Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Applicant’s election of Group I, claims 14-26 in the reply filed on 07/01/2026 is acknowledged. Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)).
Claims 14-28 are pending. Claims 27-28 are withdrawn by the Examiner as being drawn to nonelected subject matter. Claims 14-26 are examined herein on the merits.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 14-26 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
The term “narrow” in claim 14 is a relative term which renders the claim indefinite. The term “narrow” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Furthermore the specification does not expressly or otherwise give any parameters as to what constitutes a “narrow” population.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 14-26 are rejected, under 35 U.S.C. § 103 as being unpatentable over SMITH (Smith et al., Plant Breeding Method, United States Patent Application Publication No. 2005/0144664 A1, published June 30, 2005; see IDS filed 04/25/2017) in view of SCHULZ-STREECK (Schulz-Streeck et al., 2012, Genomic Selection using Multiple Populations, Crop Science 52: 2453-2461), BINK (Bink et al., Statistical Approach For Optimal Use of Genetic Information Collected on Historical Pedigrees, Genotyped With Dense Marker Maps, Into Routine Pedigree Analysis of Active Maize Breeding Populations, United States Patent Application Publication No. 2010/0095394 A1, published April 15, 2010), RINCENT (Rincent et al., 2012, Maximizing the Reliability of Genomic Selection by Optimizing the Calibration Set of Reference Individuals: Comparison of Methods in Two Diverse Groups of Maize Inbreds (Zea mays L.), Genetics 192: 715-728), OCHS (Ochs, 2012, Knowledge-based data analysis comes of age, Briefing in Bioinformatics II: 30-39), and WECKWERTH (Weckwerth, 2011, Green Systems Biology - From Single Genomes, Proteomes and Metabolomes to Ecosystems Research, Journal of Proteomics 75: 284-305).
The claims are drawn to a method for selecting individuals in a breeding program, which comprises growing a genetically narrow population of training individuals which are plants, phenotyping the population to generate a phenotype training data set, associating the phenotype training data set with a genotype training data set comprising genetic information across the genome of each training individual, linking the estimation of effects of genotypic markers with the biological knowledge-based model to generate an association training data set, genotyping a genetically narrow population of breeding individuals which are plants, selecting breeding pairs from the genetically diverse population using the association training data set, estimating effects of genotypic markers and linking the estimation of effects of genotypic markers with the biological model to select breeding pairs likely to generate offspring with one or more desired traits, crossing the breeding pairs to generate offspring, and growing the offspring with the one or more desired traits; further comprising crossing said selected breeding individuals; wherein said breeding individuals are homozygous.
SMITH teaches a plant breeding method (entire document), and teaches methods for using genetic marker genotype (e.g., gene sequence diversity information) to improve the process of developing plant varieties (e.g., single cross hybrids) with improved phenotypic performance (Id.; see Title, Abstract, for example). The methods have a number of applications, e.g., in applied breeding programs in plants (e.g., hybrid crop plants; similar methods can be applied for animals) (Id., see also page 7, paragraph 0074, for example). The methods can be used to predict the phenotypic performance of hybrid progeny (i.e., individuals), e.g., a single cross hybrid produced (actually or hypothetically) by crossing a given pair of inbred lines (which inbreds are genetically narrow) of known marker genotype (Id.). The methods permit selection of parental plants whose offspring have the highest probability of possessing the desired phenotype (i.e., complex trait and component traits).
SMITH teaches that the association between the genetic marker and the phenotypic trait is evaluated by performing Bayesian analysis using a linear model, a mixed linear model, or a nonlinear model. The association is evaluated by performing Bayesian analysis using a linear model, the Bayesian analysis being implemented via a reversible jump Markov chain Monte Carlo algorithm (page 2, paragraph 0015). SMITH teaches that association studies provide an alternative approach to identifying chromosomal regions and/or genes affecting phenotypes of interest using genetic linkage (page 6, paragraph 0067) (1.e., biological knowledge-based model is integrated into sampling algorithms for estimation of genotypic marker effect and linking the estimation of effects with the biological knowledge-based model to generate an association training data set). SMITH compares association studies involving plants, humans, and animals (Id., paragraph 0071).
SMITH teaches methods in which an association between at least one genetic marker and the phenotypic trait is provided (page 2, paragraph 0011). For example, an association between the phenotypic trait and a haplotype comprising two or more genetic markers can be provided. The association is evaluated in a first plant population which is an established breeding population, according to a statistical model that incorporates a genotype of the first plant population for a set of genetic markers and a value of the phenotypic trait in the first plant population (i.e., biological knowledge-based model).
