Integrated linkage map of Haliotis midae Linnaeus based on microsatellite and SNP markers.
KEY WORDS: Haliotis midae, abalone, linkage map, microsatellites, single nucleotide polymorphisms
The abalone Haliotis midae Linnaeus is 1 of 6 abalone species that inhabits the coastal waters of southern Africa, of which 5 are endemic to South Africa. Overfishing, increased predation, and destruction of natural habitat have led to a dramatic decrease in wild abalone stocks (Tarr et al. 1996, Hauck & Sweijd 1999, Sales & Britz 2001). Commercial aquaculture, therefore, provides a sustainable alternative to fisheries for maintaining the supply of abalone to the market. The abalone H. midae is the largest of the 5 local abalone species and is currently the only one to be cultured commercially in South Africa. Globally, the abalone aquaculture industry has expanded considerably during the past decade, increasing from 2,800 t produced in 2000 to 40,800 t in 2008 (Food and Agriculture Organization of the United Nations 2010). Similarly, in South Africa, abalone production has increased approximately 10-fold during the past decade (Department of Agriculture, Forestry and Fisheries 2011); currently, South Africa (together with Namibia) is the third largest producer of cultured abalone in the world (Allsopp et al. 2011). Therefore, there is great pressure to increase the productivity of abalone aquaculture for South Africa to remain competitive internationally. The economic value of H. midae has prompted various research initiatives, including optimization of husbandry practices and genetic improvement strategies.
Selective breeding programs have great potential to boost aquaculture production of abalone (Hayes et al. 2007) because strong evidence exists for a heritable component for growth-related traits. Studies on other commercially important abalone species, such as Haliotis discus hannai Linnaeus and Haliotis rufescens Swainson, demonstrated a significant increase in growth rate through selection (Hara & Kikuchi 1992, Kawahara et al. 1997, Jonasson et al. 1999). These studies also found low genetic correlation between the average daily growth rate measured at an early age and the same trait measured at a later age when abalone reached market size. Therefore, genetic improvement could be accelerated if the means for early identification of individuals with favorable production traits existed, highlighting the potential for marker-assisted selection in abalone. The application of marker-assisted selection requires the identification of quantitative trait loci (QTL)--specific gene regions responsible for variation of phenotypic traits such as growth rate or meat quality. The construction of dense linkage maps is necessary for accurate mapping and association with phenotypic traits in QTL identification (Liu et al. 2008).
Linkage maps have been constructed for various aquaculture species such as Oreochromis niloticus Linnaeus (Nile tilapia), Paralichthys olivaceus Temminck & Schlegel (Japanese flounder), Crassostrea gigas Thunberg (Pacific oyster), and Dicentrarchus labrax Linnaeus (European sea bass) (Kocher et al. 1998, Coimbra et al. 2003, Hubert & Hedgecock 2004, Chistiakov et al. 2005). In abalone specifically, linkage maps have been produced for the blacklip abalone Haliotis rubra Leach (Baranski et al. 2006), the Pacific abalone Haliotis discus hannai Ino (Liu et al. 2006, Sekino & Hara 2007), and small abalone Haliotis diversicolor Reeve (Shi et al. 2010, Zhan et al. 2011). Earlier linkage maps were constructed using random amplified polymorphic DNA (RAPD) or amplified fragment length polymorphism (AFLP) markers, but in recent years the majority of linkage maps have been constructed using microsatellite markers.
Microsatellites are polymorphic DNA sequences consisting of short tandem repeats. These short repeats are distributed throughout the genome in coding as well as noncoding regions. Microsatellites are known to have higher mutation rates than nonrepetitive DNA, causing these markers to be highly polymorphic (Weber & Wong 1993). It is this polymorphic nature that makes these markers ideal for linkage mapping because their multiallelic status allows for a high information content, facilitating the detection of segregating alleles (Hauser & Seeb 2008).
Recently, however, single nucleotide polymorphism (SNP) markers have gained popularity for linkage mapping, which is a result of the fact that single base changes may be responsible for most sequence variation among individuals and, therefore, are more frequently associated with QTLs (Beuzen et al. 2000). In addition, SNPs are the most frequent polymorphism in genomes, making them ideal for linkage map saturation (Kennedy et al. 2003). It has also been demonstrated that SNPs have lower genotyping error rates compared with microsatellites (Ewen et al. 2000) and, more recently, the developments in genotyping technology allow for high throughput SNP genotyping at comparatively lower costs (Liu et al. 2011).
This study aimed to construct a first-generation linkage map for Haliotis midae based on microsatellite and SNP markers that could be used for future QTL mapping of economically important traits in this cultured abalone.
MATERIALS AND METHODS
Ten full-sib mapping families from 3 different abalone aquaculture farms--Roman Bay Sea Farm (Pty) Ltd. in Gansbaai (7B, DS1, and DS2), HIK Abalone Farm (Pty) Ltd. in Hermanus (42A, DS5, and DS6), and Abagold (Pty) Ltd. in Hermanus (FamD, FamH, FamI, and FamJ)--were used for the construction of the integrated linkage map (Table 1). Families DS1 and DS2 share a common parent with family 7B (Table 1); however, all the families were regarded as full-sib families and were analyzed individually when constructing family-specific linkage maps. The parents of all studied families are wild-caught individuals and, therefore, are expected not to demonstrate a genetic relationship to each other. Epipodial tentacles were clipped from the respective parents of each family and mantle tissue samples were collected from the offspring for DNA isolation. Extractions using DNA were performed using the CTAB extraction method of Saghai-Maroof et al. (1984). All mapping families were subjected to parentage analysis with a panel of 7 microsatellite markers to ensure accurate family structure within the selected families.
A large number of microsatellite markers have been developed previously for Haliotis midae, including 202 genomic markers from enrichment techniques, such as the FIASCO or SNX Unilinker methods (Bester et al. 2004, Slabbert et al. 2008, Slabbert et al. 2010, Slabbert et al. 2012), as well as 47 expressed sequence tag (EST)-derived markers developed from transcriptome sequence data (Hepple 2010, Jansen 2012), and 15 markers from a bioinformatic study by Rhode (2010). In addition, 10 cross-species microsatellite markers were developed from Haliotis rubra and Haliotis discus hannai (Rhode 2010). Optimized polymerase chain reaction (PCR) conditions and fluorescent labeling to allow for multiplex genotyping has been reported in the literature cited for the development of the markers.
Recently, numerous SNP markers have also been identified in Haliotis midae, including 28 EST-derived SNPs (Bester et al. 2008, Rhode 2010) and 12 genomic SNPs identified in the flanking regions of microsatellite markers (Rhode et al. 2008). The Illumina transcriptome data by Franchini et al. (2011) also provided a useful resource for SNP discovery. Putative SNPs were developed by identifying contigs that showed significant hits to genes of known function. Primers were designed for a number of these contigs, and were subsequently amplified and sequenced using standard Sanger sequencing. Sequences were aligned using ClustalW v1.4 and resulted in the identification of 105 EST SNPs (Blaauw 2012). In addition, 412 in silico EST SNPs were designed from the transcriptome data using the SNP discovery application available in CLC Genomics workbench v4.5 (Blaauw 2012, Du Plessis 2012).
Thus, in total, 831 molecular markers (274 microsatellite markers and 557 SNP markers) were available for this study.
The genotyping of markers in the mapping populations was done in an overlapping fashion to include as many markers as possible while maintaining economy of scale with regard to genotyping costs.
The parents of families 7B, 42A, DS1, DS2, DS5 and DS6 were screened with 204 genomic microsatellite markers (including 2 previously unpublished markers listed in Table 2) and 14 EST-derived microsatellite markers (Table 3). Parents for families DS1, DS2, DS5, and DS6 were screened with the additional 33 EST-derived microsatellite markers, the 15 markers from the bioinformatics study, and 10 cross-species microsatellite markers. Markers that were informative (in which the segregation pattern of alleles could be determined) were then used for genotyping the offspring. Microsatellite markers were grouped into multiplex reactions to reduce genotyping costs. Multiplex reactions were designed specifically for each family, because families had different sets of informative markers. The color of the fluorescent dyes, the respective lengths of the PCR products, and the PCR conditions of the markers were taken into consideration when grouping markers to ensure optimal multiplex reactions. Multiplex PCRs were performed using the QIAGEN multiplex kit: 10 ng template DNA was added to 3.5 [micro]L 2x QIAGEN Multiplex PCR master mix (containing HotStartTaq DNA Polymerase, Multiplex PCR Buffer with 6 mM Mg[Cl.sub.2] and dNTP mix), 0.9 [micro]L primer mix (20 [micro]M of each primer), and dd[H.sub.2]O to a final volume of 7 [micro]L. Polymerase chain reaction cycles consisted of a 10-min denaturing step at 95[degrees]C for 15 min, followed by 35 cycles of 30 sec at 94[degrees]C, 90 sec at TA, and 1 min at 72[degrees]C, which was then completed with an elongation step of 60[degrees]C for 30 min. Capillary electrophoresis of PCR products and GeneScan 600 LIZ[R] Size Standard (Applied Biosystems) was done on the ABI 3730xl DNA Analyser (Applied Biosystems). GeneMapper v4.1 software (Applied Biosystems) was used for allele scoring and assigning genotypes.
