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. 2026 Mar 18;105(6):106817. doi: 10.1016/j.psj.2026.106817

Metabolite-based genome-wide association analysis reveals genetic loci and candidate genes affecting breast muscle metabolites in Sansui ducks

Yulong Feng a,b,†,⁎, Mengru Xu a,†, Xintong Yu c, Zhao Yang c, Jingjing Qi c, Yang Xi c, Shihao Guo c, Xu Han c, Meijuan Li a, Yuxi Lu a, Guotao Dai a, Chengcheng Tian a, Yu Zhao a, Anqi Huang c, Liang Li c, Hehe Liu c
PMCID: PMC13054014  PMID: 41903465

Abstract

Duck meat is highly appreciated for its unique flavor and rich nutritional value, and metabolites have become important phenotypic indicators of meat quality. The Sansui duck, a celebrated local breed from Guizhou, is known for its tender, savory meat, yet its metabolomic composition and underlying genetic basis remain unexplored. In our study, non-targeted LC-MS/MS metabolomics of 305 Sansui duck breast muscles detected 4,729 metabolic features (459 annotated) and identified 136 sex-differential metabolites enriched in fatty acid and amino acid metabolism pathways. Metabolite-based genome-wide association studies (mGWAS) further identified 355 significant LD-independent SNPs and 267 potential candidate genes associated with 103 metabolites. The signal peaks for amino acid metabolites were mainly concentrated on chromosomes 1, 5, and 20. A QTL on chromosome 2 (63.90–64.10 Mb), containing candidate genes ZNF407, CNDP1, and CNDP2, was identified for three methylglyoxal derivatives, with the lead SNP (chr2: 63928783) accounting for about 18.3 % of their variance. Additional QTLs on chromosomes 5, 20, 1, and 24 were associated with Carnosine, N-Acetylhistidine, Acetylcholine, N-Acetyl-L-aspartic acid, 11b-PGF2a, and 12(S)-HpETE. These intervals harbor BBOX1 and ACACA, two key rate-limiting enzymes in fatty acid metabolism. Our results revealed the genetic basis of breast muscle metabolites in Sansui ducks and identified associated genetic loci and candidate genes. These findings deepen our understanding of muscle metabolism and provide valuable insights for improving meat quality and nutritional breeding.

Keywords: Duck, Metabolics, mGWAS, SNP, candidate gene

Introduction

Duck meat is widely valued for its distinctive flavor and rich nutritional profile, making it a sought-after poultry product worldwide (Biswas et al., 2019). Meat quality is a complex trait influenced by various factors such as diet, age, and sex (Ge et al., 2023), among which the composition and content of metabolites play a key role (Muroya, 2023). Metabolites are chemical substances produced during metabolic processes, including amino acids, fatty acids, nucleotides, alcohols, ketones, and others. These compounds shape tenderness and juiciness and, directly or indirectly, influence duck meat flavor (Li et al., 2022). As a renowned local duck breed in Guizhou, the Sansui duck is characterized by tender, flavorful meat, high amino acid content, and low cholesterol levels (Zhu et al., 2025). However, the composition and genetic basis of its meat metabolites have not been systematically investigated, which hinders further research and genetic improvement of the nutritional and flavor of Sansui duck meat.

Untargeted metabolomics has been widely used to profile the composition and relative abundance of small-molecule metabolites in poultry breast muscle (Gu et al., 2024a, 2024b; Liu et al., 2022a; Shi et al., 2022; Zeng et al., 2024), providing a basis to interrogate their types, concentrations, and dynamic changes. Using LC-MS to investigate age-related metabolic changes in Yangzhou goose breast meat, it was found that adult geese exhibited higher levels of baicalin, tyrosine, and L-threonine, metabolites positively correlated with meat freshness (Wang et al., 2023). Through untargeted metabolomics analysis, metabolites such as glycine, serine, glutamic acid, betaine, glutathione, LPC, IMP, and N-acetyl-L-aspartic acid have been identified as key metabolites influencing the quality and flavor formation of chicken breast meat (Gai et al., 2023; Zhao et al., 2024). Meanwhile, it was found that the breast muscle of Liancheng White ducks contained relatively higher levels of docosahexaenoic acid (DHA), docosapentaenoic acid (DPA), eicosapentaenoic acid (EPA), and prostaglandin F3α (PGF3α), conferring it with superior nutritional and medicinal value (Zhang et al., 2023). Therefore, meat metabolites directly or indirectly influence the flavor of poultry meat.

