DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Herein, “the previous Office action” refers to the Non-Final Rejection filed 9/8/2025.
Priority
As detailed on the Filing Receipt filed 11/3/2021, the instant application claims priority to as early as 5/27/2020. Applicant’s claims for the benefit of prior-filed applications under 35 USC §§ 119(e) and 120 are acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 USC §§ 119(e) and 120 as follows:
No Common Inventorship
When a later-filed application is claiming the benefit of one or more prior-filed provisional applications, each prior-filed provisional application must have the same inventor or at least one joint inventor in common with the later-filed application. See MPEP 211.01(a) § I, referencing 35 USC § 119(e).
The instant application claims the benefit of Provisional Application No. 63/030,876 (filed 5/27/2020). The instant application names joint inventors (O'Connell, JM; Mozaffari, SV; Wang, W; Shringarpure, SS; Auton, A; Shi, J) that do not overlap with those named in the prior-filed provisional application (Polcari, M; Zhan, J; Ganesan, M; Marshall, AW; Ashenhurst, JR; Kondo, DP; Amiri, S; Sinha, S; Suresh, S; Macpherson, JM; Koelsch, BL; Blakkan, CT; Hamilton, SM).
Consequently, the instant application is not entitled to the benefit of the prior-filed provisional application under 35 USC § 119(e).
A prior-filed application and an alleged continuation-in-part application must be filed with the same inventor or at least one common joint inventor. See MPEP 201.08, referencing 35 USC § 120.
The instant application was filed as a continuation-in-part of Application No. 17/303,398. The instant application names joint inventors (O'Connell, JM; Mozaffari, SV; Wang, W; Shringarpure, SS; Auton, A; Shi, J) that do not overlap with those named in the prior-filed application (Polcari, M; Zhan, J; Ganesan, M; Marshall, AW; Ashenhurst, JR; Kondo, DP; Amiri, S; Sinha, S; Suresh, S; Macpherson, JM; Koelsch, BL; Blakkan, CT; Hamilton, SM).
Consequently, the instant application is not entitled to the benefit of the prior-filed application under 35 USC § 120.
Insufficiency of Prior Disclosure
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application for which benefit is claimed), and the disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 USC § 112(a) except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551 (Fed. Cir. 1994); see also MPEP 211.05 § I, referencing 35 USC §§ 119(e) and 120.
The disclosure of the prior-filed provisional application, Provisional Application No. 63/030,876 (filed 5/27/2020), fails to provide adequate support or enablement in the manner provided by 35 USC § 112(a) for one or more claims of the instant application.
With respect to claim 1 and dependents therefrom, the prior-filed application does not disclose at least determining a set of genetic correlations between the phenotype of interest and each of a plurality of candidate phenotypes; and filtering the candidate phenotypes based on the set of genetic correlations.
With respect to claim 21, the prior-filed application does not disclose at least identifying one or more filtered candidate phenotypes, each having a genetic correlation with the phenotype of interest.
With respect to claim 22, the prior-filed application does not disclose at least analyzing genotype data for a selected ethnic target population and one or more population-specific datasets for populations other than the ethnic target population.
Consequently, claims 1, 21-22 and dependents therefrom are not entitled to the benefit of the prior-filed provisional application under 35 USC § 119(e).
The disclosure of the prior-filed provisional application, Provisional Application No. 63/198,514 (filed 10/23/2020), fails to provide adequate support or enablement in the manner provided by 35 USC § 112(a) for one or more claims of the instant application.
With respect to claim 1 and dependents thereof, the prior-filed application does not disclose at least determining a set of genetic correlations between the phenotype of interest and each of a plurality of candidate phenotypes; and filtering the candidate phenotypes based on the set of genetic correlations.
With respect to claim 21, the prior-filed application does not disclose at least identifying one or more filtered candidate phenotypes, each having a genetic correlation with the phenotype of interest.
With respect to claim 22, the prior-filed application does not disclose at least analyzing genotype data for a selected ethnic target population and one or more population-specific datasets for populations other than the ethnic target population.
Consequently, claims 1, 21-22 and dependents therefrom are not entitled to the benefit of the prior-filed provisional application under 35 USC § 119(e).
The disclosure of the prior-filed application, Application No. 17/303,398 (filed 5/27/2021), fails to provide adequate support or enablement in the manner provided by 35 USC § 112(a) for one or more claims of the instant application.
Any claim in a continuation-in-part application which is directed solely to subject matter adequately disclosed under 35 USC § 112 in the parent nonprovisional application is entitled to the benefit of the filing date of the parent nonprovisional application. However, if a claim in a continuation-in-part application recites a feature which was not disclosed or adequately supported by a proper disclosure under 35 USC § 112 in the parent nonprovisional application, but which was first introduced or adequately supported in the continuation-in-part application, such a claim is entitled only to the filing date of the continuation-in-part application. See, e.g., In re Chu, 66 F.3d 292 (Fed. Cir. 1995); Transco Products, Inc. v. Performance Contracting Inc., 38 F.3d 551 (Fed. Cir. 1994); In re Van Lagenhoven, 458 F.2d 132, 136 (CCPA 1972).
With respect to claim 1 and dependents thereof, the prior-filed application does not disclose at least determining a set of genetic correlations between the phenotype of interest and each of a plurality of candidate phenotypes; and filtering the candidate phenotypes based on the set of genetic correlations.
With respect to claim 21, the prior-filed application does not disclose at least identifying one or more filtered candidate phenotypes, each having a genetic correlation with the phenotype of interest.
With respect to claim 22, the prior-filed application does not disclose at least analyzing genotype data for a selected ethnic target population and one or more population-specific datasets for populations other than the ethnic target population.
