Genetic marker identification in atlantic cod

This application is a U.S. National Phase filing under 35 USC 371 of PCT/CA2010/000504 filed Apr. 9, 2010 claiming priority under 35 USC 119(e) from U.S. Provisional Patent Application No. 61/167,979 filed Apr. 9, 2009, and U.S. Provisional Patent Application No. 61/285,354 filed Dec. 10, 2009 all of which are incorporated herein by reference in their entirety.

A computer readable form of the Sequence Listing “20683-8_SequenceListing.txt” (966,656 bytes), submitted via EFS-WEB and created on Oct. 4, 2011, is herein incorporated by reference.

Please replace the Sequence Listing currently of record with the Sequence Listing provided herewith in text format in computer readable form only.

This application relates to single nucleotide polymorphism (SNP) markers and associated methods for the genetic analysis of Atlantic cod ( Gadus morhua ) and more specifically to SNP markers and associated methods for desirable production traits in Atlantic cod.

The Atlantic cod ( Gadus morhua ) has a long history as a commercially important species in the North Atlantic and adjacent waters. The decline in the capture fishery has resulted in a shift towards the production of Atlantic cod using aquaculture in a number of countries, including Canada. Selective breeding is a proven, powerful approach in the enhancement of domesticated species for food production. In order to develop an efficient breeding program, it is desirable to generate suitable molecular tools in order to accelerate the selection process. The application of genetic tools in finfish aquaculture breeding programs has only recently become standard practice (De-Santis and Jerry, 2007). Currently, 10 out of 19 major aquaculture species are being produced using selective breeding for improved broodstock selection and new tools are being developed to make selection more precise with the ability to identify and locate quantitative trait loci and then use markers assisted selection to speed the selection process (Hershberger, 2006).

Accordingly, a dense linkage map linked to traits of economic importance would represent one of the most useful genomic tools for application in the selective breeding of Atlantic cod. A variety of different marker types such as amplified fragment length polymorphisms (AFLPs), microsatellite markers or SNP markers can be used for linkage mapping or association mapping. SNPs have become a focus of marker development in many species in recent years due to their abundance and low cost of genotyping allowing the construction of a high density map. In aquaculture species, SNPs are especially important if they cause differences in economic traits or are linked to such a trait. SNPs developed from collections of expressed sequence tags (ESTs) can be particularly valuable as they can be used to identify changes in the amino acid sequence of encoded proteins, also known as non-synonymous SNPs (Kim et al., 2003).

The increased number of ESTs available for fish has facilitated the detection of SNPs for different fish species. Significant numbers of SNPs have been reported in channel catfish (He et al., 2003), salmon (Hayes et al., 2007; Smith et al., 2005) and sea bream (Cenadelli et al., 2007).

With respect to Atlantic cod, Moen et al. (2008) recently developed a large number of SNPs from ESTs based on 5′ sequence reads. One SNP has also been identified for the pantophysin (Pan I) locus (Delghandi et al., 2007)

There remains a need for high quality SNP markers and SNP markers associated with traits of commercial importance suitable for the genetic analysis of Atlantic cod.

The present disclosure provides a large number of single nucleotide polymorphisms (SNPs) useful for the genetic analysis of Atlantic cod. Both a low throughput and a high throughput (automated) SNP detection pipeline were developed and a subset of SNPs identified by these alternate approaches was validated. More specifically, 17,365 putative SNPs were identified through the sequence analysis of 97,964 EST sequence reads assembled into 8,189 contigs. Of the 17,365 putative SNPs, 4,753 SNPs were identified as high quality SNPs based on frequency and quality of sequence data. The 3072 SNPs listed in Table 6 were then hand selected from the 4,753 high quality SNPs based on origin and relevance to project objectives. SEQ ID NOs: 1-3072 provide the flanking sequence with the SNP variant at nucleotide position 61 for each of the 3072 SNPs listed in Table 6.

In addition, the 47 SNPs shown in Table 1 were characterized in 22 Atlantic cod fish from two separate populations of fish from New Brunswick and Newfoundland. Of the 47 SNPs, suitable primer extension assays were developed for the 33 SNPs identified in Table 3. As shown in Table 2, 30 of these SNPs appeared to be polymorphic in the two populations. Further segregation analysis showed that 22 of the SNPs were inherited as expected according to Mendelian segregation (Table 3). Annotation of the sequences shown in Table 5 identified 9 SNPs in specific untranslated gene regions, 2 synonymous coding SNPs and 2 non-synonymous coding SNPs. Table 7 identifies SNPs that overlap between Table 1 and Table 6 and provides corresponding SEQ ID NOS. and SNP nucleotide positions for the SNPs of Table 1.

Accordingly, the disclosure provides for a method of identifying an animal associated with a specific phenotype or trait comprising testing the animal for one or more of the SNPs listed in Table 1 or Table 6, wherein the SNP is associated with a specific phenotype or trait.

The disclosure also provides a method of identifying an association between a SNP and a specific phenotype or trait in an animal comprising comparing the frequency of a SNP allele identified in Table 1 or Table 6 between a control population and a population selected for the specific phenotype or trait.

In a preferred embodiment, the animal is Atlantic cod ( Gadus morhua ), however the SNPs are expected to be useful in the genetic analysis of other ‘related’ fish species within the family Gadidae.

Genetic analysis using the SNPs described herein provides useful information for breeding programs. Accordingly, one aspect of the disclosure provides a method of selecting a fish for a breeding program comprising testing fish for one or more of the SNPs listed in Table 1 or Table 6 and selecting fish for the breeding program based on the presence or absence of the one or more SNP alleles.

In one aspect, the disclosure provides for determining the parentage of a fish comprising genotyping a sample of fish for one or more SNPs listed in Table 14 wherein the sample includes parent fish (dams/sires) and progeny. In one embodiment, the method includes selecting a fish with unknown parentage and determining the parentage of the fish by comparing the genotypes of the SNP alleles for the parent fish with the genotypes of the SNP alleles for each of the fish genotyped in the sample. In one embodiment, the sample comprises fish that are communally-reared fish. In one embodiment, the method comprises typing at least 16, 20, 24, 30 or 36 of the SNPs listed in Table 14.

The SNPs of the present disclosure are useful for the linkage analysis or association analysis of Atlantic cod. The SNPs are also useful for identifying family members or estimating relatedness of Atlantic cod.

In a further embodiment, the disclosure provides isolated nucleic acid molecules that comprise the SNPs identified herein. In one embodiment, the isolated nucleic acid molecules comprise the SNPs identified in Table 1 or Table 6. In a further embodiment, the disclosure provides the nucleic acid molecules comprising the sequences identified in Table 7.

In another embodiment, the disclosure provides a method of genotyping a sample for a SNP comprising amplifying a nucleic acid molecule from said sample using one of the primer pairs identified in Table 6 and assaying for the SNP using the corresponding interrogation primer identified in Table 6.

The 3072 SNPS identified in Table 6 were tested using two Illumina GoldenGate panels. 1620 of these SNPs were categorised as validated (V) SNPs as set out in Example 2 and identified in Table 6. Validated SNPs were tested for Mendelian segregation in two families and used to create a high-density genetic linkage map useful for the genetic analysis of cod. Accordingly, in one embodiment, the disclosure provides a linkage map for cod as shown in FIG. 5 . In another embodiment, the disclosure provides a linkage map for cod as shown in Table 10. In one embodiment, there is provided the validated SNPs identified in Table 6, with the proviso that they are not listed in Table 13.

In one aspect, the SNPs identified herein may be used for determining the geographic origin of cod. The 184 SNPs listed in Table 11 are monomorphic in Eastern Atlantic cod populations (Iceland, Ireland and Norway) and polymorphic in Western Atlantic cod populations. Accordingly, there is provided a method for determining the geographic origin of a codfish comprising genotyping the codfish for one or more of the SNPs listed in Table 11, wherein fish that have homozygous genotypes are from the Eastern Atlantic, and fish that have heterozygous genotypes are from the Western Atlantic.

