Association Analysis to Copy Number Variation (CNV) of Opn4 Gene with Growth Traits of Goats
by
LiJuan Li
1,†, 
Peng Yang
2,†,
ShuYue Shi
2,
ZiJing Zhang
3,
QiaoTing Shi
3,
JiaWei Xu
2,
Hua He
1,4,
ChuZhao Lei
2,
ErYao Wang
3,
Hong Chen
2 and
YongZhen Huang
2,* 1
Institute of Bijie Test Area, Guizhou University of Engineering Science, Bijie, Guizhou 551700, China
2
College of Animal Science and Technology, Northwest A&F University, Yangling Shaanxi 712100, China
3
Institute of Animal Husbandry and Veterinary Science, Henan Academy of Agricultural Sciences, Zhengzhou, Henan 45002, China
4
College of Veterinary Medicine, Northwest A&F University, Yangling Shaanxi 712100, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Animals 2020, 10(3), 441; https://doi.org/10.3390/ani10030441
Submission received: 2 January 2020
/
Revised: 11 February 2020
/
Accepted: 5 March 2020
/
Published: 6 March 2020
(This article belongs to the Section Small Ruminants)
Abstract
:Simple Summary
Copy number variation is a common genetic polymorphism and is mainly represented by submicroscopic levels of deletion and duplication which are caused by rearrangement of the genome. It is well known that copy number variations of genes are associated with growth traits of livestock. In this study, we detected the correlation between the copy number variation of the Opn4 gene and growth traits of Guizhou goats. We found that the copy number variation of the Opn4 gene had a significant influence on the body length and body weight of Guizhou goats. The results may provide preliminary suggestions into Guizhou goat breeding and new insights into the future of CNV as a new promising molecular marker in animal breeding.
Abstract
Extensive research has been carried out regarding the correlation between the growth traits of livestock and genetic polymorphisms, including single nucleotide polymorphisms and copy number variations (CNV). The purpose of this study was to analyze the CNV and its genetic effects of the Opn4 gene in 284 Guizhou goats (Guizhou black goat: n = 186, Guizhou white goat: n = 98). We used qPCR to detect the CNV of the Opn4 gene in Guizhou goats, and the classification results were correlated with the corresponding individual growth traits by SPSS software. The results showed that the Opn4 gene had a superior effect on growth traits with multiple copy variants in Guizhou black goats, and there was a significant correlation between copy number variation sites and body length traits. Contrary to the former conclusion, in Guizhou white goats, individuals with the Normal copy number type showed superior growth traits and copy number variant sites were significantly associated with body weight traits. Therefore, the CNV of the Opn4 gene can be used as a candidate molecular genetic marker to improve goat growth traits, speeding up the breeding process of goat elite varieties.
1. Introduction
In the past, it was thought that the mammalian visual system relied on two photoreceptors to react to light. Studies have shown that biological responses to light are not all derived from visual photoreceptors, and some light will extend to the retinal ganglion cells (ipRGCs) [1]. The melanopsin gene family (also known as Opn4) was first identified in Xenopus [2]. Melanopsin, encoded by the Opn4 gene, is one of the major candidates for mediating nonvisual photoreceptors. The Opn4 gene regulates the sleep structure by acting on the optic nerve system [3]. Studies have found that white adipose tissue is affected by a blue-sensitive current, small fat droplets, increased fat degradation rate, and changes in hormone secretion, such as adiponectin [4]. This current is produced by the transient receptor potential canonical (TRPC) channel, which is mediated by the opsin produced by the Opn4 gene [4,5]. As a G protein-coupled receptor, melanopsin is first activated by the activation of phospholipase C and the production of inositol triphosphate (IP3) and diacylglycerol (DAG). Then, the TRPC channel is activated [6]. The TRPC3 channel is expressed in normal and healthy tissues at a low level [7]. A high level of TRPC3 expression has been observed in cells isolated from adipose vascular stroma, indicating the Ca2+ channel protein TRPC3 is a determining factor of vascular endothelial growth factor (VEGF) signaling in adipose tissue cells. The above content suggests that the Opn4 gene can affect fat metabolism and sleep regulation in animals and has an important influence on the growth and development of animals. Based on our previous research, high-throughput sequencing revealed that there was a 2000-bp copy number variation (CNV) in the goat Opn4 gene, and the CNV marker was located in the copy number variation region of Opn4 gene candidate region Chr28:4401501-4403500. Based on this, we researched the relation between the Opn4 gene copy number variation and the growth trait effects of goats.
