DETAILED ACTION
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 .
Claim Status
Claims 1-11, 13-16, and 18-25 are pending.
Priority
This application is a 371 of PCT/GB2021/0530696924, filed 11/26/2021, which claims benefit of application no. 2018905, filed 12/01/2020 in GB. The instant application has the effective filing date of 01 December 2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/23/2023, 09/04/2025, and 12/16/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings, submitted on 05/31/2023, are accepted by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11, 13-16, and 18-25 are rejected under U.S.C 101 because the claimed invention is directed to abstract ideas without significantly more, as detailed in the analysis below.
Eligibility Step 1: Subject matter eligibility evaluation in accordance with MPEP § 2106:
Claims 1-11, 13-16, and 18-23 are directed to a statutory category (method).
Claim 24 are directed to a statutory category (machine).
Claim 25 is NOT directed to a statutory category (computer program product).
Claims 25 is non-statutory as it recites “a computer program product or computer-readable medium”. The claims instantly recited read on carrier waves, which are transitory propagating signals and therefore are not proper patentable subject matter because they do not fit within any of the four statutory categories of invention (In re Nuijten, Federal Circuit, 2007). It is noted that the recitation of a "non-transitory computer readable medium" would overcome the rejection with respect to claim 12 reading on signals. However, the amendment to only "non-transitory computer readable medium" would not overcome the rejection under 35 U.S.C. 101 since the claims would still be directed to a judicial exception without significantly more (see below).
Though claim 25 does not recite a statutory category of invention, in the interest of compact prosecution, the remaining steps of the subject matter eligibility analysis continues below on all claims.
Claims 1-11, 13-16, and 18-24 [Eligibility Step 1: YES]
Claim 25 [Eligibility Step 1: NO]
Eligibility Step 2A: This step determines whether a claim is directed to a judicial exception in accordance with MPEP § 2106.
Eligibility Step 2A -- Prong One: Limitations are analyzed to determine if the claims recite any concepts that could equate to a judicial exception (i.e. abstract idea, law of nature, or natural phenomenon). Possible judicial exceptions are explored below.
Recitations of Judicial Exceptions:
Claims 1 and 24: carrying out one or more iterations comprising, for each of the plurality of genetic variants: determining for which of the plurality of phenotypes or phenotype combinations the genetic variant is causal based on the plurality of input units; (mental process)
if the genetic variant is determined to be causal for one or more of the phenotypes or phenotype combinations, determining a sampled effect size of the genetic variant on each of the one or more phenotypes or phenotype combinations based on the plurality of input units and information about correlations between the plurality of genetic variants in the region of interest; (mental process)
for each genetic variant, determining a prediction effect size of the genetic variant on one or more of the phenotypes or phenotype combinations based on an average across at least a subset of the iterations of the sampled effect sizes of the genetic variant on the one or more phenotypes or phenotype combinations or of posterior effect sizes of the genetic variant for the input unit calculated using the sampled effect sizes. (mental process, mathematical concept)
Claim 2: wherein determining for which of the plurality of phenotypes or phenotype combinations the genetic variant is causal comprises calculating a plurality of probabilities comprising :a probability of the information from the plurality of input units assuming that the genetic variant is not causal for any of the phenotypes or phenotype combinations; a probability of the information from the plurality of input units assuming that the genetic variant is causal for all of the phenotypes or phenotype combinations; and for one or more subsets of the phenotypes or phenotype combinations, a probability of the information from the plurality of input units assuming that the genetic variant is causal for the subset of phenotypes or phenotype combinations, and stochastically determining for which of the plurality of phenotypes or phenotype combinations the genetic variant is causal with a probability based on the plurality of probabilities. (mental process, mathematical concept)
Claim 3: wherein the probability of the information from the plurality of input units assuming that the genetic variant is causal for one or more of the phenotypes or phenotype combinations is dependent on:a proportion of the plurality of genetic variants expected to be causal; the plurality of input units; and a correlation between the effect sizes of the genetic variant on the phenotypes or phenotype combinations. (mental process, mathematical concept)
Claim 4: wherein the probability of the information from the plurality of input units assuming that the genetic variant is not causal for any of the phenotypes or phenotype combinations is dependent on:a proportion of the plurality of genetic variants expected to be causal; and the plurality of input units. (mental process, mathematical concept)
Claim 5: wherein, for each of the one or more subsets of the phenotypes or phenotype combinations, the probability of the information from the plurality of input units assuming that the genetic variant is causal for the subset of phenotypes or phenotype combinations is dependent on:a proportion of the plurality of genetic variants expected to be causal; a subset of input units comprising the input units comprising information about the association between the plurality of genetic variants and one of the subset of phenotypes or phenotype combinations; and a correlation between the effect sizes of the genetic variant on the phenotypes or phenotype combinations. (mental process, mathematical concept)
Claim 6: wherein the proportion of the plurality of genetic variants expected to be causal is predetermined. (mental process, mathematical concept)