In one embodiment, the SNPs identified in Table 12 are useful for determining the geographic origin of cod fish, and in particular distinguishing codfish from Northern populations such as Newfoundland and Northern Norway/Barents Sea (which only have distinct ‘Northern’ genotypes present in populations), from codfish from Ireland and other populations that exhibit a distinct Southern genotype, or a mixed genotype with both Northern and Southern genotypes present in populations. Accordingly, in one embodiment there is provided a method for determining the geographic origin of codfish comprising genotyping one or more of the SNPs in Table 12. In one embodiment, a fish is identified as not from Newfoundland or Northern Norway if it has one or more Southern genotype alleles as set out in Table 12.

In one aspect, there is provided a method for identifying codfish with desirable production traits. In one embodiment, desirable production traits include increased weight, disease resistance, and resistance to stress. In one aspect, a quantitative trait locus (QTL) associated with the weight of codfish has been identified on linkage group 7 (Tables 10 and 20). In another embodiment, QTLs associated with the weight of codfish have been identified on linkage groups 1, 11, 18, 22 and 23 (Tables 10 and 21). Accordingly, in one embodiment, there is provided a method of identifying a codfish with QTL for increased weight, the method comprising genotyping the codfish for one or more of the SNPs listed in Tables 20 or 21, or genotyping a marker in linkage disequilibrium with any one of the SNP marker alleles listed in Tables 20, or 21. In one embodiment, the presence of a marker allele in linkage disequilibrium with the preferred SNP marker allele listed in Tables 20 or 21 indicates the presence of the QTL for increased weight.

In another aspect, markers associated with resistance to nodavirus infection have been identified on linkage groups 8, 19 and 23. Accordingly, in one embodiment there is provided a method of identifying a codfish resistant to nodavirus infection, the method comprising genotyping the codfish for one or more of the SNPs listed in Table 18.

In another aspect, there is provided genetic markers associated with fish that have a QTL for resistance to handling stress. In one embodiment, fish with the preferred alleles shown in Table 24 exhibit lower levels of cortisol in response to handling stress. Accordingly, in one embodiment there is provided a method of identifying a codfish resistant to stress, the method comprising genotyping the codfish for one or more of the SNPs listed in Table 24.

In one aspect, fish may be selected for a plurality of trait-associated markers as described herein. In one embodiment, a fish may be selected for SNP markers for growth, nodavirus resistance, and/or stress resistance as described herein. In one embodiment, there is provided a method for selecting fish with a plurality of genetic markers for desirable production traits such as growth, nodavirus resistance and stress resistance.

Other features and advantages of the present invention will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples while indicating preferred embodiments of the invention are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.

The invention will now be described in relation to the drawings in which:

FIG. 1 provides an overview of automated SNP detection. The ESTs are processed using Paracel Transcript Assembler (PTA) to assemble contigs and to remove unwanted sequence. Contigs in ace file format are used as PolyPhred input, to look for SNPs in areas of multi-read coverage. The output of PolyPhred is further refined using a custom perl script that can be adjusted to select various SNP output qualities. Two examples of criteria sets are shown. Both sets require a minimum of 4 read coverage. The less stringent set identifies SNPs that have a minor allele representation equal to or greater than 25%—for example, for a contig region with 4 contributing reads it would detect SNPs with 1 copy of the minor allele and 3 copies of the major allele. The more stringent set requires a minor allele representation greater than 25%, which means that each allele is represented at least twice in contigs containing 4 reads or more.

FIG. 2 is a flowchart showing the output resulting from testing of the 3072 selected SNPs. The 1620 “predicted alleles” correspond to the set of validated SNPs.

FIG. 3 shows the observed heterozygosity for polymorphic SNPs in four Canadian populations. SNPs were grouped into categories based on their values for observed heterozygosity averaged across four Canadian populations. All polymorphic SNPs were analysed, including those with high values for observed heterozygosity (predicted duplicates). The number of SNPs falling into each category is shown.

FIG. 4 shows the minor allele frequency of validated SNPs in four Canadian populations. SNPs were grouped into categories based on their minor allele frequency (MAF) averaged across four Canadian populations. Only the validated SNPs have been included in this analysis, with the number of SNPs falling into each MAF category shown.

FIG. 5 is a genetic linkage map for Atlantic cod. The 23 major linkage groups are shown. These have been arbitrarily numbered CGP1-23 based on the order generated by JoinMap 4 , and to distinguish them from the linkage groups generated by Moen and colleagues (Moen, 2009). Distances in centimorgans are indicated on the left of each linkage group, with SNP identifiers on the right. A revised version of the genetic linkage map used for QTL analyses is provided in Table 10.

FIG. 6 provides a schematic showing the selection process for SNPs for the parentage panel as described in Example 3.

FIG. 7 shows crosses used to generate simulated progeny for in silico parental assignment testing of selected SNPs as described in Example 3. PLOCI was used to randomly generate a set of crosses using genotyping data for 58 SNPs generated from a population study of fish from collections used to generate NB YC1, NB YC2, NL YC2 and NL YC3 families. Fifty simulated progeny were generated per cross.

The present inventors have identified a large number of SNPs useful for the genetic analysis of Atlantic cod presented in Tables 1 and 6.

Referring to FIG. 1 , SNP selection was completed as follows: 97,964 3′ EST (Expressed Sequence Tag) reads were assembled with PTA (Paracel Transcript Assembler—used for EST cleaning, clustering and assembly) resulting in 8,189 contigs. SNP detection was performed with PolyPhred which is a program that compares sequence information to identify heterozygous sites for single nucleotide substitutions. Analysis outputs included a ranking which indicates quality of the SNPs estimated by bioinformatic analysis (not shown), which was used towards SNP selection. Initial SNP detection yielded 17,365 putative SNPs. This list was trimmed using various criteria to identify 4,753 SNPs that were high quality. Specifically, “predicted frequent” SNPs were selected from contiguous sequences (contigs) which contained 4 or more sequence reads. In addition, from these “predicted frequent” SNPs, for 4,753 of these SNPs, the minor allele was represented in a minimum of 2 of the 4 reads. From these 4,753 SNPs, the 3072 SNPs listed in Table 6 were then hand-selected based on origin and relevance to project objectives.

The 47 SNPs identified in Table 1 were identified and selected for further characterization as set out in Example 1. Primer extension assays for these SNPs were developed and amplification and interrogation primers suitable for the amplification and genotyping of the SNPs are shown in Table 6. The SNPs shown in Table 2 were genotyped and further characterized with respect to segregation in two populations of Atlantic Canadian cod. Sequences containing the SNPs were also annotated with respect to gene location and putative SNP functionality (synonymous vs. nonsynonymous changes).

The term “SNP” as used herein, means a single nucleotide polymorphism which is a single nucleotide position in a nucleic acid sequence for which two or more alternative alleles are present in a given population.

The term “allele” means any one of a series of two or more different gene sequences that occupy the same position or locus on a chromosome.

The term “genotype” means the specification of an allelic composition at one or more loci within an individual organism. In the case of diploid organisms such as codfish, there are two alleles at each locus; a diploid genotype is said to be homozygous when the alleles are the same, and heterozygous when the alleles are different.

As used herein “genotyping” refers to determining the genotype of a organism at a particular locus, such as a SNP.

As used herein, “quantitative trait locus” or “QTL” refers to a genetic locus that contributes, at least in part, to the phenotype of an organism for a trait that can be numerically measured.

SNP Markers and Linkage and Association Studies

In one embodiment, the SNPs of the present disclosure can be used as markers for identifying animals that contain a SNP allele associated with a specific trait or phenotype. In a further embodiment, the SNPs can be used as markers that are associated with a quantitative trait locus (QTL). In a preferred embodiment, the animals are Atlantic cod fish.