Single nucleotide polymorphism markers were genotyped using the Illumina GoldenGate assay with VeraCode technology on the BeadXpress platform. The mapping families DS1, DS2, DS5, and DS6 were included initially on a 48-plex GoldenGate assay that included 36 in vitro SNPs and 12 in silico SNPs (Blaauw 2012). The in silico SNPs were included to determine the efficiency and performance of SNPs identified using CLC workbench. Consequently, the mapping families DS1, DS2, FamD, FamH, FamI, and FamJ were genotyped on a 192-plex GoldenGate assay that contained an additional 186 in silico SNPs and 6 control SNPs (Du Plessis 2012). The control SNPs were SNPs that were included in the first GoldenGate assay, and were included in the second assay as positive controls. As a result of the assay setup, only 234 of the 557 available SNP markers could be tested in the mapping populations (Table 3). Genotyping analysis was performed using GenomeStudio Genotyping Module v1.0 software. Individuals that did not score reliable genotypes (GenCall rate < 0.80) or SNPs that failed in the initial clustering analysis (GenTrain rate < 0.45) were excluded before performing the final clustering analysis. After clustering analysis, genoplots were generated for each SNP separately, and SNPs that illustrated ambiguous clustering were omitted from further analysis.
The genotype data for both SNP and microsatellite markers were converted into the appropriate Joinmap v4.1 format for outcross (CP) populations (Van Ooijen 2011). Markers or individuals that had more than 20% missing data were excluded from the data set. Polymorphic markers were tested for segregation distortion from expected Mendelian ratios with the chi square goodness-of-fit test (P < 0.05). These markers were not excluded, but rather were noted for further analyses. Markers were grouped into linkage groups based on a test for independent segregation, using a LOD score threshold of 3. Sex-specific maps were constructed using the Create Maternal and Paternal Node function in Joinmap, whereas sex-average maps were constructed using the Create Population Node function. Subsequently, the sex-specific and sex-average maps of each family were compared with each other to determine consensus marker order within linkage groups. In addition, both regression and maximum likelihood mapping algorithms were used and compared to confirm consensus marker order. Recombination rate estimates were converted to genetic distances in centiMorgans (cM) using the Kosambi mapping function. Individual linkage groups of the sex-average maps for each family were then combined using the Combine Groups for Map Integration function to generate an integrated linkage map. Average marker spacing per linkage group was calculated by dividing the total length of the linkage group by the number of intervals (Liu et al. 2006).
Genome Size Estimation and Genome Coverage
The genome length was estimated using 2 methods, taking into account the different calculations for approximating telomeric size, and are as follows:
[G.sub.e] = [summation] [G.sub.oi] ([[K.sub.i] + 1]/[[K.sub.i] - 1]), (1)
where [G.sub.oi] represents the observed length of the linkage group i, and [K.sub.i] represents the number of loci on linkage group i (Chakravarti et al. 1991) and
[G.sub.e] = [G.sub.o] + 2t[G.sub.o]/n, (2)
where [G.sub.o] represents the total observed length, t represents the number of linkage groups, and n denotes the number of intervals between the markers (Fishman et al. 2001).
Subsequently, genome coverage was calculated using the equation [G.sub.o]/[G.sub.e ave], where [G.sub.e ave] was the average expected genome length as calculated by Eqs (1) and (2) (Cervera et al. 2001).
Functional Annotation of Markers
After linkage map construction, all the mapped markers were subjected to BLAST analysis to determine whether markers were associated with known genes. The bioinformatics protocol of Farber and Medrano (2003, 2004) was followed. In brief, repeat regions were masked using RepeatMasker v3.3.0 to ensure hits were the result of homologous flanking sequences and not the result of superfluous hits because of repetitive motifs. The masked sequences were then used to conduct BLASTx and BLASTn searches (Altschul et al. 1997) against the nr-nucleotide and nr-protein databases of NCBI (http://blast.ncbi.nlm.nih. gov/Blast.cgi). Masked sequences were also screened against the Repbase database via the program CENSOR v4.2.27 (Jurka et al. 2005, www.girinst.org/repbase) to identify possible associations to dispersed repetitive elements. Hits with either the lowest e value (cutoff, e value = 1.0[e.sup.-4]), or the highest similarity or score were assumed to be the most likely homolog.
Across all 10 mapping families, a total of 314 markers were informative, of which 178 were microsatellites and 136 were SNPs. The informative microsatellites consisted of 141 genomic, 32 EST-derived, and 5 cross-species markers. Two genomic microsatellite markers that were found to be informative have not been published before and are listed in Table 2. Several markers were excluded from further analyses, either because they were noninformative or because of problematic genotyping (Table 3). Uninformative markers (59%), in which the segregation of alleles could not be traced through the pedigree, included markers that were monomorphic, markers for which both parents were homozygous for different alleles, and markers that could not be amplified within specific families. Markers that were excluded because of problematic genotyping included duplicated markers (4%) and markers with null alleles (4%). A total of 136 EST SNPs were found to be informative in the mapping population. None of the genomic SNPs were informative, but this could be attributed to the small number that was genotyped in the mapping families. Similarly, with regard to the microsatellite markers, several markers had to be excluded from linkage analysis (Table 3). On average, 22% of SNP markers were excluded because of failed genotyping, and 51% of SNP markers were excluded for not being informative (monomorphic or homozygous for different alleles in both parents).
Sex-specific and sex-average maps were constructed for each of the mapping families. Consensus order within each linkage group, for both the sex-specific and sex-average maps, was obtained using the regression and maximum likelihood mapping algorithms. The number of linkage groups among the different families ranged from 9-21, depending on the number of informative markers. The integrated map consisted of 186 markers that mapped to 18 linkage groups (Fig. 1). Linkage groups were arranged from the longest (in centiMorgans) to the shortest (in centiMorgans) and were named accordingly. The linkage groups ranged in length from 87.1 cM to only 1.3 cM, with an average marker spacing of 6.88 cM (Table 4). The number of loci per linkage group ranged from 3-20, with an average of 9 loci per linkage group.
Genome Size Estimation and Genome Coverage
Equation (1) estimated genome length at 1,327 cM, whereas Eq (2) provided an estimate of 1,297 cM. Average estimated genome length was thus calculated to be 1,312 cM. The observed genome length was 1,038 cM (Table 4). Genome coverage of approximately 80% was achieved. The female map had a total length of 757 cM and an estimated genome length of 1,080 cM (Eq (1)) and 1,019 cM (Eq (2)). The male map was considerably shorter, with a total map length of 650 cM and a genome length estimated at 895 cM (Eq (1)) and 859 cM (Eq (2)).
Functional Annotation of Markers
Molecular markers that mapped to the linkage map was subjected to BLAST analysis and subsequently identified 84 markers that showed significant association to known genes (indicated on Fig. 1, Table 7). In addition, the mapped markers were also investigated for associations with repetitive elements and recognized 55 markers showing significant hits with known transposable elements (indicated on Fig. 1, Table 8).
The construction of linkage maps require numerous informative molecular markers in which the segregation of alleles can be traced through pedigrees. In the current study, several markers within families were excluded from further analyses because they were monomorphic or demonstrated aberrant genotypes. On average, within a family only 29% of microsatellites and 29% of SNPs were found to be informative for linkage analysis. This was resolved in part by including multiple families for linkage map construction, which increased the number of informative markers to 64% (178 informative markers of a possible 274 markers) of the total microsatellites and 58% (136 informative SNPs from a possible 234 SNP markers) of the total SNPs across families. This clearly illustrates the benefit of multifamily testing in linkage mapping experiments, which aids in negating the effect of a small number of markers being informative in individual families.
Linkage Map Construction
The integrated map was obtained by amalgamating sex-average maps from the separate families, and resulted in a final map that contains 186 markers mapped to 18 linkage groups. The haploid chromosome number for Haliotis midae has been confirmed as 18 (Van der Merwe & Roodt-Wilding 2008), and was thus the expected number of linkage groups for map construction. Although the integrated map contains 18 linkage groups, the separate families seldom displayed this exact number. The families screened for the microsatellite markers (7B, 42A, DS1, DS2, DS5, and DS6) were more likely to generate approximately 18 linkage groups, which could be attributed to the larger number and polymorphic nature of microsatellite markers (Hauser & Seeb 2008). On the contrary, families (FamD, FamH, FamI, and FamJ) that were screened exclusively using the fewer number of biallelic SNP markers demonstrated fewer linkage groups. Preliminary maps often display a number of linkage groups different from the expected haploid number as a result of the underrepresentation of markers on particular chromosomes or chromosomal segments (Yu & Guo 2003, Chistiakov et al. 2005, Baranski et al. 2006). In this study, anchor loci were used to connect linkage groups across individual family sex-average maps for the construction of the integrated map, and thus resulted in the expected 18 linkage groups. However, the integration of linkage group 18 (INT_LG_18) remained problematic. This linkage group contains anchor loci that are informative across the families (HmNS6, HmidPS1.193, and HmidPS1.559), but these markers seem to be linked to different groups of markers in the separate families. Therefore, with map integration, 4 different maps can be drawn, each containing different markers, but not connecting all the markers. This situation is most likely attributed to the fact that recombination information of informative markers across families is absent and exists only between informative markers within a family.