Claim 7: wherein the correlation between the effect sizes of the genetic variant on the phenotypes or phenotype combinations is predetermined. (mental process, mathematical concept)
Claim 8: wherein the proportion of the plurality of genetic variants expected to be causal is updated at each iteration. (mental process, mathematical concept)
Claim 9: wherein the correlation between the effect sizes of the genetic variant on the phenotypes is updated at each iteration. (mental process, mathematical concept)
Claim 11: wherein determining the sampled effect size of the genetic variant comprises calculating a probability distribution for example a multivariate normal distribution, of effect sizes of the genetic variant on the one or more phenotypes or phenotype combinations, and sampling values of the effect sizes from the probability distribution. (mathematical concept)
Claim 13: wherein the sampling of values of the effect size is performed using a Monte-Carlo Gibbs sampler. (mathematical concept)
Claim 14: wherein the sampling of values of the effect size in each iteration is dependent on the sampled effect sizes from one or more previous iterations. (mathematical concept)
Claim 15: wherein the probability distribution is dependent on a correlation between the effect sizes of the genetic variant on the phenotype or phenotype combinations. (mathematical concept)
Claim 16: wherein the correlation between the effect sizes of the genetic variant on the phenotypes or phenotype combinations is either predetermined or updated at each iteration. (mathematical concept)
Claim 18: wherein determining the sampled effect sizes comprises using a model of causal relationships between the plurality of phenotypes or phenotype combinations. (mental process)
Claim 19: wherein: each of the one or more iterations further comprises, for each genetic variant determined to be causal, subtracting weighted effect sizes from the information about the association between each other genetic variant and the phenotype or phenotype combination of each input unit; the weighted effect sizes being the sampled effect size of the genetic variant on the phenotype or phenotype combination of the input unit weighted by respective correlation factors between the genetic variant and each other genetic variant; and the correlation factors are determined based on the information about correlations between the plurality of genetic variants in the region of interest. (mental process, mathematical concept)
Claim 20: wherein carrying out one or more iterations comprises carrying out a predetermined number of iterations. (mental process)
Claim 21: wherein each of the one or more iterations further comprises a step of evaluating a convergence parameter, and carrying out one or more iterations comprises carrying out iterations until a predetermined condition on the convergence parameter is met. (mental process)
Claim 23: determining the polygenic risk score based on the genetic information for the target individual and the prediction effect sizes. (mental process)
Claim 25: carry out the method of claim 1 (mental process)
Step 2A – Prong One Analysis:
Analysis techniques such as drawing conclusions, correlations, and determining data based on sets of information, requiring nothing more than the human mind and pen/paper, read on observations, evaluations, judgments, and opinions, and fall under the mental process grouping of abstract ideas. Limitations that merely provide additional information regarding the data being analyzed in this manner are similarly categorized (claim 6-9).
Analysis techniques such as scoring using equations, calculations, weighting, subtraction, and distributions recite mathematical calculations, formulas and/or relationships that fall under the mathematical concept grouping of abstract ideas. Limitations that merely provide additional information regarding the data being analyzed in this manner are similarly categorized (claims 6-9 and 14-16).
Therefore, the claims appear to recite judicial exceptions.
[Eligibility Step 2A – Prong One: YES]
Eligibility Step 2A – Prong Two: A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. If the claim contains no additional claim elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)). Additional elements are recited, categorized, and analyzed below.
Data Gathering Elements:
Claims 1 and 24: receiving a plurality of input units, wherein each input unit comprises information about the association between a plurality of genetic variants in a region of interest of the genome of the organism and one of a plurality of phenotypes or phenotype combinations of the organism;
Claim 10: wherein the input units are determined from respective groups of individuals, and each of the plurality of probabilities is dependent on one or more parameters quantifying an overlap in the groups of individuals between respective pairs of input units.
Claim 22: wherein the information about the association between the plurality of genetic variants and each of the phenotypes or phenotype combinations comprises, for each of the plurality of genetic variants, an estimate of a strength of association between the genetic variant and the phenotype or phenotype combination and an error in the estimate of the strength of association.
Claim 23: receiving genetic information about a region of interest of the genome of the target individual; receiving prediction effect sizes on the target phenotype or target phenotype combination of a plurality of genetic variants in the region of interest
Computer Components Elements:
Claim 1: computer-implemented method
Claim 24: apparatus for analysing genetic data about an organism, the apparatus comprising: a receiving unit configured to receive a plurality of input units
Claim 25: computer program or a computer-readable medium comprising instructions which, when executed by a computer, causes the computer to
Step 2A – Prong Two Analysis:
Limitations that merely gather data necessary to complete the judicial exceptions are classified as insignificant extra-solution activity and do not integrate the judicial exceptions into practical application per MPEP 2106.05(g).
