Whole genome scanning and association mapping identified a significant association between growth and a SNP in the IFABP-a gene of the Asian seabass
© Xia et al.; licensee BioMed Central Ltd. 2013
Received: 2 October 2012
Accepted: 25 April 2013
Published: 1 May 2013
Aquaculture is the quickest growing sector in agriculture. However, QTL for important traits have been only identified in a few aquaculture species. We conducted QTL mapping for growth traits in an Asian seabass F2 family with 359 individuals using 123 microsatellites and 22 SNPs, and performed association mapping in four populations with 881 individuals.
Twelve and nine significant QTL, as well as 14 and 10 suggestive QTL were detected for growth traits at six and nine months post hatch, respectively. These QTL explained 0.9-12.0% of the phenotypic variance. For body weight, two QTL intervals at two stages were overlapped while the others were mapped onto different positions. The IFABP-a gene located in a significant QTL interval for growth on LG5 was cloned and characterized. A SNP in exon 3 of the gene was significantly associated with growth traits in different populations.
The results of QTL mapping for growth traits suggest that growth at different stages was controlled by some common QTL and some different QTL. Positional candidate genes and association mapping suggest that the IFABP-a is a strong candidate gene for growth. Our data supply a basis for fine mapping QTL, marker-assisted selection and further detailed analysis of the functions of the IFABP-a gene in fish growth.
KeywordsSingle nucleotide polymorphism Growth trait Candidate gene Quantitative trait locus
Growth traits are the major interest in aquaculture. Growth traits are complex traits that regulated by many loci that have quantitative effects on the phenotype. Due to the ease of measurement, the economic importance and intermediate heritabilities for growth traits, phenotypic selection has been commonly and effectively applied in fish breeding programs. However, the process of developing new varieties or lines is labor intensive, time consuming and costly by using traditional breeding methods. Marker-assisted selection (MAS) by using markers tightly linked to quantitative trait loci (QTL) as a substitute to assist phenotypic screening, can overcome the shortcomings in traditional breeding, thus increasing the accuracy and efficiency of selection . This technique is especially valuable for traits with low to moderate heritability, which are difficult to be improved by traditional selection.
Identification of QTL related to growth is the basis for application of genetic markers in fish breeding. A key step in QTL analysis of complex traits is the establishment of large collections of genetic markers . A few studies are available on linkage and QTL mapping for growth traits in fish, such as salmonids [3–6], sea bream [7–11], rainbow trout , tilapia [13, 14], Asian and European seabass [15–20] and turbot . However, the identification of QTL underlying complex traits have proved to be very challenging due to gene-gene and gene-environment interactions, low proportion of explained phenotypic variation for each locus [22, 23], the large number of QTL, and many additional sources of variation . Linkage-based QTL mapping and linkage disequilibrium (LD)-based genetic association mapping are two major approaches to locate genes that control phenotypes of interest. Combined linkage and association analysis showed significant improvement in detection of QTL and have shown the ability to unravel the secrets of complex traits in animal breeding . Few such reports exist for fish to date.
Many traits with importance in agriculture change with time or other independent variables . In animal, many factors such as skeletal structure, organ, muscle, and fat mass contribute to overall body size  and family-based, age-related and strain-specific growth differences are likely to co-exist [28–30]. Genetic factors affecting growth are expected to vary among individuals, families and different stages of development. The power of QTL analysis is therefore seriously limited, since only segregating QTL in one or both parents that were used in constructing mapping families can be detected . Although some family/population-specific QTL have been reported in the Asian seabass [15–17] and other fishes [28–30], the molecular and genetic basis of growth traits remains fragmentary, highlighting the need for additional studies of these important trait in fish.
The Asian seabass Lates calcarifer is an important farmed marine foodfish species, widely distributed in the Indo-West Pacific region (FishBase: http://www.fishbase.org). Currently a second generation linkage map for the Asian seabass was developed with 790 markers . Based on the genetic map information, some QTL for growth traits were mapped [15–17]. Significant QTL for growth traits measured at the age of 3 months were mapped onto linkage groups 2 and 3 [16, 17] using F1 families. Fine mapping of QTL on linkage groups 2 and 3 identified a QTL cluster underlying growth traits in the Asian seabass . These studies provided preliminary data for application of MAS selection in the Asian seabass. However, due to the low analytic power of F1 families for QTL mapping, some QTL for growth might be missed.
