J Appl Genetics DOI 10.1007/s13353-014-0232-y
ANIMAL GENETICS • ORIGINAL PAPER
QTL mapping for economically important traits of common carp (Cyprinus carpio L.) Muhammad Younis Laghari & Punhal Lashari & Xiaofeng Zhang & Peng Xu & Naeem Tariq Narejo & Baoping Xin & Yan Zhang & Xiaowen Sun
Received: 26 September 2013 / Revised: 6 May 2014 / Accepted: 7 July 2014 # Institute of Plant Genetics, Polish Academy of Sciences, Poznan 2014
Abstract Quantitative trait loci (QTL) were analyzed for three economically important traits, i.e., body weight (BW), body length (BL), and body thickness (BT), in an F1 family of common carp holding the 190 progeny. A genetic linkage map spanning 3,301 cM in 50 linkage groups with 627 markers and an average distance of 5.6 cM was utilized for QTL mapping. Sixteen QTLs associated with all three growthrelated traits were scattered across ten linkage groups, LG6, LG10, LG17, LG19, LG25, LG27, LG28, LG29, LG30, and LG39. Six QTLs for BW and five each for BL and BT explained phenotypic variance in the range 17.0–32.1 %. All the nearest markers of QTLs were found to be significantly (p≤0.05) related with the trait. Among these QTLs, a total of four, two (qBW30 and qBW39) related with BW, one (qBL39) associated with BL, and one (qBT29) related to BT, were found to be the major QTLs with a phenotypic variance of >20 %. qBW30 and qBW39 with the nearest markers HLJ1691 and HLJ1843, respectively, show significant values of 0.0038 and 0.0031, correspondingly. QTLs qBL39 and qBT29 were found to have significant values of 0.0047 and 0.0015, respectively. Three QTLs (qBW27, qBW30, qBW39) of BW, two for BL (qBL19, qBL39), and two for BT (qBT6, qBT25) found in this study were similar to populations with different genetic M. Y. Laghari : B. Xin School of Chemical Engineering and Environment, Beijing Institute of Technology, 100081 Beijing, China M. Y. Laghari : P. Lashari : X. Zhang : P. Xu : Y. Zhang (*) : X. Sun (*) Centre for Applied Aquatic Genomics, Chinese Academy of Fishery Sciences, 100141 Beijing, China e-mail: [email protected] e-mail: [email protected] M. Y. Laghari : P. Lashari : N. T. Narejo Department of Fresh Water Biology and Fisheries, University of Sindh, 76080 Jamshoro, Pakistan
backgrounds. In this study, the genomic region controlling economically important traits were located. These genomic regions will be the major sources for the discovery of important genes and pathways associated with growth-related traits in common carp. Keywords Body length . Body thickness . Body weight . QTL . Common carp
Introduction Aquaculture has been growing rapidly in the agriculture sector throughout the world for the last three decades (Food and Agriculture Organization of the United Nations, FAO 2012). Aquatic species have become a major interest of farmers and the fish industry, not only in terms of public perception, product marketing, and acceptance, but also the effective production of both quantity and quality (Ashley 2007). Several aquaculture species have been selectively bred, with the aim of improving traits of economic interest, showing better genetic improvement (Wang et al. 2010). Traditionally, that selection was based on an inheritance model considering individual phenotypes. Such an approach is considered to be time-consuming, laborious, and not accurate due to the involvement of many other environmental factors. Genetic breeding is the best way to achieve fast production at a reasonable price. The aim of genetic breeding is to select the animals with a favorable set of genes to produce animals for the next generation. There is good progress toward marker development, genetic mapping, and qualitative trait loci (QTL) detection for economically important/production-related traits among aquaculture species; briefly, progress is considered in Salmonidae and Tilapia (Liu 2007). Exploiting the individual QTLs, using
J Appl Genetics
genetic markers, through genetic selection, fish production could be significantly increased (Liu 2001). A genetic map of more than 40 aquaculture species has been constructed and mapping QTLs for important traits has been completed in over 20 species (Yue 2014). QTLs are chromosomal region(s) determining a quantitative character (Geldermann 1975) that can identify genes affecting economic traits. Hence, through QTL study, the numbers and effects of genes can be determined and will be practiced in selective breeding to accelerate the genetic improvement of important traits (Naish and Hard 2008). Therefore, QTL mapping is the best approach to determine candidate genes controlling the trait that will be useful for marker-assisted selection (MAS). Common carp (Cyprinus carpio L.) is one of the major fishery production sources, and is widely distributed. Many strains with different economic traits have been found and these stains are the best genetic resource to improve some less well known strains. So, QTL study is a better method to improve economic traits through genetic breeding. Numerous molecular markers are available in the common carp genome and have been used extensively for QTL mapping and linkage map construction (Sun and Liang 2004; Cheng et al. 2010; Zhang et al. 2011, 2013b; Jin et al. 2012; Wang et al. 2012; Sun et al. 2013; Zhao et al. 2013; Zheng et al. 2013). QTL studies on some important traits such as standard length and body mass, head size, feed conversion ratio, cold tolerance, activity of lactate dehydrogenase (LDH), body shape and growth, eye cross and diameter, muscle fiber, and growth rate have been recorded in common carp (Sun and Liang 2004; Zhang et al. 2007, 2008, 2011, 2013b; Li et al. 2009; Liu et al. 2009; Mao et al. 2009; Jin et al. 2012; Wang et al. 2012; Laghari et al. 2013; Zheng et al. 2013). QTL detection of the cold tolerance trait (Sun and Liang 2004) was the first record which is considered as the starting point for QTL identification in the common carp. Although a small number of markers and individuals influences the accuracy of QTLs, an increasing number of samples and density of the genetic map increases the accuracy. In previous QTL studies, small numbers of samples, ranging from 43 to 159, and markers (285–507) were utilized. Taking advantage of the recently constructed high-density genetic map, made up of 627 markers covering an average distance of 5.6 cM comprising 190 individuals (Zhang et al. 2013a), the QTL mapping of economically important traits was carried out. Hence, the high-density genetic marker map with increased numbers of samples in this study will increase the precision and power of QTLs. The body weight (BW), body length (BL), and body thickness (BT) have great influence on the fish production, which is why they are considered as the most important economic traits. Therefore, we aimed to identify QTLs associated with these economic traits (BW, BL, and BT). They are quantitative in nature and termed as polygenic traits. They are considered
as prime traits for production and likely to be candidates for the future breeding of common carp that may be implemented for effective population sizes in selection lines to ensure good growth and best production.
