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Genome-wide association analysis of body conformation traits in Chinese Holstein Cattle

BMC Genomics volume 25, Article number: 1174 (2024) Cite this article

Abstract

Background

The body conformation traits of dairy cattle are closely related to their production performance and health. The present study aimed to identify gene variants associated with body conformation traits in Chinese Holstein cattle and provide marker loci for genomic selection in dairy cattle breeding. These findings could offer robust theoretical support for optimizing the health of dairy cattle and enhancing their production performance.

Results

This study involved 586 Chinese Holstein cattle and used the predicted transmitting abilities (PTAs) of 17 body conformation traits evaluated by the Council on Dairy Cattle Breeding in the USA as phenotypic values. These traits were categorized into body size traits, rump traits, feet/legs traits, udder traits, and dairy characteristic traits. On the basis of the genomic profiling results from the Genomic Profiler Bovine 100 K SNP chip, genotype data were quality controlled via PLINK software, and 586 individuals and 80,713 SNPs were retained for further analysis. Genome-wide association studies (GWASs) were conducted via GEMMA software, which employs both univariate linear mixed models (LMMs) and multivariate linear mixed models (mvLMMs). The Bonferroni method was used to determine the significance threshold, identifying gene variants significantly associated with body conformation traits in Chinese Holstein cattle. The single-trait GWAS identified 24 SNPs significantly associated with body conformation traits (P < 0.01), with annotation leading to the identification of 21 candidate genes. The multi-trait GWAS identified 54 SNPs, which were annotated to 57 candidate genes, including 39 new SNPs not identified in the single-trait GWAS. Additionally, 14 SNPs in the 86.84–87.41 Mb region of chromosome 6 were significantly associated with multiple traits, such as body size, udder, and dairy characteristics. Four genes—SLC4A4, GC, NPFFR2, and ADAMTS3—were annotated in this region.

Conclusions

A total of 63 SNPs were identified as significantly associated with 17 body conformation traits in Chinese Holstein cattle through both single-trait and multi-trait GWAS analyses. Sixty-six candidate genes were annotated, with 12 genes identified by both methods, such as SLC4A4, GC, NPFFR2, and ADAMTS3, which are involved in pathways such as growth hormone synthesis and secretion, sphingolipid signaling, and dopaminergic synapse pathways. These findings provide potential genetic marker information related to body conformation traits for the breeding of Chinese Holstein cattle.

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Background

Chinese Holstein cattle constitute the first dairy breed developed in China and constitute the dominant breed in the country’s dairy cattle population, accounting for more than 85% of the national herd [1]. Although the body conformation traits of dairy cattle do not directly translate into economic benefits, these traits are closely related to the milk production capacity and overall health of cattle [2, 3]. These traits are essential components for evaluating the overall performance of dairy cattle. Identifying candidate genes associated with body conformation traits in Chinese Holstein cattle is therefore crucial for identifying effective molecular markers for genomic selection in breeding programs, further optimizing breeding strategies, and improving the production performance of dairy cattle. The main body conformation traits of dairy cattle include (1) udder traits (such as fore udder attachment, front teat placement, teat length, rear udder height, rear udder width, and rear teat placement), (2) feet/legs traits (such as foot angle, heel depth, bone quality, rear legs side view, and rear legs rear view), (3) body size traits (such as stature, body depth, chest width, and strength), (4) rump traits (such as rump width and rump angle), and (5) dairy characteristics (such as angularity) [4]. Since 1990, many countries have incorporated body conformation traits into dairy cattle breeding programs [5] to improve the overall performance of dairy cattle by optimizing these traits.

To gain a deeper understanding of the genetic basis of body conformation traits, genome-wide association studies (GWASs) are commonly used to identify candidate genes for economically important traits in dairy cattle. The mixed linear model proposed by Yu et al. [6] has been widely recognized as the best GWAS analysis model currently available, as it effectively accounts for population structure and complex relationships within populations. The application of this model provides robust support for understanding the genetic mechanisms underlying body conformation traits in dairy cattle. Recently, Nazar et al. [7] conducted a GWAS analysis on Chinese Holstein cattle via the mixed linear model and identified 18 SNPs significantly associated with five udder traits. Haque et al. [8] were the first research team to report GWAS results for body conformation traits in Korean Holstein cattle, wherein they identified 24 SNPs significantly associated with 24 body conformation traits. Nazar et al. [9] also conducted a GWAS analysis on three udder traits in Chinese Holstein cattle and detected nine SNPs significantly associated with udder traits after Bonferroni correction. Čítek et al. [10] identified 32 SNPs significantly or nearly significantly associated with body conformation traits in the GWAS results of 25 body conformation traits in Czech Holstein cattle.

To date, most GWAS on body conformation traits in dairy cattle have been based on independent analyses of single traits. However, the potential correlations between multiple phenotypes are often overlooked because factors such as genetic pleiotropy (directly related factors), shared environmental influences, and linkage disequilibrium (which may act as confounding factors) [11]. In contrast, Multi-trait GWAS integrates multiple traits into a single statistical test, accounting for both the intra- and inter-trait variance components, thereby reducing the number of errors caused by multiple testing. This approach enables the rigorous identification of interactions and pleiotropic loci within a statistical framework that accounts for population structure, enhancing detection efficiency and accuracy [12, 13]. Numerous studies have shown that different body conformation traits are genetically correlated [4, 14, 15]. To more comprehensively reveal the genetic variation and underlying mechanisms of body conformation traits in dairy cattle, multi-trait GWAS methods are more advantageous. The present study conducted single-trait and multi-trait GWAS analyses to determine the genetic variants associated with body conformation traits in Chinese Holstein cattle and provide marker information for genomic selection in dairy cattle; the findings of this study could offer strong theoretical support to further optimize the health of dairy cattle and enhance their production performance.

