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. 2021 Mar 12;12:602583. doi: 10.3389/fgene.2021.602583

Genome-Wide Association Study-Based Identification of SNPs and Haplotypes Associated With Goose Reproductive Performance and Egg Quality

Guangliang Gao 1,2,3,*,†, Dengfeng Gao 4,†, Xianzhi Zhao 1,3,†, Songsong Xu 5, Keshan Zhang 1,3, Rui Wu 1,3, Chunhui Yin 1,3, Jing Li 1,3, Youhui Xie 1,3, Silu Hu 2,*, Qigui Wang 1,3,*
PMCID: PMC7994508  PMID: 33777090

Abstract

Geese are one of the most economically important waterfowl. However, the low reproductive performance and egg quality of geese hinder the development of the goose industry. The identification and application of genetic markers may improve the accuracy of beneficial trait selection. To identify the genetic markers associated with goose reproductive performance and egg quality traits, we performed a genome-wide association study (GWAS) for body weight at birth (BBW), the number of eggs at 48 weeks of age (EN48), the number of eggs at 60 weeks of age (EN60) and egg yolk color (EYC). The GWAS acquired 2.896 Tb of raw sequencing data with an average depth of 12.44× and identified 9,279,339 SNPs. The results of GWAS showed that 26 SNPs were significantly associated with BBW, EN48, EN60, and EYC. Moreover, five of these SNPs significantly associated with EN48 and EN60 were in a haplotype block on chromosome 35 from 4,512,855 to 4,541,709 bp, oriented to TMEM161A and another five SNPs significantly correlated to EYC were constructed in haplotype block on chromosome 5 from 21,069,009 to 21,363,580, which annotated by TMEM161A, CALCR, TFPI2, and GLP1R. Those genes were enriched in epidermal growth factor-activated receptor activity, regulation of epidermal growth factor receptor signaling pathway. The SNPs, haplotype markers, and candidate genes identified in this study can be used to improve the accuracy of marker-assisted selection for the reproductive performance and egg quality traits of geese. In addition, the candidate genes significantly associated with these traits may provide a foundation for better understanding the mechanisms underlying reproduction and egg quality in geese.

Keywords: goose, GWAS - genome-wide association study, marker-assisted selection (MAS), egg production, egg yolk color, SNP

Introduction

As one of the most economically important waterfowl, geese are widely raised for their eggs, meat, liver, feathers, and other byproducts (Boz et al., 2017). Geese are a seasonally breeding waterfowl; that is, their reproduction is regulated by natural light, and they experience a non-laying period during the year (Wang et al., 2005; Liu et al., 2020), resulting in low reproductive performance. Broodiness can further deteriorate goose reproduction, hindering the development of the goose industry (Yu J. et al., 2016).

Eggs are inexpensive source with essential vitamins and minerals (Pires et al., 2019). In an egg, the yolk is rich in various amino acids, fatty acids, vitamins, and minerals, which provide enough nutrition for the developing embryo (Zaheer, 2017; Zhu et al., 2020). The shell is also an essential component of the egg, as it provides calcium for embryonic development (Samiullah and Roberts, 2014). Additionally, the shell quality affects critical economic indexes such as embryo viability, hatchability, egg production, and the breakage rate (Yamak et al., 2016; Guoxian et al., 2017; Farghly et al., 2019). Given the importance of goose reproductive performance and egg quality, researchers focused on improving the production and quality of egg of goose (Ouyang et al., 2020; Zhao et al., 2020).

In birds, the body weight at birth, egg production and egg yolk color traits are quantitative traits, presumed to be controlled by numerous genes or loci in the genome with minor effects, and also influenced by multiple factors, including genetic factors, diet, housing, environment, management, disease, and other factors, such as environment, diet, housing practice (Dikmen et al., 2016; Mwaniki et al., 2018; Gou et al., 2020; Nemati et al., 2020; Zhao et al., 2020). Compared with other technologies, whole-genome resequencing technology is a high-throughput sequencing approach with efficient, accurate, full information characteristics (Fuentes-Pardo and Ruzzante, 2017). Moreover, whole-genome resequencing is one of the efficient and powerful strategies to screen the genes or loci in the genome widely, especially for the unknown gene and structural variation, which have been widely applied in marker-assisted selection in animals, such as the chicken (Liu et al., 2018b, 2019), duck (Rowland et al., 2018; Zhou et al., 2018), goose (Zhao et al., 2020), cattle (Freebern et al., 2020), pig (Zhang et al., 2019), and sheep (Kominakis et al., 2017).

Genome-wide association studies (GWASs) are often used to screen for genes or markers associated with specific traits (Zhang et al., 2012). To date, a number of genes or markers associated with reproductive performance, egg quality, and other economically important traits have been identified in poultry. These genes or markers could improve these economic traits through marker-assisted selection (Liu et al., 2018a; Zhou et al., 2018; Du et al., 2020). However, few genes and markers associated with the reproductive performance and egg quality of geese have been identified.

In this study, we employed a whole-genome resequencing approach to identify single nucleotide polymorphisms (SNPs) in the genome of the Sichuan goose; a GWAS analysis was then used to screen the corresponding genomic regions and candidate genes for the reproductive performance and egg quality traits. Moreover, MALDI-TOP MS method were used to verify the SNPs and haplotype blocks that are significantly associated with the traits in goose population. This work will lay a foundation for further studies on goose traits to facilitate genetic selection.

Materials and Methods

Ethics Statement

All experiments involving animals were performed according to the laws and regulations established by the Ministry of Agriculture of China (Beijing, China). This study was approved by the Animal Care and Welfare Committee of the Chinese Chongqing Academy of Animal Science (Chongqing, China).

Experimental Animals and Phenotypic Traits

A total of 209 Sichuan white geese were used in this study. Each bird was reared from birth to the non-laying period (65 weeks). All individuals were housed in single cages (600 mm × 800 mm × 900 mm) at the AnFu Waterfowl Breeding Base in Chongqing City, China (105.478°N, 29.343°E). All geese were raised under natural temperatures and artificial lighting (12 h of light per day) and fed the same diet with free access to water. At week 28, blood samples were collected from the wings of all geese using vacuum tubes containing ethylenediaminetetraacetic acid. These blood samples were used for DNA extraction.

During the egg-laying period (weeks 28–64), we collected eggs four times a day and marked the eggs, which allowed us to trace the traits to the specific one in the future study. The body weight at birth (BBW) and the number of eggs at 48 weeks of age (EN48) or 60 weeks of age (EN60) were individually recorded and statistic. The three consecutive laid eggs from 209 individuals in a week during the experimental period (from 35 to 40 weeks) were used to evaluate the egg yolk color (EYC) using the Roche Yolk Color Fan (1–15: light yellow to orange) (Islam et al., 2017).