The synthesis and accumulation of metabolites are finely regulated by upstream gene expression, which is itself modulated by genetic variations and epigenetic modifications. Gene expression in eukaryotes is subject to complex temporal and spatial regulation, with expression patterns varying across developmental stages (Heidarpour et al., 2011; Khabiri et al., 2023). Within this regulatory network, the epigenome plays a pivotal role. Comprising diverse mechanisms such as DNA methylation, chromatin remodeling, histone tail modifications, microRNAs, and long non-coding RNAs, the epigenome interacts with environmental factors-including nutrition, pathogens, and climate-to influence gene expression profiles and the emergence of specific phenotypes (Barazandeh et al., 2016; Mohammadabadi et al., 2010). Multi-level interactions between the genome, epigenome, and environmental factors might occur (Amiri Roudbar et al., 2020), and accumulating evidence suggests that epigenome variation significantly affects animal health and production (Alavi et al., 2022). The execution of these regulatory mechanisms ultimately traces back to genetic loci within the genome-DNA sequence variations serve as the fundamental drivers determining regulatory efficiency and metabolic flux. Therefore, investigating metabolic-related genetic loci and genes at the chromosomal level constitutes a fundamental approach to elucidating the molecular mechanisms underlying complex traits (Mohammadabadi et al., 2017).

Despite increased focus on meat quality in breeding programs, the genetic basis of muscle metabolite variation remains incompletely defined. Metabolite-based genome-wide association studies (mGWAS) enable the simultaneous identification of numerous loci influencing metabolite abundance (Luo, 2015), offering insight into the genetic architecture of metabolite diversity and its relationships with meat quality traits. Tang conducted an mGWAS analysis of six glucosinolate metabolites in rapeseed and identified 187 candidate genes involved in glucosinolate biosynthesis (Tang et al., 2023). In human studies, mGWAS analyses of blood metabolites have identified numerous genetic loci that influence metabolite levels, offering new insights into many complex diseases (Köttgen et al., 2018; Ouyang et al., 2021). In poultry research, metabolomics approaches (Liu et al., 2023) were applied to analyze metabolites in the skeletal muscle of Pekin duck × Liancheng White duck crosses, identifying 2,862 metabolite-based genome-wide association signals and 48 candidate genes potentially modulating metabolite and volatile levels. Furthermore, mGWAS revealed that a splicing mutation in AADAT influences lysine degradation in duck skeletal muscle (Liu et al., 2025). Therefore, understanding the genetic basis of the metabolome is essential for genetic mechanism dissection of disease or complex traits. Therefore, elucidating the genetic basis of the meat metabolome and revealing the genetic loci that regulate key metabolites is essential for improving poultry meat quality and flavor.

In this study, we performed untargeted metabolomics to profile metabolites in Sansui duck breast meat and pioneered mGWAS analysis to identify genetic loci and candidate genes associated with key metabolites. Our findings provide theoretical reference data for uncovering the genetic and biochemical basis of breast meat metabolites in Sansui ducks, offering valuable insights for the genetic improvement of nutritional and flavor traits in duck meat.

Materials and methods

Animals and sampling

A total of 350 ducks (180 males and 170 females) were provided by the Sansui Duck Breeding Farm of Guizhou Academy of Agricultural Sciences. The feeding and management processes for all the Sansui ducks were the same. The whole process involves mixed breeding of male and female ducks, and at the same time, immunization prophylaxis is carried out according to the regular duck immunization program. At 20 weeks of age, 4 mL of blood samples were collected from 305 experimental ducks (155 males and 150 females) and stored at −20°C. All ducks were sacrificed at 20 weeks of age by exsanguination. Breast muscles were collected and stored in a 4°C refrigerator.

The animal study protocol was reviewed and approved by the Animal Care and Welfare Committee of the Institute of Animal Husbandry and Veterinary Medicine, Guizhou Academy of Agricultural Sciences (Grant No. 202304/2023-07) and was performed in accordance with the guidelines of the National Institute of Animal Health for animal experiments.

