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Genome-wide association study identifies SNPs for growth performance and serum indicators in Valgus-varus deformity broilers (Gallus gallus) using ddGBS sequencing



Valgus-varus deformity (VVD) is a lateral or middle deviation of the tibiotarsus or tarsometatarsus, which is associated with compromised growth, worse bone quality and abnormal changes in serum indicators in broilers. To investigate the genetic basis of VVD, a genome wide association study (GWAS) was performed to identify candidate genes and pathways that are responsible for VVD leg disease, serum indicators and growth performance in broilers.


In total, VVD phenotype, seven serum indicators and three growth traits were measured for 126 VVD broilers (case group) and 122 sound broilers (control group) based on a high throughput genome wide genotyping-by-sequencing (GBS) method. After quality control 233 samples (113 sound broilers and 120 VVD birds) and 256,599 single nucleotide polymorphisms (SNPs) markers were used for further analysis. As a result, a total of 5 SNPs were detected suggestively significantly associated with VVD and 70 candidate genes were identified that included or adjacent to these significant SNPs. In addition, 43 SNPs located on Chr24 (0.22 Mb - 1.79 Mb) were genome-wide significantly associated with serum alkaline phosphatase (ALP) and 38 candidate genes were identified. Functional enrichment analysis showed that these genes are involved in two Gene Ontology (GO) terms related to bone development (cartilage development and cartilage condensation) and two pathways related to skeletal development (Toll−like receptor signaling pathway and p53 signaling pathway). BARX2 (BARX homeobox 2) and Panx3 (Pannexin 3) related to skeleton diseases and bone quality were obtained according to functional analysis. According to the integration of GWAS with transcriptome analysis, HYLS1 (HYLS1 centriolar and ciliogenesis associated) was an important susceptibility gene.


The results provide some reference for understanding the relationship between metabolic mechanism of ALP and pathogenesis of VVD, which will provide a theoretical basis for disease-resistant breeding of chicken leg soundness.

Peer Review reports


Valgus-varus deformity (VVD), a common leg disorder in broilers and turkeys, is characterized by a lateral (outward) or middle (inward) angulation of the tibiotarsal bone that results in deviation of the tarsometatarsus [1]. It has caused severe economic losses for the broiler industry and physical distress in broilers [2, 3]. Previous study showed that VVD had compromised growth, worse bone quality, calcification disorders and abnormal changes in serum lipid indicators, serum calcium (Ca), phosphorus (P) and serum alkaline phosphatase (ALP) levels [4].

In humans and animals, the blood components are considered to reflect immune activity and nutrient metabolism. Serum Ca, P and serum ALP are important and evaluable indicators for bone metabolism and mineral status [5]. ALP is the by-production during bone formation and biochemical marker of bone metabolism, which can reflect bone development state [6]. Abnormal lipid metabolism is an important factor to leg disorder in broiler [7]. Triglyceride (TG), total cholesterol (T-CHO), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) in serum are four representational indicators for lipid metabolism. Bodyweight is an important trait to estimate the profitability of broiler production. Furthermore, strong genetic correlations have been found between body weight and VVD incidence in broiler production [3]. Shank length and girth are two important traits for the development of skeleton in broiler [8, 9]. Therefore, these traits are directly or indirectly associated with VVD in broiler.

Up to now, the precise etiology and pathogenesis of VVD still remain uncertain [3]. The incidence of VVD has been reduced effectively by genetic selection in the UK [10]. The heritability or pseudo heritability of leg disorders range from 0.10 to 0.40 [11, 12]. In our previous study, some candidate genes associated with VVD were found based on case/control design with VVD and normal broilers using RNA sequencing [10]. These data indicated that genetic factors will provide a more scientific and reasonable explanation for pathogenesis of VVD. Nevertheless, the molecular and regulatory mechanisms in VVD are very limited.

Genome-wide association studies (GWAS), a mature analysis way, have been extensively used to identify single nucleotide polymorphisms (SNPs) related to complex diseases or traits during the last decade [13, 14]. To the best of our knowledge, no large-scale GWAS has been performed to identify genomic loci and candidate genes for VVD in broilers. In this study, the objectives were to identify genetic markers and candidate genes that are associated with VVD leg disease and its related traits including seven serum indicators and three growth traits by performing a GWAS. We conducted a GWAS in 248 Hubbard broilers using genotyping-by-sequencing (GBS). The results could provide new insights into the molecular mechanism of VVD and increase the understanding of the relationship between VVD and serum indicators in healthy leg breeding in birds.