Whole-Genome Resequencing and SNP Calling

We extracted genomic DNA from whole blood samples using a genomic DNA extraction kit (DP332; Tiangen Biotech, Beijing, China). The DNA concentration and quality were determined using a NanoVue spectrophotometer (Cytiva Life Sciences, Marlborough, MA, United States) and agarose gel electrophoresis to verify the quality requirements of the Illumina sequencing platform.

Whole-genome resequencing data were obtained using the Illumina HiSeq X Ten platform (Illumina, San Diego, CA, United States). After read filtering, the high-quality reads were mapped to the goose genome at the chromosome-level (Li et al., 2020) using BWA software (Li and Durbin, 2009). SNP calling was conducted using GATK (parameters: -R -ERC GVCF –genotyping-mode DISCOVERY –tmp-dir -I -O) software (McKenna et al., 2010).

Quality Control and Population Structure Analysis

Quality control was conducted at both the individual and SNP levels using Plink (Purcell et al., 2007) with parameters of –geno 0.1 –mind 0.1 –maf 0.05 –hwe 0.0000001 (10–7). After quality control, 9,279,339 SNPs were identified of 209 individuals. Then, we performed principal component analysis (PCA) to assess goose population stratification with the application of Plink. Furtherly, the top two PCs (PC1 to PC2) were picked and plotted with R.

GWAS and SNP-Based Haplotype Blocks

In this study, we performed GWAS assuming a random population without considering the familial relationships. The GWAS analysis between the traits (BBW, EN48, EN60, and EYC) and the SNPs was performed with genome-wide efficient mixed model association (GEMMA) software (Zhou and Stephens, 2012) using a mixed linear model as follows: y = Wα + xβ + ε, where y is the phenotypic value for all individuals, W is a covariance matrix used to control the population structure (fixed effects: PC1 and PC2), α is a vector of the corresponding coefficients including the intercept, x is the genotype of the SNP or haplotype marker, β is the effect size of the SNP or haplotype marker for the phenotypes, and ε is a vector of random residuals. The Wald test statistic was used to calculate the significance of the associations between the SNPs and phenotypes, while the association analysis results were corrected using Bonferroni’s correction (Threshold value was 1e–7).

The genotypes of the significantly associated SNPs identified by GWAS were determined by iPLEX matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) of the MassARRAY genotyping platform (Agena Bioscience, San Diego, CA, United States). Amplification and extension primers (Supplementary Table 1) were designed to amplify the target sequences and to hybridize and elongate the fragments at the nucleotide of interest, respectively.

For all of the traits, Manhattan plots were generated using the GAP package1, and quantile–quantile (QQ) plots were drawn using the qqman package2; both were performed in R project software. We used the GenABEL package to calculate the extent of false-positive signals in the results using the genomic inflation factor (k) (Karssen et al., 2016).

We assess the Linkage disequilibrium (LD) structure between significantly associate SNPs identified by MALDI-TOF MS method using Haploview software (version 4.2)3 (Barrett et al., 2005). Association of the SNPs and haplotypes with phenotypic data was carried out using a general linear model in JMP version 13.0 as follows: y = μ + x + ε, where y is the phenotypic value for each goose, μ is the population mean, x represents genotype, and ε is the random error. The least-squares mean of each genotype or haplotype block was then calculated, and differences between the genotypes were analyzed using the Bonferroni test.

SNP Annotation

BEDTools software (Quinlan, 2014) was used to extract genetic information from 500-kb regions upstream and downstream of each potential SNP in the goose genome, while SNP annotation was conducted using Annovar software (Wang et al., 2010) (SnpEff, Annovar, VEP, and Oncotator). Functional annotation analysis of the candidate genes was performed using the Metascape website4 (Zhou et al., 2019).

Results

Phenotypic Description and SNP Calling

The body weight (BBW), laying performance (EN48 and EN60), and egg quality (EYC) of all 209 Sichuan white geese were recorded during the laying period (Table 1). A total of 2.896 Tb of raw whole-genome resequencing data were acquired from 209 female Sichuan white geese, with an average depth coverage of 12.44×. After filtering, 2.891 Tb of high-quality sequencing data were mapped to the goose reference genome sequence, with an average mapping rate of 98.15% (96.58–98.38%) (Supplementary Table 2). A total of 9,279,339 SNPs identified in the goose genome were used for further analysis (Supplementary Figure 1).

TABLE 1.

The statistic of the BBW, EN48, EN60, and EYC in Sichuan White goose.

Traits Number Mean Standard deviation Minimun Maximun CV (%)
BBW, g 160 88.89 9.07 66 109 10.20
EN48 207 35.37 10.76 8.00 58.00 30.42
EN60 208 61.16 17.57 16.00 95.00 28.73
EYC 209 5.60 1.52 2.00 18.09 27.14
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Genome-Wide Association Study

Principal component analysis revealed slight stratification within the goose population (Figure 1). Therefore, we used a mixed linear model in the GEMMA software package to adjust for variations in population structure and conducted a GWAS analysis for the selected traits. In total, 26 SNPs were found to be significantly associated with the traits (Table 2) and were located within 14 genes (Table 2). The Manhattan and QQ plots for these traits are shown in Figure 2.

FIGURE 1.

FIGURE 1

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Principle component analysis for goose population using SNP markers. (The points denote individuals).

TABLE 2.

Summary of the 26 SNP markers BBW, EN48, EN60, and EYC s in goose.

