1. Introduction
With global meat production exceeding 350 million tons in 2021 and projected to rise to 460 to 570 million tons by 2050 [1,2], the urgency of finding sustainable sources of meat is clear. Despite its modest share in the global market and a stagnating production trend since 1997 [2] (Figure 1), horsemeat represents a viable alternative, particularly in light of the environmental challenges posed by resource-intensive meat production methods [2,3,4]. Horsemeat production is characterized by lower methane emissions due to the horse’s unique digestive system [5].
It is estimated that the per capita supply of horsemeat for 2021 (calculated as total production plus imports minus exports divided by population size) is approximately 0.10 kg per person worldwide. Certain countries are well above this global average, including Mongolia (17.15 kg), Kazakhstan (8.00 kg), Kyrgyzstan (4.28 kg), Uruguay (1.54 kg), Iceland (1.43 kg), Australia (0.97 kg), Mexico (0.58 kg), Chile (0.54 kg), Canada (0.48 kg), Haiti (0.43 kg), Belgium (0.42 kg), Senegal (0.42 kg), Italy (0.42 kg), Russia (0.36 kg), Argentina (0.33 kg), Switzerland (0.28 kg), Nicaragua (0.28 kg), and Finland and Paraguay (0.22 kg, each) [2]. All of these countries, with the exception of Belgium, Italy, and Switzerland, are also among the leading per capita producers of horsemeat. These figures illustrate the considerable regional differences in the production and consumption of horsemeat and highlight the generally low global consumption of this resource.
The nutritional composition of horsemeat, which is significantly lower in fat and cholesterol and richer in n-3 fatty acids and heme iron compared to pork, beef, or poultry, shows that it has the potential to improve human health by improving lipid profiles and iron status [6,7]. Its unique fatty acid composition, particularly its high polyunsaturated fatty acid (PUFAs) content, not only offers a healthier alternative to traditionally consumed red meat but also suggests its role in the dietary management of disease. The higher content of palmitoleic and alpha-linolenic acids compared to beef and pork could protect against cardiovascular disease, stroke, and neurodegeneration [8]. In addition, the high content of high-quality proteins, essential amino acids, vitamins, and minerals suggests that horsemeat should be included in the diet of patients with metabolic obesity, as it offers potential therapeutic benefits in weight control and improving liver function [9]. The promising results on horsemeat hydrolysates suggest possible protection against muscle atrophy [10]. Overall, the nutritional benefits of horsemeat contribute significantly to its potential as a valuable dietary component for the prevention and treatment of various health conditions.
In Spain, where 15.3% of horse farms are dedicated to meat production, the traditional rearing of native breeds for meat production emphasizes the potential to conserve genetic diversity and native and/or endangered breeds, as well as to support the rural economy [11]. These practices contribute to the conservation of rural landscapes by providing important environmental services and preserving mosaic landscapes and biodiversity. In addition, extensive horse breeding helps to reduce the risk of forest fires, as less fuel is used for machinery [12]. The nutritional value of horsemeat and the environmental benefits of horsemeat production, including the contribution to the conservation of disadvantaged grazing lands, make horsemeat a viable solution to sustainability challenges in the meat industry [5,13,14]. In addition, the processing of horsemeat into various products such as “cecina”, dry-cured loin, and salami offers an attractive, gastronomically rich alternative to conventional meat products, promoting dietary diversification and preserving traditional culinary practices [15]. Additionally, Kołodziejczyk et al. (2019) highlight that horse breeding, especially cold-blooded horses, can be economically beneficial due to cheaper feed for horses, excellent feed conversion rates, rapid growth and weight gain, and high slaughter efficiency, making horse meat production potentially more profitable than other livestock such as cattle [16].
The traditional system of meat production is based on the rearing of native breeds that were formerly used as draft animals and were converted to meat production after the mechanization of agriculture [11]. The benefits of horsemeat production are overshadowed by the cultural view that horses are mainly bred for sport and leisure activities, which has resulted in few genomic studies on equine production traits compared to the extensive studies in cattle. Closing this gap will require overcoming cultural reservations and developing markets that are receptive to the benefits of horsemeat, underlining the urgency of further genomic research.
Beef production plays a central role in the global meat industry, supported by intensive research to improve various aspects of production, including nutrition, selective breeding, and genotyping. The quest for genetic improvement of cattle breeds depends on the identification of genetic markers associated with desirable traits. Key techniques such as genomic selection, genome-wide association studies (GWAS), and quantitative trait loci (QTL) mapping play an essential role in this process [17], facilitating the discovery of genetic variations associated with economically important traits such as feed efficiency and carcass quality. In addition, advances in genotyping of cattle populations enable the analysis of large cohorts, allowing accurate estimation of genomic breeding values for specific traits established in national breeding programs. These estimates are made possible by advanced genomic Best Linear Unbiased Prediction (BLUP) models, which significantly improve the accuracy and efficiency of breeding strategies [18].
Conversely, efforts to genotype horses, particularly concerning their production potential, have been neglected (Figure 2). Moreover, the most comprehensive publicly available database on QTLs and single-nucleotide polymorphism/gene association data of livestock species does not contain QTLs or associated genes/markers related to equine production traits.
To address this gap, we conducted a comparative genomic analysis with cattle, which have significant physiological and genetic similarities to horses, particularly with respect to traits such as body size and weight that are critical for meat production. Cattle have a wealth of well-annotated genomic and QTL data, making them a robust model for identifying relevant genes and markers in horses. Although pigs are another important livestock species, they are less suitable as a model for horses due to their different dietary requirements and housing systems. Pigs are omnivores and are typically kept in intensive production systems, which differ significantly from the extensive grazing systems used for horses. Sheep and goats, on the other hand, have more similarities with horses in terms of grazing behavior and certain physiological characteristics. However, there is relatively little research on these species compared to cattle, making them less suitable as primary models. Therefore, this study aims to apply the knowledge gained in cattle (meat production-associated QTLs) to horses by prioritizing the markers on the GGP Equine chip. This approach could help horse breeders in their decision-making processes. In addition, the results presented here could later be enriched with data from GWAS and other genomic evaluations performed on larger horse populations to provide the basis for future advances.
2. Materials and Methods
2.1. Literature Search
On 29 March 2024, a systematic search was conducted in the PubMed database (
2.2. Genes Related to Meat Production Retrieval
QTLs related to meat growth/size/meat production in cattle and horses were acquired from the Animal QTLdb (Release 52,
2.3. Identification of SNP Markers on the GGP Equine
The Ensembl BioMart tool (
2.4. Bioinformatics Analyses
The Variant Effect Predictor (VEP) (version 111,
To assess the biological significance of the identified genes, the STRING database (Version 12.0,
3. Results
In this study, we focused on the identification of SNP markers on the GGP Equine chip that are potentially important for horsemeat production traits. We began by identifying orthologous genes critical for meat production in cattle, followed by the identification of common genes within QTLs for body size and body weight. Next, we obtained markers within these regions from the GeneSeek® Genomic Profiler™ (GGP) Equine (Neogen, Lansing, MI, USA), which contains over 70,000 SNP markers. A total of 278 markers (Table S2) in 57 genes were identified. It is important to note that several SNP markers target the same genomic locations, resulting in duplicates: BIEC2_806771 and BIEC2_806771_DUP map to 3:101540587; 3-102641557-C-T and 3-102641557-C-T_DUP map to 3:102641557; CUHSNP00061902 and UKUL845 map to 3:103779615; BIEC681989 and BIEC681989_DUP map to 3:104518656; BIEC2_808543, GGP_100_BODY_SIZE_ECA3_F, and GGP_100_BODY_SIZE_ECA3_F_2 all map to 3:107374136; GGP_103_BODY_SIZE_ECA11_F and GGP_103_BODY_SIZE_ECA11_F_2 map to 11:23334511; BIEC2_438999, GGP_099_RACING_DISTANCE_MSTN_BIEC2-438999_F, and GGP_099_RACING_DISTANCE_MSTN_BIEC2-438999_F_2 all map to 18:67027762. The unique 268 positions were then evaluated considering the predicted variant consequences, GERP scores, and their positions within constrained elements and orthologous regulatory regions in pigs. This analysis resulted in 27 prioritized SNP markers for 22 genes. The validation of these markers by comparing the genotypes of one of the largest and one of the smallest horse breeds, Percheron (P) and American Miniature (AM), revealed 10 strong SNP candidate markers for 10 genes (Figure 3).
3.1. Comparative Analysis of SNP Markers on Bovine and Equine Chips
We initiated our investigation by identifying SNP markers on the GGP Equine chip within (and 5000 bp up- and down-stream) CAPN1, CAST, and MSTN, frontline genes directly associated with meat yield and quality in cows.
For CAPN1, GGP Equine contains a marker (CUHSNP00133513) for the SNP rs396701927. The SNP is a missense variant. Due to its high GERP score (4.15) and location within a constrained element, the SNP rs396701927 is an interesting candidate for horsemeat production. Two markers, BIEC2_277089 and BIEC2_277118, are for SNPs rs68930623 and rs68930652 within CAST, intronic variants of the canonical transcript ENSECAT00000052865. The rs68930652 is also an upstream variant of the ENSECAT00000031937. It is worth mentioning that the orthologous location of rs68930623 in pigs is within the enhancer. GGP Equine contains two SNP markers for MSTN, BIEC2_417365 (SNP rs69126368) and BIEC2_417369 (SNP rs69126372), which target intronic regions. The rs69126368 is located within a constrained element, potentially being an important marker in horses. In addition, there is a marker called GGP_099_RACING_DISTANCE_MSTN_BIEC2_438999 on the GGP Equine chip targeting rs69127737 within NEMP2 but with a low GERP score and also not within an orthologous regulatory element (Table S3).
3.2. QTLs Directly Associated with Meat Production in Cattle
No “meat”-associated QTLs have yet been described in the Animal QTLdb for horses. CattleQTLdb groups 19 QTLs associated with meat production into 3 categories: dressed carcass muscle percentage, dressed carcass muscle weight, and dressed carcass muscle-to-bone ratio (Table S4).
The QTLs for dressed carcass muscle percentage contain 499 genes, dressed carcass muscle weight 4 genes, and dressed carcass muscle-to-bone ratio 516 genes. While none of the genes were associated with all 3 groups, 30 were associated with 2, all of them with dressed carcass muscle weight and dressed carcass muscle-to-bone ratio: ADGRA3, ANAPC4, bta-mir-218-1, CCDC149, CCKAR, DHX15, ENSBTAG00000018885, ENSBTAG00000033327, ENSBTAG00000049001, GBA3, KCNIP4, LGI2, PACRGL, PCDH7, PI4K2B, PPARGC1A, RBPJ, SEL1L3, SEPSECS, SLC34A2, SLIT2, SMIM20, SNORA70, SOD3, STIM2, TBC1D19, three genes coding for U6, and ZCCHC4.