Materials and methods Economic trait measurement and genetic map construction The F1 family of common carp composed of the 190 progeny was utilized in this study. The economically important traits BW, BL, and BT were measured at 300 days post hatch (dph). For DNA isolation, blood samples of the family were collected and DNA was isolated with a QIAamp DNA Blood Mini Kit (QIAGEN, Shanghai, China) for genotyping. A genetic linkage map was constructed (Zhang et al. 2013a) by using JoinMap 4.0 (Van Ooijen 2006) and utilized for QTL mapping. Generally, the genetic map of common carp relied on 627 markers (617 SSR, 10 SNP) and spawning 3,301 cM, with an average distance of 5.6 cM, in 50 linkage groups. In brief, microsatellite markers were developed by the BAC end sequence and whole-genome shotgun sequences were generated from the Roche 454 plate form. Primers were designed by the Primer 3.0 software and a tailed primer protocol (Schuelke 2000) was used for polymerase chain reaction (PCR). The microsatellite was genotyped on a 3130xl genetic analyzer (Applied Biosystems, Foster City, CA, USA). The genotype was determined with 500 LIZ size standards (Applied Biosystems), using GeneMapper 4.0 software (Applied Biosystems) (Zhang et al. 2013a). The genetic linkage map was constructed using JoinMap 4.0 software(Van Ooijen 2006) with default significance levels from 4.0 to 10.0 logarithm of odds (LODs). A threshold of 5.0 was set to detect suspect linkage possibly resulting from allele coding errors. Marker positions were explored using up to three rounds of the mean Chi-square contribution test (Stam 1993). QTL mapping The MapQTL 4.0 program (Van Ooijen et al. 2002) was used for QTL analysis. Multiple QTL mapping methods were employed to detect any significant associations between growth-related traits and marker loci. Significant LOD thresholds for every trait were calculated by the permutation test of α20 % with significance values of 0.0038 and 0.0031, respectively. Therefore, these are assumed to be the major QTLs. There were five QTLs, qBL10, qBL19, qBL29, qBL30, and qBL39, associated with BL, which were located on LG10, LG19, LG29, LG30, and LG39, with nearest loci of
were defined as (m1f1, m1f2, m2f1, and m2f2), where (m1, m2) and (f1, f2) denote the genotypes of the dam and sire, respectively.
Results Economic traits A total of three economically important traits, BW, BL, and BT, of 190 F1 individuals were measured. The average values of BW, BL, and BT at 300 dph were recorded as 167.591± 26.974 g, 16.1±1.324 cm, and 3.522±0.315 cm, respectively. QTL analyses In QTL mapping, QTLs related to BW, BL, and BT were located on various linkage groups on the linkage map. A total of 16 QTLs associated with all three growth-related traits were found on ten linkage groups, LG6, LG10, LG17, LG19, LG25, LG27, LG28, LG29, LG30, and LG39 (Fig. 1). Among these ten LGs, LG29 and LG39 were allocated to QTLs associated with all of the three traits. LG10 and LG30 were found to be allocated to QTLs associated with two traits (BW and BL), while all the other linkage groups were linked to a QTL of a single trait. LG10 and LG39 were found to have two QTLs in similar regions, while no other linkage groups
LG6
LG10
24.5
HLJ2148
31.6 32.1 37.1 40.8 46.3 49.1 49.7 53.1 54.4 59.5