Copy number variation is a common genetic polymorphism and is mainly represented by submicroscopic levels of deletion and duplication, which are caused by the rearrangement of the genome, generally ranging from 50 bp to 5 Mb in length [8]. Commonly used detection methods fall into two main categories. One is mainly used to detect unknown CNVs in the genome-wide range, including genomic chips and high-throughput metrology techniques, while the other is mainly used for fixed-point detection or verification of known CNVs. Among them, the chip method mainly includes comparative Genomic Hybridization (CGH) [9] and SNP chips [10]. A new technology, single-cell transcriptome resequencing (SCRS), is now available. The single-cell transcriptome study is the basis and starting point for gene function and structure research, analyzing gene expression differences between cells at the single-cell level. Through a new generation of high-throughput sequencing, the information of gene expression in a single cell can be obtained comprehensively and quickly. Almost all transcript sequence information of a cell in a certain state has been widely used in basic research, including the detection of CNV [11]. In the comparative genomic chip, the oligonucleotide probe chip is widely used because of its high precision, high sensitivity, and small sample requirement. In the various methods for detecting known CNVs, qPCR is a widely used technique [12]. The method is simple in operation, high in sensitivity, and fast in speed. A conservative gene is selected in the PCR as an internal reference gene, and then the copy number variation type and the relative copy number of the target gene is determined by the method of 2−ΔΔCt [13].
In this study, we selected 284 Guizhou black goats and white goats from Guizhou Province as experimental samples to study the copy number distribution of the Opn4 gene in the Guizhou black goat and white goat populations. The Guizhou black goat and Guizhou white goat are both native breeds and bred for meat. We determined the correlation of the results with growth traits, such as body length (BL), body weight (BW), and so on.
2. Materials and Methods
2.1. Experimental Animals
The protocols used in this study and for the animals were recognized by the Faculty Animal Policy and Welfare Committee of Northwest A&F University (FAPWC-NWAFU, Protocol number, NWAFAC1008).
In order to study the distribution of the Opn4 gene copy number variation in Guizhou goats, 284 adult goats of the two breeds, Guizhou black goats (n = 186) and Guizhou white goats (n = 98) in the Guizhou Province, were selected. All of the above sample individuals underwent the same feeding management conditions. The growth data of article contained body weight, body length, height, heart girth, and cannon bone circumference. The growth data and the procedure followed for data collection are reported in Table 1.
2.2. Sample Collection and DNA Extraction
Goat ears were used as sampling sites. The ear tissues were collected into 1.5 centrifuge tubes filled with storage liquid (alcohol) and brought back to the laboratory using an ice box. In the laboratory, ear tissues were stored at −80 °C. Next, we weighed 30 mg of the tissue sample, placed it in a 2.0-mL centrifuge tube, cut it into powder with scissors, digested with proteinase K, and extracted genomic DNA using the phenol-chloroform method. The DNA concentration was measured using a spectrophotometer. The dilution volume was calculated by measuring the specific concentration of each sample to ensure each available DNA sample concentration was 10 ng/uL after dilution.
2.3. Design and Detection of Primers
In this study, we used the goat Opn4 gene sequence (NC_030835.1), which was published in the NCBI database, as the reference sequence. The region of the Opn4 gene CNVs, the chr28: 4401501 bp-4403500 bp, was determined by the results of sequencing. The real-time fluorescent quantitative PCR primer pairs (primer pair P1) were designed by the Primer 5.0 and used to detect the copy number variation of Opn4 gene in different sample individuals. At the same time, we used the goat Opn4 gene sequence (NC_030825.1) as the reference sequence, and designed the qPCR primer (primer pair P2) of the MC1R gene using the same method (primer pair sequence information is shown in Table 2). When we detected primers, the total volume of the PCR amplification reaction system was 10 µL, including 5 µL 2× Taq PCR Master Mix, 3 µL ddH2O, 0.5 µL per each primer (100 pmol/µL), and 1.0 μL DNA sample pool (10 ng/µL). The DNA sample pool was composed by the genome DNA of 30 samples. The PCR procedure for detecting primers was as follows: Pre-denaturation at 95 °C for 5 min. Then, 35 cycles were conducted: 94 °C for 30 s, 60 °C for 30 s, 72 °C for 30 s, and elongation was conducted for 5 min at 72 °C. Finally, primers were stored at 12 °C.