SNP Chromosome Position (bp) P-value Traits Location/nearest gene Gene description
SNP16 chr35 4525296 8.29 × 10–09 EN64 TMEM161A Transmembrane protein 161A
SNP17 chr35 4525312 8.29 × 10–09 EN64 TMEM161A Transmembrane protein 161A
SNP222 chr33 1516391 1.01 × 10–07 BBW SMAP2 Stromal membrane-associated protein 2
SNP284 chr13 20688708 6.79 × 10–08 EN48 PGM2L Glucose 1,6-bisphosphate synthase
SNP283 chr3 49267770 4.03 × 10–08 EN48 DP13B DCC-interacting protein 13-beta
SNP285 chr3 49260834 7.62 × 10–08 EN48 DP13B DCC-interacting protein 13-beta
SNP286 chr3 38384739 8.20 × 10–08 EN48 RTJK RNA-directed DNA polymerase from mobile element jockey
SNP281 chr35 4541709 1.64 × 10–08 EN48 TMEM161A Transmembrane protein 161A
SNP282 chr35 4530609 3.59 × 10–08 EN48 TMEM161A Transmembrane protein 161A
SNP18 chr35 4512855 1.08 × 10–07 EN60 S2542 Mitochondrial coenzyme A transporter SLC25A42
SNP287 chr35 4525296 8.29 × 10–09 EN60 TMEM161A Transmembrane protein 161A
SNP288 chr35 4525312 8.29 × 10–09 EN60 TMEM161A Transmembrane protein 161A
SNP198 chr26 443100 9.69 × 10–08 EYC ZBT46 Zinc finger and BTB domain-containing protein 46
SNP188 chr30 3532845 8.33 × 10–09 EYC MBB1A Myb-binding protein 1A-like protein
SNP189 chr30 3536078 1.45 × 10–08 EYC MBB1A Myb-binding protein 1A-like protein
SNP190 chr30 3536533 1.45 × 10–08 EYC MBB1A Myb-binding protein 1A-like protein
SNP191 chr36 1514622 2.44 × 10–08 EYC CB042 Uncharacterized protein C2orf42
SNP192 chr36 1514623 2.44 × 10–08 EYC CB042 Uncharacterized protein C2orf42
SNP196 chr36 1510734 5.65 × 10–08 EYC CB042 Uncharacterized protein C2orf42
SNP193 chr5 25366779 2.91 × 10–08 EYC CACB2 Voltage-dependent L-type calcium channel subunit beta-2
SNP194 chr5 25366791 2.91 × 10–08 EYC CACB2 Voltage-dependent L-type calcium channel subunit beta-2
SNP183 chr5 21235021 2.95 × 10–13 EYC CALCR Calcitonin receptor
SNP184 chr5 21235070 9.24 × 10–11 EYC CALCR Calcitonin receptor
SNP186 chr5 21249496 7.45 × 10–10 EYC CALCR Calcitonin receptor
SNP187 chr5 21363580 6.31 × 10–09 EYC CALCR Calcitonin receptor
SNP185 chr5 21232365 2.43 × 10–10 EYC GLP1R Glucagon-like peptide 1 receptor
SNP197 chr5 21069009 6.13 × 10–08 EYC TFPI2 Tissue factor pathway inhibitor 2
SNP195 ctg2610 34314 3.30 × 10–08 EYC CCM2 Cerebral cavernous malformations protein 2 homolog
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FIGURE 2.

FIGURE 2

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Manhattan and Q-Q plots for BBW (A,B), EN48 (C,D), EN60 (E,F), and EYC (G,H) traits.

We performed functional enrichment analysis for the reproductive (EN48 and EN60) and egg quality traits (EYC). The 37 Gene Ontology (GO) terms were significantly enriched (p < 0.05) for the EN48 and EN60 traits and included “response to radiation” (GO: 0009314, p = 2.05 × 10–5), “response to light stimulus” (GO: 0009416, p = 7.87 × 10–3), and “cellular response to hormone stimulus” (GO: 0032870, p = 2.16 × 10–2) (Supplementary Table 3 and Figure 3). The 21 GO terms were significantly enriched (p < 0.05) for the EYC trait, including “regulation of epidermal growth factor-activated receptor activity” (GO: 0007176, p = 2.00 × 10–5), “regulation of signaling receptor activity” (GO: 0010469, p = 3.20 × 10–4) and “regulation of epidermal growth factor receptor signaling pathway” (GO: 0042058, p = 6.20 × 10–4) (Supplementary Table 4 and Figure 3).

FIGURE 3.

FIGURE 3

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Gene ontology (GO) Biological Processes analysis for the regional candidate genes of EN48 and EN60 (A) and EYC (B) traits (top10).

Association Analysis of Haplotypes With EN48, EN60, and EYC

Haplotypes were constructed for the regions containing the significantly associated SNPs. We found that 5 SNPs in transmembrane protein 161A (TMEM161A), located within a 28.85 kb genomic region (45,128,55 bp ∼ 45,417,09 bp) of chromosome 35, were significantly associated with EN48 and EN60 (Figure 4A). The 5 SNPs and genotypic distributions were statistically analyzed, and associations between genotypes and the EN48 and EN60 traits were determined (Table 3 and Figure 4A). Haplotype association analysis confirmed that this region’s corresponding haplotype was significantly associated with both the EN48 and EN60 traits. Individuals with the genotype CCAAAGAA produced fewer eggs at 48 and 60 weeks than individuals with other genotypes (Supplementary Table 5).

FIGURE 4.

FIGURE 4

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Linkage Disequilibrium (LD) analyses of SNPs in the significant region for EN48, EN60 (A) and EYC (B) traits.

TABLE 3.

Effects of the SNPs and genotypes on EYC, EN48, and EN60 traits in goose.

Traits Genotypes (least-squares means) P-value
EYC
SNP183 6.86 ± 0.31a (AG) 5.45 ± 0.11a (AG) / <0.0001**
SNP184 6.86 ± 0.31a (CT) 5.43 ± 0.11b (TT) / 0.0001**
SNP185 6.86 ± 0.31a (GA) 5.45 ± 0.11b (GG) / <0.0001**
SNP187 5.44 ± 0.11b (GG) 6.68 ± 0.29a (AG) / <0.0001**
SNP197 5.42 ± 0.11b (GT) 6.66 ± 0.27a (GG) / <0.0001**
EN48
SNP17 36.86 ± 0.23a (CC) 30.87 ± 1.67b (TC) 26.71 ± 3.95c (TT) 0.0019**
SNP18 36.56 ± 0.85a (AA) 33.85 ± 1.81b (AG) 28.42 ± 2.42c (GG) 0.0049**
SNP281 37.57 ± 0.78a (AA) 36.00 ± 0.99ab (AG) 27.00 ± 1.51b (GG) <0.0001**
SNP282 28.25 ± 0.59b (AA) 27.85 ± 1.59b (AT) 37.45 ± 0.79a (TT) <0.0001**
EN60
SNP17 64.55 ± 1.30a (CC) 50.78 ± 2.61b (TC) 43.57 ± 6.24c (TT) <0.0001**
SNP18 64.49 ± 1.34a (AA) 54.26 ± 2.85b (AG) 47.25 ± 3.72c (GG) <0.0001**
SNP281 66.00 ± 1.67a (AA) 49.67 ± 2.54b (AG) 64.14 ± 1.30a (GG) <0.0001**
SNP282 53.75 ± 0.84b (AA) 50.15 ± 2.65b (AT) 64.06 ± 1.31a (TT) <0.0001**
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** extremely significant association P < 0.01; Different letters in the same row indicate a significant difference between the genotypes (P < 0.05); the same letter indicates no significant difference between genotypes (P > 0.05).

On chromosome 5, a haplotype within a 294.57 kb genomic region (21,069,009 ∼ 21,363,580 bp) was constructed based on 5 SNPs (Figure 4B and Table 3) in the glucagon-like peptide 1 receptor (GLP1R), calcitonin receptor (CALCR), and tissue factor pathway inhibitor 2 (TFPI2) genes that were significantly associated with EYC. The 5 SNPs and genotypic distributions were statistically analyzed, and associations between the genotypes and EYC traits were determined (Table 3). Haplotype association analysis showed that the haplotype was also significantly associated with EYC and that there were significant differences among the genotypes. For example, individuals with the genotypes AGCTGAGAGT and GGTTGGAAGG showed significantly higher and lower EYC values, respectively, than individuals with other genotypes (Supplementary Table 5).