Consequently, claims 1, 21-22 and dependents therefrom are not entitled to the benefit of the prior-filed application under 35 USC § 120.
Conclusion
The instant application was filed on 10/22/2021, and the claims are not entitled to the benefit of a prior-filed application. Claims 1-19 and 21-22 are thus accorded the filing date of 10/22/2021.
Information Disclosure Statement
The Information Disclosure Statement filed on 10/3/2025 is in compliance with the provisions of 37 CFR 1.97 and has been considered in full. A signed copy of the IDS is included with this Office Action.
Claim Status
Claims 20 and 23-45 are canceled.
Claims 1-19 and 21-22 are pending, and under examination.
Withdrawn Objections/Rejections
The objection to the specification is hereby withdrawn in view of Applicant’s amendment of the specification to remove an embedded hyperlink.
The objection to claim 18 is hereby withdrawn in view of Applicant’s amendment of the claim to resolve a grammatical informality.
The rejection of claims 3, 9 and 19 under 35 USC §112(b), as being indefinite, is hereby withdrawn in view of Applicant’s amendment of the claims to remove the term “about”.
The rejections of claims 1-19 and 21-22 under 35 USC § 103, as being unpatentable over combinations of Zhang, in view of Bulik-Sullivan, Krapohl, Zheng and/or Privé have been withdrawn in view of Applicant’s amendment of the claims and persuasive argument that the applied combination of references does not teach particular limitations of the amended claims (Remarks filed 12/5/2025 at pg. 13, para. 4; pg. 14, para. 2 – pg. 15, para. 2).
Response to Arguments - Drawings
In the Remarks filed 12/5/2025, Applicant states that the drawings were determined to be in black and white upon review. Accordingly, Applicant requests withdrawal of the objection to the drawings (pp. 8-9, spanning para.).
The file wrapper includes an element labeled ‘Drawings-other than black and white line drawings’, with document code DRW.NONBW, which opens a panel displaying all black and white figures. This wrapper element, to which Applicant refers in the remarks, downloads as a pdf with a default filename (‘File.pdf’).
The file wrapper also includes an element labeled ‘Drawings-black and white and/or other drawings’, with document code DRW.SUPP. This wrapper element opens a panel noting receipt on 10/22/2021 of, and allowing download of, an embedded file named ‘23MEP056A_Figures.pdf’. The Examiner downloaded and reviewed the embedded file, and found it to include both black and white figures (Figs. 1-9 and 19) and color figures (Figs. 10-18).
In the understanding of the Examiner, the DRW.NONBW element here provides a grayscale view of the figures contained in the uploaded drawings file. This explains why the element is labeled with ‘other than black and white’ despite displaying all black and white figures. It appears that Applicant submitted the uniquely-named supplemental drawings file (23MEP056A_Figures.pdf), and so provided color drawings in the absence of a granted petition under 37 CFR 1.84(a)(2) (Petition to Accept Color Drawings). Thus, the objection is maintained.
If Applicant believes that color drawings are necessary to convey the subject matter sought to be patented, Applicant is encouraged to file a petition under 37 CFR 1.84(a)(2).
Objection to the Drawings
The Drawings submitted by Applicant (filed 10/22/2021, file labeled ‘23MEP056A_Figures.pdf’) contain multiple figures (Figs. 10-18) executed in color. Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
“The patent or application file contains drawings executed in color. Copies of this patent
or patent application publication with color drawings will be provided by the Office upon
request and payment of the necessary fee.”
Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
Response to Arguments - Claim Rejections Under 35 USC § 101
In the Remarks filed 12/5/2025, Applicant traverses the rejection under 35 USC § 101 and presents supporting arguments.
Applicant alleges that the claims reflect improvements over prior art that provide a technical solution to a technical problem, and points to description in the instant specification (at paras. 83-84) of alleged technical problems and alleged provision of corresponding technical improvements over prior art by the claimed invention (pg. 9, para. 5 – pg. 11, para. 1).
The cited portions of the specification discuss advantages of the cross-traits PRS modeling technique in terms of efficiency, scalability and flexibility regarding incorporation of additional GWAS statistics. The portions particularly note that the cross-traits technique does not require retraining models when updated meta-analyses are generated, thus proving relatively efficient as compared to other modeling techniques that retrain model weights when incorporating additional GWAS statistics.
The nature of the technical problem is unclear. The relatively lower computational efficiency of prior modeling techniques resulting from their inclusion of additional computational operations, rather than recognized limitations of employed technology, does not amount to a technical problem. The courts have held that improvements in usage of a computer are not, by themselves, equivalent to improvements to the functioning of the computer, another technology, or technical field (FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089 (Fed. Cir. 2016)). Hence, the argument is found unpersuasive.
Applicant alleges that the claims reflect a further improvement in that the claimed system function of filtering candidate phenotypes, to select only those with stronger GWAS signals and larger sample sizes, has the effect of discarding low-quality data such that processor(s) only expend resources on high-value inputs that borrow strength from established datasets (pg. 11, para. 2).
Applicant’s argument posits that only processing a subset of (high-quality) data, rather than a broader set of data, improves the technical functioning of an implementing computer system. Simply filtering input data does reduce processing time, but does not inherently improve the functioning of computer technology that implements analysis of that data, despite the relative efficiency, usefulness, etc. of analyzing filtered data as compared to analyzing unfiltered data. Thus, the argument is found unpersuasive.
Applicant points to reasoning from the decision of Ex parte Desjardins, Appeal 2024-000567 (PTAB Sept. 26, 2025, Appeals Review Panel Decision; hereafter “Desjardins”) citing to the decision of Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016; hereafter, “Enfish”), and alleges that, similarly to claims held eligible in Enfish and Desjardins, the instant claims recite software that makes non-abstract improvements to computer technology, (pg. 11, para. 4 – pg. 12, para. 2).
