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. 2009 Apr;181(4):1649–1660. doi: 10.1534/genetics.108.098855

Construction of Genetic Linkage Maps and Comparative Genome Analysis of Catfish Using Gene-Associated Markers

Huseyin Kucuktas *, Shaolin Wang *, Ping Li *, Chongbo He *, Peng Xu *, Zhenxia Sha *, Hong Liu *, Yanliang Jiang *, Puttharat Baoprasertkul *, Benjaporn Somridhivej *, Yaping Wang *, Jason Abernathy *, Ximing Guo †, Lei Liu ‡, William Muir §, Zhanjiang Liu *,1
PMCID: PMC2666527  PMID: 19171943

Abstract

A genetic linkage map of the channel catfish genome (N = 29) was constructed using EST-based microsatellite and single nucleotide polymorphism (SNP) markers in an interspecific reference family. A total of 413 microsatellites and 125 SNP markers were polymorphic in the reference family. Linkage analysis using JoinMap 4.0 allowed mapping of 331 markers (259 microsatellites and 72 SNPs) to 29 linkage groups. Each linkage group contained 3–18 markers. The largest linkage group contained 18 markers and spanned 131.2 cM, while the smallest linkage group contained 14 markers and spanned only 7.9 cM. The linkage map covered a genetic distance of 1811 cM with an average marker interval of 6.0 cM. Sex-specific maps were also constructed; the recombination rate for females was 1.6 times higher than that for males. Putative conserved syntenies between catfish and zebrafish, medaka, and Tetraodon were established, but the overall levels of genome rearrangements were high among the teleost genomes. This study represents a first-generation linkage map constructed by using EST-derived microsatellites and SNPs, laying a framework for large-scale comparative genome analysis in catfish. The conserved syntenies identified here between the catfish and the three model fish species should facilitate structural genome analysis and evolutionary studies, but more importantly should facilitate functional inference of catfish genes. Given that determination of gene functions is difficult in nonmodel species such as catfish, functional genome analysis will have to rely heavily on the establishment of orthologies from model species.

LINKAGE maps are powerful research tools for mapping quantitative trait loci (QTL) to complement marker-assisted selection in many species, including aquaculture species (Lander and Botstein 1989; Sakamoto et al. 2000; Fishman et al. 2001; Nichols et al. 2003; Hubert and Hedgecock 2004; Moen et al. 2004, 2008; Chistiakov et al. 2005; Lee et al. 2005; Gharbi et al. 2006; Liu et al. 2006; Sekino et al. 2006; Phillips et al. 2007; Sekino and Hara 2007; for a recent review, see Danzmann and Gharbi 2007). However, marker density for all aquacultured species is still low. Aquaculture genome research can greatly benefit from genome studies of model species through comparative genome analysis, transferring genome information from fully sequenced and functionally well-characterized model species to aquacultured species (Sarropoulou et al. 2008).

Comparative genome analysis can be facilitated if a draft genome sequence is available for the species of interest. This area is rapidly expanding because whole-genome sequences are becoming available from many species, including five teleost species: zebrafish (Danio rerio), fugu (Fugu rubripes), Tetraodon (Tetraodon nigroviridis), medaka (Oryzias latipes), and three-spined stickleback (Gasterosteus aculeatus). To date, no whole-genome sequence exists for any aquaculture species. Major progress, however, has been made in the generation of other genome resources for some economically important aquaculture species such as tilapia (Katagiri et al. 2005; Lee et al. 2005; Ferreira and Martins 2008), rainbow trout (Rexroad and Palti 2003; Guyomard et al. 2006, 2007), Atlantic salmon (Moen et al. 2004, 2008), gilthead seabream (Sparus aurata) (Franch et al. 2006; Senger et al. 2006; Sarropoulou et al. 2008), and the European sea bass (Dicentrarchus labrax) (Chistiakov et al. 2005; Whitaker et al. 2006).

Channel catfish (Ictalurus punctatus) is the most economically important catfish species in the United States. It is anticipated that it will become one of the most important aquaculture fish species in Asia as well. Blue catfish (I. furcatus) is also economically important because the hybrid between channel catfish and blue catfish exhibits superior performance for several commercial traits (Dunham et al. 1987, 1990, 1993; Dunham and Argue 1998; He et al. 2003). Since these interspecific hybrids are fertile, it is possible to generate synthetic breeds using introgression strategies (Liu 2003; Liu et al. 2003).

Major progress has been made in catfish genome research, particularly in the area of genome resource development, including a large number of polymorphic markers (Serapion et al. 2004a,b; Xu et al. 2006; Somridhivej et al. 2008), construction of genetic linkage maps (Waldbieser et al. 2001; Liu et al. 2003), construction and characterization of BAC libraries (e.g., Wang et al. 2007), construction of BAC contig-based physical maps (e.g., Xu et al. 2007), generation of 63,000 BAC end sequences (Xu et al. 2006; Z. Liu, unpublished data), understanding of the genomic architecture (Liu et al. 1998; Liu 2007; Nandi et al. 2007), and a large number of ESTs (Ju et al. 2000; Cao et al. 2001; Karsi et al. 2002; Kocabas et al. 2002; Li et al. 2007). However, these genome resources are as yet nonintegrated and thus underutilized due to the lack of a platform for comparative genome analysis. A gene-based linkage map would provide a start for such a platform. In addition to these traditional genome resources, the use of single nucleotide polymorphism (SNP) markers is gaining significant momentum in aquacultured species (Hayes et al. 2007; Moen et al. 2008; Wang et al. 2008).

