Gene expression profiles that identify genetically elite ungulate mammals

This application claims priority from U.S. Ser. No. 60/474,577, filed May 30, 2003.

BACKGROUND OF DISCLOSURE

Animal improvement has been achieved through selective breeding since the beginning of animal agriculture. In recent times, animal breeding employs a quantitative genetics approach, where improvement is based on evaluation of production records of progeny and relatives, e.g. records of milk production and carcass quality, followed by breeding pedigreed animals whose phenotypes are closest to a desired phenotype. Most improvement in dairy cattle, for example, is made through use of sire lines selected in this manner.

Marker-assisted selection using genetic markers that identify chromosomal regions containing genes (genetic loci) that affect quantitative traits (QTL) is an approach that is currently being developed by the animal breeding industry, e.g. for cattle traits. For example, a polymorphism in the somatotropin gene causing a change at amino acid position 126 provides a marker that can be correlated to the trait of superior milk production, but does not necessarily identify the polymorphism as the cause of the trait. The actual cause of the increased milk production may be due to some other closely linked (i.e. in close proximity) genetic factor or gene in the cattle genome, and not to the existence of the somatotropin polymorphism. Consequently, these statistics-based animal breeding methods are generally slow, expensive and inaccurate because the genes themselves underlying the traits of interest have not been identified, so selection does not achieve completely successful or predictable outcomes.

Further, complex gene action and interactions among genes serve to complicate objectives of traditional breeding programs. Selection based purely on phenotypic characteristics does not efficiently take into account such genetic variability, and is therefore not optimal.

For example, these traditional approaches are used for the purpose of selecting and breeding dairy cows capable of superior milk production. Although such programs have improved milk production, there are disadvantages because of the significant costs and time involved before the success of the program can be determined. For example, a traditional breeding program requires the breeding of many cows with a particular bull and subsequent analysis of the milk production of the female-progeny of these cows to determine whether the bull is of superior genetic value. A particularly successful breeding family of cattle is the Holstein line derived from the bull Carlin-M Ivanhoe Bell.

Female progeny must be raised, become pregnant, allowed to give birth and milked for a minimum length of time before milk production capabilities can be analyzed. Although this type of improvement program has improved milk production, there are disadvantages because of the significant costs and time involved before the success of the program can be determined. A breeding program relying on traditional techniques and selection criteria typically requires the investment of 4 or more years in a group of cattle before significant analysis of the program can be undertaken. It would, therefore, be advantageous if additional methods or criteria were available that were quicker and cheaper to determine whether a bull, heifer or cow should be included in a breeding program designed for superior milk production.

Boosting the level of growth hormones via introduction of additional hormones can improve cattle performance. An example is the use of bovine growth hormone or somatotropin. This has been made possible by the cloning and isolation of genes that express such proteins and then adding the resulting products of these commercially produced proteins to animals via feeds, injections, drugs, and the like. This method of boosting production of essential proteins however is inherently limited by the underlying genetics of the animal and because the effects are not heritable, does not offer anything in the way of selection of genetically superior animals for optimum genetic capabilities.

Furthermore, qualified administration of multiple injections of growth hormone keep costs high, and sick animals cannot be given growth hormone injections. There is also a concern that animals that are treated with growth hormone are more susceptible to mastitis. In addition, public acceptance of growth hormone is still uncertain. The results of bovine growth hormone injection include an increase in overall milk production, with no change in milk composition. This is a significant disadvantage because a dairy producer is paid on the basis of three milk characteristics, total volume of milk, total pounds of fat in the milk, and total pounds of protein in the milk, thus quality is as important as quantity. Producers may be paid more for protein than fat. Thus it can be seen that there is a continuing need for means of efficiently selecting and breeding cattle for improved milk production without concomitant decrease in milk composition, particularly protein content. In general, better methods of identifying animals with desirable predicted transmitting ability (PTA) for desirable phenotypes, such as high milk production and yield of protein and fat, are needed for long term benefits.

Microarrays are being developed for many research applications in animals, e.g. to study responses of genes to external stimuli. Microarray technology is revolutionizing biology by permitting the simultaneous analysis of transcript levels of thousands of genes in different physiological states of an organism, tissue or cell. Construction of microarrays is most efficient when information is utilized from annotated genome or EST sequencing projects. Evaluation of transcript levels using microarray technology has led to new insights into animal development, cancer, infectious diseases and aging. Microarrays have recently been produced for studying the functions of cattle genes and gene expression changes in different physiological states, although results to date have been quite limited.

In summary, a need exists in the art for a method of genetically evaluating animals such as ungulate (hoofed) mammals to enable breeders to more accurately select those animals which not only phenotypically express desirable traits, but those which express favorable underlying genetic criteria leading to the desired phenotypes. Therefore, it would be advantageous to find ways to more accurately predict quantitative traits from genomic information.

SUMMARY OF DISCLOSURE

Methods and compositions to identify and select genetically elite animals, e.g. ungulate (hoofed) mammals, with a desired phenotype for breeding, in particular, a quantitative trait such as high milk production, carcass quality and resistance to disease, include creating gene expression profiles from individual animals, developing a Gene Expression Index for phenomic selection by comparing gene expression profiles of animals whose phenotypes are at the extreme ends of a continuous distribution of the phenotype, and using the index to identify and select elite animals for breeding to improve economically important traits.

A method of making a gene expression index for phenomic selection of elite ungulate mammals, e.g. cattle, sheep, goats, horses, and deer, includes the steps of defining and selecting a phenotype that has multiple levels, for example, quantitative complex traits, especially those that are economically important, such as milk production levels, and other traits such as high protein values, carcass quality, fertility and resistance to disease. A plurality of genes is selected for which gene expression can be determined to create gene expression profiles of individual animals. In an embodiment, the first group of animals differs from the second group of animals in predicted transmitting ability (PTA) for the desired phenotype, e.g. high milk production, fertility, disease, resistance. cDNA is prepared from RNA isolated from biological samples such as blood or liver from a first group of animals that has a first level of a defined phenotype (e.g., high genetic potential for milk production or fertility). The cDNAs are hybridized to the plurality of nucleic acids representing the genes. Hybridization can take place on a microarray which may be designated a DNA microchip or biochip. cDNAs from RNA isolated from biological samples from a second group of animals that has a second level of the phenotype (such as low genetic potential for milk production or fertility), are also hybridized to the plurality of nucleic acids, either on the same or a different array. The expression profiles of the two groups of animals are compared statistically and the genes that differ significantly between the two groups form the Gene Expression Index (FIG. 1) This index can be used for phenomic selection, a method that involves comparing gene expression profiles of candidate animals for animal breeding to the Gene Expression Index or to a Reference Expression Profile created from an optimal subset of genes in the Gene Expression Index.

The method of creating a Gene Expression Index that can be used to identify genetically elite ungulate mammals then includes the steps of:

• • (a) comparing expression levels of genes in tissues (e.g., blood) of ungulate mammals classified according to multiple levels of a selected phenotype (e.g. genetic potential for milk production);
  • (b) determining a set of genes that differ significantly in expression levels at different levels of the the selected phenotype; and
  • (c) using statistical criteria, creating a list of genes that are differentially expressed (Gene Expression Index) in animals classified according to the different level(s) of the phenotype (e.g., high and low genetic potential for milk production).

A Gene Expression Index that includes genes whose GenBank accession numbers are listed in Table I, Table II, Table III or a combination or a subset thereof is disclosed. In the Gene Expression Index, an optimal subset to create a Reference Expression Profile includes 1 to about 100 nucleic acid sequences, whose GenBank accession numbers are selected from Table I, Table II, or Table III.

A method of determining whether an ungulate mammal (candidate animal) is genetically elite for a phenotype/trait of interest includes the steps of:

• • (a) determining a gene expression profile for the animal using RNA collected from one or more tissues;
  • (b) comparing the expression profile with the gene expression index or a Reference Expression Profile for that tissue(s); and
  • (c) identifying the animal as an elite animal if the gene expression profile is similar to expression levels of genes the Gene Expression Index.

For example, a method for predicting milk production or genetic potential for milk production in a cow (candidate animal) prior to her first lactation includes the steps of:

• • (a) obtaining a gene expression profile of the candidate cow as a heifer; and
  • (b) comparing the profile to a Gene Expression Index created from heifers or cows with known milk production levels, or known genetic potential for milk production; and
  • (c) predicting milk production or genetic potential for milk production of the candidate animal by ranking similarity to the Gene Expression Index.

For example, a method for phenomic selection of a breeding bull predicted to transmit high genetic potential for milk production to his offspring includes the steps of:

• • (a) selecting an optimal subset of nucleic acids from the Gene Expression Index representing genes whose sequences are designated by GenBank accession numbers listed in TABLE I, II, and III, to create a Reference Expression Profile, wherein the Reference Expression Profile accounts for a significant fraction of the variation in the phenotype of interest;
  • (b) creating a gene expression profile of the subset of nucleic acids for the the candidate bull;
  • (c) designating the candidate bull as genetically elite if the gene expression profile is similar to the Reference Expression Profile for the phenotype of interest, e.g., genetic potential for milk production (measured as predicted transmitting ability, or PTA).

(d) selecting a bull for a breeding program if the Candidate Expression Profile shows has a high similarity to the Reference Expression Profile (FIG. 2).

A microarray that includes nucleic acids derived from cattle RNA, whose nucleic acid sequences are designated by GenBank accession numbers listed in TABLES I or II or III or a combination thereof is within the scope of this disclosure. Any other suitable gene expression detection methods such as as PCR and Northern blots can also be used to test the expression levels of genes in the Gene Expression Index and is in the scope of the disclosure.

An optimal subset of nucleic acids whose expression levels in blood leukocytes are useful for predicting genetic potential for milk yield includes genes encoding for a factor upregulated during skeletal muscle growth (SEQ ID NO: 1), Sjogren syndrome antigen B (SEQ ID NO: 2), ribosomal protein L22 (SEQ ID NO: 4), pre-mRNA branch site protein p14 (SEQ ID NO: 5) and other genes represented by for example, SEQ ID NOS: 1-10 in TABLE I. New functions related to lactation are provided for these genes by this disclosure.

An optimal subset of nucleic acids whose expression levels in liver are useful for predicting genetic potential for milk yield includes genes encoding for histone 1 (SEQ ID NO: 359), epithelial v-like antigen 1 (SEQ ID NO: 360), poly (A) binding protein (SEQ ID NO: 361), and any other genes represented by SEQ ID NOS: 358-367 in TABLE II.

An optimal subset of nucleic acids whose expression levels in both liver and blood are useful for predicting genetic potential (TABLE III) includes genes encoding for a core promoter element binding protein (SEQ. ID. NO: 368), a low density lipo protein receptor-related protein (SEQ. ID. NO: 369), a ubiquitin conjugating enzyme E2L3 (SEQ. ID. NO: 370) and any other genes designated by SEQ ID NOS: 371-408.

A kit for detecting gene expression profile differences includes in discrete compartments:

• • (a) at least one microchip comprising nucleic acids whose sequences are designated by genes having GenBank accession numbers listed in TABLE I, II or III;
  • (b) reagents to perform a microarray analysis; and optionally
  • (c) a computer program that can compare expression profiles and identify genetically elite ungulate mammals for breeding or for production traits

Kits may also include a subset of oligonucleotides, whose sequences represent a part of the sequences designated by GenBank accession numbers listed in TABLE I, II or III.

Reagents to perform quantitative PCR and other methods of detecting differences in Gene expression profiles, are also suitable in kits. A kit that utilizes any suitable method for detecting gene expression is within the scope of this disclosure. Such methods also include conventional reverse transcriptase (RT)-PCR, and Northern hybridizations.

The Gene Expression Index is created by statistical comparison of gene expression patterns in tissues collected from animals differing in a particular phenotype or trait, e.g., genetic potential for milk production (FIG. 1). A summary of the approach is given here, with details given in the Detailed Description of the Disclosure. Microarray gene expression data are processed for spot quality, and intensity values are normalized. Gene expression in the tissue (e.g. liver or blood) is measured relative to a standard reference control (pool of RNA from different sources) for all samples and all genes. The gene expression intensity values relative to the intensity values in the standard reference (the gene expressio ratios) are calculated for all samples for all genes on the array. Ratios are compared for animals in each group, e.g., high and low for a trait, using ANOVA. The relative difference in gene expression between the two groups of samples is measured as a “ratio-of-ratios” (see Definitions) for each gene. The probabilities of the differences being due to chance are corrected for the number of comparisons made using the false discovery rate (FDR). The genes with the highest significance value falling below a certain FDR threshold are considered “significant” and used to create the Gene Expression Index (FIG. 1). A Gene Expression Index can be created for more than one trait, or a weighted index can be created for multiple traits simultaneously. More than two groups of animals may be compared. An optimal subset of genes can then be selected to streamline the testing of candidate animals for breeding or retention in a herd (FIG. 2). The optimal subset is created by selecting those genes with the highest significance for predicting the trait, i.e., those genes whose expression level account for a large amount of phenotypic variation in the trait among the animals tested. A gene expression pattern created using an optimal subset of genes is called the Reference Expression Profile (FIG. 2).

A method of determining whether an animal is a genetically elite ungulate mammal, suitable for phenomic selection of any trait, includes the steps of determining a candidate expression profile (see Definitions) for a candidate ungulate mammal and comparing the candidate expression profile to a Reference Expression Profile. The animal is designated an elite animal for purposes of breeding or production, if its gene expression profile is similar to the Reference Expression Profile. Similarity is determined by comparing the expression profile values using statistical methods. Phenomic selection may be for high milk production or other economically important traits, such as health traits, fertility, or carcass quality.

In an embodiment, a method for selecting a genetically elite animal predicted to have high genetic potential for milk production, includes the steps of selecting an optimal subset of nucleic acids from TABLE I-III, to create a Reference Expression Profile. Genes included are those encoding for a factor upregulated during skeletal muscle growth (SEQ ID NO: 1), Sjogren syndrome antigen B (SEQ ID NO: 2), ribosomal protein L22 (SEQ ID NO: 4), pre-mRNA branch site protein p14 (SEQ ID NO: 5). The Reference Expression Profile accounts for the greatest amount of variation in the phenotype or a predetermined amount of variation in the phenotype, for example in TABLE I, genetic potential for milk production. cDNA from RNA obtained from a biological sample from the animal is used to create a gene expression profile. For a specific phenotype, a trait for which gene expression profiles are derived from RNA from various tissues, the predictive set of genes for the phenotype may overlap among the tissues or be unique. For example, for genetic potential for milk production in cattle, TABLE I shows predictive genes identified from leukocytes, TABLE II shows predictive genes from liver. TABLE III shows predictive genes from blood and liver. The expression levels ranked by significant differences between high and low genetic potential for milk production (or PTA), may not be identical when RNA is derived from leukocytes versus liver.

The GenBank accession numbers of cattle DNA sequences whose expression profiles are predictive of cattle milk production, are listed as genes in TABLE I and include both unannotated e.g. BF040830 (SEQ ID NO: 139), BF041863 (SEQ ID NO: 127) and known genes e.g. BF040826 (SEQ ID NO: 10), BM366099 (SEQ ID NO: 22) in cattle and in other species. That is, the list in TABLE I includes those genes whose expression level in peripheral blood leukocytes is different between the high and low PTA groups of cattle at a significance level of less than or equal to an FDR adjusted p-value of 0.29 (see Materials and Methods). Significance level cut-offs vary, and that will alter the number of genes used for selection.

Nucleic acids whose GenBank accession numbers of cattle DNA are listed as genes in TABLE II, whose expression profiles in liver are predictive of genetic potential for milk production include both unannotated e.g. SEQ ID NO: 358 (AW464111) and known e.g. SEQ ID NO: 359 (AW464166) genes. The unannotated genes are of unknown specific function, but their utility is that their gene expression profile is predictive of milk production. “Gene” used herein refers to sequences which are derived from sequencing cattle cDNAs and/or ESTs. Gene quanitity was determined by comparing sequences with the human and mouse UniGene databases and other GenBank resources.

A Gene Expression Index may include nucleic acids selected from the group consisting of genes in TABLE I, genes in TABLE II, genes in TABLE III, or a unique combination or a subset thereof.

A method of increasing milk production in cattle also includes selecting a gene or genes from TABLE I or TABLE II or TABLE III and modulating expression of the gene in the target cow, heifer or bull to increase milk production. Modulation indicates the variation in the level of the protein in the cattle or the level of gene expression of other genes that affect lactation as a result of transgenic and non-transgenic manipulation, e.g. somatotropin injection.

A gene expression profile-based phenotypic selection is not limited by the number of phenotypic markers, such as enzyme levels or blood groups. Gene expression data can be correlated with the expression of complex phenotypes. Also, gene expression profiling can be applied to any sex (e.g. identify elite bulls for milk production), life stage, including embryos, and targeted at specific tissues that determine particular phenotypes. In addition, correlations between expression profiles and phenotypes selected provide insights into metabolic and signaling pathways that affect complex traits.

Gene expression profile-based selection is useful to lower the high cost of progeny testing currently used to prove a sire's genetic merit, because young sires that are determined by genetic testing to have undesirable Gene Expression Index values would not have to be progeny tested, thus saving the seedstock industry millions of dollars and increasing the rate of genetic improvement for targeted traits. In addition to identification of genetically elite bulls, the gene expression profile of individual females can identify those animals that will have the highest lactation levels and also those that can serve as bull dams for the production of the next generation of elite dairy cows.

Thus, methods of the present disclosure for identifying genetically elite animals based on gene expression profiles using any tissue as a source of RNA greatly reduces the time and expense for identifying breeding animals and improves the accuracy of selection.

Definitions

Array, microarray: molecules connected to a matrix or support in a specific arrangement relative to each other.

Accession numbers: relate to sequences that represent cattle genes in GenBank. The UniGene database provides unique indentification numbers of the corresponding genes in human or mouse databases. Cattle gene sequences are aligned to human or mouse sequences to determine “gene” identification and or similarity.

Biochip: also known as a chip, DNA chip, DNA microarray, DNA array, peptide chip or peptide array; includes array of biological molecules such as DNA fragments, peptides, proteins, lipids, and tissues connected to a matrix.

Biological sample: a biological material obtained from blood, liver, skin, tissues, saliva, tears, bodily fluids or bodily secretions.

Candidate animal: an animal that is screened for a desired molecular phenotype, e.g. expression profile, to determine if it is a genetically elite animal.

Candidate Expression Profile: an expression profile obtained from a biological sample of a candidate animal whose phenotype is to be predicted.

• • cDNA expression array: also known as cDNA array or gene expression array or gene expression microarray. The ordered alignment of different complementary DNAs (cDNAs), or fragments of cDNAs, or oligonucleotides immobilized on a support (e.g. a nylon-based membrane or a glass slide). Such arrays may contain tens of thousands of different cDNAs on a small space (e.g. 1×1 cm, or less), and are used to determine differential gene expression patterns. cDNA arrays can be produced by different techniques. For example, one method uses PCR amplified partial sequences of cDNAs.

Elite: an animal with desired or improved characteristics (traits).

Expression profile (gene expression profile): a gene expression dataset generated by simultaneous detection in a sample from an animal of expression of a plurality of genes, whose genomic DNA, cDNA or oligonucleotide fragments thereof are determined by methods including microarrays wherein the DNA is immobilized onto a matrix or support, to which labeled cDNA from a target sample(s) are hybridized.

Expression ratio: ratio of expression value of a gene from at least two biological sources or at least two different time points.

False Discovery Rate (FDR): an approach to statistically analyze false positives in multiple samples. Instead of controlling the chance of any false positives, FDR controls the expected percent of false predictions in a set of predictions. A FDR threshold is determined from the observed p-value distribution.

Gene: a specific sequence of nucleic acids that generally includes introns and exons and regulatory regions. A “gene” referred to herein also includes ESTs cDNAs or fragments thereof, which include exons.

Gene index: (Gene Expression Index): a selective list of genes based on their differential expression profiles that correlate to the genetic potential for a desired phenotype. A Gene Expression Index represents expression values arranged or ranked using a specific classifying scheme such as hierarchical clustering or p-value (probability level) sorting.

Genotype: the complete genetic complement at a locus or of an organism.

High PTA ratio: normalized fluorescence intensity level of a singe DNA sequence on the microarray for an individual or group of high genetic potential animals divided by the normalized fluorescence intensity value for the same DNA sequence expressed in the reference standard control. Fluorescence intensity is directly related to the level of sequence-specific mRNA in a cell, cells or tissues (other measures of relative expression are within the scope of the disclosure).

Hybridization: the formation of duplex molecules from complementary single strands (e.g., DNA-DNA, DNA-RNA, RNA-RNA). A single stranded nucleic acid molecule is generally labeled, e.g. with a detectable dye (radioactive or fluorescent) and used as a probe that may anneal to molecules with similar sequences that are single stranded. Conditions are varied to detect degrees of similarity, i.e. the more stringent the conditions, the greater the similarity needed for hybridization to occur.

Low PTA ratio: normalized gene expression ratio from a biological source with low predicted transmitting ability for a particular phenotype. (See also, High PTA Ratio.)

Marker: any specific DNA segment whose base sequence is polymorphic and is used as a diagnostic tool to identify a particular phenotype or a method of detecting the presence of a linked gene. Markers used herein refer to molecular markers and markers determined by expression profile analysis.