The conclusion of eligibility in Enfish was supported by discussion in the considered specification of how the claimed implementation of a specific, recited logical structure provided improved data storage and retrieval functionality to employed computer technology (see 822 F.3d at 1339). The instant specification does not describe analogous improvements to functionality of implementing computer technology, but rather discusses the relative efficiency of the claimed statistical technique as compared to techniques involving repeated model retraining. Hence, the argument of analogy to Enfish (and, by extension, Desjardins) is found unpersuasive.
For the above reasons, the arguments are found unpersuasive and the rejection is maintained.
Claim Rejections - 35 USC § 101
35 USC § 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 1-19 and 21-22 are rejected under 35 USC § 101 because the claimed invent ion is directed to an abstract idea without significantly more, i.e., non-statutory subject matter. This rejection is maintained from the previous Office action, and has been revised to address the amended claims (filed 12/5/2025).
"Claims directed to nothing more than abstract ideas, natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 § I).
Abstract ideas include mathematical concepts (including formulas, equations and calculations), and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
The claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
Step 1: The Four Categories of Statutory Subject Matter (MPEP 2106.03)
The claims are directed to methods, which fall within the ‘process’ category of statutory subject matter.
Step 2A, Prong One: Whether the Claims Set Forth or Describe a Judicial Exception (MPEP 2106.04 § II.A.1)
‘Mathematical concepts’ are relationships between variables and numbers, numerical formulas or equations, or acts of calculation, which need not be expressed in mathematical symbols (MPEP 2106.04(a)(2) § I). The claims recite elements which encompass mathematical concepts, at least under their broadest reasonable interpretation, including:
“determining a set of genetic correlations between the phenotype of interest and each candidate phenotype of the plurality of candidate phenotypes” (claim 1), i.e., calculating pairwise covariance values, wherein:
“the set of genetic correlations comprises p-values between the phenotype of interest and each candidate phenotype” (claim 2);
“determining the cross-traits PRS model for the phenotype of interest based at least in part on the plurality of PRS models” (claims 1 and 21), i.e., deriving an equation based on a set of equations, wherein:
“the cross-traits PRS model comprises a weight factor for each PRS model of the plurality of PRS models” (claim 10), and
“each PRS model outputs a PRS, and the cross-traits PRS is a linear or logistic combination of the PRS from the plurality of PRS models” (claim 13);
“determining a genetic correlation between the phenotype of interest and a candidate phenotype based on the set of summary statistics for the phenotype of interest and the set of summary statistics for the candidate phenotype” (claim 4);
“determining a genetic correlation between the phenotype of interest and a candidate phenotype based on determining a genetic covariance between the plurality of single nucleotide polymorphism sites for the phenotype of interest and the candidate phenotype” (claim 6), wherein:
“the genetic correlation is determined based on a function of the genetic covariance among the plurality of single nucleotide polymorphism sites for the phenotype of interest and the candidate phenotype and a heritability of the phenotype of interest and the candidate phenotype.” (claim 7), and
“the genetic correlation is determined according to [a recited] following formula” (claim 8);
“determining the weight factor by a penalized linear or logistic regression” (claim 11), wherein:
“the penalized linear or logistic regression includes elastic net regularization” (claim 12);
“executing the cross-traits PRS model to generate a PRS for the phenotype of interest” (claim 14);
“generating each of the plurality of PRS models” (claim 17), wherein:
“generating… is performed by a stacked clumping and thresholding (SCT) method” (claim 18);
“analyzing the genotype data for the ethnic target population and one or more population specific genetic datasets to determine one or more sets of SNPs that are statistically associated with a phenotype of interest, wherein the population-specific genetic datasets are for populations other than the ethnic target population” (claim 22), i.e., calculating data subsets that satisfy statistical constraints;
“training a plurality of PRS models based on the genotype data for the one or more population-specific genetic datasets and the plurality of training SNP sets to generate a PRS model for each of the one or more populations in the one or more population-specific genetic datasets” (claim 22), i.e., calculating optimized equations; and
“determining the transethnic PRS model for the ethnic target population based at least in part on the plurality of PRS models” (claim 22).
The recited acts of calculation constitute mathematical concepts.
‘Mental processes’ are processes that can be performed in the human mind at least with use of a physical aid, e.g., a slide rule or pen and paper (MPEP 2106.04(a)(2) § III). The recited acts of calculation are practicably performable in the human mind, rendering them as mental processes. Additionally, the claims recite elements that encompass further processes that are practicably performable in the human mind, at least under their broadest reasonable interpretation, including:
“selecting the phenotype of interest having a set of summary statistics from a genome wide association study (GWAS)” (claim 1), i.e., selecting a data attribute, wherein:
“the set of summary statistics from a GWAS comprise a p-value for each of a plurality of single nucleotide polymorphism (SNP) sites” (claim 5);
“selecting a plurality of candidate phenotypes, each candidate phenotype having a set of summary statistics from a corresponding GWAS for that candidate phenotype” (claim 1), i.e., selecting data attributes, wherein:
“the plurality of candidate phenotypes comprises more than 100 phenotypes” (claim 9);
“filtering the plurality of candidate phenotypes based on the set of genetic correlations to assemble a cohort of filtered candidate phenotypes” (claim 1), i.e., organizing information, wherein:
“the set of summary statistics for each of the cohort of filtered candidate phenotypes has a stronger GWAS signal and a larger sample size than that of the phenotype of interest” (claim 1),
“filtering… is further based on a p-value threshold” (claim 2), and
“the p-value threshold is less than 1e-3” (claim 3);
“identifying one or more filtered candidate phenotypes to form a cohort of filtered candidate phenotypes” (claim 21), wherein:
“each filtered candidate phenotype has GWAS statistical data” (claim 21),
“each filtered candidate phenotype has a genetic correlation with the phenotype of interest… [that] exceeds a defined threshold” (claim 21), and
“the GWAS statistical data for each of the filtered candidate phenotypes has a stronger GWAS signal and a larger sample size than that of the phenotype of interest” (claim 21);
“selecting an ethnic target population of interest having genotype data available for individuals within the ethnic target population” (claim 22); and
“applying SNP filtering criteria to the one or more set of SNPs to generate a plurality of training SNP sets with each training SNP set corresponding to a different population of the one or more population-specific genetic datasets” (claim 22), i.e., filtering data.
