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Wang Y, Ma J, Wang J, Zhang L, Xu L, Chen Y, Zhu B, Wang Z, Gao H, Li J, Gao X. Genome-Wide Detection of Copy Number Variations and Their Potential Association with Carcass and Meat Quality Traits in Pingliang Red Cattle. Int J Mol Sci 2024; 25:5626. [PMID: 38891814 PMCID: PMC11172001 DOI: 10.3390/ijms25115626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/16/2024] [Accepted: 05/17/2024] [Indexed: 06/21/2024] Open
Abstract
Copy number variation (CNV) serves as a significant source of genetic diversity in mammals and exerts substantial effects on various complex traits. Pingliang red cattle, an outstanding indigenous resource in China, possess remarkable breeding value attributed to their tender meat and superior marbling quality. However, the genetic mechanisms influencing carcass and meat quality traits in Pingliang red cattle are not well understood. We generated a comprehensive genome-wide CNV map for Pingliang red cattle using the GGP Bovine 100K SNP chip. A total of 755 copy number variable regions (CNVRs) spanning 81.03 Mb were identified, accounting for approximately 3.24% of the bovine autosomal genome. Among these, we discovered 270 potentially breed-specific CNVRs in Pingliang red cattle, including 143 gains, 73 losses, and 54 mixed events. Functional annotation analysis revealed significant associations between these specific CNVRs and important traits such as carcass and meat quality, reproduction, exterior traits, growth traits, and health traits. Additionally, our network and transcriptome analysis highlighted CACNA2D1, CYLD, UBXN2B, TG, NADK, and ITGA9 as promising candidate genes associated with carcass weight and intramuscular fat deposition. The current study presents a genome-wide CNV map in Pingliang red cattle, highlighting breed-specific CNVRs, and transcriptome findings provide valuable insights into the underlying genetic characteristics of Pingliang red cattle. These results offer potential avenues for enhancing meat quality through a targeted breeding program.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Junya Li
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Xue Gao
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
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Delledonne A, Punturiero C, Ferrari C, Bernini F, Milanesi R, Bagnato A, Strillacci MG. Copy number variant scan in more than four thousand Holstein cows bred in Lombardy, Italy. PLoS One 2024; 19:e0303044. [PMID: 38771855 PMCID: PMC11108207 DOI: 10.1371/journal.pone.0303044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 04/18/2024] [Indexed: 05/23/2024] Open
Abstract
Copy Number Variants (CNV) are modifications affecting the genome sequence of DNA, for instance, they can be duplications or deletions of a considerable number of base pairs (i.e., greater than 1000 bp and up to millions of bp). Their impact on the variation of the phenotypic traits has been widely demonstrated. In addition, CNVs are a class of markers useful to identify the genetic biodiversity among populations related to adaptation to the environment. The aim of this study was to detect CNVs in more than four thousand Holstein cows, using information derived by a genotyping done with the GGP (GeneSeek Genomic Profiler) bovine 100K SNP chip. To detect CNV the SVS 8.9 software was used, then CNV regions (CNVRs) were detected. A total of 123,814 CNVs (4,150 non redundant) were called and aggregated into 1,397 CNVRs. The PCA results obtained using the CNVs information, showed that there is some variability among animals. For many genes annotated within the CNVRs, the role in immune response is well known, as well as their association with important and economic traits object of selection in Holstein, such as milk production and quality, udder conformation and body morphology. Comparison with reference revealed unique CNVRs of the Holstein breed, and others in common with Jersey and Brown. The information regarding CNVs represents a valuable resource to understand how this class of markers may improve the accuracy in prediction of genomic value, nowadays solely based on SNPs markers.
