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Identification of candidate genomic regions for egg yolk moisture content based on a genome-wide association study



Eggs represent important sources of protein and are widely loved by consumers. Egg yolk taste is an important index for egg selection, and the moisture content of the egg yolk affects the taste. To understand the molecular mechanism underlying egg yolk moisture content, this study determined the phenotype and heritability of egg yolk water content and conducted a genome-wide association study (GWAS) using a mixed linear model.


We determined the phenotype and heritability of thermogelled egg yolk water content (TWC) and found that the average TWC was 47.73%. Moreover, significant variations occurred (41.06–57.12%), and the heritability was 0.11, which indicates medium-low heritability. Through the GWAS, 48 single nucleotide polymorphisms (SNPs) related to TWC (20 significantly, 28 suggestively) were obtained, and they were mainly located on chromosomes 10 and 13. We identified 36 candidate genes based on gene function and found that they were mainly involved in regulating fat, protein, and water content and embryonic development. FGF9, PIAS1, FEM1B, NOX5, GLCE, VDAC1, IGFBP7, and THOC5 were involved in lipid formation and regulation; AP3S2, GNPDA1, HSPA4, AP1B1, CABP7, EEF1D, SYTL3, PPP2CA, SKP1, and UBE2B were involved in protein folding and hydrolysis; and CSF2, SOWAHA, GDF9, FSTL4, RAPGEF6, PAQR5, and ZMAT5 were related to embryonic development and egg production. Moreover, MICU2, ITGA11, WDR76, BLM, ANPEP, TECRL, EWSR1, and P4HA2 were related to yolk quality, while ITGA11, WDR76, BLM, and ANPEP were potentially significantly involved in egg yolk water content and thus deserve further attention and research. In addition, this study identified a 19.31–19.92 Mb genome region on GGA10, and a linkage disequilibrium analysis identified strong correlations within this region. Thus, GGA10 may represent a candidate region for TWC traits.


The molecular genetic mechanism involved in TWC was revealed through heritability measurements and GWAS, which identified a series of SNPs, candidate genes, and candidate regions related to TWC. These results provide insights on the molecular mechanism of egg yolk moisture content and may aid in the development of new egg traits.

Peer Review reports


Eggs are rich in fat, minerals, and vitamins and considered an excellent source of protein [1]. The internal quality of eggs is an important index used by the poultry industry to evaluate eggs [2], and associated index factors include the protein height, Hastelloy unit, yolk texture, and yolk taste. Boiling can retain the nutrients in eggs to the greatest extent compared with other cooking processes and thus facilitates the absorption of nutrients from the eggs. However, the yolk taste of boiled eggs is often criticized by consumers [3]. Consumers may find that chewed thermogelled gel yolks are difficult to swallow because in the process of chewing and swallowing, thermogelled gel yolks absorb water from the mouth and throat, resulting in a dry throat and difficulty swallowing [4]. The moisture content in thermogelled gel yolks affects the fineness of the yolk texture, with a higher the moisture content leading to a finer texture [5]. Therefore, studying the thermogelled egg yolk water content (TWC) can help resolve the problem of poor yolk taste and provide a better understanding of the yolk texture.

Egg yolk is a valuable material component not only because of its nutritional components but also because of its sensory characteristics [6]. The complex composition of egg yolks endows these components with a unique texture and flavor, thereby promoting their wide use in food processing. During heat treatment, egg yolks become folded into a three-dimensional network structure and undergo irreversible deformation, changing from liquid to solid, which leads to the characteristic texture [7]. The substances in egg yolk play an important role in this process. The protein structure is destroyed under high temperature, which changes the spatial structure of yolk. Fat particles act as active fillers in the protein gel network based on interactions between proteins in the network [8]. Water is wrapped by proteins and lipids, which affects the fineness of the texture [9]. The water content in egg yolk is not only affected by the protein and lipid contents and environment of the egg but also by genetic factors [10]. However, the effects of genetic factors on egg yolk water content have not been previously studied.

Heritability can be used to evaluate the proportion of traits controlled by genetic factors. The higher the heritability, the greater the proportion of genetic factors and the smaller the proportion of environmental factors involved in a trait. Determining heritability can provide insights on the genetic mechanisms underlying traits. Whole genome sequencing is a sensitive, fast, and accurate technology [11]. Genome wide association analysis (GWAS) is a statistical tool and one of the effective methods of identifying important single nucleotide polymorphisms (SNPs) and candidate gene [12]. Compared with genotyping chips, the genes covered by high-throughput sequencing are more comprehensive. GWAS can identify important SNPs and candidate genes and may also be used in molecular breeding to shorten the period of poultry breeding [13]. At present, GWAS has been widely used to study the growth, reproduction and genetic structure of various species [14].