Materials and methods

Ethics statement

The animals and experimental procedures used in this study followed the guidelines of the Animal Care and Use Committee of the Institute of Animal Science and Veterinary Medicine, Tianjin Academy of Agricultural Sciences (TAAS) (Tianjin, China). The experimental animals were not anesthetized or euthanized in this study. We confirmed that all methods were reported in accordance with ARRIVE guidelines (https://arriveguidelines.org) for the reporting of animal experiments.

Genotype data and quality control

In this study, 586 Chinese Holstein cattle from two farms at Fuyou Agricultural Technology Co., Ltd. (Tianjin, China) were selected for tail vein blood collection, and genomic DNA was extracted from 586 Chinese Holstein cattle via the Tiangen Blood Genome Extraction Kit. Genotyping was performed via the GeneSeek Genomic Profiler Bovine 100 K single-nucleotide polymorphism (SNP) chip. Quality control of the individual and SNP data was conducted via PLINK (v1.9) software [16], according to the following criteria: (1) individuals with an SNP missing rate of > 10% were excluded; (2) SNPs with a call rate of < 90% were excluded; (3) SNPs with a minor allele frequency (MAF) of < 5% were excluded; and (4) SNPs with a P value of < 1.0 × 10–7 were excluded. LD analysis was performed via Haploview software [17]. A total of 80,713 high-quality SNP markers from 586 individuals were ultimately selected. These SNP markers were evenly distributed across the chromosomes and were suitable for subsequent GWAS analysis of Chinese Holstein cattle (Fig. 1).

Fig. 1
figure 1

SNP density map following quality control with the 100K SNP chip

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Phenotypic data collection

In accordance with the updated standard methods of the Dairy Cattle Breeding Committee, the predicted transmitting abilities (PTAs) of 17 body conformation traits, as assessed by the Council on Dairy Cattle Breeding, USA, were used as the phenotypic data for this study on 586 Chinese Holstein cattle. Using PTA as a phenotypic measure effectively minimizes the influence of the environment [18, 19]. These traits were categorized into five groups: (1) body size traits: stature (STA), strength (STR), and body depth (BDE); (2) rump traits: rump angle (RPA) and rump-thurl width (RTW); (3) feet/legs traits: rear legs side view (RLS), rear legs rear view (RLR), foot angle (FTA), feet/legs score (FLS), and feet/legs composite (FLC) index; (4) udder traits: fore udder attachment (FUA), rear udder height (RUH), rear udder width (RUW), udder cleft (UCL), udder depth (UDP), and udder composite (UDC) index; and (5) dairy form traits: dairy form (DFM).

Estimation of genetic parameters for body conformation traits

Genetic correlations were determined via the “-reml-bivar” parameter in GCTA software [20]. Descriptive statistical analysis for the 17 body conformation traits, including maximum, minimum, mean, variance, and standard deviation, was conducted via SPSS 19 software. The frequency distribution histograms for each trait were plotted via the R package.

 Single-trait and multi-trait GWAS

Before conducting a GWAS, principal component analysis (PCA) was performed via PLINK (v1.9) software to correct for potential false positives caused by population stratification. GWAS analysis between single conformation traits and genome-wide SNPs was performed via the univariate linear mixed model (LMM) in GEMMA software [21] via the following model:

$$\mathrm y=\mathrm X\beta+Z\kappa\gamma\kappa+\xi+\varepsilon$$

where y is the PTA vector, Χβ represents age and population structure effects (the first five principal components), Ζκγκ represents the effect of the marker to be tested, ξ ~ N (0,Kφ2) represents the polygenic effect, and ε ~ N (0,Iσ2) represents the residual effect. In the polygenic effect, K is the kinship matrix inferred from the markers.

Multi-trait GWAS analysis between multiple traits and genome-wide SNPs was conducted via the multivariate linear mixed model in GEMMA software [22] via the following model:

$$\mathrm Y=\mathrm X\mathrm\beta+\mathrm {Z\kappa\gamma\kappa} +\mathrm\xi+\mathrm E$$

where Y is the n × d PTA matrix, n is the number of individuals in the population, and d is the number of traits analyzed; Χβ represents age and population structure effects (the first five principal components); Ζκγκ represents the effect of the marker to be tested; ξ ~ N (0,K,Vg) represents the polygenic effect; and Ε ~ N (0,In×n,Ve) represents the residual effect. In the polygenic effect, K is the kinship matrix inferred from the markers, Vg is the d × d polygenic variance‒covariance matrix, and Ve is the d × d residual variance‒covariance matrix. The number of independent SNPs was calculated via PLINK software with the “–indep pairs 50 5 0.2” command. The significance threshold was determined via Bonferroni correction (P value < 0.05/number of independent SNPs). Manhattan plots and QQ plots were generated via the CMplot function in the R package.

Gene annotation and functional enrichment analysis

The SNP position information in the GeneSeek Genomic Profiler Bovine 100 K SNP chip was based on the Bos taurus UCD version 1.2. The ARS-UCD 1.2 bovine reference genome information was downloaded from the ENSEMBL website. Significant SNPs within a 50-kb upstream and downstream range were annotated via ANNOVAR software [23], and potential candidate genes related to the traits were identified. Functional enrichment analysis, including GO and KEGG pathway enrichment, was conducted on the annotated candidate genes via the DAVID database (https://davidbioinformatics.nih.gov/).

Results

Descriptive statistical analysis of the phenotypic data

Descriptive statistical analysis was performed on the 17 body conformation traits of 586 Chinese Holstein cattle (Table 1). The phenotypic values of each trait generally followed a normal distribution (Figure S1).

