Marine Biotechnology

, Volume 13, Issue 1, pp 74–82

Mapping QTL for an Adaptive Trait: The Length of Caudal Fin in Lates calcarifer

  • C. M. Wang
  • L. C. Lo
  • Z. Y. Zhu
  • H. Y. Pang
  • H. M. Liu
  • J. Tan
  • H. S. Lim
  • R. Chou
  • L. Orban
  • G. H. Yue
Original Article

DOI: 10.1007/s10126-010-9271-5

Cite this article as:
Wang, C.M., Lo, L.C., Zhu, Z.Y. et al. Mar Biotechnol (2011) 13: 74. doi:10.1007/s10126-010-9271-5

Abstract

The caudal fin represents a fundamental design feature of fishes and plays an important role in locomotor dynamics in fishes. The shape of caudal is an important parameter in traditional systematics. However, little is known about genes involved in the development of different forms of caudal fins. This study was conducted to identify and map quantitative trait loci (QTL) affecting the length of caudal fin and the ratio between tail length and standard body length in Asian seabass (Lates calcarifer). One F1 family containing 380 offspring was generated by crossing two unrelated individuals. One hundred and seventeen microsatellites almost evenly distributed along the whole genome were genotyped. Length of caudal fin at 90 days post-hatch was measured. QTL analysis detected six significant (genome-wide significant) and two suggestive (linkage-group-wide significant) QTL on seven linkage groups. The six significant QTL explained 5.5–16.6% of the phenotypic variance, suggesting these traits were controlled by multiple genes. Comparative genomics analysis identified several potential candidate genes for the length of caudal fin. The QTL for the length of caudal fin detected for the first time in marine fish may provide a starting point for the future identification of genes involved in the development of different forms of caudal fins in fishes.

Keywords

Fish Teleosts Tail QTL Evolution 

Introduction

An important question in evolutionary biology is whether adaptation involves the accumulation of many genetic changes with small phenotypic effects or at least initially with large effects (Orr and Coyne 1992). A promising way to examine which hypothesis is correct is to determine the genetic basis of adaptive traits that vary among populations within a species or between closely related species (Jermstad et al. 2003). With the advent of technologies for developing DNA markers and constructing a linkage map (Botstein et al. 1980), as well as sophisticated statistical tools for mapping quantitative traits (QTL) (Paterson et al. 1990), it is now possible to estimate the number of QTL affecting specific traits and pinpoint the location of these factors on a whole genome. In addition, the effects of QTL can be estimated within experimental populations (Calboli et al. 2003; Haidle et al. 2008; Lallias et al. 2009). Previous studies on QTL affecting adaptive quantitative traits have detected QTL with large effects and small effects (Tanksley 1993; Rogers and Bernatchez 2007; Li et al. 2009). Although fishes represent 50% of vertebrates on the Earth (Nelson 2006), only less than 300 species have been cultured. Among the 300 cultured fish species, QTL analyses have been conducted in less than 30 species. Most QTL studies focused on economically important traits, such as growth (Wang et al. 2006; Moghadam et al. 2007; Wang et al. 2008a; Liu et al. 2010), disease, and stress resistance (Perry et al. 2001; Somorjai et al. 2003; Cnaani et al. 2004; Lallias et al. 2009). In fish species, only a few studies analyzed QTL for adaptive traits, such as skin pigmentation and shape in Atlantic salmon (Boulding et al. 2008) and plated morphs in threespine stickleback (Cano et al. 2006). Knowledge about QTL affecting adaptive traits in fishes is still very limited.