Matrix: a support such as glass slide, silicon, gold slide, gel pad, nylon membrane or other similar structures on which an array or microarray of molecules is formed. A matrix or support may contain functional groups to attach biomolecules.

Modulating: refers to a controlled increase or decrease of transcript or protein levels of any specific gene through genetic or non-genetic methods.

Nucleic acids: DNA, cDNA, mRNA and any other modified nucleic acids. Nucleic acids also include single stranded DNA, double stranded DNA, RNA-DNA hybrids, complements and reverse complements.

Optimal subset: a selective list of genes whose expression profiles account for the greatest amount of variation in a desired phenotype, or a predetermined amount of variation. An optimal subset may include as few as one gene.

P-value: represents the probability that a deviation as great as, or greater than, that obtained from the experiment will occur by chance alone. In other words, p-value is the probability of observing a test statistic that is as extreme or more extreme than currently observed, assuming that the null hypothesis (H₀) is true.

Phenomic selection: selection of animals for breeding or production on the basis of one or more phenotypic markers that directly contribute through molecular processes to a particular phenotype. Phenotypic markers can include profiles of RNA transcripts (transcriptome), protein profile (proteome) and metabolites (metabolome).

Phenotype: the observable structural and functional properties of an organism which results from interactions of both genotype and environment. The phenotype can be exhibited in multiple levels or degrees. The term phenotype also includes improved phenotype, desired phenotype, favorable phenotype, preferred phenotype and target phenotype.

Pleiotropy: multiple effects of a gene which can result in distinct, apparently unrelated phenotypes.

Polynucleotide: any single stranded or double stranded molecule with a sequence of more than ten nucleic acids. The cDNA spots on a microarray are all double stranded, but are denatured during the process of hybridization.

Predictive genes: a subset of genes from a gene index selected to account for a predetermined amount of variation in a phenotype, based on gene expression profiles.

Printing: a process by which a biomolecule such as DNA, RNA or peptides are immobilized or attached to a matrix.

Proteome: the complete set of proteins in a cell at a given time.

PTA: predicted transmitting ability of an animal in reference to phenotypic traits; a measure of genetic merit.

Quantitative trait locus (QTL): a genomic region controlling expression of a quantitative trait, the locus may have several alleles; or two or more separate genetic loci that contribute cooperatively to the establishment of a specific phenotype or trait.

Ratio-of-ratios: ratio of normalized gene expression ratio from a biological source with high predicted transmitting ability for a particular phenotype to the normalized gene expression ratio from a biological source with low predicted transmitting ability for the particular phenotype. The ratio-of-ratios gives the absolute difference in expression for a particular gene among the two sources or groups.

Reference Expression Profile: a gene expression profile obtained from an optimal subset of predictive genes from a Gene Expression Index. Variation in expression of genes in the Reference Expression Profile accounts for all, some or a predetermined amount of variation of a trait.

Similar: a measure is similar if the correlation between it and another measure is statistically significant.

Statistical significance: statistical methods allow an estimate to be made of the probability of the observed degree of association between variables, and from this statistical significance can be expressed, commonly in terms of the p value.

Target animal (candidate animal): an animal that is potentially an elite animal.

Traits: phenotypes, e.g., genetic potential for milk production.

Transcriptome: an entire set of mRNAs and non-coding RNAs expressed by a given cell, tissue or organism.

Weighted Gene Expression Index: a list of genes organized according to a weighting factor. A weighted index can be used for phenomic selection on the basis of its ability to predict a phenotype(s). One method to create a weighted index is to multiply the p-value for a given gene (in the comparison of two phenotype classes) by the percentage of correct predictions when the gene is used to identify high or low PTA animals at the population level.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic illustration of creation of a Gene Expression Index for phenomic selection of an ungulate mammal.

FIG. 2 shows a schematic illustration of phenomic selection of genetically elite ungulate mammals for production or breeding using a Gene Expression Index or Reference Expression Profile.

FIG. 3 shows that a scatter plot of the first two canonical discriminant functions (Can1 and Can2) of gene expression separates cows into high (Hi) and low (Lo) PTA groups number represent individual cow identification. Table IV shows the data for the cows.

DETAILED DESCRIPTION OF THE DISCLOSURE

Methods of the present disclosure (“phenomic selection”) will enhance or replace progeny testing, significantly increasing the rate and efficiency of genetic improvement in animals. Multiple phenotypic traits may also simultaneously be improved by these methods. Quantitative traits may be controlled either by the action of a major gene, or by many genes interacting with environmental factors. Construction of a Gene Expression Index for a specific trait permits use of that index for selective breeding. The gene expression profile for a particular trait may be linked to a single gene with a major effect or associated with a number of genes with additive effects. Premises of the disclosure are that 1) the genes whose expression levels are under artificial selection include genes that directly affect the quantitative trait of interest, and/or 2) genes that might not physically be associated with a mapped quantitative trait locus (QTL; trans effects) can be used as a reliable predictor of the trait. In other words, using “polymorphism” in gene expression levels as a correlated indicator(s), alleles that control a particular trait, despite being present at a chromosomally distinct locus or loci, will also be selected. This represents an effective approach for “marker assisted selection,” because of the possible roles of pleiotropy and epistasis in determining complex phenotypes. For example, an important predictor of a complex phenotype may be the expression level of a transcription factor that is regulated by five polymorphic QTL. Monitoring the expression level of the transcription factor directly controls for many favorable alleles at the QTL level. Because individual animals will have different expression patterns for the QTL identified by expression profiling, the Gene Expression Index can be used to identify genetically elite individuals. Using this scheme, the animals with the maximum number of QTL positively affecting a trait and the minimum number of QTL negatively affecting the trait would be selected for breeding. Values might range from +100, which would represent 100 percent positively associated expression levels of all genes positively affecting a trait, to −100, which would represent 100 percent negatively associated expression levels of all genes negatively affecting a trait. This is an example of a weighted index. Selection can be performed on either sex using indices that are common to the sexes or be sex-specific. Selection can be performed at any stage of development, depending on the trait. Even embryos may be tested.

Gene expression patterns that are associated with phenotypic variation in a quantitative trait may be part of upstream or downstream regulatory pathways and are thus potential candidates for drug targets or genetic modification. Expression patterns may vary both quantitatively or qualitatively to have value in predicting a specific phenotype.

The present methods can reduce the number of animals needed to achieve a production goal and reduce breeding costs. Gene expression profiles from young male calves can be tested before entry into sire evaluation programs. Young bulls whose expression profiles most closely match the “ideal” phenotype predicted by the gene index are advanced in breeding programs while those that have lower overall index values are culled from the program. The bulls' expression index value allows ranking of that bull for genetic merit, thus permitting prediction of milk yield (and other traits) among his daughters. For young female calves (heifers) the expression profile is used to predict milk production (and other traits) during the animal's individual future lactation or lifetime. Heifers that have a low expression index value are then culled from the herd. Animals may be tested in any life stage assuming tissues are available, but the index used may need to be matched to each specific life stage.

The Gene Expression Index provides information as a supplement to other traditional tools for selection. However, in cases of equal pedigree merit, the Gene Expression Index or Reference Expression Profile will help distinguish among the lines and thus lead to substantial improvements at much lower cost, and much more quickly. It is contemplated gene expression profiles could replace current statistical methods for animal breeding.

Extension of comparisons to more than two phenotypic levels is straightforward under the ANOVA approach or any other equivalent statistical method. Phenotypes for which these methods are useful are the basis of three milk characteristics, total volume of milk, total pounds and percentage of fat in the milk, and total pounds and percentage of protein in the milk. Thus, quality is as important as quantity. Producers may be paid more for protein than fat.

A suitable starting number of genes for an expression index is 10-300. The more stringent the p-value [or false discovery rate (FDR) adjusted p-value] used as a cut-off, the less number of genes that are included in the index. Sufficient number of genes need to be included to account for a large proportion of variation in each phenotype. The various factors that determine the number of genes included in the Gene Expression Index for trait analysis may depend on the technical and practical feasibility (e.g, handling of multiple genes in an array or multiple samples in a quantitative PCR), the robustness of the statistical significance of the expression data for a subset of genes that correlates with a particular trait, and nature of the trait (e.g., monogenic vs polygenic).

A method of making a Gene Expression Index for phenomic selection of elite animals such as ungulates, in an embodiment, cattle, includes the following steps: First, a phenotype to be improved is selected, such as high milk production, high milk protein levels, high milk fat, fertility, disease resistance, carcass quality. PTAs are available for these traits in many breeds of cattle and pigs. Next, a plurality of nucleic acids with measurable expression levels is selected, and hybridized to cDNAs created from RNAs of biological samples from a first group of animals expressing the phenotype at a first level, and compared to a reference standard or to other samples, directly (e.g., in a loop design). The same is done for a second group of animals expressing the phenotype at a second level. Expression ratios are then calculated for nucleic acids from each group. It is then determined whether there is a statistically significant difference between the expression ratios of the two groups (FIG. 1). A Gene Expression Index is created in which the nucleic acids (genes) are ranked according to their statistical significance. Multiple groups and multiple levels are within the scope of the disclosure. The methods are extendable to more than two groups using ANOVA.

An optimal subset of nucleic acids from a Gene Expression Index is used to create a Reference Expression Profile that accounts for the greatest amount of variation in the phenotype, or for a predetermined amount of variation (FIG. 2). The Reference Expression Profile is compared to a gene expression profile created from a biological sample, such as blood or liver tissue, from a candidate (target) animal. Comparison is made by microarrays or relatively inexpensive assays such as quantitative real-time RT (reverse transcriptase) PCR, e.g. TaqMan®. An animal is selected for breeding if the two expression profiles are correlated.

A cDNA library was created using mRNA from bovine placenta. Approximately 17,000 clones were partially sequenced and are termed expressed sequence tags (ESTs). Using standard normalization and computational techniques, a collection of 12,620 ESTs was selected after eliminating redundant clones. Further refinement was performed by eliminating repeats, clones with bad sequence reads, clones having sequence length of less than 300 bp, and multiple ESTs representing the same gene by BLAST analysis against the human UniGene databases. This reduced the collection to 7,653 ESTs representing approximately 6,000 unique genes. These 7,653 ESTs, represent 6,000 genes were printed in duplicate on a glass slide (microarray). The microarray was used in hybridization analysis of blood and liver RNA samples from cattle. Based on the microarray results from cattle blood samples, genes whose expression levels were significantly different among heifers grouped by PTA for milk production were included in TABLE I. Similarly, based on the microarray results from liver samples, genes whose expression levels were significantly different among the same heifers grouped by PTA for milk production were included in TABLE II. Based on the microarray results from liver and blood samples, genes whose expression levels were significantly different among the same heifers grouped by PTA for milk production were included in TABLE III. Thus, TABLES I-III do not contain all the genes present on the microarray. The tables contain genes whose expression levels have predictive values for the trait of interest.

An embodiment of the disclosure is a method for selecting genetically elite cattle predicted to have a phenotype of interest. This is accomplished by selecting an optimal subset of nucleic acids from TABLE I or TABLE II or TABLE III (a Reference Expression Profile) and comparing against a gene expression profile created from a biological sample of a candidate animal using a microarray, or quantitative PCR or another method for determining mRNA levels in a cell, tissue or organ. Afterward, the candidate cow or bull is designated as elite if the two expression profiles are statistically similar. The degree of similarity to the expression pattern obtained with Reference Expression Profile provides a relative measure of genetic merit of the candidate animal.

An embodiment of the disclosure is increasing milk production in target cattle or genetic potential for milk production (in bulls and cows). This method includes selecting a gene from TABLE I or TABLE II or TABLE III or a unique combination or subset thereof, for a Gene Expression Index. Furthermore, modulating expression of specific genes in the target animal is contemplated.

Subsets of genes whose GenBank accession numbers listed in TABLE I or II or III are also aspects of the disclosure. TABLES I-III show genes that differ significantly in expression levels for milk production in cattle. Another aspect is a group of polynucleotides (polydideoxynucleotides) selected from a group designated by GenBank accession numbers listed in TABLES I-III, for which there are no known functions. These include novel cattle genes, that is, genes with no existing human or mouse equivalent, or whose ortholog cannot yet be identified by sequence comparison.

A subset of approximately 1 to about 100 genes from the gene expression indices (TABLES I-III) can be used to predict a desired phenotype. The number in the subset is related to the contribution of any gene to the variations in the trait of interest. The greater the contribution, the fewer genes are needed.

EXAMPLES

Example 1

Development of Microarrays for Predicting Milk Production

A microarray printed with about 7,500 cattle genes was used to profile gene expression in liver and peripheral blood leukocyte samples collected from two groups of heifers selected for significant differences in predicted transmitting ability (PTA) for milk production. The equations (“animal model”) for predictive PTAs are published and used by DHIA as part of their routine summaries. http://www1.uwex.edu/ces/nubs/pdf/A3473.PDF. A web page describing PTAs can be found at http://www.ext.vt.edu/pubs/dairy/404-082/404-082.html. The PTAs of 20 heifers selected from the extremes of the PTA distributors were obtained from the DHIA records of milk yield (also correlated with fat and protein yields). The two groups of animals were age-matched (all heifers) and of the same breed (Holstein Friesian).

The microarray for gene expression profiling included a plurality of approximately 7,500 cDNAs spotted in duplicate representing a unique gene (cDNA) set of approximately 6,000 genes (see Materials and Methods). Creation of the arrays was performed essentially as described by Brown and Botstein (1999), although any microarray production method for expressing profiles is suitable for practice of the disclosure. The genes represent amplified inserts from a cattle placenta cDNA library referenced in Band et al. (2002) as disclosed herein. A cDNA library from which approximately 17,000 ESTs were sequenced and screened to reduce redundant gene sets through computational methods. The resulting unique gene set was amplified for printing on the microarray. Not all the genes that are on the array are shown in TABLE I. Only those genes that are significant between PTA groups in blood are shown in TABLE I. TABLE II shows genes expressed in liver that exhibit significant differences between PTA groups. Table III lists genes whose expression patterns differ in both blood leukocytes and liver.

Microarray Construction

A 7,653 gene cDNA microarray was created, representing an expansion of the 3,800 element microarray described by Band et al. (2002). The microarray was created according to the methods in Band et al. (2002). Briefly, a collection of 12,620 ESTs from a normalized and subtracted cattle placenta cDNA library were used as to select all new cDNA inserts that were added to the 3800 element array (http://titan.biotec.uiuc.edu/cattle/cattle_project.htm). Normalization equalizes the representation of the cDNAs in a cDNA library, thus reducing the over representation of genes, whose mRNAs are expressed to higher levels. Subtraction involves the reduction in the number of new cDNA clones to be sequenced by hybridizing the previously sequenced cDNA molecules against the cDNA libraty to eliminate duplicates. The placenta cDNA inserts were unidirectionally cloned and sequenced from the 5′ end using the M13 reverse-48 primer (AGCGGATAACAATTTCACAC). Sequences were trimmed of vector, low-quality reads and minimum length of >300 bp using a local script and filtered for repeats using RepeatMasker (Smith and Green, 1999). Clusters of ESTs were then created using CAP3, (Huang and Madan, 1999) using default parameters, except that 40 bp was set as the minimum size of the overlap between clones. Sequences entering CAP3 had an average Phred score of 20. After CAP3 assembly, all clusters and singlets containing sequences present on the 3,800 gene array were removed from the data set. New sequences were selected for the array using an approach that combined BLAST with evaluation of clone position in the transcript. First, all sequences were analyzed by BLASTN against human UniGene database (build 155-Oct. 25, 2002) and mouse UniGene database (build 116-October 2002) and checked for duplicates on the basis of human UniGene identification numbers of the best BLAST hits. Second, a representative clone was picked from each cluster with a UniGene identification number not represented on the 3,800 gene array (clusters without UniGene hits were also used). Clones were selected from clusters with the longest and most 3′ high quality read that were available. Third, singletons with and without a human UniGene hit were selected. With the few remaining places in the rearrayed grid of clones, a low level of redundancy (for intended control) was introduced by selecting clones with stronger similarity scores to human UniGene clusters than original clones used for the 3,800 set. The total number of selected sequences for the microarray was 7,653.

Amplification of clone inserts, clean-up of PCR products and spotting of the microarray were performed as described by Band et al. (2002) with some modifications. Amplification of inserts employed M13-FWD (GTTTTCCCAGTCACGACGTTG) and Ml 3-REV (TGAGCGGATAACAATTTCACACAG) oligonucleotide primers (Hegde et al., 2000). After purification, PCR products were redissolved in 3×SSC supplemented with 1 M betaine (Diehl et al., 2001). A row of 32 control spots was placed in every grid of the array. Controls include the endogenous housekeeping genes encoding beta actin (ACTB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and hypoxanthine phosphoribosyltransferase (HPRT), BLV genes env and tax, exogenous soybean control genes chlorophyll ab binding protein (CAB), Rubisco small chain 1 (RBS1) and major latex protein (MSG). Negative controls are Cot1 DNA, genomic DNA, spotting buffer, poly-A and H₂O. Duplicate spots were placed in different blocks instead of adjacent to each other as in the 3,800 gene array. This layout permits identification of the true experimental variation over the entire slide, thus facilitating interpretation of statistical analyses. Spot and printing quality were assessed on one slide by hybridizing a Cy3-labeled random nonamer (Operon, Alameda, Ca). The accuracy of the reracking, spotting and clone annotation was evaluated by resequencing the entire set of clones of the original 3,800 gene microarray and sample sequencing 8 clones per plate of the new clone set. Analysis of the sequence data revealed an error rate of 2% for the first set and 0% for the second set. Mislabeled clones were reannotated on the basis of the sequences obtained.

Functional Annotation of Microarray Sequences

All sequences were masked for repeats using RepeatMasker prior to BLAST analysis. Similarity searches were conducted for all sequences against the human UniGene database (build 166) using BLASTN. The remaining sequences with no significant similarity to human UniGene sequences (E-value threshold of e⁻⁵) were analyzed by TBLASTX against the human UniGene database followed by BLASTN against the human genome draft sequence (build 34.2, Jan. 12, 2004). In all searches, best hits were used to annotate the cattle sequences as putative orthologs. Previous comparative mapping studies have shown that such predictions are at least 95% accurate (Band et al., 2000). The GO annotations associated with human UniGene numbers were parsed from LocusLink (Mar. 1, 2004 release). GeneOntology (GO) terms (March, 2004 release) were downloaded from http://www.geneontology.org/ (Ashburner et al., 2000) and used for GO annotation of the sequences. Many of the steps of the process, including BLAST and GO annotation, were automated with Perl computer programs.

Example 2

Hybridization Analysis for Predicting Milk Production

cDNA samples from 10 heifers (see Materials and Methods) that were predicted to have high predicted transmitting ability for milk production based on breeding values of their parents, were tested 2 times (once with Cy3 dye and once with Cy5 dye) using a microarray slide printed with ˜15,000 spots, or approximate 7500 cDNA molecules in duplicate. Cy3 and Cy5 dyes are used to label cDNAs. Similarly, cDNA samples from 10 heifers, that were predicted to have low predicted transmitting ability for milk production based on breeding values of their parents were tested 2 times (once with Cy3 dye and once with Cy5 dye) using microarray printed with 7,500 genes in duplicate. Therefore, every low PTA ratio (normalized gene expression ratio for heifers with low PTAs) or high PTAs (normalized gene expression ratio for heifers with high PTAs) shown in TABLE I or TABLE II (see Sequences for clones in Tables I and II) refers to an average of up to 40 data points. For example, every experiment involves isolating RNA from cow blood, synthesizing and labeling the cDNAs with a fluorescent dye (Cy3), and co-hybridizing on a microarray with Cy5-labeled cDNA prepared from the reference standard RNA. The experiment is replicated by dye-swapping (labeling sample with Cy5 and the reference standard with Cy3). The reference standard is produced by pooling RNA from brain tissue and three different bovine cell lines. Thus, the reference standard is the same for all comparisons. Following labeling, the cDNA samples (blood and reference standard) are hybridized to the same microarray containing approximately 7,500 gene spots in duplicates. Likewise, the experiments are repeated for other cows whose predicted transmitting ability (PTA) for milk production is known.

A “reference standard” RNA pool was created to enable comparison of expression profiles obtained using the 7,653 gene array. RNA collected from brain tissue of three cows (two Angus x Hereford and one Hereford) and the following cell lines were used to create the reference standard: B-lymphocyte cell line BL3⁰ (ATCC: CRL-8037), bovine tracheal epithelial cell line EBTr (ATCC: CCL-44; provided by Dr. M. Abrahamsen, University of Minnesota, St. Paul, Minn.) and bovine kidney cell line MDBK (ATCC: CCL-22). EBTr was grown in MEM whereas MDBK and BL3⁰ were grown in L-15. Both culture media were supplemented with 10% FBS, 100 U/ml penicillin and 0.1 mg/ml streptomycin (Sigma, St. Louis, Mo.). After checking the quality and quantity of RNA, 8 mg of total RNA from each source were mixed to create the pool and frozen at −80° C. until use. Labeling of the reference RNA with Cy3 and Cy5 and subsequent co-hybridization revealed 70% (Cy3) to 75% (Cy5) of spots were 3 standard deviations above background signal.

