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29 January 2023

QTL Mapping of Growth Traits in Yellow River Carp (Cyprinus carpio haematopterus) at 5–17 Months after Hatching

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1
College of Fisheries, Henan Normal University, Xinxiang 453007, China
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Engineering Lab of Henan Province for Aquatic Animal Disease Control, Henan Normal University, Xinxiang 453007, China
3
Engineering Technology Research Center of Henan Province for Aquatic Animal, Henan Normal University, Xinxiang 453007, China
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Authors to whom correspondence should be addressed.
Fishes2023, 8(2), 79;https://doi.org/10.3390/fishes8020079
This article belongs to the Section Genetics and Biotechnology
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Abstract

To screen the quantitative trait loci (QTL) and genes related to the growth of Yellow River carp (Cyprinus carpio haematopterus) after removing the maternal effect, we established a family of these carps. Four growth-related traits (body length, height, thickness, and weight) of the family at 5 and 17 months after hatching (MAH) were measured. Analysis of QTL mapping for the four growth-related traits was conducted using the genetic linkage map constructed in our laboratory. We identified 47 QTL that were related to the four growth traits and three consensus QTL (cQTL). A total of 10, 14, 10, and 13 QTL were associated with body length, height, thickness, and weight, respectively; cQTL-1, cQTL-2, and cQTL-3 contained 11, 2, and 2 QTL, respectively. We detected 17 growth-related candidate genes within 50 Kb upstream and downstream of the five main-effect QTL (phenotypic variation explained > 10%, logarithm of odds > 5.5). Two genes (cbfa2t2 and neca1) that may be affected by maternal effects were identified by comparing the main-effect QTL at 17 and 5–17 MAH. This study was the first attempt to eliminate growth-related QTL and genes affected by maternal effects in Yellow River carp. These results can be used in molecular marker-assisted breeding and provide valuable genomic resources for the genetic mechanisms underlying growth in Yellow River carp.

1. Introduction

The common carp (Cyprinus carpio) is the most economically important bony fish in China, with a farming history of more than 8000 years [1]. In the past 20 years, there has been a consistent growth in the value chains of freshwater aquaculture, alongside advances in fish nutrition and genetics. Consequently, aquaculture has been the fastest-growing food production sector in the world [2]. As one of the most important edible and ornamental fishes, the common carp has been widely farmed worldwide, with a production of more than 4.23 million tons in 2020 [3]. From the first establishment of the common carp reference genome in 2014 to its recent resequencing, the continuous development of genome sequencing has promoted corresponding genomic research on common carp [4,5].
The Yellow River carp (Cyprinus carpio haematopterus), an important subspecies in Asia, has adapted to the natural environment of the Yellow River. It exhibits characteristics such as strong cold tolerance, a high feed conversion rate, and rapid growth [6]. Quantitative trait loci (QTL) are genetic regions that influence phenotypic variation of a complex trait. Mapping of QTL is of great significance in research surrounding breeding strategies and trait-related markers [7]. Over the past few decades, the development of different genomic resources and genetic tools has accelerated the process of genetic improvement and breeding, resulting in great progress in QTL mapping for the quantitative traits of carp. Since the establishment of the first genetic linkage map of carp [8], multiple versions of linkage maps have been constructed using multiple populations and families; such QTL mapping studies have been conducted for traits such as growth [7,9], food conversion rate [10,11], intramuscular fat content [12,13] and muscle fiber [14] of common carp. Subsequently, various QTL have been detected in multiple families. Although there are some QTL mapping studies of Yellow River carp [15,16,17], there are few studies investigating genetic linkage maps and conditional QTL.
Growth directly affects yield in aquaculture and is one of the most important economic traits of aquaculture fishes [18]. Compared to traditional genetic improvement of growth-associated traits, QTL mapping based on high-density genetic linkage maps can increase the accuracy of genetic selection and accelerate genetic improvement in marker-assisted selection (MAS) [19]. Some growth-related QTL studies have been conducted on Yellow River carp. For example, Wang et al. [7] mapped body length, thickness, height, and weight of Yellow River carp during overwintering, 5–9 months after hatching (MAH); a total of 29 QTL were obtained, including two main-effect QTL and three genes. Chen et al. [15] applied genome-wide association analysis to find 12 loci and nine candidate genes that were significantly related to the head type of Yellow River carp. Peng et al. [16] selected 28,194 single nucleotide polymorphism (SNP) markers and identified 22 QTL and candidate genes related to growth. Finally, Wang et al. [17] used an F2 family to construct a high-density genetic linkage map of Yellow River carp; 12 chromosome-wide and 2 growth-associated QTL, and 15 candidate genes were identified. However, owing to the influence of maternal effects on the growth of Yellow River carp, many growth-related QTL localization results could not eliminate this factor.
Classical quantitative genetic studies have demonstrated the significance of maternal effects in the genetics of complex phenotypes [20]. The maternal effect [21,22,23] has a significantly great impact on the early growth traits of fish; however, this effect decreases over time and can be ignored 5 MAH because of the increasing influence of the offspring genome and environmental quality. Although there have been several studies on the other factors affecting fish growth [24,25], such as temperature, salinity, gonadal maturation, and breeding time [26,27], few studies have considered the influence of maternal effects. Nonetheless, Doupé and Lymbery [21], using a factorial mating design, observed a gradual decline in the maternal effect with age in black sea bream (Acanthopagrus schlegelii), from 9.4% (75 days old, 0.6 g) to 1.8% (180 days old, 17.2 g). Falica et al. [28] adopted a full-factorial breeding design to track the contribution of additive and non-additive genetic effects, alongside maternal effects, on phenotypic trait variation in Chinook salmon (Oncorhynchus tshawytscha) from larval to adult stages. Maternal and non-additive effects are important drivers of larval size, but additive effects may play a more important role with age.
Maternal effects have a significant influence on early growth traits. However, existing QTL mapping studies rarely consider maternal effects. Therefore, we mapped growth-related QTL during 5–17 MAH based on high-density genetic maps of Yellow River carp [9] established by our group; this was conducted to reveal the QTL and genes that definitively affect growth and to provide references for molecular marker-assisted breeding of Yellow River carp.