Phenotype information and SNP characteristics after quality control

The phenotype information for growth performance, serum indicators and leg disorder were listed in Table S1. In this study, a total of 233 birds were retained after quality control of the genotype, including 120 VVD birds (case group) and 113 sound birds (control group). A number of 256,599 SNPs were obtained for further association analysis. All SNPs were uniformly distributed on each chromosome and these SNPs are mostly located in intron (Fig. S1) and intergenic regions (Fig. S2).

GWAS analysis for VVD trait

Pairwise kinship was estimated using autosomal SNP information (Fig. S3), which indicated no population stratification. For VVD, the manhattan and QQ plots indicated that there was no significant SNP associated with VVD clinical phenotype (Fig. 1). At the suggestive significant level, a total of 5 SNP was detected, which were located on Chr 1 (rs80839937, rs192358715), Chr 7(rs18584148), Chr 6 (rs12086380) and Chr 8 (rs20132188), respectively (Fig. 2 and Table 1). The most significant SNP was rs20132188 (P = 8.49 × 10 − 6) at Chr 8. A total of 70 putative candidate genes were obtained by annotating each significant locus (Table S2).

Fig. 1
figure 1

Manhattan and quantile-quantile (QQ) plot on VVD traits at genome-wide level. Manhattan plot is on the left and QQ plot is on the right. The horizontal red lines indicated the whole-genome significant (P = 1.51e-6)

Fig. 2
figure 2

Manhattan and quantile-quantile (QQ) plot on VVD traits at suggestive level. Manhattan plot is on the left and QQ plot is on the right. The horizontal red lines indicated the whole-genome significant (P = 3.02e-5)

Table 1 Loci associated with VVD trait

GWAS analysis for serum indicators and growth performance

A total of 43 genome-wide significant SNPs were identified for serum ALP, which were located region at 0.22 Mb to 1.79 Mb on Chr 24 (Fig. 3 and Table 2). The most significant SNP was rs249241 (P = 5.36 × 10− 15, Position: 0.47 Mb). The estimated genomic inflation factor (λ) for ALP was 1.0, which indicated the low false positives to analysis results. For H-HDL, a significant SNP (rs71661, P = 1.14 × 10− 6) was identified on Chr 2 at position 61.93 Mb (Fig. 4 and Table 3). A Manhattan plot revealed that univariate analysis generated a P-value that exceeded the Bonferroni cutoff on Chr 23, which illustrates that SNP was significantly associated with Serum Ca (rs248988, P = 8.34 × 10− 7, Position: 4.68 Mb) (Fig. 4 and Table 3). The manhattan and QQ plots for TG, T-CHO, LDL-C, serum P were indicated in Fig. S4. Association analysis results revealed that these SNP were not significant by Manhattan plot. A top SNP (rs174201, P = 2.06 × 10− 6, Position: 24.62 Mb) was focused for TG on Chr 2 (Fig. S4). For T-CHO, the top SNP was rs121566 (P = 3.60 × 10− 6) on Chr 3 at position 92.15 Mb (Fig. S4). As the top SNP for LDL-C, the rs90406 (P = 3.74 × 10− 6) was detected which was located on Chr 2 at 132.19 Mb (Fig. S4). For HDL-C level, Fig. S4 showed that a top SNP rs71661 (P = 1.14 × 10− 6) was located on Chr 2 at 61.3 Mb. The genomic control inflation factor (λ) calculated for serum indicators ranged from 0.99 to 1.01, indicating acceptably low false positives, and revealing relatively good consensus between the observed distributions of the P-value and expected.

Fig. 3
figure 3

Manhattan and quantile-quantile (QQ) plot revealing the significance signal associated with ALP levels in broiler. Manhattan plot is on the left and QQ plot is on the right. The horizontal red lines indicated the whole-genome significant (P = 1.51e-6)

Table 2 Loci associated with ALP
Fig. 4
figure 4

Results of genome-wide association analysis for Shank length, HDL-C and Serum Ca. Manhattan and quantile-quantile (QQ) is on the left and right, respectively

Table 3 Loci associated with Shank length, Serum Ca and H-HDL

The GWAS identified a SNP that was significantly associated with shank length, and the QQ plot for this trait supported the results shown in the Manhattan plot (Fig. 4). The significant SNP (rs72002, P = 1.04 × 10− 6) was identified on Chr 2 at position 63.02 Mb (Table 3). The manhattan and QQ plots for body weight and shank girth were indicated in Fig. S5. Association analysis results revealed that these SNP were not significant by Manhattan plots. For body weight, Fig. S5 indicated that a top SNP was rs230254 (P = 4.02 × 10− 6) located on Chr 15 at position 5.79 Mb. A top SNP site (rs199378, P = 1.20 × 10− 5) for shank girth was detected on Chr 9 at 14.63 Mb (Fig. S5). The genomic control inflation factor (λ) calculated for the growth performance ranged from 0.99 to 1.11.