HLJ668 CAFS999 HLJ2688 CAFS882 CAFS1572 HLJ2599 HLJ3611 HLJ3356 HLJ2572 HLJ1254
96.0
CAFS670 HLJ2439
HLJ2678 CAFS771 CAFS1586 CAFS2414 HLJ3727 HLJE93 HLJ1233 CAFS1692 HLJ1946 HLJ3466 HLJ3426 HLJ3938
52.9
HLJ3953
69.0
HLJ3937
79.1
HLJ3543
HLJ3004
5.8
HLJ1288
15.3 20.8 25.0 26.0 27.2 30.9
HLJ3999 HLJ434 HLJ3995 HLJ2988 CAFS735 HLJ3822
44.2
CAFS230
49.3
HLJ2695
59.4
HLJ2316
79.5
HLJ3371
0.0
HLJ2270
8.4 12.1
CAFS2055 CAFS2238
24.9
CAFS2227
29.7
HLJ456
35.7
CAFS1317
40.5 44.1
HLJ2818 HLJ3633
48.9 53.1
HLJ2248 HLJ3554
59.3
CAFS1932
66.0
HLJ3768
76.2
HLJ2312
83.9
HLJ1482
qBL19
73.4 74.8
9.0 18.2 19.8 21.6 24.1 24.9 25.8 29.5 30.9 34.1 38.1 41.1
0.0
LG19
qBW17
HLJ1856
CAFS2266
qBL10
18.6
0.0
qBW10
HLJ3537 HLJ2744 HLJ3621 HLJ594
BT6
0.0 4.2 5.4 7.3
LG17
HLJ2994
Fig. 1 Ten LGs consisted of QTLs associated with economic traits. The red color indicates body weight QTLs, green indicates body length QTLs, and blue indicates body thickness QTLs. These
linkage groups were constructed by Zhang et al. (2013a) in an F1 family with 627 markers consisting of 190 progeny
J Appl Genetics
LG25
LG27
HLJ2458
0.0
HLJ3612
86.8
HLJ2825
97.9
CAFS1119
HLJE331
LG30
75.2
HLJ1123
92.5
HLJ2103
HLJ2225
42.8
CAFS2392
52.7 54.0 54.6 55.1 56.2 58.0 61.6 65.1 69.8
CAFS174 HLJ3275 CAFS2299 CAFS2152-1 CAFS1846 CAFS1950 HLJ3812 HLJ2110 HLJ2300
79.4
CAFS2208
50.7 53.1
HLJ3219 HLJ3223
qBT39
HLJ2170 HLJ1275 CAFS1808 CAFS1568 HLJ534 HLJ639 CAFS2320 CAFS1340 HLJ1691
CAFS1116 HLJ3811 HLJ1843 HLJ3366 HLJ3577 HLJ3573 CAFS1751 HLJ3171 CAFS963 CAFS94 CAFS159 HLJ334 CAFS787 HLJ526
qBL39
HLJ3697
30.4 34.6 37.6 37.9 39.1 40.0 40.8 44.4 49.2
0.0 9.3 12.0 13.1 14.2 15.5 16.7 18.9 20.3 22.4 31.8 34.0 36.1 37.8
qBW39
20.7
qBL30
HLJ2490
22.7
LG39
qBW30
0.0
HLJ3444
72.4
HLJE748
qBT29
80.1
65.0
0.0
qBL29
HLJ3952
HLJ2992
HLJ3827 HLJ3874 SNP0189 HLJ3524 HLJ2164 HLJ3625 HLJ3587 CAFS2424 CAFS2134 CAFS2005 CAFS1569 HLJ519 HLJ2351 HLJ2391
0.0 2.7 5.1 12.9 16.3 18.0 18.3 19.4 20.8 21.4 22.6 24.4 24.6 34.1
qBW29
69.5
57.3
LG29
qBT28
CAFS675 HLJ3969 HLJ3634
HLJ2596 HLJ3989 CAFS1623 HLJ3976 CAFS1993 CAFS1850 HLJ476 HLJ1842 HLJ392 HLJ3272 HLJ1093 CAFS1417 CAFS997 HLJ3012 HLJ3751
0.0 9.7 14.2 18.4 21.3 22.0 22.3 23.5 24.2 24.7 25.3 27.5 30.1 31.5 33.7
qBW27
56.8 58.5 62.6
qBT25
CAFS2009 HLJ2345 CAFS198 HLJ518
34.2 37.7 40.0 42.5
LG28
Fig. 1 (continued)
CAFS1586, HLJ1482, HLJ2225, CAFS1340, and HLJ3366, respectively (Fig.2b). The LOD was 4.76, 3.9, 5.22, 4.87, and 4.49 corresponding to each detected QTL of BL. The observed phenotypic variance was 17, 20, 20.1, 17.7, and 20.8 for each nearest locus, respectively. The confidential interval distances of qBL10, qBL19, qBL29, qBL30, and qBL39 were 2, 9.5, 22.5, 8, and 5 cM, respectively. The shortest confidential interval among BL was recorded at qBL10 with a distance of 2 cM. The ANOVA test of neighbored loci for BL QTLs
resulted in a significant (p≤0.05) increase in length. QTL qBL39 with nearest marker HLJ3366 was observed with a higher phenotypic variance >20 % and significant value p=0.0047. Hence, this was considered as the major QTL associated with the BL trait. Five QTLs, qBT6, qBT25, qBT28, qBT29, and qBT39, were found to be related to BT on LG6, LG25, LG28, LG29, and LG39, with nearest loci HLJ594, CAFS198, HLJ2391, CA1950, and HLJ1843, respectively (Fig. 2c).