The caudal fin represents the distal region of a fish, where fluid accelerated anteriorly is shed into the surrounding medium. The caudal fin is the main propelling fin used for propulsion (Flammang and Lauder 2009). A previous study demonstrated that the morphological variation in caudal area exhibited by wild juvenile brook charr from microhabitats differing in water velocity could be a consequence of phenotypic plasticity in response to hydrological conditions (Imre et al. 2002). A recent study showed that the dorsal fin length and caudal span width were positively associated with swimming speed in several fish species (Leavy and Bonner 2009). The evolution of the caudal fin has become such an important tool in systematics that nearly every investigator who has discussed the evolution of vertebrates has commented on the caudal fin of ray-finned fishes (Actinopterygii) (Lauder 1989). The caudal skeleton is a major character complex used in the evaluation of teleostean interrelationships (Mehta et al. 1989; Schultze and Arratia 1989). However, as compared to the extensive analyses of genes involved in fin and limb development in model organisms that have been conducted using mutation screening and transgenic techniques over the last two decades (Tabin 1992; White et al. 1994; Wills et al. 2008; Kizil et al. 2009; Sims et al. 2009), comparatively little is known about how the underlying developmental determinants of skeletal morphology change during evolution in fish species, leaving a large gap in our understanding of the evolution of adaptive traits. Therefore, it would be useful to know whether the phenotypic variation in caudal fish structure has a small or a large genetic component and whether the genetic component is determined by many genes of small effect or by a few major loci of large effect in non-model fish species (Slate 2005). Analysis of QTL will locate QTL related to the structure of caudal fin and estimate the effects of QTL; thus, it is the first step towards understanding the genes involved in adaptive evolution of the caudal fin.

Asian seabass (Lates calcarifer), known as barramundi in Australia, is a member of the Latidae family. It is widely distributed in the Indo-West Pacific region from the Arabian Gulf to China, Taiwan, Papua New Guinea, and Northern Australia (Nelson 2006). Its caudal fin showed large length variation (Wang et al. 2008b). Our previous studies showed that body weight and length were affected by several significant QTL with large effect and several putative QTL with small phenotypic effects (Wang et al. 2006; Wang et al. 2008a). The objective of this study was to conduct a whole genome search for QTL affecting the length of caudal fin and the ratio between tail length and standard body length using 117 microsatellites and a segregating F1 family. This study allowed us to locate the positions of QTL affecting the length of caudal fin and estimate effects of these QTL on phenotypic variations. This study would provide a basis for identifying genes involved in the development of different forms of caudal fins in fish.

Methods

Reference Family, Traits, and DNA

A broodstock consisting of 94 Asian seabass brooders from the Marine Aquaculture Center, Singapore, was genotyped with nine polymorphic microsatellites (Lca08, Lca20, Lca21, Lca58, Lca64, Lca69, Lca70, Lca74, and Lca98). The genetic relationships among individuals were determined by using the similarity of microsatellite genotypes. One F1 reference family was generated by crossing two unrelated brooders (Wang et al. 2006). Fertilized eggs were cultured in a 1-ton tank following standard culture protocols (Pillay 1990) with modification (i.e., conducting a size grading at 26 days post-hatch) until 30 dph. As juveniles are highly carnivorous, grading is necessary. From 26 dph on, these larvae were subsequently graded into three distinct sizes (large, medium, and small) every week up to 65 dph and transferred into cylindrical 3-ton capacity tanks with continuous flow-through of optimum quantity of sand-filtered seawater. Juveniles were raised in tanks with same stocking density and were fed to satiation three times (9:30 am, 1:30 pm, and 5:30 pm) daily on stringently sized pellets (INVE, PHICHIT, Thailand) until the experiment ended at 90 dph. Other conditions (i.e., air flow, water flow, and light intensity) in the tanks were maintained as constant as possible.

At 90 dph, tail length, body length, and standard length of 380 progeny randomly collected from tanks was measured. Since the ratio between tail length and standard length (RTS) may be important in fish swimming (Videler 1993), in this study, we considered RTS as a trait for QTL mapping. Fin clips of the parents and their progeny were collected and kept in absolute ethanol. DNA was isolated from fin clips according to Yue and Orban (2005) and arrayed into 96-well PCR plates for later use.

Microsatellites Genotyping

One hundred and seventeen microsatellite markers almost evenly covering the 24 linkage groups, selected from the linkage map of Asian seabass (Wang et al. 2007), were genotyped using an automated DNA sequencer ABI 3730xl (Applied Biosystems, Foster City, CA, USA). Allele sizes were determined against the size standard ROX-500 (Applied Biosystems) with software GeneMapper V3.5 (Applied Biosystems) as described previously (Wang et al. 2006).

Linkage Mapping and QTL Analysis

Linkage analyses were conducted through a series of pairwise comparisons between loci using LINKMFEX version 1.5 (Danzmann 2006). Map graphics were drawn with MapMaker software (Lander et al. 1987).