SMITH teaches that the association between the at least one genetic marker and the phenotypic trait is evaluated by performing Bayesian analysis using a linear model, a mixed linear model, or a nonlinear model (i.e., biological modeling method; biological knowledge- based model) (page 2, paragraph 0015, for example).
SMITH teaches Bayesian analysis (i.e., biological modeling method; biological knowledge based model) for QTL mapping in complex plant populations, which methods incorporate genotypic, phenotypic, and family pedigree information for complex plant populations (e.g., a first plant population). Use of such complex populations offers a number of advantages. For example, a large number of single cross hybrids (or a large number of segregating F2 progeny from a biparental cross, or the like) need not be generated and phenotyped to perform the analysis; instead, plants and/or lines can be chosen from the breeding population, where phenotypic evaluation of large numbers of progeny of different types is a normal part of the breeding program (paragraph 0072, bridging pages 6-7, for example).
SMITH teaches that Bayesian analysis, QTL mapping, optimal sampling of a population to determine QTL location, variance, and allelic number, and the like, are also extensively described in the art (page 9, paragraph 0094; references omitted herein).
SMITH teaches that the method comprises a genetically diverse complex plant population (i.e., a genetically diverse population) of training individuals (of phenotypically and genotypically characterized parent plants (of training individuals), phenotyping the training individuals to generate a phenotype training data set, providing values for phenotypic data for the members of the population to generate a phenotypic information data set (i.e., phenotyping the genetically diverse population of training individuals to generate a phenotype training data set) (pages 6-7, paragraphs 0072-0075), genotyping the training individuals using a set of genome wide genetic markers to generate a genotype training data set, providing marker genotype information for a set of markers for the population to generate a genotype training data set (i.e., genotyping the training individuals using a set of genome wide genetic markers to generate a genotype training data set) (Id.), and associating the phenotype training data set with the genotype training data set (i.e., associating the phenotype training data set with the genotype training data set (Id.). SMITH teaches estimating effects of the genome wide genetic markers from the genotype training data set, and predicting a value of a phenotypic trait in a target population from the genetic marker data from the first population (1.e., estimating effects of the genome-wide genetic markers from the genotype training data set (Id.). SMITH teaches linking the estimation of the effects of the genome wide genetic markers with a (knowledge-based) biological model, using a statistical model, including family relationships to link the predicted phenotype with the genotypic information of the first plant population (i.e., linking the estimation of the effects of the genome-wide genetic markers with a biological model) (Id.) to generate an association training data set, to generate a map of the effect of the values of the genetic loci on the inherited phenotypic traits (1.e., to generate an association training data set) (Id.).
SMITH teaches that the phrase “phenotypic trait” refers to the appearance or other detectable characteristic of a plant, resulting from the interaction of its genome with the environment (page 5, paragraph 0052). A quantitative trait is typically the result of a genetic locus interacting with the environment or of multiple genetic loci (QTL) interacting with each other and/or with the environment. Examples of quantitative traits include plant height and yield (page 5, paragraph 0058).
SMITH teaches that the target plant population can comprise actual living plants and/or hypothetical plants (e.g., hypothetical single cross hybrids produced by crossing given pairs of inbred lines of known genetic marker genotype) (page 10, paragraph 0098). The methods are particularly applicable to hybrid crop plants (page 10, paragraph 0102).
SMITH teaches and claims (claim 1 of SMITH, for example) a method of predicting a value of a phenotypic trait in a target plant population (.e., selecting individuals in a breeding program), which comprises (a) providing an association between at least one genetic marker and the phenotypic trait, wherein the association is evaluated in a first established plant breeding population, wherein the association is evaluated in the first plant population according to a statistical model that incorporates a genotype of the first plant population for a set of genetic markers and a value of the phenotypic trait, and (b) providing the value of the phenotypic trait in a member of the target plant population, wherein the providing comprises predicting the value from the association of (a) and from a genotype of the member for a genetic marker associated with the phenotypic trait.
SMITH does not explicitly teach every single step of the instant claims. However, such claimed methods and the recited compositions for practicing the methods would have been prima facie obvious to a person of ordinary skill in the art at the time the application was filed for the following reasons.
SCHULZ-STREECK teaches genomic selection using multiple maize (i.e., plant) populations (entire document).