Table 1 Descriptive statistics of body conformation traits
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Genetic correlation

The results of genetic correlation between the phenotypic traits indicated a strong positive correlation (r > 0.72) among the body size traits (STA, BDE, and STR; Table 2). For the rump traits, a weak negative correlation was observed between RTA and RPA (r = −0.02) (Table 3). Among the feet/legs traits, RLS showed varying degrees of negative correlation with other traits (−0.5 < r < −0.2), whereas strong positive correlations (r > 0.6) were found between RLR, FTA, FLS, and the FLC index, except for RLS (Table 4). For the udder traits, varying degrees of positive correlation (0.1 < r < 1) were noted among the UDC index, FUA, RUH, RUW, central ligament, and UDP (Table 5).

Table 2 Genetic correlation between body size traits
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Table 3 Genetic correlation between rump traits
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Table 4 Genetic correlation between feet/legs traits
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Table 5 Genetic correlation between udder traits
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PCA

As shown in Fig. 2, population stratification was observed. The explained variance percentage of the first 10 principal components (PCs) was calculated, and the results indicated that the first 5 PCs accounted for 80% of the variance. Therefore, in this study, the first 5 PCs were selected as covariates and included in the LMM for GWAS analysis.

Fig. 2
figure 2

PCA plot after genotyping quality control

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Single-trait and multi-trait GWAS analyses

A single-trait genome-wide association study (GWAS) was conducted for 17 body conformation traits, which led to the identification of 24 significant SNPs across 12 traits (Table 6). To uncover additional significant SNPs, a multi-trait GWAS was performed, resulting in the identification of 54 significant SNPs (Tables 7 and S1). Compared with the single-trait GWAS, 39 novel SNPs were identified in the multi-trait analysis. The corresponding QQ plots and Manhattan plots for both analyses are presented in Figs. 3 and 4.

Table 6 Significant loci and genes identified by single-trait GWAS
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Table 7 Significant loci and genes identified by multi-trait GWAS
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Fig. 3
figure 3

Manhattan and QQ plots for single-trait GWAS. A, C, E, G, and I Manhattan plots for body size, rump, feet/legs, udder, and dairy traits, respectively. B, D, F, H, and (J) represent the QQ plots for body size, rump, feet/legs, udder, and dairy traits, respectively

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Fig. 4
figure 4

Manhattan and QQ plots for the multi-trait GWAS. A, C, E, and G Manhattan plots for body size, rump, feet/legs, and udder traits, respectively. B, D, F, and H represent the QQ plots for body size, rump, feet/legs, and udder traits, respectively

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For body size traits, the single-trait GWAS identified a significant SNP on chromosome 11, with the candidate gene LDAH annotated. In the multi-trait GWAS, 19 significant SNPs were identified across three trait combinations (STR-BDE, STA-STR, and STA-BDE), which are located on chromosomes 5, 6, 7, 8, 11, 19, and 29. Fourteen candidate genes were annotated, including SLC4A4, GC, NPFFR2, and ADAMTS3. Notably, 18 of these 19 SNPs were newly identified.

For rump traits, the single-trait GWAS identified four significant SNPs on chromosome 7, with annotations of six candidate genes, including OR2T4_1, ELAVL1, and LMAN2. In the multi-trait GWAS, two significant SNPs were identified in the RPA-RTW combination on chromosome 7, with four candidate genes annotated, including OR2T4_1, ATP8B3, and KLF16. Both SNPs were newly identified.

For the feet/legs traits, the single-trait GWAS identified two significant SNPs on chromosomes 8 and 29, with annotations of two candidate genes (PIP5K1B and NTM). In the multi-trait GWAS, 11 significant SNPs were identified across five trait combinations (FLC-RLR, RLS-FTA-FLS, and FLC-RLS-FTA), located on chromosomes 8, 13, 21, 28, and 29. Fourteen candidate genes were annotated, including GNAQ, SAMHD1, and SOGA1. Among these 11 SNPs, 9 were newly identified.

For udder traits, the single-trait GWAS identified eight significant SNPs on chromosomes 5, 6, 7, and 19, with annotations of seven candidate genes, including ADGRE5, CCND2, and ARAP2. In the multi-trait GWAS, 33 significant SNPs were identified across 41 trait combinations (FUA-RUH, FUA-RUH-RUW, and FUA-RUH-RUW-UCL), located on chromosomes 4, 6, 7, 11, 16, 17, 19, and 28. Thirty candidate genes were annotated, including BMT2, IGFBP1, and IGFBP3. Among these 33 SNPs, 28 were newly identified.

For dairy characteristic traits, the single-trait GWAS identified nine significant SNPs on chromosome 6, with annotations of four candidate genes, including SLC4A4, GC, NPFFR2, and ADAMTS3. Additionally, one SNP on chromosome 19 (BovineHD1900013254) was significantly associated with UDC, RUH, and RUW. In the multi-trait GWAS, 32 SNPs were repeatedly detected across multiple trait combinations. Nine SNPs were associated with body size and udder traits, and two SNPs were associated with both feet/legs and udder traits, suggesting the potential pleiotropic nature of these loci.

Furthermore, the multi-trait GWAS identified a region on chromosome 6 (86.84–87.41 Mb) containing 14 significant SNPs associated with both udder and body size traits. Six haplotype blocks, comprising 3, 2, 2, 9, 2, and 5 SNPs, were observed (Fig. 5). Notably, eight of these SNPs, which are significantly associated with milk production-related traits, were also detected in the single-trait GWAS.

Fig. 5
figure 5

Haplotypes in the 86.84–87.41 Mb region of chromosome 6

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GO and KEGG analyses

We performed GO and KEGG pathway enrichment analyses on a total of 66 genes located within a 50 kb range upstream and downstream of the significant SNP loci. GO analysis revealed significant enrichment (p < 0.05) for a total of 10 GO terms, including 4 biological process terms, 1 cellular component term, and 5 molecular function terms (Fig. 6). KEGG pathway analysis revealed that five pathways were enriched. Among them, MAPK10 and GNAQ were enriched in four pathways; PPP2R5C was enriched in two pathways (Table 8).