Discussion

Sichuan white goose is a popular Chinese goose breed for meat and egg purposes. The number of eggs at 60 weeks for the experimental geese were about 60 per individual, which was consistent with that described previously and laid the foundation for the further study (Xiao et al., 2010). While egg laying and egg quality traits have been studied intensively in avians, including chickens (Liu et al., 2018b, 2019), ducks (Rowland et al., 2018; Zhou et al., 2018), and quail (Wu et al., 2018), few studies have determined the associations between genetic markers and these traits (Zhao et al., 2020). In this study, we identified many SNPs and haplotypes that were associated with these traits.

There were two advantages than most of the previous studies. (1) The goose is an economically important waterfowl with distinctive characters. However, the habitat preference for water, large body size and the seasonal reproduction restricts most of the goose rearing in free-range or large groups. Compared with most of the previous studies, one of advantages in this study is that the individuals were reared in single cages (600 mm × 800 mm × 900 mm), which allowed the traits to be accurately recorded and statistically analyzed for each goose. (2) Our lab has generated a chromosome-level de novo assembly of the goose genome (Li et al., 2020). Our assembly has more continuity, completeness, and accuracy; the annotation of core eukaryotic genes and universal single-copy orthologs has also been improved, which significant improvement compared to previous assemblies. For example, our contig N50 value is more than fifty-fold that of prior studies. In this study, we mapped high-quality whole-genome resequencing data to the goose genome sequence and performed the GWAS analysis, which led to the results from the GWAS being more accurate than those of previous similar studies.

There was a population stratification phenomenon in the experimental goose population in this study maybe due to the limitation of the population size (209). Considering the number maybe the limitation for this study, to identify the SNPs associated with the EN48, EN60, and EYC traits, we employed the MALDI-TOF MS method to detect some selected SNP sites, and analyzed the frequency of candidate SNPs, which was consistent with the GWAS analysis results. In addition, the haplotype blocks constructed by some SNPs from GWAS analysis were also significantly associated with the traits. For instance, SNP16, SNP17, SNP18, SNP281, and SNP282 were significantly associated with the EN48 or EN60 (Supplementary Table 5), and the haplotype block (Figure 4A) constructed by the five SNPs were also significantly with the EN48 and EN60 traits (Supplementary Table 5), suggesting that the results were reliable and robust and avoided false positives.

In this study, A total of 2.896 Tb of raw whole-genome resequencing data were generated from the 209 individuals, and the average GC content was 42.85%, consistent with that previously reported for the goose genome (Lu et al., 2015; Gao et al., 2016; Li et al., 2020), which supply the robust sequencing data for the further study. A total of 26 SNPs in 209 geese were identified to be significantly associated with the BBW, EN48, EN60, and EYC traits by GWAS. As one of the most economically important traits, BBW is a crucial index of goose breeding and has been correlated with a number of other traits in birds (Sarangi et al., 2016). A SNP located within stromal membrane-associated protein 2 (SMAP2) was significantly associated with BBW. SMAP2 is a member of the ArfGAP subfamily. The protein encoded by this gene exerts a vital function in vesicle trafficking and plays a crucial role in sperm formation (Natsume et al., 2006; Funaki et al., 2013). In mammals, SMAP2 is necessary for spermiogenesis (Han et al., 2020), where SMAP2-deficient mice have been shown display male infertility (Sumiyoshi et al., 2015), globozoospermia (Funaki et al., 2013), asthenozoospermia (Funaki et al., 2013), and abnormal acrosome formation (Funaki et al., 2013). Therefore, we speculated that SMAP2 might also play a key role in reproduction.

Notably, four SNPs located within TMEM161A were grouped into a haplotype block, and association analysis confirmed that both the SNPs and the haplotype were significantly associated with EN48 and EN60. TMEM161A, also known as adaptive response to oxidative stress protein 29 (AROS-29), is involved in several cellular processes, including the adaptive response to oxidative stress (Montesano Gesualdi et al., 2006), the response to ultraviolet radiation, and the response to retinoic acid; in addition, TMEM161A is a negative regulator of the intrinsic apoptotic signaling pathway in response to DNA damage and is a positive regulator of the DNA repair response. In birds, retinoic acid promotes embryonic development and egg production and can regulate the number of eggs (Fu et al., 2000; Scheider et al., 2018). Sichuan white geese display seasonal reproduction as an adaptation to the migration habits of domesticated wild geese and are therefore sensitive to UV radiation. Because TMEM161A plays a vital role in the response to UV light (Montesano Gesualdi et al., 2006), it is possible that the artificial selection pressures resulting from the intensification of animal reproduction systems contributed to the appearance of the four SNPs in TMEM161A, satisfying the demand for increased egg production and aiding in reproductive environmental adaptation. Moreover, we identified five other candidate genes for the EN48 or EN60 traits, including glucose 1,6-bisphosphate synthase (PGM2L), DCC-interacting protein 13-beta (DP13B), RNA-directed DNA polymerase from mobile element jockey (RTJK), and S2542 (the mitochondrial coenzyme A transporter SLC25A42). Previous studies have identified many candidate genes corresponding to goose reproductive traits (Shigang et al., 2015; Yu S. et al., 2016; Ouyang et al., 2020; Zhao et al., 2020). However, the candidate genes for the number of eggs identified in the current study did not overlap with previous genes. The number of eggs in poultry is a quantitative trait regulated by numerous genes and environmental factors (Goto and Tsudzuki, 2016). The candidate genes and SNPs identified here were all associated with egg production despite their minor effects on the examined traits. Second, the breeds used in previous studies were raised under diverse conditions with artificial selection pressures, which would influence the different variants associated with the number of eggs trait in Sichuan white geese. Third, while the population sizes used in those studies were sufficient to identify characteristics specific to the examined breeds, they were not large enough to form a general conclusion that can be applied to all goose breeds. Finally, the goose genome sequence previous studies. For example, the value of contig N50 were improved fifty-fold than that of prior studies (Lu et al., 2015; Gao et al., 2016; Li et al., 2020). In this study, we mapped high-quality whole-genome resequencing data to the goose genome sequence and performed the GWAS analysis, which may be another reason why the GWAS results of this study were different from those of previous studies.

The pathway analysis of the candidate genes for EN48 and EN60 showed that the genes were highly enriched in response to radiation (GO: 0009314), response to a light stimulus (GO: 0009416), cellular response to a hormone stimulus (GO: 0032870), and response to a peptide hormone stimulus (GO: 0043434) (Supplementary Table 3). Geese are a seasonal breeding avian species regulated by light, and reproduction-related hormones control their reproductive activities. We speculated that the candidate genes for EN48 or EN60 were involved in the response to light or reproductive hormones, thus participating in the goose reproductive activities. However, the mechanism needs to be further studied.