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Affiliation(s)
- Andrea Delledonne
- Department of Veterinary Medicine and Animal Science, Università degli Studi di Milano, Lodi, Italy
| | - Chiara Punturiero
- Department of Veterinary Medicine and Animal Science, Università degli Studi di Milano, Lodi, Italy
| | - Carlotta Ferrari
- Department of Veterinary Medicine and Animal Science, Università degli Studi di Milano, Lodi, Italy
| | - Francesca Bernini
- Department of Veterinary Medicine and Animal Science, Università degli Studi di Milano, Lodi, Italy
| | - Raffaella Milanesi
- Department of Veterinary Medicine and Animal Science, Università degli Studi di Milano, Lodi, Italy
| | - Alessandro Bagnato
- Department of Veterinary Medicine and Animal Science, Università degli Studi di Milano, Lodi, Italy
| | - Maria G. Strillacci
- Department of Veterinary Medicine and Animal Science, Università degli Studi di Milano, Lodi, Italy
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Cendron F, Cassandro M, Penasa M. Genome-wide investigation to assess copy number variants in the Italian local chicken population. J Anim Sci Biotechnol 2024; 15:2. [PMID: 38167097 PMCID: PMC10763469 DOI: 10.1186/s40104-023-00965-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Copy number variants (CNV) hold significant functional and evolutionary importance. Numerous ongoing CNV studies aim to elucidate the etiology of human diseases and gain insights into the population structure of livestock. High-density chips have enabled the detection of CNV with increased resolution, leading to the identification of even small CNV. This study aimed to identify CNV in local Italian chicken breeds and investigate their distribution across the genome. RESULTS Copy number variants were mainly distributed across the first six chromosomes and primarily associated with loss type CNV. The majority of CNV in the investigated breeds were of types 0 and 1, and the minimum length of CNV was significantly larger than that reported in previous studies. Interestingly, a high proportion of the length of chromosome 16 was covered by copy number variation regions (CNVR), with the major histocompatibility complex being the likely cause. Among the genes identified within CNVR, only those present in at least five animals across breeds (n = 95) were discussed to reduce the focus on redundant CNV. Some of these genes have been associated to functional traits in chickens. Notably, several CNVR on different chromosomes harbor genes related to muscle development, tissue-specific biological processes, heat stress resistance, and immune response. Quantitative trait loci (QTL) were also analyzed to investigate potential overlapping with the identified CNVR: 54 out of the 95 gene-containing regions overlapped with 428 QTL associated to body weight and size, carcass characteristics, egg production, egg components, fat deposition, and feed intake. CONCLUSIONS The genomic phenomena reported in this study that can cause changes in the distribution of CNV within the genome over time and the comparison of these differences in CNVR of the local chicken breeds could help in preserving these genetic resources.
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Affiliation(s)
- Filippo Cendron
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale Dell'Università 16, 35020, Legnaro, PD, Italy.
| | - Martino Cassandro
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale Dell'Università 16, 35020, Legnaro, PD, Italy
- Federazione Delle Associazioni Nazionali Di Razza E Specie, Via XXIV Maggio 43, 00187, Rome, Italy
| | - Mauro Penasa
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale Dell'Università 16, 35020, Legnaro, PD, Italy
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Wu J, Wu T, Xie X, Niu Q, Zhao Z, Zhu B, Chen Y, Zhang L, Gao X, Niu X, Gao H, Li J, Xu L. Genetic Association Analysis of Copy Number Variations for Meat Quality in Beef Cattle. Foods 2023; 12:3986. [PMID: 37959106 PMCID: PMC10647706 DOI: 10.3390/foods12213986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Meat quality is an economically important trait for global food production. Copy number variations (CNVs) have been previously implicated in elucidating the genetic basis of complex traits. In this article, we detected a total of 112,198 CNVs and 10,102 CNV regions (CNVRs) based on the Bovine HD SNP array. Next, we performed a CNV-based genome-wide association analysis (GWAS) of six meat quality traits and identified 12 significant CNV segments corresponding to eight candidate genes, including PCDH15, CSMD3, etc. Using region-based association analysis, we further identified six CNV segments relevant to meat quality in beef cattle. Among these, TRIM77 and TRIM64 within CNVR4 on BTA29 were detected as candidate genes for backfat thickness (BFT). Notably, we identified a 34 kb duplication for meat color (MC) which was supported by read-depth signals, and this duplication was embedded within the keratin gene family including KRT4, KRT78, and KRT79. Our findings will help to dissect the genetic architecture of meat quality traits from the aspects of CNVs, and subsequently improve the selection process in breeding programs.