Fig. 6
figure 6

GO analysis of candidate genes for body conformation traits

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Table 8 KEGG analysis of candidate genes related to body conformation traits
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Discussion

Genetic correlation among body conformation traits in Chinese Holstein Cattle

A very strong positive genetic correlation (r > 0.72) was observed between STA, BDE, and STR in terms of body size traits, which is consistent with the findings of Ning [15] and Degroot et al. [25]. For the feet/legs traits, negative genetic correlations were observed between RLS and the other foot and leg traits (−0.47 < r < −0.22). This finding was similar to the genetic correlation (−0.34) between RLS and RLR reported by Huang et al. [26] in their estimation of genetic parameters for body conformation traits of dairy cattle, although RLS was positively correlated with other foot and leg traits in their study. Because foot and leg traits are easily influenced by farm management and external environmental factors, the foot and leg structure may vary among different cattle populations. For the rump traits, the genetic correlation between RTA and RPA was weak (r = −0.02), which is similar to the findings of Peng [27] on the genetic correlation between RTA and RPA in Holstein cattle in Hebei Province. However, the genetic correlations reported by Huang et al. [26] and An et al. [28] differed significantly (0.22 < r < 0.38). For udder traits, the genetic correlations ranged from 0.22 (RUW and UDP) to 1 (UDC index and FUA); this finding is similar to the results reported by Degroot et al. [25]. The genetic correlations observed among body conformation traits suggest that the selection of one trait can indirectly influence other traits. Additionally, the positive correlations between traits indicate that these traits may share some common genetic basis, thus implying that certain genes or gene combinations may simultaneously affect multiple body conformation traits. Therefore, this genetic correlation can be used to develop more effective selection strategies.

Advantages of multi-trait GWAS

Body conformation traits are often controlled by multiple genes. Multi-trait GWAS can leverage the correlation between traits and combine weak genetic effects to increase the statistical power of GWASs and improve the ability to detect new SNP loci [29, 30]. In the present study, body conformation traits were genetically correlated. Compared with single-trait GWAS, 39 new SNPs were identified in the multi-trait GWAS. Using a similar strategy, Li et al. [30] conducted a multi-trait GWAS on weaning weight and yearling weight in sheep and identified 93 new SNPs. Gao et al. [24] discovered three new SNPs related to carcass weight, carcass length, and chest depth in Huaxi cattle. These results suggest that when traits show genetic correlations, multi-trait GWAS can complement the findings of single-trait GWAS, thereby increasing the statistical power of GWAS.

Candidate genes

By using a combination of single-trait and multi-trait GWAS analyses, we detected 63 significant SNP loci and annotated 66 candidate genes. Among these, 12 genes were identified by both single-trait and multi-trait GWAS, including SLC4A4, GC, NPFFR2, ADAMTS3, LDAH, OR2T4_1, NTM, ADGRE5, ARAP2, TLK2, SAO, and LOC100138645. Four genes were located in the 86.84–87.41 Mb region of chromosome 6. Among these genes, SLC4A4 is a solute transporter and a member of a major transporter superfamily involved in active glucose transport [31]. The secondary bicarbonate transporters of the SLC4 family mediate the transport of HCO3−, CO32−, Cl−, Na+, K+, NH3, and H+, which are critical for pH regulation and ion homeostasis [32]. SLC4A4 may support the proper development of muscle and adipose tissues by ensuring cellular functional integrity. GC is a gene encoding a vitamin D-binding protein that is specifically expressed in tissues such as the liver and plays a crucial role in calcium absorption and bone development [33, 34]. NPFFR2 is a member of the G-protein-coupled neuropeptide receptor subfamily activated by the neuropeptides A-18-amide and F-8-amide [35]. This gene plays a regulatory role in cardiovascular and neuroendocrine functions and is involved in energy metabolism and the stress response [36]. It contributes to the modulation of the growth rate and body conformation of cattle, allowing them to adapt to changes in environmental conditions and management practices. The ADAMTS3 gene activates vascular endothelial growth factors and promotes lymphangiogenesis [37]. SNPs in this region were associated with dairy traits in single-trait GWAS and with body size and udder traits in multi-trait GWAS. Jiang et al. [38] also reported that the four genes in this region were related to milk yield and milk protein content in Holstein cattle. Liang et al. [39] conducted a GWAS of more than one million U.S. Holstein cattle and reported that the SLC4A4, GC, and NPFFR2 genes are related to fertility traits. Wu et al. [40] reported that SLC4A4 and NPFFR2 are candidate genes for mastitis susceptibility in Danish Holstein cattle. Wirth et al. [41] identified an association of the NPFFR2 and ADAMTS3 genes with longevity in German Brown cattle through continuous homozygous segment analysis and gene mapping. Additionally, the SLC4A4, NPFFR2, and GC genes were found to be linked to udder health and morphology. These results suggest that the four genes in this region may exhibit pleiotropy.