Generic computer components and implementations provide mere instructions to implement the abstract ideas onto a technological environment per Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
As such, the additional elements, when viewed separately and in the context of a whole claimed invention, do not integrate the judicial exceptions into practical application.
[Eligibility Step 2A – Prong Two: NO]
Eligibility Step 2B: Claim elements are probed for inventive concept equating to significantly more than the judicial exception (MPEP 2106.04(II)).
Step 2B Analysis:
The data gathering elements are further found to be well-understood, routine, and conventional per Sebastiani et al. (Am J Hematology; Vol. 84; 2009), which reviews Genome-wide association studies and the genetic dissection of complex traits.
The computer components are further found to be well-understood, routine, and conventional per Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 for storing and retrieving information in memory and Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (MPEP 2106.05 (a)).
As such, the additional elements are further found to lack inventive concept.
[Eligibility Step 2B: NO]
Therefore, claims 1-11, 13-16, and 18-23 are directed to judicial exceptions without significantly more and are rejected under 35 U.S.C 101.
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 (i.e., changing from AIA to pre-AIA ) 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 11, 14, 20, and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Andreassen et al. (2015/0356243) in view of Sanyal et al. (Bioinformatics; Vol. 35: 1; 2019).
Andreassen et al. describes systems and methods of identifying polymorphisms.
Claim 1, 24, and 25 are directed to a computer-implemented method, apparatus, and computer-readable medium with code for performing the steps of: receiving a plurality of input units, wherein each input unit includes information about the association between a plurality of genetic variants in a region of interest of the genome of the organism and one of a plurality of phenotypes or phenotype combinations of the organism.
Andreassen et al. teaches the first step is inputting the GWAS data of a particular train or disease as one data file or individual chip/sequence data; in which the data file includes the p-values that represent the significance of association with disease for each SNPs from the GWAS [0093]; assigning a linkage disequilibrium (LD) score to each SNP [0006]; and obtaining information about the enrichment factor from the literature or public databases, such as location of the SNP within a region of the genome [0095].
Claims 1 and 24-25 are further directed to carrying out one or more iterations, for each of the plurality of genetic variants that include: determining for which of the plurality of phenotypes or phenotype combinations the genetic variant is causal based on the plurality of input units; and if the genetic variant is determined to be causal for one or more of the phenotypes or phenotype combinations, determining a sampled effect size of the genetic variant on each of the one or more phenotypes based on the input units and correlation information of the plurality of genetic variants in the region of interest.
Andreassen et al. teaches weighting the enrichment factor by a function of the linkage equilibrium (LD) of the observed said gene variant with underlying potential causal variants [claim 47]; and combining one or more said enrichment factors within a linear or non-linear regression model to predict relative effect size [claim 1], wherein enrichment factors include location of the SNP within a region of the genome [0095].
Claims 1 and 24-25 are further directed to for each genetic variant, determining a prediction effect size of the genetic variant on one or more of the phenotypes based on an average across at least a subset of the iterations of the sampled effect sizes of the genetic variant on the one or more phenotypes or of posterior effect sizes of the genetic variant for the input unit calculated using the sampled effect sizes.
Andreassen et al. teaches calculating the expected a posteriori effect size across the same 120 equally sized z-score bins ranging from −5.33 to 5.33, corresponding to the GWAS p-value of 5×10.sup.−8 [0280]; averaging the results across 70 iterations; and plotting them as a function of discovery z-score independently for each genic annotation category [0280].
Claim 23 is directed to receiving genetic information about a region of interest of the genome of the target individual; receiving prediction effect sizes on the target phenotype or target phenotype combination of a plurality of genetic variants in the region of interest determined using the method of analysing genetic data of claim 1; and determining the polygenic risk score based on the genetic information for the target individual and the prediction effect sizes.
Andreassen et al. obtaining information about the enrichment factor from the literature or public databases, such as location of the SNP within a region of the genome [0095]; calculating the expected a posteriori effect size [0280]; and computing a polygenic risk score as a linear or nonlinear function of the estimated statistical parameters, including per SNP allele effect size mean and/or estimates of variability [0119].
Andreassen et al. does not explicitly teach only determining sampled effect size if the genetic variant is determined to be causal for one or more iterations (claims 1 and 24-25).
Sanyal et al. describes GWASinlps, a non-local prior based iterative SNP selection tool for genome-wide association studies.
Sanyal et al. teaches the GWASinlps method is designed to select SNPs iteratively in steps; given an initial list of SNPs, S, the genotype matrix X and the phenotype vector y, the procedure begins in iteration 1 by determining those SNPs that have highest ranking in association with the phenotype (page 4, column 1), which conceptually amounts to determining all those SNPs that are in LD with Sj with a strength at least rxx (page 4, column 1); and independently generating standardized effect sizes from the N(0, 1) distribution for the causal SNPs (page 5, column 2).