In this study, we selected a population of Rhode Island Red (RIR) chickens to determine the phenotype and heritability of the TWC. A GWAS of the whole genome was performed through the second generation, and high-throughput sequencing was conducted to obtain candidate genes that significantly affect the TWC. The findings should provide insights for domestic poultry breeding, and the identified molecular markers may assist in molecular breeding programs.

Materials and methods

Experimental animals

In this study, we selected 754 eggs from the RIR population to determine the water content of the yolks, and these eggs were randomly selected during the sampling process. In this experiment, all hens were raised in separate cages under the same conditions, and they were fed the same feed throughout the experiment. From 159 hens, blood sampling was performed from the vein under the wing, and the samples were used for DNA extraction.

Phenotype measurement

The pretreatment procedure for hot gel eggs was as follows: at one minute after a pot of water had come to a boil, the eggs were added and allowed to boil for five minutes, and then three minutes after turning off the flame, the eggs were removed. Whole yolks were then separated from the egg white and prepared for the experiment. We accurately weighed 2.0 g of gel sample from each egg and recorded it as M1. The sample was placed into an aluminum box, dried in an oven to a constant weight, and recorded as M2. The formula for calculating the water content was as follows: \(\text{w}\text{a}\text{t}\text{e}\text{r} \text{c}\text{o}\text{n}\text{t}\text{e}\text{n}\text{t}/\text{\%}=\frac{\text{M}1-\text{M}2}{\text{M}1}\ast 100\). This process was repeated three times for each sample.


DMU software (Version: 6 swMATH, Berlin, Germany) was used to estimate the variance and covariance components. An animal threshold model was used to analyze the heritability of TWC.

The animal model was constructed as follows:

$$y = X{\rm{ }}\beta + Za + e$$

where y represents the vector of phenotypic value, β represents the vector of “fixed” effects, a represents the vector of random additive genetic effects of all individuals, e represents the vector of random residuals, and X and Z are appropriate correlation matrices. The Gibbs sampling module included in the DMU software package was used to analyze the animal threshold [15], and the DMUAI module in the DMU program was used to analyze the classical animal model using the average information limited maximum likelihood algorithm [16].

High flux sequencing and quality control

We isolated individual genomic DNA from blood samples by the classical phenol chloroform method. DNA purity was assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA) and the A260/280 ratio. The whole genome was sequenced using the T7 platform of the Huada Company. Quality control was performed using Plink v1.9 [17], and the quality control standard included retaining SNPs with an allele frequency ≥ 1% and genotyping rate ≥ 98%. The individual quality control level was based on eliminating individuals whose genotype deletion rate was > 5%. SNPs in Hardy Weinberg equilibrium with P < 10 − 6 were excluded. After filtration, 159 chickens were retained for further analysis.

Population structure analysis

Before the GWAS, we first evaluated the population structure through a principal component analysis (PCA) implemented in PLINK 1.9 [18], which determined the overall structure and generate eigenvectors and eigenvalues. The plink ‘-- indep pairwise 25 5 0.2’ command was used to retain the relatively dependent SNPs. The PCA results were visualized using the “ggplot2” package in R studio. A PCA diagram was created using the first two principal components as horizontal and vertical coordinates.

Genome wide association study

The GWAS of TWC traits was carried out using the univariate linear mixed model in GEMMA [19]. The model was as follows:

y = W α + x β + u + ε.

where y represents the n × 1 dimensional quantitative trait phenotype value vector, W represents the n × c covariate matrix j, α represents the population structure matrix calculated by ADMIX software, x represents the marked genotype, β represents the corresponding effect of SNP, u represents the vector of random effect (its covariance structure follows the normal distribution of u ~ N (0, KVg), where K is the genome relationship matrix derived from independent SNP), Vg represents the additive variance of multiple genes, and ɛ represents the error vector. In this study, the Wald test was used as a criterion for selecting SNPs associated with metabolic efficiency traits.

The “qqman” package in R was used to generate Manhattan and quantile-quantile (Q-Q) plots [20]. The genome expansion factor (λ) was calculated with R. The traditional Bonferroni correction was too strict, which resulted in a high false negative rate and led to the exclusion of SNPs that were truly related to the traits. Therefore, simpleA was used as the independent test, and the effective number of independent tests was 8,685,008. The genome-wide significance level and genome-wide suggestive significance level were set to 5.75 × 10− 9 (0.05/8,685,008) and 8.24 × 10− 8 (0.72/8,685,008), respectively. Therefore, SNPs with P values below 8.24 × 10− 8 were considered and may be associated with TWC traits. A linkage disequilibrium (LD) analysis of significant SNPs was performed using the solid spin algorithm implemented in Haploview version 4.2.

Bioinformatics analysis of candidate genes

We annotated SNPs according to the Galgal 6.0 assembly supported by the Biomart tool in the Ensembl database ( and then searched for the nearest genes located within 400 kb upstream or downstream of important related SNPs to identify candidate genes. Then, we searched PubMed ( for the biological functions of these genes. To provide insights into the functional enrichment of candidate genes, we carried out Gene Ontology (GO) analyses using Metascape .