Metabolite profiling

We collected a total of 305 breast muscle samples, each corresponding to a blood sample. After screening and quality assessment, 296 breast muscle samples were retained for metabolite analysis (9 breast muscle samples with missing identifiers could not be matched to their corresponding blood samples and were therefore excluded from metabolite detection. These 9 samples were recorded as "NA" in subsequent genomic analyses, which still included all 305 individuals). Samples were extracted with 1000 µL tissue extract (75 % [9:1 methanol: chloroform]: 25 % H₂O), homogenized, sonicated (room temperature, 30 min), and further treated in an ice bath (30 min). After centrifugation (12,000 rpm, 10 min, 4°C), the supernatant was collected, concentrated, and dried. Residues were re-dissolved in 200 µL 50 % acetonitrile containing 2‑chloro-L-phenylalanine (4 ppm), filtered (0.22 µm), and transferred to vials for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis.

The liquid chromatography (LC) analysis was performed on a Vanquish UHPLC System (Thermo Fisher Scientific, USA) using an ACQUITY UPLC ® HSS T3 column (2.1 × 100 mm, 1.8 µm) (Waters, Milford, MA, USA) maintained at 40 °C. The flow rate was 0.3 mL/min with a 2 μL injection volume. For LC-ESI (+)-MS analysis, the mobile phases were 0.1 % formic acid in water (A2) and acetonitrile (B2), with the gradient: 0-1 min, 8 % B2; 1-8 min, 8-98 % B2; 8-10 min, 98 % B2; 10-10.1 min, 98-8 % B2; 10.1-12 min, 8 % B2. For LC-ESI (-)-MS analysis, the mobile phases were 5 mM ammonium formate (A3) and acetonitrile (B3), with the same gradient program as described previously (Zelena et al., 2009).

Metabolite detection was performed on an Orbitrap Exploris 120 (Thermo Fisher Scientific, USA) with an ESI source in Full MS-ddMS2 mode. Parameters were: sheath gas, 40 arb; aux gas, 10 arb; spray voltage, +3.50 kV/−2.50 kV; capillary temperature, 325°C; MS1 range, m/z 100–1000; MS1 resolution, 60,000 FWHM; MS/MS resolution, 15,000 FWHM; data-dependent scans per cycle, 4; normalized collision energy, 30 %; dynamic exclusion, automatic (Want et al., 2013).

Metabolite identification

Raw mass spectrometry files were converted to mzXML format using the MSConvert tool (ProteoWizard v3.0.8789). Peak detection, filtering, and alignment were performed with XCMS (Navarro-Reig et al., 2015) (bw = 2, ppm = 15, peakwidth = c (5, 30), mzwid = 0.015, mzdiff = 0.01, method = “centWave”), producing a quantitative compound list. QC-based support vector regression was applied to correct systematic errors, and only compounds with CV < 30 % in QC samples (Want et al., 2013) were retained. Metabolite identification was conducted using Human Metabolome Database (HMDB) (Wishart et al., 2007), MassBank (Horai et al., 2010), LipidMaps (Sud et al., 2007), mzCloud (Abdelrazig et al., 2020), and Kyoto Encyclopedia of Genes and Genomes (KEGG) (Ogata et al., 1999), with compounds possessing MS/MS spectra matched against database fragment ions for secondary-level identification. According to the Metabolomics Standards Initiative (MSI) guidelines, this annotation corresponds to MSI Level 2 (putatively annotated compounds). It is important to note that all identifications were putative, as no metabolites were confirmed with authentic standards (MSI Level 1) in this study.

Genomic DNA sequencing and genotyping

DNA was extracted from duck blood samples (n = 305) using the phenol-chloroform protocol. DNA quality was assessed using a NanoDrop-2000 and agarose gel electrophoresis. Qualified samples were used to construct paired-end libraries following standard protocols, with an average insert size of 450 bp. Sequencing was performed on the DNBSEQ-T7 platform (BGI, China) to generate 150 bp paired-end reads at an average depth of 10 × . The raw data still contained adapters and low-quality reads. To ensure data quality for subsequent analyses, raw reads were processed with Trimomatic to obtain clean reads under the following criteria (Bolger et al., 2014): (1) removal of adapter sequences; (2) exclusion of reads with >10 % unidentified bases (N); (3) removal of reads shorter than 25 bp; and (4) exclusion of low-quality reads with base quality < Q20. Clean reads were compared with the duck reference genome (ZJU1.0) using Burrows-Wheeler Aligner (BWA aln) (Li and Durbin, 2009). SNPs were identified using GATK HaplotypeCaller and further filtered with VCFtools (Danecek et al., 2011; DePristo et al., 2011). In total, 11,947,837 SNPs were retained for GWAS. SNPs were filtered based on the following criteria: (1) minor allele frequency > 0.05 and major allele frequency < 0.99; (2) the maximum missing rate was < 0.1; and (3) SNPs could only have two alleles.