The SNPs of the present disclosure may be used in genetic linkage or association studies to identify SNP alleles that are associated with a specific phenotype or QTL. In one embodiment, the linkage maps provided in FIG. 5 or Table 10 are useful for the genetic analysis of Atlantic cod. A person skilled in the art would readily be able to design linkage or association studies using the SNPs of Table 1 or Table 6 in order to identify SNPs that are linked or associated with a specific trait, phenotype or QTL. For example, a SNP associated with a trait or phenotype may be identified by comparing the frequency of a particular SNP allele in a control population and a test population selected for the presence of a specific trait or phenotype. Alleles that are present in a higher frequency in the test population compared to the control population are associated with the specific trait or phenotype. Examples of such traits or phenotypes include rapid growth, disease resistance, tolerance to handling stress, tolerance to thermal stress, high survival rates, low incidence of deformity or other traits of interest which would benefit aquaculture production. Methods of identifying QTL associated with a specific SNP allele are also known in the art. Examples of quantitative traits for which the SNPs of the present disclosure may be associated also include rapid growth, disease resistance, tolerance to handling stress, tolerance to thermal stress, high survival rates, low incidence of deformity or other traits of interest which would benefit aquaculture production.

Marker Assisted Selection

In a further embodiment, the SNP markers of the present disclosure may be used in Marker Assisted Selection (MAS), wherein fish enrolled in a breeding program are checked for the presence or absence of one or more SNP marker alleles. For example, fish having a specific SNP allele associated with a particular trait may be identified and put into a breeding program in order to select for offspring that also carry that marker. In one embodiment, the SNPs alleles or molecular markers identified herein will be associated with fish that perform well or poorly under aquaculture conditions. Accordingly, the SNPs can be used to non-lethally screen potential broodstock for traits of interest. For example, a piece of a fin can be obtained from a fish from a breeding program, and DNA can be extracted and analyzed to determine whether a SNP allele associated with a trait of interest is present. If the SNP allele is present, that fish would be desirable to include in a breeding program. In one embodiment, fish enrolled in a breeding program are checked for the presence or absence of a plurality of SNP markers alleles associated with a trait as described herein.

Identification of Family Members and Estimation of Relatedness

The SNP markers of the present disclosure may also be used to design targeted panels of markers used for the estimation of measures of relatedness. In one embodiment, measures of relatedness derived from the SNPs of the present disclosure may be used to ensure that crosses in breeding programs are not made between individuals that are too closely related.

In another embodiment, the SNPs of the present disclosure may be used to identify family members associated with a particular fish. For example, the SNPs may be used to identify parents of untagged progeny. As set out in Example 3, the present disclosure provides a set of SNPs listed in Table 14 that can be used to determine the parentage of fish, and in particular codfish. In a further embodiment, the SNPs listed in Table 14 can be used to estimate the relatedness of two or more fish. In one embodiment, the methods and SNPs described herein can also be used to identify siblings. In one embodiment, the SNPs identified in Table 14 can be used to identify the parentage of a fish by genotyping as few as 16 SNPs. In other embodiments, more than 16, more than 20 or more than 24 of the SNPs listed in Table 14 can be used to identify the parentage or estimate the relatedness of two or more fish.

Population Management and Analysis—Geographic Origin

The SNPs of the present disclosure may also be used in population management for fisheries, or in population analysis of Atlantic cod stocks. In a further embodiment, the SNPs may be used in forensics/compliance activities, such as determining where fish originated. For example, the SNPs may be used to differentiate between fish caught in different geographic areas that are associated with a specific allele or combinations of alleles.

In one embodiment, there is provided a method for determining the geographic origin of cod based on SNP genotypes. As set out in Example 2 and Tables 11 and 12, codfish from specific geographic areas have been shown to carry a specific set of genotypes which distinguishes fish caught in different areas.

As used herein “Eastern Atlantic Cod” refers to cod fish that are endemic to the European Atlantic seaboard including Iceland, Norway, Ireland and Great Britain. As used herein “Western Atlantic Cod” refers to fish that are endemic to the Canadian Atlantic seaboard including Newfoundland, Nova Scotia and New Brunswick as well as the Atlantic seaboard of the United States of America.

As used herein, a fish with that is not from Newfoundland or Northern Norway (Barents Sea), refers to codfish that are endemic to other areas of the Atlantic such as Ireland, Iceland, Georges Bank, or Cape Sable.

Genetic Markers for Production Traits

The present disclosure also provides genetic markers for production traits for Atlantic cod. Screening codfish for these markers can help identify commercially important breeding stocks or fish with more desirable production traits suitable for use in commercial aquaculture.

As set out in Example 4 and Table 18, SNP alleles that are associated with resistance to nodavirus infection in codfish have been identified. Identifying fish harboring the alleles identified in Table 18 can therefore be used to select cod fish suitable for breeding stocks or acquaculture that are less susceptible to nodavirus infection.

SNPs and specific alleles associated with growth and/or weight in Atlantic cod have also been identified as set out in Example 5. Identifying fish that harbour these QTLs for growth and/or weight can allow the selection of fish for use as parents in breeding programs, or the selection of fish at a young age that are more likely to develop into adult fish with desirable traits such as increased weight or size. Accordingly, in one embodiment fish harbouring growth and/or weight QTLs can be identified by screening for the SNPs alleles associated with increased weight or growth identified in Tables 20 or 21.

In another embodiment, SNPs and specific alleles associated with resistance to stress have been identified as set out in Example 7 and Table 24. Fish that are resistant to stress are better suited for breeding stocks and commercial aquaculture.

A person of skill in the art will appreciate that a fish can be identified that have a particular trait by genotyping the fish for one of the SNPs identified herein and determining whether they have one of the alleles shown to be associated with the trait as described herein, it is also possible to identify fish that have a particular trait by genotyping for markers that are in linkage disequilibrium (LD) wherein one of the alleles of that marker is in LD with one of the alleles shown to be associated with the trait as described herein. As used herein “linkage disequilibrium” refers to the co-segregation of alleles at different loci on a chromosome such that the presence of one allele is indicative of the presence of the other allele. Furthermore, a person of skill in the art would readily be able to identify markers and alleles in linkage disequilibrium on a common halpotype with the SNPs shown to be associated with particular traits identified herein.

A person of skill in the art will also appreciate that the methods and genetic markers described herein can be used to select fish for multiple markers. For example, in one embodiment the fish are selected for genetic markers associated with growth, nodavirus resistance and/or resistance to handling stress. When applying marker assisted selection (MAS) to breeding programs, single trait selection is not necessarily a desirable method to apply in production systems. It is possible, for example that selecting only for one beneficial trait could have the unexpected and undesirable result of selecting for a linked undesirable trait if precautions are not taken. For example, selecting only for improved growth when developing a breeding population for animal production could result in fast growing, and immune susceptible animals. When using MAS, best practices in development of broodstock for production include selection for multiple traits, where possible, such as improved growth, disease resistant and stress tolerance.

Isolated SNP-Containing Nucleic Acid Molecules

Table 6 identifies the corresponding SEQ ID NO. for each of the 3,072 SNPs identified in the present disclosure. Table 7 identifies the corresponding SEQ ID NO: for each of the SNPs of Table 1. Accordingly, one embodiment provides a nucleic acid sequence identified in Table 6 or 7 that comprises either one of the SNP alleles. In a further embodiment, the disclosure provides isolated nucleic acid molecules that comprise one or more of the SNPs identified in Tables 6 or 7. A further embodiment includes amplification primers and interrogation primers for the sequences and SNPs identified in Tables 6 or 7. A person skilled in the art would readily identify suitable amplification primers and interrogation primers based on the sequences identified in Tables 6 or 7 and common general knowledge in the art.