Phenotypic basic statistics and heritability test

The descriptive statistics and heritability test results for TWC are presented in Table 1. The average value of TWC was 47.73%, the minimum value was 41.06%, and the maximum value was 57.12%. These findings show that the TWC traits varied greatly from individual to individual. The heritability of the TWC traits was 0.11, which indicates medium-low heritability. The standard error of heritability shows that heritability had statistical significance. The determination of heritability showed that the TWC traits had a genetic basis.

Table 1 Descriptive statistics and heritability determination of the TWC traits

Population structure

Genetic analyses were conducted for 159 individuals. After a series of strict quality control procedures, all samples satisfied the criteria and could be used for subsequent research. In GWAS, population stratification may lead to false positive results. The PCA showed that the RIR population was somewhat stratified (Fig. 1). Thus, we conducted a GWAS and calculated the λ value. The value of λ was 1.024, which was close to 1, indicating that population stratification was not obvious and the population could be used for the GWAS analysis.

Fig. 1
figure 1

PCA plot of the population structure. The abscissa represents pc1, the ordinate represents pc2, and the point represents each individual

Genome-wide association analysis

The Q-Q and Manhattan plots of TWC are shown in Fig. 2. The Q-Q diagram and λ value indicated that obvious stratification did not occur in the population and the GWAS results were reliable. The Manhattan plot showed a global view of the P values (expressed in - log10 (P value)) of all SNPs. As shown in Figs. 2a and 48 SNPs were potentially related to TWC, of which 20 were significantly related and 28 were suggestively related (Table 2). Among the 48 SNPs, 25 genome-wide significant SNPs spanned a narrow 0.61 Mb region (19.31–19.92 Mb) on GGA10 (GGA for Gallus gallus), which represented the highest peak. Based on the analysis of significant and suggestive SNPs on GGA10 (Table 2), we found that 2 SNPs were located in the intergenic region, 7 important SNPs were located in the intron variant, 9 were located in the downstream gene variant, and 5 were located in the upstream gene variant. Based on the analysis of significant SNPs (Table 2), we found that 6 SNPs were located in the intergenic region, 17 important SNPs were located in the intron variant, 12 were located in the downstream gene variant, and 8 were located in the upstream gene variant. Significant and suggestive SNPs were mainly located on chromosomes 10 and 13. Ensembl was used to annotate the relevant SNPs, and genes located 400 kb upstream and downstream of important SNPs were searched (Table 2). The LD analysis showed that all significant SNPs within the whole genome were at high LD (Fig. 3), which increases the difficulty of identifying causal SNPs.

Table 2 Genome-wide SNPs around significant peaks associated with TWC traits
Fig. 2
figure 2

Q-Q plot and TWC traits in Manhattan Plot. The Q-Q plot shows the expected -log10 P-value (x-axis) versus the observed -log10 P-value (y-axis). In the Manhattan plot, the x-axis is the position of each SNP on the chicken chromosome (40 means Z chromosome), and the y-axis is the -log10 P-value. The horizontal red dashed line at the top represents the genome-wide significance threshold of 5.75 × 10− 9, and the bottom line represents the genome-wide implication threshold of 8.24 × 10− 8. a: Manhattan plot of TWC; b: Q-Q of TWC.

Fig. 3
figure 3

LD plots for significant SNPs. The top row represents the SNP position, and darker colors indicate greater LD intensity

SNP annotation and promising genes related to TWC

Using Ensembl to annotate related SNPs, we found a total of 151 potential candidate genes around the significant peaks (Table S1). We performed a GO analysis on the candidate genes (Fig. 4) and found that these genes were mainly enriched in the following terms: regulation of tyrosine phosphorylation of STAT protein (GO: 0042509), intracellular protein transport (GO: 0006886), positive regulation of protein catabolic process (GO: 0045732), and negative regulation of intracellular signal transduction (GO: 1,902,532). A search of the 151 candidate genes on PubMed revealed some important candidate genes (Table 3). FGF9, PIAS1, FEM1B, NOX5, GLCE, VDAC1, IGFBP7, and THOC5 are involved in the formation and regulation of lipids [21,22,23,24,25,26,27,28]; AP3S2, GNPDA1, HSPA4, AP1B1, CABP7, EEF1D, SYTL3, PPP2CA, SKP1, and UBE2B are involved in protein folding and hydrolysis [25, 29,30,31,32]; CSF2, SOWAHA, GDF9, FSTL4, RAPGEF6, PAQR5, and ZMAT5 are related to embryonic development and egg number laid [33,34,35,36,37,38,39]; CCDC157, Vps33B and SNX9 are involved in the endocytosis cycle and transport [40,41,42]; MICU2, ITGA11, WDR76, BLM, ANPEP, TECRL, EWSR1, and P4HA2 are associated with meat quality; and ITGA11, WDR76, BLM and ANPEP are related to meat quality water content [43,44,45,46,47,48,49] (Table 4).