mGWAS and heritability estimation

mGWAS were conducted in which each metabolite (n = 459) was considered a phenotype. We estimated the SNP heritability (H2) of each metabolite using GCTA (v1.94.1) (Yang et al., 2011). mGWAS were performed using the mixed-effects linear model program Emmax to identify SNPs (Eisen et al., 1998). The model was defined as follows:

y=Xα+Zβ+Wμ+e

In this formula, y represents the vector of phenotypic observations; Xα represents fixed effects correcting for population stratification, including gender and the first three principal components of genome-wide SNP genotypes (Price et al., 2006); Zβ denotes SNP effects; Wμ represents random animal effects, and the variance covariance matrix is based on a kinship matrix based on genotype-based whole-genome SNP genotype estimation; e indicates the random residual vector. Manhattan and QQ plots were generated using the R package (version 4.5.1). A genome-wide significance threshold of -log10P = 8.38 (P = 0.05 / total number of SNPs) was applied to identify significant SNPs, and QQ plots were used to assess potential false-positive inflation due to population stratification.

LD analysis

VCFtools was used to extract individual genotypes in regions of interest. Linkage disequilibrium (LD) analysis among the most significant SNPs within the candidate region was analyzed using PLINK (version 1.90). LocusZoom plots were generated in R (version 4.5.1), and Haploview was used to assess the overall LD structure.

Gene annotation and enrichment analysis

Significant SNPs were functionally annotated using SnpEff based on the duck reference genome (ZJU1.0) to identify candidate genes (Cingolani et al., 2012). Gene Ontology (GO) and KEGG pathway enrichment were conducted using KOBAS 3.0 (http://bioinfo.org/kobas) (Bu et al., 2021). A threshold of P < 0.05 was applied to determine statistically significant enrichment for both GO terms and KEGG pathways.

Statistical analysis

Microsoft Excel 2021 was used to calculate the mean and standard deviation for each trait. Statistical significance of trait differences between males and females was assessed using SPSS software (version 22.0). Correlation analyses and correlation heatmaps among various traits were conducted and visualized using RStudio (version 4.5.1). Graphs were generated with GraphPad Prism (version 8.0.2) and RStudio.

Results and analysis

Statistical analysis of breast muscle metabolites

Using a widely targeted LC-MS/MS approach, a total of 4,729 metabolic features were detected (Table S1), of which 459 metabolites were annotated and classified into 8 categories (Fig. 1A) (Table S2). Lipids were the most abundant, accounting for 24.18 %, followed by amino acids and organic acids, which represented 20.04 % and 13.73 %, respectively. The correlation analysis of 459 annotated metabolites (Fig. 1B) indicates that metabolites within the same metabolic pathway or network exhibit higher correlations. For example, the metabolites with high correlation in the lower-right corner are all lipid oxidation products and prostaglandins.

Fig. 1.

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Metabolomic analysis of duck breast muscle samples. (A) Classification information of the 459 annotated metabolites. (B) Correlation analysis of the 459 annotated metabolites. Red indicates a positive correlation, and blue indicates a negative correlation. (C) The volcano plot of differential metabolites between males and females. The horizontal and vertical coordinates represent -log2 (Fold change) and -log10P value, respectively. (D) Classification information of differential metabolites between males and females. (E) Cluster heatmap of differential metabolites between males and females. The F represents female, and M represents male. (F) KEGG enrichment analysis of differential metabolites between males and females.

We identified 136 differential metabolites by comparing the relative metabolite levels of male and female (P < 0.05, |log2(Fold Change) | > 1), including 42 upregulated and 94 downregulated metabolites (Fig. 1C). Among these, lipids and amino acids contained the most significant number of differential metabolites (Fig. 1D). For example, Cortexolone, Stearic acid, and Creatine were significantly upregulated in male breast muscle (P < 0.05), while Alpha-Linolenic acid, L-Cysteine, and N-Acetyl-L-aspartic acid were significantly higher in female breast muscle (P < 0.05) (Table S3). Additionally, 16 downregulated metabolites were identified among the organic acids. Cluster heatmap analysis (Fig. 1E) revealed clear clustering of differential metabolites by sex, highlighting both similarities and differences in the breast muscle metabolomes. Enrichment analysis of the 136 differential metabolites (Fig. 1F) revealed significant enrichment in 20 KEGG pathways (P < 0.05), including Cysteine and methionine metabolism, Glutathione metabolism, and Alanine, aspartate, and glutamate metabolism (Table S4), indicating sex-specific remodeling of redox and amino-acid/energy metabolism that may underpin duck sex differences in immune/oxidative-stress handling and in the abundance of meat-quality flavor precursors.