QTL analysis was carried out using the program MapQTL 4.0 (Van Ooijen et al. 2002) with genotype data of the 117 markers and phenotypic data of the 380 progeny. Interval mapping and multiple QTL model (MQM) mapping were utilized to detect any significance associating growth-related traits and marker loci in the data sets. The LOD score significance thresholds were calculated by permutation tests in MapQTL 4.0, with a genome-wide significance level of α < 0.05, n = 1,000 for significant linkages and a linkage-group-wide significance level of α < 0.05, n = 1,000 for suggestive linkages.

An analysis of variance (ANOVA) was carried out to determine the differences among the genotypes of markers nearest to each QTL based on the genotypes (m1f1, m1f2, m2f1, and m2f2) of the marker locus lying closest to the peak in each of the QTL-containing genomic regions. A simple Bonferroni correction, to reduce the chance of type 1 error to 0.01 across all tests, gave a significance threshold of 0.0025, since we consider that tests were performed among four genotypes of each marker. This was conducted by using the general linear model (GLM) procedure of SAS (SAS Institute) and the Bonferroni method of multiple comparisons with α < 0.01. For the marker nearest to each QTL, effects of alleles within two parents and the interaction between paternal and maternal alleles were analyzed through ANOVA using GLM of SAS at α < 0.05.

Identification of Potential Candidate Genes in QTL Regions by Blast Against Known Genome Sequences

To identify potential candidate genes in QTL regions, we did a blast search of the sequences of microsatellites located in the significant QTL regions against the whole genome sequences of zebrafish and medaka in the current ENSEMBL release version (www.ensembl.org) and against all known sequences in GenBank.

Results

Trait Values

The phenotypic values of two traits (tail length and the ratio between tail length and standard length) in the F1 family at 90 dph were evaluated. Continuous variations were observed for both traits (Fig. 1). Tail lengths (TAILL) of these fish ranged from 9.3 to 30.7 mm with an average of 21.5 ± 3.2 mm. The ratios between tail length and standard length (RTS) were between 0.12 and 0.36, with an average of 0.21 ± 0.02. The correlation coefficient between tail size and standard length is significant (r = 0.406, df = 378, P < 0.05)
Fig. 1

Distribution of tail length (upper) and ratio between tail length and standard length (lower) in Asian seabass

QTL Analysis

The 117 microsatellite markers selected were mapped into 24 linkage groups with a map length of 690.6 cM. Twenty-one of the 24 linkage groups were covered with at least two markers. The markers were well-dispersed throughout the genome and the average distance between markers was 7.12 cM. Thus, QTL affecting TAILL and RTS were identified on a genome-wide scale with this linkage map.

The genome-wide LOD significance thresholds were 4 and 4.2 for TAILL and RTS, respectively, while the linkage-group-wide LOD significance thresholds varied from 1 to 2.9 (Table 1). Six significant (genome-wide significant) and five suggestive (linkage-group-wide significant) QTL were detected by the whole genome scan. The phenotypic variance explained (PVE) each QTL ranged from 1.5 to 16.6% (Table 1). Four significant QTL (qTAILL2-a, qTAILL2-b, qTAILL2-c, and qTAILL2-d) controlling tail length, were detected on LG 2 (Figs. 2 and 3), showing one QTL cluster on LG2. Two suggestive QTL affecting tail lengths were detected on LGs 17 and 21. Three QTL underlying RTS were detected on LGs 10, 14, and 21, respectively.
Table 1

Locations and effects of QTL for tail length (TAILL) and the ratio between tail length and standard body length (RTS) in Asian seabass

Trait

QTL name

LG

Nearest marker

LOD

LOD threshold

PVE (%)