For these 30 genes, 22 horse orthologous genes were retrieved, all located on chromosome 3, in a syntenic block (Figure 4) rich in horse growth-related QTLs, such as body weight, size, and chest circumference. In total, 96 SNP markers for 18 genes are present on the GGP Equine chip (Table S5).
Most markers may target regions that influence transcription. Of particular note is a 3 prime UTR variant (rs1147560021) in LGI2 with a GERP score of 2.7. This variant may alter the miRNA binding site. Another variant is the intron variant rs68672779 in PPARGC1A with a GERP score of 3.57. In addition, the orthologous location of a 5 prime UTR variant rs68670656 and an upstream gene variant rs68508249 of SEPSECS and ADGRA3 are located within the promoter and enhancer, respectively, in pigs. Variants that may alter protein sequence and function include two missense variants rs68555658 and rs1147724321 (markers UKUL834 and UKUL835) in PCDH7 with GERP scores 2.83 and 3.84, respectively, a stop-lost variant rs1141209077 (marker CUHSNP00004551) in PI4K2B, and a splice donor region variant rs68593800 (marker CUHSNP00004551) in SLIT2, located in constrained element.
3.3. Analysis of QTLs Indirectly Involved in Meat Production
There are 35 QTLs associated with production in cows, but none in horses. However, when examining QTLs associated with horse growth, there are three main traits in common related to production: body weight, body length, and (overall) body size. In the following subsections, we focused on body weight and body size.
3.3.1. Body Weight
There are 1135 genes within the body weight QTLs in cows, but only 26 in horses, 2 of which are common to both species, ITPRID2 and FAM184B. The remaining 24 genes in horses are as follows: ABI2, C18H2orf88, COL5A2, DNAH7, RAPH1, ENSECAG00000033069, ENSECAG00000043662, ENSECAG00000043877, ENSECAG00000046295, ENSECAG00000046532, ENSECAG00000052525, ENSECAG00000053791, ENSECAG00000057875, GULP1, LDB2, MED28, MFSD6, MYO1B, NAB1, NEMP2, SLC39A10, TFPI, and ZNF804A.
For the genes in horses, we obtained SNP markers on the GGP Equine chip. There are 148 markers for the above genes, including the markers of GGP_099_RACING_DISTANCE_MSTN_BIEC2-438999 and GGP_100_BODY_SIZE_ECA3 for body size mentioned above. The summarized table of the variant consequences of the body weight markers is given in Table S6.
Of note are three missense variants in DNAH7 (rs396935555), ZNF804A (rs69192315), and GULP1 (18_65173441_T/C). A not-yet-reported variant of GULP1 is also a 5 prime UTR variant of another transcript. Its high GERP score (4, 81) and the location within a constrained element make this SNP a likely candidate. High GERP scores are also for SNPs rs68525653 (3.0) and 18_65288583_A/C (3.2), located in an intron of LDB2 and in the 3 prime UTR of GULP1, respectively. Four SNPs are within the orthologous enhancer: rs68454110 and rs68534807 in FAM184B, rs68603064 in ENSECAG00000052525 (GGP_100_BODY_SIZE_ECA3), and rs68525607 in LDB2. Other SNPs located in constrained elements, although having low GERP scores, are rs69126368 in C18H2orf88 and MSTN and rs69153418 and rs69153424 in ABI2 and RAPH1.
The STRING tool enriched these genes based on the bovine orthologs [28] as genes involved in muscularity: GULP1, NAB1, MFSD6, MYO1B, and COL5A2. Interestingly, MFSD6, NAB1, and MSTN are in strong linkage disequilibrium in mammals [29].
3.3.2. Body Size
There are only three QTLs for body size in cattle. Two of them are intergenic regions, and a QTL 57,073 overlaps with only one gene, IGF1R. In contrast, 12 QTLs were mapped to body size in horses, which overlap with 6 genes: LASP1, OXCT1, FAM184B, MED28, QDPR, and ENSECAG00000052525. Here, it is worth emphasizing ENSECAG00000052525, FAM184B, and MED28 that are in body weight QTL as well.
For the genes in horses, we obtained SNP markers on the GGP Equine chip. There are 37 unique targets for the above genes, including the “cow” gene IGF1R. There are also two markers for body size on the chip, GGP_100_BODY_SIZE_ECA3 for lncRNA ENSECAG00000052525 and GGP_103_BODY_SIZE_ECA11 (intron of LASP1) (Table S7).
Here, we would like to mention potential regulatory variants, with the exception of those mentioned above for ENSECAG00000052525 and FAM184B. Interestingly, three SNPs within LASP1 are in the orthologous enhancer: rs68875002, rs68876315, and a marker for body size rs68876319 (GGP_103_BODY_SIZE_ECA11). While rs68520444 is also localized in the enhancer of QDPR, rs68514854 is within the orthologous open chromatin region of IGF1R.
3.4. Gene Annotations
3.4.1. Protein–Protein Interaction Networks and Biological Pathway of Candidate Genes for Horsemeat Production
Of the 57 candidate genes, 43 were successfully imported into the STRING tool, with 27 edges among them. Compared to the expected five edges, the PPI enrichment was significant, with a p-value of 5.31 × 10−11. In total, 2 major PPI clusters were identified, consisting of 12 and 7 proteins, respectively. Interestingly, 6 genes (out of 12) with prioritized SNPs are included in the larger of the 2 clusters (Figure 5).
While no single gene ontology term was enriched, 19 genes are annotated in the KEGG database. Analysis of the KEGG pathways reveals that the genes studied are predominantly involved in signal transduction pathways that are central to the regulation of growth, nutrient sensing, and energy homeostasis (ANAPC4, IGF1R, CAPN1, MSTN, PI4K2B, PPARGC1A, RBPJ, STIM2). Associated with this pathway, a significant subset is implicated in the endocrine system, highlighting roles in reproductive function, hormone signaling, and calcium regulation (ANAPC4, IGF1R, PPARGC1A, SLC34A2). Additionally, a notable group is involved in carbohydrate metabolism, emphasizing their contribution to energy production and management in the organism (GBA3, HIBCH, OXCT1, PI4K2B). Other important pathways include nutrient absorption (COL5A2, SLC34A2), transcription (DHX15), translation (SEPSECS), amino acid metabolism (HIBCH, SEPSECS), and protein turnover (ANAPC4, CAPN1). In addition, signaling pathways related to the nervous system (IGF1R, CAPN1, DNAH7, RBPJ, PPARGC1A) and the immune system (RBPJ, TFPI) were detected (Figure 6).
3.4.2. Mammalian Phenotype
Subsequently, 18 genes (of the aforementioned 57) were identified that are associated with mammalian phenotypes related to body weight, growth, size, body fat, muscle development, feed uptake, and absorption. Twelve of these genes are associated with body size/growth/length. Also of interest are six genes related to feed/nutrient absorption and digestion. Perhaps more importantly, Ppargc1a and Mstn are associated with all five trait groups, while Igf1r and Itprid2 are associated with four (Figure 7). Of the 22 genes for which SNPs were prioritized, 8 are associated with screened mammalian phenotypes: GULP1, IGF1R, LASP1, MSTN, PPARGC1A, RAPH1, SEPSECS, and SLIT2 (Figure 7).
Table 1 summarizes the prioritized SNP markers and the corresponding genes, focusing on the reasons for their prioritization. The markers were selected based on potential effects on protein function, GERP scores greater than 2, their presence within constrained elements, and their location within orthologous regulatory elements. Notably, the gene GULP1 has SNP markers with potential effects on protein function, high GERP scores, and is located within constrained elements, emphasizing its importance in the study. Other genes such as CAPN1, MSTN, and SLIT2 also show multiple criteria for prioritization, underscoring their potential significance in horsemeat production traits.
Finally, the potential involvement of the prioritized SNPs in meat production in horses was validated by comparing alleles between one of the largest and one of the smallest horse breeds, P and AM. The analysis revealed ten SNPs in ten genes that differ between the two breeds, including rs68603064 (ENSECAG00000052525/LCORL, P: C|C, AM: T|T), rs68454110 (FAM184B, P: C|C, AM: T|T), rs68514854 (IGF1R, P: G|A, AM: A|A), rs68875002 (LASP1, P: T|T, AM: C|C), rs69126368 (C18H2orf88/MSTN, P: A|G, AM: G|G), rs68555658 (PCDH7, P: T|G, AM: G|G), and rs68520444 (QDPR, P: A|A, AM: A|G), among others. These markers are marked in bold in Table 1, and the corresponding genotypes are listed in Table S8. To further validate the relevance of these SNPs, we compared the sequence similarities of the orthologous genes between horses and cattle. Proteins with more than 60% sequence similarity are likely to have similar functions [30]. Table S9 provides an orthologous gene comparison between horses and cattle, showing that all genes have sequence similarities above 60%, with many genes such as IGF1R, LASP1, and MSTN having identity percentages exceeding 94%.
4. Discussion
In this study, we focused on the identification of SNP markers on the GGP Equine chip, which contains over 70,000 SNP markers, potentially relevant to horsemeat production traits. We began by identifying orthologous genes related to meat yield in cattle and QTLs for body weight and body size. Using these identified regions, we obtained 268 markers for 57 genes from the chip. These 268 markers were then evaluated considering predicted variant consequences, GERP scores, and their positions within constrained elements and orthologous regulatory regions in pigs. The predicted variant effects help us to identify SNPs that may affect protein function or gene regulation. High GERP scores and positions within constrained elements suggest that these SNPs are in evolutionarily significant regions, indicating their crucial role in biological processes. In addition, SNPs that match orthologous regulatory elements in pigs might affect gene transcription.