Genetic architecture of the breast muscle metabolome

The heritability of the relative abundance of 459 metabolites in breast muscle was estimated at 0.23. Only 11.98 % of the metabolites had heritability greater than 0.4, and 29.41 % had heritability greater than 0.2 (Fig. 2A). Overall, the metabolite levels can therefore be considered traits with low to moderate heritability. Compared with other categories (Fig. 2B), amino acids (0.32) and nucleosides and nucleotides (0.28) exhibited higher heritability, which may be because they are more closely associated with DNA and thus more directly influenced by genetic factors. Then, we identified 109 metabolites that were both phenotypically and genetically correlated (Fig. 2C). Most of the metabolites clustered in the center were associated with lipid metabolism, while those in the lower-left corner were primarily associated with amino acid and energy metabolism.

Fig. 2.

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Genetic basis of the breast muscle metabolites in ducks. (A) Heritability statistics of 459 metabolites. (B) Heritability statistics of different types of metabolites. (C) Heatmap of metabolite pairs that are both phenotypically and genetically correlated.

Metabolome genome‑wide association study (mGWAS)

The contents of 459 annotated metabolites were used as phenotypic traits for genome-wide association analysis with SNPs. A total of 551 significantly associated SNPs (-log10P > 8.38) were identified for 103 metabolites (Table S5), among which 355 were LD-independent SNPs (Table S6) (Fig. 3A). The largest number of genetically regulated metabolites was detected in lipids and amino acids, while the highest number of LD-independent SNPs was detected in benzenoids and Lipids (Fig. 3B). These genetically regulated metabolites were significantly enriched in Linoleic acid metabolism, Arachidonic acid metabolism, Pentose phosphate pathway, and Glutathione metabolism (Fig. 3C) (Table S7). This may indicate that lipid metabolism, energy metabolism, and oxidative stress responses in individual breast muscle exhibit genetic variation.

Fig. 3.

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Overview of metabolites and SNPs identified in the mGWAS. (A) Manhattan plot of different classes of metabolites. The gray line represents the Bonferroni-corrected significance threshold (- log10P = 8.38). (B) Statistics of LD-independent SNP numbers for different classes of metabolites. (C) KEGG enrichment analysis of all genetically regulated metabolites.

Significant signals for amino acid metabolites were mainly located on chromosomes 1, 5, and 20 (Fig. 3A). Specifically, the levels of N5-(L-1-Carboxyethyl)-L-ornithine, Allocystathionine, and (2S)-2-1-(R)-Carboxyethyl amino pentanoate were significantly associated with a LD-independent SNP (63928783 bp, A > G) on chromosome 2 (Fig. 4A). (2S)-2-1-(R)-Carboxyethyl amino pentanoate and N5-(L-1-Carboxyethyl)-L-ornithine are derivatives of pentanoic acid and ornithine, respectively, while Allocystathionine is a sulfur-containing amino acid derivative. These three metabolites share a common metabolic precursor, Methylglyoxal (Fig. 4F), and exhibit a significant strong positive correlation both phenotypically and genetically (P < 0.001) (Fig. 4E). Analysis of this lead SNP showed that GG and AG genotypes had significantly higher metabolite levels than AA (P < 0.05) (Fig. 4B), with the SNP explaining an average of 18.3 % of the variation in these metabolites (Table S9). Then, we delineated a quantitative trait locus (QTL) interval spanning 63.90- 64.10 Mb based on pairwise LD analysis around the lead SNP (Fig. 4C), which contains four genes (DIPKIC, CNDP2, CNDP1, and ZNF407) (Fig. 4D). Using the same approach, QTLs on chromosome 5 (28.90-28.95 Mb) and chromosome 20 (1.77-1.88 Mb) were associated with carnosine/N-acetylhistidine and N-acetyl-L-aspartic acid levels, respectively (Fig. S1 and S3). For lipids metabolites, QTLs for 11b-PGF2a (Chr1: 126.84-126.97 Mb) and 12(S)-HpETE (Chr24: 2.11-2.15 Mb) were identified (Fig. S2). Finally, a QTL for acetylcholine (Cofactors and Vitamins) was detected on Chr5: 57.98-58.01 Mb (Fig. S3).