2. Materials and Methods

2.1. Family and Phenotypic Measurement

Nine families of Yellow River carp were established in 2015. After two years of breeding, one female was selected from the fast-growing Yellow River carp family, and one male was selected from the slow-growing Yellow River carp family; the selected individuals were mated to obtain the target family (F1). When the average body length reached approximately 10 cm (October 2017), 300 individuals from F1 were randomly selected to be tagged with passive integrated transponder tags (Shenzhen Yu Jing Technology Co., Ltd, Shenzhen, China) . These individuals were anesthetized with MS-222 (tricaine methanesulfonate) at 5 and 17 MAH; then, their body length, height, thickness, and weight were measured. During the final measurement, the caudal fins of each individual were cut for DNA extraction; 207 individuals were selected as the mapping population. Genomic DNA was extracted using proteinase K (Sigma) digestion and phenol-chloroform (Phenol-chloroform-isoamyl alcohol mixture, Sigma) extraction, and the quality of the genomic DNA was assessed using a NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Waltham, MA, USA). The basic statistics (mean and standard deviation) and Pearson correlations between each trait were calculated using SPSS 24.0 (IBM Corp, Armonk, NY, USA).

2.2. QTL Mapping

Ethanol-preserved Yellow River carp fin samples were sent to Shanghai Ouyi Biotechnology Co., Ltd. (Shanghai, China) for sequencing. Using the Super-GBS (Genotyping-by-Sequencing) method, a GBS library was constructed with the genomic DNA of two parents and 207 F1 progeny. DNA was digested with PstI-HF/MspI (New England BioLabs, Ipswich, MA, USA), and then circularized with T4 ligase (New England BioLabs, cat. no. M0202, Ipswich, MA, USA) and adapters with corresponding barcodes. Using a modified magnetic beads recovery system, the recovered fragment size was adjusted by adjusting the volume ratio of the magnetic bead solution to the ligated product; then, the recovered fragments were amplified using PCR. Reads of GBS data were submitted to NCBI-SRA (Acc. PRJNA788161). Additionally, clean reads were mapped to a reference genome using Bowtie 2 (v2.3.4.1), followed by SNP/Indel detection using the Genome Analysis Toolkit (v3.8-1) [29]. Deviations of marker frequencies from the expected Mendelian ratios were tested using a chi-square test. Maternal and paternal datasets were set to “backcross” and HH datasets to “F2 intercross” [29]. The HH dataset contained SNPs that were heterozygous in both parents with progeny segregated as A:H:B (1:2:1). Next, SNPs with genotypic scores A or B in parent 1 and H in parent 2 and segregated as A:H or B:H (1:1) were combined; B scores were converted to A scores to yield the paternal (AH) dataset. Similarly, H:A and H:B markers were combined, and B scores were converted to A scores to form the maternal (HA) dataset. This was conducted according to a previously established protocol [29].
A combination of MSTMap [30] and MAPMAKER [31,32] was used to construct HA, AH, and HH genetic maps. An in-house Python script was used to add co-segregating SNPs to the framework map at the same location as their representative marker. MapChart [33] was used to draw genetic linkage maps of the Yellow River carp constructed in our laboratory [9]. Based on these linkage maps, phenotypic data for body length, height, and thickness for the F1 progeny aged 5–17 months were comprehensively mapped using WinQTLCart 2.5 [34] using the composite interval mapping method. The threshold for significant QTL detection was determined with 1000 permutations at α = 0.05. On the physical map, all QTL within 1 Mb of each other were identified as the consensus. Quantitative trait loci were named as follows: ‘q’ (abbreviation of QTL), followed by an abbreviation for the growth trait and the map type (m: maternal map, p: paternal map, h: HH map), and then the QTL number.