Egg yolk color is a crucial economic egg quality index because consumers link yolk color to the nutrition contained in an egg (Galobart et al., 2004). Studies have shown that EYC is affected by genetic (Goto and Tsudzuki, 2016), environmental (Spada et al., 2016), housing (Sokołowicz et al., 2018), and dietary (Moreno et al., 2020) factors. In the current study, twelve SNPs were significantly associated with the EYC of Sichuan white goose eggs. Notably, five of the SNPs constituted a haplotype, leading to the identification of the candidate genes CALCR, TFPI2, and GLP1R. CALCR is a receptor that binds to the peptide hormone calcitonin and is involved in calcium homeostasis, bone formation and metabolism, and lipid metabolism (Davis et al., 2019). GLP1R, a member of the glucagon receptor family of G protein-coupled receptors, is involved in controlling the blood sugar level by enhancing insulin secretion (Cardoso et al., 2020). Interestingly, CALCR and GLP1R can regulate peptides with anorexic effects, thus suppressing long-term food intake and promoting significant weight loss (Yamaguchi et al., 2015; Adams et al., 2018). TFPI-2 belongs to the Kunitz-type family of protease inhibitors and regulates matrix metalloproteinase activation and extracellular matrix degradation. Deficiency of this gene in mice accelerates atherosclerotic lesions (Hong et al., 2016). In addition, TFPI-2 overexpression strongly inhibits vascular smooth muscle cell proliferation and migration and affects smooth muscle cell proliferation and migration (Zhao et al., 2011; Hong et al., 2016). Therefore, these positional candidate genes may control the EYC trait by affecting food intake or lipid metabolism of geese. EYC is a quantitative trait regulated by many genes in poultry (Zhang et al., 2021). In agreement with previous studies, we also identified multiple other positional candidate genes that cover the twelve SNPs, including Myb-binding protein 1A-like protein (MBB1A), CB042 (known as uncharacterized protein C2orf42), voltage-dependent L-type calcium channel subunit beta-2 (CACB2), cerebral cavernous malformations protein 2 homolog (CCM2), and zinc finger and BTB domain-containing protein 46 (ZBT46). The effects of these genes on EYC require further study.

The GO pathway analysis revealed that the “regulation of epidermal growth factor-activated receptor activity” (GO: 0007176) and “regulation of epidermal growth factor receptor signaling pathway” (GO: 0042058) (Supplementary Table 4) were remarkably enriched for EYC traits in goose. The two GO terms are involved in modulating epidermal growth factor (EGF)-activated receptor activity. In mammals or birds, EGF-like growth factors play an essential role in the ovulatory follicle (Park et al., 2004), maturation of the cumulus-oocyte complex (Su et al., 2010), and oocyte maturation and development (Yang and Jia, 2019). Genetics is one of the critical factors affecting EYC (Mori et al., 2020). We propose that the candidate genes for the EYC trait and the EGF-activated receptor might regulate the processes of oocyte maturation and development and determine the EYC trait in geese.

Conclusion

In the current study, a total of 26 SNPs significantly associated with egg production and quality trait were identified by GWAS. Additionally, these SNPs with significant effects were further verified using the MALDI-TOP MS method in the same population. Furthermore, 14 annotated genes were detected as candidate genes for the four traits based on their basic function. These results supplied the new candidate genetic markers and genes for the marker-assisted selection of geese, and laid the foundation for the genetic basis of the reproduction performance and egg quality traits.

Data Availability Statement

The raw whole-genome resequencing data have been deposited in the NCBI Short Read Archive (SRA) under accession numbers: SRR10687319–SRR10687346, SRR10687348–SRR1068 7383, SRR10687385–SRR10687397, SRR10687399–SRR106874 03, SRR10687405–SRR10687410, SRR10687412–SRR10687417, SRR10687419, SRR10687421–SRR10687423, SRR10687425, SRR 10687427–SRR10687433, SRR10687436–SRR10687445, SRR106 87447–SRR10687464, SRR10687466–SRR10687473, SRR106874 75, SRR10687478, SRR10687483–SRR10687486, SRR10687488–SRR10687510, SRR10687513–SRR10687517, SRR10687519–SRR 10687528, SRR10687530, SRR10687532–SRR10687535, and SRR 10687537–SRR10687554.

Ethics Statement

The animal study was reviewed and approved by all experiments involving animals were performed according to the laws and regulations established by the Ministry of Agriculture of China (Beijing, China). The study was approved by the Animal Care and Welfare Committee of the Chinese Chongqing Academy of Animal Science (Chongqing, China).

Author Contributions

GG, QW, and XZ provided funds, and designed and supervised the projects. GG, XZ, JL, and YX collected the samples and the phenotypic data. GG, DG, SH, SX, and RW performed the bioinformatics analysis, data uploaded, and drawn the figures. GG, KZ, RW, and CY wrote the manuscripts. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer YW declared a shared affiliation, with no collaboration, with one of the authors DG to the handling editor at the time of review.

Acknowledgments

The authors would like to gratefully thank all of the staffs whose hard work in AnFu waterfowl-breeding base for collecting the phenotypic data and the reviewers whose valuable insights helped to remarkably upgrade the manuscript, and the Professor TuoYu Geng from Yangzhou University for elaborative language editing on our manuscript.

Abbreviations

AROS-29

oxidative stress protein 29

BBW

birth body weight

CACB2

voltage-dependent L-type calcium channel subunit beta-2

CALCR

calcitonin receptor

CB042

uncharacterized protein C2orf42

CCM2

cerebral cavernous malformations protein 2 homologs

EN48

the number of eggs at 48 weeks

EN60

the number of eggs at 60 weeks

GWAS

genome-wide association study

GEMMA

Genome-wide Efficient Mixed Model Association

GLP1R

glucagon-like peptide 1 receptor

LD

Linkage disequilibrium

MALDI-TOP MS

matrix-assisted laser desorption-ionization time-of-flight mass spectrometry

MBB1A

Myb-binding protein 1A-like protein

QQ plot

quantile–quantile plots

SNPs

single nucleotide polymorphisms (SNPs)

SMAP2

stromal membrane-associated protein 2

SRA

Short Read Archive

TMEM161A

transmembrane protein 161A

TFPI2

tissue factor pathway inhibitor 2

ZBT46

zinc finger and BTB domain-containing protein 46.