6 25 28 29 39
Body thickness (cm) BT qBT6 qBT25 qBT28 qBT29 qBT39 4.9 4.38 5.38 3.94 4.49
4.76 3.9 5.22 4.87 4.49
4.4 4.61 4.3 5.1 4.5 4.9
LOD
4.4 3.8 4.4 3.5 4.1
4.3 3.6 4.5 4.3 4
3.7 4.2 3.8 4.7 4.1 4.4
LOD threshold
6.2 8.5 15 3.8 2.2
2 9.5 22.5 8 5
4.6 14 2.3 20 15 4.5
CI cM
22.7 26.3 32.1 26 27.1
17 20 20.1 17.7 20.8
27.5 17.5 21.5 26.4 20.8 20.4
PVE%
3.528±0.346 3.731±0.309 3.522±0.298 3.493±0.35 3.561±0.336
15.94±1.102 15.841±1.091 16.188±1.091 16.63±1.078 15.92±1.062
164.483±22.919 154.0±19.441 171.558±23.303 169.3±22.244 162.78±24.613 173.918±22.547
3.655±0.3 3.412±0323 3.733±0.359 3.605±0.313 3.48±0.289
16.298±1.03 16.518±1.093 15.691±1.052 15.931±1.047 16.656±1.096
171.849±20.917 169.333±19.453 165.962±21.25 155.974±15.173 168.535±17.789 160.765±20.71
3.504±0.32 3.51±0.322 3.906±0.315
3.497±0.303 3.521±0.306 3.385±0.246
15.826±1.063 16.22±1.04 15.849±1.107
171.762±29.21 183.495±25.465
174.317±21.085 159.542±17.874
16.107±1.133 16.368±1.03 16.159±1.143
175.354±20.008
167.534±25.094
LG linkage group; CI confidential interval (QTL interval above 2 LOD); PVE percent variance explained; f1, f2 father alleles; m1, m2 mother alleles
Analyzed phenotypic means for each allele and significant mean phenotypic differences between alleles are noted in the “Significance” column
*Major QTLs
m2f2
0.047 0.0501 0.049 0.0015 0.0435
0.0485 0.0732 0.0406 0.0288 0.0047
0.0236 0.0146 0.0372 0.0426 0.0038 0.0031
p-Value
m2f1
m1f1
m1f2
Significance
Phenotype mean
Phenotypic means (± standard error) are listed for each genotype at the marker with the peak LOD score for each trait
HLJ594 CAFS198 HLJ2391 CAFS1950* HLJ1843
CAFS1586 HLJ1482 HLJ2225 CAFS1340 HLJ3366*
10 19 29 30 39
Nearest marker
CAFS1586 HLJ2316 HLJ1093 HLJ2225 HLJ1691* HLJ1843*
LG
10 17 27 29 30 39
QTL
Body weight (g) BW qBW10 qBW17 qBW27 qBW29 qBW30 qBW39 Body length (cm) BL qBL10 qBL19 qBL29 qBL30 qBL39
Trait
Table 1 Detail of QTLs related to economic traits
J Appl Genetics
J Appl Genetics
74.4
79.4
79.5
74.8
79.4
69.4
69.8
47.8
64.4
65.1
42.8
59.4
58 61.6
37.8
54.3
59.3
49.3
44.2
49.2
40.9
25
20.8
20.3
15.3
0
74
79 79.1
69
62.9
67.9
57.9
52.9
46.1
51.1
38.1
41.1
34.1
30.9
0
29.5
0
25.8
1
24.9
1
24.1
2
21.6
2
19.8
3
5
3
9 14 18.2
4
0
4
5 5.8 10.8
qBW10
35.9
qBW17
5
30.9
5
26 27.2
a
6
5
qBW27
qBW29
5
4
4 3
3 2
2 1
1
56.2
55.1
54 54.6
52.7
47.8
42.8
42.7
37.7
32.7
27.7
15
20 22.7
0
65
57.3
62.3
53.7
43.7
48.7
38.7
33.7
31.5
30.1
27.5
25.3
24.7
24.2
23.5
22 22.3
18.4
21.3
5 9.7 14.2
0
5
5 10
0
0
6
qBW30
qBW39
5
4
4
3 3
2 2
1
1
53.1
50.7
34 36.1
31.8
27.4
22.4
20.3
18.9
16.7
15.5
14.2
12 13.1
5 9.3
92.5
90.2
85.2
75.2 80.2
74.2
69.2
64.2
54.2 59.2
44.4 49.2
40 40.8
37.9 39.1
37.6
34.6
30.4
25.7
20 20.7
15
5 10
0
0
0
0
Fig. 2 a QTL detected for the BW trait in 190 progeny of common carp. The LOD of the QTL is shown on the y-axis and the marker positions (cM) are shown on the x-axis. b QTL detected for the BL trait in 190 progeny of common carp. The LOD of the QTL is shown on the y-axis
and the marker positions (cM) are shown on the x-axis. c QTL detected for the BT trait in 190 progeny of common carp. The LOD of the QTL is shown on the y-axis and the marker position (cM) are shown on the x-axis
The LOD scores were calculated to be 4.9, 4.38, 5.38, 3.94, and 4.49 for each QTL, respectively. For the BT trait, QTLs explained 22.7, 26.3, 32.1, 26, and 27.1 % of phenotype variation, respectively. The confidential interval distance of
BT QTLs ranged from 3.5 to 4.4 cM. The nearest loci of the QTLs indicated a significant (p≤0.05) increase in BT. qBT29 was observed as the major QTL with a phenotypic variance of >20 % and a significant value of p=0.0015.
J Appl Genetics
b
6
5
qBL10
5
qBL19 4
4 3
3 2
2
1
1
6
81 83.9
76
71
66
64.3
59.3
58.1
48.9
53.1
44.1
35.7
40.5
34.7
29.7
22.1
24.9
17.1
5 8.4 12.1
0
74
0
79 79.1
69
62.9
67.9
57.9
52.9
51.1
41.1
46.1
38.1
34.1
29.5
30.9
25.8
24.1
24.9
19.8
21.6
9 14 18.2
5
0
0
6
92.5
90.2
85.2
75.2 80.2
74.2
69.2
64.2
54.2 59.2
44.4 49.2
40 40.8
37.9 39.1
34.6
30.4
25.7
20 20.7
15
0
5 10
79.4
74.8
65.1
69.8
58 61.6
56.2
55.1
54 54.6
0
47.8
0
52.7
0
42.8
1
42.7
1
37.7
2
32.7
2
20 22.7
3
27.7
3
15
4
5 10
4
5
qBL30
5
37.6
qBL29
5
qBL39
4
3
2
1
53.1
50.7
47.8
42.8
37.8
34 36.1
31.8
27.4
22.4
20.3
18.9
15.5
16.7
14.2
12 13.1
5 9.3
0
0
Fig. 2 (continue)
Statistical analysis In addition a one-way ANOVA for four allele combinations (m1f1, m1f2, m2f1, m2f2) and a t-test for two allele combinations (m1f1,m2f1), from markers nearest to the QTLs, was performed on 190 progeny to investigate the association between the phenotype trait and genotype.