Linkage disequilibrium (LD) blocks analysis

For Serum ALP, we performed LD blocks analysis of 43 significant SNPs on Chr 24. After haplotype construction with these SNPs, two smaller blocks were obtained at 0.23–0.31 Mb and 0.36–0.55 Mb (Fig. 5). Strong linkage disequilibrium was observed among 5 SNPs, which were located in the other haplotype. In addition, 10 SNPs were located in one haplotype block. The result hinted that 2 regions were important to find the true causal mutations SNP.

Fig. 5
figure 5

LD blocks with SNPs which affect ALP levels

Candidate genes and functional annotation

For VVD trait, 70 candidate genes that included or adjacent to the 5 significant SNPs with a distance less than 300 kb were annotated (Table S3). GO and pathway analysis were performed to determine the biological functions of the 70 protein coding genes. Uniplex complex GO term (GO:1990246) was the most significant GO term in GO enrichment analysis results (Table S4). KEGG enrichment analysis results revealed that Hedgehog signaling pathway (gga04340) and Wnt signaling pathway (gga04310) were important to skeleton development and leg disorder (Table S5).

Putative candidate genes of each significant locus were annotated. A total of 38 protein coding genes were obtained for ALP (Table S6). GO and KEGG pathway enrichment analysis were performed on 38 protein coding genes (Fig. 6, Table S6 and Table S7). Among GO enrichment analysis results, nuclear body organization (GO: 0030575) was the most significant GO term in biological process. Within molecular function, pseudouridine synthase activity (GO: 0009982) was the most dominant GO subcategories. Regarding the cellular component category, axon (GO: 0030424) was the most significant GO term. It is worth mentioning that BARX2 (BARX homeobox 2) was enriched to cartilage development (GO: 0051216) and cartilage condensation (GO: 0001502) GO term. Pathway enriched analysis results showed that Toll−like receptor signaling pathway (gga04620) and p53 signaling pathway (gga04115) were important to bone development and bone disease. By integrating analysis of GWAS and previous transcriptome results, we identified HYLS1 (centriolar and ciliogenesis associated) as an important susceptibility gene.

Fig. 6
figure 6

Comparison of functional annotations for the candidate genes with ALP. Histogram representing GO classification results for t the candidate genes on the top. The most enriched KEGG pathways on the underneath


A total of 5 SNPs were detected for VVD trait at the suggestive significant level. Among these SNPs, the rs18584148 was located on Chr7. Notably, a study indicated that the regions related to chicken leg disease score on chromosome 7 by the ssGWAS methods [15]. Thus, the findings illustrated that the chromosome 7 merit closer attention to leg disorder in further search. Furthermore, five SNPs were performed relevant gene annotation, which obtained 70 candidate genes. Among these candidate genes, the low-density lipoprotein receptor-related protein (LRP2) plays important role in lipoprotein metabolism progress. Our previous study indicated that an association between VVD and lipid metabolism in broilers [4]. In LRP2 knockout mice, bone disorder and Vitamin D deficiency were found. In human, LRP2 is closely related to bone mineral density and bone quality. These data revealed that LRP2 gene merits closer attention in our future study. A total of 70 candidate genes were performed functional enrichment analysis. Our previous study demonstrated that VVD mainly reduces skeletal quality and development to broilers [4]. Significantly, Hedgehog signaling pathway (gga04340) and Wnt signaling pathway (gga04310) were closed related to skeletal development and bone homeostasis. Therefore, these SNPs, genes and pathways may provide new insights for understanding the relationship between VVD and genetics in further study.