Funding. This work was supported by grants from the National Natural Science Foundation of China (grant number 31572386), the Earmarked Fund for China Agriculture Research System (grant number CARS-42-51), the Chongqing Scientific Research Institution Performance Incentive Project (grant numbers cstc2018jxjl80036, cstc2019jxjl80009, and cstc2019jcyj-msxmX0072), and the Chongqing Key Project of Technology Innovation and Application Development (grant number cstc2019jscx-gksbX0097).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2021.602583/full#supplementary-material

Click here for additional data file. (10.4KB, xlsx)
Click here for additional data file. (27.3KB, xlsx)
Click here for additional data file. (13.4KB, xlsx)
Click here for additional data file. (11.7KB, xlsx)
Click here for additional data file. (11.5KB, xlsx)
Click here for additional data file. (607.3KB, TIFF)

References

  1. Adams J. M., Pei H., Sandoval D. A., Seeley R. J., Chang R. B., Liberles S. D., et al. (2018). Liraglutide modulates appetite and body weight through glucagon-like peptide 1 receptor-expressing glutamatergic neurons. Diabetes 67 1538–1548. 10.2337/db17-1385 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Barrett J. C., Fry B., Maller J., Daly M. J. (2005). Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21 263–265. 10.1093/bioinformatics/bth457 [DOI] [PubMed] [Google Scholar]
  3. Boz M., Sarica M., Yamak U. (2017). Production traits of artificially and naturally hatched geese in intensive and free-range systems: I. Growth traits. Br. Poult. Sci. 58 132–138. 10.1080/00071668.2016.1261997 [DOI] [PubMed] [Google Scholar]
  4. Cardoso J. C., Félix R. C., Ferreira V., Peng M., Zhang X., Power D. M. J. S. R. (2020). The calcitonin-like system is an ancient regulatory system of biomineralization. Sci. Rep. 10:7581. 10.1038/s41598-020-64118-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Davis R. B., Ding S., Nielsen N. R., Pawlak J. B., Blakeney E. S., Caron K.M.J.a.P, et al. (2019). Calcitonin-receptor-like receptor signaling governs intestinal lymphatic innervation and lipid uptake. ACS Pharmacol. Transl. Sci. 2 114–121. 10.1021/acsptsci.8b00061 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Dikmen B. Y., Ipek A., Şahan Ü, Petek M., Sözcü A. (2016). Egg production and welfare of laying hens kept in different housing systems (conventional, enriched cage, and free range). Poult. Sci. 95 1564–1572. 10.3382/ps/pew082 [DOI] [PubMed] [Google Scholar]
  7. Du Y., Liu L., He Y., Dou T., Jia J., Ge C. J. B. P. S. (2020). Endocrine and genetic factors affecting egg laying performance in chickens: a review. Br. Poult. Sci. 61 538–549. 10.1080/00071668.2020.1758299 [DOI] [PubMed] [Google Scholar]
  8. Farghly M. F., Mahrose K. M., Rehman Z. U., Yu S., Abdelfattah M. G., El-Garhy O. H. J. P. S. (2019). Intermittent lighting regime as a tool to enhance egg production and eggshell thickness in Rhode Island Red laying hens. Poult. Sci. 98 2459–2465. 10.3382/ps/pez021 [DOI] [PubMed] [Google Scholar]
  9. Freebern E., Santos D. J., Fang L., Jiang J., Gaddis K. L. P., Liu G. E., et al. (2020). GWAS and fine-mapping of livability and six disease traits in Holstein cattle. BMC Genomics 21:41. 10.1186/s12864-020-6461-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Fu Z., Kato H., Sugahara K., Kubo T. J. B. O. R. (2000). Retinoic acid accelerates the development of reproductive organs and egg production in Japanese quail (Coturnix coturnix japonica). Biol. Reprod. 63 1795–1800. 10.1095/biolreprod63.6.1795 [DOI] [PubMed] [Google Scholar]
  11. Fuentes-Pardo A. P., Ruzzante D. E. (2017). Whole-genome sequencing approaches for conservation biology: advantages, limitations and practical recommendations. Mol. Ecol. 26 5369–5406. 10.1111/mec.14264 [DOI] [PubMed] [Google Scholar]
  12. Funaki T., Kon S., Tanabe K., Natsume W., Sato S., Shimizu T., et al. (2013). The Arf GAP SMAP2 is necessary for organized vesicle budding from the trans-Golgi network and subsequent acrosome formation in spermiogenesis. Mol. Biol. Cell. 24 2633–2644. 10.1091/mbc.e13-05-0234 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Galobart J., Sala R., Rincón-Carruyo X., Manzanilla E. G., Vila B., Gasa J. (2004). Egg yolk color as affected by saponification of different natural pigmenting sources. J. Appl. Poult. Res. 13 328–334. 10.1093/japr/13.2.328 [DOI] [Google Scholar]
  14. Gao G., Zhao X., Li Q., He C., Zhao W., Liu S., et al. (2016). Genome and metagenome analyses reveal adaptive evolution of the host and interaction with the gut microbiota in the goose. Sci. Rep. 6:32961. 10.1038/srep32961 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Goto T., Tsudzuki M. J. (2016). Genetic mapping of quantitative trait loci for egg production and egg quality traits in chickens: a review. J. Appl. Poult. Res. 54 1–12. 10.2141/jpsa.0160121 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gou Z., Fan Q., Li L., Jiang Z., Lin X., Cui X., et al. (2020). Effects of dietary iron on reproductive performance of Chinese Yellow broiler breeder hens during the egg-laying period. Poult. Sci. 99 3921–3929. 10.3382/ps/pez006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Guoxian Z., Di L., Lin W., Jie T., Xu Z. (2017). Influencing factors and controlling measures of egg quality in late stage of egg production. Feed Industry 38 1–6. 10.13302/j.cnki.fi.2017.19.001 [DOI] [Google Scholar]
  18. Han Y., Liang C., Yu Y., Manthari R. K., Cheng C., Tan Y., et al. (2020). Chronic arsenic exposure lowered sperm motility via impairing ultra-microstructure and key proteins expressions of sperm acrosome and flagellum formation during spermiogenesis in male mice. Sci. Total Environ. 734:139233. 10.1016/j.scitotenv.2020.139233 [DOI] [PubMed] [Google Scholar]
  19. Hong J., Liu R., Chen L., Wu B., Yu J., Gao W., et al. (2016). Conditional knockout of tissue factor pathway inhibitor 2 in vascular endothelial cells accelerates atherosclerotic plaque development in mice. Thromb. Res. 137 148–156. 10.1016/j.thromres.2015.11.010 [DOI] [PubMed] [Google Scholar]