2.3. Screening for Candidate Genes

Candidate genes related to growth within the main-effect QTL with logarithm of odds (LOD) value > 5.5 and phenotypic variation explained (PVE) > 10% were identified [9]. Briefly, the ±50 kb genomic regions surrounding the significant SNPs were scanned on the basis of the Yellow River carp reference genome that was released in the BIGD Genome Warehouse [35] (https://bigd.big.ac.cn/gwh/Assembly/497/show, accessed on 27 October 2021); the candidate genes were annotated by conducting a BLAST search using the Swiss-Prot database.

3. Results

3.1. Phenotypic Analysis of Growth-Related Traits

Four growth-related traits of Yellow River carp at 5 and 17 MAH were measured and analyzed (Table S1). The mean ± standard deviation of body length, height, thickness, and weight of Yellow River carp at 5–17 MAH were 10.3 ± 2.4 cm, 3.0 ± 0.8 cm, 2.1 ± 0.5 cm, and 210.3 ± 133.3 g, respectively. Pearson correlation analysis revealed a significant (p < 0.01) correlation of 0.714–0.846 (Table 1) between each trait. The correlation between BH and BL was the highest (r = 0.846), whereas the correlation between BH and the other two traits was relatively low ( r ¯ = 0.749)
Table 1. Pairwise correlation for all 4 growth-related traits in Yellow River carp at 5–17 months after hatching.

3.2. QTL Related to Body Length Traits

Ten QTL associated with the body length of Yellow River carp 5–17 MAH (Figure 1 and Table 2) were distributed across seven linkage groups (LGs: LG6, LG11, LG13, LG16, LG19, LG28, and LG35). There were l, 2, 2, 1, 1, 1, and 2 QTL in each linkage group, respectively; additionally, their corresponding distribution was relatively uniform. Among these QTL, qBL-p-2, located in LG11, had the highest LOD value (7.70); the corresponding additive effect value was −0.87, and PVE was 11.94% for qBL-p-2. qBL-m-1, located in LG6, had the lowest LOD value (3.48); the corresponding additive effect value was −0.62, and PVE was 6.20% for qBL-m-1. As shown in Table 2, QTL mapping for body length had two main-effect QTL (qBL-p-1 and qBL-p-2), with PVEs of 10.85% and 11.94% and additive effect values of −0.82 and −0.87, respectively. Interestingly, both qBL-p-1 and qBL-p-2 QTL are located on chromosome 12.
Figure 1. Distribution of body length-related QTL across 50 linkage groups of Yellow River carp from the maternal (A), paternal (B), and HH maps (C).
Table 2. Body length-associated QTL identified in Yellow River carp 5–17 months after hatching.

3.3. QTL Related to Body Height Traits

A total of 14 QTL associated with body height of Yellow River carp 5–17 MAH, (Figure 2 and Table 3) were distributed across 11 LGs (LG2, LG11, LG13, LG14, LG16, LG18, LG19, LG27, LG29, LG30, and LG46). The numbers of QTL in each linkage group were l, 3, 2, 1, 1, 1, 1, 1, 1, 1, and 1, respectively. Among these QTL, qBH-p-5, located in LG13, had the highest LOD value (6.45), with a corresponding additive effect value of 0.25, and PVE of 10.25%. qBH-p-8, located in LG27, had the lowest LOD value (3.04), with an additive effect value of −0.16, and a PVE of 4.30%. As shown in Table 3, there were two main-effect QTL associated with body height (qBH-p-5 and qBH-h-2) with PVE values of 10.25% and 11.20% and additive effect values of 0.25 and −0.34, respectively. These two QTL were found to be located on chromosomes 13 and 12, respectively.
Figure 2. Distribution of body height–related QTL across 50 linkage groups of Yellow River carp from the maternal (A), paternal (B), and HH maps (C).
Table 3. Body height-associated QTL identified in Yellow River carp 5–17 months after hatching.

3.4. QTL Related to Body Thickness Traits

A total of 10 QTL associated with body thickness of Yellow River carp 5–17 MAH (Figure 3 and Table 4) were distributed across eight LGs: LG2, LG9, LG11, LG16, LG24, LG31, LG32, and LG34; the numbers of QTL in each linkage group were 2, 1, 1, 1, 1, 2, 1, and 1, respectively. Among these QTL, qBT-m-3, located in LG31, had the highest LOD value (4.08), with an additive effect value of −0.14, and PVE of 7.02%. qBT-m-1, located in LG24, had the lowest LOD value (3.25); the corresponding additive effect value and PVE of this QTL were 0.13 and 5.56%, respectively. As shown in Table 4, thickness-related QTL mapping did not locate the main-effect QTL.
Figure 3. Distribution of body thickness–related QTL across 50 linkage groups of Yellow River carp from the maternal (A), paternal (B), and HH maps (C).
Table 4. Body thickness-associated QTL identified in Yellow River carp 5–17 months after hatching.