A major utility of gene-based linkage maps is comparative genomics, which is used to assist in the understanding of genome evolution (Meyers et al. 2005; Woods et al. 2005; Mousel et al. 2006; Sasazaki et al. 2006; Sawera et al. 2006). Because gene-associated markers are conserved through a wide evolutionary spectrum of species, they have become the most desirable type of marker for comparative mapping (Martin et al. 2005; Moretzsohn et al. 2005; Smith et al. 2005; Snelling et al. 2005; Casasoli et al. 2006; Kim et al. 2006; Sasazaki et al. 2006; Sawera et al. 2006). We have previously reported the identification of a large number of EST-associated microsatellites and SNPs (He et al. 2003; Serapion et al. 2004a; Wang et al. 2008). Here we present a gene-based linkage map of the catfish genome constructed with EST-associated microsatellites and SNPs. We report map locations for a total of 331 gene-based markers including 259 microsatellites and 72 SNPs. The linkage map is composed of 29 linkage groups (LGs). Significant differences in recombination frequencies between males and females were noted. Conserved syntenies were identified between the catfish and three model fish species: zebrafish, Tetraodon, and medaka.

MATERIALS AND METHODS

Catfish resource families:

F1 interspecific hybrid catfish were made by mating channel catfish females with blue catfish males. These F1 catfish and their parents were screened prior to the 1997 spawning season to determine which matings were most informative. Backcross families were made in the spawning season of 1997 by mating the F1 fish with channel catfish (channel catfish backcross). A specific family, F1-2 × channel catfish-6, was used for this project. The resource family was reared in 1000-liter tanks until collection of blood samples for genotyping. Individuals that were sampled for genotyping were heat-branded for future identification.

Genomic DNA:

Blood samples (0.5–1 ml) were collected in a 1-ml syringe and immediately expelled into a 50-ml tube containing 20 ml of DNA extraction buffer (100 mm NaCl, 10 mm Tris, pH 8, 25 mm EDTA, 0.5% SDS, and 0.1 mg/ml freshly made proteinase K), and DNA was isolated as previously described using standard protocols (Liu et al. 1998). Briefly, the blood samples were incubated at 55° overnight and DNA was extracted twice with phenol and once with chloroform. DNA was precipitated by adding a half volume of 7.5 m ammonium acetate and 2 vol of ethanol. DNA was collected mostly by spooling onto a micropipette tip, or in some cases by brief centrifugation, and washed twice with 70% ethanol, air-dried, resuspended in TE buffer (Tris–HCl, 10 mm, EDTA, 1 mm, pH 7.5), and quantified with a spectrophotometer.

Identification of microsatellites, primers, and PCR amplification:

EST-based microsatellites were previously published (Serapion et al. 2004b). FastPCR (Kalendar 2008) was used for the design of PCR primers. A tailed primer protocol (Oetting et al. 1995; Boutin-Ganache et al. 2001) was used to amplify microsatellite alleles. The following conditions were used to amplify the microsatellites: 1× PCR buffer, 1.5 mm MgCl2, 0.2 mm each of dNTPs, 4 ng upper PCR primer, 6 ng lower PCR primer, 20 fmol labeled primer, 0.25 units of JumpStart Taq polymerase (Sigma, St. Louis), and 20 ng genomic DNA, in a total reaction volume of 5 μl. A touchdown PCR was performed with the following thermo profile: after an initial denaturation at 94° for 3 min, PCR amplification was carried out at 94° for 30 sec, 57° for 30 sec, and 72° for 30 sec for 20 cycles as the first step and at 94° for 30 sec, 53° for 30 sec, and 72° for 30 sec for 15 cycles as the second step. A final extension at 72° for 10 min was included. The PCR products were analyzed on 7% polyacrylamide sequencing gels using LI-COR automated DNA sequencers.

SNP markers:

A total of 384 EST-derived SNP markers were genotyped as described in Wang et al. (2008) using the F1-2 × channel catfish-6 hybrid catfish.

Genotyping:

For microsatellites, after running through the LI-COR automated sequencers, genotypes were called by recording the amplified fragment sizes (in base pairs) in a Microsoft Excel spreadsheet. The fragment sizes were determined by using labeled size markers (LI-COR). Loci that did not show any polymorphism were recorded as nonpolymorphic. The complex loci and parental type microsatellites were also recorded. A chi-square goodness-of-fit test was used to assess the departures from the expected Mendelian segregation patterns. Genotype configurations of markers were categorized into three expected segregation types when null-allele segregation was allowed: 1:1:1:1-ratio type (♀ × ♂: AB × CD or AB × AC), 1:1 ♀ type (AB × AA or CC), and 1:1 ♂ type (AA or CC × AB). All statistical analyses described below were completed using JoinMap 4.0 (Kyazma B.V., Wageningen, The Netherlands) with the cross-pollinating coding scheme, which handles the data containing various genotype configurations with unknown linkage phases (Sekino et al. 2006). Segregation data from expected 1:1:1:1-type markers into 1:1 ♀- and 1:1 ♂-type markers were partitioned by creating maternal and paternal data sets using JoinMap 4.0 to perform linkage analysis for each sex (Jacobs et al. 1995; Viruel et al. 1995). This option in JoinMap 4.0 creates maternal and paternal genotypes by converting genotypes from 1:1:1:1-ratio type (♀ × ♂: AB × CD or AB × AC) to 1:1 ♀ type (AB × AA or CC) and to 1:1 ♂ type (AA or CC × AB).