Table 3 Details on the candidate genes that influence TWC traits in different ways
Table 4 Details on the 36 candidate genes that influence egg number traits in different ways
Fig. 4
figure 4

Heatmap of the top 20 candidate gene clusters and their representative enriched terms for TWC.


The texture of boiled egg yolk is an important factor affecting consumer acceptance and preference [3]. TWC affects the texture of egg yolk [5] and thus is an important character of egg yolk. At present, more studies have focused on the factors that influence chicken meat sensory characteristics [48], while fewer have investigated hot gel yolk characteristics. Moreover, the genetic mechanisms underlying TWC remain unclear. The lipid and protein content in the yolk also affect the water content in the yolk [50]. We measured the TWC of 754 hens’ eggs in the RIR population. The average TWC value was 47.73%, and the proportion of water in the yolk was high. The results showed that these TWC traits had significant variations (41.06–57.12%), which provides a basis for genetic research on TWC.

Here, we report the heritability of TWC traits for the first time. The heritability of TWC was 0.11, which indicates medium-low heritability. A previous study of chicken heritability showed that many traits presented medium-low heritability [51, 52]. The low heritability of TWC traits indicates that TWC is a complex trait that is affected by many factors. TWC is affected by protein and lipid contents. During the gelling process, the secondary structure of yolk protein is destroyed. The interactions between proteins and proteins, between proteins and lipids, and between proteins and water changed the spatial structure of yolk [53]. The spatial structure of protein and lipid during heating also affected water evaporation [54]. Another reason for the low heritability of TWC may be due to lack of selection for the trait in the breed. The disadvantages of this study are that the heritability of TWC traits of multiple varieties of chickens has not been tested and heritability estimates of the same character from different varieties of chicken will present deviations [55]. The determination of TWC heritability showed that TWC has a genetic basis and can be used for heritability research. Therefore, it is a heritable trait, which provides insights for poultry breeding.

To our knowledge, this is the first study to explore the TWC trait from a genetic perspective, and the results provide new ideas for the study of yolk texture. We performed a GWAS through the second generation and then conducted high-throughput sequencing. Moreover, the genetics of the yolk water content of 159 pure bred hens from the RIR population were studied. Some SNP sites and candidate genes related to the TWC trait were found. A significant peak was found on GGA10, and strong correlations were observed within the region from 19.31 to 19.92 Mb on GGA10. We found 48 SNPs that may be related to TWC (20 significantly, 28 suggestively) and identified 36 candidate genes according to their functions, which mainly affect TWC through the following three pathways: lipid and protein decomposition (FGF9, PIAS1, FEM1B, NOX5, AP3S2, GNPDA1, HSPA4, and AP1B1) [21,22,23,24,25,26,27,28], embryonic development (CSF2, SOWAHA, GDF9, FSTL4, RAPGEF6, PAQR5, and ZMAT5) [33,34,35], and texture and water content (MICU2, ITGA11, WDR76, BLM, ANPEP, TECRL, EWSR1, and P4HA2) [43,44,45,46,47,48,49]. ITGA11, WDR76, BLM, and ANPEP are located on GGA10 and likely have a significant impact on the TWC trait. ITGA11 encodes integrin subunit α 11 (same as β subunit 1) and dimerizes and forms cell surface collagen receptors that are involved in cell migration and collagen recombination and play a role in cell surface adhesion and signal transduction [56]. Studies have shown that ITGA11 plays an important role in regulating changes of drip loss in pigs and represents an important candidate gene for regulating water content [47]. Therefore, it is reasonable to speculate that ITGA11 has an important impact on TWC. WD40 repeat domain-containing protein 76 (WDR76) is a new Ras-regulated E3 ligase interacting protein, which controls 3T3-L1 adipocyte differentiation through HRAS stability regulation [57]. In zebrafish, BLM is necessary during the exponential proliferation of female GC and male meiosis, and the loss of BLM function will affect somatic and germ line cells [58]. Guo et al. found that WDR76 and BLM have an important impact on the water content of duck meat [48]. ANPEP is a digestive enzyme that cleaves amino acids from the N-terminal of peptides [59] and is involved in the biochemical process of developing flavor and texture attributes [60]. ANPEP may affect the moisture of the egg yolk and subsequently the texture of the yolk. Trans-2, 3-enoyl CoA reductase-like enzyme (TECRL) located on GGA4 is a protein coding gene involved in fatty acid metabolism, fatty acid elongation, and PUFA biosynthesis, and it has significant contribution to fatty acid accumulation and metabolic regulation [61]. Moreover, TECRL is involved in the regulation of water content. P4HA2 is related to collagen synthesis, meat tenderness, and protein content [49]. THOC5 is a newly identified gene related to lipid levels and involved in high-density lipoprotein cholesterol metabolism, which affects the generation and regulation of fat [62]. These genes may play an important role in controlling TWC.