The recited steps of evaluating information, which are practicably performable in the human mind, constitute mental processes.
Hence, the claims recite elements that, individually and in combination, constitute an abstract idea. The claims must therefore be examined further to determine whether they integrate this abstract idea into a practical application (MPEP 2106.04(d)).
Step 2A, Prong Two: Whether the Claims Contain Additional Elements that Integrate the Judicial Exception(s) into a Practical Application (MPEP 2106.04 § II.A.2)
The claims recite additional elements that gather data necessary for performance of claimed method steps, including:
“retrieving a plurality of PRS models, each PRS model corresponding to a phenotype of the cohort of filtered candidate phenotypes” (claims 1 and 21), i.e., gathering mathematical models based on organized data, wherein:
“each PRS model is based at least in part on the set of summary statistics from the corresponding GWAS” (claim 15),
“the plurality of PRS models includes a PRS model for the phenotype of interest” (claim 16), and
“each of the plurality of PRS models includes greater than 50,000 SNPs” (claim 19); and
“obtaining, for the phenotype of interest, GWAS statistical data relating the phenotype of interest to genetic information” (claim 21).
Necessary data gathering is considered to be insignificant pre-solution activity, and as such insufficient to integrate an abstract idea into a practical application (MPEP 2106.05(g)).
The claims do not recite any additional non-abstract elements.
When the claims are considered as a whole: they do not improve the functioning of a computer, other technology, or technical field (MPEP 2106.04(d)(1) and 2106.05(a)); they do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (MPEP 2106.04(d)(2)); they do not implement the abstract idea with, or in conjunction with, a particular machine (MPEP 2106.05(b)); they do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)); and they do not apply or use the abstract idea in some other meaningful way beyond linking the use of the abstract idea to a particular technological environment and/or field of use (e.g., polygenic risk assessment; MPEP 2106.05(e) and 2106.05(h)).
Therefore, the claims do not integrate the abstract idea into a practical application. See MPEP 2106.04(d) § I.
Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are directed to that abstract idea. Claims that are directed to abstract ideas must be examined further to determine whether the additional elements besides the abstract idea render the claims significantly more than the abstract idea. Claims that are directed to abstract ideas and that raise a concern of preemption of those abstract ideas must be examined to determine what elements, if any, they recite besides the abstract idea, and whether these additional elements constitute inventive concepts that are sufficient to render the claims significantly more than the abstract idea (MPEP 2106.05).
Step 2B: Whether the Claims Contain Additional Elements that Amount to an Inventive Concept (MPEP 2106.05)
As noted above, several recited additional elements amount to insignificant extra-solution activity. Mere addition of insignificant extra-solution activity does not amount to an inventive concept that would render the claims significantly more than the recited abstract idea, particularly when the activities are well-understood or conventional (MPEP 2106.05(g)). The conventionality of recited additional elements that amount to insignificant extra-solution activity must be further considered.
The specification indicates that the following additional elements, which amount to data gathering (i.e., insignificant extra-solution) activity, may be performed using computer hardware:
retrieving PRS models (para. 162: “In certain embodiments… [r]etrieving a PRS model from a database”; para. 183: “Each model… may be associated with various metadata… includ[ing]: Model parameters… All metadata associated with a model may be saved in a repository, allowing for the reproduction of the model… Models may be defined in a git repository… and made available in a performant and scalable web service”; para. 189: “a PRS Machine repository may contain shared code… A specification file or other form of storing parameter information may be used to provide parameters for each part of the PRS model training process”); and
obtaining GWAS statistical data relating a phenotype of interest to genetic information (para. 106: “in some embodiments, a GWAS is run for [a] chosen phenotype… The results may be stored in a database and accessible to downstream systems”; para. 121: “In some embodiments, the individual level information is retrieved from one or more databases. The individual level information may include genetic information… [and] phenotypic information… of the members”).
The courts have held that computer-implemented functions of retrieving data, both via a computer network and from system memory, are coextensive with a general purpose computer and/or well-understood, routine, and conventional. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Versata Dev. Group, Inc. v. SAP America, Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015).
In this way the specification indicates that recited additional elements amounting to insignificant extra-solution activity encompass well-understood, routine, and conventional activity (see MPEP 2106.07(a) § III). Well-understood, routine and conventional activity is insufficient to constitute an inventive concept that would render the claims significantly more than judicial exceptions (MPEP 2106.05(d)).
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment (e.g., polygenic risk assessment; MPEP 2106.05(a) and 2106.05(h).
Conclusion: Claims are Directed to Non-statutory Subject Matter
For these reasons, the claims, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept. Hence, the claimed invention does not constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments - Claim Rejections Under 35 USC § 103
In the Remarks filed 12/5/2025, Applicant traverses the rejections under 35 USC § 103 and presents supporting arguments.
Applicant points to discussion in Bulik-Sullivan of a process of pruning clusters of correlated phenotypes, and alleges that said process amounts to filtering of phenotypes regardless of underlying genetic basis which is fundamentally different than the claimed process of filtering of phenotypes based on genetic correlations between a phenotype of interest and each of a plurality of candidate phenotypes (pg. 13, paras. 2-3).