Out of 268 SNP markers, 27 for 22 genes were prioritized, including 2 already known markers for body size, GGP_100_BODY_SIZE_ECA3 for rs68603064 in lncRNA ENSECAG00000052525 and GGP_103_BODY_SIZE_ECA11 for rs68876319 in LASP1. However, the marker GGP_100_BODY_SIZE_ECA3 is usually assigned to the LCORL/NCAPG genes [31,32]. Both LCORL and LASP1 are involved in osteogenesis [33]. The rs68603064 overlaps with the putative transcription factor binding site (3′-ATAAA-5′) for TFIID, which is involved in skeletal bone development and affects LCORL (ligand dependent nuclear receptor corepressor like) transcript levels [34]. Lower expression of LCORL was observed in horses with the C allele at rs68603064, which was associated with larger animals compared to those with the T allele [34]. This is consistent with the fact that rs68603064 is localized in the orthologous enhancer, a regulatory element that is known to not always affect the nearest promoter but can skip neighboring genes to control genes located further away on a chromosome. In addition, certain enhancers have been shown to regulate multiple genes [35]. For example, a human enhancer ENSR00000718725, located upstream of LCROL, regulates four genes: MED28, LCORL, piR-32461-120, and HSALNG0033171, according to the GeneCards database [36]. Remarkably, the same polymorphism was also most strongly associated with osteochondritis dissecans in GWAS [37], which should be considered in genetic selection for larger horses. As with GGP_103_BODY_SIZE_ECA11, the marker targets rs68876319 of LASP1 (LIM and SH3 protein 1), which is also located within an orthologous enhancer. Ponies with the alternative genotypes for rs68876319 (A/G and G/G) have smaller measures for head length, neck length, wither height, croup height, and body length [33]. Two additional markers targeting polymorphic sites in orthologous enhancers within LASP1 are present on the chip, BIEC2_144152 (rs68875002) and BIEC2_144165 (rs68876315). Among the three markers for LASP1, it is interesting to note that rs68875002 has the highest GERP score (0.72); however, no information on this SNP is currently available in the scientific literature. Nevertheless, the genotypes for this marker differ between the P (T|T) and American Miniature (C|C) breeds. Furthermore, comprehensive genome and transcriptome analyses of the small-sized Korean native Jeju horse revealed positive selection for LCORL and LASP1. RNAseq analysis indicated differential expression of these genes, with Jeju horses having a higher proportion of type I muscle fiber (slow twitch) compared to Thoroughbred horses [31]. This also supports the role of LCORL and LASP1 in muscle structure and their potential impact on meat yield and quality traits, as enhancing meat tenderness and quality can be achieved by increasing the proportion of type I muscle fibers [38]. Further studies are needed to validate these markers in different horse breeds.
Three markers were also prioritized for the potential key genes IGR1R, MSTN, and PPARGC1A. According to the bioinformatic analysis, these genes are involved in signal transduction and endocrine system pathways. The GHRH-GH-IGF axis influences the mammalian growth and development [39]. A variant (marker UKUL191) within the IGF1R (insulin like growth factor 1 receptor) has been associated with both weight and size in horses [40]. In the present study, an intronic variant rs68514854 (marker BIEC2_44702) within the orthologous regulatory region was prioritized as possibly affecting gene expression (P: G|A, AM: A|A). Expression of IGF1R was higher in the lamellar tissue (LT) of lean ponies compared to obese ponies and in LT of ponies fed a low nonstructural carbohydrate (low-NSC) diet compared to ponies fed a high NSC diet. Similarly, more IGF1R was measured in the livers of ponies fed a low-NSC diet [41]. In cattle, IGF1R has been demonstrated to be involved in the IGF1R/PI3K/AKT signaling pathway to promote muscle proliferation and differentiation [42], while in dogs, a nonsynonymous variant was associated with smaller size [43]. Furthermore, exercise-induced modifications in skeletal muscle transcriptomes of Arabian horses and the effects of maternal overnutrition on foal muscle development highlight the role of IGF1R in muscle growth, structure, and performance [44,45]. Considering this, it is worth mentioning that there are no markers for the exonic regions of IGF1R on the analyzed chip. Anyhow, further studies are needed to establish a link between IGF1R polymorphism and meat yield in horses. The MSTN (myostatin) gene encodes a protein known to be a negative regulator of muscle mass in mammals [46]. In horses, it has been analyzed for its association with muscularity, strength [46], and race distance [47]. In the present study, we prioritized rs69126368 (marker BIEC2_417365) within the intron five of MSTN and with a high GERP score of 3.57 (P: A|G, AM: G|G). Interestingly, this SNP also overlaps with another protein-coding gene C18H2orf88 (chromosome 2 open reading frame 88). Although little is known about C18H2orf88, it is located in a genomic region that distinguishes horse breeds with contrasting risk for degenerative suspensory desmitis [48], a severity that is potentially at risk with increasing body size/weight. It is perhaps noteworthy that the orthologous gene in human is associated with diseases such as muscle hypertrophy and myostatin-related muscle hypertrophy, according to the GeneCards database [36], making the rs69126368 locus an interesting candidate region for future investigation. PPARGC1A (PPARG coactivator 1 alpha) regulates mitochondrial biogenesis and oxidative energy metabolism in muscle and has been recognized as a candidate gene for equine athletic performance [49,50]. Recent research has shown significant exercise-induced changes in PPARGC1A expression in equine skeletal muscle, highlighting its role in muscle adaptation and function [51,52]. In cattle, PPARGC1A has been identified as one of the key genes associated with intramuscular fat content and reproductive traits [53,54]. These findings suggest that PPARGC1A variants may influence meat yield and quality traits in horses. Here, we have prioritized an intronic variant rs68672779 (marker BIEC2_848001) with a high GERP score of 3.5.
Three more genes have been previously associated with mammalian phenotypes possibly important for horsemeat production: GULP1, SLIT2, and RAPH1. In previous studies, two markers for GULP1 (GULP PTB domain containing engulfment adaptor 1) have been associated with withers height in racing lines of Quarter Horses; BIEC2_417120 and BIEC2_417135 [55]. For the first marker, animals with the genotype TT were higher than genotypes TC or CC, while for BIEC2_417135, AA genotype was identified as the “favorable” genotype [55]. However, based on our data, both markers target intronic regions without orthologous regulatory elements or high conservation scores. In the present study, we prioritized UKUL3220, which targets a missense variant 18_65173441_T/C (canonical transcript) and the 5 prime UTR of other protein-coding transcripts of GULP1 within the constrained element and with a very high GERP score of 4.81. Also worth mentioning is the marker CUHSNP00150635, which targets a 3 prime UTR variant 18_65288583_A/C with a GERP score of 3.2. GULP1 positively regulates TGF-β signaling, resulting in cell growth inhibition [56]. Furthermore, up-regulated Gulp1 expression decreases adipose tissue expansion in mice [57]. This makes GULP1 and its markers interesting candidates for horsemeat production that should be explored in the future. SLIT2 (Slit guidance ligand 2) is a gene involved in neuronal development. The gene has been associated with backfat thickness in Landrace and Yorkshire pigs [58] and beef production [59]. In the present study, we prioritized a marker CUHSNP00004551 for a splice donor region variant rs68593800 within the constrained element. As for a newly identified gene in horses, RAPH1 (ENSECAG00000022376), we prioritized a marker BIEC2_421054 that targets the intronic rs69153424 within a constrained element. The gene is involved in nutrient absorption by rumen epithelial cells [60], possibly via the phosphatidylinositol (3,4) bisphosphate signaling pathway [61]. Interestingly, we also prioritized a marker CUHSNP00004551 for a high-impact stop-lost variant rs1141209077 in PI4K2B (phosphatidylinositol 4-kinase type 2 beta), which likely alters protein length and disrupts protein function.
In addition to the above genes, other genes with prioritized markers likely contribute to the desirable traits important for horsemeat production, which will be discussed below. Potential regulatory variants were identified in six other genes: ADGRA3, FAM184B, QDPR, SEPSECS, LDB2, and CAST. The rs68508249 (BIEC2_848365) is located upstream of ADGRA3 (adhesion G protein-coupled receptor A3) within an orthologous enhancer. These sites are common regions for promoters, agreeing that the orthologous position of rs68508249 also overlaps the mouse and human promoter. ADGRA3/GPR125 participates in cell communication and positively regulates osteoclastogenesis [62], which is involved in bone remodeling and formation that is particularly vigorous during the growth period to increase body size [63]. In mice, reduced osteoclastogenesis led to smaller and lighter animals [64]. Similarly, ADGRA3 has been associated with body weight in chickens [65]. In addition, the Adgra3 expression was higher in the adipose tissue of obese mice, and its siRNA-mediated silencing resulted in significantly reduced lipid storage in adipocytes. Therefore, the prioritized SNP may affect body growth and possibly also meat yield and quality.
Another bone-related gene is FAM184B (family with sequence similarity 184 member B). Variants within the gene have been associated with height at the withers in German warmblood horses (marker BIEC2_808608) [66] and with body length in Spanish purebred horses (marker BIEC2_808617) [67]. However, a variant rs68534803 (marker BIEC2_808579) in FAM148B was associated with osteochondrosis in the tarsocrural joint of Dutch Warmblood and French Trotter horses [68,69]. In the present study, two markers within the orthologous enhancer were prioritized, BIEC2_808625 for rs68454110 (P: C|C, AM: T|T) and BIEC2_808581 for rs68534807, but have not yet been reported in the literature.
In SEPSECS (Sep (O-phosphoserine) tRNA:Sec (selenocysteine) tRNA synthase), the prioritized marker BIEC2_808653 targets an SNP rs68520444 (P: A|A, AM: A|G) within the orthologous promoter in the 5 prime UTR and thus has a possible effect on transcription. The gene encodes a protein that participates in selenium metabolism and produces selenocysteine. Selenium primarily plays an important role in immune function and antioxidant capacity [70,71,72]. However, selenoproteins, which contain selenium in the form of selenocysteine, are critical for bone remodeling, and selenium deficiency leads to growth retardation [73]. In cattle, SEPSECS is associated with carcass weight [74], so the gene and its polymorphism should be investigated in the future.
Another metabolic gene, QDPR (quinoid dihydropteridine reductase), regenerates tetrahydrobiopterin (BH4), which is an essential cofactor for the biosynthesis of catecholamine and serotonin (5-hydroxytryptamine, 5-HT) [75]. BH4 influences the level of phenylalanine. Treatment of patients with phenylketonuria (phenylalanine build-up in the body) with BH4 improves growth, development, and nutrient status [76,77,78]. In chickens, higher expression of QDPR was associated with body weight [79]. In the present study, we prioritized an intronic variant rs68520444 (marker BIEC2_808653), which is located within an orthologous enhancer and could therefore affect the expression of QDPR and surrounding genes.
The CAST (calpastatin) gene is primarily associated with meat quality parameters [80]. In sheep, however, it codes for growth factors, their receptors, and transport and regulatory proteins that contribute to the formation of muscle mass and influence growth rates [81]. The CAST gene has not been studied in horses. Recently, however, Belousova et al. (2023) found a polymorphism in the CAST of Vyatka horse lines [81]. In chickens, higher expression of CAST was found in a commercial meat chicken breed (S01) compared to the black-bone mountain chicken breed (MB) [82], and gene expression was higher in fast-growing rainbow trout than in fish with the slowest growth rate [83]. In the present study, a prioritized marker BIEC2_277089 targets the intronic variant rs68930623, which is located within an orthologous enhancer and potentially affects gene transcription. CAST is an inhibitor of CAPN1 (calpain 1), and together they form a system that regulates skeletal muscle growth, development, and atrophy [84]. In the present study, we also prioritized a marker CUHSNP00133513 targeting a missense variant rs396701927 of CAPN1. Considering its location within a constrained element and a very high GERP score of 4.15, this polymorphism is likely to be functionally relevant.