LDAH, a lipid droplet-associated hydrolase, is highly expressed in tissues that primarily store triacylglycerol and plays a key role in lipogenesis [42]. Previous studies have shown that it is associated with hoof and leg diseases in Danish Holstein cattle [43]. NTM is a neurotrivial protein with an important role in neurodevelopment. Xu et al. [44] analyzed the imprinting status of the NTM gene in cattle and identified an SNP (rs42185569) within the NTM gene through direct sequencing of PCR products. RT‒PCR amplification revealed monoallelic expression of the NTM gene in bovine placenta and adult tissues, suggesting that NTM is an imprinted gene in cattle. ADGRE5 functions primarily in cell adhesion and transport proteins and is associated with angiogenesis in cattle [45]. ARAP2, which is involved in the endocytosis pathway, could be a candidate gene affecting loin strength in Chinese Holstein cattle [46]; we also speculate that it could be a candidate gene influencing udder traits in dairy cattle. TLK2 is a Tousled-like kinase whose main function involves the phosphorylation of the histone chaperones ASF1a and ASF1b and the promotion of DNA replication-coupled nucleosome assembly, which is crucial for genome maintenance and proper cell division in both plants and animals [47]. SAO encodes a copper-containing amine oxidase that oxidizes spermine and plays an important role in polyamine metabolism in cattle [48]. Polyamine metabolism is essential for cell proliferation and growth, potentially impacting muscle and skeletal development. LOC100138645 is a primary amine oxidase and a liver isoenzyme that functions in amine metabolism.

GO and KEGG enrichment analyses revealed that several pathways involving specific genes may influence body conformation traits in Chinese Holstein cattle. The growth hormone synthesis and secretion pathway, which includes genes such as MAPK10, IGFBP3, and GNAQ, plays a critical role in growth rates and muscle development [49]. The sphingolipid signaling pathway and dopaminergic synapse pathway, both involving genes such as MAPK10, GNAQ, and PPP2R5C, suggest that these genes may play versatile roles in body conformation traits. Within the sphingolipid pathway, lipid signaling supports cell growth and structural integrity [50], whereas in the dopaminergic synapse pathway, these genes likely contribute to growth regulation through energy balance and cellular signaling mechanisms. Additionally, GO terms related to insulin-like growth factor receptor signaling and amine oxidase activity, with genes such as IGFBP1, IGFBP3, SAO, and LOC100138645, highlight the roles of metabolic and structural regulation in muscle and bone development [51]. Collectively, these pathways and genes reveal a complex network of biological processes shaping body conformation. Notably, the four genes (SLC4A4, GC, NPFFR2, and ADAMTS3) in the 86.84–87.41 Mb region of chromosome 6 exhibit significant pleiotropy and may play roles in multiple economically important traits in dairy cattle, including milk production, body conformation, and reproductive health. These findings provide valuable genetic markers for further research on molecular breeding and functional validation in dairy cattle.

Comparison with other GWAS studies

There is an extensive body of GWAS research on body conformation traits in ruminants. For example, previous GWAS have identified key candidate genes such as CCND2, KCNS3, SLC4A4, NTM, GC [52], NPFFR2 [53], EML6 [54], LDAH [55], GNAQ [56], and IGFBP3 [57]. Our study revealed consistent associations for several of these genes, underscoring their potential role across various populations and environments. However, we also identified novel loci, such as TRNAC-GCA_192, TRNAC-GCA_141, LOC112444734, and LOC104973438, which offer new insights into the genetic basis of body conformation traits.

Despite these findings, it is important to acknowledge the limitations of this study, which may have constrained the detection of certain loci with potential influence, such as DGAT1. The relatively limited sample size could have reduced the statistical power to identify loci with small-to-moderate effects. Furthermore, variations in allele frequency across different cattle populations might have led to the underrepresentation of population-specific genetic variants [58]. Additionally, the strict multiple testing corrections applied in this study, while necessary to control false-positive rates, may have inadvertently excluded loci with weaker signals that still play biologically meaningful roles. These constraints underscore the need for future research involving larger, more diverse populations, multi-environment data to account for gene-environment interplay, and advanced analytical methods, such as Bayesian approaches or machine learning techniques, to enhance detection sensitivity. By addressing these limitations, future studies could provide a more comprehensive understanding of the complex genetic architecture underlying body conformation traits in cattle, paving the way for more precise genomic selection strategies.

Conclusion

In this study, we conducted both single-trait and multi-trait GWAS to investigate the genetic basis of 17 body conformation traits in 586 Chinese Holstein cattle. A total of 63 significant SNP loci were identified, and 66 candidate genes were annotated, with 12 genes detected by both GWAS methods. These candidate genes are involved in various biological pathways identified through GO and KEGG enrichment analyses, such as growth hormone synthesis and secretion (MAPK10, IGFBP3, and GNAQ), sphingolipid signaling (MAPK10, GNAQ, and PPP2R5C), and dopaminergic synapse pathways. These pathways play critical roles in growth regulation, cell signaling, and metabolic processes. Additionally, a genomic region significantly associated with body conformation traits was identified in the 86.84–87.41 Mb region on chromosome 6; this region included four candidate genes (SLC4A4, GC, NPFFR2, and ADAMTS3), which may be significantly related to body conformation traits in Chinese Holstein cattle. The results of this study provide potential genetic markers for breeding for genomic selection and related analyses in dairy cattle.

Data availability

The variation data reported in this paper have been deposited in the Genome Variation Map (GVM) in National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, under accession number GVM000846 (https://bigd.big.ac.cn/gvm/getProjectDetail?Project=GVM000846). For more detailed information, please contact the corresponding author.

Abbreviations

PTAs:

Predicted Transmitting Abilities

GWAS:

Genome-Wide Association Study

SNP:

Single-Nucleotide Polymorphism

FLC:

Feet/Legs Composite

UDC:

Udder Composite

STA:

Stature

STR:

Strength

BDE:

Body Depth

DFM:

Dairy Form

RPA:

Rump Angle

RTW:

Rump-Thurl Width

RLS:

Rear Legs Side View

RLR:

Rear Legs Rear View

FTA:

Foot Angle

FLS:

Feet/Legs Score

FUA:

Fore Udder Attachment

RUH:

Rear Udder Height

RUW:

Rear udder width

UCL:

Udder Cleft

UDP:

Udder Depth

PCA:

Principal Component Analysis

LD:

Linkage disequilibrium

References

  1. Shengli Z, Dongxiao S. The past, present, and future of the dairy cattle breeding industry. China Animal Husbandry Industry. 2021;15:22–6.