2.4. Statistical Analysis
We amplified each sample by qPCR with primers of target sequence and primers of reference sequence and set three replications per pairs of primer. Subsequently, we analyzed the qPCR results using log2(2−ΔΔCt). The specific calculation method was as follows: ΔΔCt = ΔCt (experimental group) −ΔCt (reference group), ΔCt (experimental group) = Ct (experimental target gene) −Ct (experimental group Internal reference gene), ΔCt (reference group) = Ct (reference group target gene) −Ct (reference group reference gene). Ct is the Cycle threshold, which is the number of amplification cycles. The amplification cycle is the PCR product, which experiences cycling when the fluorescence signal of the amplified product reaches a set threshold. According to the −ΔΔCt method, the quantitative results were divided into three categories: Duplication: −ΔΔCt > 0.5, Deletion: −ΔΔCt < −0.5, and Normal: −0.5 ≤ −ΔΔCt ≤ 0.5. In the study, all analyses were done in two steps. First, a full animal model was used, and then a reduced animal model was used. The full animal model included fixed effects of marker genotype, birth year, season of birth, age of dam, sire, herd, sex, and random effects of the animal. The reduced model was used in the final analysis. The effects associated with herd, sex, sire, age of dam, and season of birth were not matched in the linear model because the preliminary statistical analyses indicated that these effects did not have a significant influence on the variability of traits in the analyzed populations. Thus, we used a fixed effect reduced model. IBM SPSS Statistics 23.0 software was used to analyze the connection between growth traits and CNV types.
The complete model is as follows:
where Yijk is an individual phenotypic record, μ is the population mean, Ai is the age effect, Gj is the copy number variation type of each point, and Eijk is a random error. The gene expression abundance was derived from the 2−ΔΔCt method, where ΔCt = Ct (target gene) and −Ct (reference genes). There are three types of copy number variations: Duplication (>0.5), Deletion (<−0.5), and Normal (<| ± 0.5|).
Yijk = μ + A + Gj + Eijk
3. Results
3.1. Distribution of Different CNV Types in Goats
As described above, in order to determine the distribution of Opn4 gene copy number in Guizhou goats, we chose two goat breeds as sample species: Guizhou black goats and Guizhou white goats. According to the log2(2−ΔΔCt) values, we divided the CNV types into three classes as Deletion type (<0.5), Normal type (<|±0.5|), and Duplication type (>0.5). Then, we converted the classes to Deletion (0~2), Normal (2), and Duplication (>2). As revealed in Figure 1, in Guizhou black goats, the popular type was the Deletion type, followed by the Normal type and the Duplication type. The Normal type only accounted for one-third of the Duplication type. Meanwhile, in Guizhou white goats, the gap about distribution of three copy number variants was not large. The Duplication type accounted for 35.7%, the Normal type accounted for 33.7%, and the Deletion type accounted for 30.6%.
3.2. Correlation Analysis of CNV Type and Goat Growth Traits
In this study, we correlated the Opn4 gene copy number variation of the above 284 Guizhou goat samples with the corresponding individual growth traits using the one-way ANOVA method. As shown in the Table 3 and Table 4, in the Guizhou black goat group, individuals with the Deletion copy number variation type had a better growth performance, and the variation site was significantly correlated with body length (p < 0.05). In the white goat group, the normal-type individuals grew better, and the variant sites were significantly connected with body weight (p < 0.05).