The etiology and pathogenesis of VVD were complex and multiple factors, including genetics, exercise, nutrient distribution, growth rate and lighting program [3]. The heritability or pseudo heritability of leg disorders range from 0.10 to 0.40 [11, 12]. The incidence of VVD has been reduced effectively decreased by genetic selection in the UK. These researches indicated that genetic factor was important for VVD. We firstly exploit double-digest genotyping by sequencing (ddGBS) method to conduct GWAS based on case/control experience in this study. However, the result revealed that these SNPs were not found to significantly associate with VVD at genome wide level in broilers. As we all know, most diseases and complex traits are controlled by multiple genes, which cause these intractable traits to be controlled difficultly by the major genes or a single SNP [16]. Meanwhile, the association between a single gene and phenotypic traits is relatively weak due to the quantitative trait was controlled by multiple minor genes, which some SNP often fail to significant after Bonferroni tests in GWAS analysis. The developmental processes of skeletons are regulated by genetic factors and their interactions with environmental factors [1, 2]. Consequently, we speculated that VVD is a complex trait and is affected by a large number of small-effect variants.

The sample size is insufficient which causes the result was limited in this study. However, seven significant SNPs and four genes associated with Piglet splay leg (PSL) syndrome were obtained for the limited sample size (73 cases, 112 controls) using the linear mixed model in GWAS [17]. In a host genetic resistance to Marek’s disease study, GWAS was performed on 57 susceptible chickens (case group) and 10 resistant chickens (control group), which result in two SNPs were found to be associated with host resistance to MD [18]. In the present study, we conducted GWAS on 233 samples based on a case and control design, including 126 VVD broilers in the case group and 122 sound broilers as controls. Comprehensive consideration, we concluded that the SNPs without significant difference might be independent of the sample size.

In addition, as one of the Reduced-Representation Genome Sequencing (RRGS) methods, ddGBS was double-digest genotyping by sequencing method with high genotyping accuracy and high-density SNP markers to chickens and pigs [19]. However, there must be a great deal of Gap using ddGBS. If these sites associated with VVD are located in these Gap, which affects the sequencing results using ddGBS. Therefore, we speculated that these SNP sites associated with VVD might be affected by these gaps. In conclusion, the reason why no significant SNPs were found still needs to be further exploration. Of course, we will try to using whole genome markers to explored the leg disorder in broilers in the future study.

Hematological parameters are a valuable tool for assessing the individual’s state of health in human diagnostics. Certain economic traits are indirectly measured using serum components in animal breeding. Serum ALP, a by-product in osteoblast activity or bone formation mainly derived from liver and bone, is essential in bone mineralization and vascular calcification [6, 20]. Our previous results indicated that ALP activity was markedly inhibited and bone calcification disorders in VVD broilers [4]. Therefore, ALP is merit to the molecular mechanism of VVD and healthy leg breeding in birds. The genomic region on Chr 24 (0.22 Mb - 1.79 Mb) contains 43 SNPs associated with the ALP levels in this study. A total of 38 putative candidate protein coding genes were obtained. Among these loci and genes, we pay attention to the rs475471 (G > A) at ST3GAL4 (ST3 beta-galactoside alpha-2, 3-sialyltransferase 4). ST3GAL4 is associated with the pathogenesis of osteoarthritis (OA) and LDL-C level [21, 22]. In our subsequent study, we explored the relationship between ST3GAL4 genetic variation loci and serum ALP in chicken. We determined 9 SNPs within ST3GAL4 and ST3GAL4 were closely related to ALP in VVD broilers (unpublished data). The findings indicated that ST3GAL4 plays an important role in abnormal serum ALP in VVD birds (unpublished data). Furthermore, among 38 putative candidate genes, Panx3 (Pannexin 3) was important at the early stages of bone development [23]. As a potential therapeutic target for OA, Panx3 plays a specific catabolic role in articular cartilage [24] and can improve chondrocyte differentiation. Panx3 merits closer attention in our follow-up study. To gain insight into the function of 38 candidate genes, GO and KEGG enrichment analyses were performed. The cartilage development, cartilage condensation GO term and candidate genes in two GO terms merit our attention in further study. According to GO enrichment analysis results, BARX2 was focused. BARX2, a new homeobox gene of the Bar class, is expressed in the articular cartilage of the developing limb, suggesting that it is involved in regulation of chondrogenesis, which acts downstream of BMP signaling and in concert with Sox proteins to regulate chondrogenesis [25]. Of course, further evidence is needed to understand the specific mechanism of these candidate genes for ALP level and VVD. In pigs and chickens, the direct relationships between ALP levels and candidate genes and corresponding QTLs have not been reported at present [26]. In mice, the regions for ALP levels were located on chromosome 1 and 6 [23]. This study first time identified that a genomic region on Chr 24 (0.22 Mb - 1.79 Mb) associated with the ALP levels in Hubbard VVD birds. These results will provide some in-depth reference to the interaction relationship between VVD and ALP level.