    Google Scholar 

  2. Abo-Ismail MK, Brito LF, Miller SP, Sargolzaei M, Grossi DA, Moore SS, Plastow G, Stothard P, Nayeri S, Schenkel FS. Genome-wide association studies and genomic prediction of breeding values for calving performance and body conformation traits in Holstein cattle. Genet Sel Evol. 2017;49(1):82.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kock A, Ledinek M, Gruber L, Steininger F, Fuerst-Waltl B, Egger-Danner C. Genetic analysis of efficiency traits in Austrian dairy cattle and their relationships with body condition score and lameness. J Dairy Sci. 2018;101(1):445–55.

    Article  CAS  PubMed  Google Scholar 

  4. Xue X, Hu H, Zhang J, Ma Y, Han L, Hao F, Jiang Y, Ma Y. Estimation of genetic parameters for conformation traits and milk production traits in Chinese holsteins. Animals-Basel. 2022;13(1):100.

  5. Wu X, Fang M, Liu L, Wang S, Liu J, Ding X, Zhang S, Zhang Q, Zhang Y, Qiao L, et al. Genome wide association studies for body conformation traits in the Chinese Holstein cattle population. BMC Genomics. 2013;14:897.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Yu J, Pressoir G, Briggs WH, Vroh BI, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. 2006;38(2):203–8.

    Article  CAS  PubMed  Google Scholar 

  7. Nazar M, Abdalla IM, Chen Z, Ullah N, Liang Y, Chu S, Xu T, Mao Y, Yang Z, Lu X. Genome-wide association study for udder conformation traits in Chinese holstein cattle. Animals-Basel. 2022;12(19):2542.

  8. Haque MA, Alam MZ, Iqbal A, Lee YM, Dang CG, Kim JJ. Genome-wide association studies for body conformation traits in Korean holstein population. Animals-Basel. 2023;13(18):2964.

  9. Nazar M, Lu X, Abdalla IM, Ullah N, Fan Y, Chen Z, Arbab A, Mao Y, Yang Z. Genome-wide association study candidate genes on mammary system-related teat-shape conformation traits in Chinese holstein cattle. Genes-Basel. 2021;12(12):2020.

  10. Citek J, Brzakova M, Bauer J, Tichy L, Sztankoova Z, Vostry L, Steyn Y. Genome-wide association study for body conformation traits and fitness in Czech holsteins. Animals-Basel. 2022;12(24):3522.

  11. Korte A, Vilhjalmsson BJ, Segura V, Platt A, Long Q, Nordborg M. A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nat Genet. 2012;44(9):1066–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Porter HF, O’Reilly PF. Multivariate simulation framework reveals performance of multi-trait GWAS methods. Sci Rep-Uk. 2017;7:38837.

    Article  Google Scholar 

  13. Klei L, Luca D, Devlin B, Roeder K. Pleiotropy and principal components of heritability combine to increase power for association analysis. Genet Epidemiol. 2008;32(1):9–19.

    Article  PubMed  Google Scholar 

  14. Yuetong S, Rumei Z, Yanqin L, Rongling L, Yundong G, Jifeng Z, Guanghui X, Yudong W, Jianbin L, Dongxiao S. Estimation of genetic parameters for body conformation traits and the influence of pedigree generation in Shandong Holstein cattle. Acta Veterinaria et Zootechnica Sinica. 2022;53(05):1384–95.

    Google Scholar 

  15. Jianghua N, Jiying T. Correlation analysis of body conformation traits in Beijing Holstein cattle. Shaanxi Agricultural Science. 2023;69(11):91–4.

    Google Scholar 

  16. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, 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 

  17. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21(2):263–5.

    Article  CAS  PubMed  Google Scholar 

  18. Cole JB, Waurich B, Wensch-Dorendorf M, Bickhart DM, Swalve HH. A genome-wide association study of calf birth weight in Holstein cattle using single-nucleotide polymorphisms and phenotypes predicted from auxiliary traits. J Dairy Sci. 2014;97(5):3156–72.

    Article  CAS  PubMed  Google Scholar 

  19. Li B, VanRaden PM, Null DJ, O’Connell JR, Cole JB. Major quantitative trait loci influencing milk production and conformation traits in Guernsey dairy cattle detected on Bos taurus autosome 19. J Dairy Sci. 2021;104(1):550–60.

    Article  CAS  PubMed  Google Scholar 

  20. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88(1):76–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 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 

  22. Zhou X, Stephens M. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat Methods. 2014;11(4):407–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16): e164.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Han G, Fei G, Zezhao W, Hongwei L, Bingxing A, Haipeng L, Lingyang X, Bo Z, Wentao C, Lupei Z, et al. GWAS analysis of carcass traits in Huaxi cattle. China Animal Husbandry Journal. 2022;58(11):92–9.

    Google Scholar 

  25. DeGroot BJ, Keown JF, Van Vleck LD, Marotz EL. Genetic parameters and responses of linear type, yield traits, and somatic cell scores to divergent selection for predicted transmitting ability for type in Holsteins. J Dairy Sci. 2002;85(6):1578–85.

    Article  CAS  PubMed  Google Scholar 

  26. Yuechuan H, Hailing Z, Wei X, Liyun H, Jiamin Z, Liqin M, Wan W, Yachun W. Estimation of genetic parameters for body conformation traits in dairy cattle in the Ningxia region. China Animal Husbandry & Veterinary Medicine. 2024;51(07):2908–22.