TWC is a complex trait determined by multiple genes and environmental factors. Previous studies showed that the genes ITGA11, WDR76, BLM, and ANPEP have a significant impact on water content and meat quality [48, 59]. This study also confirmed that ITGA11, WDR76, BLM, and ANPEP genes have a significant impact on TWC and likely represent the most important candidate genes for TWC. Additional research is needed before the genetics of TWC are fully understood. Moreover, the heritability and GWAS evaluations of TWC performed in this study provided helpful information for understanding the factors that influence yolk texture.


In this study, the phenotype and heritability of TWC traits in the RIR population were determined, indicating that TWC is a measurable, variable, and heritable trait with a genetic basis. A GWAS of TWC revealed a series of SNP sites and candidate genes related to TWC. ITGA11, WDR76, BLM, and ANPEP may play a significant role in TWC. These results may help us better understand the molecular mechanisms underlying egg yolk water content and provide insights for poultry breeding.

Data Availability

The data that support the findings of this study are openly available on the. SRA database under Bioproject accession PRJNA916197.




thermogelled egg yolk water content


genome-wide association study


single nucleotide polymorphisms


Rhode Island Red


  1. Liu Z, Sun C, Yan Y, Li G, Shi F, Wu G, Liu A, Yang N. Genetic variations for egg quality of chickens at late laying period revealed by genome-wide association study. Sci Rep. 2018;8(1):10832.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Perini F, Cendron F, Rovelli G, Castellini C, Cassandro M, Lasagna E. Emerging Genetic Tools to Investigate Molecular Pathways Related to Heat Stress in Chickens: A Review. In: Animals., vol. 11; 2021, 46.

  3. Zhang R, Li X, Ma Y, Liu Y, Zhang Y, Cheng X, Ning Z. Identification of candidate genomic regions for thermogelled egg yolk traits based on a genome-wide association study.POULTRY SCI2022:102402.

  4. Li J, Xu L, Su Y, Chang C, Yang Y, Gu L. Flocculation behavior and gel properties of egg yolk/κ-carrageenan composite aqueous and emulsion systems: Effect of NaCl. FOOD RES INT. 2020;132:108990.

    Article  CAS  PubMed  Google Scholar 

  5. Li J, Li X, Wang C, Zhang M, Xu Y, Zhou B, Su Y, Yang Y. Characteristics of gelling and water holding properties of hen egg white/yolk gel with NaCl addition. FOOD HYDROCOLLOID. 2018;77:887–93.

    Article  CAS  Google Scholar 

  6. Razi SM, Fahim H, Amirabadi S, Rashidinejad A. An overview of the functional properties of egg white proteins and their application in the food industry. FOOD HYDROCOLLOID. 2023;135:108183.

    Article  CAS  Google Scholar 

  7. Zhang R, Deng J, Li X, Shang W, Ning Z. Research note: comparison of the texture, structure, and composition of eggs from local chinese chickens and a highly selected line of egg-type chickens and analysis of the effects of lipids on texture. Poult SCI. 2022;101(8):101934.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kalkani A, Paraskevopoulou A, Kiosseoglou V. Protein interactions and filler effects in heat-set gels based on egg. FOOD HYDROCOLLOID. 2007;21(2):191–7.

    Article  CAS  Google Scholar 

  9. Goto T, Shimamoto S, Takaya M, Sato S, Takahashi K, Nishimura K, Morii Y, Kunishige K, Ohtsuka A, Ijiri D. Impact on genetic differences among various chicken breeds on free amino acid contents of egg yolk and albumen. SCI REP-UK. 2021;11(1):2270.

    Article  CAS  Google Scholar 

  10. Franco D, Rois D, Arias A, Justo JR, Marti-Quijal FJ, Khubber S, Barba FJ, López-Pedrouso M, Manuel Lorenzo J. Effect of Breed and Diet Type on the Freshness and Quality of the Eggs: A Comparison between Mos (Indigenous Galician Breed) and Isa Brown Hens. Foods (Basel, Switzerland) 2020, 9(3):342.

  11. Jiang L, Liu X, Yang J, Wang H, Jiang J, Liu L, He S, Ding X, Liu J, Zhang Q. Targeted resequencing of GWAS loci reveals novel genetic variants for milk production traits. BMC Genomics. 2014;15(1):1105.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Zhang GX, Fan QC, Wang JY, Zhang T, Xue Q, Shi HQ. Genome-wide association study on reproductive traits in Jinghai Yellow Chicken. ANIM REPROD SCI. 2015;163:30–4.