Phenotype means

Genome-wide

Linkage-group-wide

m1f1

m1f2

m2f1

m2f2

Significance

Tail length (mm)

qTAILL2-a

2

Lca287

5.27a

4

2.9

10

22.5

21.1

24

22.6

d

qTAILL2-b

2

Lca371

6.86a

4

2.9

13.7

20.9

22.9

22.6

24.4

bcd

qTAILL2-c

2

Lca418

5.20a

4

2.9

5.7

22.6

23.5

21.3

22.5

cd

qTAILL2-d

2

Lca825

5.35a

4

2.9

8.1

23.7

22.7

22.3

21

c

qTAILL3

3

Lca359

4.38a

4

2.3

7.7

22.7

22.3

23.3

20.9

bcd

qTAILL15

15

Lca392

4.04a

4

2.3

16.6

24.2

21.1

23.2

23.4

b

qTAILL17

17

Lca312

2.96

4

2.1

7.1

24.4

22.3

23.1

23.1

c

qTAILL21

21

Lca228

2.64

4

2.5

5.9

23.5

23.7

22

23.5

b

Ratio between tail length and standard length

qRTS10

10

Lca149

1.02

4.2

1

1.5

0.210

0.215

0.210

0.215

c

qRTS14

14

Lca192

2.2

4.2

2.2

2.5

0.212

0.208

0.211

0.204

d

qRTS21

21

Lca228

2.61

4.2

2.2

4.6

0.212

0.220

0.211

0.214

b

For each QTL detected, the linkage group maximum LOD score, and percentage of the phenotypic variance explained (PVE) are indicated. The genome-wide LOD significance thresholds are 4.0 for tail length and 4.2 for ratio between tail length and total length Mean phenotypic values of each trait were also calculated for those progeny with the alternate alleles at the most closely linked microsatellite markers, inherited from the male parent (m1 or m2), alleles inherited from the female parent (f1 or f2)

aSignificant QTL

bSignificant effect within male alleles

cSignificant effect within female alleles

dSignificant interaction between alleles from male and female

Fig. 2

Locations of QTL affecting tail traits in Asian seabass. Locations of QTL are circled. The names of QTL are shown in italic and bold

Fig. 3

Mapping of QTL for tail length (TAILL) and the ratio between tail length and standard body length (RTS) in Asian seabass

Multiple QTL peaks were located on LG-2. ANOVA was performed on the 380 progeny using four allelic combinations (m1f1, m1f2, m2f1, and m2f2) from markers nearest to all the significant QTL in order to investigate effects of QTL allele substitution. As a result, the four QTL on LG2, namely, qTAILL2-a, b, c, and d, showed different patterns of significant effects within the male and female alleles and interaction between alleles from male and female (Table 2). On the other hand, Lca064 on this linkage group does not exhibit a QTL effect, while flanking markers do, so we checked Lca064 marker, which showed good polymorphism with four different alleles of the two parents with PCR product lengths of 276, 278, 288, and 290 bp, and the genotypes of the male 276/278 and female 288/290, where the scores of alleles were the product length of PCR products, read by GeneMapper V3.5. But we detected no QTL effect at this marker by using interval mapping and/or multiple QTL model (MQM) mapping. Thus, it is concluded that there are four separate QTL on LG2.
Table 2

Mean tail length (TAILL) and ratio between tail length and standard body length (RTS) classified by genotypes of marker Lca228 in Asian seabass

Genotypea

TAILL

RTS

No. of individuals

Mean (mm)

SD

Bonferroni T tests

No. of individuals

Mean

SD

Bonferroni T tests

269/277

198

21.16

0.25

P = 0.029

191

0.208

0.001

P = 0.039

275/277

171

21.76

0.23

 

162

0.212

0.001

 

aThe scores of allele were the product length of PCR products

Effects of QTL Linked to Lca228

It is notable that qRTS21 affecting RTS were located on the same position of qTAILL21, which affects TAILL, i.e., Lca228 on LG21. To further determine associations between the tail traits and genotypes, ANOVA was performed on the 380 progeny using allelic combinations of marker Lca228, which is nearest to the QTL. The male and female parent’s allelic combinations were 269/275 and 277/277, respectively; thus, the allelic combinations of offspring were 269/277 and 275/277. The phenotype values of the allelic combination are listed in Table 2. Progeny with 275/277 possess longer tail and higher ratio between tail and standard length than those with 269/277 by Bonferroni t test at P < 0.05, suggesting that marker Lca228 linked to the QTL underlying tail growth on LG21.