Shank length is an important indicator for leg health in chicken. Identification of SNPs and genes affecting shank length traits were valuable for leg health in birds. For shank length, a SNP was obtained mapped on Chr 2 at position 63.02 Mb in our study. A study found that shank length was mapped to Chr 2, indicating the common effect of the chromosome on shank length and leg disease [8]. Lipid metabolism disorders was involved in leg disorder, which cause the bone swelling and deformation in VVD birds [7]. For HDL-C and serum Ca, a SNP was obtained respectively mapped on Chr 2 at position 61.93 Mb and Chr 23 at position 4.68 Mb, indicating the common effect of the Chromosome on leg disease. These results indicated that these loci and annotated genes merits closer attention.

Identification of SNP sites and candidate genes involved in VVD, growth performance and serum indicators may provide insight into disease mechanisms. For VVD, growth performance and serum indicators, several association signals and some putative genes were detected in the current study. Future study is need to confirm this finding. Our current research has provided some in-depth understanding of the interaction relationship between VVD and growth performance or serum indicators.


In summary, we firstly exploited GWAS method to explore VVD trait, growth performance and serum indication in Hubbard broiler. This study excavated that some SNPs and functional candidate genes may play significant roles in the interaction relationship between ALP and VVD, which provides some reference for understanding of the pathogenesis of VVD and improving leg healthy in poultry industry.


Animals and sample preparation

Hubbard broilers (n = 56,000) were reared in Zhonghong Sanrong Group Co., Ltd.; Liaoning, China. A total of 15 individuals with leg weakness were anesthetized by an intravenous wing injection of sodium pentobarbital (0.2%, 40 mg/kg body weight) in the wing vein. Subsequently, these individuals were euthanized by intravenous KCl (1–2 mg/kg). Subsequently, these individuals were anatomical. According to clinical examination, anatomical results and the bent degree of abnormal leg, Hubbard broilers with leg weakness were diagnosed as VVD at 38 -day- old [1, 3]. Then, 126 VVD broilers (case group) and 122 sound broilers (control group) were randomly selected, respectively. The leg disorder in the phenotype broiler was visually classified as normal or affected (0 / 1, respectively). Blood samples were collected from wing vein and stored at − 20 °C until DNA isolation. A total of seven serum indicators including serum alkaline phosphatase (ALP), calcium (Ca), phosphorus (P), triglyceride (TG), total cholesterol (T-CHO), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) were measured [4]. Three growth traits including body weight, shank length, and shank girth of these birds were measured using a weighing scale and caliper [4].

DNA isolation and sequencing

DNA was extracted from blood tissue using the Qiagen DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) and diluted to a normal 50 ng/μL. The DNA quality satisfied the requirements for library construction of double-digest genotyping-by-sequencing (ddGBS) [19]. The quality of DNA libraries was estimated by Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, U.S.A.) and Qubit2.0 Fluorometer (Thermo, MA, U.S.A.). Ultimately, these libraries were sequenced on the Illumina Hiseq X Ten platform with 2 × 150-bp paired-end reads were generated.

Genotyping and quality control

Removing these reads of that were polluted by adapter sequence and the low-quality reads from raw reads. The clean reads were aligned to the chicken reference genome (Gallus_gallus-6.0) using Bowtie2 and TASSEL GBS analysis pipeline was used to call SNP [24, 27]. The quality control of these genotyping data was performed through VCFtools [28] to remove the SNPs, in accordance with these strict parameters: a minor allele frequency (MAF) > 0.05; the quality of genotypes above 98 (GQ ≥ 98%) and depth ≥ 5; consistent with Hardy-Weinberg equilibrium; only biallelic markers were retained; animals with missing rate < 0.4. According to the information of the remaining SNPs to imputed the missing genotypes using Beagle 4.0 software [19, 29]. Meanwhile, SNPs on sex chromosomes were removed. Finally, a total of 233 samples (120 case and 113 control) and 256, 599 markers were kept for further analysis after quality control.