3.5. QTL Related to Body Weight Traits

A total of 13 QTL associated with the body weight of Yellow River carp 5–17 MAH (Figure 4 and Table 5) were distributed across eight LGs: LG11, LG16, LG26, LG30, LG33, LG35, LG44, and LG50; For each LG, there were 3, 1, 1, 1, 2, 3, 1, and 1 QTL, respectively. Primarily, LG11 and LG35 had the most QTL. Among the QTL, qBW-p-2, located in LG11, had the highest LOD value (6.32), with a corresponding additive effect value and PVE of −44.31 and 10.62%, respectively. qBW-m-1, located in LG26, had the lowest LOD (3.02); for this QTL, the additive effect value was −30.82, and the PVE was 5.24%. As shown in Table 5, QTL mapping for body weight located a single main-effect QTL (qBW-p-2).
Figure 4. Distribution of body weight–related QTL across 50 linkage groups of Yellow River carp from the maternal (A), paternal (B), and HH maps (C).
Table 5. Body weight-associated QTL identified in Yellow River carp 5–17 months after hatching.

3.6. Consensus QTL

Multiple QTL loci were located on the same chromosome for the four traits of Yellow River carp: body length, height, thickness, and weight (Table 6). Three colocalized loci were detected in this experiment. On chromosome 13, two loci controlling body length and height (qBL-p-4 and qBH-p-6) were detected, with a PVE of 5.55% and 9.30% and LOD of 3.54 and 5.80, respectively. On two loci of chromosome 39, qBL-p-7 and qBW-p-5, which control body length and body weight, respectively, were detected; qBL-p-7 and qBW-p-5 possessed PVEs of 5.44% and 5.48% and LODs of 3.57 and 3.78, respectively. Additionally, cQTL-1 on chromosome 12 contained eleven QTL (qBL-p-2, qBL-h-1, qBH-p-3, qBH-p-4, qBH-h-2, qBT-p-3, qBT-h-2, qBW-p-1, qBW-p-2, qBW-p-3, and qBW-h-1) that simultaneously influenced four growth traits (BL, BH, BT, and BW), indicating pleiotropy.
Table 6. Consensus QTL identified in Yellow River carp 5–17 months after hatching.

3.7. Candidate Genes

We identified 17 candidate genes associated with growth 5–17 MAH (Table 7). Mitochondrial fission 1 protein (fis1), telomerase cajal body protein 1 (wap53), and PERQ amino acid–rich with GYF domain-containing protein 1 (perq1) genes are located on chromosome 13 and are associated with body height. The other 14 candidate genes were located on chromosome 12. The phospholipid transfer protein (pltp), adapter protein CIKS (ciks), dynamin-like 120 kDa protein, mitochondrial (opa1), ERAD-associated E3 ubiquitin-protein ligase HRD1A (hrd1a), and chromodomain-helicase-DNA-binding protein 1-like (chd1l) are associated with body length. The remaining eight candidate genes are associated with qBW-p-2. Most of these candidate genes are related to cell proliferation, energy metabolism, immunity, fat formation, and growth.
Table 7. Candidate genes related to main-effect QTL in Yellow River carp at 5–17 months after hatching.
The 17 MAH main-effect QTL obtained by our group [9] were compared with the 5–17 MAH main-effect QTL in this study; consequently, a QTL (BH-PM-T2-2) that may be affected by maternal effect was observed. Among the screened candidate genes, two may be affected by maternal effects (Table 8). Among them, the protein CBFA2T2 (cbfa2t2) is an ETO (mtg8) gene family member and a key regulator of adipogenesis [36]. This protein mediates the involvement of Prdm14 in stem cell maintenance and primordial germ-cell formation [37]. Alternatively, the N-terminal EF-H and calcium-binding protein 1 (neca1) is a protein-coding gene involved in blastocyst hatching, acting upstream of or within this process.
Table 8. Candidate genes associated with maternal effects in Yellow River carp.