Linkage analysis:

Linkage between markers, recombination rate (Θ), and map distances was calculated using the Kosambi mapping function. Significance was tested by JoinMap, which tests for independence of segregation using LOD scores. LOD scores were generated using the G2 statistic, which was multiplied by 0.5 × log10e to convert into a normalized LOD scale. Significance was determined at a LOD threshold of 3.0, and a threshold Θ of 0.6 was set to detect suspect linkage possibly resulting from allele-coding errors. Six individuals were omitted from the analyses because they were missing too many genotypes.

Markers were linearly aligned in each linkage group, converting the recombination rates into the Kosambi's map distance (in centimorgans). The position of markers was developed using a sequential map buildup (Stam 1993). With this method, the most informative pair of markers was selected, followed by sequential addition of other markers. The “ripple” was performed after adding each marker. The best fitting position of an added marker was examined on the basis of the goodness-of-fit test (chi-square) for the resulting map, which is the normalized difference in chi-square value before and after adding the marker. Mean chi-square contribution values were used to determine if genotyping errors were suspected. Suspect loci were manually regenotyped. When a marker generated a negative map distance, or a large jump in goodness-of-fit, the marker was removed, and map construction was continued as a first-round map. After the first-round marker ordering, the previously removed markers were added back and again subjected to the goodness-of-fit testing. In this manner, the marker ordering was continued to a third round until an optimum order of markers was found.

Genome size and coverage:

Genome length from the linkage map was calculated according to Hubert and Hedgecock (2004). Telomeric regions were added to the map distance by adding 2x to the length of each linkage group (Fishman et al. 2001), where x is the average spacing between markers, which was calculated by dividing the total length of all linkage groups by the number of markers minus the number of linkage groups (29).

Comparative genome analysis:

The ESTs containing the microsatellites or SNPs used for linkage mapping were used as queries for BLAST searches to locate their genomic location in zebrafish, Tetraodon, and medaka genome sequences (E-value < e−10). The chromosomal locations and linkage groups of the microsatellites and SNPs were recorded. Putative conserved syntenies were identified when the genes were located in the same chromosome and the same linkage group. The distances among genes on the same chromosome of zebrafish, Tetraodon, and medaka are given in base pairs, whereas the distances among markers on the linkage groups of catfish are given in centimorgans.

RESULTS AND DISCUSSION

EST-derived microsatellite markers:

As summarized in Table 1, a total of 992 EST-derived microsatellites were used for PCR analysis. Nine of these loci were later found to represent duplicate ESTs of the same genes and therefore were removed; of the 983 remaining microsatellite loci, 450 were unsuccessful in PCR. A total of 533 EST-derived microsatellites were successful in PCR amplifications. Of these 533 successful amplifications, 120 were not polymorphic. One hundred three of the remaining 413 microsatellites could not be scored, mostly due to duplicated gene loci and non-Mendelian segregation patterns, leaving a total of 310 microsatellite markers for linkage mapping analysis. The overall success rate from the identification of the EST-derived microsatellites to successful genotyping was 31.3%. This low success rate was attributed to several major factors related to the nature of the microsatellites. A large fraction (45.4%) of EST-derived microsatellites failed in PCR amplification. The major reason for failures at this step was most likely caused by the involvement of introns. In spite of the efforts to limit the PCR product size to <300 bp for genotyping using automated sequencers, the unknown involvement of introns could have made the PCR amplification impossible or the size of amplicons was too large to be analyzed on the automated sequencers. Approximately 12% of the microsatellites were nonpolymorphic in the resource family. In addition, amplification of duplicated gene loci made it impossible to call the genotypes in almost 10% of the EST-derived microsatellites. Overall, EST-derived microsatellites had a much lower success rate as compared to microsatellites identified from genomic survey sequences, e.g., BAC end sequences (Xu et al. 2006; Somridhivej et al. 2008). The advantage of representing genes by microsatellites is severely limited by this low success rate. However, such problems can be alleviated using full-length cDNAs or draft genome sequences for accurate predictions of intron locations. As discussed by Massault et al. (2008), such gene-based maps should not only facilitate QTL analysis in aquaculture, but also set the foundation for orthology establishment, thus enabling functional inference of genes in aquaculture species where direct functional genomics work is difficult.

TABLE 1.

EST-derived microsatellites and SNPs and their performance in genotyping analysis

Category Microsatellites SNPs
Total no. of markers 992 384
Duplicated EST contigs 9 0
No. of failed markers 450 118
Nonpolymorphic markers 120 143
Markers not scored due to gene duplication 103 0
Validated in mapping family 310 125
Over five parent–parent–children genotyping errors NA 27
Markers used for linkage mapping analysis 310 98
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NA, not applicable.

EST-derived SNP markers:

Wang et al. (2008) described the factors that are significant for validation of EST-derived SNPs. One hundred eighteen of 384 SNP markers failed, and 125 were polymorphic in the mapping family. Twenty-seven of these polymorphic SNP markers were not included in the mapping analysis because they contained a P-P-C error value (parent–parent–children genotyping error) >5, resulting in a total of 98 SNPs for the linkage mapping. These markers were subjected to BLAST searches against the zebrafish, medaka, and Tetraodon nigrovirides cDNA databases in ENSEMBL with genome location information. A total of 72 SNPs of 98 SNP markers with significant hits were associated with linkage groups at a minimum LOD score of 3.0 while 26 of them remained unassigned.