The term “isolated nucleic acid” refers to a nucleic acid substantially free of cellular material or culture medium, for example, when produced by recombinant DNA techniques, or chemical precursors, or other chemicals when chemically synthesized. An “isolated nucleic acid” is also substantially free of sequences which naturally flank the nucleic acid (i.e. sequences located at the 5′ and 3′ ends of the nucleic acid) from which the nucleic acid is derived. The term “nucleic acid” is intended to include DNA and RNA and can be either double stranded or single stranded.

Detecting SNP Variants

A person skilled in the art will appreciate that a number of methods can be used to measure or detect the presence of the SNPs identified in the present disclosure. For example a variety of techniques are known in the art for detecting a SNP within a sample, including genotyping, microarrays, Restriction Fragment Length Polymorphism, Southern Blots, SSCP, dHPLC, single nucleotide primer extension, allele-specific hybridization, allele-specific primer extension, oligonucleotide ligation assay, and invasive signal amplification, Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry, and Fluorescence polarization (FP). Such methods optionally employ the isolated nucleic acid molecules of the disclosure.

Accordingly, the SNPs are detected in one embodiment by genotyping. Methods of genotyping are well known in the art. In one method, primers flanking the SNP are selected and used to amplify the region comprising the SNP. The amplified region is then sequenced using DNA sequencing techniques known in the art and analyzed for the presence of the SNP alleles.

In another embodiment, the method of detecting a SNP comprises using a probe. For example, in one embodiment an amplified region comprising the SNP is hybridized using a composition comprising a probe specific for the SNP allele under stringent hybridization conditions.

Accordingly, one aspect of the disclosure includes isolated nucleic acids that bind to SNP alleles at high stringency that are used as probes to determine the presence of the allele. In a particular embodiment, the nucleic acids are labeled with a detectable marker. The marker or label is typically capable of producing, either directly or indirectly, a detectable signal.

For example, the label may be radio-opaque or a radioisotope, such as 3 H, 14 C, 32 P, 35 S, 123 I, 125 I, 131 I; a fluorescent (fluorophore) or chemiluminescent (chromophore) compound, such as fluorescein isothiocyanate, rhodamine or luciferin; an enzyme, such as alkaline phosphatase, beta-galactosidase or horseradish peroxidase; an imaging agent; or a metal ion.

The term “probe” refers to a nucleic acid sequence that will hybridize to a nucleic acid target sequence. In one example, the probe hybridizes to a sequence comprising a specific SNP allele or its complement under stringent conditions, but will not to the corresponding alternative allele or its complement. The length of probe depends on the hybridization conditions and the sequences of the probe and nucleic acid target sequence. In one embodiment, the probe is 8-100, 8-200 or 8-500 nucleotides in length, such as 1-7, 8-10, 11-15, 16-20, 21-25, 26-50, 51-75, 76-100, 101-150 or 151-200 nucleotides in length or at least 200, 250, 400, 500 or more nucleotides in length. In other embodiments, 10, 15, 20 or 25 nucleotides provide a lower end for the aforementioned nucleotide ranges.

In a further embodiment, the SNPs are detected using a primer extension assay. Briefly, an interrogation primer is hybridized to the sequence nucleotides immediately upstream of the SNP nucleotide. A DNA polymerase then extends the hybridized interrogation primer by adding a base that is complementary to the SNP. The primer sequence containing the incorporated base is then detected using methods known in the art. In one embodiment, the added base is a fluorescently labeled nucleotide. In another embodiment, the added base is a hapten-labelled nucleotide recognized by antibodies.

The SNPs described herein are optionally detected using restriction enzymes. For example, amplified products can be digested with a restriction enzyme that specifically recognizes sequence comprising one of the SNP alleles, but does not recognize the other allele. In one embodiment PCR is used to amplify DNA comprising a SNP, amplified PCR products are subjected to restriction enzyme digestion under suitable conditions and restriction products are assessed. If for example a specific SNP allele corresponds to a sequence digested by the restriction enzyme, digestion is indicative of detecting that particular SNP allele. Restriction products may be assayed electrophoretically as is common is the art.

SNP alleles can also be detected by a variety of other methods known in the art. For example, PCR and RT-PCR and primers flanking the SNP can be employed to amplify sequences and transcripts respectively in a sample comprising DNA (for PCR) or RNA (for RT-PCR). The amplified products are optionally sequenced to determine which of the SNP alleles is present in the sample.

Accordingly, the disclosure provides in one aspect, methods and nucleic acid molecules useful for detecting SNPs. In one embodiment, a sample comprising genomic DNA is obtained and primers flanking the SNP are used to amplify the region comprising the mutation. Sequencing is optionally employed to determine which SNP allele is present in the sample. In another embodiment, a sample comprising RNA is reverse transcribed, primers flanking the SNP are used to amplify the region comprising the SNP, and sequencing is employed to determine which SNP allele is present in the sample. In another embodiment the SNP is detected using a composition comprising a probe specific for the mutated sequence.

Alternatively SNP alleles are optionally detected by a variety of other techniques known in the art including microarrays, hybridization assays, PCR based assays, molecular beacons, Dynamic allele-specific hybridization (DASH) and/or combinations of these.

In one embodiment, the disclosure includes isolated nucleic acid molecules that selectively hybridize under stringent conditions to one of the sequences listed in Tables 7 or 8. A further embodiment includes an isolated nucleic acid molecule that selectively hybridizes to a nucleic acid comprising a SNP allele or its complement.

The phrase “specifically hybridizes to a SNP allele or its complement” means that under the same conditions, the isolated nucleic acid sequence will preferentially hybridizes to one of the SNPs alleles or its complement, as compared to the other allele.

The term “hybridize” refers to the sequence specific non-covalent binding interaction with a complementary nucleic acid. In a preferred embodiment, the hybridization is under high stringency conditions.

By “high stringency conditions” it is meant that conditions are selected which promote selective hybridization between two complementary nucleic acid molecules in solution. Hybridization may occur to all or a portion of a nucleic acid sequence molecule. The hybridizing portion is typically at least 5-14, 15-20, 21-25, 26-30, 31-40, 41-50 or 50, 50-100, 100-200 or 200 or more or more nucleotides in length. Those skilled in the art will recognize that the stability of a nucleic acid duplex, or hybrids, is determined by the Tm, which in sodium containing buffers is a function of the sodium ion concentration and temperature (Tm=81.5° C.−16.6 (Log 10 [Na+])+0.41(% (G+C)−600/l), or similar equation). Accordingly, the parameters in the wash conditions that determine hybrid stability are sodium ion concentration and temperature. In order to identify molecules that are similar, but not identical, to a known nucleic acid molecule a 1% mismatch may be assumed to result in about a 1° C. decrease in Tm, for example if nucleic acid molecules are sought that have a >95% identity, the final wash temperature will be reduced by about 5° C. Based on these considerations those skilled in the art will be able to readily select appropriate hybridization conditions. In preferred embodiments, stringent hybridization conditions are selected. By way of example the following conditions may be employed to achieve stringent hybridization: hybridization at 5× sodium chloride/sodium citrate (SSC)/5× Denhardt's solution/1.0% SDS at Tm-5° C. for 15 minutes based on the above equation, followed by a wash of 0.2×SSC/0.1% SDS at 60° C. It is understood, however, that equivalent stringencies may be achieved using alternative buffers, salts and temperatures. Additional guidance regarding hybridization conditions may be found in: Current Protocols in Molecular Biology, John Wiley & Sons, N.Y., 1989, 6.3.1-6.3.6 and in: Sambrook et al., Molecular Cloning, a Laboratory Manual, Cold Spring Harbor Laboratory Press, 1989, Vol. 3.