The final marker prioritized for its potential role in regulating gene transcription is BIEC2_808833, which maps to the intronic variant rs68525607 in the orthologous enhancer of LDB2 (LIM domain binding 2). LDB2 is a transcriptional regulator responsible for the assembly of the transcriptional regulatory complex [85]. In humans, it is associated with neuronal development [86] and the cell cycle [87]. In chickens, however, it was found to be most strongly associated with body weight and late growth [88]. Interestingly, in Hucul mares, LDB2 was found to be one of the age-related differentially methylated genes in blood cells [89]. In addition to BIEC2_808833, another marker, BIEC2_808856, targeting rs68525653, was prioritized due to its high GERP score (3), making it potentially relevant. Similarly, LGI2 (leucine rich repeat LGI family member 2) has been suggested to regulate neuron synapse development and maturation [90]. Mice double mutants for Lgi2/3 exhibited poor postnatal growth [91]. For LGI2, we prioritized a marker UKUL843 targeting a 3 prime UTR variant rs1147560021 with a GERP score of 2.7. Given this, the variant could affect miRNA-mediated LGI2 regulation (P: C|T, AM: T|T).
Three additional markers were prioritized based on the high GERP score of their polymorphic target sites or their location within a constrained element. These markers were identified in ABI2 and PCDH7. ABI2 (Abl interactor 2), which is involved in actin remodeling and maintenance of cell morphology, has been demonstrated to be under the regulatory control of PRR16/Largen, a cell size regulator that is independent of mTOR and Hippo signaling pathways [92]. In black Bohai cattle, ABI2 has been linked to muscle development [93], while the prioritized marker BIEC2_421048 targeting intronic rs69153418 has been associated with body weight in Japanese Thoroughbred racehorses [94]. Two markers were prioritized for PCDH7 (protocadherin 7), UKUL834 and UKUL835. Both target missense variants with high GERP scores of 2.83 (rs68555658, P: T|G, AM: G|G) and 3.84 (rs1147724321), respectively. In Simmental beef cattle, PCDH7 has been proposed as a candidate for regulating body size [95]. The gene may be involved in residual feed intake as suggested in Nelore cattle [96] and feed efficiency in Duroc pigs [97].
Finally, missense variants for DNAH7 (dynein axonemal heavy chain 7) and ZNF804A (zinc finger protein 804A) were prioritized: marker Affx-102281324 for rs396935555 (P: C|T, AM: T|T) in DNAH7 and marker BIEC2_416678 for rs69192315 in ZNF804A. While DNAH7, another gene encoding a cytoskeletal protein, is thought to be primarily responsible for fertility [98,99,100], ZNF804A regulates pre-messenger RNA processing and gene expression [101]. In cattle, ZNF804A has been identified as one of the markers differentiating adipose tissue types, which influences intramuscular fat [102].
5. Conclusions
Bioinformatic analysis of the SNP chip helped us to find potentially functional SNP markers for several genes that could be important for horsemeat production, such as LCORL, LASP1, IGF1R, and MSTN. This study will benefit smallholder farmers by providing genetic insights for breeding decisions that promote desirable traits for meat production. Identifying specific SNP markers related to body size and weight enables selective breeding for improved meat yield, supporting efficient breeding without extensive genotyping resources. Empirical studies show that targeted weighting improves prediction accuracy, supporting precise and effective breeding decisions [103,104,105,106,107]. The information from this study will be valuable for large-scale genotyping and analyses such as GWAS and GBLUP. Therefore, integrating these prioritized SNP markers with genotypic data from larger horse populations will improve the accuracy of genomic breeding value predictions and support more effective breeding programs aimed at improving meat production, and the principles used in this study could be extended to other economically important traits in horses. Overall, this research not only helps individual breeders and small farmers but also lays the foundation for further development of the broader field of equine genomics. However, this study has some limitations, including reliance on bioinformatics and the literature without experimental validation. In addition, not all traits related to meat yield were examined, such as chest circumference, body length, growth rate, average daily gain, myopathies, muscle mass, number of thoracic vertebrae, and bone strength. Important meat quality parameters, such as fat deposition, fat composition, tenderness, firmness, marbling, and water holding capacity, were also not included. The strict criteria for inclusion or exclusion of SNP markers may also have resulted in relevant SNPs being omitted. In addition, we have focused on canonical transcripts, although other transcripts of the same gene may also play significant roles in meat production traits and should be considered in future analyses. Therefore, future research should experimentally validate these markers, examine a wider range of traits, and include quality parameters to enhance the accuracy of horse breeding schemes using methods such as GWAS and other genomic approaches. Furthermore, it will be essential to assess the variability within individual populations and incorporate this information into breeding programs or provide guidelines for breeders. This approach will ensure that the genetic insights gained from this study are effectively translated into practical breeding strategies.
Conceptualization, K.P.; investigation, M.Š.; data curation, M.Š.; writing—original draft preparation, M.Š.; writing—review and editing, A.K. and K.P.; supervision, A.K. and K.P. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The data that support the findings of this study are available from the corresponding author, A.K., upon reasonable request.
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
![View Image - Figure 1. Trends and statistics in global meat production (1961–2021). Top Left: Total global meat production by type (in million tons) from 1961 to 2021. Top Right: Share of horse meat in total meat production (in percentage) from 1961 to 2021, showing a declining trend over the years. Bottom Left: Horse meat production by region (in million tons) in 2021, highlighting Asia as the leading producer. Bottom Right: Annual global horse meat production (in thousand tons) from 1961 to 2021, illustrating fluctuations and an overall increasing trend in recent years [2].](https://media.proquest.com/media/hms/THUM/2/NZZeZ?_a=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%2BgIBWYIDA1dlYooDHENJRDoyMDI1MDgyOTEyMzUxMTYxNzo0MDQzOTA%3D&_s=%2FMmLvaF%2FQjMN7vk%2FHlopBW8Ljrg%3D "View Image - Figure 1. Trends and statistics in global meat production (1961–2021). Top Left: Total global meat production by type (in million tons) from 1961 to 2021. Top Right: Share of horse meat in total meat production (in percentage) from 1961 to 2021, showing a declining trend over the years. Bottom Left: Horse meat production by region (in million tons) in 2021, highlighting Asia as the leading producer. Bottom Right: Annual global horse meat production (in thousand tons) from 1961 to 2021, illustrating fluctuations and an overall increasing trend in recent years [2].")
Enlarge this image.Figure 1. Trends and statistics in global meat production (1961–2021). Top Left: Total global meat production by type (in million tons) from 1961 to 2021. Top Right: Share of horse meat in total meat production (in percentage) from 1961 to 2021, showing a declining trend over the years. Bottom Left: Horse meat production by region (in million tons) in 2021, highlighting Asia as the leading producer. Bottom Right: Annual global horse meat production (in thousand tons) from 1961 to 2021, illustrating fluctuations and an overall increasing trend in recent years [2].
Enlarge this image.Figure 2. Comparative analysis of genetic research in cattle and horses for meat production (1783–2024).
Enlarge this image.Figure 3. Workflow for identifying and prioritizing SNP markers for horsemeat production traits. Note: The markers in this figure represent unique SNP locations, excluding duplicate SNP probes.
Enlarge this image.Figure 4. Syntenic block on chromosome 3 in horses. Red rectangles on the horse chromosome indicate the region of orthologous genes associated with dressed carcass muscle weight and dressed carcass muscle-to-bone ratio in cattle.
Enlarge this image.Figure 5. Protein–protein interaction (PPI) network of 43 candidate genes for horsemeat production analyzed using the STRING tool. Nodes circled in black indicate candidate genes with prioritized markers.
Enlarge this image.Figure 6. KEGG pathway analysis of 19 candidate genes for horsemeat production analyzed using the KEGG database. Nodes circled in red indicate candidate genes with prioritized markers.
Enlarge this image.Figure 7. Mammalian phenotype associations of 18 genes from the study. These genes are linked to various traits possibly important for horsemeat production. Nodes circled in black indicate candidate genes with prioritized markers.
Summary of prioritized SNP markers, their associated genes, variant consequences, and the criteria for their prioritization. A ✓ indicates the presence of the respective characteristic: potential effect on protein function, GERP > 2 (indicating evolutionary conservation), location within a constrained element, or location within an orthologous regulatory element. SNPs that differ between Percheron and American Miniature horse breeds are marked in bold.
| Gene Symbol | Marker | SNP ID | Variant | Potential Effect on Protein | GERP > 2 | Constrained Element | Regulatory Element | Mammalian Phenotype |
|---|---|---|---|---|---|---|---|---|
| ABI2 | BIEC2_421048 | rs69153418 | intronic variant | ✓ | ||||
| ADGRA3 | BIEC2_848365 | rs68508249 | upstream gene variant | ✓ | ||||
| C18H2orf88 | BIEC2_417365 | rs69126368 | intronic variant | ✓ | ||||
| CAPN1 | CUHSNP00133513 | rs396701927 | missense variant | ✓ | ✓ | ✓ | ||
| CAST | BIEC2_277089 | rs68930623 | intronic variant | ✓ | ||||
| DNAH7 | Affx-102281324 | rs396935555 | missense variant | ✓ | ||||
| ENSECAG00000052525 | GGP_100_BODY_SIZE_ECA3 | rs68603064 | intronic variant | ✓ | ||||
| FAM184B | BIEC2_808625 | rs68454110 | intronic variant | ✓ | ||||
| FAM184B | BIEC2_808581 | rs68534807 | intronic variant | ✓ | ||||
| GULP1 | UKUL3220 | 18_65173441_T/C | missense variant | ✓ | ✓ | ✓ | ✓ | |
| GULP1 | CUHSNP00150635 | 18_65288583_A/C | 3 prime UTR variant | ✓ | ||||
| IGF1R | BIEC2-44702 | rs68514854 | intronic variant | ✓ | ✓ | |||
| LASP1 | BIEC2_144152 | rs68875002 | intronic variant | ✓ | ✓ | |||
| LASP1 | BIEC2_144165 | rs68876315 | intronic variant | ✓ | ||||
| LASP1 | GGP_103_BODY_SIZE_ECA11 | rs68876319 | intronic variant | ✓ | ||||
| LDB2 | BIEC2_808856 | rs68525653 | intronic variant | ✓ | ||||
| LDB2 | BIEC2-808833 | rs68525607 | intronic variant | ✓ | ||||
| LGI2 | UKUL843 | rs1147560021 | 3 prime UTR variant | ✓ | ||||
| MSTN | BIEC2_417365 | rs69126368 | intronic variant | ✓ | ✓ | |||
| PCDH7 | UKUL834 | rs68555658 | missense variant | ✓ | ✓ | |||
| PCDH7 | UKUL835 | rs1147724321 | missense variant | ✓ | ✓ | |||
| PI4K2B | CUHSNP00004551 | rs1141209077 | stop-lost | ✓ | ||||
| PPARGC1A | BIEC2_848001 | rs68672779 | intronic variant | ✓ | ✓ | |||
| QDPR | BIEC2_808653 | rs68520444 | intronic variant | ✓ | ||||
| RAPH1 | BIEC2_421054 | rs69153424 | intronic variant | ✓ | ✓ | |||
| SEPSECS | BIEC2_806764 | rs68670656 | 5 prime UTR variant | ✓ | ✓ | |||
| SLIT2 | CUHSNP00004551 | rs68593800 | splice donor region variant | ✓ | ✓ | ✓ | ||
| ZNF804A | BIEC2_416678 | rs69192315 | missense variant | ✓ |
Supplementary Materials
The following supporting information can be downloaded at:
References
1. TWC World Consumption of Meat. Available online: https://www.theworldcounts.com/challenges/consumption/foods-and-beverages/world-consumption-of-meat%0A (accessed on 27 March 2024).