4. Discussion
Goats are an important animal resource in China. People provide plentiful products, such as goat milk, goatskin, and goat meat. In order to improve the production efficiency and product quality of goats, people focus on the breeding of goats. The detection of SNP and CNV in goat genes and using them for breeding has gained attention. The CNV has been associated with the treatment of certain diseases [14,15]. Currently, researchers are conducting classification studies on the copy number by identifying the difference in the threshold number of times in the qPCR loop. However, there may be a correlation between CNV and SNP [16], and the two techniques can both be considered in association analyses by identifying the SNP markers for common CNV regions [17].
Opsins are a group of retinal-dependent G protein-coupled receptors. Among them, most studies have focused on the Opn1 and Opn2 in the cone cells and the rod cells present in the retinal photoreceptors [18]. To date, most studies on the Opn4 gene have been related to the circadian rhythm [19]. The Opn4 gene encodes the opsin, and it is the third protein that transmits light signals except the two photoreceptor cells. This function may have a regulatory effect on the physiological activities of the animal. In particular, the protein responds to blue light and thus produces a specific current which is transmitted to the fat cells, thereby regulating the secretion of hormones, such as adiponectin, and affecting the size of the lipid droplets [19].
In recent years, more and more research has been conducted on the link between CNV and livestock growth, reproduction, and disease [20,21,22]. In this study, we examined the copy number variation of the Opn4 gene in Guizhou black goats and Guizhou white goats. We found that the CNV of Opn4 gene was significantly associated with the growth traits of Guizhou black goats and Guizhou white goats. For Guizhou black goats, individuals of Duplication copy number had an advantage in body length, and there was a great difference among the number of the three copy number types. The individuals of Deletion type accounted for 69.9% of the population, the individuals of Normal type accounted for 25.3% of the population, and the Duplication type only accounted for 5.8%. In the Guizhou white goat population, there was no such great difference in distribution. The difference in the number of three copy number types was small, and individuals of Deletion type accounted for 30% of the total, the Normal type accounted for 33.7%, and the Duplication-type individuals accounted for 35.7% of the population. The individuals with the Normal copy number had more body weight than individuals with the other two types, suggesting that Normal-type individuals performed significantly better than the other two individuals in terms of body weight. Whether the Opn4 gene has an effect on the growth and development of adipose tissue, the function of the gene in animal growth and reproduction, and a series of signal transduction and regulation mechanisms in the body are still unknown, and further research is needed. The Chinese demand for goat-related products is extremely large, especially mountain wool, cashmere, and so on. Goat products have a great impact on the national economy, which has extremely high requirements for the production and growth performance of goats. With the current gene chip technology, it is possible to make early selections of goats and select good individuals to speed up the selection process.
5. Conclusions
The results of the association analysis indicated that individuals with multiple copy variants of the Opn4 gene in Guizhou black goats had superiority in growth traits, and there was a significant correlation between copy number variation sites and body length traits. In Guizhou white goats, individuals with a Normal copy number variation were superior in growth traits, and copy number variation sites were significantly associated with body weight traits. Therefore, the type of Opn4 gene CNV can be used as a candidate molecular genetic marker to improve goat growth traits and to improve the speed of breeding process of goat elite varieties.
In summary, we examined the copy number variation of the Opn4 gene in Guizhou black goats and Guizhou white goats, and demonstrated that this degree of variation affects the growth traits in a certain way clearly. This conclusion was confirmed by correlation analysis, suggesting that the Opn4 gene plays an important role in the process of Guizhou goat growth. Our research demonstrates the effects of Opn4 CNV in Guizhou goats for the first time in order to provide evidence that the CNV of the Opn4 gene may be a potential factor for growth traits of other livestock species like bovines.
Author Contributions
Methodology, J.X. and Y.H.; software, C.L.; validation, H.C.; formal analysis, Q.S.; investigation, H.H.; resources and data curation, Z.Z. and L.L.; writing—original draft preparation, S.S., L.L. and Y.H.; writing—review and editing, P.Y. and Y.H.; visualization and supervision, E.W.; project administration and funding acquisition, Y.H. and L.L. All authors have read and agreed to the published version of the manuscript.