SCHULZ-STREECK teaches an approach in which the main plus population-specific marker effects are simultaneously estimated in a single mixed model. Cross-validation is used to compare the predictive ability of this model to that of the ridge regression best linear unbiased prediction (RR-BLUP) method involving only either the main marker effects or the population- specific marker effects (i.e., biological modeling method; biological knowledge based model) (Id.; see Abstract, for example).
SCHULZ-STREECK teaches the use of a maize data set with 312 genotypes derived from five biparental populations, which were genotyped with 39,339 markers. A combined analysis incorporating genotypes for all the populations and hence using a larger training set was better than separate analyses for each population. Modeling the main plus the population-specific marker effects simultaneously improved predictive ability only slightly compared with modeling only the main marker effects. The performance of the RR-BLUP method was comparable to that of two regularization methods, namely the ridge regression and the elastic net, and was more accurate than that of the least absolute shrinkage and selection operator (LASSO). Overall, combining information from related populations and increasing the number of genotypes improved predictive ability (Id.).
SCHULZ-STREECK teaches that statistical methods have been applied to both plant and animal populations (i.e., biological modeling method; biological knowledge based model), and teaches that in animal breeding, more accurate genomic predictions can be achieved by combining genotypes from different breeds into one training set (page 2453, right-hand col., last paragraph; page 2454, left-hand col., first paragraph). In animal breeding, the art teaches a breed- specific single nucleotide polymorphism (SNP) allele model in which breed--specific substitution effects for alleles are estimated based on the population origin of the allele and whether the alleles originate from a sire or a dam (/d.). SCHULZ-STREECK combines the idea of modeling the main marker effects, which are consistent across all populations, with that of modeling population-specific marker effects, into one mixed model (/d., right-hand col., first paragraph). SCHULZ-STREECK then compares the predictive ability of this combined model with that of (i) the common ridge regression best linear unbiased prediction (RRBUJP) model, which involves only the main marker effects, and (ii) a population-specific analysis, involving only population-specific marker effects. Additionally, SCHULZ-STREECK compares the different models by varying the number of markers (Id.).
SCHULZ-STREECK teaches that statistical methods can be particularly useful if the training set for one of the pooled populations is too small to reliably perform within-population evaluation (page 2453, right-hand col., last paragraph).
SCHULZ-STREECK teaches that conducting separate analyses for each population may not always improve predictive accuracy and may sometimes be less accurate than a combined analysis that exploits information from related populations (page 2454, left-hand col., first paragraph). See also the Data Set section in the right-hand col. on page 2454, which describes
both a completely pooled set of data and a partially pooled set of data from maize for use in the analysis.
SCHULZ-STREECK teaches the use of various markers, including SNP markers (page 2454, right-hand col., penultimate paragraph), and teaches the use of heterozygous (and conversely homozygous) individuals (/d.). SCHULZ-STREECK also teaches the use of hybrid and genetically diverse populations in the data set (Id., second-third paragraphs, for example).
SCHULZ-STREECK teaches model comparison, evaluating model performance by cross-validation, and teaches repeating the process multiple times to generate 25 different replicate training and validation sets from the combined data set (page 2456, left-hand col., last paragraph).
BINK teaches a statistical approach for optimal use of genetic information collected on historical pedigrees, genotyped with dense marker maps, into routine pedigree analysis of active maize breeding populations (entire document; see Title, Abstract, for example).
BINK teaches means of predicting plant phenotypes that incorporates marker data. The method operates by collecting information from a population pertaining to one or more loci, which is used to build one or more matrices by calculating, for the alleles present at the measured loci, the probability that the alleles are identical by descent. These matrices are then used to develop a second set of matrices in which each value represents the probability that a certain individual in the population descended from a certain ancestral (founder) genotype. This set of second matrices can then be used as part of a breeding program for selecting and breeding individuals from the population (i.e., selecting individuals in a breeding program and breeding selected individuals) (see Abstract; see page 1, paragraph 0007, Summary, for example).