Analysis of the Data

Fluorescence intensity data were available in a total of 40 microarrays, 20 pertaining to 10 high PTA animals (including dye-swap) and 20 pertaining to 10 low PTA animals with dye-swap. Dye-swap refers to switching of the dyes used to label the cDNAs derived from mRNAs representing the high PTA and the low PTA groups. Dye-swap accounts and corrects for differences in the fluorescence intensity due to dye instability or labelling efficiency. The fluorescence intensities from half of the animals within each PTA level were obtained at the first stages of the experiment by one person and the remaining half were obtained at a later stage by another person. This potential variation was considered a sub-experiment effect and was accounted for in the model. Each sequence was available in duplicated spots within the microarray. The intensity record was the median of all pixels in the foreground subtracted from the associated median background intensity. A filtering process was implemented to remove suspicious observations and use only data that were reproducible. When the foreground minus background intensity was lower than one, the difference was set equal to one. All background-subtracted spot intensities lower than the mean plus 3 standard deviations of the corresponding slide-dye background intensities in both dye systems were removed from the analysis. The logarithmic transformation (base 2) of the background-subtracted intensities was used in the follow-up analysis. A spot intensity was removed from the data if the corresponding sample:reference ratio was extreme with respect to the rest of the ratios available for an animal, i.e. an outlier.

The Cook's Distance (a metric for deciding whether a particular point alone affects regression estimates), a well-known influence statistic that measures the change to the estimates that results from deleting each observation (Cook, 1977) was used to identify suspicious ratios. Sequences with less than 10 sample:universal control ratios per PTA-sub-experiment group (from a maximum of 20 ratios) and with less than two high or low PTA animals per sub-experiment were removed from the analysis. A global Loess normalization based on 20% of the data in the neighborhood of each spot average fluorescence intensity was applied within slide to the remaining sequences. In addition to the within-array normalization for fluorescence intensity effects, an across-array scaling of the normalized ratios for dye effects was implemented. The resulting normalized ratios were analyzed using a linear mixed effects models and an analysis of variance (ANOVA) approach for each sequence. The model included the fixed effects of sub-experiment and PTA group nested within sub-experiment and the random effect of cow. False-discovery-rate (FDR, Benjamini and Hochberg, 1995) adjusted significance p-values of the PTA effect were obtained for each sequence and the contrast between PTA estimates represented the adjusted ratio-of-ratios due to the logarithmic transformation of the fluorescence intensities. “Ratio-of-ratios” was used to estimate the absolute differences in gene expression between the two groups.

The statistical model used was conservative and powerful for detecting differences in gene expression levels between the highPTA and lowPTA groups. LOESS transformation of the data for normalization was used. The FDR was used to reduce the number of false positives. An FDR of <10% is reasonable, although higher FDR can also be used. Raw probabilities for the ANOVA are also given in TABLES I-III, in addition to FDR adjusted p-value. In TABLE I, the number of genes with effects at each FDR p-value cutoff is: <0.1 (number of genes 50); <0.2 (number of genes 200); and <0.3 (number of genes 357).

The model used in calculating the difference in expression level of each gene on the array was y_ijk_l=m+P_i+PTA(P)_ij+C(PTA)_ijk+e_ijkl, where y_ijk_l=log2 transformed and LOESS normalized ratio of cow/reference intensities “1” for data set i (e.g., experiment 1 or experiment 2), PTA level “j” (high or low) nested within data set, and cow “k” nested within a PTA group and data set. m equals overall mean; P_i equals fixed effect of data set i; PTA(P) equals fixed effect of PTA level j nested within data set; C(PTA) equals random effect of cow k nested within PTA level; eijkl=random residual associated with observations y_ijk_l.

TABLE I provides gene expression data from blood for approximately 357 DNA fragments representing approximately 357 genes that have varying levels of significance in predicting a desirable phenotype (e.g., milk production).

The “GenBank ID” column in TABLE I refers to GenBank accession numbers of cattle ESTs/DNA fragments representing a cDNA clone on the microarray. In TABLE 1, the data set was generated from two sets of separate experiments (e.g., first set of 10; animals; and a second set of 10 animals) and the data from the two experiments were combined to generate the gene expression profile from blood. ANOVA was performed on LOESS transformed fluorescence intensity values for the combined gene expression data (fluorescence intensity equates to expression level of a gene). The standard errors were calculated for expression data of each gene on the microarray. The “fold-change” ratio-of-ratios column in TABLE I shows the change of gene expression level of highPTA group relative to lowPTA group. The differences were transformed to a linear scale for easy comparison (a value of 1.5 means that expression level was 1.5 fold-greater in highPTA vs. lowPTA group). Comparisons are always for highPTA-to-lowPTA ratio-of-ratios. The “ratio-of-ratios” is calculated by dividing the “high-PTA-ratio” by “low-PTA-ratio”. This quotient, referred to as the “ratio-of-ratios,” represents the ratio of high-PTA-ratio to the low-PTA-ratio values. The normalized ratio is calculated as follows:

• • 1) compute the log2 (tissue/reference) (after background subtraction and removal of spots <3SD)
  • 2) normalize the log2 ratio using Ab binding gene
  • 3) find the average of normalized log2 ratio for all spots pertaining to the sequence

The “raw_pvalue” column shows the probability estimate of ANOVA for difference between high and low PTA groups. The “FDR adjusted p value” column shows the FDR for ANOVA as disclosed herein. The FDR was calculated for the combined data (from experiment 1 and 2), unless a gene's expression data was absent in either experiment 1 or experiment 2.

In TABLE I, the “Gene Name” column provides a descriptive name of the gene associated with that particular GenBank accession number. Other details of the gene can be obtained from the GenBank database by using the appropriate GenBank accession numbers. The “gene symbol” column provides relevant gene symbols if available. The gene identification was determined by BLAST analysis of the cattle sequence on the microarray against the public domain DNA sequence databases. If a human gene was identified, the human gene name was given. If there was no similarity to a human gene, but significant similarity to a gene from another species (e.g., mouse), then gene name was given from the species with the highest significant BLAST score. The “GenBank ID best hit” column provides GenBank accession numbers of nucleic acid sequences that returned the best similarity when compared against the cattle sequences. A GenBank ID representing a cattle sequence will appear only if there was no hit in the human or mouse genomes. The “UniGene” column provides identification numbers for the best hit of cattle sequence on the microarray against human unigene databases. Human UniGene release 166 (Jan. 12, 2004); Mouse UniGene release 135 (Feb. 27, 2004); human genome Build #34.2 (January 2004); Cow UniGene Build 55 (Mar. 9, 2004); TIGR9 (Sep. 3, 2003); LocusLink of Mar. 1, 2004 were used to update the “UniGene” column.

In TABLE I, for genes differentiated expressed in blood samples, the list was sorted based on the FDR adjusted p-value. Presumably, the genes appearing on the top of the list have a higher predictive value compared to those appearing at the bottom of the list. The order of the genes in the list may vary depending upon the trait tested and tissue analyzed. However, the genes in the list provide a statistically robust method for milk yield using gene expression data.

TABLE II shows the data and calculations, based on liver samples, using similar column headings as used for TABLE I. Testing and analysis of gene expression ratios as described for blood samples in TABLE I were performed using liver samples. TABLE II provides a list of 10 genes, whose expression profile in liver is predictive of high milk production and associated traits in cattle.

TABLE III contains a list of genes expressed in both liver and blood that have significant effects on PTA. The analysis was done as described for the blood data in TABLE I. The gene expression levels from both blood and liver predict PTA across various tissue types. This provides confirming evidence that these genes are involved in regulating the traits of interest.

In TABLE III, the “contrast blood” and “contrast” test statistics column provides a comparison between two levels (i.e. transformed and normalized estimated ratio of cow: reference intensities) of an independent variable (PTA high and low) for blood and liver samples only. ANOVA was performed on LOESS transformed fluorescence intensity values. Standard errors were calculated. The “raw p-value blood” column represents an unadjusted p-value for ANOVA of blood data. The “contrast liver” column represents a comparison between two levels (transformed and normalized estimated ratio of cow: reference intensities) of an independent variable (PTA high and low) for liver samples. ANOVA was performed on LOESS transformed fluorescence intensity values. The “raw p-value liver” column provides an unadjusted p-value for ANOVA of liver data. The “Gene Name”, “Gene Symbol”, “GenBank Best hit” and the “UniGene” columns are as described for TABLE I.

The genes in the list provided in TABLE III are arranged based on the raw p-value for both blood and liver. For example, a listing in TABLE III was generated by comparing genes with raw p-values of ≦0.10 for both blood and liver. The genes appearing on the top of the list have a higher predictive value compared to those appearing at the bottom of the list. The order of the genes in the list may vary depending upon the trait tested and tissue analyzed. However, the genes in the list provide a statistically robust method (see peripheral blood).

Results from the analysis of fluorescence intensities collected from high and low PTA animals were used to identify the sequences with the highest potential as biomarker predictors of PTA level (FIG. 3). Only genes significant at FDR-adjusted p-value <0.1 (50 genes) and with information on all 20 animals (14 genes) were considered repeatable and highly likely to be true positives. The predicted values for the 20 animals from the linear mixed effects model corresponding to genes represented by the 14 sequences (AW461980, AW464526, AW465165, AW465571, AW466043, BF039168, BF044446, BF044893, BF046007, BF046202, BF440243, BF440261, AW466044, BF039212) were used in a discriminant analysis to identify the sequences that can most accurately classify animals into high and low PTA groups. These 14 genes define an optimal subset to create a Reference Expression Profile for a candidate mammal. A stepwise discriminant analysis (Klecka, 1980) further reduced the number of genes used to distinguish PTA groups to five represented by Gen Bank (AW466043, BF044446, BF039168, BF046202, AW461980). Stepwise selection started with no genes in the model and in an iterative process, the variable that contributed most to the discriminatory ability of the model was entered and the ones that contributed least and did not meet the statistical criterion (p-value <0.15) were removed. Disjoint cluster analysis using Euclidean distances, also known as k-means model, divided the animals into clusters with centers based on least-squares estimation. Canonical variables were used to depict the cluster of animals based on the selected sequences (FIG. 3). Canonical discriminant analysis was used for the purpose of graphical representation of the cluster of cows by PTA group. Canonical discriminant analysis is a dimension-reduction technique that finds the linear combinations of the quantitative variables that provide maximal separation between the classes or groups (SAS online manual 2002, The SAS Institute Inc.). This approach successfully discriminated animals by PTA.

Example 3

Developing a Gene Expression Index for Phenomic Selection

The expression profiles of the genes in high PTA cows and low PTA cows (TABLES I-III) were ranked according to their p-values or FDR-adjusted p-value, i.e. gene expression profiles represented in TABLES I-III were classified based on statistical significance. For example, in blood samples from cows with predicted transmitting ability for high and low milk production, a total of 357 genes (or partial DNA fragments representing those 357 genes) were found to differ significantly between the groups at <FDR adjusted p-value of 0.29 and approximately 25 genes at <0.051 FDR-adjusted p-value (TABLE I). Any other relevant statistical method can be used to rank the genes.

In liver samples from cows with predicted transmitting ability for high and low milk production, a total of 10 genes were found to differ significantly between the groups at ≦0.4 FDR-adjusted p-value (TABLE II). The genes listed in TABLES I-III are part of a Gene Expression Index useful for identifying a candidate animal as predicted to be elite, that is, to have increased (high) milk production.

These results demonstrate that genetically elite cows can be identified prior to their first lactation on the basis of gene expression profiles of liver or peripheral blood leukocyte RNA. When applied to bulls, this method may enhance or replace progeny testing, significantly increasing the rate and efficiency of genetic improvement by breeding. Multiple phenotype traits may be improved simultaneously using this method. This method is generally referred to as phenomic selection.

Example 4

Developing a Weighted Gene Expression Index for Phenomic Selection

Based on the expression profiles of all the genes in the microarray, a Gene Expression Index is developed, where the genes ranked higher in the index according to statistical significance between high and low PTA for milk production (or any 2 levels of a different trait) account for a greater fraction of the phenotypic variation in the trait. More levels could also be added. One method to further refine the Gene Expression Index is to create a weighted Gene Expression Index. This is accomplished by comparing actual milk production records from the high and low PTA cows from genetically distinct backgrounds and in different herds to their gene expression profiles for the genes in non-weighted index. Weighting of the index is accomplished by ranking the actual production values or PTAs of cows or bulls in the population and adjusting the p-value-based ranking of individual genes by a defined multiple. The weighted index can then be used to rank candidate animals in a breeding program. The genes with a greater weighted average will have the most predictive power for the trait, such as high milk production.

Example 5

Identifying a Candidate Animal for Selective Breeding

Expression profiles of genes selected from a gene index are evaluated in the cattle population for their ability to predict high milk production. The RNA isolation, synthesis of cDNA and labeling are performed as described in MATERIALS AND METHODS. Hybridization can be carried out with a microarray containing genes selected from the disclosed gene index. Alternatively, particularly useful at field level, quantitative PCR analysis for measuring gene expression or an equivalently sensitive method can be performed for a limited number of genes selected from the gene index. For example, quantitative real-time RT (reverse transcription) PCR analysis is less expensive for a limited number of genes and diagnosis is portable.

Following microarray hybridization or quantitative PCR, the expression profiles of tested genes are compared to the expression profiles of genes in the Gene Expression Index or Reference Expression Profile (FIG. 2). Similarity of the expression profile of a sample from the candidate animal to the Gene Expression Index indicates higher probability for increased milk production.

The Gene Expression Index and the weighted Gene Expression Index disclosed herein can also be used to identify bulls for predicted transmitting ability for high milk production. This is based on the premise that a strong selection pressure for increased milk production must have influenced the fundamental gene expression pattern, and this gene expression pattern that correlates with increased milk production must be present in bulls as well. Consequently, the gene expression indices disclosed herein can provide a basis to correlate expression profiles from cows at the population level, as well as a specific bull's daughters for increased milk production. Therefore, the need for expensive progeny testing can be minimized. An aspect of the disclosure is that blood, indeed any tissue for which expression profiles can be obtained, at any stage of development, can be used for creating the index. With complex traits such as lactation that involve hundreds of genes, many of which will be involved in intermediary metabolism, RNA levels of such genes are expected to be consistently different in animals of different PTA levels. That is, an animal with a high potential for milk production would have a different “metabolic set point” that is reflected in leukocytes as well as in other tissues.

Example 6

Identification of Novel Genes Involved in Milk Production

Several genes with differential expression levels in high milk producing cows and low milk producing cows were identified through a microarray analysis. Based on the sequence information of these genes, several genes do not have counterparts or functional annotations in humans and may represent genes unique to milk production in cattle. The data provided in this disclosure presents an insight to analyzing functions of several unannotated genes.

Example 7

Genetic Manipulation of a Desired Gene

A method to manipulate gene expression in a ungulate mammals is through in vitro fertilization of a genetically altered egg followed by an embryo transfer. Methods to generate transgenic ungulate mammals include microinjection of DNA into the oocyte under conditions which permit the transfection of the oocyte, and contacting the transfected oocyte with sperm under conditions which permit the fertilization of the transfected oocyte to produce an embryo. Following the fertilization of the transfected oocyte, the embryo is transferred into a hormonally synchronized non-human recipient animal generally of the same species (i.e., a female animal hormonally synchronized to stimulate early pregnancy). The transferred embryo is allowed to develop to term. A transgenic offspring is then identified based on the expression of the desired gene or by detecting the presence of a recombinant protein. An altered gene expression or a difference in the amount of protein indicates a successful gene transfer to generate a transgenic offspring expressing a desired characteristic such as increased milk production. Genes suitable for manipulation through transgenesis are those disclosed herein.

Example 8

Selecting for Traits in Dairy Cattle and Beef Cattle Using a Gene Expression Index

The following are traits other than milk production that are suitable for developing gene indices for cattle selection:

A. Dairy Cattle

1. Health Traits in Dairy Cattle

The primary problems are mastitis, digestive and reproductive disorders. Use of antibiotics and drugs is already limited for lactating cows. Therefore the ability to predict better health traits in breeding cows is valuable for the dairy industry to minimize economic losses due to diseases. Adequate evidence indicates that the variability in disease incidences and health disorders is under some degree of genetic control.

(a) Somatic Cell Score:

Somatic cell concentration is a quantitative trait in cattle with moderate heritability and is apparently affected by many different loci. Somatic cells in milk consist primarily of leukocytes and neutrophils secreted in response to invasion by an infectious pathogen. Somatic cell counts in milk serve as an indicator of udder health and are elevated during mastitis infections. Research has indicated that somatic cells in milk are elevated when heifers give birth to their first offspring, although clinical symptoms may or may not be present. The relative roles of pre-calving infections, or even calfhood infections, environmental stressors, or onset of lactation in causing elevated cells counts is not clear. Correlations between somatic cell score and mastitis infection and QTL are available. The methods to create gene expression indices disclosed herein can be used to predict somatic cell counts in cattle and to predict disease resistance for mastitis infections.

(b) Immunity:

Overall immunity is a measure of healthier and longer productive life for cattle. The methods for creating gene expression indices disclosed herein can be used to estimate the predictive transmitting ability for disease resistance in cattle.

(c) Fertility and Longevity:

Reproductive traits such as fertility can also be predicted using the methods of the present disclosure. Longevity of dairy cattle is a result of overall fitness and a Gene Expression Index can be used to predict longevity of the animal in a herd. Correlations between somatic cell score and fertility in dairy cattle have been detected and can be used with a gene according to the present discussion to predict longevity.

2. Type Traits in Dairy Cattle:

The standard type traits that can be used in methods for creating the gene expression indices disclosed herein include stature, chest, width, body depth, angularity, rump angle, rump width, rear legs set, rear legs rear view, foot angle, fore udder attachment, rear udder height, central ligament, and teat length.

B. Beef Cattle

In addition to some of the traits such as health, disease resistance, and reproductive fitness, beef cattle are selected for higher carcass and meat quality and growth traits. Beef cattle traits may also include customer satisfaction traits, such as marbling, tenderness and composition. These beef traits can also be predicted using methods of the present disclosure.

Example 9

Phenomic Selection for Traits in Swine Using a Gene Expression Index

Health traits including immunity or disease resistance (e.g., resistance to intestinal E. coli associated diarrhea), reproductive traits such as fertility, and meat quality (tenderness and intramuscular fat content) in swine (an ungulate mammal) can be predicted using the Gene Expression Index methods disclosed herein. The methods described herein for analyzing PTA for milk production in cattle can also be used to evaluate PTA for the above-mentioned traits in swine. These methods include isolating a suitable tissue sample such as blood from swine and comparing the expression profiles of genes from the swine samples with reference to a Gene Expression Index created by methods disclosed herein. Boars and sows with superior genetic merit as assessed by comparison to the relevant expression index, can be used for selective breeding.

Example 10

Analyzing Phenotypic Traits in Sheep/Goats Using a Gene Expression Index

Analyzing traits in sheep or goats (ungulate mammals) includes predicting traits for better meat quality, increased milk production, wool quantity and quality and other health traits such as immunity or disease resistance and fertility. The methods disclosed herein for analyzing PTA for milk production in cattle can also be used to evaluate PTAs or phenotypes of the above-mentioned traits in sheep or goats. These methods include isolating a suitable tissue sample such as blood from sheep or goats and comparing the expression profiles of genes from the sheep or goats samples with reference to a Gene Expression Index created by methods disclosed herein for a specific trait. A high correlation of the trait with the expression of genes present in a Gene Expression Index created by methods disclosed herein indicates superior genetic merit for the chosen trait. Sheep, goats, deer, horses and other ungulate mammals can be selected for breeding according to the methods of the disclosure to improve production efficiency, health and profitability.

Example 11

Race Horses

Racing ability in horses can be predicted by creating a Gene Expression Index and comparing gene expression levels in horses that have known racing ability, or phenotypes that are correlated with racing ability. Gene expression profiling is performed on RNA collected from muscle tissue collected from foals. When the horses reach racing age, the patterns established when they are foals is correlated with racing ability or racing-related traits (e.g., speed). An index associated with speed can therefore be established. Candidate foals are then tested at birth and the patterns most closely matching the patterns of horses with greater speed can be identified.

Materials and Methods

Experimental Animals. A total of 20 Holstein Friesian dairy heifers were selected for study, 10 with extreme low breeding values for milk production and/or composition traits and 10 with extreme high breeding values. All animals were housed at the University of Illinois Dairy Farm, operated by the Department of Animal Sciences. A cow's genetic merit is determined from the predicted transmitting ability (PTA) of her dam, and the daughter yield deviation (DYD) of the sire. Only offspring of sires and dams with high accuracy predictions (>0.80) were used. Blood and tissue (liver punch biopsy) samples were collected in pairs (one high potential, one low genetic potential). All samples were collected at the same time of day, in the same season, all prior to the heifer's first lactation. Animals were fed identical diet and housed under identical conditions.