Segregation of markers and linkage analysis:

One hundred sixteen (28%) of the 408 markers exhibited a segregation ratio of 1:1:1:1, serving as the most useful markers segregating in codominant fashion. Two hundred ninety-two (72%) of the 408 markers were segregating with a ratio of 1:1 ♀ type (AB × AA or CC) and 1:1 ♂ type (AA or CC × AB). Eighteen markers showing a significant level of distorted segregation were excluded from initial map construction, but 9 of them were later added back manually to the linkage groups using the “strongest cross-link” feature of the software. The remaining 9 markers could not be assigned to any linkage groups at the threshold LOD score. Two hundred seventy-six markers were organized in linkage groups at an initial LOD score of 15.0. Using the “strongest cross-link” feature in JoinMap 4.0, initially ungrouped and excluded markers were assigned to groups to which they have linkage with a minimum LOD score of 3.0. Similarly, markers in smaller groups were also assigned to the groups that have the strongest linkage with a minimum LOD score of 3.0. No suspect linkages were detected. As the DNA for grandparents was not available, the linkage phase of the mapping family was unknown. Therefore, we first obtained the genotypes of the polymorphic microsatellite markers of the female and the male separately to construct sex-specific linkage maps. The software made the best estimate of the linkage phases. A total of 331 EST-derived microsatellites and SNPs were mapped on the combined map.

The genetic linkage map:

A linkage map for channel catfish was constructed with 331 markers composed of 259 type I microsatellites and 72 type I SNP markers. The linkage map contains 29 linkage groups (Figure 1) with 3–18 markers/linkage group; the number of linkage groups is consistent with expectations from the 29 haploid chromosomes of catfish. The largest linkage group contained 15 loci and spanned almost 131.2 cM, while the smallest linkage group contained 14 loci and spanned 7.9 cM. The linkage map covers a genetic distance of 1811 cM with an average of one marker every 6.0 cM. An additional 348.0 cM for the telomeric regions increased the estimated genome size to 2159.0 cM.

Figure 1.—

Figure 1.—

Figure 1.—

Figure 1.—

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Linkage map of catfish constructed from EST-derived microsatellites and SNPs. Location of a locus relative to the neighboring locus (LOD = 3) is indicated on the left side of each linkage group in centimorgans, and names of the loci are indicated on the right side of each linkage group.

Two linkage maps were previously published:

One linkage map was constructed using AFLP markers (Liu et al. 2003), and the other was constructed using microsatellite markers (Waldbieser et al. 2001). While the current EST-based map is not comparable with an AFLP-based map because of the dominant nature of AFLPs, the current map had similar genome coverage to the previously published microsatellite map. The number of mapped markers was similar and the mapped genome size was similar as well, suggesting similar recombination frequencies within the intraspecific and interspecific mapping populations. However, a direct comparison of the two mapping populations should be conducted using a common set of microsatellite markers.

Of the 408 polymorphic markers, 358 were segregating female markers and 150 were segregating male markers. Linkage mapping analysis using JoinMap allowed the mapping of 315 female segregating markers into 29 linkage groups, while 43 markers were ungrouped. Similarly, of the 150 segregating male markers, 123 were mapped into 27 groups (5 of them were 2-point linkage groups), while 27 were ungrouped. Clearly, in the case of the male map, the markers were not sufficient to cover all the chromosomes (N = 29). The male and female linkage groups with shared markers are shown in supplemental Figure 1. The female map spanned 2009.8 cM with an average marker spacing of 6.15 cM. The male map spanned 761.0 cM with an average marker spacing of 4.95 cM. Clearly, the number of polymorphic markers was much larger in the female than in the male parent; this was at least in part due to the greater levels of polymorphism between channel catfish and blue catfish than within channel catfish. The female parent used for the resource family production was an F1 hybrid catfish made from mating a channel catfish female with a blue catfish male, whereas the male parent was a channel catfish.

Differences in recombination between sexes:

The sexes show substantial differences in recombination rate, both in general and for specific pairs of linked markers. In general, there is less recombination and genetic distance in the male linkage map (supplemental Figure 1). When common informative markers were selected (90 loci; 18 linkage groups), a higher recombination rate was observed in the female map (supplemental Figure 1, Table 3). Summing up the map distances for common markers for each LG resulted in a total length of 1891.2 and 3403.2 cM in the male and female maps, respectively. Thus, the ratio of female:male recombination rates for shared markers was 1.6:1. In 5 of the 22 linkage groups, recombination frequency was larger in the male than in the female, and the ratio of male-to-female recombination frequency in these 22 linkage groups varied greatly from 0.3 to 4.7. In contrast, in 17 of the 22 linkage groups, the recombination frequency was greater in the female than in the male with a female-to-male recombination frequency ratio of 0.2 to 3.4 (Table 2).

TABLE 2.