Fig. 4.

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QTL candidate intervals for amino acid metabolites on chromosome 2. (A) Manhattan and Q-Q plots of mGWAS of these three metabolites. The gray line represents the Bonferroni corrected significance threshold (-log10P = 8.38). (B) Relative abundance differences of the three metabolites across Genotypes at chr2:63928783 (A > G). (C) Locuszoom in the candidate region (chr2: 63.90- 64.10 Mb). (D) LD analysis and gene annotation in the region (R2>0.4). (E) Pairwise correlation analysis among the three metabolites. (F) Metabolic pathway connections of the three metabolites with Methylglyoxal.

Identification of candidate genes

All SNPs reaching the significant threshold (-log10P > 8.38) in the mGWAS were annotated to 267 genes (Table S5), primarily associated with pathways including amino metabolism, neuronal development, and regulation of hormone levels (Table S8). The largest number of candidate genes was found for lipids and benzenoids (Table S10), with genes involved in multiple pathways related to metabolite synthesis, metabolism, and regulation listed in Table 1. Notably, the SNP (Chr2: 63,928,783) located within the carnosine dipeptidase 2 gene (CNDP2), which participates in histidine and β-alanine metabolism, is significantly associated with several amino acid metabolites, while a position at 2,377,767 bp on chromosome 20 is annotated to the amino acid transporter solute carrier family 7 member 5 (SLC7A5). In lipid metabolism, γ-butyrobetaine hydroxylase 1 (BBOX1), the rate-limiting enzyme for carnitine biosynthesis, is annotated to SNP (Chr5: 57,994,830) significantly associated with acetylcholine, while acetyl-CoA carboxylase alpha (ACACA), the rate-limiting enzyme for fatty acid synthesis, is annotated to SNP (Chr20: 1,876,024) significantly associated with N-Acetyl-L-aspartic acid. Both genes influence fatty acid β-oxidation. Additionally, SNP (Chr5: 57,994,830) significantly associated with 12(S)-HpETE is located within the phospholipase D family member 5 (PLD5), which catalyzes the conversion of phosphatidylcholine to phosphatidic acid and choline. Finally, some genes closely related to energy metabolism were also annotated, such as ATPase H⁺ transporting accessory protein 1 (ATP6AP1), oxidative stress responsive kinase 1 (OXSR1), and GDP-D-glucose phosphorylase 1 (GDPGP1).

Table 1.

Summary of genes associated with metabolite metabolism in the mGWAS.