Nearly all the linkage groups on the integrated sex-average map contain both microsatellite and SNP markers, illustrating good integration of both marker types. In the families screened with microsatellites only, only 2 markers mapped repeatedly to INT_LG_16 (HmNS21 and HmRS80); however, with the inclusion of SNPs, this linkage group consists of 10 markers. There are only 2 linkage groups that contain 1 marker type only: INT_LG_9 (only microsatellites) and INT_LG_11 (only SNPs). Future studies could attempt to saturate these linkage groups by screening the currently placed markers in mapping families that have been screened with the other marker type. For example, the SNP mapping families can be screened with the microsatellites on INT_LG_9 and the microsatellite mapping families can be genotyped with the SNPs on INT_LG_11.
The current map has an average intermarker spacing of 6.88 cM. Massault et al. (2008) found that a marker interval of 10 cM was sufficient for initial QTL detection. Therefore, this map provides a foundation for future QTL identification studies in Haliotis midae. It should, however, be noted that there are areas on the map that could be improved with regard to marker density, because the marker interval is not uniform throughout the map and there are linkage groups that are still poorly saturated. In addition, more markers could help to merge the separate groups of INT_LG_18.
Informative markers were tested for segregation distortion using a chi square goodness-of-fit test. Markers that displayed segregation distortion (P < 0.05) were included for linkage analysis, and their map positions were investigated. Most of the markers that displayed segregation distortion were distorted in 1 family only and not across multiple families. One SNP marker (HmSNP1834_464) was, however, distorted in all the families for which it was informative (DS1, DS5, and DS6), but could not be assigned to any linkage group in these families. The locations of the distorted markers were investigated, because clusters of markers that display segregation distortion may be an indication of viability selection (Charlesworth & Charlesworth 1998). No clusters of distorted markers were found, however, on the sex-average maps of the individual families, suggesting that segregation distortion is probably a result of statistical bias or scoring error (Plomion et al. 1995). Another marker that demonstrated distortion in more than 1 family (HmidILL2.8738) did map to the integrated map; although it was informative in 4 families (DS1, DS2, DS5, and DS6), it displayed segregation distortion in only 2 (DS1 and DS5). As mentioned earlier, the segregation distortion is likely indicative of a genotyping error, and therefore the distance between HmidILL2.8738 and Hmid2015 might be inaccurate. Microsatellite markers are prone to genotyping errors and should be inspected carefully to avoid skewing of recombination rates and, consequently, estimates on map length and marker order. Ball et al. (2010) estimated that an error rate of 5% could cause map inflation of up to 50%. Nonetheless, HmidILL2.8738 mapped to the same position on LG-2 in all families where it was found to be informative, regardless of being distorted in some families.
Recombination Rates Between Sexes
Integrated sex-specific maps were also constructed to compare differences between the sexes (Table 5). The female map is 1.16-fold larger than the male map. Considering that approximately equal numbers of markers mapped to both sex-specific maps (118 on the male map and 121 on the female map), the difference in length could be attributed to a recombination rate differential between the sexes. This finding is in accordance with observations in other mollusc species, including other abalone species, which also demonstrated longer map lengths for female maps (Yu & Guo 2003, Hubert & Hedgecock 2004, Baranski et al. 2006, Liu et al. 2006, Sekino & Hara 2007, Zhan et al. 2011). Although sex-specific recombination rates are observed frequently, the biological mechanism remains poorly understood. Various hypotheses have been proposed, including the differences in meiotic prophase progression during spermatogenesis and oogenesis, variations in transcriptional activity of certain genes in each of the sexes, or the presence of sex-specific enzymes (Lindahl 1991). More recently, Singer et al. (2002) determined that the shorter male map is usually compressed at the centromeres and that distances closer to the telomeres are expanded and more comparable with the female map. The mechanism behind this phenomenon, however, remains elusive and further investigation is needed to explain its occurrence.
Comparison with Linkage Maps from Other Haliotis Species
Currently, there are linkage maps available for 3 haliotid species--Haliotis diversicolor, Haliotis rubra, and Haliotis discus hannai (Table 6). These linkage maps are based on AFLP, RAPD, and microsatellite markers. The current study produced the first linkage map for any haliotid species to include SNPs and is best compared with the integrated linkage map of Zhan et al. (2011). The studies by Baranski et al. (2006), Liu et al. (2006), Sekino and Hara (2007), and Shi et al. (2010) constructed sex-specific maps only. The genetic map presented for Haliotis midae has an average intermarker spacing of 6.88 cM. The resolution of the map by Zhan et al. (2011) was slightly higher, with a marker interval of 4.6 cM, but this could be the result of the smaller number of linkage groups (n = 16) found for H. diversicolor. The current map has an approximate genome coverage of 80%, which is similar to the genome coverage (approximately 78.1%) of the map by Zhan et al. (2011). In general, the estimated genome length of Haliotis midae was within the expected range as observed in other haliotids (e.g., Baranski et al. 2006, Zhan et al. 2011). Genome size for H. midae based on C value calculations via flow cytometry estimates it to be 1.43 pg or 1,398.54 Mb (Franchini et al. 2010), which equals roughly 1,398 cM, comparable with the currently calculated 1,312 cM.
Functional Annotation of Markers
This study used numerous markers that demonstrate associations to genes and/or transposable elements (indicated on Fig. 1). Such functional molecular markers are of particular importance in highlighting genomic regions that may contribute to phenotypic variation and may also elucidate structural and functional characteristics of the Haliotis midae genome. Although definitive conclusions cannot yet be drawn, the current data suggest a relatively even distribution of genes across the genome, with some gene clusters on, for example, LG_1, LG_4, LG_5, LG_8, LG_13, and LG_16 (Fig. 1). This information could also be used to correlate physical maps with genetic maps, and possibly enable the pairing of linkage groups with chromosomes if the chromosome positions of genes are known. A further point of interest could be marker H.rub13F06 (on linkage group 4), a transferred marker from the sister taxon, Haliotis rubra (Australia's blacklip abalone), which is also a gene-associated marker. This chromosomal segment may represent a region of synteny between the H. midae and H. rubra genomes, and warrants further investigation. Markers that show significant hits with known genes are shown in Table 7. There are numerous markers that displayed significant hits against genes involved in muscle formation. These genes include both the myosin heavy and light chain, and the phosphoglycerate mutase 2 (pgam2) gene. These markers could be of interest when looking for regions that could improve growth rates in abalone. When investigating growth rate traits, it would also be interesting to consider markers that associated with genes that could influence metabolic rate, such as the cellulase gene, hexokinase, and the beta 1,3-glucan binding protein. In addition, a marker that could be involved with cell growth (HMC1449_847) could also play a role in growth rate. On linkage groups INT_LG_2, -5, and -8, there are markers that could be targets for reproduction studies, because these markers showed significant association with fertilization protein and sperm lysin precursor genes. Another important trait of interest for abalone aquaculture is disease resistance. Genes that are involved in cell signaling and apoptosis regulation could be potential targets for such investigations.
Transposable elements are ubiquitous genomic features in most eukaryotic genomes (Biemont & Vieira 2005, Bennetzen 2000). In Haliotis midae, Rhode and Roodt-Wilding (2011) illustrated that approximately 21% of microsatellite loci showed similarity to known transposable elements, and recent transcriptomic work demonstrated that these transposable elements are active transcriptionally (unpublished data, Rhode et al. in prep.), which suggests that transposable elements may play a significant role in the genome dynamics of abalone. Microsatellites that showed significant hits with known transposable elements are distributed on various linkage groups (INT_LG_1, -3, -6, -8, -9, -10, -12, -14, -18), illustrating the widespread distribution of mobile elements in the H. midae genome. Interestingly, a significant number of loci that demonstrate association to genic regions also show association to transposable elements (Table 8). This is, however, not uncommon with various DNA transposons and LTR retrotransposons locating in both promoter and coding regions of genes. Such transposable elements have also been found to influence gene function by creating or abolishing regulatory motifs (Bennetzen 2000, Feschotte & Pritham 2007). These loci may prove to be interesting candidate loci for future genotypephenotype association- and gene expression studies. The presence of transposable elements--in particular, the association to commonly used molecular markers like microsatellites--may pose some technical challenges for linkage mapping, because transposable elements often facilitate unequal recombination (Lira & Simmons 1994). An example in point is INT_LG_18, which contains 5 markers and has shown significant hits with transposons, and could explain why this linkage group is divided across 4 groups. It is possible that these markers could be part of mobile elements and therefore link to various groups of markers, thus representing duplicated genomic regions. Subsequently, this could result in groups of markers being grouped together that have sufficient linkage with the transposon-associated markers but have insufficient linkage to the other markers in that linkage group, causing conflict in map construction.
Identification of Anchor Loci for Future Studies
The current study used numerous mapping families to construct the integrated linkage map for Haliotis midae and, consequently, provides a measure of marker informativeness across families. Markers that were mapped in 4 or more families have been identified that could be useful for future linkage or QTL mapping studies (Table 9, indicated on Fig. 1). These markers could function as anchor loci, enabling the integration of additional markers within the current linkage map. These markers could also be of use for studies in closely related species by testing the transferability of markers and, consequently, screening these markers in mapping families of those species. The absence of anchor loci on INT_LG_18 could explain further why this linkage group failed to generate 1 map.