    Google Scholar 

  27. Peng P, Guie J, Chendong Y, Jianming L, Yabin M, Junqing N, Dongxiao S. Genetic parameter analysis of body conformation traits in Holstein cattle in Hebei Province. China Dairy Cattle. 2021;08:23–7.

    Google Scholar 

  28. Yongfu A, Peijuan L, Jihua W, Fan P, Baokui X. Parameter analysis of body conformation traits in Hebei Holstein cattle. Chinese Cattle Science. 2011;37(02):6–10.

    Google Scholar 

  29. Zhou X, Xiang X, Cao D, Zhang L, Hu J. Selective sweep and GWAS provide insights into adaptive variation of Populus cathayana leaves. Forestry Research. 2024;4(1):e012.

  30. Li Y, Yang H, Guo J, Yang Y, Yu Q, Guo Y, Zhang C, Wang Z, Zuo P. Uncovering the candidate genes related to sheep body weight using multi-trait genome-wide association analysis. Front Vet Sci. 2023;10:1206383.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Pedrosa VB, Schenkel FS, Chen SY, Oliveira HR, Casey TM, Melka MG, Brito LF. Genome-wide association analyses of lactation persistency and milk production traits in holstein cattle based on imputed whole-genome sequence data. Genes-Basel. 2021;12(11):1830.

  32. Zhekova HR, Ramirez ED, Sejdiu BI, Pushkin A, Tieleman DP, Kurtz I. Molecular dynamics simulations of lipid-protein interactions in SLC4 proteins. Biophys J. 2024;123(12):1705–21.

    Article  CAS  PubMed  Google Scholar 

  33. Freebern E, Santos D, Fang L, Jiang J, Parker GK, Liu GE, VanRaden PM, Maltecca C, Cole JB, Ma L. GWAS and fine-mapping of livability and six disease traits in Holstein cattle. BMC Genomics. 2020;21(1):41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lee YL, Takeda H, Costa MMG, Karim L, Mullaart E, Coppieters W, Appeltant R, Veerkamp RF, Groenen M, Georges M, et al. A 12 kb multi-allelic copy number variation encompassing a GC gene enhancer is associated with mastitis resistance in dairy cattle. Plos Genet. 2021;17(7): e1009331.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Sun YL, Zhang XY, Sun T, He N, Li JY, Zhuang Y, Zeng Q, Yu J, Fang Q, Wang R. The anti-inflammatory potential of neuropeptide FF in vitro and in vivo. Peptides. 2013;47:124–32.

    Article  CAS  PubMed  Google Scholar 

  36. Schwarz L, Krizanac AM, Schneider H, Falker-Gieske C, Heise J, Liu Z, Bennewitz J, Thaller G, Tetens J. Genetic and genomic analysis of reproduction traits in holstein cattle using SNP chip data and imputed sequence level genotypes. BMC Genomics. 2024;25(1):880.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Janssen L, DuPont L, Bekhouche M, Noel A, Leduc C, Voz M, Peers B, Cataldo D, Apte SS, Dubail J, et al. ADAMTS3 activity is mandatory for embryonic lymphangiogenesis and regulates placental angiogenesis. Angiogenesis. 2016;19(1):53–65.

    Article  CAS  PubMed  Google Scholar 

  38. Jiang J, Ma L, Prakapenka D, VanRaden PM, Cole JB, Da Y. A Large-Scale Genome-Wide Association Study in U.S. Holstein Cattle. Front Genet. 2019;10:412.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Liang Z, Prakapenka D, VanRaden PM, Jiang J, Ma L, Da Y. A Million-Cow Genome-wide association study of three fertility traits in U.S. holstein cows. Int J Mol Sci. 2023;24(13):10496.

  40. Wu X, Lund MS, Sahana G, Guldbrandtsen B, Sun D, Zhang Q, Su G. Association analysis for udder health based on SNP-panel and sequence data in Danish Holsteins. Genet Sel Evol. 2015;47(1):50.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Wirth A, Duda J, Emmerling R, Gotz KU, Birkenmaier F, Distl O. Analyzing runs of homozygosity reveals patterns of selection in German brown cattle. Genes-Basel. 2024;15(8):1051.

  42. Goo YH, Son SH, Paul A. Lipid Droplet-Associated Hydrolase Promotes Lipid Droplet Fusion and Enhances ATGL Degradation and Triglyceride Accumulation. Sci Rep-Uk. 2017;7(1):2743.

    Article  Google Scholar 

  43. Wu X, Guldbrandtsen B, Lund MS, Sahana G. Association analysis for feet and legs disorders with whole-genome sequence variants in 3 dairy cattle breeds. J Dairy Sci. 2016;99(9):7221–31.

    Article  CAS  PubMed  Google Scholar 

  44. Da X, JunLiang L, Cui Z, WeiNa C, DongJie L, ShiJie L. The analysis of splice variants and genomic imprinting status of NTM gene in cattle (Bos taurus). Journal of Agricultural Biotechnology. 2018;26(10):1707–13.

    Google Scholar 

  45. Talker SC, Barut GT, Lischer H, Rufener R, von Munchow L, Bruggmann R, Summerfield A. Monocyte biology conserved across species: Functional insights from cattle. Front Immunol. 2022;13: 889175.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 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):1927.

  47. Simon B, Lou HJ, Huet-Calderwood C, Shi G, Boggon TJ, Turk BE, Calderwood DA. Tousled-like kinase 2 targets ASF1 histone chaperones through client mimicry. Nat Commun. 2022;13(1):749.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Cervelli M, Leonetti A, Cervoni L, Ohkubo S, Xhani M, Stano P, Federico R, Polticelli F, Mariottini P, Agostinelli E. Stability of spermine oxidase to thermal and chemical denaturation: comparison with bovine serum amine oxidase. Amino Acids. 2016;48(10):2283–91.