Table 1 shows the phenotype values of each allelic combination. Among the nearest QTL markers related to the BW trait, HLJ2316 and HLJ1691 show significantly (p
ANIMAL GENETICS • ORIGINAL PAPER
QTL mapping for economically important traits of common carp (Cyprinus carpio L.) Muhammad Younis Laghari & Punhal Lashari & Xiaofeng Zhang & Peng Xu & Naeem Tariq Narejo & Baoping Xin & Yan Zhang & Xiaowen Sun
Received: 26 September 2013 / Revised: 6 May 2014 / Accepted: 7 July 2014 # Institute of Plant Genetics, Polish Academy of Sciences, Poznan 2014
Abstract Quantitative trait loci (QTL) were analyzed for three economically important traits, i.e., body weight (BW), body length (BL), and body thickness (BT), in an F1 family of common carp holding the 190 progeny. A genetic linkage map spanning 3,301 cM in 50 linkage groups with 627 markers and an average distance of 5.6 cM was utilized for QTL mapping. Sixteen QTLs associated with all three growthrelated traits were scattered across ten linkage groups, LG6, LG10, LG17, LG19, LG25, LG27, LG28, LG29, LG30, and LG39. Six QTLs for BW and five each for BL and BT explained phenotypic variance in the range 17.0–32.1 %. All the nearest markers of QTLs were found to be significantly (p≤0.05) related with the trait. Among these QTLs, a total of four, two (qBW30 and qBW39) related with BW, one (qBL39) associated with BL, and one (qBT29) related to BT, were found to be the major QTLs with a phenotypic variance of >20 %. qBW30 and qBW39 with the nearest markers HLJ1691 and HLJ1843, respectively, show significant values of 0.0038 and 0.0031, correspondingly. QTLs qBL39 and qBT29 were found to have significant values of 0.0047 and 0.0015, respectively. Three QTLs (qBW27, qBW30, qBW39) of BW, two for BL (qBL19, qBL39), and two for BT (qBT6, qBT25) found in this study were similar to populations with different genetic M. Y. Laghari : B. Xin School of Chemical Engineering and Environment, Beijing Institute of Technology, 100081 Beijing, China M. Y. Laghari : P. Lashari : X. Zhang : P. Xu : Y. Zhang (*) : X. Sun (*) Centre for Applied Aquatic Genomics, Chinese Academy of Fishery Sciences, 100141 Beijing, China e-mail: [email protected] e-mail: [email protected] M. Y. Laghari : P. Lashari : N. T. Narejo Department of Fresh Water Biology and Fisheries, University of Sindh, 76080 Jamshoro, Pakistan
backgrounds. In this study, the genomic region controlling economically important traits were located. These genomic regions will be the major sources for the discovery of important genes and pathways associated with growth-related traits in common carp. Keywords Body length . Body thickness . Body weight . QTL . Common carp
Introduction Aquaculture has been growing rapidly in the agriculture sector throughout the world for the last three decades (Food and Agriculture Organization of the United Nations, FAO 2012). Aquatic species have become a major interest of farmers and the fish industry, not only in terms of public perception, product marketing, and acceptance, but also the effective production of both quantity and quality (Ashley 2007). Several aquaculture species have been selectively bred, with the aim of improving traits of economic interest, showing better genetic improvement (Wang et al. 2010). Traditionally, that selection was based on an inheritance model considering individual phenotypes. Such an approach is considered to be time-consuming, laborious, and not accurate due to the involvement of many other environmental factors. Genetic breeding is the best way to achieve fast production at a reasonable price. The aim of genetic breeding is to select the animals with a favorable set of genes to produce animals for the next generation. There is good progress toward marker development, genetic mapping, and qualitative trait loci (QTL) detection for economically important/production-related traits among aquaculture species; briefly, progress is considered in Salmonidae and Tilapia (Liu 2007). Exploiting the individual QTLs, using
J Appl Genetics
genetic markers, through genetic selection, fish production could be significantly increased (Liu 2001). A genetic map of more than 40 aquaculture species has been constructed and mapping QTLs for important traits has been completed in over 20 species (Yue 2014). QTLs are chromosomal region(s) determining a quantitative character (Geldermann 1975) that can identify genes affecting economic traits. Hence, through QTL study, the numbers and effects of genes can be determined and will be practiced in selective breeding to accelerate the genetic improvement of important traits (Naish and Hard 2008). Therefore, QTL mapping is the best approach to determine candidate genes controlling the trait that will be useful for marker-assisted selection (MAS). Common carp (Cyprinus carpio L.) is one of the major fishery production sources, and is widely distributed. Many strains with different economic traits have been found and these stains are the best genetic resource to improve some less well known strains. So, QTL study is a better method to improve economic traits through genetic breeding. Numerous molecular markers are available in the common carp genome and have been used extensively for QTL mapping and linkage map construction (Sun and Liang 2004; Cheng et al. 2010; Zhang et al. 2011, 2013b; Jin et al. 2012; Wang et al. 2012; Sun et al. 2013; Zhao et al. 2013; Zheng et al. 2013). QTL studies on some important traits such as standard length and body mass, head size, feed conversion ratio, cold tolerance, activity of lactate dehydrogenase (LDH), body shape and growth, eye cross and diameter, muscle fiber, and growth rate have been recorded in common carp (Sun and Liang 2004; Zhang et al. 2007, 2008, 2011, 2013b; Li et al. 2009; Liu et al. 2009; Mao et al. 2009; Jin et al. 2012; Wang et al. 2012; Laghari et al. 2013; Zheng et al. 2013). QTL detection of the cold tolerance trait (Sun and Liang 2004) was the first record which is considered as the starting point for QTL identification in the common carp. Although a small number of markers and individuals influences the accuracy of QTLs, an increasing number of samples and density of the genetic map increases the accuracy. In previous QTL studies, small numbers of samples, ranging from 43 to 159, and markers (285–507) were utilized. Taking advantage of the recently constructed high-density genetic map, made up of 627 markers covering an average distance of 5.6 cM comprising 190 individuals (Zhang et al. 2013a), the QTL mapping of economically important traits was carried out. Hence, the high-density genetic marker map with increased numbers of samples in this study will increase the precision and power of QTLs. The body weight (BW), body length (BL), and body thickness (BT) have great influence on the fish production, which is why they are considered as the most important economic traits. Therefore, we aimed to identify QTLs associated with these economic traits (BW, BL, and BT). They are quantitative in nature and termed as polygenic traits. They are considered
as prime traits for production and likely to be candidates for the future breeding of common carp that may be implemented for effective population sizes in selection lines to ensure good growth and best production.