It is true, as Applicant notes, that Bulik-Sullivan does not expressly disclose clustering or pruning of phenotypes on the basis of genetic correlations. Bulik-Sullivan describes clustering of “correlated phenotypes” (pg. S2, Summary statistic data sets) to producing “clusters of highly correlated traits” (pg. 1238, Fig. 2 caption), then selecting a representative phenotype from each cluster for further analysis. Bulik-Sullivan does not further describe the clustering methodology or correlations utilized therein.
Bulik-Sullivan does expressly disclose calculation of genetic correlations between phenotypes via cross-traits LD score regression (pg. 1236, Abstract; pg. 1238, Fig. 2), and before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to utilize the disclosed genetic correlations as basis for the disclosed process of clustering ‘correlated phenotypes’. In this way, although Bulik-Sullivan does not anticipate the cited claim feature, Bulik-Sullivan does render the claim feature as obvious in combination with the teachings of Zhang. Hence, the argument is found unpersuasive.
Applicant also points to the claim limitation of “the set of summary statistics for each of the cohort of filtered candidate phenotypes has a stronger GWAS signal and a larger sample size than that of the phenotype of interest”, newly incorporated by amendment, and alleges that this feature is absent from the cited references (pg. 13, para. 4).
This argument is found persuasive with respect to amended claims 1-19 and 21, since no art of record discusses embodiments wherein a filtered phenotype has a larger sample size than a phenotype of interest as required by these claims. Consequently, the previous rejections of claims 1-19 and 21 have been withdrawn.
Applicant’s amendment (filed 12/5/2025) of claims 1-19 and 21 to incorporate new limitations necessitated search and application of additional prior art. The applied reference by Cai et al is considered to remedy this deficiency of the previously-cited art, including Bulik-Sullivan.
Applicant points to discussion in Zhang of a training a multi-class classifier to determine the ethnic composition of an individual based on genetic data, and alleges distinction between these teachings of Zhang and the determination of a transethnic model for an ethnic target population based at least in part on a plurality of PRS models, as claimed (pg. 14, para. 2 – pg. 15, para. 1).
This argument is found persuasive with respect to amended claim 22. Consequently, the previous rejection of claim 22 has been withdrawn. Applicant’s amendment (filed 12/5/2025) of claims 1-19 and 21 to incorporate new limitations necessitated search and application of additional prior art. The applied reference by Cai et al is considered to remedy the deficiency of Zhang.
Applicant also points to discussion in Krapohl of multi-polygenic scoring based on genetic traits, and alleges distinction between these teachings of Krapohl and polygenic scoring based on genetic populations, as claimed (pg. 15, para. 2).
This argument is considered moot in view of the withdrawal of the previous rejection, and the issuance of a new rejection containing additional prior art (Cai) that describes construction of a transethnic PRS model for an ethnic target population.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 USC §§ 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 USC § 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 USC § 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 USC § 102(b)(2)(C) for any potential 35 USC § 102(a)(2) § prior art against the later invention.
Claims 1-8, 10-17, 19 and 21-22 are rejected under 35 USC § 103 as being unpatentable over Zhang et al (previously cited), in view of Bulik-Sullivan et al (previously cited), Krapohl et al (previously cited) and Cai et al (The American Journal of Human Genetics 108(4): P632-655; published 4/1/2021). The new grounds of rejection presented herein were necessitated by Applicant’s amendment of the claims (filed 12/5/2025).
Claim 1 recites a method for generating a cross-traits polygenic risk score (PRS) model for a phenotype of interest, comprising: selecting the phenotype of interest having a set of summary statistics from a genome wide association study (GWAS); selecting a plurality of candidate phenotypes, each having a set of summary statistics from a corresponding GWAS; determining a set of genetic correlations between the phenotype of interest and each candidate phenotype; filtering the candidate phenotypes based on the set of genetic correlations to assemble a cohort of filtered candidate phenotypes, wherein the set of summary statistics for each of the cohort of filtered candidate phenotypes has a stronger GWAS signal and a larger sample size than that of the phenotype of interest; retrieving a plurality of PRS models, each corresponding to a phenotype of the cohort; and determining the cross-traits PRS model for the phenotype of interest based at least in part on the plurality of PRS models.
With respect to claim 1, Zhang discloses a method for predicting a phenotypic trait in an individual (para. 4), comprising: obtaining genetic datasets, comprising genotypes over a plurality of SNP loci, for a plurality of training individuals reported to have, and reported to not have, a phenotypic trait in consideration, conducting a GWAS, and calculating tabulated predictive scores (paras. 5 and 59-60), i.e., summary statistics; filtering SNP loci based on the predictive scores to identify subsets of SNP loci (para. 4); and calculating a PRS for each training individual (i.e., retrieving a plurality of PRS models) based on the individual’s genotypes at an identified subset of SNP loci (Fig. 6B, label 670).
Zhang further discloses embodiments wherein the phenotypic trait comprises a plurality of labels (e.g., communities of interest such as known ethnic origins, or eye colors) and the GWAS is conducted iteratively by calculating scores for each considered SNP locus indicating predictive ability of each considered label (para. 61). This is considered equivalent to selecting candidate phenotypes, each having a set of summary statistics from a corresponding GWAS.
Zhang additionally discloses training a multi-class classifier to determine whether a target individual’s genetic dataset most likely belongs to one of several possible genetic communities, e.g., known ethnic origins (paras. 48-49). Thus, Zhang is considered to disclose determining a cross traits model.
However, Zhang does not specifically disclose determining a cross-traits PRS model based at least in part on the plurality of PRS models. Neither does Zhang disclose determining a set of genetic correlations between the phenotype of interest and candidate phenotypes; or filtering candidate phenotypes based on the genetic correlations.