    Article  CAS  PubMed  Google Scholar 

  13. Eltaher S, Baenziger PS, Belamkar V, Emara HA, Nower AA, Salem KFM, Alqudah AM, Sallam A. GWAS revealed effect of genotype × environment interactions for grain yield of Nebraska winter wheat. BMC Genomics. 2021;22(1):2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Cole JB, Wiggans GR, Ma L, Sonstegard TS, Lawlor TJ, Crooker BA, Van Tassell CP, Yang J, Wang S, Matukumalli LK, et al. Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary U.S. Holstein cows. BMC Genomics. 2011;12(1):408.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Ødegård J, Meuwissen THE, Heringstad B, Madsen P. A simple algorithm to estimate genetic variance in an animal threshold model using bayesian inference. Genet selection evolution: GSE. 2010;42(1):29.

    Article  PubMed Central  Google Scholar 

  16. Li X, Nie C, Zhang Z, Wang Q, Shao P, Zhao Q, Chen Y, Wang D, Li Y, Jiao W, et al. Evaluation of genetic resistance to Salmonella Pullorum in three chicken lines. Poult SCI. 2018;97(3):764–9.

    Article  CAS  PubMed  Google Scholar 

  17. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, et al. PLINK: a Tool Set for whole-genome Association and Population-Based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. William A, David JB. Population structure and cryptic relatedness in Genetic Association Studies. STAT SCI. 2009;24(4):451–71.

    Google Scholar 

  19. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. NAT GENET. 2012;44(7):821–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Shen M, Qu L, Ma M, Dou T, Lu J, Guo J, Hu Y, Yi G, Yuan J, Sun C et al. Genome-Wide Association Studies for Comb Traits in Chickens. PLOS ONE 2016, 11(7):e159081.

  21. Cui S, Li X, Li R, Zhang H, Wang Y, Li Y, Zhu J, Li Z, Lin Y. FGF1 promotes the differentiation of goat intramuscular and subcutaneous preadipocytes.ANIM BIOTECHNOL2021:1–13.

  22. Akil A, Song P, Peng J, Gondeau C, Samuel D, Gassama-Diagne A. PIAS1 Regulates Hepatitis C Virus-Induced Lipid Droplet Accumulation by Controlling Septin 9 and Microtubule Filament Assembly. In: Pathogens., vol. 10; 2021.

  23. Silva ÉF, Lopes MS, Lopes PS, Gasparino E. A genome-wide association study for feed efficiency-related traits in a crossbred pig population. ANIMAL. 2019;13(11):2447–56.

    Article  CAS  PubMed  Google Scholar 

  24. García JG, Ansorena E, Milagro FI, Zalba G, de Miguel C. Endothelial Nox5 Expression Modulates Glucose Uptake and Lipid Accumulation in Mice Fed a High-Fat Diet and 3T3-L1 Adipocytes Treated with Glucose and Palmitic Acid. In:International Journal of Molecular Sciences., vol. 22; 2021.

  25. Guan X, Zhao S, Xiang W, Jin H, Chen N, Lei C, Jia Y, Xu L. Genetic Diversity and Selective Signature in Dabieshan Cattle Revealed by Whole-Genome Resequencing. In: Biology., vol. 11; 2022.

  26. Yang L, Zheng X, Mo C, Li S, Liu Z, Yang G, Zhao Q, Li S, Mou C. Transcriptome analysis and identification of genes associated with chicken sperm storage duration. Poult SCI. 2020;99(2):1199–208.

    Article  CAS  PubMed  Google Scholar 

  27. Mazloomi S, Sanoeei FM, Tayebinia H, Karimi J, Amiri I, Abbasi E, Khodadadi I. The Association of mitochondrial translocator protein and voltage-dependent Anion Channel-1 in Granulosa cells with estradiol levels and Presence of immature follicles in polycystic ovary syndrome. J Reprod Infertil. 2022;23(3):148–59.

    PubMed  PubMed Central  Google Scholar 

  28. Xu Z, Mei S, Zhou J, Zhang Y, Qiao M, Sun H, Li Z, Li L, Dong B, Oyelami FO et al. Genome-Wide Assessment of Runs of Homozygosity and Estimates of Genomic Inbreeding in a Chinese Composite Pig Breed.FRONT GENET2021,12.

  29. Vastagh C, Solymosi N, Farkas I, Liposits Z. Proestrus differentially regulates expression of Ion Channel and Calcium Homeostasis genes in GnRH neurons of mice. FRONT MOL NEUROSCI; 2019. p. 12.

  30. Han J, Ma S, Liang B, Bai T, Zhao Y, Ma Y, MacHugh DE, Ma L, Jiang L. Transcriptome Profiling of Developing Ovine Fat Tail Tissue Reveals an Important Role for MTFP1 in Regulation of Adipogenesis.Frontiers in Cell and Developmental Biology2022,10.

  31. Zhao L, Zhang D, Li X, Zhang Y, Zhao Y, Xu D, Cheng J, Wang J, Li W, Lin C, et al. Comparative proteomics reveals genetic mechanisms of body weight in Hu sheep and Dorper sheep. J Proteom. 2022;267:104699.