4. Discussion

Because we constructed three independent genetic linkage maps (maternal, paternal, and HH), the corresponding QTL results for the same trait varied greatly across these maps. Overall, 47 growth-related QTL were identified and found to be distributed across 15 chromosomes. The features of QTL abundance and dispersion were similar to the other studies on growth QTL in common carp [16,38,39]. In the current study, 47 QTL related to four growth traits were identified in 23 LGs; this demonstrated that growth-associated traits were determined by many different genes. Five growth-related QTL with LOD > 5.5 and PVE > 10% were located on chromosomes 12 (qBL-p-1, qBL-p-2, qBH-h-2, qBW-p-2) and 13 (qBH-p-5), indicating that growth-related genes were mainly located on these chromosomes. However, these results differ from those reported by Wang et al. [17] and Peng et al. [16]; these differences may be due to a variation in the pedigree, methods, and period of the tests conducted.
There was a strong correlation between the four growth traits, suggesting that weight gain at 5–17 MAH was caused by multiple traits rather than by a single trait. Analysis of Atlantic salmon (Salmo salar) [40], blunt snout bream (Megalobrama amblycephala) [41], and Yangtze River carp (Cyprinus carpio haematopterus) [39] demonstrated similar results. Consensus QTL have been observed for some growth traits. In this study, 11 QTL related to four growth traits were identified in cQTL-1, thereby showing pleiotropy. In previous studies, some QTL have shown pleiotropy across various periods or families [9,42]. If the PVE of a QTL is very low, these consensus QTL may be associated with housekeeping genes. In this study, it was suggested that growth may be less affected by cQTL-2 and cQTL-3 due to this low PVE.
The maternal effect on fishes gradually weakens with growth and development, and the maternal effect becomes effectively negligible 5 MAH [21]. Unlike previous studies on growth-associated QTL mapping in Yellow River carp at a certain point after hatching [16,17], this study identified the growth-related QTL from the end of early growth to maturity (5–17 MAH). Hence, we could exclude the influence of maternal effects during the first five months; therefore, this study revealed the QTL and genes that definitively affect the growth of Yellow River carp.
In this study, we compared the main-effect QTL 17 MAH with the main-effect QTL 5–17 MAH; consequently, we observed two genes that may be affected by maternal effects. Co-repressor protein cbfa2t2 is a regulator of pluripotency and is important for germline development [43]. This protein participates in the formation of primordial germ cells and has been implicated in acute myeloid leukemia. Additionally, CBFA2T2 has been observed to be upregulated during the adipogenic differentiation of mesenchymal stem cells, and knockdown of CBFA2T2 in mesenchymal stem cells significantly inhibits adipogenesis [36]. Alternatively, neca1 is a calcium-binding protein, which is predominantly expressed in small and medium-sized neurons. This protein is the only currently known binding protein for the C2A-domain of Syt1 [44] and acts before or during blastocyst development. Previous studies have indicated that maternal effects play an important role in primordial germ cell genesis [45,46] and early embryonic development [47,48,49]. Therefore, cbfa2t2 and neca1, and their related QTL, should be excluded from research on QTL related to fish growth traits, so that research on growth-trait correlation can be more accurate and helpful for breeding strategies.
In total, 47 QTL were identified in this study. The QTL with LOD > 5.5 and PVE > 10% were primarily concentrated on chromosome 12 and contained 14 candidate genes. Most of these genes are involved in cell proliferation, energy metabolism, immunity, fat formation, and growth; nonetheless, there may also be some unidentified functions. Additionally, this is the first study to date to eliminate growth-related QTL and genes affected by maternal effects in Yellow River carp. Therefore, this study provides a foundation for further accurate analysis of growth traits. In future studies, we aim to explore the relationship between growth and the alleles of these genes, and screen these corresponding growth-related molecular markers and alleles; this will be conducted in order to provide a basis for the molecular MAS of Yellow River carp.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/fishes8020079/s1, Table S1: Body length, height, thickness, and weight of the family at the age of 5, 9 and 5–9 months after hatch (MAH).