2. FAO Crops and Livestock Products. Available online: http://www.fao.org/faostat (accessed on 12 October 2023).
3. Caccialanza, A.; Cerrato, D.; Galli, D. Sustainability Practices and Challenges in the Meat Supply Chain: A Systematic Literature Review. Br. Food J.; 2023; 125, pp. 4470-4497. [DOI: https://dx.doi.org/10.1108/BFJ-10-2022-0866]
4. Noble, K.; Huaccho Huatuco, L.; Dyke, A.; Green, J. The Environmental Impacts of Cultured Meat Production: A Systematic Literature Review. Smart Innovation, Systems and Technologies; Springer Science and Business Media: Berlin/Heidelberg, Germany, 2024; Volume 377, pp. 91-101. ISBN 9789819981588
5. Belaunzaran, X.; Bessa, R.J.B.; Lavín, P.; Mantecón, A.R.; Kramer, J.K.G.; Aldai, N. Horse-Meat for Human Consumption—Current Research and Future Opportunities. Meat Sci.; 2015; 108, pp. 74-81. [DOI: https://dx.doi.org/10.1016/j.meatsci.2015.05.006] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26047980]
6. Del Bo’, C.; Simonetti, P.; Gardana, C.; Riso, P.; Lucchini, G.; Ciappellano, S. Horse Meat Consumption Affects Iron Status, Lipid Profile and Fatty Acid Composition of Red Blood Cells in Healthy Volunteers. Int. J. Food Sci. Nutr.; 2013; 64, pp. 147-154. [DOI: https://dx.doi.org/10.3109/09637486.2012.728198]
7. Lorenzo, J.M.; Sarriés, M.V.; Tateo, A.; Polidori, P.; Franco, D.; Lanza, M. Carcass Characteristics, Meat Quality and Nutritional Value of Horsemeat: A Review. Meat Sci.; 2014; 96, pp. 1478-1488. [DOI: https://dx.doi.org/10.1016/j.meatsci.2013.12.006] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24423453]
8. Lee, C.-E.; Seong, P.-N.; Oh, W.-Y.; Ko, M.-S.; Kim, K.-I.; Jeong, J.-H. Nutritional Characteristics of Horsemeat in Comparison with Those of Beef and Pork. Nutr. Res. Pract.; 2007; 1, 70. [DOI: https://dx.doi.org/10.4162/nrp.2007.1.1.70]
9. Kadyrova, R.K.; Salkhanov, B.A.; Shakieva, R.A. Vliianie Dietoterapii s Primeneniem Koniny Na Funktsional’noe Sostoianie Pecheni u Bol’nykh Obmenno-Alimentarnym Ozhireniem. Vopr. Pitan.; 1984; 3, pp. 22-27.
10. Lee, H.J.; Kim, D.; Do, K.; Yang, C.B.; Jeon, S.W.; Jang, A. Effects of Horse Meat Hydrolysate on Oxidative Stress, Proinflammatory Cytokines, and the Ubiquitin-Proteasomal System of C2C12 Cells. Food Sci. Anim. Resour.; 2024; 44, pp. 132-145. [DOI: https://dx.doi.org/10.5851/kosfa.2023.e65]
11. Insausti, K.; Beldarrain, L.R.; Lavín, M.P.; Aldai, N.; Mantecón, Á.R.; Sáez, J.L.; Canals, R.M. Horse Meat Production in Northern Spain: Ecosystem Services and Sustainability in High Nature Value Farmland. Anim. Front.; 2021; 11, pp. 47-54. [DOI: https://dx.doi.org/10.1093/af/vfab003]
12. Ascoli, D.; Plana, E.; Oggioni, S.D.; Tomao, A.; Colonico, M.; Corona, P.; Giannino, F.; Moreno, M.; Xanthopoulos, G.; Kaoukis, K. et al. Fire-Smart Solutions for Sustainable Wildfire Risk Prevention: Bottom-up Initiatives Meet Top-down Policies under EU Green Deal. Int. J. Disaster Risk Reduct.; 2023; 92, 103715. [DOI: https://dx.doi.org/10.1016/j.ijdrr.2023.103715]
13. Sebbane, M.; Vial, C.; Lamy, A. A Horse on Your Plate? A Cluster Analysis of French Consumers Hippophagy Acceptance. Meat Sci.; 2023; 203, 109220. [DOI: https://dx.doi.org/10.1016/j.meatsci.2023.109220]
14. Lamy, A.; Costa, S.; Vial, C.; Badji, I.; Carrère, M.; Rollet, P.; Amiot, M.J. Horsemeat Consumption in France: Determinants and Sustainable Market Perspectives. Meat Sci.; 2023; 198, 109083. [DOI: https://dx.doi.org/10.1016/j.meatsci.2022.109083] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36592491]
15. Lorenzo, J.M.; Munekata, P.E.S.; Campagnol, P.C.B.; Zhu, Z.; Alpas, H.; Barba, F.J.; Tomasevic, I. Technological Aspects of Horse Meat Products—A Review. Food Res. Int.; 2017; 102, pp. 176-183. [DOI: https://dx.doi.org/10.1016/j.foodres.2017.09.094]
16. Kołodziejczyk, D.; Socik, M.; Socha, S. Importance of Breeding and Management of Cold-Blooded Horses in Terms of Their Meat Utilization. Acta Sci. Pol. Zootech.; 2020; 18, pp. 63-72. [DOI: https://dx.doi.org/10.21005/asp.2019.18.4.09]
17. Blasco, A.; Toro, M.A. A Short Critical History of the Application of Genomics to Animal Breeding. Livest. Sci.; 2014; 166, pp. 4-9. [DOI: https://dx.doi.org/10.1016/j.livsci.2014.03.015]
18. Vandenplas, J.; Spehar, M.; Potocnik, K.; Gengler, N.; Gorjanc, G. National Single-Step Genomic Method That Integrates Multi-National Genomic Information. J. Dairy Sci.; 2017; 100, pp. 465-478. [DOI: https://dx.doi.org/10.3168/jds.2016-11733]
19. Hu, Z.L.; Park, C.A.; Wu, X.L.; Reecy, J.M. Animal QTLdb: An Improved Database Tool for Livestock Animal QTL/Association Data Dissemination in the Post-Genome Era. Nucleic Acids Res.; 2013; 41, pp. D871-D879. [DOI: https://dx.doi.org/10.1093/nar/gks1150]
20. Raudvere, U.; Kolberg, L.; Kuzmin, I.; Arak, T.; Adler, P.; Peterson, H.; Vilo, J. G:Profiler: A Web Server for Functional Enrichment Analysis and Conversions of Gene Lists (2019 Update). Nucleic Acids Res.; 2019; 47, pp. W191-W198. [DOI: https://dx.doi.org/10.1093/nar/gkz369]
21. McLaren, W.; Gil, L.; Hunt, S.E.; Riat, H.S.; Ritchie, G.R.S.; Thormann, A.; Flicek, P.; Cunningham, F. The Ensembl Variant Effect Predictor. Genome Biol.; 2016; 17, 122. [DOI: https://dx.doi.org/10.1186/s13059-016-0974-4] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27268795]
22. Martin, F.J.; Amode, M.R.; Aneja, A.; Austine-Orimoloye, O.; Azov, A.G.; Barnes, I.; Becker, A.; Bennett, R.; Berry, A.; Bhai, J. et al. Ensembl 2023. Nucleic Acids Res.; 2023; 51, pp. D933-D941. [DOI: https://dx.doi.org/10.1093/nar/gkac958]
23. Huber, C.D.; Kim, B.Y.; Lohmueller, K.E. Population Genetic Models of GERP Scores Suggest Pervasive Turnover of Constrained Sites across Mammalian Evolution. PLoS Genet.; 2020; 16, e1008827. [DOI: https://dx.doi.org/10.1371/journal.pgen.1008827]
24. Szklarczyk, D.; Kirsch, R.; Koutrouli, M.; Nastou, K.; Mehryary, F.; Hachilif, R.; Gable, A.L.; Fang, T.; Doncheva, N.T.; Pyysalo, S. et al. The String Database in 2023: Protein-Protein Association Networks and Functional Enrichment Analyses for Any Sequenced Genome of Interest. Nucleic Acids Res.; 2023; 51, pp. D638-D646. [DOI: https://dx.doi.org/10.1093/nar/gkac1000] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36370105]
25. Kanehisa, M.; Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res.; 2000; 28, pp. 27-30. [DOI: https://dx.doi.org/10.1093/nar/28.1.27] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10592173]
26. Bult, C.J.; Blake, J.A.; Smith, C.L.; Kadin, J.A.; Richardson, J.E.; Anagnostopoulos, A.; Asabor, R.; Baldarelli, R.M.; Beal, J.S.; Bello, S.M. et al. Mouse Genome Database (MGD) 2019. Nucleic Acids Res.; 2019; 47, pp. D801-D806. [DOI: https://dx.doi.org/10.1093/nar/gky1056]
27. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res.; 2003; 13, pp. 2498-2504. [DOI: https://dx.doi.org/10.1101/gr.1239303] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/14597658]
28. Doyle, J.L.; Berry, D.P.; Veerkamp, R.F.; Carthy, T.R.; Evans, R.D.; Walsh, S.W.; Purfield, D.C. Genomic Regions Associated with Muscularity in Beef Cattle Differ in Five Contrasting Cattle Breeds. Genet. Sel. Evol.; 2020; 52, 2. [DOI: https://dx.doi.org/10.1186/s12711-020-0523-1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32000665]
29. Grade, C.V.C.; Salerno, M.S.; Schubert, F.R.; Dietrich, S.; Alvares, L.E. An Evolutionarily Conserved Myostatin Proximal Promoter/Enhancer Confers Basal Levels of Transcription and Spatial Specificity in Vivo. Dev. Genes Evol.; 2009; 219, pp. 497-508. [DOI: https://dx.doi.org/10.1007/s00427-009-0312-x]
30. Sangar, V.; Blankenberg, D.J.; Altman, N.; Lesk, A.M. Quantitative Sequence-Function Relationships in Proteins Based on Gene Ontology. BMC Bioinform.; 2007; 8, 294. [DOI: https://dx.doi.org/10.1186/1471-2105-8-294]
31. Srikanth, K.; Kim, N.-Y.; Park, W.; Kim, J.-M.; Kim, K.-D.; Lee, K.-T.; Son, J.-H.; Chai, H.-H.; Choi, J.-W.; Jang, G.-W. et al. Comprehensive Genome and Transcriptome Analyses Reveal Genetic Relationship, Selection Signature, and Transcriptome Landscape of Small-Sized Korean Native Jeju Horse. Sci. Rep.; 2019; 9, 16672. [DOI: https://dx.doi.org/10.1038/s41598-019-53102-8]
32. Saito, I.; Nakamura, K.; Tozaki, T.; Hano, K.; Takasu, M. Genetic Characterization of Japanese Native Horse Breeds by Genotyping Variants That Are Associated with Phenotypic Traits. J. Equine Sci.; 2023; 34, pp. 115-120. [DOI: https://dx.doi.org/10.1294/jes.34.115]
