transmembrane anterior posterior transformation 1 (TAPT1) is a transmembrane transporter protein primarily associated with the regulation of gene expression. However, its precise cellular function remains elusive, and there is a possibility that it plays a role in the transduction or transmission of extracellular information necessary for the establishment of axial skeletal patterns during development (Howell et al., 2007). Symoens et al. (Symoens et al., 2015) have reported that mutations in the TAPT1 gene can lead to severe bone mineralization deficiency. The TAPT1 mutation disrupts primary ciliary function, resulting in an imbalance within chondrocytes' ciliary hedgehog signaling pathway Research has shown that primary cilia are crucial in guiding mesenchymal stem cells to differentiate into chondrocytes and osteoblasts (Soltanoff et al., 2009). Moreover, Bone marrow stromal cell antigen 1(BST1), located on chromosome 4, is a surface molecule on bone marrow stromal cells and is a GPI-anchored pr TAPT1otein that promotes the growth of pre-B cells. It also plays a role in ADP-ribosyl cyclase synthesis. Research has reported about 30% homology in amino acid sequences between human and mouse BST1 and CD38 and Aplysia ADP ribosyl cyclase Hirata et al., 1994, Takasawa, 2022. Sun et al. (Sun et al., 1999) found that CD38 (CD38 molecule) influences rabbit osteoclast function by elevating intracellular Ca2+ concentration through CD38 activation, thereby regulating bone resorption in osteoclasts. Similarly, studies suggest that CD38 is expressed in osteoclasts and regulates bone metabolism by modulating the absorption and release of calcium ions in cells (Sun et al., 2003). Therefore, it is speculated that the BST1 gene may regulate bone metabolism by controlling the absorption and release of calcium ions in osteoblasts and osteoclasts and may also affect mineral deposition in the skeleton by regulating the absorption of phosphate in the intestine. The candidate gene Fibroblast growth factor binding protein 2 (FGFBP2) encodes fibroblast growth factor binding protein and is expressed at high levels in the early stages of tibial development. It is enriched in the growth factor binding metabolic pathway within the GO classification. Fibroblast growth factors play a crucial role in endochondral ossification, and both chondrocytes and osteoblasts play significant roles in cell proliferation and differentiation (Felício et al., 2013). Studies have shown that mice lacking FGF2 exhibit reduced bone mass (Montero et al., 2000), while mice lacking FGF9 display shortened long bone development. Mice with global knockdown of FGF18 exhibit abnormal skeletal development and delayed ossification (Behr et al., 2011).

CONCLUSION

In summary, this study analyzed 9 bone quality indicators and employed GWAS for investigation. The GWAS analysis revealed a significant association at the genome-wide level with 30 SNPs located on chromosome 4 of tibia diameter. 10 candidate genes were identified near the SNP locus, among which STIM2, TAPT1, FGFBP2, and BST1 are potential key genes influencing bone diameter. Identifying these candidate genes lays the foundation for understanding the genetic mechanisms underlying the phenotypic traits of ducks' bone quality. Our study provides valuable insights into the primary stage of duck breeding.

ACKNOWLEDGMENTS

This work was supported by grants from the National Key R&D Program of China (2022YFF1000000), the Key Technology Support Program of Sichuan Province (2021YFYZ0014), China Agricultural Research System of MOF and M RA (CARS-42-4).

Author Contributions: LY, HL, and LL are responsible for constructing the article concept and drafting the manuscript, JH is responsible for the duck breeding section, BW, XxH, XH, and LB are responsible for the experimental section, and QY and QT are responsible for technical guidance. In addition, YB, TZ, SJ, and MX conducted data statistics and analysis. HL and LL participated in the writing guidance and revision of the manuscript. All listed authors have made significant contributions to this research and publication.

DISCLOSURES

The authors declare no conflicts of interest.

Footnotes

Supplementary material associated with this article can be found in the online version at doi:10.1016/j.psj.2024.103515.

Appendix. Supplementary materials

mmc1.jpg (1.2MB, jpg)
mmc2.jpg (5.1MB, jpg)
mmc3.xlsx (24.8KB, xlsx)

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