Linkage maps remain an invaluable tool for genomic annotation, particularly in nonmodel organisms with genomic sequences that are still unknown. The linkage map constructed in this study is the first reported for Haliotis midae and is the first haliotid map to include SNP markers, providing improved genome coverage. This map could also serve as framework for future QTL analyses, especially considering the number of gene-linked markers used that provides candidate regions of interest for traits of economic importance.
We acknowledge Dr. Ruhan Slabbert and Liana Swart for their technical assistance with the genotyping of markers. We thank the following abalone farms for their involvement and funding: Abagold (Pty) Ltd., Aquafarm Development (Pty) Ltd., HIK Abalone Farm (Pty) Ltd., Irvine and Johnston (I&J) Cape Cultured Abalone (Pty) Ltd., and Roman Bay Sea Farm (Pty) Ltd. This study was also funded by the Innovation Fund (Technology Innovation Agency, South Africa). Stellenbosch University is thanked for facilities provided.
Allsopp, M., F. Lafarga-De la Cruz, R. Flores-Aguilar & E. Watts. 2011. Abalone culture. In: R. Fotedar & B. Phillips, editors. Recent advances and new species in aquaculture. Oxford, UK: Blackwell Publishing. pp. 23l-251.
Altschul, S. F., T. L. Madden, A. A. Schaffer, J. Zhang, Z. Zhang, W. Miller & D. J. Lipman. 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl. Acids Res. 25:3389-3402.
Ball, A. D., J. Stapley, D. A. Dawson, T. R. Birkhead, T. Burke & J. Slate. 2010. A comparison of SNPs and microsatellites as linkage mapping markers: lessons from the zebra finch (Taeniopygia guttata). BMC Genomics 11:218.
Baranski, M., S. Loughnan, C. M. Austin & N. Robinson. 2006. A microsatellite linkage map of the blacklip abalone Haliotis rubra. Anim. Genet. 37:563-570.
Bennetzen, J. F. 2000. Transposable element contributions to plant gene and genome evolution. Plant Mol. Biol. 42:151-169.
Bester, A. E., R. Roodt-Wilding & H. A. Whitaker. 2008. Discovery and evaluation of single nucleotide polymorphisms (SNPs) for Haliotis midae: a targeted EST approach. Anita. Genet. 39:321-324.
Bester, A. E., R. Slabbert & M. E. D'Amato. 2004. Isolation and characterisation of microsatellite markers in the South African abalone (Haliotis midae). Mol. Ecol. Notes 4:618-619.
Beuzen, N. D., M. J. Stear & K. C. Chang. 2000. Molecular markers and their use in animal breeding. Vet. J. 160:45-52.
Biemont, C. & C. Vieira. 2005. What transposable elements tell us about genome organization and evolution: the case of Drosophila. Cytogenet. Genome Res. 110:25-34.
Blaauw, S. 2012. SNP screening and validation in Haliotis midae. Unpublished MS thesis, Stellenbosch University.
Cervera, M., V. Storme, B. Ivens, J. Gusmao, B. H. Liu, V. Hostyn, J. van Slycken, M. van Montagu & W. Boerjan. 2001. Dense genetic linkage maps of three Populus species (Populus deltoides, P. nigra and P. trichocarpa) based on AFLP and microsatellite markers. Genetics 158:787-809.
Chakravarti, A., L. K. Lasher & J. E. Reefer. 1991. A maximum likelihood method for estimating genome length using genetic linkage data. Genetics 128:175-182.
Charlesworth, B. & D. Charlesworth. 1998. Some evolutionary consequences of deleterious mutations. Genetica 102:3-19.
Chistiakov, D. A., B. Hellemans, C. S. Haley, A. S. Law, C. S. Tsigenopoulos, G. Kotoulas, D. Bertotto, A. Libertini & F. A. M. Volckaert. 2005. A microsatellite linkage map of the European sea bass Dicentrarchus labrax L. Genetics 170:1821-1826.
Coimbra, M. R. M., K. Kobayashi, S. Koretsugu, O. Hasegawa, E. Ohara, A. Ozaki, T. Sakamoto, K. Naruse & N. Okamoto. 2003. A genetic linkage map of the Japanese flounder, Paralichthys olivaceus. Aquaculture 220:203-218.
Department of Agriculture, Forestry and Fisheries. 2011. South Africa's aquaculture annual report. Department of Agriculture, Forestry and Fisheries. Republic of South Africa. pp 9-11.
Du Plessis, J. 2012. High-throughput SNP genotyping and linkage mapping in Haliotis midae. Unpublished MS thesis, Stellenbosch University.
Ewen, K. R., M. Bahlo, S. A. Treloar, D. F. Levinson, B. Mowry, J. W. Barlow & S. J. Foote. 2000. Identification and analysis of error types in high-throughput genotyping. Am. J. Hum. Genet. 67:727-736.
Farber, C. R. & J. F. Medrano. 2003. Putative in silico mapping of DNA sequences to livestock genome maps using SSLP flanking sequences. Anim. Genet. 34:11-18.
Farber, C. R. & J. F. Medrano. 2004. Identification of putative homology between horse microsatellite flanking sequences and cross-species ESTs, mRNAs and genomic sequences. Anim. Genet. 35:28-33.
Feschotte, C. & E. J. Pritham. 2007. DNA transposons and the evolution of eukaryotic genomes. Annu. Rev. Genet. 41:331-368.
Fishman, L., A. J. Kelly, E. Morgan & J. H. Willis. 2001. A genetic map in the Mimulus guttatus species complex reveals transmission ratio distortion due to heterospecific interactions. Genetics 159:1701-1716.
Food and Agriculture Organization of the United Nations (FAO). 2010. The State of World Fisheries and Aquaculture 2010 (SOFIA). FAO Fisheries and Aquaculture Department. Food and Agriculture Organisation of the United Nations. pp 40-41.
Franchini, P., R. Slabbert, M. van der Merwe, A. Roux & R. Roodt-Wilding. 2010. Karyotype and genome size estimation of Haliotis midae: estimators to assist future studies on the evolutionary history of Haliotidae. J. Shellfish Res. 29:945-950.
Franchini, P., M. van der Merwe & R. Roodt-Wilding. 2011. Transcriptome characterization of the South African abalone Haliotis midae using sequencing-by-synthesis. BMC Res. Notes 4:59.
Hara, M. & S. Kikuchi. 1992. Increasing the growth rate of abalone, Haliotis discus hannai, using selection techniques. NOAA Techn. Rep. NMFS 106:21-26.
Hauck, M. & N. A. Sweijd. 1999. A case study of abalone poaching in South Africa and its impact on fisheries management. ICES J. Mar. Sci. 56:1024-1032.
Hauser, L. & J. E. Seeb. 2008. Advances in molecular technology and their impact on fisheries genetics. Fish Fish. 9:473-486. Hayes, B., M. Baranski, M. E. Goddard & N. Robinson. 2007.
Optimisation of marker assisted selection for abalone breeding programs. Aquaculture 265:61-69.
Hepple, J.- A. 2010. An integrated linkage map of Perlemoen (Haliotis midae). Unpublished MS thesis. Stellenbosch University.
Hubert, S. & D. Hedgecock. 2004. Linkage maps of microsatellite DNA markers for the Pacific oyster Crassostrea gigas. Genetics 168:351-362.
Jansen, S. 2012. Linkage mapping in Haliotis midae using gene-linked markers. Unpublished MS thesis, Stellenbosch University.
Jonasson, J., S. E. Stefansson, A. Gudnason & A. Steinarsson. 1997. Genetic variation for survival and body size during the first life stages of red abalone, Haliotis rufescens, in Iceland [abstract]. Presented at the Third International Abalone Symposium, Monterey, California, October 1997.
Jurka, J., V. V. Kapitonov, A. Pavlick, P. Klownowski, O. Kohany & J. Walichiewicz. 2005. Repbase update: a database of eukaryotic repetitive elements. Cytogenet. Genome Res. 110:462-467.
Kawahara, I., T. Noro, M. Omori, O. Hasekura & A. Kijima. 1997. Genetic progress for growth in different selected populations of abalone, Haliotis discus hannai, at different hatcheries. Fish Genet. Breed. Sci. 25:81-90.
Kennedy, G. C., H. Matsuzaki, S. Dong, W. Liu, J. Huang, G. Liu, X. Su, M. Cao, W. Chen, J. Zhang, W. Liu, G. Yang, X. Di, T. Ryder, Z. He, U. Surti, M. S. Phillips, M. T. Boyce-Jacino, S. P. A. Fodor & K. W. Jones. 2003. Large-scale genotyping of complex DNA. Nat. Biotechnol. 21:1233-1237.
Kocher, T. D., W. Lee, H. Sobolewska, D. Penman & B. McAndrew. 1998. A genetic linkage map of a cichlid fish, the tilapia (Oreochromis niloticus). Genetics 148:1225-1232.
Lim, J. K. & J. M. Simmons. 1994. Gross chromosome rearrangements mediated by transposable elements in Drosophila melanogaster. Bioessays 16:269-273.