Nucleic acid sequences that are primers are useful to amplify DNA or RNA sequences containing a SNP of the present disclosure. Accordingly, in one embodiment, the disclosure provides a composition comprising at least one isolated nucleic acid sequence that is a specific primer able to amplify a sequence comprising a SNP identified in Table 1 or Table 6. A person skilled in the art would understand how to identify and test primers that are useful for amplifying sequences containing the SNPs identified in Table 6.

The above disclosure generally describes the present application. A more complete understanding can be obtained by reference to the following specific examples. These examples are described solely for the purpose of illustration and are not intended to limit the scope of the disclosure. Changes in form and substitution of equivalents are contemplated as circumstances might suggest or render expedient. Although specific terms have been employed herein, such terms are intended in a descriptive sense and not for purposes of limitation.

The following non-limiting examples are illustrative of the present disclosure:

Information provided for this example is also described in Hubert et al. 2010. With wild Atlantic cod ( Gadus morhua ) stocks declining dramatically over the last few decades (Rose, 2007), aquaculture is becoming increasingly important as a means of maintaining a market supply for this species. Cod aquaculture is currently being developed in several countries (Rosenlund et al., 2006), but has not yet reached a sustainable commercial scale (Brown et al., 2003). Applying genomics tools in the selection of elite broodstock has the potential to enhance the productivity and value of commercial production for this species (Gjedrem, 2000).

Genetic marker discovery is a necessary first step in the application of genomics to improve broodstock as these markers can be used for the creation of linkage maps and subsequent QTL identification. Marker assisted selection (MAS) can then be employed by selecting broodstock based on genotypes at QTL that are relevant to economically important traits such as rapid growth, disease resistance and the control of early maturation. Currently, a limited collection of genetic markers is available for Atlantic cod, including restriction fragment length polymorphisms (RFLP), microsatellites and single nucleotide polymorphisms (SNPs) (Pogson et al., 1995; Delghandi et al., 2008a; Delghandi et al., 2008b; and Higgins et al., 2009). Most of the studies describing genetic markers in Atlantic cod have employed microsatellite markers (Wesmajervi et al., 2007; Ruzzante, 1996). In total, 352 microsatellites have been published to date for this species, including a large, new collection of expressed sequence tag (EST) derived microsatellites (Higgins et al., 2009). However, SNPs are the most abundant type of DNA sequence polymorphism, are suitable for high-throughput genotyping, and provide enhanced possibilities for genetic and breeding applications, linkage map development, assessment of genetic variability and marker assisted breeding. As a result, SNP discovery pipelines have been recently developed for many species including fishes (Hayes et al., 2007; Cenadelli et al., 2007; He et al., 2003; Stickney et al., 2002; and Ryynanen et al., 2006). To date, a collection of 318 SNPs has been identified for Atlantic cod using 17,056 ESTs generated from a North-East Atlantic cod population, and these SNPs have been tested on several additional Norwegian cod populations (Moen et al., 2008). In total, 174 of these SNPs, together with 33 microsatellites, have been used to generate a genetic linkage map for Atlantic cod. This map comprises 25 linkage groups with an overall length of 1225 cM, and represents the first reported linkage map for this species (Moen et al., 2009).

As set out in Example 1, SNPs have been identified from sequence data generated by a large-scale expressed sequence tag (EST) program focusing on fish originating from Canadian waters. A subset (3072) of these SNPs (listed in Table 6) have been tested for polymorphism across a number of different wild stocks and has also been used to generate a preliminary linkage map for this species. The EST set was designed to provide an excellent resource for SNP marker discovery, since it is generated from several cDNA libraries representing different tissues, with three to 340 individuals contributing to each library. Furthermore, many of the SNPs developed were identified from sequence data with functional annotation potentially allowing the identification of genes contributing directly to a phenotype.

Putative SNPs identified in this study were subsequently validated for polymorphism across a number of geographically diverse Atlantic cod populations, ranging from Canada to the North-East Atlantic (Iceland, Norway and Ireland). These SNPs were also tested for Mendelian segregation in two families, and used to create a high-density genetic linkage map that can be applied in QTL analysis to facilitate cod broodstock selection.

Materials and Methods

Validation of Putative SNPs on Panel

In total, 5×96 well plates of selected DNA samples were genotyped using the two Illumina GoldenGate panels. Two plates consisted of two references families, B33 and B87 with two parents and 91 progeny. The three remaining plates consisted of wild cod populations. In total, seven populations of Atlantic cod were genotyped for this study, with an average of 23 fish genotyped per population. The geographic location of collections covers the North Atlantic with a more detailed sampling for Atlantic Canadian populations. DNA extraction methods have been described previously. In summary, fin clips or muscle tissue samples were taken and placed in 95% ethanol. DNA was extracted using the Qiagen DNAeasy 96 extraction kit (Qiagen, Mississauga, ON). The kit protocol utilizes a buffer containing proteinase K to lyse the tissue. The lysate is loaded onto a plate where the DNA binds to a silica membrane in the presence of chaotropic salt. Proteins and other contaminants are washed from the bound DNA using wash buffers and centrifugation. DNA is then eluted in water. High-throughput genotyping was performed using the GoldenGate assay.

SNP Annotation

For sequences where SNPs were detected, the consensus sequence for each contig was compared to the NCBI nr database using BLASTX (Altschul et al., 1990), with a value of 1×e −05 used as the cutoff to determine significance. All SNPs that were determined to be polymorphic after testing have been deposited in the GenBank SNP database under accession numbers ss131570222 to ss131571915. Sequences, and their associated annotation using both BLASTX and AutoFACT (Koski et al., 2005), can also be accessed via the CGP database (http://ri.imb.nrc.ca/codgene).

Identification of Synonymous and Non-Synonymous SNPs

The procedure for determining a SNP as synonymous or non-synonymous is as outlined in Example 1. Briefly, each contig consensus was compared against the NCBI protein database using BLASTX to establish a reading frame in which to assess synonymous or non-synonymous status. For those SNPs within regions of similarity, the consensus sequence was translated for each SNP allele and the resulting amino acid sequences were then compared to determine whether the SNP was synonymous or non-synonymous.

Analysis of Atlantic Cod Populations

Loci deviating from Hardy-Weinberg equilibrium (HWE) were identified in each of four Canadian populations of Atlantic cod. This was assessed separately in each population using Hardy-Weinberg exact tests calculated using GenePop v4.0 (Rousset, 2008). The 64 loci that failed Hardy-Weinberg exact tests in four Canadian populations were excluded from data used to generate the linkage map.

Genetic Linkage Map Construction

The genetic linkage map was constructed using JoinMap®4. Genotypes for progeny generated through the Illumina GoldenGate platform were converted to CP codes based on parental genotypes. Each cross was examined separately, with segregation ratios analysed for all loci, and those which showed abnormal segregation as determined using a chi-square goodness of fit test were removed (P<0.005). Markers were then associated within linkage groups using the group function of JoinMap®4, using a LOD cut-off value of 5.0 or greater. Marker orders within linkage groups were determined and map distances calculated using Haldane's mapping function. Maps generated independently for the two families were compared, and a 1:1 correspondence between linkage groups confirmed. The corresponding groups from the two families were combined using the JoinMap®4 merge function, and a consensus map generated.