Sanyal et al. teaches for the applications in the simulated and real data studies, we use a non-local prior based estimation model that generates samples from the posterior distribution of the SNP effect sizes; and the SNP effect sizes are estimated using the mean of these posterior samples (page 5, column 1).
Sanyal et al. further teaches generally, for polygenic traits the effect sizes of individual causal SNPs are low; in such situations, the pMOM prior is expected to work better; however, if the effect sizes are more dispersed, the piMOM prior might outperform the MOM prior (page 10, column 1).
Claim 11 is directed to wherein determining the sampled effect size of the genetic variant includes calculating a probability distribution, for example: a multivariate normal distribution, of effect sizes of the genetic variant on the one or more phenotypes or phenotype combinations, and sampling values of the effect sizes from the probability distribution.
Claim 14 is directed to wherein the sampling of values of the effect size in each iteration is dependent on the sampled effect sizes from one or more previous iterations.
Sanyal et al. teaches in the expressions of the priors for the effect sizes, if only one component of the effect size vector is zero, the density π is zero (page 3, column 2); this is a crucial feature of the pMOM and piMOM priors, which imposes, for variable selection, a strong penalty on the regression vector with at least one 0 component, facilitating consistent identification of the causal SNPs and for coefficient estimation, and a strong data-dependent shrinkage on the effect sizes (page 3, column 2).
Sanyal et al. teaches standardized effect sizes were independently generated from the N (0, 1) distribution (page 5, column 2); and the predicted values of the phenotype are obtained by using these effect size estimates in Equation 1 (page 5, column 1).
Claim 20 is directed to wherein carrying out one or more iterations comprises carrying out a predetermined number of iterations.
Sanyal et al. teaches for GWASinlps analysis, we used 1800 MCMC iterations after 200 burn-ins (page 6, column 2).
Sanyal et al. further teaches the proposed iterative structured screen-and-select strategy has two intuitive advantages (page 2, column 2): first, opposed to selecting all the SNPs in one step, it breaks down the selection problem into small chunks thereby making small or moderately high-dimensional methods applicable within each chunk and secondly (page 2, column 2), it performs screening hierarchically through the imposition of a structure that is informed by the dependence pattern in the data (page 2, column 2).
Therefore Sanyal et al. teaches an iterative method of selecting and determining causal SNP variants using GWAS data. Sanyal et al. further provides motivation to determine the effect size of causal SNPs, as it has an impact which prior would perform better within the model; and provides two advantages of the iterative approach. As such, it would be obvious to one of ordinary skill in the art to apply the techniques of Sanyal et al. to the method of Andreassen et al. with a reasonable expectation of success.
Claim 2-7, 10, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Andreassen et al. in view of Sanyal et al. (Bioinformatics; Vol. 35: 1; 2019), as applied to claims 1, 11, 14, 20, and 23-25 above, and in further view of Giambartolomei et al. (PLOS Genetics; Vol. 10: 5; 2014).
Andreassen et al. in view of Sanyal et al. teach an iterative causal variant screening method that accounts for effect size.
Claim 2 is directed to wherein determining for which of the plurality of phenotypes or phenotype combinations the genetic variant is causal comprises calculating a plurality of probabilities including: a probability of the information from the plurality of input units assuming that the genetic variant is not causal for any of the phenotypes or phenotype combinations, a probability of the information from the plurality of input units, assuming that the genetic variant is causal for all of phenotypes or phenotype combinations; and for one or more subsets of the phenotypes or phenotype combinations, a probability of the information from the plurality of input units assuming that the genetic variant is causal for the subset of phenotypes or phenotype combinations.
Andreassen et al. in view of Sanyal et al. do not teach calculating the plurality of probabilities of claim 2.
Giambartolomei et al. describes a Bayesian test for colocalization between pairs of genetic association studies using summary statistics.
Giambartolomei et al. teaches SNP causality in a region of Q variants can be summarised for each trait using a vector of length Q of (0, 1) values, where 1 means that the variant is causally associated with the trait of interest and at most one entry is non-zero; and each possible pair of vectors, in traits 1 and 2, which we refer to as “configurations” can be assigned to one of five hypotheses:
: No association with either trait;
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: Association with trait 1, not with trait 2;
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: Association with trait 2, not with trait 1;
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: Association with trait 1 and trait 2, two independent SNPs; and
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: Association with trait 1 and trait 2, one shared SNP (page 3, column 1); our method is Bayesian in the sense that it integrates over all possible configurations (page 3, column 1); and the result of this procedure is five posterior probabilities: PP0, PP1, PP2, PP3 and PP4 (page 3, column 1).
Claim 2 is further directed to stochastically determining for which of the plurality of phenotypes or phenotype combinations the genetic variant is causal with a probability based on the plurality of probabilities.