Lindahl, K. F. 1991. His and hers recombinational hotspots. Trends Genet. 7:273-276.
Liu, Z., B. Liu, Y. Deng & N. He. 2011. The state of field of high-throughput SNP genotyping system. Conference publications of the International Symposium on Bioelectronics and Bioinformatics, 3-5 November 2011 in Suzhou, China. pp. 174-177.
Liu, X., X. Liu, X. Guo, Q. Gao, H. Zhao & G. Zhang. 2006. A preliminary genetic linkage map of the Pacific abalone Haliotis discus hannai Ino. Mar. Biotechnol. (NY) 8:386-397.
Liu, X., H. Zhang, H. Li, N. Li, Y. Zhang, Q. Zhang, S. Wang, Q. Wang & H. Wang. 2008. Fine-mapping quantitative trait loci for body weight and abdominal fat traits: effects of marker density and sample size. Poult. Sci. 87:1314-1319.
Massault, C., H. Bovenhuis, C. Haley & D. de Koning. 2008. QTL mapping designs for aquaculture. Aquaculture 285:23-29.
Plomion, C., D. M. O'Malley & C. E. Durel. 1995. Genomic analysis in maritime pine (Pinus pinaster): comparison of two RAPD maps using selfed and open-pollinated seeds of the same individual. Theor. Appl. Genet. 90:1028-1034.
Rhode, C. 2010 Development of gene-linked molecular markers in South African abalone (Haliotis midae) using an in silieo mining approach. Unpublished MS thesis, Stellenbosch University.
Rhode, C. & R. Roodt-Wilding. 2011. Bioinformatic survey of Haliotis midae microsatellites reveals a non-random distribution of repeat motifs. Biol. Bull. 221:147-154.
Rhode, C., R. Slabbert & R. Roodt-Wilding. 2008. Microsatellite flanking regions: a SNP mine in South African abalone (Haliotis midae). Anim. Genet. 39:329.
Saghai-Maroof, M. A., K. M. Soliman, R. A. Jorgens & R. W. Allard. 1984. Ribosomal DNA spacer-length polymorphisms in barley: Mendelian inheritance, chromosomal location, and population dynamics. Proc. Natl. Acad. Sci. U S A 81:8014-8018.
Sales, J. & P. J. Britz. 2001. Research on abalone (Haliotis midae L.) cultivation in South Africa. Aquacult. Res. 32:863-874.
Sekino, M. & M. Hara. 2007. Linkage maps for the Pacific abalone (genus Haliotis) based on microsateltite DNA markers. Genetics 175:945-958.
Sekino, M., T. Kobayashi & M. Hara. 2006. Segregation and linkage analysis of 75 novel microsatellite DNA markers in pair crosses of Japanese abalone (Haliotis discus hannai) using the 5'-tailed primer method. Mar. Biotechnol. (NY) 8:453-466.
Shi, Y., X. Guo, G. U. Zhifeng, A. Wang & Y. Wang. 2010. Preliminary genetic linkage map of the abalone Haliotis diversicolor Reeve. Chin. J. Oceanol. Limnol. 28:549-557.
Singer, A., H. Perlman, Y. Yan, C. Walker, G. Corley-Smith, B. Brandhorst & J. Postlethwait. 2002. Sex-specific recombination rates in zebrafish (Danio rerio). Genetics 160:649-657.
Slabbert, R., J.- A. Hepple, A. Venter, S. Nel, L. Swart, N. C. van den Berg & R. Roodt-Wilding. 2010. Isolation and segregation of 44 microsatellite loci in the South African abalone Haliotis midae L. Anim. Genet. 41:332-333.
Slabbert, R., N. R. Ruivo, N. C. van den Berg, D. L. Lizamore & R. Roodt-Wilding. 2008. Isolation and characterisation of 63 microsatellite loci for the abalone, Haliotis midae. J. World Aquacult. Soc. 39:429-435.
Slabbert, R., J-A. Hepple, C. Rhode, A. E. Bester-Van der Merwe & R. Roodt-Wilding. 2012. New microsatellite markers for the abalone Haliotis midae developed by 454 pyrosequencing and in silico analyses. Genet. Mol. Res. 11:2769-2779.
Tarr, R. J. Q., P. V. G. Williams & A. J. Mackenzie. 1996. Abalone, sea urchins and rock lobster: a possible ecological shift that may affect traditional fisheries. S. Afr. J. Mar. Sci. 17:319-323.
Van der Merwe, M. & R. Roodt-Wilding. 2008. Chromosome number of the South African abalone Haliotis midae. Aft. J. Mar. Sci. 30:195-198.
Van Ooijen, J. W. 2011. Multipoint maximum likelihood mapping in a full-sib family of an outbreeding species. Genet. Res. 93:343-349.
Weber, J. L. & C. Wong. 1993. Mutation of human short tandem repeats. Hum. Mol. Genet. 2:1123-1128.
Yu, Z. & X. Guo. 2003. Genetic linkage map of the Eastern oyster Crassostrea virginica Gmelin. Biol. Bull. 204:327-338.
Zhan, X., F. Fan, W. You, J. Yu & C. Ke. 2011. Construction of an integrated map of Haliotis diversicolor using microsatellite markers. Mar. Biotechnol. (NY) 14:79-86.
JESSICA VERVALLE, JULI-ANN HEPPLE, SUZAAN JANSEN, JANA DU PLESSIS, PEIZHENG WANG, CLINT RHODE AND ROUVAY ROODT-WILDING *
Molecular Aquatic Research Group, Department of Genetics, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa
* Corresponding author. E-mail: firstname.lastname@example.org
TABLE 1. Summary of the origin and number of offspring for the mapping families. Family Female Male Location Offspring parent parent (n) 7B F462 M342 Roman Bay 101 42A F64 M18 HIK 105 DS1 F617 M342 Roman Bay 103 DS2 F462 M456 Roman Bay 104 DS5 88 W06 HIK 93 DS6 D06 V06 HIK 94 FamD A22 B21 Abagold 72 FamH A35 B43 Abagold 71 FamI A17 B25 Abagold 81 FamJ A24 B28 Abagold 72 TABLE 2. Microsatellite markers found to be informative in the mapping families but have not been published previously. Marker name Repeat sequence Primer Sequence (5'-3') HmNS17bT [(CACT).sub.31] F: AGTAAAGTGGCCGGCAATCGG R: GAAAGAATTCCACTGGCCAC HmidPS1.792 [(CA).sub.n] F: TCAATGTAACATGCACACACA R: AGTTTGAGCCTTGTCCTGAAT Marker name [T.sub.A] Accession no. HmNS17bT 65 EF367116 HmidPS1.792 63 JX853745 TABLE 3. Summary of information regarding microsatellite and SNP markers used in this study. Mapping families 7B 42A DS1 DS2 DS5 Microsatellites Total no. microsatellites screened 216 216 274 274 274 Null alleles 4 7 13 16 11 Duplicated 10 9 21 14 5 Noninformative 106 108 151 163 191 Informative markers 96 92 89 81 67 Mapped markers 86 75 76 67 57 SNPs Total no. SNPs screened -- -- 234 196 48 Failed to genotype -- -- 49 50 7 Monomorphic -- -- 95 56 26 Homozygous parents -- -- 14 15 5 Informative markers -- -- 76 75 10 Mapped markers -- -- 63 66 3 Mapping families DS6 FamD FamH Faml FamJ Microsatellites Total no. microsatellites screened 274 -- -- -- -- Null alleles 10 -- -- -- -- Duplicated 6 -- -- -- -- Noninformative 198 -- -- -- -- Informative markers 60 -- -- -- -- Mapped markers 52 -- -- -- -- SNPs Total no. SNPs screened 48 196 196 196 196 Failed to genotype 7 48 49 49 49 Monomorphic 30 93 86 92 89 Homozygous parents -- 6 9 9 11 Informative markers 11 49 52 46 47 Mapped markers 8 31 25 30 25 TABLE 4. Summary of integrated sex-average linkage map for H. midae. Linkage Length Loci Average spacing Largest group (cM) (n) (CM) interval 1 87.1 15 6.22 28.93 2 86.5 10 9.61 40.79 3 67.5 5 16.88 24.91 4 64.7 10 7.19 21.44 5 64.3 19 3.57 15.14 6 64.0 12 5.82 18.96 7 62.5 9 7.81 16.25 8 62.0 