    Article  CAS  PubMed  Google Scholar 

  49. Dakhlan A, Adhianto K. Sulastri, Kurniawati D, Ermawati R, Doni ST: Mapping Growth Hormone Gene of Body Weight Krui Cattle in Pesisir Barat Regency Lampung. Indonesia Pak J Biol Sci. 2022;25(8):741–7.

    Article  CAS  PubMed  Google Scholar 

  50. Hannun YA, Obeid LM. Sphingolipids and their metabolism in physiology and disease. Nat Rev Mol Cell Bio. 2018;19(3):175–91.

    Article  CAS  Google Scholar 

  51. LeRoith D, Holly J, Forbes BE. Insulin-like growth factors: Ligands, binding proteins, and receptors. Mol Metab. 2021;52: 101245.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Long M, Wang B, Yang Z, Lu X. Genome-wide association study as an efficacious approach to discover candidate genes associated with body linear type traits in dairy cattle. Animals-Basel. 2024;14(15):2181.

  53. Sousa Junior LPB, Pinto LFB, Cruz VAR, Oliveira Junior GA, Oliveira HR, Chud TS, Pedrosa VB, Miglior F, Schenkel FS, Brito LF. Genome-wide association and functional genomic analyses for body conformation traits in North American holstein cattle. Front Genet. 2024;15:1478788.

  54. Ruvinskiy D, Amaral A, Weldenegodguad M, Ammosov I, Honkatukia M, Lindeberg H, Peippo J, Popov R, Soppela P, Stammler F, et al. Adipose gene expression profiles in Northern Finncattle, Mirandesa cattle, Yakutian cattle and commercial Holstein cattle. Sci Rep-Uk. 2024;14(1):22216.

    Article  CAS  Google Scholar 

  55. Silva E, Gaia RC, Mulim HA, Pinto L, Iung L, Brito LF, Pedrosa VB: Genome-wide association study of conformation traits in Brazilian holstein cattle. Animals-Basel. 2024;14(17):2472.

  56. Rajawat D, Nayak SS, Jain K, Sharma A, Parida S, Sahoo SP, Bhushan B, Patil DB, Dutt T, Panigrahi M. Genomic patterns of selection in morphometric traits across diverse Indian cattle breeds. Mamm Genome. 2024;35(3):377–89.

    Article  CAS  PubMed  Google Scholar 

  57. Ao X, Rong Y, Han M, Wang X, Xia Q, Shang F, Liu Y, Lv Q, Wang Z, Su R et al. Combined genome-wide association study and haplotype analysis identifies candidate genes affecting growth traits of inner Mongolian cashmere goats. Vet Sci. 2024;11(9):428.

  58. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017;101(1):5–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank all the authors for their contributions to the study.

Funding

This work was supported by the Breeding Industry Special Project of Tianjin Academy of Agricultural Sciences (2023ZYCX011 and 2024ZYCX012) and the Tianjin Seed Industry Special Project (22ZXZYSN00020).

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Author notes

  1. Shuangshuang Li, Fei Ge and Lili Chen contributed equally to this work.

Authors and Affiliations

  1. Tianjin Key Laboratory of Animal Molecular Breeding and Biotechnology, Tianjin Engineering Research Center of Animal Healthy Farming, Institute of Animal Science and Veterinary, Tianjin Academy of Agricultural Sciences, Tianjin, 300381, China

    Shuangshuang Li, Lili Chen & Yi Ma

  2. Tianjin Key Laboratory of Agricultural Animal Breeding and Healthy Husbandry, College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, Tianjin, 300392, China

    Shuangshuang Li

  3. State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China

    Shuangshuang Li, Fei Ge, Yuxin Liu & Yan Chen

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  1. Shuangshuang Li
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Contributions

S.S.L. and F.G. wrote the main manuscript text, and L.L.C. and Y.X.L. prepared the supplementary materials. Y.C. and Y.M. supervised the project and provided guidance on experimental design. S.S.L., F.G., and L.L.C. performed data analysis and interpretation. Y.C. and Y.M. reviewed the manuscript and provided academic feedback and revisions. All authors reviewed and approved the final version of the manuscript.

Corresponding authors

Correspondence to Yan Chen or Yi Ma.

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Ethics approval and consent to participate

The animals and experimental procedures used in this study followed the guidelines of the Animal Care and Use Committee of the Institute of Animal Science and Veterinary Medicine, Tianjin Academy of Agricultural Sciences (TAAS) (Tianjin, China). Informed consent was obtained from Tianjin Fuyou Agricultural Technology Co., Ltd. (Tianjin, China) for data collection. There was no use of human participants, data, or tissues. We confirmed that all methods were reported in accordance with ARRIVE guidelines (https://arriveguidelines.org) for the reporting of animal experiments.

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Supplementary Information

12864_2024_11090_MOESM1_ESM.pdf

Supplementary Material 1: Figure S1 Distribution of the frequency of 17 body conformation traits. FLC, feet/legs composite index; UDC, udder composite index; STA, stature; STR, strength; BDE, body depth; DFM, dairy form; RPA, rump angle; RTW, rump − thurl width; RLS, rear legs side view; RLR, rear legs rear view; FTA, foot angle; FLS, feet/legs score; FUA, fore udder attachment; RUH, rear udder height; RUW, rear udder width; UCL, udder cleft; UDP, udder depth.

Supplementary Material 1: Table S1 Significant loci and genes identified by different combinations of multi-trait GWAS.

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Li, S., Ge, F., Chen, L. et al. Genome-wide association analysis of body conformation traits in Chinese Holstein Cattle. BMC Genomics 25, 1174 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12864-024-11090-8

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BMC Genomics

ISSN: 1471-2164

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