The potentials of MAS to improve traits are increasingly evident in aquaculture. Identification of QTL-related economic traits is a major aim of fish genome research. In Southeast Asia, the harvesting time for the aquacultured Asian seabass is starting from about nine months of age. In order to map QTL for growth traits at different farming stages of the species, we conducted a whole genome scan for growth traits at the age of six and nine months post hatch by using an F2 family including 359 individuals, and genotyping with 123 microsatellites and 22 SNPs. We identified significant and suggestive QTL that affect growth-related traits (body weight, standard length and total length measured at the age of 6 and 9 months) in the Asian seabass. In addition, we detected SNPs in 19 candidate genes related to growth, nutritional condition or environmental adaptation and mapped them in the linkage map of Asian seabass. The gene IFABP-a located on LG5 was significantly associated with growth. Our study provides a basis for identification of genes controlling growth traits and conducting of MAS in the Asian seabass.
Results and discussion
Phenotypic data in the F2mapping family
Identified QTL affecting growth traits in the Asian seabass
Peak position (cM)
227.6 (±62.0) g
340.8 (±110.3) g
206.1 (±20.3) cm
229.5 (±24.1) cm
238.9 (±22.2) cm
277.8 (±28.0) cm
Linkage and QTL mapping
One hundred and twenty-three microsatellite markers showed informativeness in the F2 mapping family. These markers distributes evenly on the 24 linkage groups that defined by Wang et al. . In addition, 22 SNP markers that developed from genes related to growth, nutritional condition, or environmental adaptation were also applied in map construction. Primer information for the SNP markers was listed in Additional file 1: Table S1. Using linkage analysis and LOD ≥ 3 as threshold for mapping data, all loci were finally assigned to 24 linkage groups (LGs), designated as LG1 – LG24, corresponding to the linkage group names of the high-resolution linkage maps [15, 16]. The largest linkage group LG17 obtained in this study containing 8 markers and had a length of 93.5 cM, and the shortest linkage group LG20 consisted of 5 markers with a length of 2.3 cM. The total length for the map was 812 cM with an average linkage group length of 33.8 (± 22.1 sd) cM. The average spacing between adjacent markers was 7.8 (± 5.5 sd) cM, therefore meeting the minimum requirements for QTL analysis . Most markers mapped in the same linear order as in the high-resolution linkage maps [15, 16]. The differences between two maps could be caused from the differences in mapping population sizes and marker numbers, genotyping errors and the nature of the population, e.g., differences in family structures and female/male specific recombination rates. Summary of linkage map information and the averaged linkage map are presented in Additional file 2: Table S2 and Additional file 3: Figure S1, respectively.
The proportions of phenotypic variance explained (PVE) by all the suggestive QTL were generally small. The highest PVE was 12.0% for the suggestive QTL tl6m_i, which were located on LG24 (8.07 cM). The PVE for the eight QTL of the trait BW6M ranged from 2.5% (bw6m_e) to 7.2% (bw6m_b); and for the nine QTL of trait SL6M, the PVE ranged from 3% (sl6m_e) to 10.3% (sl6m_i). The small PVEs of the QTL that identified per trait suggest that the expression of these traits is under the control of a number of genes. Similar results were obtained in turbot by Sánchez-Molano , in which, the PVE of the tightest linked markers to the significant QTL varied from 8.0% to 13.6% for weight and from 8.2% to 12.9% for body length. Some potential candidate genes were detected near or on some of the QTL intervals, e.g. IG2F2BP3 on LG3, IFABP-a on LG5, 2-TNNL2 on LG6, GCDH on LG11, SP on LG13 and APOCI on LG15.