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Affiliation(s)
- Jiayuan Wu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Tianyi Wu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Xueyuan Xie
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
- College of Animal Science and Veterinary Medicine, Shanxi Agricultural University, Jinzhong 030801, China
| | - Qunhao Niu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Zhida Zhao
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Bo Zhu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Yan Chen
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Lupei Zhang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Xue Gao
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Xiaoyan Niu
- College of Animal Science and Veterinary Medicine, Shanxi Agricultural University, Jinzhong 030801, China
| | - Huijiang Gao
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Junya Li
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
| | - Lingyang Xu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (J.W.); (B.Z.); (L.Z.); (J.L.)
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Zhang M, Li Q, Wang KL, Dong Y, Mu YT, Cao YM, Liu J, Li ZH, Cui HL, Liu HY, Hu AQ, Zheng YJ. Lipolysis and gestational diabetes mellitus onset: a case-cohort genome-wide association study in Chinese. J Transl Med 2023; 21:47. [PMID: 36698149 PMCID: PMC9875546 DOI: 10.1186/s12967-023-03902-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/17/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Genetic knowledge of gestational diabetes mellitus (GDM) in Chinese women is quite limited. This study aimed to identify the risk factors and mechanism of GDM at the genetic level in a Chinese population. METHODS We conducted a genome-wide association study (GWAS) based on single nucleotide polymorphism (SNP) array genotyping (ASA-CHIA Bead chip, Illumina) and a case-cohort study design. Variants including SNPs, copy number variants (CNVs), and insertions-deletions (InDels) were called from genotyping data. A total of 2232 pregnant women were enrolled in their first/second trimester between February 2018 and December 2020 from Anqing Municipal Hospital in Anhui Province, China. The GWAS included 193 GDM patients and 819 subjects without a diabetes diagnosis, and risk ratios (RRs) and their 95% confidence intervals (CIs) were estimated by a regression-based method conditional on the population structure. The calling and quality control of genotyping data were performed following published guidelines. CNVs were merged into CNV regions (CNVR) to simplify analyses. To interpret the GWAS results, gene mapping and overexpression analyses (ORAs) were further performed to prioritize the candidate genes and related biological mechanisms. RESULTS We identified 14 CNVRs (false discovery rate corrected P values < 0.05) and two suggestively significant SNPs (P value < 0.00001) associated with GDM, and a total of 19 candidate genes were mapped. Ten genes were significantly enriched in gene sets related to lipase (triglyceride lipase and lipoprotein lipase) activity (LIPF, LIPK, LIPN, and LIPJ genes), oxidoreductase activity (TPH1 and TPH2 genes), and cellular components beta-catenin destruction complex (APC and GSK3B genes), Wnt signalosome (APC and GSK3B genes), and lateral element in the Gene Ontology resource (BRCA1 and SYCP2 genes) by two ORA methods (adjusted P values < 0.05). CONCLUSIONS Genes related to lipolysis, redox reaction, and proliferation of islet β-cells are associated with GDM in Chinese women. Energy metabolism, particularly lipolysis, may play an important role in GDM aetiology and pathology, which needs further molecular studies to verify.
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Affiliation(s)
- Miao Zhang
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032, China
- Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032, China
| | - Qing Li
- Department of Obstetrics and Gynecology, Anqing Municipal Hospital, Anqing, 246003, China
| | - Kai-Lin Wang
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032, China
- Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032, China
| | - Yao Dong
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032, China
- Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032, China
| | - Yu-Tong Mu
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032, China
- Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032, China
| | - Yan-Min Cao
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032, China
- Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032, China
| | - Jin Liu
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032, China
- Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032, China
| | - Zi-Heng Li
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032, China
- Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032, China
| | - Hui-Lu Cui
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032, China
- Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032, China
| | - Hai-Yan Liu
- Department of Clinical Laboratory, Anqing Municipal Hospital, Anqing, 246003, China.
| | - An-Qun Hu
- Department of Clinical Laboratory, Anqing Municipal Hospital, Anqing, 246003, China.
| | - Ying-Jie Zheng
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032, China.
- Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai, 200032, China.
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, 200032, China.