BINK teaches that the first matrix of IBD probabilities (ie., probability that the QTL alleles are identical by descent) is calculated using standard statistical techniques, including those based on Markov Chain Monte Carlo algorithms. The genotypes in the second matrix are automatically generated using mathematical models. In a preferred embodiment, the second matrix is calculated using a latent (ancestral) class model, although other models such as a threshold model, distance model, a vector model, or a cluster analysis model may be used. In a preferred embodiment, the second matrix is used in a Bayesian statistical approach for more accurate QTL detection and prediction of plant phenotypes with calculations performed using a Markov Chain Monte Carlo based algorithm while keeping computational efforts to a minimum. These calculations may also be computer-implemented (Id., page 2, paragraph 0010; Fig. 2 and accompanying text; see also page 2, paragraphs 0019-0029, for example, describing how the first matrix depends on the specified locus and is typically calculated from the pedigree data and genetic marker data, and may contain identity by descent probabilities measured across multiple loci, chromosomal segments, haplotypes, or the whole genome; see also the section on Developing a Latent Ancestral Class Model for an Identity by Descent Matrix, spanning pages 2- 3, paragraphs 0030-0034).
As an example of a numerical implementation of the modified Markov Chain Monte Carlo algorithm, BINK teaches drawing a new set of alleles for each parent, by first sampling an appropriate P matrix from which each ancestor each parent inherited and then assigning, for each parent, the allele of the ancestor that it inherited (page 5, paragraph 0053) (1.e., sampling algorithm for estimation of genotypic marker effects).
BINK teaches that the classification of the progeny into groups using highly accurate IBD information can be used to augment a statistical analysis of the progeny, including the mapping of quantitative trait loci (QTL) as the statistical analysis technique (page 8, paragraph 0071). BINK further teaches the collection of phenotypic data were collected on numerous traits using standard practices (page 9, paragraphs 0080-0082).
BINK teaches obtaining genomic estimated breeding values for the genotyped breeding individuals using the phenotypes of the candidates, which, enables the selection of individuals for use in further crosses in the breeding program (page 6, paragraph 0063 — page 7, paragraph 0067; Fig. 1 and accompanying text). The classification of individuals into groups, coupled with the knowledge of the breeding value of the loci of interest, enables the selection of individuals for use in further crosses in the breeding program (Table 6, last column; page 7, paragraph 0066). New populations generated from the selected individuals will be biased toward producing favorable progeny (ie., selected individuals in a breeding program) (page 7, paragraph 0066; page 8, paragraph 0073).
BINK teaches that a whole genome selection technique can be used as the statistical technique for evaluating the Fl hybrid progeny (paragraph 0083, bridging pages 9-10). See also many relevant references listed in paragraphs 0086-0105 on page 10; references omitted).
BINK teaches that the genetic markers used can be any that are used in traditional QTL mapping analysis such as SNPs (Id., paragraph 0008); see also page 9, paragraph 0082 for a description of the recording up to 18 generations of pedigree relationships and genotypic information at 768 SNP markers throughout the genome.
BINK teaches an embodiment of the method where each F1 hybrid is produced by crossing two inbred parents together (ie., the inbred individuals are homozygous) (page 9, paragraph 0081, for example).
BINK teaches practice of the methods as applied to plant breeding, wherein the population consists of diploid plants, either hybrid or inbred; and also the population consists of polyploid plants. BINK contemplates the use of a variety of plants, wherein said breeding individual is selected from the group consisting of: maize, soybean, sunflower, sorghum, canola, wheat, alfalfa, cotton, rice, barley, millet, sugar cane and switchgrass (page 1, paragraph 0009).
BINK also references a mixed model quantitative trait loci (QTL) analysis for multiple environment trial data using environmental covariables for QTL-by-environment interactions, with an example in maize (the biological knowledge-based model models the functional relationship between a complex trait and underlying component traits and incorporates environmental and/or plant management information) (page 10, paragraph 0090).
RINCENT teaches maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals, using diverse groups of maize inbreds genotyped with a SNPs array (entire document; see Title, Abstract, for example). Genomic selection refers to the use of genotypic information for predicting breeding values of selection candidates. A prediction formula is calibrated with the genotypes and phenotypes of reference individuals constituting the calibration set. The size and the composition of this set are essential parameters affecting the prediction reliabilities (i.e., prediction parameters to predict a complex trait and component traits) Id., see Abstract, for example).
RINCENT teaches a way to estimate the marker effects in the calibration set and then to predict the breeding values of the selection candidates by multiplying their genotypes by the marker effects (page 716, left-hand col., second paragraph). Powerful statistical tools and relevant data sets (genotypes and phenotypes to train the prediction model) are key factors for the predictive efficiency (Id.).
RINCENT teaches computation of the correlations for various levels of heritability (page 722, left-hand col., second paragraph).