33. Bartholazzi Junior, A.; Quirino, C.R.; Vega, W.H.O.; Rua, M.A.S.; David, C.M.G.; Jardim, J.G. Polymorphisms in the LASP1 Gene Allow Selection for Smaller Stature in Ponies. Livest. Sci.; 2018; 216, pp. 160-164. [DOI: https://dx.doi.org/10.1016/j.livsci.2018.07.015]
34. Metzger, J.; Schrimpf, R.; Philipp, U.; Distl, O. Expression Levels of LCORL Are Associated with Body Size in Horses. PLoS ONE; 2013; 8, e56497. [DOI: https://dx.doi.org/10.1371/journal.pone.0056497] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23418579]
35. Pennacchio, L.A.; Bickmore, W.; Dean, A.; Nobrega, M.A.; Bejerano, G. Enhancers: Five Essential Questions. Nat. Rev. Genet.; 2013; 14, pp. 288-295. [DOI: https://dx.doi.org/10.1038/nrg3458]
36. Safran, M.; Rosen, N.; Twik, M.; BarShir, R.; Stein, T.I.; Dahary, D.; Fishilevich, S.; Lancet, D. The GeneCards Suite. Practical Guide to Life Science Databases; Springer Nature: Singapore, 2021; pp. 27-56. ISBN 9789811658129
37. Finno, C.J. Science-in-Brief: Genomic and Transcriptomic Approaches to the Investigation of Equine Diseases. Equine Vet. J.; 2022; 54, pp. 444-448. [DOI: https://dx.doi.org/10.1111/evj.13549]
38. Mo, M.; Zhang, Z.; Wang, X.; Shen, W.; Zhang, L.; Lin, S. Molecular Mechanisms Underlying the Impact of Muscle Fiber Types on Meat Quality in Livestock and Poultry. Front. Vet. Sci.; 2023; 10, 1284551. [DOI: https://dx.doi.org/10.3389/fvets.2023.1284551] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38076559]
39. Fang, J.; Zhang, D.; Cao, J.W.; Zhang, L.; Liu, C.X.; Xing, Y.P.; Wang, F.; Xu, H.Y.; Wang, S.C.; Ling, Y. et al. Pathways Involved in Pony Body Size Development. BMC Genom.; 2021; 22, 58. [DOI: https://dx.doi.org/10.1186/s12864-020-07323-1]
40. Pozharskiy, A.; Abdrakhmanova, A.; Beishova, I.; Shamshidin, A.; Nametov, A.; Ulyanova, T.; Bekova, G.; Kikebayev, N.; Kovalchuk, A.; Ulyanov, V. et al. Genetic Structure and Genome-Wide Association Study of the Traditional Kazakh Horses. Animal; 2023; 17, 100926. [DOI: https://dx.doi.org/10.1016/j.animal.2023.100926] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37611435]
41. Kullmann, A.; Weber, P.S.; Bishop, J.B.; Roux, T.M.; Norby, B.; Burns, T.A.; McCutcheon, L.J.; Belknap, J.K.; Geor, R.J. Equine Insulin Receptor and Insulin-like Growth Factor-1 Receptor Expression in Digital Lamellar Tissue and Insulin Target Tissues. Equine Vet. J.; 2016; 48, pp. 626-632. [DOI: https://dx.doi.org/10.1111/evj.12474] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26095356]
42. Shen, X.; Tang, J.; Jiang, R.; Wang, X.; Yang, Z.; Huang, Y.; Lan, X.; Lei, C.; Chen, H. CircRILPL1 Promotes Muscle Proliferation and Differentiation via Binding MiR-145 to Activate IGF1R/PI3K/AKT Pathway. Cell Death Dis.; 2021; 12, 142. [DOI: https://dx.doi.org/10.1038/s41419-021-03419-y]
43. Hoopes, B.C.; Rimbault, M.; Liebers, D.; Ostrander, E.A.; Sutter, N.B. The Insulin-like Growth Factor 1 Receptor (IGF1R) Contributes to Reduced Size in Dogs. Mamm. Genome; 2012; 23, pp. 780-790. [DOI: https://dx.doi.org/10.1007/s00335-012-9417-z]
44. Ropka-Molik, K.; Stefaniuk-Szmukier, M.; Zukowski, K.; Piórkowska, K.; Bugno-Poniewierska, M. Exercise-Induced Modification of the Skeletal Muscle Transcriptome in Arabian Horses. Physiol. Genom.; 2017; 49, pp. 318-326. [DOI: https://dx.doi.org/10.1152/physiolgenomics.00130.2016]
45. Bradbery, A.N.; Coverdale, J.A.; Hammer, C.J.; Dunlap, K.A.; Leatherwood, J.L.; Satterfield, M.C. Effect of Maternal Overnutrition on Predisposition to Insulin Resistance in the Foal: Foal Skeletal Muscle Development and Insulin Signaling. Domest. Anim. Endocrinol.; 2021; 77, 106648. [DOI: https://dx.doi.org/10.1016/j.domaniend.2021.106648] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34314944]
46. Dall’Olio, S.; Wang, Y.; Sartori, C.; Fontanesi, L.; Mantovani, R. Association of Myostatin (MSTN) Gene Polymorphisms with Morphological Traits in the Italian Heavy Draft Horse Breed. Livest. Sci.; 2014; 160, pp. 29-36. [DOI: https://dx.doi.org/10.1016/j.livsci.2013.12.002]
47. Binns, M.M.; Boehler, D.A.; Lambert, D.H. Identification of the Myostatin Locus (MSTN) as Having a Major Effect on Optimum Racing Distance in the Thoroughbred Horse in the USA. Anim. Genet.; 2010; 41, pp. 154-158. [DOI: https://dx.doi.org/10.1111/j.1365-2052.2010.02126.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21070290]
48. Momen, M.; Brounts, S.H.; Binversie, E.E.; Sample, S.J.; Rosa, G.J.M.; Davis, B.W.; Muir, P. Selection Signature Analyses and Genome-Wide Association Reveal Genomic Hotspot Regions That Reflect Differences between Breeds of Horse with Contrasting Risk of Degenerative Suspensory Ligament Desmitis. G3 Genes Genomes Genet.; 2022; 12, jkac179. [DOI: https://dx.doi.org/10.1093/g3journal/jkac179]
49. Eivers, S.S.; McGivney, B.A.; Gu, J.; MacHugh, D.E.; Katz, L.M.; Hill, E.W. PGC-1α Encoded by the PPARGC1A Gene Regulates Oxidative Energy Metabolism in Equine Skeletal Muscle during Exercise. Anim. Genet.; 2012; 43, pp. 153-162. [DOI: https://dx.doi.org/10.1111/j.1365-2052.2011.02238.x]
50. Polasik, D.; Rogers, B.; Bobrowska-Chwat, A.; Pikuła, R. Detection and Analysis of Polymorphism in the Promoter Region of Equine Pparcg1a Gene. J. Anim. Plant Sci.; 2017; 27, pp. 691-695.
51. Murphy, B.A.; Wagner, A.L.; McGlynn, O.F.; Kharazyan, F.; Browne, J.A.; Elliott, J.A. Exercise Influences Circadian Gene Expression in Equine Skeletal Muscle. Vet. J.; 2014; 201, pp. 39-45. [DOI: https://dx.doi.org/10.1016/j.tvjl.2014.03.028]
52. Eivers, S.S.; McGivney, B.A.; Fonseca, R.G.; MacHugh, D.E.; Menson, K.; Park, S.D.; Rivero, J.-L.L.; Taylor, C.T.; Katz, L.M.; Hill, E.W. Alterations in Oxidative Gene Expression in Equine Skeletal Muscle Following Exercise and Training. Physiol. Genom.; 2010; 40, pp. 83-93. [DOI: https://dx.doi.org/10.1152/physiolgenomics.00041.2009] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19861432]
53. Ramayo-Caldas, Y.; Fortes, M.R.S.; Hudson, N.J.; Porto-Neto, L.R.; Bolormaa, S.; Barendse, W.; Kelly, M.; Moore, S.S.; Goddard, M.E.; Lehnert, S.A. et al. A Marker-Derived Gene Network Reveals the Regulatory Role of PPARGC1A, HNF4G, and FOXP3 in Intramuscular Fat Deposition of Beef Cattle. J. Anim. Sci.; 2014; 92, pp. 2832-2845. [DOI: https://dx.doi.org/10.2527/jas.2013-7484]
54. Komisarek, J.; Walendowska, A. Analysis of the PPARGC1A Gene as a Potential Marker for Productive and Reproductive Traits in Cattle. Folia Biol.; 2012; 60, pp. 171-174. [DOI: https://dx.doi.org/10.3409/fb60_3-4.171-174]
55. de Matteis, R.; Pereira, G.L.; Casarotto, L.T.; Tavernaro, A.J.S.; Silva, J.A.I.V.; Chardulo, L.A.L.; Curi, R.A. Variants in the Chromosomal Region of the Myostatin Gene and Their Association with Lines, Performance, and Body Measurements of Quarter Horses. J. Equine Vet. Sci.; 2018; 71, pp. 75-83. [DOI: https://dx.doi.org/10.1016/j.jevs.2018.09.007]
56. Ma, C.I.J.; Martin, C.; Ma, Z.; Hafiane, A.; Dai, M.; Lebrun, J.J.; Kiss, R.S. Engulfment Protein GULP Is Regulator of Transforming Growth Factor-β Response in Ovarian Cells. J. Biol. Chem.; 2012; 287, pp. 20636-20651. [DOI: https://dx.doi.org/10.1074/jbc.M111.314997] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22451657]
57. Mahmassani, Z.S.; Reidy, P.T.; McKenzie, A.I.; Petrocelli, J.J.; Matthews, O.; de Hart, N.M.; Ferrara, P.J.; O’Connell, R.M.; Funai, K.; Drummond, M.J. Absence of MyD88 from Skeletal Muscle Protects Female Mice from Inactivity-Induced Adiposity and Insulin Resistance. Obesity; 2020; 28, pp. 772-782. [DOI: https://dx.doi.org/10.1002/oby.22759]
58. Lee, Y.-S.; Shin, D. Genome-Wide Association Studies Associated with Backfat Thickness in Landrace and Yorkshire Pigs. Genom. Inform.; 2018; 16, pp. 59-64. [DOI: https://dx.doi.org/10.5808/GI.2018.16.3.59] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30309204]
59. Sanchez, M.P.; Tribout, T.; Kadri, N.; Chitneedi, P.K.; Maak, S.; Hozé, C.; Boussaha, M.; Croiseau, P.; Philippe, R.; Spengeler, M. et al. 523. Sequence-Based GWAS Meta-Analyses for Beef Production Traits. Proceedings of the 12th World Congress on Genetics Applied to Livestock Production (WCGALP); Rotterdam, The Netherlands, 3–9 July 2022; Wageningen Academic Publishers: Wageningen, The Netherlands, 2022; pp. 2170-2173.