Gene name Chr Position Metabolite name Gene description
EXOC4 1 2028957 Caproic acid Exocyst Complex Component 4
CACNA2D1 1 10283847 3-Dehydro-4-phospho-D-erythronate Calcium Voltage-Gated Channel Auxiliary Subunit Alpha2delta1
ATP6AP1 1 23519890 Diosmetin ATPase H+ Transporting Accessory Protein 1
IGSF3 1 96925948 O-Acetylcarnitine Immunoglobulin Superfamily Member 3
DMD 1 119452175 3—Hydroxymethylglutaric acid Dystrophin
SCML2 1 126921305 11b-PGF2a, Prostaglandin G2 Scm Polycomb Group Protein Like 2
RAI2 1 126944514 11b-PGF2a Retinoic Acid Induced 2
IL18R1 1 140857990 3-Dehydro-4-phospho-D-erythronate Interleukin 18 Receptor 1
ARHGEF7 1 146415103 Pyrophosphate Rho Guanine Nucleotide Exchange Factor 7
DIPK1C 2 63899588 N5-(L-1-Carboxyethyl)-L-ornithine, Allocystathionine, (2S)-2-1-(R)-Carboxyethyl amino pentanoate Divergent Protein Kinase Domain 1C
CNDP2 2 63928783 N5-(L-1-Carboxyethyl)-L-ornithine, Allocystathionine, (2S)-2-1-(R)-Carboxyethyl amino pentanoate Carnosine Dipeptidase 2
OXSR1 2 63928783 (2S)-2-1-(R)-Carboxyethyl amino pentanoate Oxidative Stress Responsive Kinase 1
ZNF407 2 64030059 (2S)-2-1-(R)-Carboxyethyl amino pentanoate Zinc Finger Protein 407
VSTM2A 2 74836895 Acetylcholine V-Set and Transmembrane Domain Containing 2A
ANKH 2 92798473 9,12,13-TriHOME ANKH Inorganic Pyrophosphate Transport Regulator
FBXO11 3 16727475 Palmitic acid F-Box Protein 11
PLD5 3 28243640 12(S)-HpETE Phospholipase D Family Member 5
PTPRK 3 62950070 15-Deoxy-D-12,14-PGJ2 Protein Tyrosine Phosphatase Receptor Type K
SLC35F1 3 67398085 5-HETE Solute Carrier Family 35 Member F1
BST1 4 60853775 Xanthine Bone Marrow Stromal Antigen 1
TSPAN32 5 28922030 Carnosine, N-Acetylhistidine Tetraspanin 32
BBOX1 5 57994830 Acetylcholine Gamma-Butyrobetaine Hydroxylase 1
MAP3K2 7 6278959 Deoxycholic acid Mitogen-Activated Protein Kinase Kinase Kinase 2
RPL37A 7 6579103 Deoxycholic acid Ribosomal Protein L37a
PBX1 8 10308377 12(S)-HpETE, 5-HETE PBX Homeobox 1
MAP2K5 11 20814362 3-Dehydro-4-phospho-D-erythronate Mitogen-Activated Protein Kinase Kinase 5
GDPGP1 11 22159387 Betaine GDP-D-Glucose Phosphorylase 1
SLC7A5 12 2377767 Diosmetin Solute Carrier Family 7 Member 5
FHIT 13 8998905 O-Acetylcarnitine Fragile Histidine Triad Diadenosine Triphosphatase
CA10 19 4633733 Alpha-Tocotrienol Carbonic Anhydrase 10
ACACA 20 1876024 N-Acetyl-L-aspartic acid Acetyl—CoA Carboxylase Alpha
COL26A1 20 7615850 Spermidine Collagen Type XXVI Alpha 1 Chain
PSMB2 24 2142801 12(S)-HpETE Proteasome 20S Subunit Beta 2
CD34 27 5839798 S-Adenosylmethionine CD34 Molecule
SLC25A42 29 1395351 11b-PGF2a, 13(S)-HpOTrE Solute Carrier Family 25 Member 42
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Discussion

In recent years, research on poultry meat quality has become a growing area of interest. Metabolomics has been widely applied to screen and identify differential metabolites associated with meat quality (Chen et al., 2024; Yogeswari et al., 2024; Zhang et al., 2024), thereby establishing a high-quality meat evaluation system. In this study, we identified 459 annotated metabolites in the breast muscle of Sansui ducks by comparing multiple databases. The metabolites with the highest relative abundances, including lipids, amino acids, and benzenoids, serve as key precursors in the biochemical processes underlying meat flavor formation. During cooking, amino acids participate in the Maillard reaction to produce aroma compounds (Liu et al., 2022b). Fatty acids and their derivatives undergo oxidation to generate volatile compounds that influence meat flavor, particularly unsaturated fatty acids, which form aromatic compounds during heating and directly affect meat flavor (Khan et al., 2015). Benzenoids, derived from aromatic amino acids, also form aroma compoundsduring thermal processing (Lee et al., 2024). Their high abundance in the Sansui duck breast muscle may provide a crucial basis for the savory and aromatic qualities of their meat (Park and Choi, 2025). Sex is also an important factor influencing meat flavor in poultry (Ou et al., 2022). In this study, a large number of differential metabolites were identified in the breast muscle of male and female ducks, primarily in pathways including cysteine and methionine metabolism, glutathione metabolism, and the metabolism of alanine, aspartate, and glutamate. This suggests that sex-specific remodeling of amino acids, redox, and energy metabolism may underpin duck sex differences in immune and oxidative stress responses, as well as in the abundance of meat-quality flavor precursors. These metabolites are key determinants of the nutritional and flavor properties of duck meat. However, their underlying genetic basis and mechanisms remain largely unexplored. In this study, the average heritability of breast muscle metabolites in Sansui ducks was 0.23, with 29.41 % exceeding 0.2, consistent with estimates in Pekin duck (Liu et al., 2025) and chicken (Tian et al., 2023). These findings suggest that metabolite abundances are moderately to lowly heritable and shaped in part by genetic variation. mGWAS is widely used to analyze associations between large numbers of metabolites and genetic variants (Ren et al., 2024). This study identified 355 LD-independent SNPs affecting the relative abundances of 103 metabolites, providing a comprehensive view of how genetic variation influences metabolite levels in duck breast muscle. Lipid and amino acid metabolites were most strongly genetically regulated, indicating that lipid and amino acid metabolism, as well as oxidative stress pathways, are under substantial genetic influence in breast muscle. Amino acids, as key building blocks of protein, are also important indicators of meat quality (Luo et al., 2022). QTLs for amino acid metabolite levels were primarily concentrated on chromosomes 1, 5, and 20. Among the annotated genes, ZNF407 (Charrier et al., 2016) and CNDP1/2 (Kalbe et al., 2023)may influence methylglyoxal production by affecting glycolysis and amino acid metabolism. Increased glycolysis during enhanced energy metabolism elevates methylglyoxal levels, potentially inducing muscle protein denaturation and increased drip loss (Przybylski et al., 2022). SLC7A5, as an amino acid transporter, mediates the uptake of leucine, thereby activating the mTORC1 signaling pathway (Ni et al., 2024). The composition and content of lipid metabolites influence the color, juiciness, flavor, and nutritional value of meat (Kamal et al., 2022). Among the QTLs associated with lipid metabolite levels, BBOX1 (Ringseis et al., 2012) and ACACA (Stachowiak et al., 2013), both rate-limiting enzymes in fatty acid metabolism, play key catalytic roles in the synthesis and β-oxidation of fatty acids. These two genes have also been identified as candidate genes for beef quality traits (Shin et al., 2011; Zhou et al., 2018). These results are of great significance for potential marker-assisted breeding; however, the functional mechanisms of these genetic loci and candidate genes require further investigation.