Genome wide association study

Principal component analysis (PCA) was conducted using GCTA software to determine whether stratification exists in our study population. All SNPs were pruned to ensure their independence using the option of “indep-pairwise 25 5 0.2” in PLINK software to avoid the highly correlated SNP may distort the resulting PCs. A total of 33,030 independent effective tests were therefore obtained. GWAS was accomplished with the single-locus mixed linear model procedure implemented in GEMMA software using effective SNPs.

$$\boldsymbol{y}=\boldsymbol{W}\boldsymbol{\alpha } +\boldsymbol{X}\boldsymbol{\beta } +\boldsymbol{g}+\boldsymbol{e}$$

where Y is the vector of phenotypes (VVD trait, serum indicator and growth trait), α is the fixed effect vector including gender (male; female), chicken house (1; 2), β is the effect size of the genotype, g is a n-vector of random individual genetic effects, e is a vector of residuals; W, X are incidence matrix corresponding to the fixed effects in α and the genotype effect of β. Association analysis results were adjusted using Bonferroni correction (P < 0.05/ N). The significance threshold of GWAS analysis was 0.05/33030 = 1.51e-6, where 33,030 is the number of independent markers. The significance threshold of GWAS analysis was 1/33030 = 3.02e-5 at the suggestive levels. The manhattan and Q-Q plots were obtained from the GWAS results with the CMplot package within the R software.

Post-GWAS analysis

To identify the positional candidate genes that are potentially associated with VVD trait, serum indicator and growth trait, the genomic regions that were close to the significant SNPs with a distance less than 300 Kb were selected. These regions were then referenced against the Gallus gallus reference genome (GRCg6a) using the Ensembl Genome Browser to find genes that are located in the vicinity of the significant SNPs [16]. The candidate genes were performed Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes database (KEGG) enrichment analysis with the cutoff of P-values < 0.05 [30,31,32]. In order to screen VVD susceptibility genes, the candidate genes from GWAS and candidate genes from previous RNA-seq [2] were overlapped.

Availability of data and materials

The GWAS datasets generated and/or analyzed during the present study are available at NCBI project PRJNA720768 with accession number SRR14251137–39.



Valgus-varus deformity


Single nucleotide polymorphism


Alkaline phosphatase








Total cholesterol


High-density lipoprotein cholesterol


Low-density lipoprotein cholesterol


Body weight


Shank length


Shank girth


Double-digest genotyping by sequencing (ddGBS)


Genome-wide association study


Reduced-Representation Genome Sequencing




Linkage disequilibrium


Principal components




BARX homeobox 2


Centriolar and ciliogenesis associated


Gene Ontology


Kyoto Encyclopedia of Gene and Genomes database


ST3 beta-galactoside alpha-2,3-sialyltransferase 4


  1. 1.

    Shim MY, Karnuah AB, Anthony NB, Pesti GM, Aggrey SE. The effects of broiler chicken growth rate on valgus, varus, and tibial dyschondroplasia. Poult Sci. 2012;91(1):62–5.

    CAS  Article  Google Scholar 

  2. 2.

    Guo Y, Tang H, Li Z, Zhang Y, Han R. High-throughput transcriptome analysis reveals potentially important relationships between LncRNAs and genes in broilers affected by Valgus-varus deformity (Gallus gallus). Gene. 2020;743:144511.

    CAS  Article  Google Scholar 

  3. 3.

    Bradshaw RH, Kirkden RD, Broom DM. A review of the Aetiology and pathology of leg weakness in broilers in relation to welfare. Avian Poult Biol Rev. 2002;13(2):45–103.

    Article  Google Scholar 

  4. 4.

    Guo YP, Tang HH, Wang XN, Li WT, Yan FB. Clinical assessment of growth performance, bone morphometry, bone quality, and serum indicators in broilers affected by valgus-varus deformity. Poult Sci. 2019;98(10):4433-40.

  5. 5.

    Reyer H, Oster M, Wittenburg D, Murani E, Ponsuksili S, Wimmers K. Genetic contribution to variation in blood calcium, phosphorus, and alkaline phosphatase activity in pigs. Front Genet. 2019;10(590):1-12.

  6. 6.

    Brzoska MM, Moniuszko-Jakoniuk J. Disorders in bone metabolism of female rats chronically exposed to cadmium. Toxicol Appl Pharmacol. 2005;202(1):68–83.

    CAS  Article  Google Scholar 

  7. 7.

    Erken HY, Ofluoglu O, Aktas M, Topal C, Yildiz M. Effect of pentoxifylline on histopathological changes in steroid-induced osteonecrosis of femoral head: experimental study in chicken. Int Orthop. 2012;36(7):1523–8.