60. Terry, S.A.; Basarab, J.A.; Guan, L.L.; McAllister, T.A. Strategies to Improve the Efficiency of Beef Cattle Production. Can. J. Anim. Sci.; 2021; 101, pp. 1-19. [DOI: https://dx.doi.org/10.1139/cjas-2020-0022]
61. Li, H.; Marshall, A.J. Phosphatidylinositol (3,4) Bisphosphate-Specific Phosphatases and Effector Proteins: A Distinct Branch of PI3K Signaling. Cell. Signal.; 2015; 27, pp. 1789-1798. [DOI: https://dx.doi.org/10.1016/j.cellsig.2015.05.013]
62. Tang, C.Y.; Wang, H.; Zhang, Y.; Wang, Z.; Zhu, G.; McVicar, A.; Li, Y.P.; Chen, W. GPR125 Positively Regulates Osteoclastogenesis Potentially through AKT-NF-ΚB and MAPK Signaling Pathways. Int. J. Biol. Sci.; 2022; 18, pp. 2392-2405. [DOI: https://dx.doi.org/10.7150/ijbs.70620]
63. Duncan Bassett, J.H.; Williams, G.R. Role of Thyroid Hormones in Skeletal Development and Bone Maintenance. Endocr. Rev.; 2016; 37, pp. 135-187. [DOI: https://dx.doi.org/10.1210/er.2015-1106]
64. Yamashita, J.; Datta, N.S.; Chun, Y.H.P.; Yang, D.Y.; Carey, A.A.; Kreider, J.M.; Goldstein, S.A.; McCauley, L.K. Role of Bcl2 in Osteoclastogenesis and PTH Anabolic Actions in Bone. J. Bone Miner. Res.; 2008; 23, pp. 621-632. [DOI: https://dx.doi.org/10.1359/jbmr.071211]
65. Cha, J.; Choo, H.; Srikanth, K.; Lee, S.-H.; Son, J.-W.; Park, M.-R.; Kim, N.; Jang, G.W.; Park, J.-E. Genome-Wide Association Study Identifies 12 Loci Associated with Body Weight at Age 8 Weeks in Korean Native Chickens. Genes; 2021; 12, 1170. [DOI: https://dx.doi.org/10.3390/genes12081170]
66. Tetens, J.; Widmann, P.; Kühn, C.; Thaller, G.A. Genome-Wide Association Study Indicates LCORL/NCAPG as a Candidate Locus for Withers Height in German Warmblood Horses. Anim. Genet.; 2013; 44, pp. 467-471. [DOI: https://dx.doi.org/10.1111/age.12031]
67. Sevane, N.; Dunner, S.; Boado, A.; Cañon, J. Polymorphisms in Ten Candidate Genes Are Associated with Conformational and Locomotive Traits in Spanish Purebred Horses. J. Appl. Genet.; 2017; 58, pp. 355-361. [DOI: https://dx.doi.org/10.1007/s13353-016-0385-y] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27917442]
68. Teyssedre, S.; Dupuis, M.C.; Guérin, G.; Schibler, L.; Denoix, J.M.; Elsen, J.M.; Ricard, A. Genome-Wide Association Studies for Osteochondrosis in French Trotter Horses. J. Anim. Sci.; 2012; 90, pp. 45-53. [DOI: https://dx.doi.org/10.2527/jas.2011-4031] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21841084]
69. Orr, N.; Hill, E.W.; Gu, J.; Govindarajan, P.; Conroy, J.; Van Grevenhof, E.M.; Ducro, B.J.; Van Arendonk, J.A.M.; Knaap, J.H.; Van Weeren, P.R. et al. Genome-Wide Association Study of Osteochondrosis in the Tarsocrural Joint of Dutch Warmblood Horses Identifies Susceptibility Loci on Chromosomes 3 and 10. Anim. Genet.; 2013; 44, pp. 408-412. [DOI: https://dx.doi.org/10.1111/age.12016]
70. Mehdi, Y.; Dufrasne, I. Selenium in Cattle: A Review. Molecules; 2016; 21, 545. [DOI: https://dx.doi.org/10.3390/molecules21040545]
71. Richardson, S.M.; Siciliano, P.D.; Engle, T.E.; Larson, C.K.; Ward, T.L. Effect of Selenium Supplementation and Source on the Selenium Status of Horses. J. Anim. Sci.; 2006; 84, pp. 1742-1748. [DOI: https://dx.doi.org/10.2527/jas.2005-413] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16775058]
72. Cao, J.J.; Gregoire, B.R.; Zeng, H. Selenium Deficiency Decreases Antioxidative Capacity and Is Detrimental to Bone Microarchitecture in Mice. J. Nutr.; 2012; 142, pp. 1526-1531. [DOI: https://dx.doi.org/10.3945/jn.111.157040]
73. Kim, H.; Lee, K.; Kim, J.M.; Kim, M.Y.; Kim, J.-R.; Lee, H.-W.; Chung, Y.W.; Shin, H.-I.; Kim, T.; Park, E.-S. et al. Selenoprotein W Ensures Physiological Bone Remodeling by Preventing Hyperactivity of Osteoclasts. Nat. Commun.; 2021; 12, 2258. [DOI: https://dx.doi.org/10.1038/s41467-021-22565-7] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33859201]
74. Bhuiyan, M.S.A.; Lim, D.; Park, M.; Lee, S.; Kim, Y.; Gondro, C.; Park, B.; Lee, S. Functional Partitioning of Genomic Variance and Genome-Wide Association Study for Carcass Traits in Korean Hanwoo Cattle Using Imputed Sequence Level SNP Data. Front. Genet.; 2018; 9, 217. [DOI: https://dx.doi.org/10.3389/fgene.2018.00217]
75. Suganuma, Y.; Sumi-Ichinose, C.; Kano, T.; Ikemoto, K.; Matsui, T.; Ichinose, H.; Kondo, K. Quinonoid Dihydropteridine Reductase, a Tetrahydrobiopterin-Recycling Enzyme, Contributes to 5-Hydroxytryptamine-Associated Platelet Aggregation in Mice. J. Pharmacol. Sci.; 2022; 150, pp. 173-179. [DOI: https://dx.doi.org/10.1016/j.jphs.2022.08.006]
76. Singh, R.H.; Quirk, M.E.; Douglas, T.D.; Brauchla, M.C. BH4 Therapy Impacts the Nutrition Status and Intake in Children with Phenylketonuria: 2-Year Follow-Up. J. Inherit. Metab. Dis.; 2010; 33, pp. 689-695. [DOI: https://dx.doi.org/10.1007/s10545-010-9224-1]
77. Aldámiz-Echevarría, L.; Llarena, M.; Andrade, F.; Couce, M.L.; Lamuño, D.G.; Bueno, M.A.; Aldámiz-echevarría, L. Phenylketonuria Treatment ’ s Impact on Physical Growth: A Spanish Retrospective Longitudinal Study Abstract Study Population. J. Rare Disord. Diagn. Ther.; 2015; 1, pp. 1-8.
78. Trefz, F.K.; Scheible, D.; Frauendienst-Egger, G.; Korall, H.; Blau, N. Long-Term Treatment of Patients with Mild and Classical Phenylketonuria by Tetrahydrobiopterin. Mol. Genet. Metab.; 2005; 86, pp. 75-80. [DOI: https://dx.doi.org/10.1016/j.ymgme.2005.06.026]
79. Cai, K.; liu, R.; Wei, L.; Wang, X.; Cui, H.; Luo, N.; Wen, J.; Chang, Y.; Zhao, G. Identification of Candidate Genes for Feed Efficiency and Growth Traits in Wenchang Chickens. 2024; Available online: https://www.researchsquare.com/article/rs-3931439/v1 (accessed on 27 March 2024).