Giambartolomei et al. further teaches broadly, in the case of either a single common causal variant or two distinct causal variants, our proposed method could infer the simulated hypotheses correctly (PP4 or PP3 >0.9) with good confidence; and a key advantage in our Bayesian approach is the ability to distinguish evidence for colocalisation (i.e. high PP4) from a lack of power (i.e. high PP0, PP1 or PP2), where in both of these cases (high PP4 or high PP0/PP1/PP2), the use of the proportional approach leads to failure to reject the null even though the interpretation of these situations should differ (page 5, column 2).
Claim 3 is directed to the probability of the information from the plurality of input units assuming that the genetic variant is causal for one or more of the phenotypes or phenotype combinations is dependent on: a proportion of the plurality of genetic variants expected to be causal; the plurality of input units; and a correlation between the effect sizes of the genetic variant on the phenotypes or phenotype combinations.
Giambartolomei et al. teaches assigning a prior probability of
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for
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, which encodes the probability that a variant affects both traits (page 9, column 2), where priors can be interpreted as an estimate for the proportion of SNPs that we expect to be associated with the trait in question (page 12, column 2); integration of GWAS results using only p-values and the sample size of the datasets, where results include a list of potentially causal genes with the associated PP4 (page 8, column 2); and under
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, the effect sizes of the shared variant on both traits are independent (page 9, column 2); however, [citation 30] has proposed a framework to deal with correlated effect sizes, and these ideas could potentially be incorporated in our colocalisation test (page 9, column 2).
Claim 4 is directed to wherein the probability of the information from the plurality of input units assuming that the genetic variant is not causal for any of the phenotypes or phenotype combinations is dependent on: a proportion of the plurality of genetic variants expected to be causal; and the plurality of input units.
Giambartolomei et al. teaches the probability space for a single SNP can be fully partitioned into (p0,p1,p2,p12), where p0 is the prior probability that a SNP is not associated with either trait (page 16, column 1); and the integration of GWAS results using only p-values and the sample size of the datasets, where results include a list of potentially causal genes (page 8, column 2).
Claim 5 is directed to wherein, for each of the one or more subsets of the phenotypes or phenotype combinations, the probability of the information from the plurality of input units assuming that the genetic variant is causal for the subset of phenotypes or phenotype combinations is dependent on: a proportion of the plurality of genetic variants expected to be causal; a subset of input units including the input units with information about the association between the plurality of genetic variants and one of the subset of phenotypes phenotype combinations; and a correlation between the effect sizes of the genetic variant on the phenotypes or phenotype combinations.
Giambartolomei et al. teaches assigning a prior of
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for
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and
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, the probability that a SNP is associated with either of the two trait; and since all SNPs are assumed to have the same prior probability of association, this prior can be interpreted as an estimate for the proportion of SNPs that we expect to be associated with the trait in question (page 12, column 2); integration of GWAS results using only p-values and the sample size of the datasets, where results include a list of potentially causal genes with the associated PP4 (page 8, column 2); and [citation 30] has proposed a framework to deal with correlated effect sizes, and these ideas could potentially be incorporated in our colocalisation test (page 9, column 2).
Claim 6 is directed to wherein the proportion of the plurality of genetic variants expected to be causal is predetermined.
Giambartolomei et al. teaches to assess the role of the prior, varying the critical parameter
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, which codes for the prior probability that a variant is associated with both traits; however here we report the results using
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(page 7, column 1).
Claim 7 is directed to wherein the correlation between the effect sizes of the genetic variant on the phenotypes or phenotype combinations is predetermined.
Giambartolomei et al. teaches prior standard deviation of the additive effect parameter β was set to 0.15 for a continuous trait (page 19, column 1).
Claim 10 is wherein the input units are determined from respective groups of individuals, and each of the plurality of probabilities is dependent on one or more parameters quantifying an overlap in the groups of individuals between respective pairs of input units.
Andreassen et al. teaches the method, which includes inputting the GWAS data, quickly and reliably analyzes large amounts of data and provides knowledge about genotype-phenotype associations both in groups and individuals [0091] [fig. 20]; and shows stratified Q-Q plots for schizophrenia conditioned on nominal p-values of association with bipolar disorder [0012].
Giambartolomei et al. teaches a natural question to ask is whether two independent association signals at the same locus, typically generated by two GWAS studies, are consistent with a shared causal variant; if the answer is positive, we refer to this situation as colocalised traits, and the probability that both traits share a causal mechanism is greatly increased (page 1, column 2). Giambartolomei et al. further teaches using this technique for the systematic comparison between a new GWAS dataset and a large catalogue of association studies in order to identify novel shared mechanisms, such as a meta-analysis of blood lipids and liver expression, although any two datasets resulting from association studies can be used (page 2, column 1).
Claim 16 is directed to wherein the correlation between the effect sizes of the genetic variant on the phenotypes or phenotype combinations is either predetermined or updated at each iteration.