  20. Islam K. M., Khalil M., Männer K., Raila J., Rawel H., Zentek J., et al. (2017). Lutein specific relationships among some spectrophotometric and colorimetric parameters of chicken egg yolk. J. Poult. Sci. 54 271–277. 10.2141/jpsa.0160065 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Karssen L. C., Van Duijn C. M., Aulchenko Y. (2016). The GenABEL project for statistical genomics. F1000Res. 5:914. 10.12688/f1000research.8733.1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kominakis A., Hager-Theodorides A. L., Zoidis E., Saridaki A., Antonakos G., Tsiamis G. (2017). Combined GWAS and ‘guilt by association’-based prioritization analysis identifies functional candidate genes for body size in sheep. Genet. Sel. Evol. 49:41. 10.1186/s12711-017-0316-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Li H., Durbin R. (2009). Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25 1754–1760. 10.1093/bioinformatics/btp324 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Li Y., Gao G., Lin Y., Hu S., Luo Y., Wang G., et al. (2020). Pacific biosciences assembly with Hi-C mapping generates an improved, chromosome-level goose genome. Gigascience 9:giaa114. 10.1093/gigascience/giaa114 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Liu G., Chen Z., Zhao X., Li M., Guo Z. (2020). Meta-analysis: supplementary artificial light and goose reproduction. Anim. Reprod. Sci. 214:106278. 10.1016/j.anireprosci.2020.106278 [DOI] [PubMed] [Google Scholar]
  26. Liu Z., Sun C., Yan Y., Li G., Shi F., Wu G., et al. (2018a). Genetic variations for egg quality of chickens at late laying period revealed by genome-wide association study. Sci. Rep. 8:10832. 10.1038/s41598-018-29162-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Liu Z., Sun C., Yan Y., Li G., Wu G., Liu A., et al. (2018b). Genome-wide association analysis of age-dependent egg weights in chickens. Front. Genet. 9:128. 10.3389/fgene.2018.00128 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Liu Z., Yang N., Yan Y., Li G., Liu A., Wu G., et al. (2019). Genome-wide association analysis of egg production performance in chickens across the whole laying period. BMC Genet. 20:67. 10.1186/s12863-019-0771-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lu L., Chen Y., Wang Z., Li X., Chen W., Tao Z., et al. (2015). The goose genome sequence leads to insights into the evolution of waterfowl and susceptibility to fatty liver. Genome Biol. 16:89. 10.1186/s13059-015-0652-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. McKenna A., Hanna M., Banks E., Sivachenko A., Cibulskis K., Kernytsky A., et al. (2010). The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20 1297–1303. 10.1101/gr.107524.110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Montesano Gesualdi N., Chirico G., Catanese M. T., Pirozzi G., Esposito F. (2006). AROS-29 is involved in adaptive response to oxidative stress. Free Radic. Res. 40 467–476. 10.1080/10715760600570547 [DOI] [PubMed] [Google Scholar]
  32. Moreno J., Díaz-Gómez J., Fuentes-Font L., Angulo E., Gosálvez L., Sandmann G., et al. (2020). Poultry diets containing (keto) carotenoid-enriched maize improve egg yolk color and maintain quality. Anim. Feed Sci. Technol. 260:114334. 10.1016/j.anifeedsci.2019.114334 [DOI] [Google Scholar]
  33. Mori H., Takaya M., Nishimura K., Goto T. (2020). Breed and feed affect amino acid contents of egg yolk and eggshell color in chickens. Poult. Sci. 99 172–178. 10.3382/ps/pez557 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Mwaniki Z., Neijat M., Kiarie E. (2018). Egg production and quality responses of adding up to 7.5% defatted black soldier fly larvae meal in a corn–soybean meal diet fed to Shaver White Leghorns from wk 19 to 27 of age. Poult. Sci. 97 2829–2835. 10.3382/ps/pey118 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Natsume W., Tanabe K., Kon S., Yoshida N., Watanabe T., Torii T., et al. (2006). SMAP2, a novel ARF GTPase-activating protein, interacts with clathrin and clathrin assembly protein and functions on the AP-1-positive early endosome/trans-Golgi network. Mol. Biol. Cell 17 2592–2603. 10.1091/mbc.e05-10-0909 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Nemati Z., Alirezalu K., Besharati M., Amirdahri S., Franco D., Lorenzo J. M. (2020). Improving the quality characteristics and shelf life of meat and growth performance in goose fed diets supplemented with vitamin E. Foods 9:798. 10.3390/foods9060798 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Ouyang Q., Hu S., Wang G., Hu J., Zhang J., Li L., et al. (2020). Comparative transcriptome analysis suggests key roles for 5-hydroxytryptamlne receptors in control of goose egg production. Genes 11:455. 10.3390/genes11040455 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Park J. Y., Su Y. Q., Ariga M., Law E., Jin S., Conti M. (2004). EGF-like growth factors as mediators of LH action in the ovulatory follicle. Science 303 682–684. 10.1126/science.1092463 [DOI] [PubMed] [Google Scholar]
  39. Pires P. G. S., Pires P. D. S., Cardinal K. M., Leuven A. F. R., Kindlein L., Andretta I. (2019). Effects of rice protein coatings combined or not with propolis on shelf life of eggs. Poult. Sci. 98 4196–4203. 10.3382/ps/pez155 [DOI] [PubMed] [Google Scholar]
  40. Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M. A., Bender D., et al. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81 559–575. 10.1086/519795 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Quinlan A. R. (2014). BEDTools: the Swiss-Army Tool for genome feature analysis. Curr. Protoc. Bioinformatics 47:11.12.1-34. 10.1002/0471250953.bi1112s47 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Rowland K., Wolc A., Gallardo R. A., Kelly T., Zhou H., Dekkers J., et al. (2018). Genetic analysis of a commercial egg laying line challenged with Newcastle disease virus. Front. Genet. 9:326. 10.3389/fgene.2018.00326 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Samiullah S., Roberts J. (2014). The eggshell cuticle of the laying hen. Worlds Poult. Sci. J. 70 693–708. 10.1017/S0043933914000786 [DOI] [Google Scholar]
  44. Sarangi N. R., Babu L., Kumar A., Pradhan C., Pati P., Mishra J. (2016). Effect of dietary supplementation of prebiotic, probiotic, and synbiotic on growth performance and carcass characteristics of broiler chickens. Vet. World 9 313–319. 10.14202/vetworld.2016.313-319 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Scheider J., Afonso-Grunz F., Jessl L., Hoffmeier K., Winter P., Oehlmann J. (2018). Morphological and transcriptomic effects of endocrine modulators on the gonadal differentiation of chicken embryos: the case of tributyltin (TBT). Toxicol. Lett. 284 143–151. 10.1016/j.toxlet.2017.11.019 [DOI] [PubMed] [Google Scholar]