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Identification of Copy Number Variations in Four Horse Breed Populations in South Korea. Animals (Basel) 2022; 12:ani12243501. [PMID: 36552421 PMCID: PMC9774267 DOI: 10.3390/ani12243501] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 11/21/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
In this study, genome-wide CNVs were identified using a total of 469 horses from four horse populations (Jeju horses, Thoroughbreds, Jeju riding horses, and Hanla horses). We detected a total of 843 CNVRs throughout all autosomes: 281, 30, 301, and 310 CNVRs for Jeju horses, Thoroughbreds, Jeju riding horses, and Hanla horses, respectively. Of the total CNVRs, copy number losses were found to be the most abundant (48.99%), while gains and mixed CNVRs accounted for 41.04% and 9.96% of the total CNVRs, respectively. The length of the CNVRs ranged from 0.39 kb to 2.8 Mb, while approximately 7.2% of the reference horse genome assembly was covered by the total CNVRs. By comparing the CNVRs among the populations, we found a significant portion of the CNVRs (30.13%) overlapped; the highest number of shared CNVRs was between Hanla horses and Jeju riding horses. When compared with the horse CNVRs of previous studies, 26.8% of CNVRs were found to be uniquely detected in this study. The CNVRs were not randomly distributed throughout the genome; in particular, the Equus caballus autosome (ECA) 7 comprised the largest proportion of its genome (16.3%), while ECA 24 comprised the smallest (0.7%). Furthermore, functional analysis was applied to CNVRs that overlapped with genes (genic-CNVRs); these overlapping areas may be potentially associated with the olfactory pathway and nervous system. A racing performance QTL was detected in a CNVR of Thoroughbreds, Jeju riding horses, and Hanla horses, and the CNVR value was mixed for three breeds.
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Copy Number Variation (CNV): A New Genomic Insight in Horses. Animals (Basel) 2022; 12:ani12111435. [PMID: 35681904 PMCID: PMC9179425 DOI: 10.3390/ani12111435] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/25/2022] [Accepted: 05/30/2022] [Indexed: 12/04/2022] Open
Abstract
Simple Summary This study aimed to contribute to our knowledge of CNVs, a type of genomic marker in equines, by producing, for the first time, a fine-scale characterization of the CNV regions (CNVRs) in the Pura Raza Española horse breed. We found not only the existence of a unique pattern of genomic regions enriched in CNVs in the PRE in comparison with the data available from other breeds but also the incidence of CNVs across the entire genome. Since these regions could affect the structure and dose of the genes involved, we also performed a gene ontology analysis which revealed that most of the genes overlapping in CNVRs were related to the olfactory pathways and immune response. Abstract Copy number variations (CNVs) are a new-fangled source of genetic variation that can explain changes in the phenotypes in complex traits and diseases. In recent years, their study has increased in many livestock populations. However, the study and characterization of CNVs in equines is still very limited. Our study aimed to investigate the distribution pattern of CNVs, characterize CNV regions (CNVRs), and identify the biological pathways affected by CNVRs in the Pura Raza Española (PRE) breed. To achieve this, we analyzed high-density SNP genotyping data (670,804 markers) from a large cohort of 654 PRE horses. In total, we identified 19,902 CNV segments and 1007 CNV regions in the whole population. The length of the CNVs ranged from 1.024 kb to 4.55 Mb, while the percentage of the genome covered by CNVs was 4.4%. Interestingly, duplications were more abundant than deletions and mixed CNVRs. In addition, the distribution of CNVs across the chromosomes was not uniform, with ECA12 being the chromosome with the largest percentage of its genome covered (19.2%), while the highest numbers of CNVs were found in ECA20, ECA12, and ECA1. Our results showed that 71.4% of CNVRs contained genes involved in olfactory transduction, olfactory receptor activity, and immune response. Finally, 39.1% of the CNVs detected in our study were unique when compared with CNVRs identified in previous studies. To the best of our knowledge, this is the first attempt to reveal and characterize the CNV landscape in PRE horses, and it contributes to our knowledge of CNVs in equines, thus facilitating the understanding of genetic and phenotypic variations in the species. However, further research is still needed to confirm if the CNVs observed in the PRE are also linked to variations in the specific phenotypical differences in the breed.
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