    Article  CAS  Google Scholar 

  32. Dai W, Wang Q, Zhao F, Liu J, Liu H. Understanding the regulatory mechanisms of milk production using integrative transcriptomic and proteomic analyses: improving inefficient utilization of crop by-products as forage in dairy industry. BMC Genomics. 2018;19(1):403.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Yurchenko AA, Daetwyler HD, Yudin N, Schnabel RD, Vander JC, Soloshenko V, Lhasaranov B, Popov R, Taylor JF, Larkin DM. Scans for signatures of selection in russian cattle breed genomes reveal new candidate genes for environmental adaptation and acclimation. Sci Rep. 2018;8(1):12984.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Zhong C, Liu Z, Qiao X, Kang L, Sun Y, Jiang Y. Integrated transcriptomic analysis on small yellow follicles reveals that sosondowah ankyrin repeat domain family member a inhibits chicken follicle selection. Anim Biosci. 2021;34(8):1290–302.

    Article  CAS  PubMed  Google Scholar 

  35. Xiang X, Huang X, Wang J, Zhang H, Zhou W, Xu C, Huang Y, Tan Y, Yin Z. Transcriptome Analysis of the Ovaries of Taihe Black-Bone Silky Fowls at Different Egg-Laying Stages. In: Genes., vol. 13; 2022.

  36. Lu X, Abdalla IM, Nazar M, Fan Y, Zhang Z, Wu X, Xu T, Yang Z. Genome-Wide Association Study on Reproduction-Related Body-Shape Traits of Chinese Holstein Cows.Animals (Basel)2021, 11(7).

  37. Sun Z, Zhang Z, Liu Y, Ren C, He X, Jiang Y, Ouyang Y, Hong Q, Chu M. Integrated Analysis of mRNAs and Long Non-Coding RNAs Expression of Oviduct That Provides Novel Insights into the Prolificacy Mechanism of Goat (Capra hircus). In: Genes., vol. 13; 2022.

  38. Wang Y, Ding X, Tan Z, Xing K, Yang T, Wang Y, Sun D, Wang C. Genome-wide association study for reproductive traits in a large White pig population. ANIM GENET. 2018;49(2):127–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zhao Q, Chen J, Zhang X, Xu Z, Lin Z, Li H, Lin W, Xie Q. Genome-Wide Association Analysis Reveals Key Genes Responsible for Egg Production of Lion Head Goose.FRONT GENET2020,10.

  40. Bassaganyas L, Popa SJ, Horlbeck M, Puri C, Stewart SE, Campelo F, Ashok A, Butnaru CM, Brouwers N, Heydari K, et al. New factors for protein transport identified by a genome-wide CRISPRi screen in mammalian cells. J CELL BIOL. 2019;218(11):3861–79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Galmes R, Ten Brink C, Oorschot V, Veenendaal T, Jonker C, van der Sluijs P, Klumperman J. Vps33B is required for delivery of endocytosed cargo to lysosomes. TRAFFIC. 2015;16(12):1288–305.

    Article  CAS  PubMed  Google Scholar 

  42. Soulet F, Yarar D, Leonard M, Schmid SL. SNX9 regulates dynamin assembly and is required for efficient clathrin-mediated endocytosis. MOL BIOL CELL. 2005;16(4):2058–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Li Y, Chen Z, Fang Y, Cao C, Zhang Z, Pan Y, Wang Q. Runs of Homozygosity Revealed Reproductive Traits of Hu Sheep. In: Genes., vol. 13; 2022.

  44. Chen C, Shiue Y, Yen C, Tang P, Chang H, Lee Y. Laying traits and underlying transcripts, expressed in the hypothalamus and pituitary gland, that were associated with egg production variability in chickens. Theriogenology. 2007;68(9):1305–15.

    Article  CAS  PubMed  Google Scholar 

  45. San J, Du Y, Wu G, Xu R, Yang J, Hu J. Transcriptome analysis identifies signaling pathways related to meat quality in broiler chickens – the extracellular matrix (ECM) receptor interaction signaling pathway. Poult SCI. 2021;100(6):101135.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wang X, Ran X, Niu X, Huang S, Li S, Wang J. Whole-genome sequence analysis reveals selection signatures for important economic traits in Xiang pigs. SCI REP-UK. 2022;12(1):11823.

    Article  CAS  Google Scholar 

  47. Zhao X, Wang C, Wang Y, Lin H, Wang H, Hu H, Wang J. Comparative gene expression profiling of muscle reveals potential candidate genes affecting drip loss in pork. BMC GENET. 2019;20(1):89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Guo Q, Huang L, Bai H, Wang Z, Bi Y, Chen G, Jiang Y, Chang G. Genome-Wide Association Study of Potential Meat Quality Trait Loci in Ducks. In: Genes., vol. 13; 2022.