Total RNA purification for gene expression analysis. Two tissues were sampled from each animal: peripheral blood leukocytes and liver. Liver tissue was collected using a standard approved biopsy procedure. Total RNA isolation was performed using optimized protocols developed for this project. Briefly, blood samples were collected in a 250 ml bottle containing 100 ml of 0.5M EDTA and mixed well. The sample was thoroughly mixed with an equal amount of a lysis buffer (8.9 g NH₄Cl, 0.1 g KHCO₃ in one liter of H₂O) and centrifuged at 3,500 rpm for 10 min at 4° C. to separate blood leukocytes. The procedure was repeated with one-third the amount of the lysis buffer until clean leukocytes were obtained. Liver biopsies were collected and stored in liquid nitrogen and/or RNALater (Ambion, Inc., Austin, Tex.). Frozen liver tissue was ground into fine powder in liquid nitrogen using a mortar and pestle. Tissue preserved in RNALater was cut into small pieces before being used for RNA isolation. Total RNA was extracted using TRIzol reagent (Life Technologies, Carlsbad, Calif.). Briefly, each sample of leukocytes or liver was homogenized in TRIzol using a mechanical homogenizer, debris was precipitated by centrifugation and discarded. The RNA was further purified by extractions using chloroform, acid phenol:chloroform, precipitated by adding isopropanol, and incubated at −20° C. The isolated RNA was cleaned using 75% ethanol, resuspended in RNA storage buffer, aliquoted and stored at −80° C. until use. The integrity of total RNA was examined by denaturing agarose gel electrophoresis. RNA concentrations and purity were measured by spectrophotometry.

RNA probe labeling. RNA labeling procedures were adapted from Hegde et al. (2000) with modifications. Based on the optimized protocol disclosed herein, 10 μg of total RNA for each sample were used for each labeling reaction. In a typical 30 μl reverse transcription reaction, 10 μg of total RNA in 7.5 μl is mixed with 1 μl of oligo (dT)s (2 μg/μl) and 1 μl of exogenous control gene mix, and incubated at 70° C. for 10 min. After cooling on ice, 15 μl of labeling buffer is added followed by 3 Ill of Cy3-dUTP or Cy5-dUTP, separately, 2 μl of SuperScriptII (200 U/μl) and 0.5 μl of RNasin (40 U/l). The mix is then incubated in the dark at 42° C. for 2 h. Each of the labeled Cy3- and Cy5-dUTP reactions are purified separately using a column purification system (QIAGEN, Valencia, Calif.).

The length of labeled cDNA and the quality of the paired probes was examined by electrophoresis. The probes incorporated with Cy3 and Cy5 are measured using spectrophotometry. Total dye incorporations are calculated using the following formula: Cy3-dUTP (pmol)=(OD₅₅₀×volume)/(E₅₅₀×10⁻⁶); and Cy5-dUTP (pmol)=(OD₆₅₀×volume)/(E₆₅₀×10⁻⁶), where E₅₅₀ is the molar extinction coefficient of Cy3 (150,000 cm⁻¹ M⁻¹) and E₆₅₀ is molar extinction coefficient of Cy5 (250,000 cm⁻¹ M⁻¹).

Hybridization to microarrays. Prior to hybridization, the arrays were treated with prehybridization buffer (5×SSC, 0.1% SDS and 0.1% BSA) by immersing into the prehybridization solution at 42° C. for 45 min. The prehybridized slide was then washed five times by sequential dipping in MilliQ water at room temperature with a final dip wash once in isopropanol. The washed slide was spun dry at 500 rpm for 1 min and used immediately for hybridization. The purified labeled probe was combined with 20 μg of bovine Hyblock DNA (Applied Genetics Laboratories Inc., Melbourne, Fla.) and 1 μg of poly (A) in μl volume. This hybridization mix was denatured in a water bath at 95° C. for 3 min and subsequently mixed with an equal amount of 2× hybridization buffer (50% formamide, 10×SSC and 0.2% SDS) and hybridized at 42° C. overnight in a CMT-hybridization chamber (Corning, Inc. Corning, N.Y.) using a LifterSlip (Erie Scientific Company, Portsmouth, N.H.) for each array slide. The hybridization chamber contained a small piece of moist filter paper next to the array to maintain proper moisture. The chamber was sealed and incubated at 420 C in a water bath in the dark overnight. The hybridized slide was washed in a buffer containing 1×SSC and 0.2% SDS at 42° C. for 5 min with agitation. Then it was washed in a second buffer containing 0.1×SSC and 0.2% SDS at room temperature for 5 min with agitation, followed by a third wash in 0.1×SSC at room temperature for 5 min with agitation. The slides were spun dry at 2,000 rpm for one min after final wash before they were scanned. All samples were hybridized to duplicate slides and repeated with reverse labeling for a total of 2 slides, or 4 spots per gene per experiment. Slides were scanned for both dye channels using an Axon 4,000B Scanner (Axon Instruments, Inc., Union City, Calif.) and data acquisition was done using Gene Pix 3.5 and raw data saved in files of gpr format.

Spiking control using exogenous nucleic acids. Exogenous nucleic acids were spiked into labeling and hybridization reactions for use as a reference for normalization and validation. The spiking control consists of three soybean (Glycine max) genes, photo system I chlorophyll ab binding protein (Cab), major latex protein (MSG), and ribulose bisphosphate carboxylase small chain I precursor (RBS1). The polyadenylated RNA of the exogenous nucleic acids was prepared by in vitro transcription using MAXIscript (Ambion, Inc. Austin, Tex.) and quantified by spectrophotometry and gel electrophoresis. One microliter of spiking control contains 10, 100 and 1,000 μg of mRNA from MSG, Cab and RBS1, respectively.

TagMan® Analysis. One microgram of total RNA from each sample was denatured at 70° C. for 10 min and reverse transcribed in 20 μL reactions containing 500 ng oligo-dT primer, 500 KM dNTP, 100 μM DTT, 40 U RNasin, 1× first strand buffer and 200 U reverse transcriptase (SuperScript II, GIBCO BRL, Rockville, Md.) at 42° C. for 1 hour. Quantitative PCR was carried out on an ABI7700 PRISM sequence detector in 25 μL reactions containing 1× Quantitative PCR Universal PCR Master Mix (Applied Biosystems, Foster City, Calif.) 1 μL cDNA, 200 nM each primer and 100 nM probe. The PCR protocol consisted of denaturation at 95° C. for 9 min followed by 40 cycles of 95° C. for 15 s and 60° C. for 1 min. All reactions were carried out in triplicate. Standard curves for both target and reference genes were constructed using 2-fold serial dilutions of adult spleen cDNA. Relative amounts of transcripts are calculated.

p-values. p-values represent the probability that a deviation as great as, or greater than, that obtained from the experiment will occur by chance alone. In other words, p-value is the probability of observing a test statistic that is as extreme or more extreme than currently observed, assuming that the null hypothesis (H₀) is true. This can be expressed as the conditional probability P(data/H₀ true), where “P” is read “the probability of and “/” is read as “given” or “conditional upon.” If the p-value is small, it is concluded that the deviations are not entirely due to chance, and the null hypothesis is rejected. If the p-value is greater than the predetermined value (e.g., 0.05), the data confirm reasonably well with the predictions of the hypothesis, and the null hypothesis is accepted.

False Discovery Rate: This method is used to adjust for multiple comparisons by setting an acceptable proportion of false positives for an experiment (Benjamini and Hochberg, 1995). In this case the p-values are ranked in increasing order of p_i (p₁<p₂<p_k . . . <p_m, m=total number of comparisons). The p-values for each comparison are then sequentially compared to an adjusted cutoff for each p_k equals to (FDR)*(i/m), where FDR=the false discovery rate. The null hypotesis is rejected for any comparison in which p_k<(FDR)*(i/m), until a comparison is made in which the observed p-value is greater than the adjusted cut-off. All larger p-values (p_k . . . p_m) are not statistically significant. If the smallest p-value yields a false discovery rate higher than the selected FDR, the null hypothesis cannot be rejected for any test.