Bulik-Sullivan discusses cross-traits LD score regression, a method of estimating genetic correlations among traits (i.e., phenotypes) based on GWAS summary statistics (pg. 1236, Abstract), and exemplifies filtering steps including pruning clusters of correlated traits (pg. S2, Summary statistic data sets). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to utilize genetic correlations, calculated between phenotypes as disclosed, as basis for the disclosed process of clustering correlated phenotypes. In this way, Bulik-Sullivan is considered to make obvious filtering phenotypes based on genetic correlations. Additionally, Bulik-Sullivan teaches that their methodology is robust to oversampling, computationally fast, and scales easily to studies of thousands of pairs of traits (pp. 1239-40, Discussion).
Bulik-Sullivan discloses removing clustered phenotypes with a heritability z score below 4, and selecting a representative phenotype having the highest heritability z-score from each phenotype cluster (pg. S2, Summary statistic data sets). In this way, Bulik-Sullivan discloses filtering to select a cohort of representative candidate phenotypes based on strongest GWAS signals. However, Bulik-Sullivan does not disclose embodiments wherein a selected representative phenotype has a larger sample size.
Bulik-Sullivan also presents a cross-traits LD score regression equation (pg. 1237, l. column), and the disclosed process of performing LD score regression by solving this equation is considered equivalent to determining a cross-traits model. However, Bulik-Sullivan does not teach determining a cross-traits PRS model based on a plurality of models.
Krapohl et al discusses a multi-polygenic score (MPS) approach for predicting individual-level genetic propensities and outcomes based on GWAS summary statistics (pg. 1368, Abstract), and teaches generation of a series of single-polygenic score (i.e., PRS) models followed by optimization of a final MPS model including the polygenic scores as predictors (pg. 1369, Models). In this way, Krapohl teaches determining a cross-traits PRS model based on a plurality of PRS models. Krapohl further teaches that their MPS approach yields better individual-level phenotype prediction than single-score predictor models for independent test data (pg. 1372, Discussion).
Krapohl does not disclose embodiments wherein a selected representative phenotype has a larger sample size.
Cai discusses a cross-population analysis framework including a summary-level embodiment, called XPASS, that utilizes GWAS summary statistics (i.e., z-scores) and SNP correlation matrices from target and auxiliary populations to construct a PRS for an under-represented target population (pg. 632, Abstract; pg. 633, r. column). Cai teaches that sample size limits the accuracy of PRS, and integrating small-to-medium scale data from an underrepresented target population with large-scale data from a EUR (i.e., auxiliary) population can robustly improve PRS prediction accuracy due to genetic correlation between populations (pg. 633, r. column).
Cai describes the purpose of their framework as improving genetic prediction of under-represented populations by leveraging their trans-ancestry genetic correlations with a large and well-powered auxiliary GWAS dataset from another population (pg. 647, l. column). In this way, Cai advantageously teaches performance of cross-population PRS analysis wherein auxiliary populations have a larger sample size than a target population.
Cai presents findings that XPASS outperforms existing summary-level PRS models and, when auxiliary data is available and genetic correlation is non-zero, achieves the highest prediction R2 among all compared methods (pg. 642, l. column). Cai teaches that XPASS can be applied to a wide class of phenotypes, including complex diseases and molecular phenotypes (pg. 647, r. column).
With respect to claim 2, Zhang discloses identifying a subset of SNP loci with predictive ability by conducting a genome-wide association study (GWAS) for the trait and identifying (i.e., filtering) SNP loci having a p-value score below a threshold (pg. 1, para. 4). Zhang does not disclose filtering candidate phenotypes.
Bulik-Sullivan teaches pruning clusters of correlated traits (i.e., filtering phenotypes) based on a z-score threshold (pg. S2, Summary statistic data sets).
With respect to claim 3, Zhang exemplifies p-value cutoffs of 1e-5 and lower (para. 68). See MPEP 2144.05 § I.
With respect to claim 4, Bulik-Sullivan teaches a method of estimating genetic correlation among traits (i.e., phenotypes) based on GWAS summary statistics (pg. 1236, Abstract).
With respect to claim 5, Zhang discloses identifying (i.e., filtering) SNP loci having a p-value score below a threshold (pg. 1, para. 4).
With respect to claims 6-7, Bulik-Sullivan teaches a method of estimating genetic correlation comprising estimating pairwise genetic covariance via regression, which is computationally very fast, and normalizing by SNP heritability (pg. 1237, Overview).
With respect to claim 8, Bulik-Sullivan teaches calculation of genetic correlation between two phenotypes, among a set of SNPs, based on the claimed formula (pg. S1, l. column).
With respect to claim 10, Zhang discloses embodiments wherein a PRS function is a linear function with a weight for each SNP locus, and weights are determined based on GWAS predictive scores (para. 64). Zhang further discloses training a multi-class classifier to determine whether a target individual’s genetic dataset most likely belongs to one of several possible genetic communities (paras. 48-49), i.e., a cross traits model.
However, Zhang does not specifically disclose a cross-traits PRS model comprising a weight factor for each of a plurality of PRS models.
Krapohl teaches generating a series of single-polygenic score models followed by optimizing predictor coefficients of a final MPS model including the polygenic scores as predictors (pg. 1369, Models), i.e., a cross traits PRS model comprising a weight factor for each of a plurality of PRS models. Krapohl further teaches that their MPS approach yields better individual-level phenotype prediction than single-score predictor models for independent test data (pg. 1372, Discussion).
With respect to claim 11, Zhang discloses embodiments wherein penalized regression techniques are employed in calculating a PRS (para. 65).
With respect to claim 12, Krapohl exemplifies use of elastic net regularized regression to optimize predictor coefficients, and teaches that conventional multiple linear regression models are subject to overfitting while elastic net regularized regression prevents overfitting (pg. 1369, Models).