Results

A total of 97976 ESTs were assembled to generate 13448 contigs. 4753 SNPs were detected that met our selection criteria, which included a depth of coverage of at least 4 reads with a minor allele frequency higher than 25%. From the 4753 SNPs identified, the 3072 SNPs listed in Table 6 were selected for testing using two Illumina GoldenGate panels. The percentage of successful assays was 75% for these panels, with 2291 SNPs amplifying correctly. From the successful assays, 607 (26%) of the SNPs were found to be monomorphic for all populations tested. In total, 64 (4%) of SNP assays that scored as polymorphic are likely to represent duplicated genes or highly similar members of gene families, rather than alternative alleles of the same gene, since they showed a high frequency of heterozygosity in the samples tested. The remaining polymorphic SNPs (1620) were categorised as validated SNPs and are also identified in Table 6. The mean minor allele frequency among the validated loci was 0.258 (+−0.141). The ratio of transition to transversion types of substitution was 1.11:1 for validated SNPs. Of the 1514 contigs from which validated SNPs were selected, 31% have a significant blast hit. Of the 141 of these SNPs that are predicted to occur in coding regions, we determined that 64% (90) are synonymous. When comparing different populations, including those from North America and Europe, a large number of loci (1033 SNPs; 64%) are polymorphic for all populations originating from the North Atlantic. However a small number of SNPs (184), shown in Table 11 that were shown to be polymorphic in the Western Atlantic were monomorphic in all fish tested from three European populations. The large set of validated, polymorphic SNPs has been used to construct a preliminary linkage map using two families, with two parents and 91 progeny genotyped in each case ( FIG. 5 ). This map has 23 major linkage groups and 924 mapped SNPs.

In addition, a set of associated SNPs was also found to distinguish some Northern and Southern populations of cod. It was noted that linkage group 7 (Table 10) had characteristics that can be associated with low recombination. A set of 21 SNPs (identified on 20 contigs) were found to co-segregate on the linkage map. This was the largest set of co-segregating SNPs on the linkage map. Genotypes for different individuals included in the population analysis were obtained. Individuals from the most extreme populations, Norway and Ireland, were used to identify the “North” and the “South” genotype respectively. Then genotypes for other populations were identified using homozygotes with North or South-type genotypes (Table 12). For the Canadian population tested, it is noteworthy that fish from populations near New Brunswick harboured the South and the North-type genotype, but fish from populations near Newfoundland harboured only the north-type genotype for all fish tested.

Validation of Putative SNPs on Panel

Out of the pool of 4753 predicted good quality SNPs, 3677 SNPs satisfied the criteria for the Illumina Golden Gate platform in that they appeared to be bi-allelic, with 100 bp of flanking sequence and less than 60 bp from a selected neighbouring SNP, and these SNPs were scored for primer design. Two Golden Gate panels, each comprising 1536 SNPs (3072 SNPs total; listed in Table 6), were created from the best-scoring SNPs (CGP Panel 1 and CGP Panel 2) and these were tested against a large number of Atlantic cod sampled from a number of sites (multiple populations from Canada, and single collections from Iceland, Ireland and Norway). Parents and progeny from two reference families selected from the CGP breeding program in New Brunswick were also genotyped to test for non-Mendelian segregation and for the creation of a genetic linkage map (Table 9).

The success rate for SNP assays was 75% for the two panels tested, with a total of 781 assays that failed to give good quality genotypes ( FIG. 2 ). From the 2291 successful assays, 607 SNPs (26%) were monomorphic (i.e. only one SNP variant was identified) in all individuals from four Canadian populations that were tested (Table 8), and therefore are either incorrectly identified as SNPs, or are rare SNPs within the populations analysed. The majority of these have a minor allele represented by two sequences, the minimum allowed by our selection criteria; only a small number of monomorphic SNPs have more than 2 reads representing the minor allele. In total, 1684 SNP assays identified both SNP variants, with at least one individual tested carrying the predicted minor allele. However, 64 of these SNPs showed a high proportion of heterozygotes in all individuals tested ( FIG. 3 ), indicating that they might represent sequence variation between duplicated genes, or members of closely related gene families, rather than different alleles from the same gene; the total number of SNPs predicted as corresponding to bi-allelic loci was 1620 ( FIG. 2 ; identified in Table 6). For the purpose of this study, we define validated SNPs as those having a value for observed heterozygosity greater than zero but lower than 0.9. Therefore this study has identified 1684 polymorphic SNPs, with 1620 of these being validated SNPs as we predict they correspond to a base change at a single locus. The average observed heterozygosity for these validated SNPs was 0.332 (+/−0.148) and ranged from 0.01 to 0.69. The mean minor allele frequency among the validated SNPs was 0.258 (+/−0.141) and ranged from 0.005 to 0.5. The number of SNPs observed with varying MAFs in the four Canadian populations enrolled in the CGP breeding programs is shown in FIG. 4 . The number of SNPs in different MAF ranges from 0.05 to 0.5 is relatively consistent for these populations, with 141 validated SNPs having a MAF lower than 0.05, and 169 SNPs with a MAF from 0.45 to 0.5 for example.

SNPs for CGP panel 1 (1536 SNPs) were chosen such that only one SNP per contig was included. The second panel selection consisted of various categories of SNPs chosen based on a prioritized strategy. Initially, SNPs from remaining contigs not represented on the first panel were selected. The panel was then completed by selecting SNPs that are neighbours on the same contig to SNPs that failed, or were monomorphic, on panel 1, and also SNPs that are neighbours to successful SNPs on panel 1 (or a small number of SNPs included on panel 2 which had not yet been tested) but having a different haplotype. A few SNPs were selected manually based on contig annotations, with some of these identified on SSH EST contigs. Thus the final set of validated SNPs (1620) were selected from 1514 contigs, with several contigs having multiple validated SNPs. These can be identified in the SNP set as they have an identical number, but a different suffix, as in cgpGmo-S177a and S177b for example.

SNPs neighbouring failed panel 1 SNPs had slightly lower success rate (69%) when compared to other SNP categories, which ranged from 72 to 79%. Analysis of the polymorphism of successful panel 2 SNPs showed that SNPs neighbouring panel 1 failures have a higher number of polymorphic SNPs (78%) when compared to the “unique SNPs/contig” category (71%). Two categories showing the smallest number of polymorphic loci are the manually picked SNPs (35%) and the neighbours of monomorphic panel 1 SNPs (47%). Some of the manually picked SNPs were selected from SSH libraries which had each been generated from a single family, and thus this subset may contain a greater proportion of SNPs which are rare within the population as a whole.

The ratio of transition substitutions (A/G and C/T) to transversions among predicted alleles of the 3072 putative SNPs chosen for inclusion on the two Illumina panels was examined. The base substitution frequency was compared with that seen in the validated SNPs. Little variation was found between the ratio of transition:transversion substitution types for the different SNP categories (putative SNPs and validated SNPs), with a ratio of 1.18:1 for the SNPs selected for validation, which decreased slightly to 1.11:1 for validated SNPs (Table 10).

Functional Annotation of SNPs

The SNPs described herein are particularly valuable as they are linked to expressed sequences. However, because a large fraction of the 3′ sequence in which the SNPs were detected is likely to originate from the 3′ UTR of each transcript, most SNPs were expected to fall in non-coding regions. This resulted in a relatively low percentage of sequences for which a function could be inferred based on sequence similarity. Of the 1514 contigs from which at least one validated SNP was selected, 474 (31%) had a significant blast hit (e value<=1e −05 ) in the NCBI non-redundant dataset. In total, 514 SNPs (32%) were associated with sequences having significant similarity to an entry in the NCBI nr database.

After analysis based on sequence homology, a subset of the SNPs identified was found to fall within coding regions; these SNPs were analysed to determine if substitutions encoded by the two allelic variants would result in an amino acid change, i.e., if the substitutions are non-synonymous or synonymous. Only 9% of validated SNPs occur on a known reading frame within coding regions (i.e. they have similarity with a protein sequence present in public databanks). Of these 141 SNPs, 90 (64%) were predicted to generate synonymous substitutions, while 51 (36%) were non synonymous.