Although this study revealed the genetic regulation of duck breast muscle metabolites and identified candidate genes, it primarily focused on a single tissue and did not address complex traits or environmental influences, thereby limiting a comprehensive understanding of metabolic variation. Metabolite measurements were taken at a single time point with a relatively small sample size, which may not capture dynamic or developmental changes. Additionally, as we applied a per-metabolite Bonferroni correction in our analysis, these findings should be regarded as candidate associations requiring validation in independent populations or through functional experiments. Future studies integrating larger cohorts, multi-tissue and longitudinal sampling, and functional validation of candidate genes are needed to clarify the causal mechanisms and genetic relationships between metabolites and complex traits, ultimately providing a more robust framework for molecular breeding and improving meat quality in ducks.

Conclusion

In summary, we conducted a non-targeted metabolomic analysis of breast muscle in the Sansui duck population, detecting 459 annotated metabolites and comprehensively characterizing its metabolic profile through classification statistics. Using mGWAS, we identified 355 significant LD-independent SNPs associated with 103 metabolites and 267 candidate genes. Annotation of QTL regions for amino acid and lipid metabolites revealed key genes, including CNDP1/2, ZNF407, SLC7A5, BBOX1, and ACACA, all of which play critical roles in metabolic regulation. The findings of this study provide a comprehensive framework for metabolomic research in the Sansui duck breast muscle, deepen our understanding of the genetic basis of muscle metabolic traits, and offer important insights into muscle nutritional components and the development of breeding strategies for meat quality.

CRediT authorship contribution statement

Yulong Feng: Writing – review & editing, Writing – original draft, Methodology, Funding acquisition. Mengru Xu: Writing – review & editing, Writing – original draft, Visualization, Investigation, Formal analysis, Data curation. Xintong Yu: Investigation, Data curation. Zhao Yang: Investigation, Data curation. Jingjing Qi: Formal analysis, Data curation. Yang Xi: Software, Methodology. Shihao Guo: Data curation. Xu Han: Software. Meijuan Li: Visualization. Yuxi Lu: Resources. Guotao Dai: Formal analysis. Chengcheng Tian: Investigation. Yu Zhao: Investigation. Anqi Huang: Resources. Liang Li: Supervision, Resources. Hehe Liu: Supervision, Methodology, Conceptualization.

Disclosures

No conflict of interest exists in the submission of this manuscript.

Acknowledgements

This research was supported by the grants from the Guizhou Province Science and Technology Plan Project (QKHJC ZD [2026]090), the Guizhou Provincial Key Laboratory of Livestock and Poultry Genetic Resources Innovation and Utilization (Qiankehe Platform ZSYS[2025]034), and the China Agriculture Research System (CARS-42-53).

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psj.2026.106817.

Appendix. Supplementary materials

mmc1.docx (2MB, docx)
mmc2.xlsx (21MB, xlsx)

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mmc2.xlsx (21MB, xlsx)

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