  49. Wen Y, Li S, Bao G, Wang J, Liu X, Hu J, Zhao F, Zhao Z, Shi B, Luo Y. Comparative Transcriptome Analysis Reveals the Mechanism Associated With Dynamic Changes in Meat Quality of the Longissimus Thoracis Muscle in Tibetan Sheep at Different Growth Stages.Frontiers in Veterinary Science2022,9.

  50. Xu L, Gu L, Su Y, Chang C, Wang J, Dong S, Liu Y, Yang Y, Li J. Impact of thermal treatment on the rheological, microstructural, protein structures and extrusion 3D printing characteristics of egg yolk. FOOD HYDROCOLLOID. 2020;100:105399.

    Article  CAS  Google Scholar 

  51. Rozempolska-Rucinska I, Zieba G, Lukaszewicz M. Heritability of individual egg hatching success versus hen hatchability in layers. Poult SCI. 2013;92(2):321–4.

    Article  CAS  PubMed  Google Scholar 

  52. Berthelot F, Beaumont C, Mompart F, Girard-Santosuosso O, Pardon P, Duchet-Suchaux M. Estimated heritability of the resistance to cecal carrier state of Salmonella enteritidis in chickens. Poult SCI. 1998;77(6):797–801.

    Article  CAS  PubMed  Google Scholar 

  53. Zhao Y, Chen Z, Li J, Xu M, Shao Y, Tu Y. Formation mechanism of ovalbumin gel induced by alkali. FOOD HYDROCOLLOID. 2016;61:390–8.

    Article  CAS  Google Scholar 

  54. Xiang X, Liu Y, Liu Y, Wang X, Jin Y. Changes in structure and flavor of egg yolk gel induced by lipid migration under heating. FOOD HYDROCOLLOID. 2020;98:105257.

    Article  CAS  Google Scholar 

  55. Dou D, Shen L, Zhou J, Cao Z, Luan P, Li Y, Xiao F, Guo H, Li H, Zhang H. Genome-wide association studies for growth traits in broilers. BMC Genom Data. 2022;23(1):1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Wu P, Wang Y, Wu Y, Jia Z, Song Y, Liang N. Expression and prognostic analyses of ITGA11, ITGB4 and ITGB8 in human non-small cell lung cancer. PEERJ. 2019;7:e8299.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Yang J, Wang F, Chen B. The role of WDR76 protein in human diseases. Bosn J Basic Med Sci. 2021;21(5):528–34.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Annus T, Müller D, Jezsó B, Ullaga G, Németh B, Harami GM, Orbán L, Kovács M, Varga M. Bloom syndrome helicase contributes to germ line development and longevity in zebrafish. CELL DEATH DIS. 2022;13(4):363.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Jiang S, El-Senousey HK, Fan Q, Lin X, Gou Z, Li L, Wang Y, Fouad AM, Jiang Z. Effects of dietary threonine supplementation on productivity and expression of genes related to protein deposition and amino acid transportation in breeder hens of yellow-feathered chicken and their offspring. Poult SCI. 2019;98(12):6826–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Pena RN, Gallardo D, Guàrdia MD, Reixach J, Arnau J, Amills M, Quintanilla R. Appearance, flavor, and texture attributes of pig dry-cured hams have a complex polygenic genomic architecture1. J ANIM SCI. 2013;91(3):1051–8.

    Article  CAS  PubMed  Google Scholar 

  61. Xie Y, Liu Z, Guo J, Su X, Zhao C, Zhang C, Qin Q, Dai D, Zhao Y, Wang Z et al. MicroRNA-mRNA Regulatory Networking Fine-Tunes Polyunsaturated Fatty Acid Synthesis and Metabolism in the Inner Mongolia Cashmere Goat.FRONT GENET2021,12.

  62. Loja-Chango R, Salazar-Pousada D, Escobar-Valdivieso GS, Ramírez-Morán C, Espinoza-Caicedo J, Pérez-López FR, Gavilanes AWD, Chedraui P. Polymorphism of the THOC5 of the transcription/export multiprotein complex and its correlation with the lipid and metabolic profile in middle-aged women. GYNECOL ENDOCRINOL. 2020;36(3):243–6.

    Article  CAS  PubMed  Google Scholar 

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We are grateful for all of our colleagues of the Poultry Team of the National Engineering Laboratory for Animal Breeding of China Agricultural University.


This work was funded in part by grants from the China Agricultural Research System (CARS-40).

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ZN conceived and designed the experiment and revised the manuscript; RZ, FY, XC and MY collected the experimental samples and data; RZ, FY and XC analyzed the data; and RZ wrote the manuscript. All authors read and approved the final draft.

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Correspondence to Zhonghua Ning.

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Zhang, R., Yao, F., Cheng, X. et al. Identification of candidate genomic regions for egg yolk moisture content based on a genome-wide association study. BMC Genomics 24, 110 (2023).

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  • Thermogelled egg yolks
  • Water content
  • Heritability
  • Genome-wide association study