Population Comparison

Provided herein is a description of SNP characteristics in several populations of Atlantic cod shown in Table 9. From our analysis, the number of monomorphic loci varied greatly between the Canadian populations and more distant populations such as Ireland and Norway. A large number of loci (1033) are polymorphic in all populations. As anticipated, the greatest number of monomorphic loci from this SNP set is seen in the East Atlantic populations (Iceland, Ireland and Norway). This is likely to be due to ascertainment bias rather than a real underlying difference in variability between West and East Atlantic populations, as SNPs were selected based on their frequent occurrence in Cape Sable and Bay Bulls fish. A number of SNPs (184; listed in Table 11) have been identified as diagnostic SNPs as they can be used to distinguish between Western and Eastern Atlantic cod populations, being monomorphic in fish tested from the Eastern Atlantic cod populations (Iceland, Ireland and Norway) but polymorphic in Western Atlantic cod populations. In addition, a set of associated SNPs were also identified that can be used to distinguish some Northern and Southern Atlantic populations (see Table 12).

A few of the polymorphic SNPs were not in Hardy Weinberg equilibrium (HWE) in one or more of the four Canadian populations tested. We determined that 65 of the total collection of polymorphic SNPs significantly deviate from HWE in all four populations (P<=0.05), and the vast majority of these (64) were screened out from the set of validated SNPs as they had values for observed heterozygosity greater than 0.9. An additional 136 SNPs show significant deviations from HWE in one population only, and this is also true for 19 SNPs in two of the four populations and two SNPs in three of the four populations tested.

Mendelian Inheritance and Informativeness of SNPs for Linkage Mapping

Segregation patterns of SNPs (Mendelian/non-Mendelian) were tested by genotyping the parents and progeny from 2 CGP families, to ensure that SNPs can be used reliably for linkage analysis. In each case, patterns of segregation were assessed in the 91 progeny genotyped for each family. Most of the SNPs that were predicted to represent differences between genes (paralogs or members of gene families) were removed prior to analysis of segregation patterns, although a small number were included in the initial analysis, and all of these demonstrated non-Mendelian inheritance patterns. Additional SNPs also showing non-Mendelian inheritance were screened out prior to generating the linkage groups used for map generation. Different, overlapping sets of SNPs could be assessed for segregation in each of the two families. In family B33 it was possible to examine the inheritance for 858 SNPs, whereas 832 SNPs were informative in family B87. A total of 19 SNPs in family B33 showed a significant departure from Mendelian segregation (P<=0.005) however nine of these are predicted to represent gene duplications/multigene families rather than real SNPs since both parents and all progeny are heterozygous. For family B87, 38 SNPs showed significant departure from the Mendelian expectation, with 12 SNPs predicted to arise from duplications rather than segregating alleles in this family. Combining the results from the two families, a total of 46 SNPs show non-Mendelian segregation, with 14 of these giving results indicative of gene duplication in at least one family.

On comparison, 64 of the 65 SNPs that deviate from HWE in all four of the Canadian populations tested in this study were either screened out as potential duplicates prior to analysis, or showed non Mendalian inheritance (P<=0.005) in one or both of those crosses. It was possible to map a single SNP, cgpGmo-S89a, from this category. Of the 157 SNPs that deviated from HWE in one, two or three Canadian populations, two failed the test for Mendalian segregation in both families used for mapping (cgpGmo-S1835 and S1962), with a further three SNPs showing departure from Mendelian segregation in family B33 but segregating correctly (within the parameters allowed for Mendelian inheritance) in B87 (cgpGmo-S1219b, S626a and S2232). However, the majority of the SNPs in this second category could be successfully placed on the linkage map.

Generation of a Preliminary Genetic Linkage Map for Atlantic Cod

The generation of genomics resources as described herein is tightly integrated with family-based selective breeding programs based in New Brunswick and Newfoundland. As part of these programs, individual crosses are generated with known parental contribution, with the progeny from these crosses reared in separate tanks until they reach a suitable size for surgical implantation of a passive integrated transponder tag. Parents and 91 progeny from each of two independent crosses, families B33 and B87, were genotyped using the two Illumina GoldenGate panels described in this study (Table 8) with the aim of generating a SNP-based genetic linkage map.

After removal of the loci with highly skewed segregation ratios (P<0.005) described earlier, JoinMap®4 (Van Ooijen, 2006) was used to generate linkage groups of associated loci for each family independently, and to order loci within linkage groups to create a preliminary map. For both families, 23 major linkage groups were generated using an LOD threshold value of 5.0, which is in good agreement with the haploid chromosome number of 23 usually reported for Atlantic cod (Fan et al., 1991). A small number of SNPs that failed to be assigned to these 23 linkage groups, as well as a few additional linkage groups generated by JoinMap®4 containing 2-3 loci, were not incorporated in further analyses. Marker content of linkage groups, and marker order within those groups, was in good agreement when the maps for the two families were compared. Therefore, the family maps were combined to generate a consensus map using the merge function of JoinMap®4. The consensus map produced is shown in FIG. 5 , and contains 924 loci on 23 linkage groups, ranging from to 41 to 79.5 cM in length, and a total map length of 1421.92 cM. The number of markers per linkage group ranges from 23 to 58, with an average of 40.2.

Discussion

The present description provides a large collection of SNP markers suitable for the genetic analysis of cod. After screening 13448 contigs generated from 97976 ESTs, 4753 SNPs were identified using the criteria of 4 reads minimum and a MAF>25%. Assays have been developed for 3072 SNPs using 465 fish, which were genotyped using a GoldenGate assay. The success rate for this set of SNP assays was 75%. The SNPs were assessed for polymorphism by testing against Canadian and European populations and it was determined that 26% of SNPs were monomorphic. Table 6 lists the 3072 SNPs for which assays were developed, and also identifies the 1620 SNPs that were validated according to the present Example. However, on analysis, a small number of the SNPs were found to be identical to SNPs identified independently in a previous study (Moen et al., 2008). The SNPs of the present disclosure that overlap with those described by Moen et al. are listed in Table 13.

The frequency of the present set of selected SNPs in Atlantic cod is 1/516 bp, which is similar to the frequency reported in Atlantic salmon of 1/614 bp (Hayes et al., 2007). It is somewhat lower than the frequency observed in Oncorhynchus keta (chum salmon; 1/175 bp) or in Oncorhynchus tshawytscha (Chinook salmon; 1/301 bp) (Smith et al., 2005). SNP selection strategy is likely to play a large role in the observed frequency of SNPs within the genome, but it also might reflect the fact than in case of Atlantic cod and Atlantic salmon SNPs have been detected in fish originating from a limited number of populations.

To maximize the detection of real SNPs, stringent criteria were used to reduce the likelihood of selection of false or rare SNPs. It was postulated that selecting SNPs having the minor allele represented in at least two reads will generate a set of markers that are useful for gene mapping and parental assignment. However, the present SNP set has been selected based on SNPs that are expected to be frequent within populations being used for selective breeding in Atlantic Canada and in related populations located in the area surrounding Atlantic Canada. Therefore, this set of SNPs is likely to be less useful for the estimation of genetic variation in populations with different geographical distribution, such as populations originating in North-East Atlantic The present SNP collection likely contains few rare SNPs because of the selection criteria employed. These rare alleles can be useful for the analysis of certain populations since they may prove to be specific to, and thus diagnostic for, these populations. The fact that 184 SNPs were found that are polymorphic in Canadian populations but monomorphic in North-East Atlantic populations is a clear indication that, due to the ascertainment bias intrinsic within the selection procedure, the present collection of SNPs might be regarded as less useful resource for characterizing the genetic structure of European populations.

The ratio of transition (A/G, C/T) to transversion (interchange of purine for pyrimidine bases) substitutions among validated SNPs was 1.11:1. This is lower than the values obtained in Atlantic salmon, (1.37:1) (Hayes et al., 2007) and for O. tshawystsha (1.49:1) (Smith et al., 2005). These values are both slightly higher than that reported for zebrafish of 1.20:1 (Stickney et al., 2002). It was also observed that the ratio is slightly different between predicted SNPs and validated SNPs, which are 1.18:1 and 1.11:1 respectively, showing that the success rate of validation for transition substitutions is marginally lower.