Author Contributions

Conceptualization, L.W. and Z.Q.; methodology, Y.C. and J.H.; software, L.W.; validation, L.W.; formal analysis, Y.C. Z.J. and J.C.; investigation, L.W. and Y.C.; resources, L.W. and Z.Q.; data curation, L.W. and Y.C.; writing—original draft preparation, Y.C. and L.W.; writing—review and editing, Y.C. and L.W.; visualization, J.H., M.Z., M.Y. and H.J.; supervision, Z.Q.; project administration, L.W.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (grant 31602149).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Academic Committee of Henan Normal University (HNSD-SCXY-2116BS1029).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Nakajima, T.; Hudson, M.J.; Uchiyama, J.; Makibayashi, K.; Zhang, J. Common carp aquaculture in Neolithic China dates back 8000 years. Nat. Ecol. Evol. 2019, 3, 1415–1418. [Google Scholar] [CrossRef] [PubMed]
  2. Naylor, R.L.; Hardy, R.W.; Buschmann, A.H.; Bush, S.R.; Cao, L.; Klinger, D.H.; Little, D.C.; Lubchenco, J.; Shumway, S.E.; Troell, M. A 20-year retrospective review of global aquaculture. Nature 2021, 591, 551–563. [Google Scholar] [CrossRef] [PubMed]
  3. FAO. FAO Yearbook. Fishery and Aquaculture Statistics 2020; FAO: Roma, Italy, 2022. [Google Scholar]
  4. Xu, P.; Zhang, X.; Wang, X.; Li, J.; Liu, G.; Kuang, Y.; Xu, J.; Zheng, X.; Ren, L.; Wang, G.; et al. Genome sequence and genetic diversity of the common carp, Cyprinus carpio. Nat. Genet. 2014, 46, 1212–1219. [Google Scholar] [CrossRef] [PubMed]
  5. Li, J.T.; Wang, Q.; Huang, Y.M.; Li, Q.S.; Cui, M.S.; Dong, Z.J.; Wang, H.W.; Yu, J.H.; Zhao, Y.J.; Yang, C.R.; et al. Parallel subgenome structure and divergent expression evolution of allo-tetraploid common carp and goldfish. Nat. Genet. 2021, 53, 1493–1503. [Google Scholar] [CrossRef]
  6. Jiang, Y.; Zhang, S.; Xu, J.; Feng, J.; Mahboob, S.; Al-Ghanim, K.A.; Sun, X.; Xu, P. Comparative transcriptome analysis reveals the genetic basis of skin color variation in common carp. PLoS ONE 2014, 9, e108200. [Google Scholar] [CrossRef]
  7. Wang, L.; Zhang, L.; Chen, Y.; Chen, J.; Zhang, M.; Yu, M.; Jiang, H.; Qiao, Z.; Li, X. Mapping Growth-Related Quantitative Trait Locus (QTL) in Commercial Yellow River Carp (Cyprinus carpio haematopterus) during Overwintering. Fishes 2022, 7, 166. [Google Scholar] [CrossRef]
  8. Sun, X.; Liang, L. A genetic linkage map of common carp. J. Fish. Sci. China 2000, 7, 1–5. [Google Scholar]
  9. Wang, L.; Jia, S.; Zhang, Y.; Jiang, S.; Chen, Y.; Chen, J.; Yu, M.; Zhang, L.; Qiao, Z.; Li, X. Construction of Three High-Density Genetic Linkage Maps and Dynamic QTL Mapping of Growth Traits in Yellow River Carp (Cyprinus carpio haematopterus). Curr. Issues Mol. Biol. 2021, 43, 2276–2288. [Google Scholar] [CrossRef]
  10. Lu, C.; Laghari, M.Y.; Zheng, X.; Cao, D.; Zhang, X.; Kuang, Y.; Li, C.; Cheng, L.; Mahboob, S.; Al-Ghanim, K.A.; et al. Mapping quantitative trait loci and identifying candidate genes affecting feed conversion ratio based onto two linkage maps in common carp (Cyprinus carpio L). Aquaculture 2017, 468, 585–596. [Google Scholar] [CrossRef]
  11. Wang, X.; Zhang, X.; Li, W.; Zhang, T.; Li, C.; Sun, X. Mapping and genetic effect analysis on quantitative trait loci related to feed conversion ratio of conmmon carp (Cyprinus carpio L). Acta Hydrobiol. Sin. 2012, 36, 177–196. [Google Scholar]
  12. Kuang, Y.; Zheng, X.; Lv, W.; Cao, D.; Sun, X. Mapping quantitative trait loci for flesh fat content in common carp (Cyprinus carpio). Aquaculture 2015, 435, 100–105. [Google Scholar] [CrossRef]
  13. Zheng, X.; Kuang, Y.; Lv, W.; Cao, D.; Sun, Z.; Sun, X. Genome-Wide Association Study for Muscle Fat Content and Abdominal Fat Traits in Common Carp (Cyprinus carpio). PLoS ONE 2016, 11, e169127. [Google Scholar] [CrossRef]
  14. Zhang, Y.; Xu, P.; Lu, C.; Kuang, Y.; Zhang, X.; Cao, D.; Li, C.; Chang, Y.; Hou, N.; Li, H.; et al. Genetic linkage mapping and analysis of muscle fiber-related QTLs in common carp (Cyprinus carpio L.). Mar. Biotechnol. 2011, 13, 376–392. [Google Scholar] [CrossRef]