      SEQ. ID. NO:1       BM362588

      GCACGAGGCGGGATCGAGGGGCAGCAGCGTACGGTGAAGGACACAGGCCGTGGAGTTTGAACCCCT

TGAAAGATTGAAATCATGGCAGGTGCAGAAGCTGATGCCCAGTTCCATTTCACTGGTATCAAAAAATATTTCA

ACTCTTACACTCTCACAGGGAGAATGAATTGTGTGCTGGCCACATACGGAAGTATTGCTTTGATAGTCTTATA

CTTTCAAGTTAAGGTCTAAAAAAACTCCAGCTGTGAAAGCAACATAAACAGATTCTGAGCTGTACATTATCTGT

TAAGTTCCCATGCCTGAAGAAGCTAATGTCAACTCATCATGTGATACTCAATTTGTACAATAAATTATGAACC

TGGAAAAAAAAAAAAAAAAAAAAAAAA

      SEQ. ID. NO:2       BF440243

      TTTTTTTTTTTTTTTTTGACTATCTACAAAAATTTATTGTCTATTTACAGAAGAAAAGCATGCGTATCA

TTAAAACAAATAAAATGTGTTTTCTCACAGCGCAGTACATTTTTNNNNAAAAAAATTTTTTTAAGCTGTATCA

CAGAAACAAGACACAAGGATTTTTTAAAAGAGCTAAACACTCATCATTCGAGGTGCAATACTCATGGACATG

AGTTCCTGAAACAACAGTTTGCACGCATAAGGCATTCGAACCAAAGAGATCTGGGTTTTATTTCGGCAGCCCC

TGCATTCGTATGTATGGGTCCTGNNGTTCGCAATTGCCATTATTCCACAAAGATTGCAAACGTGAACCTGATA

CGGATCTGACGCCTCAAACAACCTCTCCCTTNNAAACTGGGCTGCTCCATGCGCGATCTGACAGTCTCGTTCC

ATCTCTCCAAAACGCAAGCCACCATCACGAGATCTACCCTCCATCGGCTGNNTATTTAGAATCTGAATAGGTC

CCCGAGCACGAGAATGAATCTTATCATCCACCATATGCTTCAAACGCTGGTAGTAAGTA

      SEQ. ID. NO:3       BM361928

      GCACGAGAGCCGGCGTCTCAGAGGAGTGCAGACGCTGCTGGTGACCCTGTGGCGCGTCTCTGTGGGG

CCAGGAACTGAAAGAGAGCCAAAATGGCTGAAAATGGTGATAATGAAAAAATGGCTGCTCTGGAGGCCAAA

ATCTGTGATCAAATTGAGTATTATTTTGGAGACTTCAATTTGCCACGGGACAAATTTTTAAAGGAACAGATCA

AACTGGATGAAGGCTGGGTACCTTTGGAGATAATGATAAAGTTTAATAGGTTAAACCGTTTAACGACAGACT

TTAATGTAATAGTAGAGGCCCTGAGCAAATCAAAGGCAGAACTCATGGAAATAAGTGAAGATAAAACTAAA

ATTAGAAGATCTCCAAGCAAACCTCTCCCTGAAGTGACTGATGAGTATAAAAATGATGTAAAAAACAGATCT

GTTTATATTAAAGGCTTCCCGACAGATGCAGCTCTTGATGACATAAAAGAAT

      SEQ. ID. NO:4       BM364471

      GCACGAGCGGCGGAGAGCGGCACCCACACCGTGTGTCGGCGGTGAGTCCCGGCCAGCCCGAGCTGC

ACGTCCCAGCCCCGGGGAGACGCCGGAAAAAACGGAAGGACCTGGGATTCCAGAGCAGTCGCCGCTGACTG

CTGCTCTCCTGCCGTTGCCGCGGCGGAGGCTTCCGCACTCGCCGCTGAAGACGCGGCCCTGACAGGCCTAGA

GGCCTAGGCGCGGCCCTCCGAGCCCGACGTGTTGCCGCCGGTGCAGCTGTGAGTAATCCGAGCGCTCTCTCC

ACGGCCGTTTACAGATTAAAATGGAGGAAATTTCCTTGGCTAACCTGGATACTAACAAGCTGGAGGCCATCG

CTCAGGAGATATACGTAGACCTGATAGAGGATTCTTGTTTGGGCTTCTGCTTTGAGGTGCACCGGGCAGTCAA

GTGTGGCTACTTCTACCTGGAATTCGCAGAGACTGGTAACGTGAAGGATTTTGGCATTCAGCCAGTTGAAGAT

AAAGGAGCGTGTCGCCTCCCGCTTTGCTCCCTTCCTGGAGAATCTGGGAATGGGCCTGATCAGCAGC

      SEQ. ID. NO:5       BM365159

      GCACGAGATGGCGCCTGTGAAAAAGCTTGTGGCGAAGGGGGGCAAAAAAAAAGAAGCAAGTCCTA

AAATTCACTCTGGACTGTACCCACCCTGTAGAAGATGGAATCATGGATGCTGCCAATTTTGAGCAGTTTCTTC

AGGAGAGGATCAAGGTGAATGGAAAAGCTGGCAACCTGGGCGGCGGTGTTGTAACAATTGAAAGAAGCAAG

AGCAAGATTACTGTAACTTCCGAGGTGCCCTTTTCCAAAAGGTATTTGAAATATCTTACCAAAAAATATTTGA

AGAAGAATAATCTACGAGATTGGTTACGCGTAGTCGCTAACAGCAAAGAAAGTTACGAATTGCGTTACTTCC

AGATTAATCAAGATGAAGAAGAGGAGGAAGATGAGGATTAAAACTCAATCTGGAATATTTGTATAAGTTCTT

AAATAAAATTTATCAACTGAAAAAAAAAAAAAAAAAAAA

      SEQ. ID. NO:6       BM365446

      GCACGAGGTTGATGTCCTGTATCTGATGCCTTGTTGCGGTCGAAAGTAAGCGAGCTCCAGAGGAGTG

CGGAGAAATTCAAGTCTTTCCTGCTGTAACTTCATCAGCCCGCCAAGATGGCGATGCAAGCGGCCAAGAGGG

CGAACATTCGACTTCCACCAGAAGTAAATCGGATTTTGTATATAAGAAATTTGCCTTACAAAATCACAGCTGA

AGAAATGTATGATATATTTGGGAAATATGGACCTATTCGTCAAATCAGAGTGGGGAACACACCTGAAACTAG

AGGAACAGCTTATGTGGTCTATGAGGACATCTTTGATGCCAAGAATGCATGTGATCACCTGTCAGGATTCAAT

GTTTGTAACAGATACCTT

      SEQ. ID. NO:7       BM365732

      GCACGAGGGACGCCATGGCGACCAACATCGAGCAGATTTTTAGGTCTTTCGTGGTCAGTAAATTCCG

GGAAATTCAACAGGAACTATCAAGTGGAAGGAGTGAAGGACAGCTCAATGGTGAAACAAACACACCTATTG

AAGGAAACCAGGCAGGTGATGCAGCTGCCTCTGCCAGGAACCTACCAAATGAAGACATAGTTCAGAAGATA

GAGGAAGTACTTTCTGGGGTCTTAGATACAGAATTACGATATAAGCCAGACTTGAAGGAGGCATCCAGAAAA

AGTAGATGTGTGTCTGTCCAAACAGATCCTACTGATGAAATTCCTACNNNAAAGTCGAAGAAGCATAAAAAG

CACAAAAAAAAAAAAAAAAAA

      SEQ. ID. NO:8       BF046007

      TCTCTGTCTGCAGTGGTCCACACCCATCCTCCGCCATACCCGGCCTCAGCCTGGCTGCCACGGGCGCC

GCACCTGCCGGGGCTGCCTCTAGAGGCGGCAAGATGTTGAGCGCGGCACGGAGGATCCAGGCCTACTGGCCT

AGTCGGGCCGAGAGCCGGAAGGCCACGTGGTTCTCAGGCGCAGTGGAAGAAGCCGGACCCTGTGTGGGCGA

CGCCCTGCTGCACGCGCAGGCCCTCGCGGCCCTGTGCTGCGGTGTGACGGTTTCCAGAATGTCCCAGTAGATG

AGTCCTGACACACAGGATTTAGTTGTGCCAGAAGATTCCAGGATGACTGAAGCTAACCTTTCTGGTGAGTGA

AGAGGACATGACAGGGATGGAACGAAAGCCTCAGGACCCGGTTGCCCCCCGTTTTTTAACTGGCAGTGCCTG

ACACTGAAGTAACTGAAAATACCACCTTGTCACTGGAGCCGTCCTTTAGAATAAGACCTGTTGCCAGTAAAG

CTGTCTTCATCTGTGCGGATCTACAGAGTTGGGAGAGAACCAAAAA

      SEQ. ID. NO:9       BF044446

      TTTTTTTTTTTTCTTCTTCTTGTCCCCGTCCTTCTTCTCTTCCTCCTCTTCATCTGAGCCAACATCTTCTA

TCTCGGGCTTGTCATCAAACTCCTTTTCTTCTTTCTCCTTCTCTTCCTCTTTTGTNTTCCTTCTCTTCTGCTTCATC

ATCACTAACTTCTTTATCACGTTCCTTTTCCACCTTCAAAGGAGGACAAATCTTGTCACCTCTCCATCACAAAA

CCGGGGGAAAAAAGCTAAAGGAGACTGCAGCACTTACACCAACGCCACCTG

      SEQ. ID. NO:10       BF040826

      TTTTTTTTTTTTTTAAGGCAACAAAAAGCTICAATCTCTTCTCCAAGTAAACAGAACTAGTACAGTAT

ATTATTTTCTGGAACATGTACCCCCGGAGAAGTAACACAAGAGTTAAAGGGGGGCCTCTCTGAACACACTCA

CACACTCCCCCCTACCCCAATGAACCAGTCTCTCTCTCACACCCACGCACACACAGAGCTATTCACAGGCGCA

AATGTATACTATGTACAAACACACAGATCCGGGTTTCCCCTCAAGTCTCCTGGCAGACTGCCCACCAGAGAG

GAGGGATGGGCTAAGGCAAGGGGAAGAAACAAGGAACCAGTCTCTGGAAGGAAACCAGCTGAGCCGTGAGT

TGTGAGGTGCTTAGGGGCGTGTCTCTTCTCGTATTCCAAGATGCAGCATTGTAGAGTTGGGGTTGGGCGGTTT

GGAATCAACAAAAGGAAAACAAAAGAACCCGAGGAGAATGGTCGGGATGGATACGAGTGCACAGGGTTTGG

GCCCAGNGACAGAGATGTCTGAGCGTTCACACAGAGACTAGGCGAGGAGGAAAAAGTGCAAATCGAGGCAA

CGTGTTTGCAGTCTTTCTTGTTTGATTTGGGTGC

      SEQ. ID. NO:11       BF039212

      GCATTTGTTTTTTTTTTTTTTTTTTAAGAGGCAGTCTGTTCCTTCCATGAACCGTGTTCTCTCCTTAAAG

CTCTGGGGGTTGGGGGGACCGATCATGGCTTGCAGCGCTGGACAAACCGAGGGTACAAACACACATCTCGGA

TGTGGTATCTGTCCAGAATCCAGGTTAAGAATCGTTCCAAGCCCAGGCCATAGCCGCCATGTGGACACGTGC

CATATTTTCTCTGATCCGTGTACCAGTAATAGGGAGTGGGATCAATCCCTTCTCTTTTGTAACCTGCCAGGATC

TCTTCATTATCCCAGATACGCATGGAGCCACCCACGATCTCACCAACATTGGGCATCAACACGTCGACAGATT

CGGTGAGCCGGGGGTGATGACGGAGCGCGCCTTCANNATTTTGGACATGTTCTCCCCGCGGATCATCATGTGT

CTGTTGTTGAGCTGGACG

      SEQ. ID. NO:12       AW461477

      TTTTTTTTTTTTTTTTTTTTTGAGGGAAACATAAGCAGAGTGGCGTCACTTGGTTTATTGTATTCTGAA

GTGTCATGGGGGGCCGGGGAGGGGTGCTGAAAACAAGCCTGCTTTATCAGCAGTTCTAAAGCCTTATCACCT

GAGATTTGCATTCTGGAAACAAAATCATGATTGCAGTATCAGCACATATGTCCTGTGAGATCTGGGTTCCAGC

CCTGGTGGATGGCTGGACTAACCTGACGTGAGGTCAC

      SEQ. ID. NO:13       AW464361

      ATCCGACCACGTGAGGACTGGCATCCTCGGCTCAAGCCCCAGAGTCCTGGGCGGGACACCGGACTGG

CCCAGCCCAGCTTGGGGGCCCCGTCGTAGCTGCCAAAGGAGAAGGAAACACGNNCCGTGGAGCTNNCACCA

AGCGGGTGCGNNNCCGCCCCACCGAAACCGGCAGAGTCTGCTTCTNNCCCAGTGAGGAGCAGCGTCTGTGAG

GTGCAGCCAAACGGGGACAAAGTGAACAACGCCCGCTGCCCGCAGTCACACTCAGNCCGTAGTACACCTCCG

AGAACAAGGAGCTCCCCAGGGACAGACGAGCAGCCTTGTGCCGGAAGCTCGTGGGCA

      SEQ. ID. NO:14       AW466044

      GTTACAGAATAATATGCAGAAATGCCACTATCTAGTTAGTTGGCTCTACATAAGCAAAGACTATCCT

GTCTTTGTGGTGACTGAACTTAGAATGTCCTCCTCTGGAAATGGAAAAGTACCTGTATTAGGAGCTAAGTGAC

AGAAGGAGTTATCAAACTTGACTCCATATAGATAATGAACAGTTTAGGGGAAGCATTTTTCTTGGAACTAAG

AAGGACTTACCTGACATTGGCCTCATTCTGGCCTTCACTTGTTCATAAGAATCATGGAACCAGAGTTTGAGTT

AAGAAACTTGGAAAAAGCAGCTGAAAACATCTCNGNNNCCACTTAACAATTTAAAAATTCCACTTAAGATGC

TAAATAATCCATTGCTTATGTAGCAACTCAACGATGTTCTCAA

      SEQ. ID. NO:15       BF039490

      TTTATGAGCAGCTAGAATGTGCTGCAACAGAAGAAGCTGCGACACGCCAGTGGGGCCAACATCACT

AACGCAGCCACTGCTGCTACCACAGCGGCCACGGCCACTGCCACCACCACCAGCACCGAGGGCAGCAACAG

CGAGAGCGAGGCTGAGAGCACCGAGAACAGCCCTACCCCGTCTCCTCTGCAGAAGAAGGTCACTGAGGATTT

GTCCAAAACCCTCTTGATGTACACTGTCCCTGCTGTCCAGGGCTTCTTCCGTTCCATCTCCTTGTCACGAGGCA

ACAACCTCCAGGACACGCTCAGAGTCCTCACCTTATGGTTTGATTATGGTCACTGGCCAGATGTAAATGAAGC

CTTAGTGGAAGGGGTGAAAGCAATCCAGATTGACACTTGGTTACAGGTTATACCACAGCTCATTGCAAGAAT

TGATACGCCAAGGCCCCTGGTGGGACGTCTTATCCACCAGCTTCTCACAGACATTGGTCGGTACCACCCCCAG

GCCCTCATCTACCCACTGACAG

      SEQ. ID. NO:16       BF042320

      GGGGTTCAGGATCACCAGGGCCGGTCTGACTTTCAACCCCATGTGTCCGGTGGGGATCTCTGCAGTG

TGTGTCTCTGGCATCTCCTACCAGGGGAGCAGGCTTGACTTCTCCCTCTCTGTGGGCTTCGTGACAGTCGAGG

TCACGGCCCAGGCAGGGCCTTGGGCCCCCTCTCTGGAAGCCGAGCTGTGGCCGTCACGTACTCGCCTCCCCCT

GCCTCCAGGACGGAGAGTCTGTTTTCCCTGCTCAGCGGGTCGGATACAAAGGTCAGGCCTGTAGGCCGCACG

CGCTGGCCCACGAACTTCCTGGGAGGCTTTCTAAAGACGTTGAACAGCTGCCCCGAGCCCGCAGGCCCCGCT

CTGGGACACCCTTGGGCCCTGAAGCT

      SEQ. ID. NO:17       BF043074

      CGCTCGGAAGGTCCCCGGTGTGCACTCCTTCAGCAGGCCTTGGGGGAAGATGGCGGCCCTGGGGGAC

GGTCAGGAGCCCCCTCATGTCCTGTCCCCGGTCAGTTTCGAGTCACCCGGGACACCTGGAGGCCACCACCATG

AAGCCCAACTTCACCTCCACCTCCATGGTCATCAACATGCAGCAGCCCTGAGGTGCCCTCCCAGCCCCCACAG

GAGCCGTCAGACCTGGACTTCCAAGAGGTGGCAGAGGTCCAGATCTGCAGAGACACCTGCTGGTCAGGTTCT

GAGTCGGAGCCGGAGCAGGCCCCGTCGTCTCCCAGCCCGCACGGTCCTAAGACGAGGTGCACCAGGCCGGA

GGCGTGCTGAGGACCCTGCTGAGGAGCCTTCCCCGCAGACCCGGGGGTGGGGACCGCTTTGGGCAGGAGCCC

AGCCTGGAGCGGTCAGGAGGCCAGACACCGAGGGCTGGGCCCCGTTCCCAGAAGAGAGACACCTGGCTCGG

GTCGCAGGGGGCC

      SEQ. ID. NO:18       BF044776

      GCCCCGGTCTACTCTGGGGTGGTGCTAAGCCGGCGCCAGATCGACCCTCGACTGAGGAGAGGCAGTG

CGGTTCCTCTAGGCGCTTCTCCGTTGGTTCCTCCGGCTTCCTCAGCCTCCTCACCACCCGCGGGGACCCGAGA

GCTCGGTGTATGCCCCACCCCTGACCCCGCTAGAGACATGTCCACCCCGGCTCGGCGGCGTCTCATGAGGGA

CTTCAAGAGGTTGCAGGAGGATCCTCCAGCCGGAGTCAGCGGGGCTCCGTCTGAGAACAACATCATGGTTTG

GAACGCGGTCATTTTTGGGCCTGAAGGGACCCCGTTTGAGGATGGAACCTTTAAGCTTACAATAGAATTCACT

GAGGAATATCCAAATAAGCCACCAACGGTTAGATTTGTCTCTAAGATGTTCCATCCAAATGTCTATGCAGATG

GTAGCATATGTCTGGACATACTTCAGAACCGTTGGAGTCCAACCTATGATGTGTCTTCCATTTTAACATCCAT

ACAGTCTCTACTGGATGAACCCAATCCCAATAGTCCAGCAAATAGCCAGGCTGCTCAGCTGTACCAGGAGAA

CAAGCGGGAGTATGAAAAACGTGTTTCTGCGATAGTAGAACAGAGCTGGCGTGACTGTTG

      SEQ. ID. NO:19       BF046287

      GGAGGAGTAGGACGGGGAAAACACTTTCTCCCCCACAAATTCATCAAAAGAGCATTTAAACGTCGA

GTAAATTCCACAAAACAACTTCTGAATGCCGGCAGAGGACATCAGGCACCGAGAAAAGCAACCCAACTCTTC

GAAAGGAGTTTTTACCAACGCTTCAGACCCGAGTTTCTGGTGAGAAATGGCTTCACTGATGCGTCATCTCCTG

CCGTGTGCAAAAGTGCTGGGCACAGCACCCAATTCCGCAGACAGTGGAGCCAAGTGGGCTGTGCGCAGTGGC

GGTATAAACAGCAGCTCTACAGGTGGCCTTCTTTAAGCAATCTGCTGGAAGCATGTTCGGCGTGTCCACCAGT

CTGTCAACTGAAGTTATGTTCAAGAATTTCCAACTCTAGGGAGAATAAATCACACAAGTTCTACCTACCTTAA

AGACGACTGTGAGATTTGAGAGGTACTGAAGATGAAAGCACTTCCAATGTGTGAGGAGTTAAAAAAAATGTT

ACTCATCATTATGATAAAAATAACCATAATGATGAAGATGTTGGTAACTGCTCTAATTGGTTTTCTTTTTGTTT

TATCTCACACAGACCATATGCAATTAAAGCTCTTATTAAATC

      SEQ. ID. NO:20       BM362351

      GCACGAGCCCAATCCTCCTCCCACCCTAGTTGCCAATGACCACACGGCTGTTATCAGGTAAATGACC

TTTAATCCAGCCCCTGCCTCGCCCACAAGGCTTCGGGGGTGACAGCCAGGCCCCAGGGGACAGGCCGGGGCA

GGGCCGGGGACCCTCAGCGGCACGATTCCCCAGCGCGCCTAGGTGTTGCGTACGACCAGGGACTGCTCCAGC

TCCTGCCTGCGCTGCTGGATCTGTAAGGAAAGGAGAGCAGGCGCAGGTGACCAGTTGCTGCGCCCCCGGAGG

CCCCCTTCACCTTGAGGTCCCGACTCTGGGAGCGGAGGAGGTCCTGGATGTAGCCCTTGGGGTCTCTGGAGA

AGCTCAGCATGAAGTCCCTCTGGATCTTGAGCTGGTTGATGGACTCAATCGTCTCGTGGATCTGCAGTGCCAG

GCTCCAGCCCCACCTTACTGTCCAGAGCGCTGATCTCCTGCTGGTTGGCCGTNGACAGCAGGAAGCTGCTCAT

CTGTCCCTTCAGGGGCTCCTCCACTTCCACGTCAATGTCGTAGCACGCCGTCTTCTTCTGGTCCGACGGGT

      SEQ. ID. NO:21       BM366715

GCACGAGGTCGCAATGGTGAAGCTGAGCAAAGAGGCCAAGCAGAGGCTGCAGCAGCTTTTCAAGGG

AGGACAATTTGCCATCCGCTGGGGTTTTATTCCTCTCGTGATTTACCTGGGATTTAAGAGGGGTGCAGATCCT

GGAATGCCTGAACCAACTGTTTTGAGCTTACTTTGGGGATAAAGGACTGTTTGGTCATCTGGTTTTGGAAGCA

GTCAATGCAGAGGAACAACATGGAAGGTGTGCTCTCTGGCTGGGATAAGAGATGGGACATCGTTCAGACGGT

CACCAGTTGGATGGCACAGGGCTCTTACTTCTCAGATGCATCTGTTGCAGAGTGGAACCTCTACTGACTTATT

TATGATAGACTGTATTAAAATAAATGTTTTTAACAATGTTAAAAAAAAAAAAAAAAAAAAACT

      SEQ. ID. NO:22       BM366099

      GCACGAGGTCGCCTGGCCCGCCGTGGTGGTGTTAAACGGATTTCTGGGCTCATCTACGAGGAGACCC

GCGGGGTGCTGAAGGTGTTCCTGGAGAACGTGATCCGGGACGCGGTCACCTACACCGAGCACGCCAAGCGCA

AAACTGTCACCGCCATGGACGTGGTCTACGCGGTGAAGCGCCAGGGACGCACTCTCTACGGCTTCGGCGGTT

AAGTTCCAGGCAGCCATTTGGCATAGTCTAATAAAACCAAAGGCCCTTTTCAGGGCCACACAA

      SEQ. ID. NO:23       AW464526

      TGTAACTTTGACCCAGTCTGACTTGGTTTTGTTTTGTTCTGTTCTTTTTCCCCCTGGAATACAGGACGG

GACCAGGGCCCTTGTACTCGGAGCCAAGCTGCTCTCCAGGCATTGTGTAAGCCTCTTGTGTTGTGCTCTCTTTC

AGGTAGGATAATTGCGGACTGAACCCTCGGGCTGCGGTCATATATGAGAACTTGCTCCGCGCGGTCCCCTTTG

CCGGGATGTTTCCATTGCTTCATGTTTCAGTAAACAAAGGAGTTTGTGACCAACTATGTTTTCTTTCTTAATTT

AATTCTTCTACATTCACTTTTCTCTCCTCCTGGTACTAGTCTCTGTAGCCTTTCTGTTCCTCTCGTTCCCAGCCT

CTGAGCAGCCCTAGGTAAGGATTATGTTGGCGTCCCCTTTCTCCTGTAGAGGGGGATCCCTCTTATCTTGCTTT

      SEQ. ID. NO:24       BF046202

      ATGATGCTCTTCAATGATGGCACCTTTCAGGTGAATTTCTACCATGATCATACAAAAATAATCATCTG

TAGCCAAAATGAAGAATACCTTCTCACCTACATCAATGAGGACAGGATATCTACAACTTTTAGACTGACAAG

TCTTCTGATGTCTGGCTGTTCGTTAGAATTAAAAAATCGAATGGAATACGCTCTGAACATGCTCTTACAGAGG