Significant differences in QTL positions for different growth traits were also found, such as sl6m_a on LG3, sl9m_b on LG5 and sl9m_c on LG6, suggesting that different sets of genes are involved in the control of these traits. In a previous study of the Asian seabass [15, 16], five significant QTL controlling growths were detected on LG2 and LG3, and explained 6.4 - 59.7% of the observed phenotypic variance, respectively. We also identified two QTL regions on these linkage groups. But, low PVEs and only suggestive significance level (p < 0.05) were found for these QTL. These differences could result from the growth variations at different development stages among different families, since we measured traits of fishes at the age of 6 and 9 months, but those used by Wang et al. [15–17] were measured at the 3 months. Specific QTL for body weight, standard length and total length that detected at different sampling time period suggest that there were dynamic expression for the traits, as in salmonids , in which some QTL were differentially expressed at different developing stages.
Co-localization of the detected QTL among traits is evident, indicating that pleiotropic effects may be playing a role in growth of the Asian seabass. This result was expected since high genetic correlations among growth traits were found in the seabass and other fish species. All suggestive QTL were located in 17 different genomic regions of 13 linkage groups. QTL regions having similar LOD profiles for multiple traits were detected on 10 linkage groups (LG2, 5, 7, 9, 11, 13, 15, 18, 21 and 24). Interestingly, one QTL region on linkage group LG5 (0~1 cM) was significantly associated to all traits (GW threshold: p < 0.01). Given that the high and sharp LOD peak, one common locus might be responsible for the different traits. The PVE of traits for this QTL ranged from 5.4% (tl9m_b) to 10.5% (sl9m_b). Near or on this QTL, eight markers including two SNP markers were located within 1.2 cM regions (Additional file 3: Figure S1 and Figure 1). Interestingly we found IFABP-a-SNP1245 marker showed a significant association to the traits. This result suggests that IFABP-a gene is of great importance in growth of fish and hence, warrant further studies.
Association of heterozygosity with growth traits in the full sib mapping family
Analysis of associations between candidate markers and growth traits in 4 populations
Significant associations between body weight and candidate markers in the four populations of Asian seabass
Marker name, linkage group and position (cM) in linkage map
Characterization of IFABP genes in the Asian seabass
Expressions of the two IFABP homologs in the fish intestines in response to fast challenge
A genome-wide QTL study was performed for growth traits at age of 6 and 9 months post hatch in the Asian seabass. Twenty-one significant QTL and 24 suggestive QTL were detected across all traits, which might be representative of the genetic architecture of growth traits in the Asian seabass. QTL mapping and association analysis revealed that IFABP-a-SNP1245 was significantly associated to growth. Characterization of IFABP gene gives additional support to our QTL findings and suggests that IFABP-a is strong candidate gene for growth in fish. Our data supply a basis for fine mapping QTL, marker-assisted selection and further detailed analysis of the functions of the IFABP gene in fish growth.
Constructing mapping populations and phenotyping
To construct an F2 population for mapping QTL affecting growth traits, an F1 population was first constructed by mass crossing of 50 wild Asian seabass that captured from Southeast Asia (Thailand, Indonesia, Malaysia and Singapore). Two F1 individuals selected as F2 parents based on their performance difference in growth traits were then intercrossed to generate the F2 mapping family (F2S2), which contained 359 full sib individuals. The F2 individuals were raised communally in a 20-ton indoor tank and maintained on standard feeding regimes until 270 days post hatch. All individuals were tagged at the age of 3 months with microchips (Opulent, Singapore). Phenotypic data including body weight, standard length and total length were recorded on each fish of the family at the age of 6 and 9 months (Table 1), using methods described in Fishbase (http://www.fishbase.org/Identification/Morphometrics/centimeters/Index.php). Fin samples were collected from each fish and kept at −80°C for DNA isolation.