80. Ropka-Molik, K.; Bereta, A.; Tyra, M.; Rózycki, M.; Piórkowska, K.; Szyndler-Nedza, M.; Szmatoła, T. Association of Calpastatin Gene Polymorphisms and Meat Quality Traits in Pig. Meat Sci.; 2014; 97, pp. 143-150. [DOI: https://dx.doi.org/10.1016/j.meatsci.2014.01.021]
81. Belousova, N.; Bass, S.; Zinov’eva, S.; Sorokin, S.; Wilkinson, M. Study of Population-Genomic Structure of Vyatka Horses in Interline Aspect. Agrar. Bull.; 2023; 229, pp. 2-8. [DOI: https://dx.doi.org/10.32417/1997-4868-2022-229-14-2-8]
82. Zhang, Z.R.; Jiang, X.S.; Du, H.R.; Zhu, Q.; Li, X.C.; Yang, C.W.; Liu, Y.P. Characterization of the Expression Profiles of Calpastatin (CAST) Gene in Chicken. Mol. Biol. Rep.; 2012; 39, pp. 1839-1843. [DOI: https://dx.doi.org/10.1007/s11033-011-0926-8]
83. Salem, M.; Yao, J.; Rexroad, C.E.; Kenney, P.B.; Semmens, K.; Killefer, J.; Nath, J. Characterization of Calpastatin Gene in Fish: Its Potential Role in Muscle Growth and Fillet Quality. Comp. Biochem. Physiol. Part B Biochem. Mol. Biol.; 2005; 141, pp. 488-497. [DOI: https://dx.doi.org/10.1016/j.cbpc.2005.05.012]
84. Kemp, C.M.; Oliver, W.T.; Wheeler, T.L.; Chishti, A.H.; Koohmaraie, M. The Effects of Capn1 Gene Inactivation on Skeletal Muscle Growth, Development, and Atrophy, and the Compensatory Role of Other Proteolytic Systems. J. Anim. Sci.; 2013; 91, pp. 3155-3167. [DOI: https://dx.doi.org/10.2527/jas.2012-5737]
85. Bach, I.; Carrière, C.; Ostendorff, H.P.; Andersen, B.; Rosenfeld, M.G. A Family of LIM Domain-Associated Cofactors Confer Transcriptional Synergism between LIM and Otx Homeodomain Proteins. Genes Dev.; 1997; 11, pp. 1370-1380. [DOI: https://dx.doi.org/10.1101/gad.11.11.1370]
86. Fassunke, J.; Majores, M.; Tresch, A.; Niehusmann, P.; Grote, A.; Schoch, S.; Becker, A.J. Array Analysis of Epilepsy-Associated Gangliogliomas Reveals Expression Patterns Related to Aberrant Development of Neuronal Precursors. Brain; 2008; 131, pp. 3034-3050. [DOI: https://dx.doi.org/10.1093/brain/awn233]
87. Yuan, C.; Wu, C.; Xue, R.; Jin, C.; Zheng, C. Suppression of Human Colon Tumor by EERAC through Regulating Notch/DLL4/Hes Pathway Inhibiting Angiogenesis in Vivo. J. Cancer; 2021; 12, pp. 5914-5922. [DOI: https://dx.doi.org/10.7150/jca.61581]
88. Sharmaa, A.; Lee, J.S.; Dang, C.G.; Sudrajad, P.; Kim, H.C.; Yeon, S.H.; Kang, H.S.; Lee, S.H. Stories and Challenges of Genome Wide Association Studies in Livestock—a Review. Asian-Australas. J. Anim. Sci.; 2015; 28, pp. 1371-1379. [DOI: https://dx.doi.org/10.5713/ajas.14.0715]
89. Ząbek, T.; Semik-Gurgul, E.; Szmatoła, T.; Gurgul, A.; Fornal, A.; Bugno-Poniewierska, M. Methylation Marks of Blood Leukocytes of Native Hucul Mares Differentiated in Age. Int. J. Genom.; 2019; 2019, pp. 1-9. [DOI: https://dx.doi.org/10.1155/2019/2839614]
90. Favuzzi, E.; Deogracias, R.; Marques-Smith, A.; Maeso, P.; Jezequel, J.; Exposito-Alonso, D.; Balia, M.; Kroon, T.; Hinojosa, A.J.; Maraver, E.F. et al. Neurodevelopment: Distinct Molecular Programs Regulate Synapse Specificity in Cortical Inhibitory Circuits. Science; 2019; 363, pp. 413-417. [DOI: https://dx.doi.org/10.1126/science.aau8977] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30679375]
91. Kozar-Gillan, N.; Velichkova, A.; Kanatouris, G.; Eshed-Eisenbach, Y.; Steel, G.; Jaegle, M.; Aunin, E.; Peles, E.; Torsney, C.; Meijer, D.N. LGI3/2–ADAM23 Interactions Cluster Kv1 Channels in Myelinated Axons to Regulate Refractory Period. J. Cell Biol.; 2023; 222, e202211031. [DOI: https://dx.doi.org/10.1083/jcb.202211031] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36828548]
92. Kang, G.J.; Park, J.H.; Kim, H.J.; Kim, E.J.; Kim, B.; Byun, H.J.; Yu, L.; Nguyen, T.M.; Nguyen, T.H.; Kim, K.S. et al. PRR16/Largen Induces Epithelial-Mesenchymal Transition through the Interaction with ABI2 Leading to the Activation of ABL1 Kinase. Biomol. Ther.; 2022; 30, pp. 340-347. [DOI: https://dx.doi.org/10.4062/biomolther.2022.066] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35719027]
93. Ma, X.; Cheng, H.; Liu, Y.; Sun, L.; Chen, N.; Jiang, F.; You, W.; Yang, Z.; Zhang, B.; Song, E. et al. Assessing Genomic Diversity and Selective Pressures in Bohai Black Cattle Using Whole-Genome Sequencing Data. Animals; 2022; 12, 665. [DOI: https://dx.doi.org/10.3390/ani12050665]
94. Tozaki, T.; Kikuchi, M.; Kakoi, H.; Hirota, K.I.; Nagata, S.I. A Genome-Wide Association Study for Body Weight in Japanese Thoroughbred Racehorses Clarifies Candidate Regions on Chromosomes 3, 9, 15, and 18. J. Equine Sci.; 2017; 28, pp. 127-134. [DOI: https://dx.doi.org/10.1294/jes.28.127]
95. An, B.; Xu, L.; Xia, J.; Wang, X.; Miao, J.; Chang, T.; Song, M.; Ni, J.; Xu, L.; Zhang, L. et al. Multiple Association Analysis of Loci and Candidate Genes That Regulate Body Size at Three Growth Stages in Simmental Beef Cattle. BMC Genet.; 2020; 21, 32. [DOI: https://dx.doi.org/10.1186/s12863-020-0837-6]
96. Tizioto, P.C.; Coutinho, L.L.; Decker, J.E.; Schnabel, R.D.; Rosa, K.O.; Oliveira, P.S.N.; Souza, M.M.; Mourão, G.B.; Tullio, R.R.; Chaves, A.S. et al. Global Liver Gene Expression Differences in Nelore Steers with Divergent Residual Feed Intake Phenotypes. BMC Genom.; 2015; 16, 242. [DOI: https://dx.doi.org/10.1186/s12864-015-1464-x]
97. Sahana, G.; Kadlecová, V.; Hornshøj, H.; Nielsen, B.; Christensen, O.F. A Genome-Wide Association Scan in Pig Identifi Es Novel Regions Associated with Feed Effi Ciency Trait. J. Anim. Sci.; 2013; 91, pp. 1041-1050. [DOI: https://dx.doi.org/10.2527/jas.2012-5643] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23296815]
98. Paumard-Hernández, B.; Calvete, O.; Inglada Pérez, L.; Tejero, H.; Al-Shahrour, F.; Pita, G.; Barroso, A.; Carlos Triviño, J.; Urioste, M.; Valverde, C. et al. Whole Exome Sequencing Identifies PLEC, EXO5 and DNAH7 as Novel Susceptibility Genes in Testicular Cancer. Int. J. Cancer; 2018; 143, pp. 1954-1962. [DOI: https://dx.doi.org/10.1002/ijc.31604]
99. Gao, Y.; Liu, L.; Shen, Q.; Fu, F.; Xu, C.; Geng, H.; Lv, M.; Li, K.; Tang, D.; Song, B. et al. Loss of Function Mutation in DNAH7 Induces Male Infertility Associated with Abnormalities of the Sperm Flagella and Mitochondria in Human. Clin. Genet.; 2022; 102, pp. 130-135. [DOI: https://dx.doi.org/10.1111/cge.14146]
100. Rabaglino, M.B.; Le Danvic, C.; Schibler, L.; Kupisiewicz, K.; Perrier, J.P.; O’Meara, C.M.; Kenny, D.A.; Fair, S.; Lonergan, P. Identification of Sperm Proteins as Biomarkers of Field Fertility in Holstein-Friesian Bulls Used for Artificial Insemination. J. Dairy Sci.; 2022; 105, pp. 10033-10046. [DOI: https://dx.doi.org/10.3168/jds.2022-22273]
101. Chapman, R.M.; Tinsley, C.L.; Hill, M.J.; Forrest, M.P.; Tansey, K.E.; Pardiñas, A.F.; Rees, E.; Doyle, A.M.; Wilkinson, L.S.; Owen, M.J. et al. Convergent Evidence That ZNF804A Is a Regulator of Pre-Messenger RNA Processing and Gene Expression. Schizophr. Bull.; 2019; 45, pp. 1267-1278. [DOI: https://dx.doi.org/10.1093/schbul/sby183] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30597088]
102. Pan, C.; Yang, C.; Wang, S.; Ma, Y. Identifying Key Genes and Functionally Enriched Pathways of Diverse Adipose Tissue Types in Cattle. Front. Genet.; 2022; 13, 790690. [DOI: https://dx.doi.org/10.3389/fgene.2022.790690]
103. Lopez, B.I.M.; An, N.; Srikanth, K.; Lee, S.; Oh, J.-D.; Shin, D.-H.; Park, W.; Chai, H.-H.; Park, J.-E.; Lim, D. Genomic Prediction Based on SNP Functional Annotation Using Imputed Whole-Genome Sequence Data in Korean Hanwoo Cattle. Front. Genet.; 2021; 11, 603822. [DOI: https://dx.doi.org/10.3389/fgene.2020.603822]
104. Ramstein, G.P.; Buckler, E.S. Prediction of Evolutionary Constraint by Genomic Annotations Improves Functional Prioritization of Genomic Variants in Maize. Genome Biol.; 2022; 23, 183. [DOI: https://dx.doi.org/10.1186/s13059-022-02747-2]
105. Ruan, D.; Zhuang, Z.; Ding, R.; Qiu, Y.; Zhou, S.; Wu, J.; Xu, C.; Hong, L.; Huang, S.; Zheng, E. et al. Weighted Single-Step GWAS Identified Candidate Genes Associated with Growth Traits in a Duroc Pig Population. Genes; 2021; 12, 117. [DOI: https://dx.doi.org/10.3390/genes12010117]
106. Naserkheil, M.; Lee, D.H.; Mehrban, H. Improving the Accuracy of Genomic Evaluation for Linear Body Measurement Traits Using Single-Step Genomic Best Linear Unbiased Prediction in Hanwoo Beef Cattle. BMC Genet.; 2020; 21, 144. [DOI: https://dx.doi.org/10.1186/s12863-020-00928-1]
107. Xiang, R.; Berg, I. van den Berg, I.; MacLeod, I.M.; Hayes, B.J.; Prowse-Wilkins, C.P.; Wang, M.; Bolormaa, S.; Liu, Z.; Rochfort, S.J.; Reich, C.M.; et al. Quantifying the Contribution of Sequence Variants with Regulatory and Evolutionary Significance to 34 Bovine Complex Traits. Proc. Natl. Acad. Sci. USA; 2019; 116, pp. 19398-19408. [DOI: https://dx.doi.org/10.1073/pnas.1904159116] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31501319]
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Potočnik, Klemen 1
1 Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Groblje 3, 1230 Domžale, Slovenia;
2 Department of Animal Science and Technology, Faculty of Agriculture, University of Zagreb, Svetošimunska 25, 10000 Zagreb, Croatia