20 3.26 11.98 9 58.7 8 8.39 12.13 10 51.0 10 5.67 9.86 11 50.9 6 10.18 27.84 12 46.4 6 9.28 17.09 13 43.2 13 3.60 10.31 14 42.6 7 7.10 10.75 15 33.6 7 5.60 10.20 16 29.3 10 3.26 10.60 17 15.2 3 7.60 11.93 18A 1.3 4 0.43 0.64 18B 64 6 12.80 25.54 18C 36.8 6 7.36 20.28 18D 5.9 3 2.95 3.57 Total 1,038 189 6.88 40.79 TABLE 5. Summary of integrated sex-specific linkage maps for Haliotis midge. Female map Average Largest Linkage group Length (cM) Loci (n) spacing (cM) interval 1 25.1 9 3.14 13.56 2 86.8 11 8.68 28.91 3 28.8 3 14.40 22.92 4 Eq (1) 24 7 8.17 14.43 Eq (2) 57 4 8.00 10.47 5 57 13 4.75 11.17 6 30.8 7 5.13 11.51 7 54.8 5 13.70 28.35 8 57.5 21 2.88 10.75 9 Eq (1) 35.6 3 17.80 34.98 Eq (2) 30.4 3 15.20 22.56 10 43.8 7 1.17 15.50 11 50.9 6 10.18 29.17 12 18.9 4 6.30 14.60 13 42.5 13 3.54 11.86 14 35.9 6 7.18 10.56 15 -- -- -- -- 16 Eq (1) 7.6 3 3.80 4.85 Eq (2) 27.1 5 6.78 25.24 17 6.8 3 3.40 4.66 18A -- -- -- -- 18B 39.3 5 9.83 24.39 18C -- -- -- -- 18D 4.4 4 1.47 1.88 Male map Length Average Largest Linkage group (cM) Loci (n) spacing (cM) interval 1 28.3 12 2.57 8.73 2 8.7 4 2.90 4.09 3 49 4 16.33 28.99 4 33.1 5 8.28 23.53 5 60.3 16 4.02 9.04 6 39.6 8 5.66 17.18 7 20.4 5 5.10 8.19 8 33.8 11 3.38 25.99 9 50.7 6 10.14 23.98 10 54 9 6.75 18.41 11 -- -- -- -- 12 45.8 5 11.45 20.78 13 35.7 16 2.38 9.93 14 44.7 6 8.94 17.52 15 18.6 6 3.72 7.26 16 25.4 6 5.08 10.06 17 13.7 3 6.85 10.74 18A 2.9 3 1.45 2.84 18B 46.8 7 7.80 15.06 18C 38.4 5 9.60 20.25 18D -- -- -- -- TABLE 6. Comparison of the current study with other abalone linkage mapping studies. Species Markers Haliotis diversicolor 412 AFLP markers H. diversicolor 182 Microsatellite markers Haliotis rubra 122 Microsatellite markers 365 AFLP markers Haliotis discus hannai 10 RAPD markers 9 Microsatellite markers H. discus hannai 180 Microsatellite markers Linkage Species Segregation families groups Haliotis diversicolor 1 family (76 offspring) 16-18 H. diversicolor 1 family (96 offspring) 16 Haliotis rubra 1 family (95 offspring) 17-20 Haliotis discus hannai 1 family (106 offspring) 19-22 H. discus hannai 3 families (60-96 offspring) 18-19 Average marker Mapped Species interval (cM) markers Haliotis diversicolor [female] 25.7 [female] 90 [male] 25.0 [male] 94 H. diversicolor 4.6 (integrated) 175 (integrated) Haliotis rubra [female] 9.8 [female] 98 [male] 7.3 [male] 102 [female] 18.3 [female] 119 Haliotis discus hannai [male] 318.2 [male] 147 H. discus hannai [female] 6.3 [female] 160 [male] 4.7 [male] 167 Expected map Species length (cM) Map coverage Haliotis diversicolor [female] 2,773.0 [female] 67.6% [male] 2,817.1 [male] 67.3% H. diversicolor 943.8 (integrated) [male] 80.7% (integrated) Haliotis rubra [female] 1,586.2 [female] 64% [male] 940.5 [male] 80% [female] 2,584.4 [female] 68.6% Haliotis discus hannai [male] 2,054.8 [male] 66.5% H. discus hannai [female] 1,156.7 [female] 76.6% [male] 899.1 [male] 78.4% Species Reference Haliotis diversicolor Shi et al. (2010) H. diversicolor Zhan et al. (2011) Haliotis rubra Baranski et al. (2006) Haliotis discus hannai Liu et al. (2006) H. discus hannai Sekino and Hara (2007) TABLE 7. BLAST alignments of markers that mapped to the integrated Haliotis midae linkage map. Linkage Marker name group BLAST hit HmC2040_1251 1 Collagen pro alpha-chain HmC300_1828 1 Myosin light-chain kinase HmC300_4738 1 Myosin light-chain kinase HmC300_4982 1 Myosin light-chain kinase HmC300_6993 1 Myosin light-chain kinase HmC5634_234 1 Hypothetical protein HmC1813_300 2 B-cell translocation gene 1 HmC4600_1745 2 Sec61 alpha 1 subunit Hmid12015 2 Sperm lysin precursor HmidILL1.140027 2 RAP1B, member of RAS oncogene family HmidILL2.76149 2 S-adenosylhomocysteine hydrolaselike 1 HmidILL2.8738 2 B-cell translocation gene 1 H.rub13F06 4 Neuron navigator 2 HmC1630_199 4 Cytosolic malate dehydrogenase HmC2122_257 4 Ependymin-related protein-1 precursor HmC387_215 4 Myoglobin HmC387_582 4 Myoglobin HmC5433_233 4 Mitochondrial cytochrome c oxidase subunit HmLCS67 4 Cysteine aspartic acid-specific protease HmRS38 4 NAC-like protein 13 HmC1462_825 5 Elongation factor 2 HmC20682_843 5 Hypothetical protein HmC22491-595 5 Translation elongation factor 2 HmC22491_727 5 Elongation factor 2 HmC911-1343 5 Mitochondrial H + ATPase a subunit HmC911_290 5 Mitochondrial H + ATPase a subunit HmidILL1.2192 5 Tyrosine 3-monooxygenase/ tryptophan 5-monooxygenase activation protein HmidILL1.47613 5 Transmembrane emp24 protein transport domain containing 7 (tmed7) HmidPS1.374 5 Sperm lysin precursor HmNR281 5 Sperm lysin precursor HmC2028_1228 6 Hypothetical protein HmC2028_1328 6 Hypothetical protein HmC2903_1043 6 Ribosomal protein s17 HmC2406_641 7 Ribosomal protein 123a HmC31_1387 7 Tyrosine 3-monooxygenase tryptophan 5-monooxygenase activation epsilon polypeptide HmC31_1488 7 Tyrosine 3-monooxygenase tryptophan 5-monooxygenase activation epsilon polypeptide HmSNP1001_388 7 Myosin heavy chain HmC1363_269 8 Na(+)/H(+) exchange regulatory cofactor NHE-RF2-like with PDZ domain HmC394_1510 8 Heat shock protein 20 HmC428_2186 8 Myosin light-chain kinase HmC428_225 8 Myosin light-chain kinase HmidILL1.72605 8 Translocase of outer mitochondrial membrane 7 homolog HmidILL2.71359 8 Ubiquitin-specific peptidase 50 HmSSRex446a 8 Cellulase gene HmSSRex489a 8 Fertilization protein HmSSRex489b 8 Fertilization protein H.rub15AO1 9 HoxBa gene cluster HmidILL1.46687 9 Calmodulin 3 HmC20267_102 10 Polcalcin bra r HmC2558_743 10 Predicted protein: Notch homolog, scalloped wings HmC327_1076 10 Beta 1,3-glucan binding protein HmC1002_85 11 Eukaryotic translation initiation factor 1 HmC1254_187 11 Beta-tubulin HmC1254_529 11 Beta-tubulin HmC6012_280 11 Mitochondrial ATP synthase subunit 9 HmSNP149.1_374 11 Heat shock protein (hsp71 gene) HmC4181_893 12 Hypothetical protein HmNR20 12 SH2 domain-containing protein 3C HmC1384_655 13 Voltage-dependent anion channel 2 HmC1384_793 13 Voltage-dependent anion channel 2 HmC14033_777 13 Myosin heavy chain HmC2141_504 13 Mitochondrial ATP synthase HmC22568_597 13 60S Ribosomal protein L3 HmC5339_366 13 Myosin heavy chain HmC618_116 13 Methionine adenosyllransferase HmC853_1199 13 Myosin heavy chain HmSNP1949_235 13 Putative 60S ribosomal protein L3 (RPL3) HmSNP449.2_110 13 Cytidine deaminase HmC4778_234 14 Fasciclin domain-containing protein HmSNP4691_183 14 Heat shock protein 70 Hm3Dl0_1 15 Hemocyaninlike HmC4791_1099 15 Hypothetical protein HmC1449_847 16 Insulinlike growth factor binding protein 7 HmC18774_676 16 3-Hydroxy-3-methylglutaryl-CoA HmC22449_261 16 Protein disulfide isomerase HmC3835_411 16 Sorbitol