Funding
Program of National Natural Science Foundation of China (Grant No.31601926), Science and Technology Project of Guizhou Province (qian ke he support, 2016-2589); Guizhou Provincial Department of Education Growth Project for Young scientific and technological talents (Qian jiao he KY zi, 2016-285), Key Science and Technology Program of Henan Province (172102110084, 182102110023).
Acknowledgments
This study was supported by Program of National Natural Science Foundation of China (Grant No.31601926), Science and Technology Project of Guizhou Province (qian ke he support, 2016-2589); Guizhou Provincial Department of Education Growth Project for Young scientific and technological talents (Qian jiao he KY zi, 2016-285), Key Science and Technology Program of Henan Province (172102110084, 182102110023).
Conflicts of Interest
The authors declare no conflict of interest.
References
- Brown, T.M.; Tsujimura, S.; Allen, A.E.; Wynne, J.; Bedford, R.; Vickery, G.; Vugler, A.; Lucas, R.J. Melanopsin-based brightness discrimination in mice and humans. Curr. Biol. 2012, 22, 1134–1141. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Provencio, I.; Jiang, G.; De Grip, W.J.; Hayes, W.P.; Rollag, M.D. Melanopsin: An opsin in melanophores, brain, and eye. Proc. Natl. Acad. Sci. USA 1998, 95, 340–345. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tsai, J.W.; Hannibal, J.; Hagiwara, G.; Colas, D.; Ruppert, E.; Ruby, N.F.; Heller, H.C.; Franken, P.; Bourgin, P. Melanopsin as a sleep modulator: Circadian gating of the direct effects of light on sleep and altered sleep homeostasis in Opn4(−/−) mice. PLoS Biol. 2009, 7, e1000125. [Google Scholar] [CrossRef] [PubMed]
- Ondrusova, K.; Fatehi, M.; Barr, A.; Czarnecka, Z. Subcutaneous white adipocytes express a light sensitive signaling pathway mediated via a melanopsin/TRPC channel axis. Sci. Rep. 2017, 7, 16332. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bailes, H.J.; Lucas, R.J. Human melanopsin forms a pigment maximally sensitive to blue light (λ max ≈ 479 nm) supporting activation of Gq/11 and Gi/o signalling cascades. Proc. R. Soc. B Biol. Sci. 2013, 280, 20122987. [Google Scholar] [CrossRef] [Green Version]
- Hankins, M.W.; Peirson, S.N.; Foster, R.G. Melanopsin: An exciting photopigment. Trends Neurosci. 2008, 31, 27–36. [Google Scholar] [CrossRef]
- Poteser, M.; Graziani, A.; Eder, P.; Yates, A.; Mächler, H.; Romanin, C.; Groschner, K. Identification of a rare subset of adipose tissue-resident progenitor cells, which express CD133 and TRPC3 as a VEGF-regulated Ca2+ entry channel. FEBS Lett. 2008, 582, 2696–2702. [Google Scholar] [CrossRef] [Green Version]
- Liu, G.E.; Hou, Y.; Zhu, B.; Cardone, M.F.; Jiang, L.; Cellamare, A.; Mitra, A.; Alexander, L.J.; Coutinho, L.L.; Dell’Aquila, M.E. Analysis of copy number variations among diverse cattle breeds. Genome Res. 2010, 20, 693–703. [Google Scholar] [CrossRef] [Green Version]
- Pinkel, D.; Segraves, R.; Sudar, D.; Clark, S.; Poole, I.; Kowbel, D.; Collins, C.; Kuo, W.L.; Chen, C.; Zhai, Y.; et al. High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat. Genet. 1998, 20, 207. [Google Scholar] [CrossRef]
- Winchester, L.; Yau, C.; Ragoussis, J. Comparing CNV detection methods for SNP arrays. Brief. Funct. Genom. Proteom. 2009, 8, 353–366. [Google Scholar] [CrossRef] [Green Version]