For LD-based association mapping, four F2 populations (F2S1, F2S5, F2S6, F2S7) were used. Each population was established from mating of unrelated 25 F1 sires with 25 F1 dams, whose parents originated from natural populations in Southeast Asian. The genetic relatedness and genetic distances among individuals were evaluated by using software PAPA  and MEGA 4 . Only unrelated individuals were used in mass crosses. Parentage assignment for each population was conducted to reconstruct pedigrees. Each population containing around 2000 fishes was transferred to a 7-ton indoor tank. The fishes were fed with commercial dry feed. After three months of growing in the tanks, two extreme trait groups (large size group: 60–200 fishes, and small size group: 60–100 fishes) from each tank were sampled and phenotypic data from each fish were measured. All experiments in this work have been reviewed and approved by the IACUC Committee on Bioethics of the Temasek Life Sciences Laboratory, Singapore.
Microsatellite and SNP genotyping
Based on the Asian seabass linkage maps [15, 16], 123 microsatellite markers shown polymorphism in parents of the mapping family (F2S2) were selected for genotyping analysis. These microsatellite markers distributed evenly on 24 linkage groups and were genotyped on a 3730xl DNA analyzer (Applied Biosystems) after PCR amplification using fluorescently labeled primers. The two parents and their offspring (N = 359) were genotyped using microsatellites as described previously .
Based on our Asian seabass transcriptome database (unpublished data), 19 ESTs showing high similarity to genes related to growth, nutritional condition or environmental adaptation were aligned with genomic sequence data from Stickleback (Gasterosteus aculeatus; http://www.ensembl.org/Gasterosteus_aculeatus/Info/Index). Primer sites in conserved exon regions were identified. One or two primer pairs for each gene (see Additional file 1: Table S1) were developed based on the EST sequences using the program PrimerSelect (DNASTAR, Wilmington, DE). Genotypes of SNPs in candidate genes were detected by sequencing of PCR products. SNP genotype was analyzed by using software Sequencher 4.9 (Gene Codes, Ann arbor, USA). To design primers for real-time PCR, the exon/intron boundaries and divergence regions from two IFABP homologs were first identified by using the software Sequencher 4.9. One primer pair that spans exon/intron boundary on the mRNA for each homolog was designed using the program PrimerSelect (DNASTAR, Wilmington, DE). This primer design allows differentiating the two homlogs and doesn’t amplify genomic DNA.
Fasting experiment and sampling
Twenty-eight fishes (at ~ 1 year old) that maintained at the fish facility of Temasek life sciences laboratory were used for functional analysis. Prior to fast experiment the fishes were housed in one tank containing 2000 L of freshwater. On day 0 (starvation day 0), seven tissues (kidney, heart, muscle, intestine, spleen, eye and liver) from four fishes were taken and kept in Trizol reagent. The remaining twenty-four fishes were then transferred to two tanks holding 1000 L of freshwater. The twelve fishes in control tank were fed with commercial dry feed twice per day. In test tank, a fast regime was imposed on twelve fishes for up to 12 additional days under the same conditions as the control except without feeding. Four fishes from each tank were sacrificed on days 3, 6 and 12 of the fast period. Intestine samples were kept in Trizol reagent (Invitrogen, CA, USA) for RNA extraction.
Cloning and gene expression analysis of the IFABP gene by quantitative real-time PCR (qPCR)
The full-length cDNA libraries described as in Xia et al. [47, 48] was used for cloning and characterization of the IFABP gene. For analysis of gene expression patterns 10 times dilution of the cDNA were assayed as template by qPCR using EF1A and RPL13A as control (see Additional file 1: Table S1), as suggested by Tang et al.  that the EF1A and Rpl13A genes are more suitable as a reference gene panel for zebrafish tissue analysis. PCR reactions were performed with the iQ SYBR Green Supermix (Bio-Rad, Hercules, CA, USA) as described by the manufacturer in an iQ™5 Real Time PCR Detection Systems (Bio-Rad, Hercules, CA, USA). PCRs were performed in four biological replicates and three technical replicates. For the analysis of the changes of gene expression, the values of qPCR reactions were normalized to EF1A and RPL13A gene expression, calculated by the ΔΔCT method  as implemented by the software Bio-Rad iQ5 (2.0) (Bio-Rad, Hercules, CA, USA). P-value significant difference was calculated using the student’s T-Test module installed in the Microsoft Office Excel 2008 program with parameters setting as two-tailed distribution and two-sample equal variance.