Materials and methods Economic trait measurement and genetic map construction The F1 family of common carp composed of the 190 progeny was utilized in this study. The economically important traits BW, BL, and BT were measured at 300 days post hatch (dph). For DNA isolation, blood samples of the family were collected and DNA was isolated with a QIAamp DNA Blood Mini Kit (QIAGEN, Shanghai, China) for genotyping. A genetic linkage map was constructed (Zhang et al. 2013a) by using JoinMap 4.0 (Van Ooijen 2006) and utilized for QTL mapping. Generally, the genetic map of common carp relied on 627 markers (617 SSR, 10 SNP) and spawning 3,301 cM, with an average distance of 5.6 cM, in 50 linkage groups. In brief, microsatellite markers were developed by the BAC end sequence and whole-genome shotgun sequences were generated from the Roche 454 plate form. Primers were designed by the Primer 3.0 software and a tailed primer protocol (Schuelke 2000) was used for polymerase chain reaction (PCR). The microsatellite was genotyped on a 3130xl genetic analyzer (Applied Biosystems, Foster City, CA, USA). The genotype was determined with 500 LIZ size standards (Applied Biosystems), using GeneMapper 4.0 software (Applied Biosystems) (Zhang et al. 2013a). The genetic linkage map was constructed using JoinMap 4.0 software(Van Ooijen 2006) with default significance levels from 4.0 to 10.0 logarithm of odds (LODs). A threshold of 5.0 was set to detect suspect linkage possibly resulting from allele coding errors. Marker positions were explored using up to three rounds of the mean Chi-square contribution test (Stam 1993). QTL mapping The MapQTL 4.0 program (Van Ooijen et al. 2002) was used for QTL analysis. Multiple QTL mapping methods were employed to detect any significant associations between growth-related traits and marker loci. Significant LOD thresholds for every trait were calculated by the permutation test of α20 % with significance values of 0.0038 and 0.0031, respectively. Therefore, these are assumed to be the major QTLs. There were five QTLs, qBL10, qBL19, qBL29, qBL30, and qBL39, associated with BL, which were located on LG10, LG19, LG29, LG30, and LG39, with nearest loci of
were defined as (m1f1, m1f2, m2f1, and m2f2), where (m1, m2) and (f1, f2) denote the genotypes of the dam and sire, respectively.
Results Economic traits A total of three economically important traits, BW, BL, and BT, of 190 F1 individuals were measured. The average values of BW, BL, and BT at 300 dph were recorded as 167.591± 26.974 g, 16.1±1.324 cm, and 3.522±0.315 cm, respectively. QTL analyses In QTL mapping, QTLs related to BW, BL, and BT were located on various linkage groups on the linkage map. A total of 16 QTLs associated with all three growth-related traits were found on ten linkage groups, LG6, LG10, LG17, LG19, LG25, LG27, LG28, LG29, LG30, and LG39 (Fig. 1). Among these ten LGs, LG29 and LG39 were allocated to QTLs associated with all of the three traits. LG10 and LG30 were found to be allocated to QTLs associated with two traits (BW and BL), while all the other linkage groups were linked to a QTL of a single trait. LG10 and LG39 were found to have two QTLs in similar regions, while no other linkage groups
LG6
LG10
24.5
HLJ2148
31.6 32.1 37.1 40.8 46.3 49.1 49.7 53.1 54.4 59.5