With respect to claim 13, Krapohl teaches generation of a series of single-polygenic score (i.e., PRS) models followed by optimization of a final MPS model including the polygenic scores as predictors (pg. 1369, Models). In other words, the MPS model is an optimized linear combination of the PRS models. Krapohl further teaches that their MPS approach yields better individual-level phenotype prediction than single-score predictor models for independent test data (pg. 1372, Discussion).
With respect to claim 14, Zhang discloses predicting whether a target individual has a phenotypic trait by calculating a PRS from genetic datasets of the target individual (para. 55), i.e., executing a PRS model to generate a PRS for a phenotype of interest. Zhang further discloses training a multi-class classifier to determine whether a target individual’s genetic dataset most likely belongs to one of several possible genetic communities (paras. 48-49), i.e., a cross traits model. However, Zhang does not specifically disclose executing a cross-traits PRS model.
Krapohl teaches optimization of a final MPS model including polygenic scores as predictors (pg. 1369, Models), i.e., a cross traits PRS model. Krapohl further teaches that their MPS approach yields better individual-level phenotype prediction than single-score predictor models for independent test data (pg. 1372, Discussion).
With respect to claim 15, Zhang discloses obtaining genetic datasets for a plurality of individuals, conducting a GWAS and tabulating predictive scores (i.e., summary statistics), identifying a subset of SNPs based on the predictive scores, and calculating a PRS for each individual based on the individual’s genotypes at the identified subset of SNP loci (para. 4; Fig. 6B, label 670). In other words, the generated PRS model based at least in part on a set of summary statistics from the corresponding GWAS.
With respect to claim 16, Krapohl discloses initial generation of single-polygenic score models for each of considered predictors (i.e., traits) followed by generation of MPS models for predicting particular traits of interest among the considered predictors, e.g., an exemplified MPS model for predicting BMI including polygenic scores corresponding to coronary artery disease, ulcerative colitis, etc (pg. 1370, MPS predictions; pg. 1371, Fig. 1C). Krapohl thus discloses that the generated single-polygenic score models include a PRS model for the phenotype of interest.
With respect to claim 17, Zhang discloses calculating a PRS for each training individual, i.e., generating each of a plurality of PRS models (para. 4).
With respect to claim 19, Zhang exemplifies embodiments wherein a genetic dataset includes at least 100,000 or more SNP loci (para. 40). See MPEP 2144.05 § I.
Claim 21 recites a method for generating a cross-traits PRS model for a phenotype of interest, comprising: obtaining, for the phenotype of interest, GWAS statistical data relating the phenotype of interest to genetic information; identifying one or more filtered candidate phenotypes to form a cohort of filtered candidate phenotypes, wherein each filtered candidate phenotype has GWAS statistical data and has a genetic correlation with the phenotype of interest that exceeds a defined threshold, and wherein the GWAS statistical data for each of the filtered candidate phenotypes has a stronger GWAS signal and a larger sample size than that of the phenotype of interest; retrieving a plurality of PRS models, each corresponding to a phenotype of the cohort of filtered candidate phenotypes; and determining the cross-traits PRS model for the phenotype of interest, based at least in part on the plurality of PRS models.
With respect to claim 21, Zhang discloses obtaining genetic datasets, comprising genotypes over a plurality of SNP loci, for a plurality of training individuals reported to have a phenotypic trait and conducting a GWAS (i.e., obtaining, for a phenotype of interest, GWAS statistical data relating the phenotype of interest to genetic information), and identifying a subset of SNPs based on p-values being below a threshold (i.e., identifying one or more filtered haplotypes, wherein each has GWAS statistical data and correlation with the phenotype of interest that exceeds a defined threshold; para. 4); calculating a PRS for each training individual (i.e., retrieving a plurality of PRS models) based on the individual’s genotypes at the identified subset of SNP loci (Fig. 6B, label 670).
Zhang further discloses embodiments wherein the phenotypic trait comprises a plurality of labels (e.g., communities of interest such as known ethnic origins, or eye colors) and the GWAS is conducted iteratively by calculating scores for each considered SNP locus indicating predictive ability of each considered label (para. 61). This is considered equivalent to selecting candidate phenotypes, each having GWAS statistical data.
Zhang additionally discloses training a multi-class classifier to determine whether a target individual’s genetic dataset most likely belongs to one of several possible genetic communities, e.g., known ethnic origins (paras. 48-49). Thus, Zhang is considered to disclose determining a cross traits model. However, Zhang does not specifically disclose determining a cross-traits PRS model based at least in part on the plurality of PRS models.
Neither does Zhang disclose identifying a cohort of filtered candidate phenotypes, wherein each filtered phenotype has a genetic correlation with the phenotype of interest that exceeds a threshold, a stronger GWAS signal than that of the phenotype of interest, and a larger sample size than that of the phenotype of interest; or retrieving a plurality of PRS models, each corresponding to a filtered candidate phenotype.
Bulik-Sullivan discusses cross-traits LD score regression, a method of estimating genetic correlations among traits (i.e., phenotypes) based on GWAS summary statistics (pg. 1236, Abstract), and exemplifies filtering steps including pruning clusters of correlated traits (pg. S2, Summary statistic data sets). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to utilize genetic correlations, calculated between phenotypes as disclosed, as basis for the disclosed process of clustering correlated phenotypes. In this way, Bulik-Sullivan is considered to make obvious filtering phenotypes based on genetic correlations. Additionally, Bulik-Sullivan teaches that their methodology is robust to oversampling, computationally fast, and scales easily to studies of thousands of pairs of traits (pp. 1239-40, Discussion).