    Article  Google Scholar 

  8. 8.

    Gao Y, Du ZQ, Feng CG, Deng XM, Li N, Da Y, et al. Identification of quantitative trait loci for shank length and growth at different development stages in chicken. Anim Genet. 2010;41(1):101–4.

    CAS  Article  Google Scholar 

  9. 9.

    Guo Y, Tang H, Wang X, Fu Y, Chen Y, Li Z, et al. Effects of Valgus-varus deformity on growth performance, serum biochemical indicators and bone morphometry in broilers (Gallus gallus) at different stages. J Agricultural Biotechnol. 2019;27(9):1644–51.

    Google Scholar 

  10. 10.

    Tang H, Guo Y, Zhang Z, Li Z, Zhang Y, Li Y, et al. Integrative analysis of long non-coding RNA and mRNA in broilers with valgus-varus deformity. Plos One. 2020;15(9):e0239450.

    CAS  Article  Google Scholar 

  11. 11.

    González-Cerón F, Rekaya R, Anthony NB, Aggrey SE. Genetic analysis of leg problems and growth in a random mating broiler population. Poult Sci. 2015;94(2):162–8.

    Article  Google Scholar 

  12. 12.

    Bihan-Duval EL, Beaumont C, Colleau J. Genetic parameters of the twisted legs syndrome in broiler chickens. Genet Sel Evol. 1996;28(2):177–0.

    Article  Google Scholar 

  13. 13.

    Visscher PM, Montgomery GW. Genome-wide association studies and human disease. JAMA. 2009;302(18):2028.

    CAS  Article  Google Scholar 

  14. 14.

    Einarsdottir E, Hafrén L, Leinonen E, Bhutta MF, Kentala E, Kere J, et al. Genome-wide association analysis reveals variants on chromosome 19 that contribute to childhood risk of chronic otitis media with effusion. Sci Rep. 2016;6(1):33240.

    CAS  Article  Google Scholar 

  15. 15.

    Fragomeni BO, Misztal I, Lourenco DL, Aguilar I, Okimoto R, Muir WM. Changes in variance explained by top SNP windows over generations for three traits in broiler chicken. Front Genet. 2014;5(332):1-7.

  16. 16.

    Zhang Y, Wang Y, Li Y, Wu J, Wang X, Bian C, et al. Genome-wide association study reveals the genetic determinism of growth traits in a Gushi-Anka F2 chicken population. Heredity. 2020;126(2):293-307.

  17. 17.

    Hao X, Plastow G, Zhang C, Xu S, Hu Z, Yang T, et al. Genome-wide association study identifies candidate genes for piglet splay leg syndrome in different populations. BMC Genet. 2017;18(1):64.

    Article  Google Scholar 

  18. 18.

    Li DF, Lian L, Qu LJ, Chen YM, Liu WB, Chen SR, et al. A genome-wide SNP scan reveals two loci associated with the chicken resistance to Marek's disease. Anim Genet. 2013;44(2):217–22.

    CAS  Article  Google Scholar 

  19. 19.

    Wang Y, Xuemin C, Yiqiang Z, Jing F, Xiaoxiang H, Ning L, et al. Optimized double-digest genotyping by sequencing (ddGBS) method with high-density SNP markers and high genotyping accuracy for chickens. Plos One. 2017;12(6):e0179073.

    Article  Google Scholar 

  20. 20.

    Schoppet M, Shanahan CM. Role for alkaline phosphatase as an inducer of vascular calcification in renal failure? Kidney Int. 2008;73(9):989–91.

    CAS  Article  Google Scholar 

  21. 21.

    Toegel S, Pabst M, Wu SQ, Grass J, Goldring MB, Chiari C, et al. Phenotype-related differential alpha-2,6-or alpha-2,3-sialylation of glycoprotein N-glycans in human chondrocytes. Osteoarthr Cartil. 2010;18(2):240–8.

    CAS  Article  Google Scholar 

  22. 22.

    Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010;466(7307):707–13.

    CAS  Article  Google Scholar 

  23. 23.

    Srivastava AK, Masinde G, Yu H, Baylink DJ, Mohan S. Mapping quantitative trait loci that influence blood levels of alkaline phosphatase in MRL/MpJ and SJL/J mice. Bone. 2004;35(5):1086–94.

    CAS  Article  Google Scholar 

  24. 24.