Comparison of male and female recombination rates in linkage groups with two or more shared markers

Sex-specific linkage groups Total shared markers Cumulative distance in female Cumulative distance in male Female:male ratio
F1_M38 2 0.00 1.93 ND
F2_M25 2 9.62 28.03 0.3
F4_M7 5 84.12 84.02 1.0
F6_M6 5 83.10 42.90 1.9
F7_M1 2 7.59 2.23 3.4
F7_M3 6 229.20 131.70 1.7
F8_M1 4 85.50 66.70 1.3
F9_M11 3 43.50 29.10 1.5
F10_M21 2 53.40 19.20 2.8
F11_M5 3 7.20 29.10 0.2
F16_M11 2 28.80 12.70 2.3
F17_M15 4 115.30 53.70 2.1
F17_M30 2 3.80 9.20 0.4
F18_M13 4 58.30 49.50 1.2
F19_M16 3 30.10 27.40 1.1
F20_M12 5 7.20 33.60 0.2
F21_M8 6 505.40 308.00 1.6
F23_M4 6 440.00 221.90 2.0
F30_M17 4 143.00 47.50 3.0
F31_M2 9 1465.30 683.90 2.1
F33_M17 2 0.00 7.10 ND
F33_M18 2 2.80 1.80 1.6
Total/average 83 3403.23 1891.21 1.6
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A differential recombination rate was reported for a number of species. In several teleost species, recombination appears to be reduced in males. For example, the female:male recombination ratios are 8.26:1 in the Atlantic salmon (Johnson et al. 1987; Moen et al. 2004, 2008), 3.25:1 in rainbow trout (Sakamoto et al. 2000), and 1.48:1 in the European sea bass (Chistiakov et al. 2005). Our finding here of a recombination rate of 1.6 (female) to 1 (male) is in line with the general pattern found in other teleost fish. However, because the recombination rates are related to specific markers corresponding to specific chromosome regions, species, and specific matings, they are expected to be variable when additional markers are analyzed in catfish.

Identification of potentially conserved syntenies:

The use of EST-derived microsatellites and gene-derived SNP markers in this work provided opportunities to compare the similarities of the genome organization in catfish with those of its closely related species such as zebrafish where whole-genome sequences are available. To identify potential conserved syntenies between the catfish and the model fish genomes, EST sequences containing the mapped microsatellites and SNPs were used as queries for BLAST analysis against the zebrafish, medaka, and Tetraodon cDNA databases in ENSEMBL with genome location information. Of the 331 loci mapped, 131 had significant hits when searched against the zebrafish cDNA database, 139 had significant hits against the medaka cDNA database, and 130 had significant hits against the Tetraodon cDNA database. As summarized in Table 3, a total of 29 conserved syntenic blocks were identified between the linked catfish EST-derived microsatellites and SNPs and the physically linked zebrafish genes. Eight of these syntenic blocks contained four or more markers mapped to linkage groups 8, 9, 10, 11, 25, 27, and 29. The largest syntenic block contained eight markers mapped to LG10. In three syntenic blocks, a linear syntenic relation was evident with distances between the mapped markers being proportional to the distances of the genes on the zebrafish chromosomes. For example, the three loci AUEST0074, AUSNP000270, and AUSNP000093 were mapped to LG12 spanning ∼100 cM, while the genes homologous to these ESTs span almost 50 million base pairs in the zebrafish genome in a linear fashion. Similarly, the three loci AUSNP000042, AUEST0070, and AUSNP000151 were mapped to LG25 spanning ∼20 cM, while the genes homologous to these ESTs span almost 22 million base pairs in the zebrafish genome in a linear fashion. However, for most of the identified syntenic blocks, the gene/marker order and orientation may not be the same (Table 3). The conservation of marker/gene positions was the highest between catfish and zebrafish, consistent with their phylogenetic relationships (Xu et al. 2006; Steinke et al. 2006). The overall annotation rate of the 331 mapped ESTs was lower than that of the average ESTs (>50%; Li et al. 2007), largely because of the location of microsatellites being associated mostly with 5′- or 3′-UTRs.

TABLE 3.

Identification of putative conserved syntenies between the catfish and zebrafish genomes