TGTAACTAGGAGATTTCTTGAACGGA

      SEQ. ID. NO:25       AW466043

      TTGATCAACCTGCCAATTTGCTACGGATGTTTTTTGATTGCCTGTATGACGAGGAGGTGATCTCTGAG

GATGCCTTCTACAAGTGGGAGAGCAGCAAGGACCCGGCAGAGCAGAATGGGAAGGGTGTGGCCCTGAAGTC

TGTCACGGCGTTCTTCACATGGCTGCGGGAAGCAGAAGAGGAGTCTGAGGATAACTAAAACTTCAAATACAC

AAAACGAAAGAAAAGAAACAATTTAAGTATTTTTTAAAAAGTTTCACGTCTTCGCCAATCACAGTGCAGCA

AGGCCAATTCTCGCAGAAACCCCCACGTGTGCACGAGTGGGAAAGGGGAAAGAGAAAAAAAAAGGTGATCA

TGGAGGAAAAAGGTACTGGAAAAAAAGTAAACTTCAAACCTGAGGGCGGGAGCACTAAAACCAAAATACAT

GTATTATTTATAGAAAATATTTTCTGTTTTAATCTTTTCTTTTTAAATGAGGACTCATACTTTAAAAAAAAACA

CATCTGTTTAGCAAAAAAAAAAAAA

      SEQ. ID. NO:26       BF040403

      AGGGTCCCTTCCACCACCTAGGCCTTTGGGTGGGGGTCTTGGTCCTGGCACCCCATGCACGTCTGCTT

CCTATGAGGCTGAGCAGGGACCAGGGCCTGAGAGGGAGGCGTGGCCCAGGTGAGAGGTGAGGCCTTGCTCA

GGGCCCTGGGCCTCAGTTTCCCTCTGTGAAATGGGGTGATGCAGGTCTGCGGGGGCCGGCAGGGTTGGAGCT

TCTGTTGTTTGGAGCTGCCCACCCCTCCACACGCCCAGGGATGACGAGGGTGGGCAGTCTCACCTCCCGGCTC

CCCAGCCAAACCTGGGGGGCCCATCTGTACCCCTCCTCGTTTTCTGGTGCTGGTTTCCTGACCGTGAGGTCAA

GCTACCTGATCTGACTGGATGTTCAGGGCCTTTTATGTCACTTCTGACCCCTGAACCCTCAGTCCCTTCCATGG

TCTGGGGGAGGGGGCCACCTGCTTCCACACCCGCTTGTGACAGGCCCAGCAGCTAGATGCGTATCAGCCAAT

AAAGGCCCCCGCCTGA

      SEQ. ID. NO:27       BF039168

      TGTGGTGTGGCGGCGAGGTGACCTCGAGGCTGCGGTGACCATGGGCCGCCAGTTTGGGCATCTGACA

CGGGTGCGGGATGTGATCACCTACAGCTTGTCGCCCTTCGAGCAGCGCGCCTTCCCGCACTACTTCAGCAAGG

GCATCCCCAACGTTCTGCGCCGAACTCGGGCGTGCATCCTTCGCGTCGCGCCGCGTGAGTGCCCTGGCCGGGC

GGAGAGGCTAGACTCCCATCCACAGAGGGTATTGCGGGTCCCCTCAGTGAGCACTCTGCAGCTCGCCATAAA

CACGCTTTCTCTTTCAGTAGTCCTGAATGCCGTTGAACTGGTCATGTCCTCCGCATCTTACAGATAAAGGAGTT

GGGTCACAGAGAAATGACTGGCTCAAGGTCACTTGTGTGATTCCAGGGTCTCCCTCTGAGCTCACCTTTACCC

ACTTCTTTCCTAGCAGTGAACTGTTTTGTGTTAAAGGTGCAGCAGATTGTGATGATAGTTGCACATTTCGACTT

TGCTAAAACCCACAGAAGTGTAGATTTC

      SEQ. ID. NO:28       BM362530

      GCACGAGCTTGGTTGGGGGCGTCCCGCATCTAAGGCAGGAAGATGGTGGCCGCAAAGAAGACGAAA

AAGTCACTGGAGTCGATCAACTCTAGGCTCCAGCTGGTTATGAAAAGTGGAAAGTACGTGCTGGGGTACAAA

CAGACTCTGAAAATGATCAGACAAGGCAAAGCGAAACTGGTCATTCTCGCCAACAACTGCCCAGCCTTGAGG

AAATCTGAAATAGAGTATTACGCCATGTTGGCCAAAATGGTGTCCATCACTACAGTGGCAATAATATTGAA

TTGGGCACAGGATGTGGAAAATACTACAGAGTATGCACACTGGCTATCATTGATCCAGGTGATTCTGATATTA

TTAGAAGCATGCCAGAACAGACTGGTGAAAAGTAAATCATGTACAATTTTTCTTTAATAAAACTGGCCAGAG

CTTGTTTTAAAAAAAAAAAAAAAAAA

      SEQ. ID. NO:29       AW461980

      TTGAAGTTGTCTGAAGAGTCTTCAATCACTATGTCAAGCGACACAGATATTTCTCTTTCTTCATATGA

TGAAGATCAGGGATCTAAACTTATCCGAAAAGCTAGAGAGGCACCATTTGTCCCCATTGGAATGGCAGGTTT

TGCAGCAATTGTTGCATATGGATTATATAGATTGAAGAGCAGGGGACATACTAAAATGTCTGTTCACCTGATC

CACATGCGTGTGGCAGCCCAAGGCTTTGTTGTGGGAGCAATGACTCTTGGTATGGGCTATTCCCTGTATCAAG

AATTCTGGGGGAAACCTAAACCTTAGAAGAGGAGATGCTGTCTTGGTCGTCTTGGTGGTGCTTGCTTTAGTTA

GACATCTCATATTGA

      SEQ. ID. NO:30       BM364411

      GCACGAGAGTGGAGCCGCTGGGCCGCAGCCGGGAAGCTTAGATGTGGAGGCGCTGAGACTTAGGAG

ACACCTGGGGCCGTTAGGGAGACTCAGGTGGCGGGACACTGGTGGGATCCCGACCTGACCCTGGGCCAGTCT

CGTTCTCGCGGCCCGCCTCCTCACCCCGCCCCCACTTGGGGCTGAAGTGGCTCCGCCTCCTGATCTGAGCCTG

GTCCCTCTTCAGGCACTGACCCTTGACCTCGGGGCGCTCCCCCATCCTTTGGGCGCGATGGCTACAGGCGCGG

ATGTCCGGGACATTCTAGAACTCGGGGGTCCAGAGGGAGACGCAGCCTCTGGGACCATCAGCAAGAAGGAC

ATTATCAATCCGGACAAGAAAAAGTCCAAGAAGTCCTCGGAGACACTGACCTTCAAGAGGCCCGAGGGCAT

GCACCGGGAGGTCTATGCACTGCTCTACTCTGACAAGAAGGACGCGCCCCCACTGCTACCCAGTGACACT

      SEQ. ID. NO:31       BF039456

      TGATTCAGAAATAGAATCGCTCTGATGTCTGAAGGTCCCATCGTCCTCAAAACTAAAAAGTTCTTATT

GAACCGTCTTCTCTCCAGGAAGCAAATGGTCTTGGAAGTCCTCCACCCAGGCCAAGCCAATGTTTCCAAGGA

AAAGCTTGGTGAACTCATTGCTAAGAAGTTCAAGGCTGACGCCAAGAACGTTGTCACCTTCGGCTTCCACACT

CACTTCGGTGGTGGCAGAAGTACTGGATTCTGCTTGGTATACGACAACCGTGACTACTTGTTGAAGTACGAAC

CCAAATACAGACTCAGAAGACTGAAAATCTTGGAACCAAAGCTTAACAACAGAAAGGCCAGAAAGGAATTG

AGAACCAAGAGAAAGAAGGTCAGAGGAAAGGAAAAGTCCAAGATCCAAGCCGGAAAGAAGAAGTAAAGTA

TCATATGCTCCCTTTATATGGCTGGTCGACTCGGAATATTTTCTGTCGTATTTGTTCTTTTCTATGTGCGCGTAT

AGTTCATGCATGAATCTGAAAACTGAAACCATAATTTAGCAAACAAAAAG

      SEQ. ID. NO:32       BF042632

      GCCGTCCAGACAGCAAGACAGAAAACCGGCGCATCACACACATCTCTGCGGAGCAGAAGAGGCGTT

TCAACATCAAGCTGGGCTTTGACACGCTGCACGGGCTGGTGAGCACACTCAGCACCCAGCCCAACCTCAAGA

TGAGCAAGGCCACCACGCTGCAGAAGACGGCCGAGTACATTGCCATGCTGCAGCAGGAGCGCGCGGCCAAG

CAGGAGGAGGCCCAGCAGCTCCGGGACCAGATCGAGGAGCTCAATGCTGCCATTAACCTGTGCCAGCAGCA

GCTGCCTGCTACCGGGGTGCCCATCACACACCAGCGGTTCGACCAAATGCGAGACATGTTCGATGACTATGT

CCGGACCCGCACGCTGCACAACTGGAAGTTCTGGGTATTCAGCATTCTCATCCGGCCCCTGTTTGAGTCCTTC

AACGGGATGGTGTCTACAGCAAGCCTGC

      SEQ. ID. NO:33       BF044457

      TTGGGAGCAGATCATAGCTGCTAGGTTAAGAAATTGATTCTCCCGCAGAAACAATGGATTTCGGTGT

AGCGGAACTGTTACGTGGAGATGCTAAGATGCAAAGGTACATTTCAAAACACGCCGGACAATATTGGCTGGA

AAATGACTTGGTTAAAACCTGATGATCTTACCAGCATTCTGCAGGTTGATGAATACTAATGAAGCTGTGGATG

TCACTGAGCAGCTTCATTTTAAATGAGGGGTTGCTGTCTGCCTGCTGTCTGCCTGGTGTGCTGTGACATTTTGA

AGGTGGAAACATTTCTGGCTAGTGCTGCGAGATTTACTTGTCTGTCTTATGAAAATCTGGTGATTGGGAAAAC

CTCCAATGGATGTGGGAAGAAAGTTCAAGATGAATTACATTTTTACATTGGTTTGTAAATAGATTCTGAACCA

GCATCGAGTCTAGATAATGCAT

      SEQ. ID. NO:34       BF040573

      GAGACCATGGCCTTAAAATACCTCTTATTAAACCCAGAACATGGTCTTAAGCAGTACCATATTACTTC

TTGATAATAGTGTTAAATCTTTTATGCTTTCAGTGAAGGAAAGGAAAAGTCTTGGATCAATGAAACCCATGTG

TGACTTGTCTTATCATCTTTCTCCAGGGCCCTCTTCTTTGATGCAGTTATGCCGCCTTAGAATTCGGAAGTGCT

TTGGGATCAAGCAGCATCATAAGATCACTGAGCTCAACCTCCCTGAGGAGCTGAAACGGTTTCTCCTCCACAT

TTAAATGTGTCAAGCGAATGGCGACACAGACAACAGACAAATGTTATTGAGTGTTGAGACCACTGGGATTTT

CAAGTTAAGTCAGGTTTATAGAGTTCAGCTAAGTTTTTGTTGTTTGCAGTGAGACGTTTATTGTAGCTTCGTAC

TAGGTTCTTTTGCGCTGTTGGTTTGGAGGGTATGAAAAATTATCTCCCCTGCCTGGAAGAGGGTGGCTANGAT

ATCCATGGTGTTGAATATCTTACCCAGCACTGAGCTGGGAACCCTTTATGCTTTGTCTAATTTAGTCCCACTCTT

      SEQ. ID. NO:35       BM364731

      GGCCTCGGGTGCCTACCCGGCGGTGTGTCGGGTGAAGATCCCCGCGGCCCTGCCCGTGGCCGCCGCC

GCCCCCTTTCCTGGGCTGGCGGAGGCCGGCGTGGCCGCGACTCTAGGTGGCGGAGCCGCTCTGGGGTCAGGC

TTCCTGGGAGCTGGGTCTGTGGCGGGGACCCCGGGGGGAGTCGGACTGTCAGCCGGAGGCGCTGCCGCCGGC

GTGGCTGGTGTCGCCGCCGCCGCCGCCGGAGCCGGCGGGGAGATGGCTTTCGCCAAGGGGACCACTTCGTTG

CCTACTGAGACCTTCGGGGCCGGCGGCGGATTCCCTCCTCTGCCGCGGCCTCCTCCTCAGTTGCCCACTTTGG

GCGCTGGCCTGGGAACAGTGGACGAAGGTGACTCTCTGGATGGACCAGNATACGAGGAGGAAGAGGTGGCC

ATCCCGCTGACCGCTCCTCCGACTAACCAGTAAGTCAAGACCGGCGTTTTGGGGGAAGCTGACTCGTCGGAA

AAAAAAAAAAAAAAAA

      SEQ. ID. NO:36       BF042198

      GTTGGCCTCAGGGTTTTTGCTCATGGTTTCCTCAGTGGTGCTGTCCGAGAAGTATTCAGGTGGTGACC

ATCACTGGTATGAGTTTCTCAGCAGGGTTAGGGCATATCTTTGCATGGACTTCGGTGGAATCATTACTGATTA

GGAGGACAGTTGTTGGGGGCCATCTGCCCTGCACAGGAAGAGATCTTGGACTCATGAAATGAGATACCCCTC

ACCCCCGAAGGGACCAAATGGAAACTGACATCAGAAACTCTGATACAAAATCATTTTAATTGCATCAAATGG

CCTTAATTCTGAGTTTGGTAGGCTTATCAATATGTTGCTTACAGTTGGGGTAGGGGAAGTAGAGGGAGAGAA

AGCAAGACATTTATTTACTAAGCACCTCTTAGGTGCCAGACGCTAGGCTAAGCACTTTACGTGAGCTGGGTCA

TATAAGCCCTGTGAGAACCCTGTAAGGAATGTTACTAGTATTTACACTTGACAGATGAA

      SEQ. ID. NO:37       BF045424

      ATGTGGGCAGACTGCCACAGTCCTCAATAGAATGGCCCTCTTGCTCCCGAACGTCCTGAAGCCACCA

GTCAGAACTGTAACGTACTGCAGTTCGAGAAAAGGCAAGAGGAAGACTGTGAAAGCTGTCATCTATAGGTTT

CTTCGACTTCATAGCGGCCTGTGGCTAAGGAGGAAGGCTGGTTATAAGAAAAAATTATGGAAAAAGACGGTT

GCAAGAAAAAGACGCTTGAGGGAATTTGTCTTCTGCAATAAGACCCAGAGTAAGCTCTTAGATAAAATGACA

ACGTCTTTCTGGAAGAGGCGAAACTGGTATGCTGATGATCCTTATCAGATGTATCATGATCGAACAAACTTGA

AAGTATAGATCAGAAGATCCATGATTTCTCAGTTATTAACTGTATATCTGTGTGTGTATGGTGTCTTTGCAAA

GATGAAGTGGTATAAGACATGATGTAAATTGTACCAACTGATACTTGGAACATGGGGTACCAACATTAAACT

TAACAATGTTTTAAAACTTAATGGA

      SEQ. ID. NO:38       BF039771

      TGGGTCGGCATAGCCATGGCGGCTCGTGTCCTTTGCGCCTGTGTCCGCCGACTTCCCACGGCCTTCGC

GCCGCTGCCCAGGCTCCCCACGCTAGCCGCGGCCCGGCCGCTCAGCACTACCCTCTTCGCCGCGGAGACCCG

GACGAGGCCTGGGGCTCCGCTGCCGGCCTTGGTGCTCGCGCAGGTTCCAGGCAGAGTTACACAGCTGTGCCG

CCAGTATAGCGATGCACCACCTTTGACATTGGAGGGAATCAAGGACCGTGTTCTTTACGTCTTGAAACTCTAT

GACAAGATTGACCCAGAAAAGCTTTCAGTAAATTCCCATTTTATGAAAGACCTGGGCTTAGACAGTTTGGAC

CAAGTGGAGATTATCATGGCCATGGAGGACGAATTTGGGTTTGAAATTCCTGATATAGATNCGGAGAAGTTA

ATGTGTCCACAAGAAATTGTAGATTACATTGCAGATAAGAAGGATGTATATGAATAAAATATCAGACCCCTT

TTCCTCATTGAGAGAAGGCTTNNNAGATGCTGGCGAGTGTCTGGCGGTGAGAACGCATTTCTGCATCATTGCT

GACTTTGCGAGTAATTCTGTTTAGACTT

      SEQ. ID. NO:39       BF041569

      ATACTGGTGTGGTCAGGCCCTTTTCCTTTGAAGAGGTAAGGTGAATCTGGCTTATTTTGAGGCTTTCA

GGTTTCAGTTTTTTTGATCTTTAAAGTATCCTTCAACCTGTGGTGCAAAAGCAGAAACTATGGCTGGATTAGN

TNATGAATATTTACGNNNNTTGTAAATTAACTTTTTACATTGAGAACAGCACTGATTAGGGAGATGATCAGAT

TCTTTTTTAAATACACTGTAATGACCTAGTGAACATAGGCATGTAGTGGTTTTGTGTGAGGGTAACCAGACAC

AGATTTACTTTTTGCCTTNAAGACAAAGGGAGATAAAAGCAACAAG

      SEQ. ID. NO:40       BM366529

      GCACGAGGTGAAGCTGAGCGTGTACTTGGATTACGCCAAGGCTGTGGGACTCTGGACCGCTCTGGTC

ATCTGTCTGCTGTATGGGGGTCAAAGCGCAGCTGCTATTGGGGCCAACGTGTGGCTCAGTGCCTGGACTGATG

AGGCTGCGGTGGACAGCCAGCAGAACAGCACCTCCTACAGACTAGGTGTCTACGCCGCCTTGGGAATTCTGC

AAGTGACTCCCTGACCCGCCCTAGCAGTCTACCTGCCTCTGGACCTGTCTGGCTCATCCTAGCTATGCCCTGC

CTTTGAGTGACATGCCCAAGGTCATTGCTAATATGAGGCAGAGCCCAGACTAGTCCCCGGGTCTTCTGATTCC

CAATGTGGCGATATTTCCACACTGTACTGCTTATAATCATTTCAAGGGATGACCTCCCTACCCCCATGATTTTT

TGTATTTTCTAGTCTGAAGTGTTTTTCGTTTTGTTTTTAAATAAAGCTTTCTCCTCTTTGAACAGAAGACTGNN

AGGTCAGGCCATCCCTAGGAACTGAGTCCAATACTCATTAAAAATGGAGCACTGATGAA

      SEQ. ID. NO:41       AW465571

      GGCGCTAAGCCTTTTTTTTTAAGATTTTTCAGGTACCCCTCACTAAAGGCACCAAAGGCTTAAAGTAG

GACAACCATGGAGTCTTCCTGTGGCAAGAGAGACAACAAAGCGCTATTAACTAAGGTCAATCAAAATGGTGT

CGGCGTCACAGCCCCATCTTCTGTTAGAAATGAGGACTTGACTCAACCCCCTTGACAATGTGCATTGAGGCTC

TCTGGGGGAGCGAGCATTTAAAGGAATGCTTGAGTACCTTGTATATATATCCCTGTGCTTGTCCTAATATTTA

ATTTGGCTGTTTTCATAGCAGCTGTTAATGAAGCCTGAACTTCAAGTGATGCTTGAAGGGGAGGGAAAGGGG

GAAAGCGGGCAACCACTTTTCCCTAGCTTTTCCAGAAGCCTGTTAAAAAGCAAGGTCTCCCCACAAGTGACTT

CTCTGCCACATCGCCACCCTGTGCCTTTGGCCTAGCGCAGNCCCTTCACCCCTCACCTCGATGCTGCTGGTAG

CTTGGATCCTTGTGGGCATGATCCATAATCGCTTT

      SEQ. ID. NO:42       BF043043

      GGCGACTATCCCTACTTTGAAACGAGTGCAAAAGATGCCACGAATGTCGCTGCAGCCTTTGAGGAAG

CAGTTCGAAGAGTGCTCGCTACCGAGGATAGGTCGGATCCCTTGATTCAGACAGACACGGTCCGTCTGCACC

GGAAGCCCAAGCCCAGCTCGTCTTGCTGTGGAAGTTAGAGAGGTAGCCAGTGCAACCTGACCAGCTCACCCA

CATGCGCAGATGGGCTCTGGGCGGAGAAGAGGGTACGCGTGTGCAGCAACGCATCACATACTCAACCATTAA

CCGTGCTGCTGCCTGTCAGTGGGTGGGGGAAGCGACACATCCCCTCATGGGAGAATCCATTTACTCAGTAAT

GGCGCCTGACACGTACCCATTGTAACGGCTGTCTAATAATGTTTAATTTAAATATGTATGTTACAGAGCTAAT

AAGTGAAATGACCAAGACTTTATAATTAAAACACTTAAGTATCCTAGAAGTTACTGTCTTTTCCCTGGGAATA

TGGAGAACTACTTTTTCTATGTGTATATTTTTATGTAATTAGCATTCTGTTCCTGGTTCAGGGAAAGCATGT

      SEQ. ID. NO:43       BF043765

      GCAAAACCCTCTTTCAGCATGGCGCATCTGGATAGCAACACTGAGCCAGGACTTACATTGGGAGGCT

ATTTCTGCCCCCAGTGTCGGGCAAAGTACTGTGAGCTTCCTGTCGAATGTAAAATCTGTGGTCTTACTTTGGT

GTCTGCTCCCCACTTAGCACGATCTTACCATCATTTATTTCCTTTGGATGCTTTTCAAGAAATTCCCCTAGAAG

AACATAATGGAGAAAGGTTTTGTTATGCCTGTCAGGGGGAATTGAAAGACCAACATGTCTATGTTTGCAGTG

TGTGCCAGAATGTGTTCTGTGTGGACTGTGATGTTTTTGTTCATGACTCTCTTCATTGTTGTCCTGGGTGTATTC

ATAAGATTCCAGTTCCTTCAGGTATTTGATTCCAGCATGTAATACACATTGAATGTATTAAAAAGAAATTTGC

AACTGTAAATAAAATGATTCTTTAGTAGAAACTCCAGTTAAAACACGAAGAACAGTTTGAAAGGANAAAAA

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

      SEQ. ID. NO:44       BF044823

      TGTTACCATTCTCTTCATTAGTTGTATCTGATCGACTGTCTTCTTCAATATCTTGCATTTGTCTGGTTTT

ACACTTAAGCTGTCAATGTCACTGATGTTGGCAGACAATAACTCAGCTAGCTCTTCCAAATATTTATTTTCTTG

CTCCCTGCGCGTCTTTTCAGTGCTTGATGCGAGTGTGTCACATGGTGACCCTTTTCTCTTATGTGAGTCTGGGT

TAGCAGGGTCAGATGAACTGTCCCCAAGGCCACTCATGTTGAAAAACTTCACACCTGGGAGACTTCTTTGTTC

ATAATGAGTCCTCAAAACAGATATGGTCAAATATGGTGCTCCGTAGGGTAGTTTCCAGTGGAATGGGGGAAC

ACTCTTTTCCAGAGATGGCTCTAGTAAGTCTCTTCACTCTTACCTCATTTAAGGCCAGTTATGCAAACAACTTG

AATACCTTAGAGTAAGTAGAAGTTATAAATGTCGGTGTCTTCATTTGGAAGCAAAAATTCTTACTGCCTGATG

TTGTCTGGTGGTCCGTGATTTTTATCATCAGAGTCG

      SEQ. ID. NO:45       BF044893

      AGGCGCTGCAGAAAGTGAAGATTGAGGTGAATGAGAGCGGCACGCTGGCGTCCTCCTCCACAGCCC

TTGTAGTCTCAGCCCGAATGGCCCCCGAGGAGATCATCATGGACAGACCCTTCCTCTTCGTGGTGCGGCACAA

TCCCACAGGAACTGTCCTGTTCATGGGCCAAGTGATGGAACCCTGACCATGGGGAAGGCAGCCCTCATCTGG

GACAGAATGGAGATGTCCAAGAGGAAGAAAGTCCGGAGCAAAGAATTTTTATTAATTCATTTTTCTGGAAAA

AGAGAAGATGTTTATTTATTTATTTTTCCATGGTAAATTCTTTTGAATCTGCCTCTTAGACCTAACTCTGGGCT

CTCTCAGGAGGGGCAAAGAGGACCTTTGAGTTAAACCCTCCAATGGAGACCCTGGGAAAGACTGGGAGGCA

TAACACCCAGCGGGCCTCCCAACTGGACTGTAGGACTCCCAGGACCGCTGGCCCAGCTGCTTCTGCCCATCGT