Giambartolomei et al. teaches prior standard deviation of the additive effect parameter β was set to 0.15 for a continuous trait (page 19, column 1).
Giambartolomei et al. further teaches our analysis focuses on a single genomic region at a time, with a major focus on interpreting the pattern of LD at that locus (page 2, column 2); and when the extent of LD is strong, there is uncertainty as to whether the data support a shared causal variant for both traits, or two distincts variants for eQTL/LDL; and because the data are consistent with both scenarios, the choice of prior becomes determinant (page 7, column 2).
Therefore Giambartolomei et al. teaches making the probability dependent on the number of expected causal variants and GWAS data; provides motivation to have the probability information further dependent on correlated effect size information using predetermined information; and provides further motivation to apply its causal SNP analysis technique using prior and posterior probabilities to explore the linkage disequilibrium pattern at specific locations. As such, it would be obvious to one of ordinary skill in the art to apply the techniques of Giambartolomei et al. with the method of Andreassen et al. in view of Sanyal et al.
Claims 8-9, 15, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Andreassen et al. in view of Sanyal et al. (Bioinformatics; Vol. 35: 1; 2019) and Giambartolomei et al. (PLOS Genetics; Vol. 10: 5; 2014) as applied to claims 2-7, 10, and 16 above, and in further view of Yang et al. (AJHG; Vol. 101: 3; 2017).
Andreassen et al. in view of Sanyal et al., and Giambartolomei et al. teach a causal variant screening method that accounts for effect size and the probability of variants expected to be causal.
Claim 8 is directed to wherein the proportion of the plurality of genetic variants expected to be causal is updated at each iteration.
Andreassen et al. in view of Sanyal et al. and Giambartolomei et al. do not teach the plurality of genetic variants expected to be causal is updated at each iteration (claim 8).
Yang et al. describes a method that models the effect-size distribution and probability of causality for variants with different annotations and jointly models genome-wide variants to account for linkage disequilibrium (LD) (abstract).
Yang et al. teaches initializing the category-specific parameters (𝜋𝑞, 𝜎2𝑞), then running the MCMC algorithm per block [E-step], summarizing the MCMC posterior estimates of (𝛽𝑖,𝑃𝑃𝑖) across all blocks to update (𝜋𝑞, 𝜎2𝑞) [M-step], (page 2, column 2), and repeating the block-wise EM-MCMC steps until the estimates of (𝜋𝑞,𝜎2𝑞) converge, where 𝜋𝑞 denotes the category-specific causal probability (page 9, column 1).
Claim 9 is directed to wherein the correlation between the effect sizes of the genetic variant on the phenotypes is updated at each iteration.
Yang et al. teaches initializing the category-specific parameters (𝜋𝑞, 𝜎2𝑞), then running the MCMC algorithm per block [E-step], summarizing the MCMC posterior estimates of (𝛽𝑖,𝑃𝑃𝑖) across all blocks to update (𝜋𝑞, 𝜎2𝑞) [M-step], (page 2, column 2), and repeating the block-wise EM-MCMC steps until the estimates of (𝜋𝑞,𝜎2𝑞) converge, where 𝜎2𝑞 denotes the category-specific effect-size variance that represents the square root of 𝜎2𝑞 reflects the magnitude of effect size (page 9, column 1).
Claim 15 is directed to the probability distribution being dependent on a correlation between the effect sizes of the genetic variant on the phenotype or phenotype combinations.
Yang et al. teaches this model implies that effect sizes are normally distributed as 𝛽𝑖∼𝑁(0,𝜏−1𝜎2𝑞) (page 2, column 1); conditional on a common set of category-specific parameters (𝜋𝑞,𝜎2𝑞), we can infer (𝛽𝑖,𝑃𝑃𝑖) by running the MCMC algorithm per genome block, where 𝜋𝑞 denotes the category-specific causal probability and 𝜎2𝑞 denotes the category-specific effect-size variance (page 9, column 1); and our MCMC algorithm uses a proposal distribution that favors variants near the “causal” variants being considered in each iteration and prioritizes among these neighboring variants based on their conditional association evidence (page 2, column 2) i.e., the probability for the variant to be associated with the phenotype (page 2, column 2).
Therefore, the effect size probability distribution is dependent or conditional to a correlation between category specific effect size variance and causal probability, i.e., the probability for the variant to be associated with the phenotype.
Claim 21 is directed to wherein each of the one or more iterations further includea step of evaluating a convergence parameter, and carrying out one or more iterations comprises carrying out iterations until a predetermined convergence parameter is met.
Yang et al. teaches repeating the block-wise EM-MCMC steps until the estimates of (𝜋𝑞,𝜎2𝑞) converge, as shown in Figure S1B (page 2, column 2); and using the potential scale reduction factor (PSRF) to quantitatively diagnose the MCMC mixing property, signifying convergence (page 9, column 2) (page 10, column 1).