dehydrogenase HmC45_3002 16 Hillarin HmC6061_1289 16 Phosphoglycerate mutase 2 HmC929_2563 17 Hexokinase Hm3B4_2 18A Ribosomal protein 1 Hm3B4_7 18A Ribosomal protein l HmC253_1545 18C PDZ and LIM domain protein Zasp Hmid2044 18C Hemocyanin Isoform 1 HmidILL2.66010a 18D MGC97814 protein Genbank Marker name Gene name/function accession no. HmC2040_1251 Synthesis of connective BAA75669 tissue HmC300_1828 Muscle formation XP_003214935 HmC300_4738 Muscle formation XP_003214935 HmC300_4982 Muscle formation XP_003214935 HmC300_6993 Muscle formation XP_003214935 HmC5634_234 Unknown ACP18834 HmC1813_300 Cell cycle ACH92125 regulation HmC4600_1745 Protein transport NP_001119639 Hmid12015 Fertilization FJ940473 HmidILL1.140027 Cell signaling AY423018 HmidILL2.76149 Hydrolysis of CAN87933 S-adenosylhomocysteine HmidILL2.8738 Cell cycle ACH92125 regulation H.rub13F06 Neural development NG_030347 HmC1630_199 Enzyme modification ACJ64673 HmC2122_257 Role in memory EF103395 function HmC387_215 Oxygen transport Q01966.2 HmC387_582 Oxygen transport Q01966.2 HmC5433_233 Electron transport XP_685495 HmLCS67 Apoptosis regulation HQ529712 HmRS38 Transcription factor EF535584 HmC1462_825 Cell signaling NP_492457 HmC20682_843 Unknown XP_001625221 HmC22491-595 Peptide synthesis ABX75376 HmC22491_727 Peptide synthesis ABX75376 HmC911-1343 Energy metabolism DQ986328 HmC911_290 Energy metabolism DQ986328 HmidILL1.2192 Signal transduction DQ437106 HmidILL1.47613 Transmembrane transport NM_203930 HmidPS1.374 Fertilization FJ940391 HmNR281 Fertilization FJ940473 HmC2028_1228 Unknown XP_002154876 HmC2028_1328 Unknown XP_002154876 HmC2903_1043 Protein synthesis AF548334 HmC2406_641 Protein synthesis EU302546 HmC31_1387 Signal transduction NP_001080705 HmC31_1488 Signal transduction NP_001080705 HmSNP1001_388 Muscle formation AF134172 HmC1363_269 Ion transport/cell signaling XP_003198121 HmC394_1510 Protein folding regulation AET13647 HmC428_2186 Muscle formation XP_003425477 HmC428_225 Muscle formation XP_003425477 HmidILL1.72605 Mitochondria NM_001113306 transmembrane transport HmidILL2.71359 Protein modification BC102596 HmSSRex446a Cellulose metabolism AB125892 HmSSRex489a Fertilization AF076827 HmSSRex489b Fertilization AF076827 H.rub15AO1 Morphogenesis DQ481665 HmidILL1.46687 Cell signaling NM_001132483 HmC20267_102 Cellular transport and XP_001449509 signaling HmC2558_743 Cell signaling XP_782555 HmC327_1076 Carbohydrate metabolism EF103355 HmC1002_85 Gene expression EU244348 HmC1254_187 Maintain cell structure BAG55008 HmC1254_529 Maintain cell structure BAG55008 HmC6012_280 Energy metabolism HQ729933 HmSNP149.1_374 Protein folding regulation AM283516 HmC4181_893 Unknown XP_001625221 HmNR20 Cell signaling XP_687225.2 HmC1384_655 Mitochondrial ion transport AD156517 HmC1384_793 Mitochondria ion transport AD156517 HmC14033_777 Muscle formation CAB64664 HmC2141_504 Energy metabolism EF103399 HmC22568_597 Protein synthesis JN997411 HmC5339_366 Muscle formation AAB03660 HmC618_116 Energy metabolism AAZ30689 HmC853_1199 Muscle formation U09782 HmSNP1949_235 Protein synthesis JN997411 HmSNP449.2_110 Deamination of cytidine EU101721 HmC4778_234 Cell adhesion GQ903764 HmSNP4691_183 Protein folding ADC32121 regulation Hm3Dl0_1 Oxygen transport EU135917 HmC4791_1099 Unknown EFX83250 HmC1449_847 Cell growth JF501214 HmC18774_676 Leucine metabolism NM_001126700 HmC22449_261 Protein folding AB026667 regulation HmC3835_411 Carbohydrate metabolism XP_413719 HmC45_3002 Cell division AF339450 HmC6061_1289 Muscle formation ABZ82035 HmC929_2563 Glucose metabolism AM076954 Hm3B4_2 Protein synthesis EF103429 Hm3B4_7 Protein synthesis EF103429 HmC253_1545 Cellular phosphate GAA56670 regulation Hmid2044 Oxygen transport GQ352369 HmidILL2.66010a Paracellular barrier BC090128 Marker name Organism e Value HmC2040_1251 Haliotis discus 1.00E-58 HmC300_1828 Anolis carolinensis 3.00E-166 HmC300_4738 Anolis carolinensis 3.00E-166 HmC300_4982 Anolis carolinensis 3.00E-166 HmC300_6993 Anolis carolinensis 3.00E-166 HmC5634_234 Chrrsontela trenuda 4.30E-19 HmC1813_300 Crasso,strea gigas 1.OOE-45 HmC4600_1745 Acvrthosiphon pisum 0 Hmid12015 Haliotis corrugate 4.00E-58 HmidILL1.140027 Danio rerio 3.00E-86 HmidILL2.76149 Dania rerio 5.00E-76 HmidILL2.8738 Crasso.strea gigas 2.00E-45 H.rub13F06 Homo sapiens 2.00E-13 HmC1630_199 Lottia pelta 3.66E-151 HmC2122_257 Haliotis discus discus 0 HmC387_215 Haliotis diversicolor 0 HmC387_582 Haliotis diversicolor 0 HmC5433_233 Danio rerio 1.28E-04 HmLCS67 Haliotis diversicolor 2.00E-23 HmRS38 Crocus sativus 2.00E-05 HmC1462_825 Caenorhabditis elegans 0 HmC20682_843 Nematostella vectensis 4.25E-33 HmC22491-595 Lycosa singoriensis 0 HmC22491_727 Lycosa singoriensis 0 HmC911-1343 Pinctada fucata 0 HmC911_290 Pinetada fucata 0 HmidILL1.2192 Bufo gargarizans 6.00E-44 HmidILL1.47613 Xenopus (Silurana) 2.00E-25 tropicalis HmidPS1.374 Haliotis discus 3.00E-08 HmNR281 Haliotis corrugate 6.00E-50 HmC2028_1228 Hydra magnipapillata 9.00E-05 HmC2028_1328 Hydra magnipapillata 9.00E-05 HmC2903_1043 Branchiostoma belcheri 1.00E-24 HmC2406_641 Lineus viridis 2.00E-73 HmC31_1387 Xenopus laevis 8.00E-135 HmC31_1488 Xenopus laeviss 8.00E-135 HmSNP1001_388 Pecten maximus 2.00E-26 HmC1363_269 Danio rerio 1.00E-47 HmC394_1510 Cyclina sinensis 7.00E-39 HmC428_2186 Nasonia vitripennis 2.76E-32 HmC428_225 Nasonia vitripennis 2.76E-32 HmidILL1.72605 Bos taurus 8.00E-30 HmidILL2.71359 Bos taurus 2.00E-38 HmSSRex446a Haliotis discus hannai 6.00E-39 HmSSRex489a Haliotis rufescens 8.00E-08 HmSSRex489b Haliotis rufescens 8.00E-08 H.rub15AO1 Takifugu rubripes 1.00E-16 HmidILL1.46687 Pongo abelii 2.00E-30 HmC20267_102 Paramecium tetraurelia 2.49E-05 strain d4-2 HmC2558_743 Strongylocentrotus 0.00E+00 purpuratus HmC327_1076 Haliotis discus discus 0 HmC1002_85 Haliotis diversicolor 0 HmC1254_187 Saccostrea kegaki 0 HmC1254_529 Saccostrea kegaki 0 HmC6012_280 Glycera tridactyla 8.00E-69 HmSNP149.1_374 Haliotis tuberculata 1.00E-65 HmC4181_893 Nematostella vectensis 9.59E-32 HmNR20 Danio rerio 3.00E-05 HmC1384_655 Haliotis diversicolor 0 HmC1384_793 Haliotis diversicolor 0 HmC14033_777 Mytilus galloprovincialis 2.23E-149 HmC2141_504 Haliotis discus discus 0 HmC22568_597 Haliotis diversicolor 0 HmC5339_366 Placopecten magellanicus 1.79E-122 HmC618_116 Haliotis rufescens 0 HmC853_1199 Argopecten irradians 4.00E-169 HmSNP1949_235 Haliotis diversicolor 1.00E-73 HmSNP449.2_110 Haliotis diversicolor 6.00E-27 HmC4778_234 Haliotis discus discus 0 HmSNP4691_183 Columba livia 4.00E-61 Hm3Dl0_1 Haliotismidae 2.00E-155 HmC4791_1099 Daphnia pulex 2.530-65 