RINCENT teaches the use of homozygous individuals (page 718, left-hand col., first full paragraph), and teaches application of the disclosed statistical methods to animal populations (page 716, left-hand col., first paragraph, and right-hand col., first paragraph, for example).
RINCENT teaches the use of hybrids and genetically diverse populations in the data set (page 717, right-hand col., first paragraph).
RINCENT teaches a model in which a trait is determined by a large number of genes, each having small and independent effects (page 718, right-hand col., first paragraph).
OCHS teaches that the knowledge-based data analysis comes of age, and teaches the emergence of high-throughput technologies for measuring biological systems (i.e., biological knowledge-based model is integrated into sampling algorithms for estimation of prediction parameters to predict a complex trait and component traits) (entire document; see Title, Abstract, for example).
OCHS reviews the emergence of techniques that match mathematical structure to the biology, the use of integrated data and prior knowledge to guide statistical analysis, and the recent emergence of analysis approaches utilizing simple biological models (see Abstract, for example). The advent of microarrays in biological research resulted in a new dynamic type of data (page 30, left-hand col.).
OCHS teaches genetic interactions, where one SNP or allele influences the effect on phenotype of another (page 30, right-hand col., first paragraph). OCHS teaches that high- throughput data is routinely generated for genome sequences (next generation sequencing), polymorphisms (SNPchips), transcripts (expression microarrays), miRNAs (microRNA arrays),
proteins (mass spectrometry and protein microarrays) and metabolites (mass spectrometry and NMR) (d., last paragraph) (i.e., analyses of gene expression or metabolite concentration).
OCHS teaches that knowledge-based analysis methods address the problem of identifying SNPs that will have an impact on disease or treatment response (page 34, right-hand col., first paragraph). OCHS teaches the effects of SNPs on gene expression, which nominates candidate SNPs to explain differences in sensitivity to treatment through integration of expression and genotype data (/d., second paragraph), and teaches that protein structure and promoter structure provide additional points of leverage for knowledge in a SNP-based study (d., last paragraph).
OCHS teaches the use of microarray data as a phenotypic measure to focus on SNPs that might drive expression changes (page 35, right-hand col., first full paragraph).
OCHS teaches methods for utilizing knowledge that have been created for expression- QTL (eQTL) (ce. biological knowledge-based), where microarray data and genotype data are integrated to improve QTL studies (Id., left-hand col., first full paragraph).
OCHS also teaches various analyses using biological models (section spanning pages 36- 37). In that context, OCHS teaches the potential of (biological) knowledge-based analysis, even when only a very limited biological model can be created (Id., page 37, left-hand col., first full paragraph).
OCHS teaches the use of orthologs and data integration in a Bayesian framework that integrates microarray, miRNA target prediction, localization, protein—protein interactions, protein expression, phylogenetic profiling, TF binding site and protein domain data from plant through human (i.e., complex trait and component traits) (page 35, right-hand col., penultimate paragraph).
OCHS teaches a mathematical solution to matrix factorization in the context of gene regulation, where prior knowledge can be used to inform an algorithm (page 32, Figure 1 and accompanying text). OCHS teaches a modified Bayesian Decomposition (BD) algorithm for microarray studies (page 31, right-hand col., last paragraph).
WECKWERTH provides a review of the green systems biology, and teaches going from single genomes, proteomes and metabolomes to ecosystems research and biotechnology (entire document; see Title, Abstract, for example). Systems biology can be summarized as integrating experimental data, genome-scale reconstruction of metabolic networks and the derivation of mathematical models that are able to predict the molecular phenotype of the plant in its natural environment (page 288, left-hand col., penultimate paragraph).
WECKWERTH teaches the need for in-depth understanding of the physiology of single plant species for practical applications as well as the translation of this knowledge into complex ecosystems (see Abstract, page 284, for example). The analysis of single model systems - plants, fungi, animals and bacteria - will finally emerge in the analysis of populations of plants and other organisms and their adaptation to the ecological niche. In parallel, this novel understanding of ecophysiology will translate into knowledge-based approaches in crop plant biotechnology and marker- or genome-assisted breeding approaches (Id., sentences bridging pages 284-285).
WECKWERTH also teaches the creation of transgenic plants and their use in advancing plant breeding (page 287, left-hand col., first paragraph).
WECKWERTH teaches the genotype-phenotype relationship in plants. In Fig. 3 on page 288, WECKWERTH teaches that a single genotype can produce several phenotypes depending on the environment. These phenotypes are subject to environment-dependent selection which
leads to different rates of reproduction and changes in genotypes (i.e.., incorporates environmental information) (Id.).