Therefore Yang et al. teaches techniques applicable to the method of Andreassen et al. in view of Sanyal et al. and Giambartolomei et al. As such, one of ordinary skill in the art could apply the techniques to the base product with a reasonable expectation of success and improvement to the system which analyses causal variants on account of effect size.
Claims 13, 18, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Andreassen et al. in view of Sanyal et al. (Bioinformatics; Vol. 35: 1; 2019), as applied to claims 1, 11, 14, 20, and 23-25 previously, and in further view of Zeng et al. (Nature Communications; Vol. 8: 456; 2017).
Andreassen et al. in view of Sanyal et al., teach an iterative causal variant screening method that accounts for effect size.
Andreassen et al. in view of Sanyal et al., do not teach the sampling of values of the effect size is performed using a Monte-Carlo Gibbs sampler (claim 13).
Zeng et al. describes non-parametric genetic prediction of complex traits with latent Dirichlet process regression models.
Zeng et al. teaches DPR. MCMC is a Markov Chain Monte Carlo version of Drichlet process regression (DPR) (page 4, column 1) that uses a Gibbs sampling algorithm (page 6, column 2).
Claim 18 is directed to wherein determining the sampled effect sizes comprises using a model of causal relationships between the plurality of phenotypes or phenotype combinations.
Zeng et al. teaches the effect size distribution for any given trait or disease is unknown a priori and varies for different diseases in terms of the number of causal variants, their minor allele frequencies (MAFs), and their individual effect sizes; therefore, to achieve robust performance, it is important to design prior distributions that are flexible enough to resemble the true effect distribution in many traits as close as possible (page 3, column 1).
Claim 22 is directed to wherein the information about the association between the plurality of genetic variants and each of the phenotypes or phenotype combination includes: an estimate of a strength of association between the genetic variant and the phenotype or phenotype combination and an error in the estimate of the strength of association.
Zeng et al. teaches estimating the probability of a SNP being in any normal components other than the smallest one as the posterior inclusion probability (PIP), where PIP computed in this way measures SNP marginal association strength in the presence of polygenic effects (page 7, column 1), and may represent a more powerful association indicator than standard single SNP association test statistics; and an important feature of using PIP in the context of Bayesian models is that PIP quantifies the uncertainty of association strength, which is a desirable feature that is not easily achieved by penalized frequentist counterparts (page 7, column 1).
Therefore Zeng et al. teaches techniques applicable to the method of Andreassen et al. in view of Sanyal et al. As such, one of ordinary skill in the art could apply the techniques with a reasonable expectation of success and improvement to the genetic variant analysis system.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Andreassen et al. in view of Sanyal et al. (Bioinformatics; Vol. 35: 1; 2019), as applied to claims 1, 11, 14, 20, and 23-25 previously, and in further view of So et al. (Scientific Reports; Vol. 7: 41262; 2017).
Claim 19 is directed to wherein: each of the one or more iterations further comprises, for each genetic variant determined to be causal, subtracting weighted effect sizes from the information about the association between each other genetic variant and the phenotype or phenotype combination of each input unit; the weighted effect sizes being the sampled effect size of the genetic variant on the phenotype or phenotype combination of the input unit weighted by respective correlation factors between the genetic variant and each other genetic variant; and the correlation factors are determined based on the information about correlations between the plurality of genetic variant since the region of interest.
Andreassen et al. teaches weighting the enrichment factor by a function of the linkage equilibrium (LD) of the observed said gene variant with underlying potential causal variants [claim 47], wherein enrichment factors include location of the SNP within a region of the genome [0095]; and the empirical replication z-scores closely match the expected a posteriori effect sizes and are strongly dependent upon genie annotation category [0040].
Andreassen et al. in view of Sanyal et al., do not explicitly teach subtracting the weighted effect sizes from the information about the association between each other genetic variant and the phenotype.
So et al. teaches a method of improving polygenic risk prediction from summary statistics with an empirical Bayes approach.
So et al. teaches assume that a GWAS or meta-analysis of GWAS was done and we tested the association of each variant with the phenotype by a regression analysis by formulating the problem as recovering the “true” z-statistic (i.e. the z-statistic one would obtain if there were no random noise; reflecting the true effect size) from a set of observed z-statistics (page 2, column 1); the z-statistics can then be converted to variance in liability (or heritability) explained as described in [citation 11], which can then be converted to corrected estimates of
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(page 2, column 1).
So et al. teaches using the Tweedie’s formula, the corrected estimates of βi (denoted as
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) can be expressed as [equation 3] (page 2, column 1); and generally, in the GWAS setting we expect only some but not all markers to be associated with the outcome, so we propose another estimator of the effect size as follows (page 3, column 1):
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where fdr is the local false discovery rate the probability of null given the observed z-statistic (page 3, column 1); this method weighs each effect size estimate from Tweedie’s formula by the probability of being non-null (page 3, column 1); and in practice, it will further shrink effect sizes towards zero and a portion of the effect sizes will become zero, as the local fdr equal one for some markers (page 3, column 1).