HLJ668 CAFS999 HLJ2688 CAFS882 CAFS1572 HLJ2599 HLJ3611 HLJ3356 HLJ2572 HLJ1254
96.0
CAFS670 HLJ2439
HLJ2678 CAFS771 CAFS1586 CAFS2414 HLJ3727 HLJE93 HLJ1233 CAFS1692 HLJ1946 HLJ3466 HLJ3426 HLJ3938
52.9
HLJ3953
69.0
HLJ3937
79.1
HLJ3543
HLJ3004
5.8
HLJ1288
15.3 20.8 25.0 26.0 27.2 30.9
HLJ3999 HLJ434 HLJ3995 HLJ2988 CAFS735 HLJ3822
44.2
CAFS230
49.3
HLJ2695
59.4
HLJ2316
79.5
HLJ3371
0.0
HLJ2270
8.4 12.1
CAFS2055 CAFS2238
24.9
CAFS2227
29.7
HLJ456
35.7
CAFS1317
40.5 44.1
HLJ2818 HLJ3633
48.9 53.1
HLJ2248 HLJ3554
59.3
CAFS1932
66.0
HLJ3768
76.2
HLJ2312
83.9
HLJ1482
qBL19
73.4 74.8
9.0 18.2 19.8 21.6 24.1 24.9 25.8 29.5 30.9 34.1 38.1 41.1
0.0
LG19
qBW17
HLJ1856
CAFS2266
qBL10
18.6
0.0
qBW10
HLJ3537 HLJ2744 HLJ3621 HLJ594
BT6
0.0 4.2 5.4 7.3
LG17
HLJ2994
Fig. 1 Ten LGs consisted of QTLs associated with economic traits. The red color indicates body weight QTLs, green indicates body length QTLs, and blue indicates body thickness QTLs. These
linkage groups were constructed by Zhang et al. (2013a) in an F1 family with 627 markers consisting of 190 progeny
J Appl Genetics
LG25
LG27
HLJ2458
0.0
HLJ3612
86.8
HLJ2825
97.9
CAFS1119
HLJE331
LG30
75.2
HLJ1123
92.5
HLJ2103
HLJ2225
42.8
CAFS2392
52.7 54.0 54.6 55.1 56.2 58.0 61.6 65.1 69.8
CAFS174 HLJ3275 CAFS2299 CAFS2152-1 CAFS1846 CAFS1950 HLJ3812 HLJ2110 HLJ2300
79.4
CAFS2208
50.7 53.1
HLJ3219 HLJ3223
qBT39
HLJ2170 HLJ1275 CAFS1808 CAFS1568 HLJ534 HLJ639 CAFS2320 CAFS1340 HLJ1691
CAFS1116 HLJ3811 HLJ1843 HLJ3366 HLJ3577 HLJ3573 CAFS1751 HLJ3171 CAFS963 CAFS94 CAFS159 HLJ334 CAFS787 HLJ526
qBL39
HLJ3697
30.4 34.6 37.6 37.9 39.1 40.0 40.8 44.4 49.2
0.0 9.3 12.0 13.1 14.2 15.5 16.7 18.9 20.3 22.4 31.8 34.0 36.1 37.8
qBW39
20.7
qBL30
HLJ2490
22.7
LG39
qBW30
0.0
HLJ3444
72.4
HLJE748
qBT29
80.1
65.0
0.0
qBL29
HLJ3952
HLJ2992
HLJ3827 HLJ3874 SNP0189 HLJ3524 HLJ2164 HLJ3625 HLJ3587 CAFS2424 CAFS2134 CAFS2005 CAFS1569 HLJ519 HLJ2351 HLJ2391
0.0 2.7 5.1 12.9 16.3 18.0 18.3 19.4 20.8 21.4 22.6 24.4 24.6 34.1
qBW29
69.5
57.3
LG29
qBT28
CAFS675 HLJ3969 HLJ3634
HLJ2596 HLJ3989 CAFS1623 HLJ3976 CAFS1993 CAFS1850 HLJ476 HLJ1842 HLJ392 HLJ3272 HLJ1093 CAFS1417 CAFS997 HLJ3012 HLJ3751
0.0 9.7 14.2 18.4 21.3 22.0 22.3 23.5 24.2 24.7 25.3 27.5 30.1 31.5 33.7
qBW27
56.8 58.5 62.6
qBT25
CAFS2009 HLJ2345 CAFS198 HLJ518
34.2 37.7 40.0 42.5
LG28
Fig. 1 (continued)
CAFS1586, HLJ1482, HLJ2225, CAFS1340, and HLJ3366, respectively (Fig.2b). The LOD was 4.76, 3.9, 5.22, 4.87, and 4.49 corresponding to each detected QTL of BL. The observed phenotypic variance was 17, 20, 20.1, 17.7, and 20.8 for each nearest locus, respectively. The confidential interval distances of qBL10, qBL19, qBL29, qBL30, and qBL39 were 2, 9.5, 22.5, 8, and 5 cM, respectively. The shortest confidential interval among BL was recorded at qBL10 with a distance of 2 cM. The ANOVA test of neighbored loci for BL QTLs
resulted in a significant (p≤0.05) increase in length. QTL qBL39 with nearest marker HLJ3366 was observed with a higher phenotypic variance >20 % and significant value p=0.0047. Hence, this was considered as the major QTL associated with the BL trait. Five QTLs, qBT6, qBT25, qBT28, qBT29, and qBT39, were found to be related to BT on LG6, LG25, LG28, LG29, and LG39, with nearest loci HLJ594, CAFS198, HLJ2391, CA1950, and HLJ1843, respectively (Fig. 2c).