WECKWERTH teaches the systematical investigation of the relationship of natural variation, QTL mapping and metabolism (page 290, right-hand col., last paragraph). Studies with genome-wide association using SNP-based microarrays enable the rapid mapping of traits. Custom-made Affymetrix genotyping arrays containing up to 250,000 SNPs are used. In combination with morphological and partly molecular phenotyping approaches, these SNP arrays reveal many common alleles with correlations to the phenotype (Id.).
WECK WERTH teaches that most of the studies are focused on DNA markers, however the successful application of these technologies was also demonstrated for proteomics and metabolomics (page 300, left-hand col., first full paragraph). WECKWERTH teaches a genotype-phenotype equation, which directly connects the covariance structure of the data - in other words the pattern recognition using multivariate statistics - with the underlying genotype (Id., right-hand col., first paragraph).
In Fig. 3 on page 288, WECKWERTH depicts how a single genotype can produce several phenotypes depending on the environment. These phenotypes are subject to environment-dependent selection which leads to different rates of reproduction and changes in genotypes (i.e., incorporate environmental information).
In Fig. 4 on page 289, WECKWERTH provides an exemplary overview of a proteomic toolbox, which includes sample pattern recognition/statistics/modeling/metabolic regulation.
In Fig. 7 on page 292, WECKWERTH provides an exemplary overview of a metabolomic platform, which includes a data matrix, and a multivariate statistics — covariance matrix.
In Fig. 9 on page 295, WECKWERTH provides an exemplary overview of a framework for the systematic investigation of the genotype-phenotype relationship and integration with genomic reconstruction and modeling approaches. The Figure includes a “data-driven mathematical model of the system”. As well, “[d]ata are integrated into a data matrix which can be analyzed with multivariate statistics. The result - the covariance matrix - is closely bound to the mathematical model of the system (see text, Figs. 10 and 11...).”
In Fig. 11 on page 297, WECKWERTH provides a genotype-phenotype equation that links the genotype characterized by the stoichiometric matrix N and the systems equations (ODEs).
WECKWERTH teaches a systematically combined proteome analysis with genetics and QTL mapping in maize, and the application of metabolomics for the characterization of interspecific introgression lines (ILs) of tomatoes and correlated these data to QTLs related to yield (page 300, left-hand col., first paragraph).
WECKWERTH teaches that, in the early times of the green revolution there was only one single trait: yield. Nowadays, the potential for selection of specific traits has dramatically changed. Due to genome-scale molecular analysis and elucidation of gene function, in the future a modern breeder will be able to select a plethora of single traits or their combinations (i.e., a functional relationship between a complex trait and underlying component traits) (page 301, right-hand col., section 13).
Given the teachings of SMITH, SCHULZ-STREECK, BINK, RINCENT, OCHS, and WECKWERTH as described above, it would have been obvious to one of ordinary skill in the art at the time the application was filed to combine and to modify their teachings, to obtain and use genotype and phenotype data to select individuals in a desired breeding program; thus
arriving at the Applicants’ invention with a reasonable expectation of success, and without any surprising results. Obviously, one of ordinary skill in the art would have been motivated to do so for the purpose of animal and/or plant genomic selection and breeding, as taught by the cited references. Taken together with the general knowledge in the art, the SMITH, SCHULZ- STREECK, BINK, RINCENT, OCHS, and WECKWERTH references provide ample mathematical, statistical and computational teachings and biological knowledge-based modeling, as well as genotypic and phenotypic analyses, which are also paralleled in the instant claims.
Although SMITH, SCHULZ-STREECK, BINK, RINCENT, OCHS, and WECKWERTH do not specifically reduce to practice a biological knowledge-based breeding program with a maize population that includes individuals with DNA edited with Cas9, these particular embodiments would be considered a design choice that would be readily apparent to one of ordinary skill in the art; the use of the CRISPR/Cas9 system for genome editing was well known in the art at the time the application was filed.
Statistical analysis and biological knowledge-based modeling, genotyping, phenotyping, bioinformatic analysis, recombinant DNA technology, marker and SNP identification, gene expression analyses, DNA editing with Cas9, transgene, marker, and SNP identification, plant and animal breeding, and biological modeling, are techniques that were routine in the art at the time the application was filed, as taught by the cited references and the state of the art in general.
No claims are allowed.
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/BRENT T PAGE/Primary Examiner, Art Unit 1663
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