The SNPs developed in the present study add significantly to the total number of validated SNPs for Atlantic cod. In a previous study, Moen and colleagues identified and validated 318 SNPs (Moen et al., 2008), however only 7 SNPs were common between the two studies and these have been listed previously in this section. The SNPs described in both analyses have been detected from EST assemblies and thus are associated with transcripts. One third of the SNPs were detected on annotated sequences in the present analysis as the ESTs on which they were detected have a high proportion of non-coding sequence, whereas in the Norwegian study 87% of the SNPs had a significant BLAST hit. Validation success was similar in both studies, with the percentage of failed assays at 29% for Moen et al. (Moen et al., 2008) and 25% for the present study. The number of polymorphic SNPs as a percentage of all putative SNPs tested was found to be 54% by Moen et al. (Moen et al., 2008) and 55% in the present study (53% for validated SNPs). The number of monomorphic loci was slightly higher in the present study than found by Moen et al. (Moen et al., 2008). The majority (91%) of predicted SNPs that were found to be monomorphic in the present study have their minor allele represented by 2 reads only. These likely fall into two categories; 1) SNPs that are rare within the populations tested, and therefore polymorphism at these loci exists but was not observed in the sample set tested, and 2) incorrect SNP predictions. This emphasizes the need for stringent selection criteria and also that validation of SNPs is a necessary step to establish the accuracy of markers.

The libraries from which the sequences used in the assembly were generated, and thus from which SNPs were identified, were created using tissue from fish originating from collections from Nova Scotia (Cape Sable) and Newfoundland (Bay Bulls), Canada. By testing these SNPs against more eastern populations such as Ireland, Iceland and Norway, we have shown that they are also informative as markers across more geographic distant populations. Some SNPs (184) were found to be polymorphic only in all Canadian populations, and therefore have the potential for use as traceability markers.

By genotyping, two reference families, SNP were checked for Mendelian segregation. A numbers of SNPs showed a significant departure from Mendelian segregation but in fact they were more likely paralogous genes coding for 2 SNPs since both parents and progeny were heterozygous. This is not uncommon when identifying SNPs in fish. In most studies around 2-4% of validated SNPs are assumed to be duplicated SNPs (Moen et al., 2008) except for salmon where 14% of SNPs were scored as heterozygotes in all individuals tested (Hayes et al., 2007). However, in addition to the set of SNPs predicted to occur on duplicated genome segments, several additional SNPs show non-Mendelian segregation patterns in the two families tested. Also, four SNP, two in family B33 and two in B87 appear to be duplicates in that family, but segregate in the other family, which could be indicative of either selective forces acting differently upon those families or, more likely, complex patterns of gene duplication and divergence.

Most of the SNPs described herein are predicted to fall within non-coding sequence. This is expected in the present dataset as all of the ESTs used in SNP identification were sequenced from the 3′ direction, and thus the majority of each sequence is likely to represent the 3′ untranslated region. Nevertheless, a minority of the SNPs identified herein are predicted to occur in coding regions. The remaining SNPs are either in non-coding sequence, or on contigs with no significant sequence similarity. For the SNPs found in coding regions, only a subset of the polymorphism, i.e., the non-synonymous substitutions will result in a variation in the amino acid sequence of the encoded protein. SNP studies have reported a higher number of synonymous SNPs (sSNPs) when compared to non-synonymous SNPs (nsSNPs); the variation at non-synonymous sites has the potential to be associated with deleterious mutations. A higher number of sSNPs is usually observed, and this is likely to be the result of evolutionary constraints preferentially eliminating variation at non-synonymous sites. For example, 80% of SNPs identified in coding regions in chicken (Kim et al., 2003) are synonymous compared to 71% for Schistosoma mansoni (Simoes et al., 2007), 68% for Anopheles funestus (Wondji et al., 2007), 60% for zebrafish (Guryev et al., 2006), and 55% for rat (Guryev et al., 2004). An even higher frequency of sSNPs has been detected in Salmo salar (82%). The frequency of sSNP observed in Atlantic cod is intermediate (64%) to that reported for other species.

A preliminary linkage map has been constructed using the SNPs presented herein. This map has been generated using the cross-pollination (CP) parameter set of JoinMap4® (Van Ooijen, 2006), which is applicable to crosses generated from wild individuals taken from an outbred population, and has also been used to generate maps from a small number of crosses in other species (Spigler et al., 2008; Tani et al., 2003). Independent maps were created from the two families B33 and B87, which gave the same number of major linkage groups (23) and a similar overall marker order. Maps generated from these two families were merged to give the consensus map shown in FIG. 5 . Preliminary analysis of additional families on a second-generation SNP panel gives additional support to this consensus map. The second generation map shown in Table 10 has been constructed using three families and comprises 23 linkage groups with 1298 mapped markers.

It is possible to generate separate male and female maps for most of the genome of Atlantic cod using the two families genotyped on the two SNP panels described here. The majority of the linkage groups in the consensus map could be identified in sex-specific maps, however these maps are less dense and, due to their bi-allelic nature, only a few informative SNPs are common between maps created with a single individual, making the merging of maps problematic. However, although there appears to be a significant difference in the recombination rates between male and female Atlantic cod (Moen et al., 2009), this has not prevented construction of an integrated map both here and in the previous study (Moen et al., 2009).

The large collection of SNPs described herein for Atlantic cod are of great utility for both the aquaculture industry, and for the management of wild fisheries. As improved automated genotyping systems have been developed, SNPs have become important markers for commercial diagnostics and parental genotyping applications. Due to lower individual information content, a higher number of SNPs is required for parental assignment (Werner et al., 2004) when compared to the microsatellite marker approach that is the current industry standard. In pigs, comparable parental exclusion probabilities have been achieved when using a panel of 60 SNPs or a 10 microsatellite marker panel, but the SNP panel was more sensitive for individual identification (Rohrer et al., 2007). In cattle, panels of 32 and 37 highly informative SNPs were powerful enough to distinguish progeny from multibreed composite populations (Werner et al., 2004; Heaton et al., 2002). In order to develop a powerful SNP panel for cod parental assignment, SNPs must have a high minor allele frequency within the population under study (Werner et al., 2004), and it is also useful if information of linkage between the marker set chosen is available. In total, 332 SNPs markers described herein have a minor allele frequency higher than 0.4 ( FIG. 4 ). This SNP panel may also be used in product traceability applications. It is also possible to apply this large marker set to increase the resolution of population structuring within wild populations of Atlantic cod, and to better monitor the genetic diversity within populations that are being actively fished.

The SNP collection presented here is useful for the genetic analysis of cod fish which may lead to the association of features showing interesting transcriptional responses with QTL intervals, potentially providing useful tools for marker assisted selection. For example, cgpGmo-S1123 is located in the sequence coding for 3-oxo-5-beta-steroid 4-dehydrogenase (AKR1D1). This gene belongs to the Aldo-keto reductase family 1, member D1 and catalyzes the reduction of progesterone, androstenedione, 17-alpha-hydroxyprogesterone and testosterone to 5-beta-reduced metabolites, as well as playing a role in bile acid biosynthesis (Palermo et al., 2008). This gene is of great interest for its role in sexual maturation, and this SNP can be used for marker assisted selection of selected variants.

The SNPs described here have been derived from ESTs, and thus can provide anchor points for more extensive comparative genomic analyses.

The present description provides an extensive resource of SNP markers for Atlantic cod, Gadus morhua . The SNPs have been validated across a panel comprising several populations of wild cod, and using two family crosses. This large collection of SNPs is valuable for developing diagnostic assays to distinguish between cod populations, as well as producing tools useful for the aquaculture industry. The genetic linkage map provided is also a valuable resource for QTL discovery and marker assisted selection.

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