Locus GID/contig ENSEMBL ID Chromosome Chromosome location (bp) E-value Linkage group Map location (cM)
AUEST0368 CF262908 ENSDART00000024945 1 37,727,657 0.74 29 44.6
AUSNP000047 Ctg_3078 ENSDART00000054230 1 22,493,244 1.00 29 131.2
AUEST0074 BM028055 ENSDART00000081134 1 4,593,682 0.84 12 100.4
AUEST0270 CF262276 ENSDART00000103588 1 5,640,711 0.27 12 82.6
AUSNP000093 Ctg_3139 ENSDART00000100195 1 55,725,417 1.00 12 7.5
AUEST0137 BM495657 ENSDART00000101881 1 31,938,503 0.23 9 3.7
AUEST0106 BM495226 ENSDART00000021158 2 12,139,259 0.29 6 79.2
AUEST0168 BM438559 ENSDART00000055792 2 2,187,626 0.15 6 57.3
AUEST0290 CB937768 ENSDART00000048277 2 35,593,551 0.16 16 22.2
AUEST0154 BE469707 ENSDART00000012487 2 17,181,829 0.25 13 11.3
AUEST0023L BM496054 ENSDART00000036997 2 40,658,639 0.42 19 13.8
AUSNP000009 Ctg_0027 ENSDART00000087086 2 25,356,938 0.18 5 0.5
AUEST0002 BM438455 ENSDART00000016407 3 35,368,255 0.25 11 27.9
AUEST0671 CK413397 ENSDART00000055360 3 28,793,198 0.68 23 37.7
AUSNP000026 Ctg_1273 ENSDART00000074561 3 41,753,186 0.44 13 54.3
AUEST0838 BE469511 ENSDART00000080075 3 13,504,775 1.00 26 7.4
AUSNP000072 Ctg_4221 ENSDART00000004305 3 13,516,452 1.00 26 21.9
AUSNP000042 Ctg_2754 ENSDART00000030890 3 23,436,798 1.00 25 14.1
AUEST0070 AF267989 ENSDART00000055675 3 19,486,003 0.71 25 33.5
AUEST0151 BM027834 ENSDART00000046995 3 1,374,688 0.77 25 35.8
AUEST0824 BE468808 ENSDART00000039572 4 16,948,230 0.20 12 3.2
AUEST0149 BM497130 ENSDART00000066929 4 21,411,838 0.28 22 31.6
AUEST0704 CK413527 ENSDART00000000020 4 15,491,685 0.32 22 0.0
AUEST0679 CK413500 ENSDART00000051554 5 14,064,654 0.48 16 73.1
AUSNP000036 Ctg_2405 ENSDART00000024676 5 62,066,731 1.00 16 25.4
AUEST0286L CF262064 ENSDART00000074117 5 70,268,448 0.26 10 27.1
AUEST0516 CK412782 ENSDART00000051236 5 30,026,995 0.30 10 10.9
AUEST0526 CK412855 ENSDART00000038929 5 68,419,340 0.31 10 38.0
AUEST0537 CK412946 ENSDART00000051135 5 38,197,794 0.44 10 0.0
AUSNP000041 Ctg_2749 ENSDART00000089992 5 12,496,309 0.19 10 8.0
AUSNP000081 Ctg_5550 ENSDART00000080919 5 12,171,859 0.61 10 9.4
AUSNP000082 Ctg_0743 ENSDART00000023554 5 51,276,820 1.00 10 12.2
AUSNP000087 Ctg_1741 ENSDART00000050949 5 51,428,185 1.00 10 10.3
AUEST0767 BM497034 ENSDART00000041882 6 26,178,677 0.56 28 0.0
AUSNP000048 Ctg_3079 ENSDART00000064904 6 26,868,281 1.00 28 2.8
AUEST0010 BE469169 ENSDART00000019845 6 27,616,006 0.67 15 32.4
AUEST0078 BM495047 ENSDART00000083670 6 37,763,322 0.11 14 70.6
AUSNP000023 Ctg_1244 ENSDART00000073780 6 3,537,041 0.36 25 4.9
AUEST0835 BE469419 ENSDART00000018503 6 9,055,562 1.00 25 15.5
AUSNP000058 Ctg_3435 ENSDART00000004656 6 13,058,847 1.00 25 26.9
AUEST0929 BM494953 ENSDART00000065502 6 3,510,301 0.84 25 39.7
AUSNP000074 Ctg_4582 ENSDART00000056319 6 53,324,672 0.25 17 3.0
AUEST0051 BM425105 ENSDART00000003898 6 14,085,842 0.20 9 3.9
AUEST0608 BM496609 ENSDART00000052318 7 24,276,447 0.54 20 39.8
AUEST0769 BM496763 ENSDART00000100149 7 29,092,776 0.17 20 23.8
AUEST0152 BE212675 ENSDART00000075757 7 34,233,565 0.72 27 0.0
AUEST0918 BM439064 ENSDART00000074463 7 24,815,798 1.00 21 13.6
AUEST0805 CB936968 ENSDART00000052539 7 32,892,667 1.00 8 36.0
AUSNP000083 Ctg_0030 ENSDART00000027000 7 2,534,611 1.00 8 21.3
AUEST0027 BM438274 ENSDART00000062702 8 26,857,785 0.45 8 24.7
AUEST0376 CF262727 ENSDART00000099025 8 28,749,949 0.38 8 17.3
AUEST0945 BM495553 ENSDART00000083790 8 33,771,883 0.23 8 28.8
AUSNP000032 Ctg_1974 ENSDART00000099708 8 22,251,295 0.41 8 24.4
AUSNP000060 Ctg_3638 ENSDART00000022074 9 41,386,855 1.00 27 23.4
AUEST0100 AF396747 ENSDART00000100386 9 34,893,437 1.00 9 3.8
AUSNP000015 Ctg_0867 ENSDART00000101338 9 21,792,056 1.00 9 3.8
AUSNP000017 Ctg_0881 ENSDART00000006948 9 27,291,927 1.00 9 0.0
AUSNP000030 Ctg_1591 ENSDART00000101985 9 15,176,111 1.00 9 2.7
AUSNP000061 Ctg_3648 ENSDART00000100022 10 25,016,652 0.58 1 60.3
AUEST0086 BM028141 ENSDART00000104260 11 9,753,944 0.64 27 25.0
AUEST0373 CF262754 ENSDART00000035560 11 26,287,029 0.12 27 47.7
AUEST0696 CK413701 ENSDART00000087597 11 31,179,554 0.47 27 37.4
AUSNP000014 Ctg_0865 ENSDART00000104360 11 5,177,192 1.00 27 47.5
AUSNP000065 Ctg_3707 ENSDART00000030103 11 9,704,698 1.00 27 25.0
AUEST0017 BE468998 ENSDART00000026017 11 33,770,768 0.26 15 21.0
AUSNP000085 Ctg_1136 ENSDART00000103368 11 27,142,612 0.48 15 33.4