6 25 28 29 39
Body thickness (cm) BT qBT6 qBT25 qBT28 qBT29 qBT39 4.9 4.38 5.38 3.94 4.49
4.76 3.9 5.22 4.87 4.49
4.4 4.61 4.3 5.1 4.5 4.9
LOD
4.4 3.8 4.4 3.5 4.1
4.3 3.6 4.5 4.3 4
3.7 4.2 3.8 4.7 4.1 4.4
LOD threshold
6.2 8.5 15 3.8 2.2
2 9.5 22.5 8 5
4.6 14 2.3 20 15 4.5
CI cM
22.7 26.3 32.1 26 27.1
17 20 20.1 17.7 20.8
27.5 17.5 21.5 26.4 20.8 20.4
PVE%
3.528±0.346 3.731±0.309 3.522±0.298 3.493±0.35 3.561±0.336
15.94±1.102 15.841±1.091 16.188±1.091 16.63±1.078 15.92±1.062
164.483±22.919 154.0±19.441 171.558±23.303 169.3±22.244 162.78±24.613 173.918±22.547
3.655±0.3 3.412±0323 3.733±0.359 3.605±0.313 3.48±0.289
16.298±1.03 16.518±1.093 15.691±1.052 15.931±1.047 16.656±1.096
171.849±20.917 169.333±19.453 165.962±21.25 155.974±15.173 168.535±17.789 160.765±20.71
3.504±0.32 3.51±0.322 3.906±0.315
3.497±0.303 3.521±0.306 3.385±0.246
15.826±1.063 16.22±1.04 15.849±1.107
171.762±29.21 183.495±25.465
174.317±21.085 159.542±17.874
16.107±1.133 16.368±1.03 16.159±1.143
175.354±20.008
167.534±25.094
LG linkage group; CI confidential interval (QTL interval above 2 LOD); PVE percent variance explained; f1, f2 father alleles; m1, m2 mother alleles
Analyzed phenotypic means for each allele and significant mean phenotypic differences between alleles are noted in the “Significance” column
*Major QTLs
m2f2
0.047 0.0501 0.049 0.0015 0.0435
0.0485 0.0732 0.0406 0.0288 0.0047
0.0236 0.0146 0.0372 0.0426 0.0038 0.0031
p-Value
m2f1
m1f1
m1f2
Significance
Phenotype mean
Phenotypic means (± standard error) are listed for each genotype at the marker with the peak LOD score for each trait
HLJ594 CAFS198 HLJ2391 CAFS1950* HLJ1843
CAFS1586 HLJ1482 HLJ2225 CAFS1340 HLJ3366*
10 19 29 30 39
Nearest marker
CAFS1586 HLJ2316 HLJ1093 HLJ2225 HLJ1691* HLJ1843*
LG
10 17 27 29 30 39
QTL
Body weight (g) BW qBW10 qBW17 qBW27 qBW29 qBW30 qBW39 Body length (cm) BL qBL10 qBL19 qBL29 qBL30 qBL39
Trait
Table 1 Detail of QTLs related to economic traits
J Appl Genetics
J Appl Genetics
74.4
79.4
79.5
74.8
79.4
69.4
69.8
47.8
64.4
65.1
42.8
59.4
58 61.6
37.8
54.3
59.3
49.3
44.2
49.2
40.9
25
20.8
20.3
15.3
0
74
79 79.1
69
62.9
67.9
57.9
52.9
46.1
51.1
38.1
41.1
34.1
30.9
0
29.5
0
25.8
1
24.9
1
24.1
2
21.6
2
19.8
3
5
3
9 14 18.2
4
0
4
5 5.8 10.8
qBW10
35.9
qBW17
5
30.9
5
26 27.2
a
6
5
qBW27
qBW29
5
4
4 3
3 2
2 1
1
56.2
55.1
54 54.6
52.7
47.8
42.8
42.7
37.7
32.7
27.7
15
20 22.7
0
65
57.3
62.3
53.7
43.7
48.7
38.7
33.7
31.5
30.1
27.5
25.3
24.7
24.2
23.5
22 22.3
18.4
21.3
5 9.7 14.2
0
5
5 10
0
0
6
qBW30
qBW39
5
4
4
3 3
2 2
1
1
53.1
50.7
34 36.1
31.8
27.4
22.4
20.3
18.9
16.7
15.5
14.2
12 13.1
5 9.3
92.5
90.2
85.2
75.2 80.2
74.2
69.2
64.2
54.2 59.2
44.4 49.2
40 40.8
37.9 39.1
37.6
34.6
30.4
25.7
20 20.7
15
5 10
0
0
0
0
Fig. 2 a QTL detected for the BW trait in 190 progeny of common carp. The LOD of the QTL is shown on the y-axis and the marker positions (cM) are shown on the x-axis. b QTL detected for the BL trait in 190 progeny of common carp. The LOD of the QTL is shown on the y-axis
and the marker positions (cM) are shown on the x-axis. c QTL detected for the BT trait in 190 progeny of common carp. The LOD of the QTL is shown on the y-axis and the marker position (cM) are shown on the x-axis
The LOD scores were calculated to be 4.9, 4.38, 5.38, 3.94, and 4.49 for each QTL, respectively. For the BT trait, QTLs explained 22.7, 26.3, 32.1, 26, and 27.1 % of phenotype variation, respectively. The confidential interval distance of
BT QTLs ranged from 3.5 to 4.4 cM. The nearest loci of the QTLs indicated a significant (p≤0.05) increase in BT. qBT29 was observed as the major QTL with a phenotypic variance of >20 % and a significant value of p=0.0015.
J Appl Genetics
b
6
5
qBL10
5
qBL19 4
4 3
3 2
2
1
1
6
81 83.9
76
71
66
64.3
59.3
58.1
48.9
53.1
44.1
35.7
40.5
34.7
29.7
22.1
24.9
17.1
5 8.4 12.1
0
74
0
79 79.1
69
62.9
67.9
57.9
52.9
51.1
41.1
46.1
38.1
34.1
29.5
30.9
25.8
24.1
24.9
19.8
21.6
9 14 18.2
5
0
0
6
92.5
90.2
85.2
75.2 80.2
74.2
69.2
64.2
54.2 59.2
44.4 49.2
40 40.8
37.9 39.1
34.6
30.4
25.7
20 20.7
15
0
5 10
79.4
74.8
65.1
69.8
58 61.6
56.2
55.1
54 54.6
0
47.8
0
52.7
0
42.8
1
42.7
1
37.7
2
32.7
2
20 22.7
3
27.7
3
15
4
5 10
4
5
qBL30
5
37.6
qBL29
5
qBL39
4
3
2
1
53.1
50.7
47.8
42.8
37.8
34 36.1
31.8
27.4
22.4
20.3
18.9
15.5
16.7
14.2
12 13.1
5 9.3
0
0
Fig. 2 (continue)
Statistical analysis In addition a one-way ANOVA for four allele combinations (m1f1, m1f2, m2f1, m2f2) and a t-test for two allele combinations (m1f1,m2f1), from markers nearest to the QTLs, was performed on 190 progeny to investigate the association between the phenotype trait and genotype.
Table 1 shows the phenotype values of each allelic combination. Among the nearest QTL markers related to the BW trait, HLJ2316 and HLJ1691 show significantly (p