Potential Candidate Genes in QTL Regions

Blasting sequences of DNA markers located in QTL regions against known whole genome sequences of zebrafish and medaka, we detected some potential candidate genes for the length of caudal fin. Among these markers, Lca312 on LG17 had significant alignment to LPHN2 gene located at the position 10.34 Mb on chromosome 4 of medaka. Lca359, Lca149, and Lca192 were syntenic to unknown genes in zebrafish or medaka genomes. The sequences of the marker Lca371 hit to genome sequences at 16.325 Mb on the chromosome 18 (Dr-18) in zebrafish, and at 2.311 Mb chromosome 3 (Ol-3) in medaka. When looking closely, we found that some of the sequences of the marker Lca371 showed very high similarity of the cathepsin D gene of medaka and zebrafish. Blasting the sequences of Lca371 against all know sequences in Genbank, we found that the Lca371 showed some similarity to the cathepsin D gene (TC168775 and TC156330) of rainbow trout. Comparing the sequences of Lca371 to all 24,000 EST sequences generated by our group (unpublished data), we found that Lca371 is located in the cathepsin D gene of Asian seabass. For DNA marker Lca418, there was no significant hit to zebrafish genome sequences, while it had a high probability of alignment at the position of 0.770 Mb on the chromosome 3 (Ol-3) of medaka. This microsatellite marker also fell with the apparent coding sequence of the gene (SHNK1) encoding the somatostatin interacting protein. For the microsatellite markers Lca287, Lca359, and Lca392 located in regions of significant QTL for tail length, there were no hits to the meddaka and zebrafish genomes, whereas for Lca825, no hits were evident within the medaka genome, but a significant alignment to chromosome 23 at 20.246 Mb was evident with zebrafish.

Discussion

Here, we report the first study on QTL affecting the length of caudal fin on a fish genome. In the F1 family generated with two unrelated parents, we detected large phenotypic variations of the two traits measured: tail length, and the ratio between tail length and body length in 380 F1 individuals supplying a solid basis for dissecting the genetic basis of continuous phenotypic variation (Wang et al. 2006).

We detected six genome-wide significant and two chromosome-wide QTL for the length of the caudal fin and three chromosome-wide QTL for the trait ratio between tail length and body length, indicating these traits were determined by multiple genes. It seems that growth-related traits are determined by a number of genes located in different regions of a genome (Calboli et al. 2003; O’Malley et al. 2003; Haidle et al. 2008). In this study, individual QTL effects varied and explained phenotypic variations ranged from 1.5% to 16.6%. In a previous study on QTL for body length (Wang et al. 2006), we found only one genome-wide significant QTL for body length, explaining large phenotypic variation (58.9% for total length and 59.7% for standard length), while in this study, we detected six genome-wide significant QTL for tail length explaining relatively small phenotypic variation (5.7–16.6%) of the tail length. Only the QTL located near the marker Lca287 on LG2 showed significant effect on both body length and tail length, suggesting this QTL displayed pleiotropic effects and body length and tail length were determined by different set of genes. It is striking that, on LG2, four genome-wide significant QTL for tail length were mapped, suggesting a number of genes on this chromosome played a role in determining the length of caudal fin. On the LG3, our previous study only detected a chromosome-wide QTL for body length located near the marker Lca154 (Wang et al. 2006), whereas in this study, we detected genome-wide significant QTL for tail length located near the marker Lca359, which is about 10 cM away from the marker Lca154, indicating that the QTL for body length and tail length might be different. On LG15, a genome-wide significant QTL for tail length was located near the marker Lca392. However, in our previous study, no QTL for body length on this linkage group was detected. In addition, we also mapped two chromosome-wide significant QTL for the length of tail. Taken together, our studies suggested that body length and tail length were determined by some genes in common and some different gene sets. For the trait ratio between tail length and body length, we only detected three chromosome-wide significant QTL explaining a relatively small amount of phenotypic variations, suggesting environmental factors may play an important role in expression of this trait. There are several caveats with respect to this study. First, in an F1 family, only the QTL whose alleles segregate in one parent or both parents can be detected; therefore, the power of detecting QTL in a F1 family is lower than in an F2 family (Andersson and Georges 2004). Second, as the QTL was detected in only one F1 family, confirmation of detected QTL in other families is required (Wang et al. 2008a). Third, in the F1 family, it is not possible to dissect the QTL effects into dominant and additive effects. Therefore, further QTL analysis in F2 or backcross families is preferred. Fine-scale mapping with additional genetic markers will improve our estimates of QTL positions and increase our confidence surrounding those positions. Further fine-scale mapping is possible in this population because 1,000 additional genetic markers are currently available (unpublished data).