So et al. further teaches In an infinitesimal model, all markers are assumed to be causal and the marker effects follow the distribution
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, where M is the total number of markers and h2 is the total heritability explained by the panel markers (page 3, column 1); the algorithm also allows for a non-infinitesimal model, in which only a fraction p of all makers are causal (page 3, column 1); the effect sizes can be computed for different proportion of causal markers p; and an approximate MCMC Gibbs sampler is used to estimate the posterior mean for the non-infinitesimal case while an analytic solution is available for the infinitesimal case (page 3, column 1).
Therefore So et al. teaches subtracting a weighted estimate of effect size (fdr multiplied by
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) from the association of each variant with the phenotype (
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) for causal variants in an iterative MCMC fashion. As such it is applicable to the Bayesian method of Andreassen et al. in view of Sanyal et al. As such, it would be obvious to one of ordinary skill in the art to apply the techniques of So et al. to the method of Andreassen et al. in view of Sanyal et al. with a reasonable expectation of success and improvement to the system.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
Claims 1-4, 6-11, 13-16, 19- 22, and 24-25 (instant) are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10, 12-15, 17, 24-26, and 29-30 of copending Application No. 18/255,249 (reference) in view of Beilsmith et al. (The Plant Journal. Vol. 97; 164-181; 2019).
Claim 1 (instant)
Claim 1 (reference)
A computer-implemented method of analysing genetic data about an organism, the method comprising:receiving a plurality of input units, wherein each input unit comprises information about the association between a plurality of genetic variants in a region of interest of the genome of the organism and one of a plurality of phenotypes or phenotype combinations of the organism; carrying out one or more iterations comprising, for each of the plurality of genetic variants: determining for which of the plurality of phenotypes or phenotype combinations the genetic variant is causal based on the plurality of input units; and if the genetic variant is determined to be causal for one or more of the phenotypes or phenotype combinations, determining a sampled effect size of the genetic variant on each of the one or more phenotypes or phenotype combinations based on the plurality of input units and information about correlations between the plurality of genetic variants in the region of interest; and for each genetic variant, determining a prediction effect size of the genetic variant on one or more of the phenotypes or phenotype combinations based on an average across at least a subset of the iterations of the sampled effect sizes of the genetic variant on the one or more phenotypes or phenotype combinations or of posterior effect sizes of the genetic variant for the input unit calculated using the sampled effect sizes.
A computer-implemented method of analysing genetic data about an organism, the method comprising:receiving a plurality of input units, wherein each input unit comprises information about the association between a plurality of genetic variants in a region of interest of the genome of the organism and a target phenotype of the organism; carrying out one or more iterations comprising, for each of the plurality of genetic variants: determining whether the genetic variant is causal for the target phenotype based on the plurality of input units; and if the genetic variant is determined to be causal, determining a sampled effect size of the genetic variant on the target phenotype for each of the input units based on the plurality of input units and information about correlations between the plurality of genetic variants in the region of interest, the sampled effect size of the genetic variant on the target phenotype being non-zero for all of the input units; and for each genetic variant, determining a prediction effect size of the genetic variant on the target phenotype for each of the input units based on an average across at least a subset of the iterations of the sampled effect sizes of the genetic variant for the input unit or of posterior effect sizes of the genetic variant for the input unit calculated using the sampled effect sizes.
Therefore, the independent claim 1s differ in scope of the phenotype by the use of “one or more phenotypes or phenotype combinations” in the instant claim; and “target phenotype” in the reference claims. However, the target phenotype can still represent one phenotype, encompassed in the scope of the instant claim; and is therefore obvious. The second difference between the claims is the reference limitation that “the sampled effect size of the genetic variant on the target phenotype being non-zero for all of the input units,” which is not present in the instant claims.
Beilsmith et al. describes genome-wide association studies on the phyllosphere microbiome. Beilsmith et al. teaches multi-trait GWAS methods typically test the hypothesis that a given SNP has a non-zero effect size on one or more traits against the null hypothesis that the SNP has no effect on any trait (page 13, column 1).
Therefore, it would be further obvious to one of ordinary skill in the art to apply this technique on a method which uses information regarding the association between a plurality of genetic variants in a region of interest of the genome of the organism and one of a plurality of phenotypes or phenotype combinations of the organism, such as a GWAS.
As such, the independent claims are patentably indistinct. The dependent claims are similarly rejected as they are identical to those in the reference application save for the differences already explained herein.
This is a provisional nonstatutory double patenting rejection.
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Conclusion
No claims are currently allowed.
Correspondence
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/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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