- Hou, Y.; Wu, K.; Shi, X.; Li, F.; Song, L.; Wu, H.; Dean, M.; Li, G.; Tsang, S.; Jiang, R.; et al. Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing. Gigascience 2015, 4, 37. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Weaver, S.; Dube, S.; Mir, A.; Qin, J.; Sun, G.; Ramakrishnan, R.; Jones, R.C.; Livak, K.J. Taking qPCR to a higher level: Analysis of CNV reveals the power of high throughput qPCR to enhance quantitative resolution. Methods 2010, 50, 271–276. [Google Scholar] [CrossRef] [PubMed]
- Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef] [PubMed]
- Zhang, F.; Gu, W.; Hurles, M.E.; Lupski, J.R. Copy number variation in human health, disease, and evolution. Annu. Rev. Genom. Hum. Genet. 2009, 10, 451–481. [Google Scholar] [CrossRef] [Green Version]
- Mefford, H.C.; Cooper, G.M.; Zerr, T.; Smith, J.D.; Baker, C.; Shafer, N.; Thorland, E.C.; Skinner, C.; Schwartz, C.E.; Nickerson, D.A.; et al. A method for rapid, targeted CNV genotyping identifies rare variants associated with neurocognitive disease. Genome Res. 2009, 19, 1579–1585. [Google Scholar] [CrossRef] [Green Version]
- McCarroll, S.A.; Kuruvilla, F.G.; Korn, J.M.; Cawley, S.; Nemesh, J.; Wysoker, A.; Shapero, M.H.; de Bakker, P.I.; Maller, J.B.; Kirby, A.; et al. Integrated detection and population-genetic analysis of SNPs and copy number variation. Nat. Genet. 2008, 40, 1166. [Google Scholar] [CrossRef]
- Bae, J.S.; Cheong, H.S.; Kim, J.O.; Lee, S.O.; Kim, E.M.; Lee, H.W.; Kim, S.; Kim, J.W.; Cui, T.; Inoue, I.; et al. Identification of SNP markers for common CNV regions and association analysis of risk of subarachnoid aneurysmal hemorrhage in Japanese population. Biochem. Biophys. Res. Commun. 2008, 373, 593–596. [Google Scholar] [CrossRef]
- Porter, M.L.; Blasic, J.R.; Bok, M.J.; Cameron, E.G.; Pringle, T.; Cronin, T.W.; Robinson, P.R. Shedding new light on opsin evolution. Proc. Biol. Sci. 2012, 279, 3–14. [Google Scholar] [CrossRef] [Green Version]
- Díaz, N.M.; Morera, L.P.; Guido, M.E. Melanopsin and the Non-visual Photochemistry in the Inner Retina of Vertebrates. Photochem. Photobiol. 2016, 92, 29–44. [Google Scholar] [CrossRef] [Green Version]
- Zhou, Y.; Utsunomiya, Y.T.; Xu, L.; Hay el, H.A.; Bickhart, D.M.; Alexandre, P.A.; Rosen, B.D.; Schroeder, S.G.; Carvalheiro, R.; de Rezende Neves, H.H.; et al. Genome-wide CNV analysis reveals variants associated with growth traits in Bos indicus. BMC Genom. 2016, 17, 419. [Google Scholar] [CrossRef]
- Xu, L.; Cole, J.B.; Bickhart, D.M.; Hou, Y.; Song, J.; VanRaden, P.M.; Sonstegard, T.S.; Van Tassell, C.P.; Liu, G.E. Genome wide CNV analysis reveals additional variants associated with milk production traits in Holsteins. BMC Genom. 2014, 15, 683. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ionita-Laza, I.; Rogers, A.J.; Lange, C.; Raby, B.A.; Lee, C. Genetic association analysis of copy-number variation (CNV) in human disease pathogenesis. Genomics 2009, 93, 22–26. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Figure 1.
The frequency of CNV in Guizhou goats. Distribution of different copy number variants of Opn4 gene in Guizhou black goats and Guizhou white goats.
Figure 1.
The frequency of CNV in Guizhou goats. Distribution of different copy number variants of Opn4 gene in Guizhou black goats and Guizhou white goats.
Table 1.
The measured method of growth data.
| The Growth Traits | The Measured Method |
|---|---|
| Heart girth | The circumference of the chest behind the rear edge of scapula. |
| Withers height | Vertical distance from the highest position of shoulder to the ground. |
| Body length | The straight distance from shoulder to rear edge of ischial tuberosity. |
| Body weight | Weighing after stop feeding for a day. |
Table 2.