    Van Tassell CP, Smith TPL, Matukumalli LK, Taylor JF, Schnabel RD, Lawley CT, et al. SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Methods. 2008;5(3):247–52.

    Article  Google Scholar 

  25. 25.

    Meech R, Edelman DB, Jones FS, Makarenkova HP. The homeobox transcription factor Barx2 regulates chondrogenesis during limb development. Development. 2005;132(9):2135–46.

    CAS  Article  Google Scholar 

  26. 26.

    Zhu F, Cui Q-Q, Yang Y-Z, Hao J-P, Yang F-X, Hou Z-C. Genome-wide association study of the level of blood components in Pekin ducks. Genomics. 2020;112(1):379–87.

    CAS  Article  Google Scholar 

  27. 27.

    Glaubitz JC, Casstevens TM, Lu F, Harriman J, Elshire RJ, Sun Q, et al. TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. Plos One. 2014;9(2):e90346.

  28. 28.

    Danecek P, Auton A, Abecasis G, Albers CA, Banks E, Depristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27(15):2156–8.

    CAS  Article  Google Scholar 

  29. 29.

    Browning BL, Browning SR. Improving the Accuracy and Efficiency of Identity-by-Descent Detection in Population Data. Genetics. 2013;194(2):459−+.

    Article  Google Scholar 

  30. 30.

    Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.

    CAS  Article  Google Scholar 

  31. 31.

    Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 2019;28(11):1947–51.

    CAS  Article  Google Scholar 

  32. 32.

    Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 2021;49(D1):D545–51.

    CAS  Article  Google Scholar 

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The authors thank Prof. Xiaoxiang Hu of China Agricultural University for technical assistance. We also thank Dr. Yuzhe Wang and Dr. Di Zhu of China Agricultural University for their valuable assistance in the data analysis, and Xiangnan Wang, Yawei Fu and Yi Chen for their help in sampling.


This work was supported by the Scientific Studio of Zhongyuan Scholars (30601985), the Key Scientific Research Projects of Henan Colleges and Universities (20A230006), the Innovation Research Team of the Ministry of Education (IRT16R23). The funding bodies had no role in study design or any aspect of the collection, analysis and interpretation of data or in writing the manuscript.

Author information




RH and XK conceived and designed the experiments. YG prepared the sequence samples, collected phenotypic information and wrote the manuscript with input from XL and WL. HH and ZL performed the bioinformatics analyses. ZZ, HT and HM conducted DNA extraction and collected phenotypic information. YM and JL revised All Figures and Tables. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Xiangtao Kang or Ruili Han.

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Ethics approval and consent to participate

Animal care and experimentation in this study were performed in strict accordance with the Regulations of the Chinese National Research Council (1994) and approved by the Institutional Animal Care and Use Committee of Henan Agricultural University, China (18–0180). All experimental procedures and sample collection methods were carried out in accordance with approved guidelines to ensure animal welfare. In this study, written informed consent was obtained from Henan Agricultural University and Zhonghong Sanrong Group Co., Ltd. to use these animals. Meanwhile, the study was carried out in compliance with the ARRIVE guidelines.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1: Table S1.

The phenotypic information of VVD and sound broile. Table S2. Annotated candidate genes to VVD trait. Table S3. Annotated candidate genes to ALP trait. Table S4. GO enrichment analysis of the candidate genes for VVD trait in Valgus-varus Deformity Broilers. Table S5. KEGG enrichment analysis of the candidate genes for VVD trait. Table S6. GO enrichment analysis of the candidate genes for ALP trait in Valgus-varus Deformity Broilers. Table S7. KEGG enrichment analysis of the candidate genes for ALP trait in Valgus-varus Deformity Broilers.

Additional file 2: Figure S1.

Distribution of SNPs on each chromosome. Figure S2. Distribution of SNPs on chromosome position. Figure S3. Population structure evaluated by the first two principal components. Figure S4. Manhattan and quantile-quantile (QQ) plot on Serum TG, T-CHO, LDL-C and P. Figure S5. Manhattan and quantile-quantile (QQ) plot on body weight and shank girth.

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Guo, Y., Huang, H., Zhang, Z. et al. Genome-wide association study identifies SNPs for growth performance and serum indicators in Valgus-varus deformity broilers (Gallus gallus) using ddGBS sequencing. BMC Genomics 23, 26 (2022).

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  • Broiler
  • Valgus-varus deformity
  • Genome-wide association study
  • Growth performance
  • Serum indicators