AUSNP000033 Ctg_2272 ENSDART00000054788 12 35,429,324 0.71 23 11.6
AUEST0265 CF262438 ENSDART00000022684 13 38,174,389 0.74 3 0.0
AUEST0758 CB938230 ENSDART00000043312 13 38,246,633 0.10 3 13.0
AUSNP000040 Ctg_2695 ENSDART00000057774 13 4,030,776 0.53 3 79.3
AUEST0678 CK413486 ENSDART00000101853 13 14,053,231 1.00 21 62.8
AUSNP000054 Ctg_3193 ENSDART00000102941 13 407,677 0.24 24 28.6
AUSNP000019 Ctg_1050 ENSDART00000061001 14 55,809,901 1.00 2 52.5
AUSNP000016 Ctg_0878 ENSDART00000061001 14 55,809,901 1.00 29 0.0
AUSNP000035 Ctg_2393 ENSDART00000039660 14 22,980,719 1.00 29 51.5
AUSNP000071 Ctg_4192 ENSDART00000079608 14 18,466,098 1.00 29 36.2
AUSNP000073 Ctg_4466 ENSDART00000023540 14 38,067,562 0.42 29 47.4
AUEST0302 CF261566 ENSDART00000020961 14 53,554,821 0.11 4 70.1
AUSNP000068 Ctg_3792 ENSDART00000105389 14 53,613,423 0.91 4 70.4
AUEST0472 BM496853 ENSDART00000063783 15 35,442,248 0.55 6 71.6
AUSNP000078 Ctg_5097 ENSDART00000019330 15 9,862,693 1.00 6 15.1
AUEST0377 CF262687 ENSDART00000020363 16 20,391,653 0.40 2 115.3
AUSNP000007 Ctg_4213 ENSDART00000081477 16 3,876,888 1.00 6 0.0
AUEST0436 CK414043 ENSDART00000081259 16 5,590,055 0.33 20 0.0
AUEST0220U CB937920 ENSDART00000081649 16 2,253,445 0.52 11 47.9
AUEST0936 BM495325 ENSDART00000049323 16 7,467,887 0.50 11 3.7
AUSNP000038 Ctg_2583 ENSDART00000058965 16 24,388,825 1.00 11 18.9
AUSNP000052 Ctg_3158 ENSDART00000058945 16 25,554,914 1.00 11 19.9
AUEST0032 BM424544 ENSDART00000058385 16 43,054,577 0.40 19 40.1
AUSNP000066 Ctg_3768 ENSDART00000078310 16 22,069,295 0.88 19 18.1
AUSNP000034 Ctg_2379 ENSDART00000064739 17 2,086,675 1.00 7 9.6
AUSNP000070 Ctg_4140 ENSDART00000064633 17 6,482,607 0.28 7 48.0
AUEST0132 BM424646 ENSDART00000053440 18 45,420,729 0.23 11 40.6
AUEST0288 CB937073 ENSDART00000032151 18 21,353,197 0.62 21 61.5
AUEST0771 BM496501 ENSDART00000052556 19 12,650,517 0.11 6 18.2
AUEST0067 BM497044 ENSDART00000062518 19 267,404 0.35 4 94.1
AUSNP000010 Ctg_0286 ENSDART00000023156 19 40,605,399 1.00 13 54.0
AUEST0138 BM028228 ENSDART00000104083 19 14,992,257 0.44 25 28.8
AUSNP000049 Ctg_3094 ENSDART00000052421 19 15,217,159 1.00 25 32.5
AUEST0266 CF262406 ENSDART00000058527 20 20,006,535 0.32 18 71.9
AUEST0406 BM439180 ENSDART00000032393 20 24,132,370 0.92 26 38.4
AUSNP000021 Ctg_1207 ENSDART00000053208 21 36,366,077 0.92 2 52.5
AUSNP000046 Ctg_3032 ENSDART00000040598 21 4,298,522 1.00 2 63.8
AUSNP000053 Ctg_3179 ENSDART00000055325 21 39,207,385 1.00 2 111.3
AUEST0052 BM495288 ENSDART00000020174 21 31,406,378 0.60 21 47.2
AUEST0635U BM496516 ENSDART00000015576 21 44,368,280 0.34 26 18.9
AUEST0451 CK424102 ENSDART00000063133 22 9,212,561 0.37 20 48.7
AUEST0417 BM494174 ENSDART00000048775 22 20,284,919 1.00 27 49.9
AUSNP000055 Ctg_3226 ENSDART00000062618 22 13,542,614 1.00 27 47.2
AUSNP000079 Ctg_5104 ENSDART00000076082 22 35,077,720 1.00 27 47.2
AUEST0210 CB939628 ENSDART00000059140 22 7,599,621 0.59 15 0.0
AUSNP000088 Ctg_1769 ENSDART00000092082 22 33,774,628 0.78 15 28.8
AUEST0509 CK412708 ENSDART00000085054 23 30,669,614 0.68 28 76.8
AUSNP000022 Ctg_1221 ENSDART00000104618 23 15,796,134 1.00 28 64.1
AUSNP000024 Ctg_1260 ENSDART00000081215 23 7,812,918 1.00 28 60.9
AUEST0066 BM027884 ENSDART00000025414 23 20,600,486 0.94 3 70.9
AUSNP000003 Ctg_0621 ENSDART00000077539 23 33,339,203 1.00 15 17.4
AUSNP000004 Ctg_1691 ENSDART00000009337 23 21,056,564 1.00 17 2.3
AUEST0814 CB937452 ENSDART00000066630 24 23,314,688 0.14 19 24.8
AUEST0062 BE469322 ENSDART00000039485 25 28,735,776 0.65 29 92.6
AUSNP000011 Ctg_0381 ENSDART00000021006 25 6,532,907 1.00 29 96.1
AUSNP000018 Ctg_1045 ENSDART00000064204 25 13,669,875 1.00 29 47.4
AUSNP000043 Ctg_2875 ENSDART00000005627 25 32,398,863 0.22 29 96.1
AUEST0043 AF063836 ENSDART00000073566 25 18,547,487 1.00 4 36.6
AUEST0126 BM028849 ENSDART00000018751 Unassigned 32,254 0.12 28 88.2
AUEST0685 CK413603 ENSDART00000092525 Unassigned 919 1.00 28 42.5
AUEST0716 CK413890 ENSDART00000082614 Unassigned 123,432 0.21 16 10.8
AUSNP000069 Ctg_4051 ENSDART00000013310 Unassigned 171,790 1.00 16 5.9
AUEST0242 CF261514 ENSDART00000097310 Unassigned 29,480 0.49 13 0.0
AUEST0064 AF410785 ENSDART00000098979 Unassigned 34,198 0.23 7 41.7
AUEST0476 BM496810 ENSDART00000098973 Unassigned 169,711 1.00 7 30.1
AUEST0383 CF262296 ENSDART00000053700 Unassigned 2,733 0.28 25 13.6
Open in a new tab