TCTGCCTGGTTGGGTTTTGGGTCCTGGATCCCACCGANNCCCTGGTAGGATGGCACCACAAGGCCTACATGAA

GGAGCTTTTGTGTGTTCA

      SEQ. ID. NO:46       BF046610

      CGTGGCCCCCAACGTGGCCCTGGCCCCGCCAGCCCAGCACAAGGCAGTGAGCAGCCCACCCTGTGCC

ACGGTCGTCTCCCGGGCCCCTGAGCCCCTCCCCGCCTGCATCCAGCCCCGGAAGCGGAAGCTGCCTGCGGAC

ACCCCTGGAGCCCCGGAGACACCAGCACCCGGGCCTGCCCCCGAGGAGGACAAGGACTCGGAGGCCGAGGT

GGAGGTGGAGAGCCGAGAGGAGTTCACCTCCTCCCTGTCCTCGCTGTCCTCTCCATCCTTTACCTCATCCAGC

TCCGCCAAGGACCTGAGCTCCCCAGGCCTGC

      SEQ. ID. NO:47       BF440261

      TTTTTTTTTTTTTTTTTTTAAGAGTCATACCATGTTTTATTGGACATCTTAACATGGGTGTGGGTGGGA

CCTATGGGTTGGACAGGGCACCAATGACAGCCTCAGTGAAGTCTTGGCAGNNNGCATAACCACCCATGTCAG

AAGTTCGAACCTGTAGGGGAGAATTGTTATCATCTCTGTGGCCTGGGCCCCATCCCTAACCCCCCACCACCTA

ACTGTTCCCTAAGGAAGCCAGCTCCCAACAACCTAGGCTCTGCCCAGAAGGTAATTATGGTCTAAAAGTATA

GGGCTCTTCTCTGGTCCACAGTACTGAAGGGAGGTGTATGGTCGTTGTGAGGTTGGAGGGAATAAAGGGCTC

TAGCCCCCATAGGGGTGCAGGTGGCCGATGACAGACTTGATGAAGTCGGTTGTGGTGCTGTAGCCGCCCATG

TCTCGAGTCCGTACCTTGCCAACTTTAATCACCTTCTTCACTGCCTCTGCAATCATGTTGGAGTGATGCTCG

      SEQ. ID. NO:48       BM362515

      AGGCCCTGCCCCATCTGAGTCGCAGGAGAAGAAGCCGCTGAAGCCCTGCTGCGCCTGCCCGGAGACC

AAGAAGGCGCGCGATGCGTGCATAATTGAGAAAGGAGAAGAGCAATGTGGACACCTAATTGAAGCCCACAA

GGAGTGCATGAGAGCCCTGGGATTTAAGATATGAAATGGTGAGCATGGTGGTCTGCTCTGGGAGTGAATAGT

TCCTGAAAAATGAAGAAGATTCAGTAACTTTGGGAGTTCCTTGCTGAAAATTGATAAATAAAAAATTATTTAT

AATTTATTAAAAAAAAAAAA

      SEQ. ID. NO:49       AW465165

      TTTTTTTTTTTTTTTTTTTTTGAATTTTTATACAAGTCTTTATTTACAACTTGTTTAACAACTGTACACT

TTTTGCAGCCTTGAAAACATTTTTGTACTTGAATGGGAAAATATAGTTTGACCAAATCTTAACTTTATTCTTCA

TATACATATACATATATTATATGCATACATATAAACATATACATATAATTAATACCATAACAAGTTGGCAGTC

ATAAAATTAAATGAATAAGTGACATCAAAAGGAAATACAATATAAGATTTCAAAAAATTAAAAATCTGTCT

TCTGGGGATTTCTTGGACTTCATGTTTTT

      SEQ. ID. NO:50       AW464987

      CTTTAGTGCCCCAAGCTCTGTATTAACATTTTGCCTGAATTATTCTATTAACCATTCTAAGTGTCCTTC

AAGAGCGGAAATGGAGGCATGGAGATGACATTAAGTGCTATATTTGCTTCTTAACATGGCAGGTCCCCACTC

TCTCAGGTAAAGCCACTTTGATGATATTTTCCTGCTCCTGTCTCAGGGAAGATGTAGGATGGAGGTACTTATG

AAAACAATATCTTTTTCATGACAGATGGGGAAAGTGAGGCATGGGGACACTGTAAGCACAGTATTATAAAAA

ACAAGAACACAGAGGATGCTGGTTCTGTTACTTATTTCCTTC

      SEQ. ID. NO:51       AW462906

      TCTTTCCCTAAGACGTACCCGGGACATGTCTTTGGTGTGTGGTGGAGGCAGTGAGACGTGCCTTGTCC

TTGGGTGCACGCCGCCTCCTGTCCACCTGTAGTTGATCGTGGTTTCATAGTGGAACTCTAGCTAGCTGGGGAG

AAAGAGAATCTCTGCAGCAGGAATCCCGTGTCTTCAGATGCAGGTCAAACCGTTAAGGAATTCCCGGAATTC

CCATCTAAATACTGAGACAGGAAGGAAGCCAGATGGCTAACGCACAGTCACTTTGTTAGTTAGGGCAGCATT

AGAAATCGAGCTTCCTAAAGTGTTTTCTTCTTCGTAGC

      SEQ. ID. NO:52       AW463449

      GAGCAGCGTCAACGTAGGCAGCGGCTGTGCAGAAAAAGGGCCCGAGGAATTGTCTCAGGAACCTGC

GCGCCCCGGCACTAACATTTCGAGGGTGAAGCTTTTCGACACCATGGTGGACACTTTCCTCCAGAAGTTGGTC

GCTGCCGGGAGCTTCCAGAGGTTCACTGACTGTTACAAGCGCTTCTACCAGTTGCAGCCTGAGATGACCCAGC

GCATCTATGACAAGTTTGTAACTCAGTTGCAGACTTCTATCCAGGAGGAAATCTCTGAAATGAAAGCTGAGG

GAAACCTGGAAGCTGTCCTGATTGCATTGGACGCGATTGTGGAAGAAAGCAAAGACCGCAAGGAGCAAGCC

TGGCGCCCCAGTGGGATCCCGGAGAAGGACCTGCGCAGCGCCATGGCGCCCTACTTGCTGCAGCAGCGGGAT

GCCCTGCAGCGTCGTGTGCAG

      SEQ. ID. NO:53       BF040406

      AGGGTGTCGGTCCGCAGCCCCTTGGGGGCGAGCGCGGCACCCCTGTGGCCTCTGCAAGTGTCCGTGG

CGCGGCCTNNNGGGTGGGTGGGGGAGGCTTTGCACGAAAGATGTCCAGACTCTGCCCCTGTCCCACCAGCCG

CTCCCCGCCCCCCGCCCCAAACAACTCAGCGACATATCCAGGCCAGTGTGGGGTGGGGAGGCCTCGTGTTAA

CCTGAGCACTGTGGGGAGGGCCCC

      SEQ. ID. NO:54       BF042130

      ACAAAATTTTATTGTAGGGTTGCTTTTTATGGGTTATTGAAATTACAAAAATAAATGAAGCATGCTTT

GTATCACCAAGGTTATTGACTTTAGTAAGGGTGATATACACGTAAAAAAGGAATTACAGTTCAGTAATCTTGC

TATAATAGAGGTATGTACACAGCACTGTTGGAATATTGAAAAAGGTGTGACTTTAATTGCAGGGTCCCTGAG

GAAGGTTTCGCAAAGTAAACATACCCTGGCCCCAAANNNTCCTTTCTCCTCTTCTTCAAATGAAAACCTTTTT

AAGTTGGAAAAATGGCACCTAAGGCAATTCTGGAGTCTAGGAAGGACCGATTGCAGTCAGCCACTGTTTGGG

CTAAGCCACTCC

      SEQ. ID. NO:55       BF043536

      GAAGTGCTGACCCGGGTTCTCTCAAGCCCCCAGGTGCCCCGGGTCTCCCACCCGCCTTCACCATGGA

CTCGGGCCCCCTGGGGCCCCCAGCTCCAGTTCCCACAACTCAGGCAGGCTGGTCCAGGCCCTGGGCTGCCTC

AGTCACCAGCCCCCCAGGGAGGAACCGGCCCCTCCCAGGGAGCCACTTCCGAGTTTTTAGAAAAAGTTATC

TCCCATTTCTTTTCAGCCAAGATGTTCAGTAAATATTTTTAGTACAGCACTTAGTGGACCACTTCCTAACTGTG

CTTTCTTGCCACACAAGTGTCCTGGCAAGAGCCCCTTCTCTTTAAGACATCAGGAAGCCAGCCAGACCCTTTT

GGGTCAGGAGCGCTNTGCAGCCCCAATAGCAAGGCTGTCTGTGTCTGAGCTGCCGGCCCCCCGGAAGCCCAG

GACCCCCAGAGGAAGGAGCCAGGAGAGCACAAGTCTCTGGAGCTGCAGCCCCACCCATGGTTG

      SEQ. ID. NO:56       AW464569

      GTTCTAGAATTTTTAAATTATGGAACATTAGTCAAGTTTTAATTTGGACATATGTTGAAATTCTTCAC

ATACACATTTTGTCCTCATATATATGAAGTTGGGGCTTAAATATCAGTATTCTCAGAATATCATTAATTAATG

AAATTAATTAATTGATTAATTGGATTTGTGTACCTATTTGTCATGCAAAAAATTCCTTCAGGTCTTCAGAATTC

TCAACTACCTATGATTTTTTTTTTACTGTCAACTCTTTTGACAGCAACTGGTTAAAGAAATTGATCCAAAGAC

TTAAGAGGCAACTTCTTCCTTGGTTTATGAGTGCACTTTCATTTATACCAGAATAAGCATGTACATATAGGCA

CTATTTAAGGTATTTAGCAGGTAGTAATATCTAGCTTGGACCTTAGTTCTCTGACAGAGTAGGTTCTGCATGT

CAGGTGTTGTCTCTGTAGTTTTTGTGGAGCATGAAATATATAAACTCTGACACCTCGGCTAGTATACATATTG

GAAGTTAACTCACTTTCAGNTGTTGAGAGTTAAATAACAATGTTTGTAAG

      SEQ. ID. NO:57       BF040351

      AGATTTCAAAAGGAACTGCTCGAGTTCTGAAGGTGAGCCCAGCCTTTCTAAACCTCTTCTCAGAAAA

GGGAAACTGACACTTGAATTTTTGTCACCCCTTTCCTCATTGGAAGGGAAGGAGCCTTAGAAGATTTTTCTTT

CTAACTCTGGTCTTAGGTAAATATATTCTAATAAAACATAGGCTACTCTAACAACATAGAATTTAGATGCCTC

ACGTACGTGAGAAAATCTTGAATATAGGACAAGGGTCCTGCTTTTTAAAAACAGACTCAACTGAGCTGATTA

GATGACGTGAGGCCGCTTTGCCTTCAATAACATGAAGTTTTGGACAGTTCCTACTCCTATTTGCAGAAGGAAA

TTGGCTGAAACATACTTTAACCATTTCAAAGAAGGTAAAATTGGACCTTAAAAGGTATCAAGAAGCCAGCAT

GGTACTTAATTACAACATAACATTTTGACCTTAATGGGAAGTCATTTTATTTGCACTAAAGGCCTTGCTTGCTG

AAGTCTCTTAACTCTTATCTGTAGAACTTTATTTCTTCCACTAGTACAAGGAGAGAGAAGAGTTCTTATAATT

GAATGTTATCATAAAGAGGGAATGGA

      SEQ. ID. NO:58       BF440195

      TTTTTTTTTTTTTTTTTTAAATCAAAGACTAAGAACAAGTCAGGAGCTAAGTGACTTCTGAGTTCAATG

ACTGACCCATCAGAGATTAGCCAAGGCCTCTCAGATGGTCTGCCAAGCCTTGCGTTGTAACACTGATCTTATA

ATGTGGCACACTTGTCCTTTTCTCACAGAAATATAGGTATGGGAAGTGGATCATATATGGGCAGTATCCAGCC

CAGAAGTAACTCAACAAAGACATTGTAAACTTCTTACTTATATGTTTAATGAGAACCATATGTATCTGCAGTA

GAACCTACCAAATAAGAGCACCTTTGTTTTCTTCTTTCTAGAGAGGTAATTCGGGGGATCTGACGGTGGAACT

GCACACATGACCAATGTAGAAGATCTAGACAAGTACCCTAGGGGTCACGTGGCCCCAAGAGTCGGGTCTGAA

GAGCCTCAGGTGACCCTTCTTACTTTGAATGTGTAAATTCTACTCCTCAGTCCTAGGGGGTGGAGCAATCA

      SEQ. ID. NO:59       BM362654

      GCACGAGGGCCGATGCTCTCAAGAGTATCAACAATGCCGAAAAGAGAGGCAAACGCCAGGTCCTTA

TTAGGCCGTGCTCCAAAGTCATCGTGAGGTTTCTAACAGTGATGATGAAGCATGGTTACATTGGCGAATTTGA

AATGATTGATGATCACAGGGCTGGGAAAATTGTTGTGAACCTCACAGGCAGGCTAAATAAGTGTGGAGTGAT

CAGGCCTAGATTTGATGTGCAACTCAAAGATCTAGAAAAATGGCAGAATAACCTGCTCCCATCCCGTCAGTTT

GGTTTCATTGTACTGACAACCTCAGCTGGCATCATGGACCATGAAGAAGCAAGACGAAAACATACAGGAGGG

AAAATCCTTGGATTCTTTTTCTAGGGATGTAATACATACAAATAAAATGCCTCAGAGGACTCTGATGCTTC

      SEQ. ID. NO:60       AW462632

      TGTCTCCGTTCGGGAGCCTCACAGACATATCTAGGTAAGATCGTTAAATAAACGCCGTCAGCCA

TCGCAATGCAAAAATAAATATCAATCCTCCGGCCACAGCGCCAGCTGCGCTGCGCCCCAAGTCCCATCGGCC

GCGCCTAACAATTATAAAAGTGTTCAGCGAGAGTGGGTCGGCGTGAGTGTGAACGGGTGTGCGCGCGGGGGGT

      SEQ. ID. NO:61       BF040216

      AGTGTTCTTGCCTAGGAAATCCCATGGACAGAGGAGTCTGCTGGGCTACAGTCCATGGGGTCACAAA

AGAATCAGAGACAACTAAGTGACTAAACAATAACATTAAGATGACAGAGAAGTTCATCAGACTCCTCATAAT

GTTGGGCCTGGAGTACTGAGCTCTGGCGTTAAGCACTGAGCTGTGGTGTAACAGCCAGAGACTAAGTGGAAC

CTTAAGACCTGAAAAGTGAAAGTGCCCTCCTCTAACTAGCTAATTTAGTGCACAGGCACATCTTAGAGTGTGG

AGAGAATGCAAGCCCATTATAAGTGAAGAGCTCTTGCTGCCCTNGAGGGGAATAAGTTAAAGAGATTCCATC

AAATGAATTTGCTCAACTGCAGCAAGNNNTCCATTTTATAAATATCAAATCTAGGCTAATAATGTNGCTGAAT

TGCTCTCTGAAATAACCGTGCCAACCAACAGCAATACTTTTATCAGTGATGAGGAATAGCTACTGCACATAAA

GTAGAATAAATGCACATAAAGTAGAATAA

      SEQ. ID. NO:62       BF045874

      AATTACTTCTTAGAAGTTTGGGAATTACCTTCCATCAATTCAGCTAAGAACGGAATGGATTCTGGTAA

CAAGACGATATAATTCTCTCTCAGTTTTTCAGCCANNNCTAACACAGTTATCAGAGCAGCAAATCGAACCTGA

AAAGATGAAGAATCATTNNNTAAAAACCAAAGAACTATTATAGCTCTGTTTGTTAATTTATGATCTAACTTGA

GACATGCTCTGAATCTTAAACTGGTATTTCACTCTCCATTCAAGCTTCATCTTAGCATACCAGTTCATTTAACA

GTTTGAGATCTGTTTAATAACACGGGCAACCTTGTAAGTCACAGCCTTTCAAT

      SEQ. ID. NO:63       AW461973

      ATGCACCTCCTAATACCATCACTTTGTGTGTGTGTGTGTGTGTGTAAGCACGCGCACACGCACGCATG

TGTATGCTCAGTCATGTCCAACTCTGTGCAGTCCTATGGACTGTAGCCCACCAGGCTTTTCTGTTAATGGAATT

TTCCAGGCAATACTGAGTGGGTTGCCATTTCCTACGCCAGGGTATCTTCCTGACCCNNNAATCGAGCCTGCAT

CTCCTGTGTCTCCTGCATTAGNGGCAGATTCTTTACCACTGAGCGACCTGGGAAGCCCCATTTTTGGAATTTA

GAATTTCCACATAGGAATTTTAAAGGGACACAAATATTCAAATCATGGACCCT

      SEQ. ID. NO:63       AW461973

      ATGCACCTCCTAATACCATCACTTTGTGTGTGTGTGTGTGTGTGTAAGCACGCGCACACGCACGCATG

TGTATGCTCAGTCATGTCCAACTCTCTGCAGTCCTATGGACTGTAGCCCACCAGGCTTTCTGTTAATGGAATT

TTCCAGGCAATACTGAGTGGGTTGCCATTTCCTACGCCAGGGTATCTTCCTGACCCNNNAATCGAGCCTGCAT

CTCCTGTGTCTCCTGGATTAGNGGCAGATTCTTTACCACTGAGCCACCTGGGAAGCGCCATTTTTGGAATTTA

GAATTTCCACATAGGAATTTTAAAGGGACACAAATATTCAAATCATGGACCCT

      SEQ. ID. NO:63       AW461973

      ATGCACCTCCTAATACCATCACTTTGTGTGTGTGTGTGTGTGTGTAAGGACGCGCACACGCACGCATG

TGTATGCTCAGTCATGTCCAACTCTCTGCAGTCCTATGGACTGTAGCCCACCAGGCTTTTCTGTTAATGGAATT

TTCCAGGCAATACTGAGTGGGTTGCCATTTCCTACGCCAGGGTATCTTCCTGACCCNNNAATCGAGCCTGCAT

CTCCTGTGTCTCCTGCATTAGNGGCAGATTCTTTACCACTGAGCCACCTGGGAAGCCCCATTTTTGGAATTTA

GAATTTCCACATAGGAATTTTAAAGGGACACAAATATTCAAATCATGGACCCT

      SEQ. ID. NO:64       AW461973

      ATGCACCTCCTAATACCATCACTTTGTGTGTGTGTGTGTGTGTGTAAGCACGCGCACACGCACGGATG

TGTATGCTCAGTGATGTCCAACTCTCTGCAGTCCTATGGACTGTAGCCCACCAGGCTTTTCTGTTAATGGAATT

TTCCAGGCAATACTGAGTGGGTTGCCATTTCCTACGCCAGGGTATCTTCCTGACCCNNNAATCGAGCCTGCAT

CTCCTGTGTCTCCTGCATTAGNGGCAGATTCTTTACCACTGAGCCACCTGGGAAGCCCCATTTTTGGAATTTA

GAATTTCCACATAGGAATTTTAAAGGGACACAAATATTCAAATCATGGACCCT

      SEQ. ID. NO:64       AW461973

      ATGCACCTCCTAATACCATCACTTTGTGTGTGTGTGTGTGTGTGTAAGCACGCGCACACGCACGCATG

TGTATGCTCAGTCATGTCCAACTCTCTGCAGTCCTATGGACTGTAGCCCACCAGGCTTTTCTGTTAATGGAATT

TTCCAGGCAATACTGAGTGGGTTGCCATTTCCTACGCCAGGGTATCTTCCTGACCCNNNAATCGAGCCTGCAT

CTCCTGTGTCTCCTGCATTAGNGGCAGATTCTTTACCACTGAGCCACCTGGGAAGCCCCATTTTTGGAATTTA

GAATTTCCACATAGGAATTTTAAAGGGACACAAATATTCAAATCATGGACCCT

      SEQ. ID. NO:64       AW461973

      ATGGACCTCCTAATACCATCACTTTGTGTGTGTGTGTGTGTGTGTAAGCACGCGCACACGCACGCATG

TGTATGCTCAGTCATGTCCAACTCTCTGCAGTCCTATGGACTGTAGCCCACCAGGCTTTTCTGTTAATGGAATT

TTCCAGGCAATACTGAGTGGGTTGCCATTTCCTACGCCAGGGTATCTTCCTGACCCNNNAATCGAGCCTGCAT

CTCCTGTGTCTCCTGCATTAGNGGCAGATTCTTTACCACTGAGCCACCTGGGAAGCCCCATTTTTGGAATTTA

GAATTTCCACATAGGAATTTTAAAGGGACACAAATATTCAAATCATGGACCCT

      SEQ. ID. NO:65       AW461973

      ATGCACCTCCTAATACCATCACTTTGTGTGTGTGTGTGTGTGTGTAAGCACGCGCACACGCACGCATG

TGTATGCTCAGTCATGTCCAACTCTCTGCAGTCCTATGGACTGTAGCCCACCAGGCTTTTCTGTTAATGGAATT

TTCCAGGCAATACTGAGTGGGTTCCATTTCCTACGCCAGGGTATCTTCCTGACCCNNNAATCGAGCCTGCAT

CTCCTGTGTCTCCTGCATTAGNGGCAGATTCTTTACCACTGAGCCACCTGGGAAGCCCCATTTTTGGAATTTA

GAATTTCCACATAGGAATTTTAAAGGGACACAAATATTCAAATCATGGACCCT

      SEQ. ID. NO:65       AW461973

      ATGCACCTCCTAATACCATCACTTTGTGTGTGTGTGTGTGTGTGTAAGCACGCGCACACGCACGCATG

TGTATGCTCAGTCATGTCCAACTCTCTGCAGTCCTATGGAGTGTAGCCCACCAGGCTTTTCTGTTATGGAATT

TTCCAGGCAATACTGAGTGGGTTGCCATTTCCTACGCCAGGGTATCTTCCTGACCCNNNAATCGAGCCTGCAT

CTCCTGTGTCTCCTGCATTAGNGGCAGATTCTTTACCACTGAGCCACCTGGGAAGCCCCATTTTTGGAATTTA

GAATTTCCACATAGGAATTTTAAAGGGACACAAATATTCAAATCATGGACCCT

      SEQ. ID. NO:65       AW461973

      ATGCACCTCCTAATACCATCACTTTGTGTGTGTGTGTGTGTGTGTAAGCACGCGCACACGCACGCATG

TGTATGCTCAGTCATGTCCAACTCTCTGCAGTCCTATGGACTGTAGCCCACCAGGCTTTTGTGTTAATGGAATT

TTCCAGGCAATACTGAGTGGGTTGCCATTTCCTACGCCAGGGTATCTTCCTGACCCNNNAATCGAGCCTGCAT

CTCCTGTGTCTCCTGCATTAGNGGCAGATTCTTTACCACTGAGCCACCTGGGAAGCCCCATTTTTGGAATTTA

GAATTTCCACATAGGAATTTTAAAGGGACACAAATATTCAAATCATGGACCCT

      SEQ. ID. NO:66       AW462202

      CAGTGTCCGCGCAGCTGAAGTGTGGATGGATGAATTTAAAGAGCTCTACTACCATCGCAATCCCCAG

GCCCGCCTGGAGCCTTTTGGGGACGTGACAGAGAGGAGACAACTCCGTGCAAGGCTTGGGTGTAAGGACTTC

AAGTGGTTCTTGAATACCGTGTATCCAGAGCTGCACGTGCCTGAGGACAGGCCTGGCTTCTTTGGGATGCTCC

AGAACAAAGGACTGAGAGATTACTGCTTTGACTACAATCCTCCCAATGAGCACGAGATCACAGGACACCAGG

TCATTCTGTACCGCTGTCACGGGATGGGTCAGAACCAGTTTTTCGAATACACATCCCAGAATGAAATACGCTA

CAACACCCACCAGCCAGAAGGCTGCGTGGCAGTGGTGGAAGGAACAGACGTCCTCATCATGCATCTGTGTGA

GAACACCACCCC

      SEQ. ID. NO:67       AW465524

      AGCAGAGACCCACATCAGACAGCTCCTACACGTGCCCGATGAACAGGGTCTTCTCCTGGGCTGGGAG