Comparatives genomics (Sarropoulou et al. 2008) and positional candidate gene method (Berger et al. 1992; Robertson et al. 1997) are effective ways to identify genes located in QTL. In this study, we blasted DNA sequences of markers located in regions of significant QTL against whole genome sequences of zebrafish and medaka and detected some hits in the genomes of zebrafish and medaka. Although some assignments may be not definitive, two of the markers (Lca371 and Lca418) on LG-2 were syntenic to the terminal end of linkage group Ol-3 in medaka. This is very intriguing as it gives some additional information on possible candidate genes that may be located on this linkage group. We identified several potential candidate genes (i.e., Cathepsin D, SHANK1, and LPHN2) for caudal fin length by comparative genomics analysis in the regions of significant QTL. The Cathepsin D gene encodes a lysosomal aspartyl protease composed of a dimer of disulfide-linked heavy and light chains, both produced from a single protein precursor (Chao et al. 2001). In frog Rana catesbeiana, cathepsin D might be involved in T3-induced tail-fin degradation (Seshimo et al. 1997). In humans, mutations in this gene are involved in the pathogenesis of several diseases, including breast cancer and possibly Alzheimer disease (Leto et al. 2004). SHANK1 encodes an adapter protein in the postsynaptic density of excitatory synapses that interconnects receptors of the postsynaptic membrane and the actin-based cytoskeleton (Sheng and Kim 2000). It plays a role in the structural and functional organization of the dendritic spine and synaptic junction (Naisbitt et al. 1999). However, its functions in fishes are almost unknown. LPHN2 gene encodes a member of the latrophilin subfamily of G-protein coupled receptors (GPCR). Latrophilins may function in both cell adhesion and signal transduction (White et al. 1998). Besides positional candidate genes based on QTL mapping, other candidate genes selected based on their physiological functions can also be applied to examine the association of polymorphisms of candidate genes and length of caudal fins. Previous studies on genes involved in the different fins and limbs in zebrafish and other model organisms clearly identified a number of genes involved in fin and limb developments, such as Hox genes (Tabin 1992), retinoic acid receptors (White et al. 1994), fgfs (Wills et al. 2008), and shh (Laforest et al. 1998). These genes could be served as candidate genes for association studies to identify QTL effects on continuous variation in length of tails, despite that these genes have not been mapped to the linkage map of Asian seabass. Future experiments to identify polymorphisms in positional candidate genes and functional candidate genes, and to examine their association with the tail length, are required. Comparative mapping with model organisms may identify candidate genes in the regions where QTL have been mapped.

In conclusion, we detected six significant (genome-wide significant) and five suggestive (linkage-group-wide significant) QTL on seven linkage groups. The six significant QTL explained each 5.5–16.6% of the phenotypic variance, suggesting these traits were controlled by multiple genes. The QTL mapping in this study is the first step towards the detection of genes that affect the length or shape of caudal fins. Additional studies in the fine mapping of the QTL and subsequent positional candidate gene selection or positional cloning are required in order to turn molecular knowledge into understanding the mechanism of adaptive evolution.

Acknowledgements

This study was funded by AVA and the internal fund of the Temasek Life Sciences Laboratory, Singapore. We thank our colleagues for technical assistance and Dr Mamta Chauhan for editing English of this manuscript.

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • C. M. Wang
    • 1
  • L. C. Lo
    • 1
  • Z. Y. Zhu
    • 1
  • H. Y. Pang
    • 1
  • H. M. Liu
    • 1
  • J. Tan
    • 2
  • H. S. Lim
    • 2
  • R. Chou
    • 2
  • L. Orban
    • 3
  • G. H. Yue
    • 1
  1. 1.Molecular Population Genetics Group, Temasek Life Sciences Laboratory, 1 Research LinkNational University of SingaporeSingaporeSingapore
  2. 2.Agri-Food & Veterinary Authority of SingaporeSingaporeSingapore
  3. 3.Reproductive Genomics GroupTemasek Life Sciences LaboratorySingaporeSingapore

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