  46. Shigang Y., Weiwei C., Lifan Z., Houming H., Rongxue Z., Wei W., et al. (2015). Identification of laying-related SNP markers in geese using RAD sequencing. PLoS One 10:e0131572. 10.1371/journal.pone.0131572 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Sokołowicz Z., Krawczyk J., Dykiel M. (2018). The effect of the type of alternative housing system, genotype and age of laying hens on egg quality. Ann. Anim. Sci. 18 541–556. 10.2478/aoas-2018-0004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Spada F. P., Selani M. M., Coelho A., Savino V. J. M., Rodella A. A., Souza M. C., et al. (2016). Influence of natural and synthetic carotenoids on the color of egg yolk. Sci. Agr. 73 234–242. 10.1590/0103-9016-2014-0337 [DOI] [Google Scholar]
  49. Su Y. Q., Sugiura K., Li Q., Wigglesworth K., Matzuk M. M., Eppig J. (2010). Mouse oocytes enable LH-induced maturation of the cumulus-oocyte complex via promoting EGF receptor-dependent signaling. Mol. Endocrinol. 24 1230–1239. 10.1210/me.2009-0497 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Sumiyoshi M., Masuda N., Tanuma N., Ogoh H., Imai E., Otsuka M., et al. (2015). Mice doubly-deficient in the Arf GAPs SMAP1 and SMAP2 exhibit embryonic lethality. FEBS Lett. 589 2754–2762. 10.1016/j.febslet.2015.07.050 [DOI] [PubMed] [Google Scholar]
  51. Wang C., Kao J., Lee S., Chen L. (2005). Effects of artificial supplemental light on the reproductive season of geese kept in open houses. Br. Poult. Sci. 46 728–732. 10.1080/00071660500395632 [DOI] [PubMed] [Google Scholar]
  52. Wang K., Li M., Hakonarson H. (2010). ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38:e164. 10.1093/nar/gkq603 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Wu Y., Zhang Y., Hou Z., Fan G., Pi J., Sun S., et al. (2018). Population genomic data reveal genes related to important traits of quail. GigaScience 7:giy049. 10.1093/gigascience/giy049 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Xiao M. W., Dong Y., Yi-Jun X. (2010). Reproductive performance comparison of sichuan white geese and huoyan geese in Wuhan. Mol. Biol. Rep. 37 4059–4066. 10.1007/s11033-010-0065-7 [DOI] [PubMed] [Google Scholar]
  55. Yamaguchi M., Watanabe Y., Ohtani T., Uezumi A., Mikami N., Nakamura M., et al. (2015). Calcitonin receptor signaling inhibits muscle stem cells from escaping the Quiescent State and the Niche. Cell. Rep. 13 302–314. 10.1016/j.celrep.2015.08.083 [DOI] [PubMed] [Google Scholar]
  56. Yamak U., Boz M., Ucar A., Sarica M., Onder H. (2016). The effect of eggshell thickness on the hatchability of guinea fowl and pheasants. Br. Poult. Sci. 18 49–53. 10.1590/1806-9061-2015-0214 [DOI] [Google Scholar]
  57. Yang X. Y., Jia Z. (2019). The role of EGF-like factor signaling pathway in granulosa cells in regulation of oocyte maturation and development. Yi Chuan 41 137–145. 10.1111/j.1432-0436.1984.tb01374.x [DOI] [PubMed] [Google Scholar]
  58. Yu J., Lou Y., He K., Yang S., Yu W., Han L., et al. (2016). Goose broodiness is involved in granulosa cell autophagy and homeostatic imbalance of follicular hormones. Poult. Sci. 95 1156–1164. 10.3382/ps/pew006 [DOI] [PubMed] [Google Scholar]
  59. Yu S., Wei W., Xia M., Jiang Z., He D., Li Z., et al. (2016). Molecular characterization, alternative splicing and expression analysis of ACSF2 and its correlation with egg-laying performance in geese. Anim. Genet. 47 451–462. 10.1111/age.12435 [DOI] [PubMed] [Google Scholar]
  60. Zaheer K. (2017). Hen egg carotenoids (lutein and zeaxanthin) and nutritional impacts on human health: a review. CyTA J. Food 15 474–487. 10.1080/19476337.2016.1266033 [DOI] [Google Scholar]
  61. Zhang H., Wang Z., Wang S., Li H. (2012). Progress of genome wide association study in domestic animals. J. Anim. Sci. Biotechnol. 3:26. 10.1186/2049-1891-3-26 [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
  62. Zhang J., Duan Z., Wang X., Li F., Chen J., Lai X., et al. (2021). Screening and validation of candidate genes involved in the regulation of egg yolk deposition in chicken. Poult. Sci. 1–26. 10.1016/j.psj.2021.101077 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Zhang Z., Chen Z., Ye S., He Y., Huang S., Yuan X., et al. (2019). Genome-wide association study for reproductive traits in a duroc pig population. Animals 9:732. 10.3390/ani9100732 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Zhao B., Luo X., Shi H., Ma D. J. (2011). Tissue factor pathway inhibitor-2 is downregulated by ox-LDL and inhibits ox-LDL induced vascular smooth muscle cells proliferation and migration. Thromb. Res. 128 179–185. 10.1016/j.thromres.2011.02.025 [DOI] [PubMed] [Google Scholar]
  65. Zhao Q., Chen J., Zhang X., Xu Z., Lin Z., Li H., et al. (2020). Genome-wide association analysis reveals key genes responsible for egg production of lion head goose. Front. Genet. 10:1391. 10.3389/fgene.2019.01391 [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Zhou X., Stephens M. (2012). Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44 821–824. 10.3109/9781420052923-16 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Zhou Y., Zhou B., Pache L., Chang M., Khodabakhshi A. H., Tanaseichuk O., et al. (2019). Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10:1523. 10.1038/s41467-019-09234-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Zhou Z., Li M., Cheng H., Fan W., Yuan Z., Gao Q., et al. (2018). An intercross population study reveals genes associated with body size and plumage color in ducks. Nat. Commun. 9:2648. 10.1038/s41467-018-04868-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Zhu W., Zhang J., He K., Geng Z., Chen X. J. (2020). Proteomic analysis of fertilized egg yolk proteins during embryonic development. Poult. Sci. 99 2775–2784. 10.1016/j.psj.2019.12.056 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Click here for additional data file. (10.4KB, xlsx)
Click here for additional data file. (27.3KB, xlsx)
Click here for additional data file. (13.4KB, xlsx)
Click here for additional data file. (11.7KB, xlsx)
Click here for additional data file. (11.5KB, xlsx)
Click here for additional data file. (607.3KB, TIFF)

Data Availability Statement

The raw whole-genome resequencing data have been deposited in the NCBI Short Read Archive (SRA) under accession numbers: SRR10687319–SRR10687346, SRR10687348–SRR1068 7383, SRR10687385–SRR10687397, SRR10687399–SRR106874 03, SRR10687405–SRR10687410, SRR10687412–SRR10687417, SRR10687419, SRR10687421–SRR10687423, SRR10687425, SRR 10687427–SRR10687433, SRR10687436–SRR10687445, SRR106 87447–SRR10687464, SRR10687466–SRR10687473, SRR106874 75, SRR10687478, SRR10687483–SRR10687486, SRR10687488–SRR10687510, SRR10687513–SRR10687517, SRR10687519–SRR 10687528, SRR10687530, SRR10687532–SRR10687535, and SRR 10687537–SRR10687554.

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