GTTTGACTGCTGACCTGTCCCCTCTCAGNGGTAGCCCCCACCCCCATCTCTCCAGTGGAAGTCTGTTGCAACA

AGCTTCCGTCCCACTCAGGGATGCAAAATGCCCACGGAGATCAAGCTGCTGGGGGAAGTGTTTACGTCTCTCT

AAACATACCCCTAAACATACTCTCTGTTAGTGTTAACGTTAGGCAAATGGAAGAAAGACCAGGTCGAATTCT

GAAATAATTATTCAGCCTCCCCTCCTTGTCCACTTCATACACCACCATGCTGCAGAATGTTCCTTATTTCTTAA

GGATGAGTGTGCCTGTTGAATACAAATGTACTGCTGCTGCTTAACTTGCGAGATGCATGGCGTATGTTACCGT

GCTGGGCCANTGTCGTTCTTAAATGCCCATCGTAAATACCATG

      SEQ. ID. NO:68       AW465958

      ACAAACCTAGACAGCGTATTAAAAAGCAGAGACATTACTTTGCCAACAAAGAGTCCACCACCCCCAC

CTCTGGCCTGGACGACCCCTCTCCTGCCAGCCTTGGGAACCTTTCGGTGCAGCCAGAGTGTGGGCCAGGGTCC

TGCAGTATCAGAGAGCTGCCTGAATCCGAGGGGCAGCCGCCTGCGGCCCCCCTGCCCCTCTTCTTCGTGACGC

TGGAGGCGGACTGGGCAGAGGCCAAGGCTCGCTGGGGTCTGGCCTGGGAGGCCCACGTGTACGGGGTAGGC

GCGCTCTTCGGCCTGGTGGCCTTGCTGGCGCTGCTGGCGCTAGCCCTCCTGCCCTGGCGCTGCCCGCCCGGCG

CTCCCTGCCTGGCGCTGCTGGACATGCTCCTGCTCTCGGCTGGGACCACGCGGGCCTTCCCGCTCTTCTACGA

CGCCTACGGGGACCGCGACCGGCTGCCGGCGCTGGCCTGGCTGCTGCTGCA

      SEQ. ID. NO:69       BF041193

      TGGTTCTTCCGGTCGTCTTTGAAACTGAGAAGTTACAGATGGAGCAACAGCAGCAGCTGCAGCAACG

GCAGATACTTCTAGGGCCTAATACAGGGCTGTCAGGAGGAATGCCGGGGGCTCTACCTTCACTTCCTGGAAA

AATCTAGATTGCTACTGCTATATTTGACCTGTCTTGGTGAAGAAGTTTGAAAATTCAATAGTGTTTGAACTGC

TGATTATTGGATTTTTTTTTTTTTTTAAACTTTGGCACATGGCTCTATAAACCTGGTGGCAGGAATTCTCCCCA

CATTGGCTCATGGAGAGACTCCTCACTTGCAGCTGTGCCCTCCACTGTCCTGACTTATTTCTTCTCTCCTCAAT

GCTGATACCAGAGAGCAGCAACGCAGACGGTTACTCCAGCTCTGGCCACCCACCCCCCCTCACTAAATTACT

CCTG

      SEQ. ID. NO:70       BF042630

      GTAATTCATGGGACTGGAGCATTTGGAGCAACAAAGTGCCCCGGTGCTACGTTCTCACCTTTGGTTAT

GAGATTTCAAGTTATTTTATCCCCTTTTCAGTGGCAATAAGAACCTTTGTTGGACTTCTTGTTTAATTCGTACA

TAATGTGTAAAACACTTTCTTTGAAAGCAAATTCAAGGCACTGAATCTGTATGTCTGTGTGGGTGCTGTGTCC

ATGTGGCTGTCCATTGGCAGGCAGACTTGATCCCTGACGCCCTGTACACCACACTGCATGAGTCAGGCCCTTG

ATCGGGTGTTCTCTGCTTGGATGGTAGGAACCACAGAGCTTATGAAAGAACACTTGTCACCTGCTCCATCGGT

TACAGTGCTAGCTGAGGAAAACAGTTCCTCACATGTATTCTTTTAACAGGACTCGTGTTCTAGTTTCCTGTAAT

TTATGTTCCTTTAATTTTAATAAAAGCTGAACTGTGAAAA

      SEQ. ID. NO:71       BF043059

      AGCNNAATGAAAGTTAAGTCTGTGATCCCTGGTGCCCCCAGCCCGCTGAGTGTGCCTCCAGGCTCAG

AGCCTTGGTTCCGAAGCTGGTCTCTGACAAGGGCCAGTGTCTCCCACCCAGGTGGAGAGCAGGTCCTGCTTGC

GGCGAAGGCCGCAGGGTTTGAAAAGTTTAATGTGAAAGACCCTCCCCAGAGCCCTGGCTTGTCTGGGAGGGC

CGTCAGTCCATGGCTATGTTGAGACCCCCGAAACCCTCCCCTGTTCCTCTAAGTGAGGAGCTGGTCTTGTGCA

GGATTTGTGTGTGTGTGTAAAGAGGATCTGATGTGTTTGTCTTACTGTCCGAGCCCTGTGCAGAAGAGNCTGG

AAGGGCAGGGGTGGGCTTGGAAAGGGGACACCCTTCCTAGGGAGAGCCCAGGGCCCTATGAGGTGTCAGAG

CTGGAGACTTGGGCTGGGCCTGGCGGGGTCTGAGTGCGGGCTCCGTCTCACCGGTTCGGGGCTGACTGGGTC

TTA

      SEQ. ID. NO:72       BF043236

      GGGGAATGAGCAGCGGCTACAGAGGGAGGCAGCCGGCACTGCTGGCCCCTCCTTCCTGCATCTCCAG

GAACCCAGGACCAGCCACTGCAACTAACGGCTTAACTCATGATACACCTTCCCTTCATTCCAAAGGGAAGAT

CGATGTCTGCTTATCTATCACCACTTGCTTCATCTGCTCTTGCTTTGTTTGCCTTCTCAGTACTTCTGCCTGCTG

TTTCCCTGGTCTGCGTTTATTGCGACGATGCTCCCTTGACTAAACGTGGTTACTGACAACTGACGTTAACTCTG

CACCYTTGTTGGCACCTGGAGTTCAGCCACTGGCTCACAGACCGCAGCTCTGGCTGAGGACCCTCATCCCCAGG

GATGCTTTCTGATCCTGTGCATTCCTCCATG

      SEQ. ID. NO:73       BF043635

      GAGGTTACTTCACAGGAACCAGGGGCAAAGGGCCACATCTTTTTTTGAACAAGGTTAATCCTTTCCT

GCAAAGTAGGGCCACACAGAATGTTGCAACTACCGGAGGTTTTTTTGATAAGAATTTGACTTCCAGCCTTAAG

CTTCTAAATTTCTGATTTAGTTGAATCTTGGTGAGAACCAGAGGCCGGAACTCAGCTGCCCCAGGACTGTCCA

AGGAGCAGGAGCAAGTGGTGGCCCTGAACTGATGCGGTGCCCGGAAAGCATGTGTGGCCAGCGTGCTGGGG

TTAACAAGACCTTGGTCATCCACCGAGGAAAGCAGGAAGTTGTTTCCAAAACAAGGAGGAAAAAATAGATG

CTGAAGAATCAGAAGCTACAGCTGTGCAGCACAGGCTGCCCTCAGACCTGGATGGACATAGCCCAAGCCCCA

AGACGAAAAGCTTCTGTGATACACTGACATGTTTATAACTGTCCGTGATCTTGGGGGCAGGGACCAGAATTCC

TCTGTCTGTTGGAGAAAATAGGCATAGAG

      SEQ. ID. NO:74       BF043736

      TGTGTGATTCCTATTTAACAACAAAAAAAGAAGCCTTTAGGAAGAGACAGGTAGAGGGGTCCCTTCA

CTTTGAACTTGGTGGAAAGCAGTGAGGGGACTCCGGGTGGGCAGCTCTGGGGTCGGCTTTGGGGCTGGTCTG

TGCGGCCGGAGAGGAAGACCCGAGCCCCTTTTCTGCTCCAAGAAGNCCTGGACGTTTCTTTCTTCCCAGTGCA

TTGGACCAGAACAGCGGACAAGGGGGTGCCCTCGAACCCAAGAAGCGTTCCCAGATCCAGCATCGCTGAAG

GGGGGCCGTCGGAACCATCCCGCTCCACGAGACCAATGCCGCCTTGTTTGAGACTCGT

      SEQ. ID. NO:75       BF044851

      GGGATCTCCAAAGAGTTAGGGGTCCCTGAGGAGGTCAGAGTGCTACCAACCACTAGGATATTCCTAA

TGGGGGCTCCTTGGTGCCAGAGCTCCTTGGTTGGGTGCCCTCAGCATCCCATCATCCTCCCCCAGGTGGGCTC

CCCCATCCCCGGGGGATGGTTCCTGAGCGACTGCCACCATGGCTCCAGCGCTACGTGGAGAAAGTGTCTGAC

CTCAGCCTTTTTGGGGGTCTCCCAGCCAACCACGTCCTTGTAAACCAGTATCTGCCTGGGGAGGGCATCATGC

CCCACGAGGATGGGCCACTGTACTACCCGACCGTCAGCACTATCAGCTTGGGCTCTCACACCATGCTGGACCT

CTACGAGCCTCGGCAGCCAGAGGATGATAACCCGACAGAGCAGCCCCGGCCCCGCCCCGGCCGGCCACCTC

TCTGCTGCTTGAACCGCGCAGCCTGCTGGTGCTCCGTGGCACCGCCTACACGCGCCTCCTCCATGGCATCGCG

GCAGCCAGCGT

      SEQ. ID. NO:76       BF045170

      GGCGAGCTCTGACGGCAAATAGGTCTTTGAAATTGAGGGCCTGCCCCTGAGGTGTGGCAAGGGGTCA

GCACCCCCGATCCCCCCGGCTGGCCTCTTTCCCTGTCCAGGGCGGTGCTCCCACCACAGTCCAAAGGCTGCTG

TCAGCAGACACCTGGGCTGAGCGGTGTCCCTTCAGGAGCCCGGGAGGCAGGGACCCCCTCCTGCTCTCGTCC

CACCCCTCACTAGCATCATCTCTGTAACCAGGTGAGCCAGGGTGGGAGAGGGACTTGGAGAGTCCAGGCATC

TGCTTCCAGTCTGCCTTGAGGGGCTCAGCTCCCTCGAGCGGCCAGGGGCTGCGAGTTCTGGGCATTGGGTGAC

AGGCCAGTAGACTGCCTTCAGCCTTTGTGCTACTGTGTCCCTCCTCTTCTGCTGCTCCGGGCCCTCCACCAAGA

GATCCTGTTGGACTTGGCCGCTGGACCGGGGCCTATGAGCTTCCCTTCCTGCCCTTGAAAATGAGGATCCTCT

GGTCCCTGCCTGCCCTGTTGCTATCCATTGAGGAAAGGGCAGTGGAGAACTTTCCCA

      SEQ. ID. NO:77       BF045305

      CCTCAAACCGCAGGCCCGATTGTCCCCAGGCGGGGGCTCCACTCTGGGTTTGGGGTCACCCCGTCCC

CTCTGGGGAGGGGCTGGAGCTTTGTTTCATTTTAATGACCTTTGAGCGTTGTAGGGAAACTGAGGCAGGGAA

GAAGTCTTGACTGCCTAAACGGTCAACTCCTCCTCTAAACTTGAAGGTGGCACCCTCTCTTCTGTTTAAGTTGT

GTGTGTGTGTGTGTGTGTGTCTGTGCGTGCGAACAGTCGGTCACTTTTGTCCCACTGTTGGACCCTGCTGCCCT

GGGGCTGTGGTTTTCCCGGGTCCAGGGCGCGCCCTTTCTCCCAGGAGCTGGCATTGAAGACTTGTCACTTCTG

GCAGGGTCCTGATGACCCCCTCTGCCCGCCAGTATTTGGGTTATGTCCAGAGGGAACTAGGTATCATGGTTTC

CTTGGACTTGTAAGCTTCAGGATTGCTCAGTAATGAATGAAAACATCACGGGAGACATGGGGAAGAGCAGTG

      SEQ. ID. NO:78       BM362735

      GCACGAGCGGAGCCGCGCGGAGGCGGAGGCTCGGGTGCATTCAAGATTCGGCTCCACCCGTAACCC

ACCGCCATGGCCGAGGAAGGCATTGCTGCTGGAGGTGTAATGGACGTTAATACTGCTCTGCAAGAGGTACTG

AAGACCGCCCTCATCCACGATGGCTTAGCACGTGGAATTCGCGAAGCTGCGAAAGCTTTAGACAAGCGCCAA

GCCCATCTGTGTGTGCTCGCATCCAACTGTGATGAGCCTATGTATGTCAAGTTGGTGGAGGCCCTTTGTGCTG

AGCATCAAATCAACCTGATTAAGGTTGATGACAACAAGAAACTAGGGGAATGGGTAGGCCTCTGTAAAATTG

ACAGAGAGGGGAAACCTCGTAAAGTGGTTGGTTGCAGTTGTGTGGTGGNNAAGGACTATGGCAAAGAATCTC

AGGCCAAGGATGTCATCGAGGAGTACTTCAAATGCAAGAAATGATGAAATAAACTGATTTCTTGTTTTCCAA

AAAAAAAAAAAAAAAAA

      SEQ. ID. NO:79       BM366522

      GCACGAGCCGTGGCGCAATGAAGGTGAAGGCCGCCCTCGCCGGCGGCCGAGGTGGTCAAGGCGAAG

GCCGGAGCGGGCTCTGCCACCCTGTCCATGGCATACGCTGGAGCCCGCTTTGTCTTCTCCCTCGTGGACGCGA

TGAATGGAAAGGAAGGAGTCGTCGAATGTTCCTTCGTTAAGTCCCAAGAAACGGACTGTCCGTATTTCTCCAC

ACCGTTGCTGCTGGGGAAAAAGGGCATCGAGAAGAATCTAGGCATCGGCAAGGTCTCCCCTTTCGAAGAGAA

GATGATTGCTGAGGCCATCCCTGAGCTGAAAGCCTCCATCAAGAAAGGAGAGGAGTTTGTCAAGAACATGAA

ATGAGAAGGCGCTTAGCGAGCAGTCGGTCTCCTTAACTTATTAAGGCATCATGTCACTGTAAAGCCGTTTCAG

ATACTTTTGTCGTTTCAATTTGCTTCGTTGAGGAGGATTGTATTAACGAACCACCCCTTTGCAATCTTGGTCAG

TCTGTCGGTGCATCAATAAAAGCAGGCTTTGATTTTCAAAAAAAAAAAAAAAAA

      SEQ. ID. NO:80       AW461513

      TTTTTTTTTTTTTTTTTAATGGATGCGTGGTACCACCTGCTAGGGCTGTCCATCCTCACAAAGCTGGGA

TTCTTGGCCACAGTGCCCCTCGCGCGGAGNNNACAGACCCTCCAGGCACACACGCAGCNGGAGAAACAGGA

AGGGACAGGCCGTCCCCTTCGCAGGCGAGCAAAGGACAAAACTCCATTTTAAGATAAAGTCATTGCAGAAGA

AAAAAAAAAAGTCTTTTAAGAGACAATCCTTCACAAAGGGGGAAACGAGCACC

      SEQ. ID. NO:81       AW462120

      TTCATTGGAAAAAAAGATTTTATTTTACCATAAAAATGCAAACTGGAATAAACACCATCTCTCCTAA

GGTGGACGTTACAGCTATTTTTAAGTATTTCCAAGCTTCCCTTGGAGAAGCTGACAATTATAAAATTTAACAA

GTTTGCAGCCTTAAATCTGAAACGTTCCAAGTAAAAATAATTTAGCAAAACGGCTTCTTAAAAAAACCACAC

AGGCTAACCTTGACTAGAAACCAAAGCTAATTTTAAACCAGCCTGCTTTTTTGTTTTATGCTGAATGACTTGA

GTTTGTAAAAAGTGAATGTGTGGGACCCCTGTGTACCACCTGACGCTCTTCTGTTGTATGCTGAATGACTTGA

ATTCGTACAAAGTGAATGTTTGGGACTCTTCTGTATCCCGTCTGCACCAGCCCACGCCCGTAAAGAG

      SEQ. ID. NO:82       AW463593

      AGGAGTCCTCACCCTGGGATCATTCCACAATCCCCATGGCTCCAGCTCCCAGCTTCCGTGGACACCA

GTGGACTTACAACCCTGTCCGAGGGTCCTGCCTGCTGCTGCTGCTGCTCATGTCTAATCTGCTCCTGTGCCAA

GGCAAATCATGCCCGTCCTGCGGTCCTGACGTGTTTGTTTCCTTACGGAAATCCTTTACAGACAGGTTTATGA

ATGCCGCCAGCCTCTCCCATGACTTCTATAACCTTTCCACAATAATGTTCAATGAGTTTGATGAAAAATATGC

CCAGGGCAAACTATACTATATCAATGTCACCAAGAGCTGCCACACCAATTCCTTCCATGCTCCCGAAGAAAG

AGATATAGTCCAGCAGACGAACATTGAAGACCTTAGTAAGTGGACACTCGTGTTGCTGTACTCCTGGAATAA

TCCTCTGCATCATCTAGTCACGGAGCTGCAGCATATGAAAGAACTGTCAAACGCCTTCCTATCAAGCGCCACAA

      SEQ. ID. NO:83       AW465056

      GGGACCAACGGGATCCCCTCTACCCCACCAACCCGGGAGGACGAGTTGGGGTCGGAGTTGGGTGGA

CTAGCTTTCCTGGTCCTCTCCCCACAGAGCTGACGTGTCCTGGGTTCCAGGCGATGGGCATTTCCACGGGGCG

GGAGGGTTCGGGTGGTGGGTACAGGCACGTCGCTGGCGCTTTCCTCCCTCCTGTCCCTGCTGCTCTTCGCTGG

GATGCAGATGTATAGTCGCCAGCTGGCGTCCACCGAGTGGCTCACCATCCAGGGCGGCCTGCTTGGTTCCGGC

CTTTCGTCTTCTCTCTCACTGCCTTCAATAATCTGGAGAATCTTGTCTTTGGCAAAGGATTCCAAGCAAAGAT

CTTCCCTGAGATTCTCCTCTGCCTCCTGTTGGCTCTGTTTGCATCAGGCCTCATCCACAGAGTCTGTGTCACCA

CTTGCTTCATCTTCTCCATGGTTGGTCTGTACTACATCAACAAGATCTCT

      SEQ. ID. NO:84       BF046404

      ATTTTTTCTTTTTTTAAATTTTCAAACACTACTGGGGAAATTATTCTTGTCCAATAATTATTAAAAGTC

TTTTCGACTTGAGCACATGGACAAATGAACTTGATTTGAAACTAGAGGCATAGGCCATGCATGTTAGTACTTT

ATTATTTGGCTTCCTGCCATGTTAGGAAAACAAAATATGAAAAAGGTCATTTTCTTTAAACCATGGAATTTTT

CTTCAACTAAGATGAATCAAATTTCCTTATGTATGTAAATTCATACATTAACACAAAGTTTTATATCATGCCA

GTTCACATAGCATAGTGGAGTCACCATTCTCTAGAATGTGTGTTTCTGCGAAACTTAACTTGCTTTAGAATTTT

AAATTTTAACCTTGCGCAGANNCCAGCTCCCGAAAGCTATGAAAAATTCCCAGTGGCTGATGTGGAAACCTC

TTTCCACTGCTGCCCAGCCCTCAGGATGTGCAACTTAGTGAAAGGAGAGAATCTTTTTCTAGGAAAAATGAGCC

      SEQ. ID. NO:85       BM366368

      GCACGAGCAGCAGCCGGACTTCCAGACCCAGATGCCAAGCAGTCTTGTGCTGGACTGCTCCATCGCT

GACTGCCTGAGGTTCCGCTGTGACATCCCCTCCTTCGGCGTCCAGGAGGAACTTGACTTCATCTTGAAGGGCA

ACCTCAGCTTCGGCTGGGCCAGCCAGTTGCTGCAGAAGAAGACATTGGTCGTGAGTATGGCTGAAGTCACAT

TCAACAGATCTGTGTACACCCAGATTTCAGGACAGGAGGCATTTTTGAGAGCCCAGGTAGAGATGGTGCTAG

AAGAGTATGAGGTCTACAGCCCCATGCCCCTCCTTGTGAGCAGCTCCATGGGAGGACTGCTGCTCCTGGCCCT

CATCACAGCCTTACTGTACAAGTGTGGCTTCTTCAAACGTCAATACAAAGAAATGATGGATAACAAGCCTGA

AAACACTGCACTCAATGGGGAAGATATCCACCATGAGACCCCAGATCTACCTTTGTCCGAATAATCCACTTTC

TCATTTATGTCTATTCCCATTGGCTGACCTTGGCTTCACCTAC

      SEQ. ID. NO:86       AW462010

      ACAAGAAAATGTTATCAACCACACGGACGAAGAAGGATTTACCCCTCTGATGTGGGCTGCAGCACAC

GGGCAAATAGCTGTGGTAGAGTTTCTACTTCAGAATGGCGCTGATCCTCAGCTTTTAGGAAAAGGTCGAGAA

AGTGCTCTGTCATTGGCCTGTAGCAAGGGCTACACAGATATTGTCAAAATGCTGCTGGATTGTGGAGTTGATG

TAAATGAATATGATTGGAATGGAGGGA

      SEQ. ID. NO:87       AW465551

      TTCTAGCAGTGGGACCAGGCAGCAGGACGAGGAGATGCTTGAACTCCCAGCTCCTGCTGCAGTGGCT

GCGAAGAGTCAGGCCTTAGAGGACGATGCAACAATGAGGGCTGCAGACCTGGCCGAGAAGAGAGGGCCCTC

TTCCAGCCCCGAGAACCCCAGAAAGAGACCTCGGGAAGACTCTGATGTGGAAATGGTGGAGGATGCATCCCG

AAAGGAGATGACAGCCGCTTGTACCCCCCGGAGAAGGATCATCAACCTTACCAGTGTTCTGAGTCTCCAGGA

GGAGATCAACGAGCGGGGCCATGAGAGTACCTCTCCGGGAGATGCTGCATAACCACTCCTTTGTGGGCTGCG

TGAATCCTCAGTGGGCCTTGGCACAGCATCAGACCAAGTTATACCTTCTCAACACCACCAGACTTAGTGAAG

      SEQ. ID. NO:88       AW465274

      TGCCCAATTCCAAATGTACAGAACTCTCCCATTCCTACAAAGCTCCCTGAACCAGTGAAAGCCAGTG

AGGCAGCTGCAAAGAAGACCCAGCCAAAGGCCAGACTGACAGATCCCATTCCCACTACAGAGACGTCAATT

GCACCCCGCCAGAGGCCTAAAGCTGGGCAGACTCAGCCCAACCCAGGAATCCTCCCCATCCAACCAGCCCTG

ACCCCTCGGAAGAGGGCCACAGTTCAGCCCCCGCCTCAGGCCGCAGGATCCAGCAATCAGCCTGGTCTTTTA

GCCAGTGTTCCTCAACCAAAAAACCCAGCCCCCACCCAGTCAACCCCTACCACAGTCTCAGCCCAAGCAGCC

TCAGGCTCCGCCCACCTCACAGCAGCCGCCTTCCGCGCCGGCCCAGGCTCTGCCCACCCAGGCCCAGGCCAC

GCCCCAGCACCAGCAGCAACTCTTCCTCAAGCAGCAGCAGCAGCAGCAGACAGCGCCGCCCGCACAGCAGC

CAGCGGGCACGTTCTACCAG

      SEQ. ID. NO:89       AW462049

      GGTATCCGCCCCCAGATCATGAACGGCCCCCTGCACCCCCGCCCCCTGGTGGCGCTGCTCGACGGCA

GAGACTGCACCGTAGAGATGCCCATCCTGAAGGACCTGGCCACCGTGGCCTTCTGCGACGCACAGTCCACCC

AGGAGATCCACGAGAAGGTTTTAAACGAGGCAGTCGGTGCCATGATGTATCACACCATCACGCTCACCAGGG

AGGACCTGGAGAAGTTCAAGGCCCTGAGAGTGATCGTGCGGATAGGCAGCGGCTATGACAACG

      SEQ. ID. NO:90       AW463986

      GTCTGGCTGAGCCTGACACCCCCAGGGGAAAGCAGTGCAGAAACCACTGGTTTCCCAGCCGCGGAG

GGATCTGCACTTTTGTTTGTTTTTGACCAAAAAAAAAAGGTTAGCAGTGAGGGGCTAAGGAGACATCCAGCC

TCTGATACCTAAGAGGAGAAGTCCCTGGACTTGGACCCTCCTATTGTGTGACCTCAGCCCAGGGTGGGAACT

GCTACCGTGAGTACCTGGGGAGGAGGGGATGGGAGTT