Marker loci were named with the prefix AUEST for gene-associated microsatellites and AUSNP for gene-associated SNPs. GID is the GenBank identifier for the gene (accession numbers for ESTs); for SNPs, the contig number from which the SNP was identified is given. The ENSEMBL ID column is the sequence ID of the zebrafish genome sequence homologous to the specific locus. The E-value column describes the similarity between the catfish gene and the zebrafish gene, but is encoded: e.g., 0.74 = e−74; 1 ≤ e−100; 0.27 = e−27, etc.

A total of 21 conserved syntenic blocks was identified between the linked catfish EST-derived microsatellites and SNPs and the physically linked Tetraodon genes (supplemental Table 1). The largest syntenic block contained five markers mapped to linkage group 9. Only one syntenic block with three markers (AUEST0767 AUSNP000048, AUSNP000024) located on LG28 had a linear syntenic relationship with chromosome 11 of Tetraodon. In the case of medaka, 29 conserved syntenic blocks were identified (supplemental Table 2). Five of these syntenic blocks contained four or more markers mapped to linkage groups 9, 10, 11, 27, and 29.

The evolutionary syntenic conservation appeared to be relatively low between the catfish genome and the genomes of the three model fish species. In spite of the identified conserved syntenic blocks, the extent to which the syntenies were conserved was limited in most cases. For example, the five markers on chromosome 1 of the zebrafish mapped to two linkage groups in catfish; the 6 genes on chromosome 2 of the zebrafish mapped to five different linkage groups with only a couple of markers linked together in catfish; and the 8 genes on chromosome 3 of the zebrafish mapped to five linkage groups (Table 3). This indicates that, among the fish genomes, much chromosome breakage and many rearrangements occurred during evolution. However, in a few instances, the syntenic conservation was extensive. For example, of the 10 genes on zebrafish chromosome 5, 8 were mapped to linkage group 10, and the other 2 were mapped to linkage group 16; of the 7 genes on zebrafish chromosome 11, 5 were mapped to linkage group 27, and the other 2 were mapped to linkage group 15 (Table 3). These findings are consistent with our previous findings that high levels of conservation were found within small genomic regions, whereas high levels of large-scale genome reshuffling were evident when comparing the genomes of catfish and zebrafish (Wang et al. 2007). These results indicate that comparative genome analysis is highly efficient when dealing with small genome segments for which conserved syntenies have been identified. Therefore, many smaller conserved syntenies in catfish may need to be used when comparing zebrafish or other model fish species for which whole-genome sequences are available. Such findings also strongly support the need to produce the whole-genome sequence of catfish for the purpose of genome evolution studies. Catfish is an economically important member of a large order of Siluriformes from which no whole-genome sequence is available.

This study represents a first-generation linkage map constructed by using EST-derived microsatellites and SNPs, laying the ground for large-scale comparative genome analysis in catfish. We previously reported a large number of BAC end sequences (XU et al., 2006) and their associated microsatellites (Somridhivej et al. 2008). Further expansion of this linkage map using physical-map-anchored polymorphic markers should enhance comparative mapping, thereby transferring genome information from model species to catfish. In spite of the apparent high levels of chromosome rearrangements between the catfish and zebrafish genomes, comparative mapping is still of great value, not only for the understanding of genome organization and genome evolution, but also for the understanding of genome functions. Given that determination of gene functions is very difficult in nonmodel species such as catfish, functional genome analysis will have to rely heavily on the establishment of orthologies from model species, such as zebrafish, to infer functions. Such comparative genomics information will be valuable in narrowing down suggestive candidate genes around significant QTL, which are expected to be easily found by use of such a dense linkage map.

Acknowledgments

We thank Rex Dunham and his students as well as Renee Beam, Karen Veverica, Esau Arana, and Randell Goodman for their excellent work in the production and maintenance of fish used in this study. We appreciate the support of the Auburn University Department of Fisheries and Allied Aquacultures, College of Agriculture, and the Vice President for Research and their matching funds to U. S. Department of Agriculture (USDA) National Research Initiative (NRI) Equipment Grants (2005-35206-15274). This project was supported by grants from the USDA NRI Animal Genome Tools and Resources Program (award no. 2006-35616-16685 and partially award no. 2003-35205-12827).

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