HmC1449_847 Haliotis diversicolor 1.00E-173 HmC18774_676 Xenopus (Silurana) 8.00E-69 tropicalis HmC22449_261 Haliotis discus discus 0 HmC3835_411 Gallus gallus 3.00E-114 HmC45_3002 Hirudo medicinales 7.00E-22 HmC6061_1289 Clonorchis sinensis 6.18E-115 HmC929_2563 Crassostrea gigas 9.48E-91 Hm3B4_2 Haliotis discus discus 6.00E-160 Hm3B4_7 Haliotis discus discus 6.00E-160 HmC253_1545 Clonorchis sinensis 2.00E-29 Hmid2044 Haliotis diversicolor 3.00E-29 HmidILL2.66010a Xenopus tropicales 2.00E-55 Marker name Similarity (%) HmC2040_1251 92 HmC300_1828 32 HmC300_4738 32 HmC300_4982 32 HmC300_6993 32 HmC5634_234 44 HmC1813_300 81 HmC4600_1745 94 Hmid12015 85 HmidILL1.140027 84 HmidILL2.76149 71 HmidILL2.8738 45 H.rub13F06 96 HmC1630_199 83 HmC2122_257 87 HmC387_215 96 HmC387_582 96 HmC5433_233 55 HmLCS67 88 HmRS38 85 HmC1462_825 83 HmC20682_843 66 HmC22491-595 88 HmC22491_727 88 HmC911-1343 79 HmC911_290 79 HmidILL1.2192 72 HmidILL1.47613 96 HmidPS1.374 87 HmNR281 84 HmC2028_1228 34 HmC2028_1328 34 HmC2903_1043 88 HmC2406_641 76 HmC31_1387 84 HmC31_1488 84 HmSNP1001_388 83 HmC1363_269 52 HmC394_1510 64 HmC428_2186 57 HmC428_225 57 HmidILL1.72605 95 HmidILL2.71359 93 HmSSRex446a 84 HmSSRex489a 79 HmSSRex489b 79 H.rub15AO1 89 HmidILL1.46687 94 HmC20267_102 51 HmC2558_743 89 HmC327_1076 83 HmC1002_85 91 HmC1254_187 100 HmC1254_529 100 HmC6012_280 83 HmSNP149.1_374 100 HmC4181_893 66 HmNR20 50 HmC1384_655 98 HmC1384_793 98 HmC14033_777 61 HmC2141_504 91 HmC22568_597 96 HmC5339_366 85 HmC618_116 97 HmC853_1199 75 HmSNP1949_235 97 HmSNP449.2_110 83 HmC4778_234 78 HmSNP4691_183 83 Hm3Dl0_1 100 HmC4791_1099 52 HmC1449_847 98 HmC18774_676 93 HmC22449_261 96 HmC3835_411 49 HmC45_3002 77 HmC6061_1289 67 HmC929_2563 67 Hm3B4_2 93 Hm3B4_7 93 HmC253_1545 39 Hmid2044 82 HmidILL2.66010a 97 TABLE 8. Mapped markers that produced significant hits with known transposable elements in the Repbase database. Marker Linkage group Class of mobile element HmC1878_506 1 LTR retrotransposon (Copia) HmC2040_1251 1 LTR retrotransposon (Gypsy) HmC300_1828 1 Endogenous retrovirus (ERV1) HmC300_4738 1 Endogenous retrovirus (ERV1) HmC300_4982 1 Endogenous retrovirus (ERV1) HmC300_6993 1 Endogenous retrovirus (ERVI) HmNS19L 1 DNA transposon (Helitron) HmC1813_300 2 Non-LTR retrotransposon R1 HmC460_1745 2 LTR retrotransposon (Copia) HmidILL1.140027 2 Non-LTR retrotransposon R4 HmidILL2.76149 2 Non-LTR retrotransposon HmidILL2.8738 2 Non-LTR retrotransposon RI HmC102_1408 3 DNA transposon (EnSpm) HmC1462_825 5 DNA transposon (Transib) HmC20682_843 5 LTR retrotransposon (Gypsy) HmC22491_595 5 DNA transposon (Transib) HmC22491_727 5 DNA transposon (Transib) HmC911_1343 5 LTR retrotransposon (Copia) HmC911_290 5 LTR retrotransposon (Copia) HmidILL1.2192 5 DNA transposon (EnSpm) HmidILL1.47613 5 LTR retrotransposon (Gypsy) HmidPS1.228 5 DNA transposon HmC2028_1228 6 DNA transposon (MuDR) HmC2028_1328 6 DNA transposon (MuDR) HmC2903_1043 6 DNA transposon (Sola) HmRS129D 6 DNA transposon (EnSpm) HmSNP1001_388 7 Non-LTR retrotransposon L2 HmC394_1510 8 DNA transposon (hAT) mC428_2186 8 LTR retrotransposon (Copia) HmC428_225 8 LTR retrotransposon (Copia) HmD59 8 DNA transposon (MuDR) HmidILL2.71359 8 DNA transposon (Kolobok) HmRS62D 8 DNA transposon (MuDR) HmSSRex446a 8 Non-LTR retrotransposon (SINE) HmidPS1.638 9 Interspersed repeat HmLCS48M 9 DNA transposon (Polinton) HmC20267_102 10 DNA transposon (EnSpm) HmC327_1076 10 Non-LTR retrotransposon (Ambal) HmC1254_187 11 LTR retrotransposon (Gypsy) HmC1254_529 11 LTR retrotransposon (Gypsy) HmC250_199 I1 Non-LTR retrotransposon (Jockey) HmC4181_893 12 DNA transposon (EnSpm) HmC14033_777 13 DNA transposon (MuDR) HmC853_1199 13 LTR retrotransposon (Copia) HmC4778_234 14 Non-LTR retrotransposon (RTE) HmidPS1.1063 14 Non-LTR retrotransposon Ll HmC18774_676 16 Non-LTR retrotransposon (Daphne) HmC22449_261 16 LTR retrotransposon (BEL) HmC3835_411 16 LTR retrotransposon (Gypsy) HmC45_3002 16 DNA transposon HmC11784_1697 18 LTR retrotransposon (Gypsy) HmC253_1545 18 Non-LTR retrotransposon Ll HmidILL2.66010a 18 LTR retrotransposon (Gypsy) HmidPS1.559 18 Non-LTR retrotransposon (CR1) HmidPS1.890 18 DNA transposon (hAT) Marker Score Similarity HmC1878_506 284 0.84 HmC2040_1251 263 0.71 HmC300_1828 325 0.75 HmC300_4738 325 0.75 HmC300_4982 325 0.75 HmC300_6993 325 0.75 HmNS19L 237 1.00 HmC1813_300 211 0.77 HmC460_1745 245 0.77 HmidILL1.140027 261 0.70 HmidILL2.76149 310 0.89 HmidILL2.8738 396 0.81 HmC102_1408 251 0.79 HmC1462_825 238 0.79 HmC20682_843 268 0.76 HmC22491_595 238 0.79 HmC22491_727 238 0.79 HmC911_1343 250 0.78 HmC911_290 250 0.78 HmidILL1.2192 214 0.72 HmidILL1.47613 258 0.72 HmidPS1.228 240 0.86 HmC2028_1228 236 0.70 HmC2028_1328 236 0.70 HmC2903_1043 241 0.75 HmRS129D 216 0.80 HmSNP1001_388 227 0.89 HmC394_1510 232 0.82 mC428_2186 243 0.75 HmC428_225 243 0.75 HmD59 217 0.86 HmidILL2.71359 346 0.77 HmRS62D 217 0.86 HmSSRex446a 274 0.70 HmidPS1.638 227 0.80 HmLCS48M 291 0.74 HmC20267_102 205 0.74 HmC327_1076 215 0.81 HmC1254_187 467 0.70 HmC1254_529 467 0.70 HmC250_199 270 0.82 HmC4181_893 238 0.72 HmC14033_777 231 0.74 HmC853_1199 256 0.78 HmC4778_234 234 0.80 HmidPS1.1063 223 0.82 HmC18774_676 314 0.86 HmC22449_261 259 0.78 HmC3835_411 238 0.84 HmC45_3002 225 0.83 HmC11784_1697 253 0.75 HmC253_1545 232 0.76 HmidILL2.66010a 264 0.79 HmidPS1.559 327 0.80 HmidPS1.890 289 0.79 TABLE 9. Identification of anchor loci information across 4 families or more in Haliotis midae. Linkage group Anchor loci 1 HmD14, HmNR54, HmNS19, HmC2040_1251, HmC300_6993 2 HmidILLI.140027, HmD55, HmidILL2.8738 3 Hmid65 4 HmidPS1.1058, HmC5433_233, HmC387_582, HmC387_215 5 HmidPS1.374, HmidPS1.228, HmidPS1.551, HmidILL1.47613, Hmid221 6 HmRS129 7 HmLCS388, HmidPS1.860, Hmid310 8 HmC1363_269, HmC428_2186, HmLCS37, HmC428_225 9 HmidPS1.638, HinidPS1.549 10 HmNR120, HmNS100 11 HmC1254_187, HmC1254_529 12 HmNR20, Hmid553, Hmid610, HmidPS1.874 13 HmSNP449.2_110, Hmid4010, HmC2141_504, Hmid563, HmSNP1949_235 14 HmidPS1.1063, HmidPS1.818, HmidPS1.247 15 -- 16 HmNS21, HmRS80 17 -- 18 --
|Printer friendly Cite/link Email Feedback|
|Title Annotation:||single nucleotide polymorphism|
|Author:||Vervalle, Jessica; Hepple, Juli-Ann; Jansen, Suzaan; Plessis, Jana Du; Wang, Peizheng; Rhode, Clint;|
|Publication:||Journal of Shellfish Research|
|Date:||Apr 1, 2013|
|Previous Article:||Sensory and physicochemical assessment of wild and aquacultured green and black lip abalone (Haliotis Laevigata and Haliotis Rubra).|
|Next Article:||Assessment of self-recruitment in a pink abalone (Haliotis corrugata) aggregation by parentage analyses.|