  15. Chen, L.; Peng, W.; Kong, S.; Pu, F.; Chen, B.; Zhou, Z.; Feng, J.; Li, X.; Xu, P. Genetic Mapping of Head Size Related Traits in Common Carp (Cyprinus carpio). Front. Genet. 2018, 9, 448. [Google Scholar] [CrossRef]
  16. Peng, W.; Xu, J.; Zhang, Y.; Feng, J.; Dong, C.; Jiang, L.; Feng, J.; Chen, B.; Gong, Y.; Chen, L.; et al. An ultra-high density linkage map and QTL mapping for sex and growth-related traits of common carp (Cyprinus carpio). Sci. Rep. 2016, 6, 26693. [Google Scholar] [CrossRef]
  17. Wang, X.; Fu, B.; Yu, X.; Qu, C.; Zhang, Q.; Tong, J. Fine mapping of growth-related quantitative trait loci in Yellow River carp (Cyprinus carpio haematoperus). Aquaculture 2018, 484, 277–285. [Google Scholar] [CrossRef]
  18. Mackay, T.F.; Stone, E.A.; Ayroles, J.F. The genetics of quantitative traits: Challenges and prospects. Nat. Rev. Genet. 2009, 10, 565–577. [Google Scholar] [CrossRef]
  19. Gjedrem, T. Genetic improvement of cold-water fish species. Aquac. Res. 2000, 31, 25–33. [Google Scholar] [CrossRef]
  20. Chai, C.K. Analysis of Quantitative Inheritance of Body Size in Mice. I. Hybridization and Maternal Influence. Genetics 1956, 41, 157–164. [Google Scholar] [CrossRef]
  21. Doupé, R.G.; Lymbery, A.J. Additive genetic and other sources of variation in growth traits of juvenile black bream Acanthopagrus butcheri. Aquac. Res. 2005, 36, 621–626. [Google Scholar] [CrossRef]
  22. Heath, D.D.; Fox, C.W.; Heath, J.W. Maternal effects on offspring size: Variation through early development of chinook salmon. Evolution 1999, 53, 1605–1611. [Google Scholar] [CrossRef] [PubMed]
  23. Green, B.S. Maternal effects in fish populations. Adv. Mar. Biol. 2008, 54, 1–105. [Google Scholar] [PubMed]
  24. Laghari, M.Y.; Zhang, Y.; Lashari, P.; Zhang, X.; Xu, P.; Xin, B.; Sun, X. Quantitative trait loci (QTL) associated with growth rate trait in common carp (Cyprinus carpio). Aquac. Int. 2013, 21, 1373–1379. [Google Scholar] [CrossRef]
  25. Nishida, H. Patterning the marginal zone of early ascidian embryos: Localized maternal mRNA and inductive interactions. Bioessays 2002, 24, 613–624. [Google Scholar] [CrossRef]
  26. Clarke, W.C.; Shelbourn, J.E.; Brett, J.R. Effect of artificial photoperiod cycles, temperature, and salinity on growth and smolting in underyearling coho (Oncorhynchus kisutch), chinook (O. Tshawytscha), and sockeye (O. nerka) salmon. Aquaculture 1981, 22, 105–116. [Google Scholar] [CrossRef]
  27. Handeland, S.O.; Imsland, A.K.; Stefansson, S.O. The effect of temperature and fish size on growth, feed intake, food conversion efficiency and stomach evacuation rate of Atlantic salmon post-smolts. Aquaculture 2008, 283, 36–42. [Google Scholar] [CrossRef]
  28. Falica, B.K.; Lehnert, S.J.; Pitcher, T.E.; Heath, D.D.; Higgs, D.M. Ontogentic shifts in genetic and maternal effects on length and survival in Chinook salmon (Oncorhynchus tshawytscha). Aquaculture 2017, 468, 218–225. [Google Scholar] [CrossRef]
  29. Qi, P.; Eudy, D.; Schnable, J.C.; Schmutz, J.; Raymer, P.L.; Devos, K.M. High Density Genetic Maps of Seashore Paspalum Using Genotyping-By-Sequencing and Their Relationship to The Sorghum Bicolor Genome. Sci. Rep. 2019, 9, 12183. [Google Scholar] [CrossRef]
  30. Wu, Y.; Bhat, P.R.; Close, T.J.; Lonardi, S. Efficient and accurate construction of genetic linkage maps from the minimum spanning tree of a graph. PLoS Genet. 2008, 4, e1000212. [Google Scholar] [CrossRef]
  31. Lander, E.S.; Green, P.; Abrahamson, J.; Barlow, A.; Daly, M.J.; Lincoln, S.E.; Newberg, L.A. MAPMAKER: An interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1987, 1, 174–181. [Google Scholar] [CrossRef]
  32. Qi, P.; Gimode, D.; Saha, D.; Schroder, S.; Chakraborty, D.; Wang, X.; Dida, M.M.; Malmberg, R.L.; Devos, K.M. UGbS-Flex, a novel bioinformatics pipeline for imputation-free SNP discovery in polyploids without a reference genome: Finger millet as a case study. BMC Plant Biol. 2018, 18, 117. [Google Scholar] [CrossRef]