Linkage and QTL analysis
A sex averaged genetic linkage map was generated for the full sib family containing 359 individuals using JoinMap linkage analysis software packages . A LOD score of 3.0 was used as a threshold value for defining linkage groups. Marker distances were evaluated according to the Kosambi formula (in cM). QTL analyses were performed with MapQTL version 5.0  by using the multiple QTL model mapping analysis with selected cofactor markers. The LOD profiles were generated at 1 cM intervals along each linkage group to identify the most likely QTL position. The linkage group wide and genome wide significance thresholds for LOD scores were determined by implementing a bootstrapping method with 10000 permutation iterations at p = 0.01 and 0.05. QTL that exceeded the linkage group-wide LOD threshold at p < 0.05 was reported as suggestive QTL, while exceeding a genome-wide LOD threshold of p < 0.05 was considered evidence for a significant QTL effect, and exceeding a genome-wide LOD threshold of p < 0.01 was considered evidence for a very significant QTL effect. QTL affecting different traits were considered to be located in the same region if the chromosomal positions of these QTL were overlapped. Linkage map and QTL regions affecting growth were visualized using MapChart software (ver. 2.1)  and shown in Additional file 3: Figure S1.
LD-based association analysis and heterozygosity effect
In association analysis, each of the 4 populations from mass crosses was analyzed separately, as there are differences in the maintenance of each population in separated tanks, which could distort the estimation of association effects if they were analyzed together. The genetic structure of each population was subdivided by using a model-based approach in the STRUCTURE software . Kinship matrices were calculated using the SPAGeDi software package . Association analysis between the haplotypes and genotypes of the markers/genes and growth traits was then carried out using general linear model (GLM) function and mixed linear model (MLM) function that implemented in the software TASSEL 2.0.1 .
In order to investigate associations between phenotypic traits and molecular genotypes, two extreme trait groups (N = 30/group) for each trait were selected from 359 full sib fishes from the family F2S2. Excluding the loci with heterozygous genotypes (ab×cd) that segregated in two parents (with 4 alleles), the observed heterozygosities and homozygosities of remaining loci that used in the construction of the linkage groups were calculated for each group. The one-tailed probability for frequency distribution differences of heterozygosities between two extreme groups of a trait was calculated using on line Fisher's exact test calculator (Ver.3; http://www.danielsoper.com/statcalc3/calc.aspx?id=29).
Sequence analysis and phylogenetic analysis
The Asian seabass IFABP gene sequences (GenBank accession no.: JX678843-JX678844) were used to perform BLAST analyses for EST annotation. Twenty one IFABP protein sequences, sixteen of which from fish databases, were retrieved from the NCBI database (http://mirrors.vbi.vt.edu/mirrors/ftp.ncbi.nih.gov/blast/db/). The protein sequences were aligned using the Clustal X 1.83 program . Phylogenetic analyses was carried out using the MEGA 4 package  with neighbour-joining bootstrap tests (1000 times). Genetic distances among protein sequences were evaluated with Poisson correction by MEGA 4. Evolutionary relationships of IFABP protein genes were shown in Additional file 5: Figure S2.
Descriptive statistics and correlation analysis
Basic descriptive statistics, including number of observations (N), minimum values, maximum values, means and standard deviations (sd) and Pearson's correlation coefficient were calculated using the ‘data analysis’ module of the software Excel.
Quantitative trait loci
Single nucleotide polymorphism
Fatty-acid-binding protein gene
Intestine fatty-acid-binding protein alpha gene.
We thank MAC, AVA for long-time co-operation on Asian seabass breeding, and our lab members for assisting in tissue sample collection. We are very grateful to our colleague Ms May Lee for English editing. This study is funded by the National Research Foundation Singapore under AQUAMARINOME 2016: Increasing Singapore’s Food Fish Production Through Aquaculture Genomics R & D (Award No: NRF2010NRF-CRP002-001). We thank MAC, AVA for long-time co-operation on breeding Asian seabass.
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