Primer information in copy number variations (CNV).
| Primer | Position | Sequence (5’->3’) | Product Length |
|---|---|---|---|
| Opn4-CNV | Forward primer | CGTGATACCAGGCTCCAGA | 19 nt |
| Reverse primer | ACGGCGAGGTTGATAATGA | ||
| MC1R | Forward primer | CTCGTTGGCCTCTTCATAGC | 21 nt |
| Reverse primer | GAAGTTCTTGAAGATGCAGCC |
Table 3.
Correlation analysis between copy number variation of Opn4 gene and Guizhou black goat growth traits.
Table 3.
Correlation analysis between copy number variation of Opn4 gene and Guizhou black goat growth traits.
| Growth Traits (Average Value ± Standard Error) | CNV Types | p Value | ||
|---|---|---|---|---|
| Deletion (n = 130) | Normal (n = 47) | Duplication (n = 9) | ||
| BW (kg) | 28.113 ± 0.749 | 28.034 ± 1.245 | 27.167 ± 2.845 | 0.95 |
| WH (cm) | 59.254 ± 0.538 | 59.426 ± 0.895 | 60.000 ± 2.045 | 0.933 |
| BL (cm) | 60.400 ± 0.588 a | 62.936 ± 0.978 b | 65.222 ± 2.235 b | 0.018 * |
| HG (cm) | 74.377 ± 0.608 | 74.170 ± 1.102 | 72.222 ± 2.312 | 0.666 |
Note: Value with * differs significantly at p < 0.05. a, b: Means with different superscripts were significantly different (p < 0.05). BW: Body weight. WH: Withers height. BL: Body length. HG: Heart girth.
Table 4.
Correlation analysis between copy number variation of Opn4 gene and Guizhou white goat growth traits.
Table 4.
Correlation analysis between copy number variation of Opn4 gene and Guizhou white goat growth traits.
| Growth Traits (Average Value ± Standard Error) | CNV Types | p Value | ||
|---|---|---|---|---|
| Deletion (n = 30) | Normal (n = 33) | Duplication (n = 35) | ||
| BW (kg) | 27.133 ± 1.331 a | 31.502 ± 1.269 b | 26.230 ± 1.232 a | 0.010 * |
| HG (cm) | 71.167 ± 1.214 | 73.773 ± 1.158 | 70.614 ± 1.124 | 0.123 |
>Note: Value with * differs significantly at p < 0.05. a, b: Means with different superscripts were significantly different (p < 0.05). BW: Body weight. HG: Heart girth.
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Li, L.; Yang, P.; Shi, S.; Zhang, Z.; Shi, Q.; Xu, J.; He, H.; Lei, C.; Wang, E.; Chen, H.; et al. Association Analysis to Copy Number Variation (CNV) of Opn4 Gene with Growth Traits of Goats. Animals 2020, 10, 441. https://doi.org/10.3390/ani10030441
AMA Style
Li L, Yang P, Shi S, Zhang Z, Shi Q, Xu J, He H, Lei C, Wang E, Chen H, et al. Association Analysis to Copy Number Variation (CNV) of Opn4 Gene with Growth Traits of Goats. Animals. 2020; 10(3):441. https://doi.org/10.3390/ani10030441
Chicago/Turabian StyleLi, LiJuan, Peng Yang, ShuYue Shi, ZiJing Zhang, QiaoTing Shi, JiaWei Xu, Hua He, ChuZhao Lei, ErYao Wang, Hong Chen, and et al. 2020. "Association Analysis to Copy Number Variation (CNV) of Opn4 Gene with Growth Traits of Goats" Animals 10, no. 3: 441. https://doi.org/10.3390/ani10030441
APA StyleLi, L., Yang, P., Shi, S., Zhang, Z., Shi, Q., Xu, J., He, H., Lei, C., Wang, E., Chen, H., & Huang, Y. (2020). Association Analysis to Copy Number Variation (CNV) of Opn4 Gene with Growth Traits of Goats. Animals, 10(3), 441. https://doi.org/10.3390/ani10030441
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