  33. Voorrips, R.E. MapChart: Software for the graphical presentation of linkage maps and QTLs. J. Hered. 2002, 93, 77–78. [Google Scholar] [CrossRef]
  34. Wang, S. Windows QTL Cartographer 2.5; North Carolina State University: Raleigh, NC, USA, 2012. [Google Scholar]
  35. Xu, P.; Xu, J.; Liu, G.; Chen, L.; Zhou, Z.; Peng, W.; Jiang, Y.; Zhao, Z.; Jia, Z.; Sun, Y.; et al. The allotetraploid origin and asymmetrical genome evolution of the common carp Cyprinus carpio. Nat. Commun. 2019, 10, 4625. [Google Scholar] [CrossRef]
  36. Luo, J.; Dou, L.; Yang, Z.; Zhou, Z.; Huang, H. CBFA2T2 promotes adipogenic differentiation of mesenchymal stem cells by regulating CEBPA. Biochem. Biophys. Res. Commun. 2020, 529, 133–139. [Google Scholar] [CrossRef]
  37. Nady, N.; Gupta, A.; Ma, Z.; Swigut, T.; Koide, A.; Koide, S.; Wysocka, J. ETO family protein Mtgr1 mediates Prdm14 functions in stem cell maintenance and primordial germ cell formation. eLife 2015, 4, e10150. [Google Scholar] [CrossRef]
  38. Laghari, M.Y.; Lashari, P.; Zhang, X.; Xu, P.; Narejo, N.T.; Xin, B.; Zhang, Y.; Sun, X. QTL mapping for economically important traits of common carp (Cyprinus carpio L.). J. Appl. Genet. 2015, 56, 65–75. [Google Scholar] [CrossRef]
  39. Feng, X.; Yu, X.; Fu, B.; Wang, X.; Liu, H.; Pang, M.; Tong, J. A high-resolution genetic linkage map and QTL fine mapping for growth-related traits and sex in the Yangtze River common carp (Cyprinus carpio haematopterus). BMC Genom. 2018, 19, 230. [Google Scholar] [CrossRef]
  40. Gjerde, B.; Gjedrem, T. Estimates of phenotypic and genetic parameters for carcass traits in Atlantic salmon and rainbow trout. Aquaculture 1984, 36, 97–110. [Google Scholar] [CrossRef]
  41. Luo, W.; Zeng, C.; Deng, W.; Robinson, N.; Wang, W.; Gao, Z. Genetic parameter estimates for growth-related traits of blunt snout bream (Megalobrama amblycephala) using microsatellite-based pedigree. Aquac. Res. 2014, 45, 1881–1888. [Google Scholar] [CrossRef]
  42. Lv, W.; Zheng, X.; Kuang, Y.; Cao, D.; Yan, Y.; Sun, X. QTL variations for growth-related traits in eight distinct families of common carp (Cyprinus carpio). BMC Genet. 2016, 17, 65. [Google Scholar] [CrossRef]
  43. Tu, S.; Narendra, V.; Yamaji, M.; Vidal, S.E.; Rojas, L.A.; Wang, X.; Kim, S.Y.; Garcia, B.A.; Tuschl, T.; Stadtfeld, M.; et al. Co-repressor CBFA2T2 regulates pluripotency and germline development. Nature 2016, 534, 387–390. [Google Scholar] [CrossRef] [PubMed]
  44. Sugita, S.; Ho, A.; Sudhof, T.C. NECABs: A family of neuronal Ca(2+)-binding proteins with an unusual domain structure and a restricted expression pattern. Neuroscience 2002, 112, 51–63. [Google Scholar] [CrossRef] [PubMed]
  45. Selman, K.; Wallace, R.A.; Sarka, A.; Qi, X. Stages of oocyte development in the zebrafish, Brachydanio rerio. J. Morphol. 1993, 218, 203–224. [Google Scholar] [CrossRef] [PubMed]
  46. Knaut, H.; Steinbeisser, H.; Schwarz, H.; Nüsslein-Volhard, C. An Evolutionary Conserved Region in the vasa 3′UTR Targets RNA Translation to the Germ Cells in the Zebrafish. Curr. Biol. 2002, 12, 454–466. [Google Scholar] [CrossRef]
  47. Dick, A.; Meier, A.; Hammerschmidt, M. Smad1 and Smad5 have distinct roles during dorsoventral patterning of the zebrafish embryo. Dev. Dyn. 1999, 216, 285–298. [Google Scholar] [CrossRef]
  48. Hild, M.; Dick, A.; Rauch, G.J.; Meier, A.; Bouwmeester, T.; Haffter, P.; Hammerschmidt, M. The smad5 mutation somitabun blocks Bmp2b signaling during early dorsoventral patterning of the zebrafish embryo. Development 1999, 126, 2149–2159. [Google Scholar] [CrossRef]
  49. Yabe, T.; Shimizu, T.; Muraoka, O.; Bae, Y.K.; Hirata, T.; Nojima, H.; Kawakami, A.; Hirano, T.; Hibi, M. Ogon/Secreted Frizzled functions as a negative feedback regulator of Bmp signaling. Development 2003, 130, 2705–2716. [Google Scholar] [CrossRef]
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