Bulik-Sullivan discloses removing clustered phenotypes with a heritability z score below 4, and selecting a representative phenotype having the highest heritability z-score from each phenotype cluster (pg. S2, Summary statistic data sets). In this way, Bulik-Sullivan discloses filtering to select a cohort of representative candidate phenotypes based on strongest GWAS signals. However, Bulik-Sullivan does not disclose embodiments wherein a selected representative phenotype has a larger sample size. Neither does Bulik-Sullivan teach retrieving a plurality of PRS models, each corresponding to a filtered candidate phenotype; or determining a cross-traits PRS model based on a plurality of PRS models.
Krapohl et al discusses a multi-polygenic score (MPS) approach for predicting individual-level genetic propensities and outcomes based on GWAS summary statistics (pg. 1368, Abstract), and teaches generating a series of single-polygenic score (i.e., PRS) models wherein polygenic scores are calculated as the weighted sums of each individual’s trait-associated alleles across all considered SNPs (pg. 1369, Predictors and Models). In other words, each polygenic score corresponds to a phenotype.
Krapohl further teaches optimization of a final MPS model including the polygenic scores as predictors (pg. 1369, Models), i.e., a cross-traits PRS model based on the plurality of PRS models. Additionally, Krapohl teaches that their MPS approach yields better individual-level phenotype prediction than single-score predictor models for independent test data (pg. 1372, Discussion). Krapohl does not disclose embodiments wherein a selected representative phenotype has a larger sample size.
Cai discusses a cross-population analysis framework including a summary-level embodiment, called XPASS, that utilizes GWAS summary statistics (i.e., z-scores) and SNP correlation matrices from target and auxiliary populations to construct a PRS for an under-represented target population (pg. 632, Abstract; pg. 633, r. column). Cai teaches that sample size limits the accuracy of PRS, and integrating small-to-medium scale data from an underrepresented target population with large-scale data from a EUR (i.e., auxiliary) population can robustly improve PRS prediction accuracy due to genetic correlation between populations (pg. 633, r. column).
Cai describes the purpose of their framework as improving genetic prediction of under-represented populations by leveraging their trans-ancestry genetic correlations with a large and well-powered auxiliary GWAS dataset from another population (pg. 647, l. column). In this way, Cai advantageously teaches performance of cross-population PRS analysis wherein auxiliary populations have a larger sample size than a target population.
Cai presents findings that XPASS outperforms existing summary-level PRS models and, when auxiliary data is available and genetic correlation is non-zero, achieves the highest prediction R2 among all compared methods (pg. 642, l. column). Cai teaches that XPASS can be applied to a wide class of phenotypes, including complex diseases and molecular phenotypes (pg. 647, r. column).
Claim 22 recites a method for generating a transethnic PRS model, comprising: selecting an ethnic target population of interest having genotype data available for constituent individuals; analyzing the genotype data and one or more population specific genetic datasets, for populations other than the ethnic target population, to determine one or more sets of SNPs that are statistically associated with a phenotype of interest; applying SNP filtering criteria to the one or more sets of SNPs to generate a plurality of training SNP sets, each corresponding to a different population of the one or more population-specific genetic datasets; training a plurality of PRS models, based on genotype data for the one or more population-specific genetic datasets and the plurality of training SNP sets, to generate a PRS model for each of the one or more populations in the one or more population-specific genetic datasets; and determining the transethnic PRS model for the ethnic target population based at least in part on the plurality of PRS models.
With respect to claim 22, Zhang discloses obtaining genetic datasets, comprising genotypes over a plurality of SNP loci, for a plurality of training individuals reported to have a phenotypic trait (i.e., selecting a target population of interest having genotype data available for constituent individuals), conducting a GWAS (i.e., analyzing the genotype data), and identifying a subset of SNPs based on p-values being below a threshold (i.e., determining one or more sets of SNPs that are statistically associated with a phenotype of interest by applying SNP filtering criteria; para. 4); calculating a PRS for each training individual (i.e., training a plurality of PRS models) based on the individual’s genotypes at the identified subset (i.e., training set) of SNP loci (Fig. 6B, label 670).
Zhang additionally discloses training a multi-class classifier to determine whether a target individual’s genetic dataset most likely belongs to one of several possible genetic communities, wherein genetic communities comprising individuals with confirmed ethnic origins can be used as training data, and further discloses training models to determine the ethnic composition of an individual (paras. 48-49). However, Zhang does not disclose determining a transethnic PRS model for an ethnic target population based on a plurality of PRS models.
Krapohl discusses a multi-polygenic score (MPS) approach for predicting individual-level genetic propensities and outcomes based on GWAS summary statistics (pg. 1368, Abstract), and teaches generation of a series of single-polygenic score (i.e., PRS) models followed by optimization of a final MPS model including the polygenic scores as predictors (pg. 1369, Models), i.e., a cross-traits PRS model based on a plurality of PRS models. Krapohl further teaches that their MPS approach yields better individual-level phenotype prediction than single-score predictor models for independent test data (pg. 1372, Discussion).
Krapohl does not disclose determining a transethnic PRS model for an ethnic target population based on a plurality of PRS models.
Cai discusses a cross-population analysis framework including a summary-level embodiment, called XPASS, that utilizes GWAS summary statistics (i.e., z-scores) and SNP correlation matrices from target and auxiliary populations to construct a PRS for an under-represented target population (pg. 632, Abstract; pg. 633, r. column). Cai characterizes this process as “construction of PRS in the trans-ancestry setting” (pg. 633, l. column), and the construction of a PRS model as disclosed is considered equivalent to determining a transethnic PRS model as claimed.
Cai teaches that sample size limits the accuracy of PRS, and integrating small-to-medium scale data from an underrepresented target population with large-scale data from a EUR (i.e., auxiliary) population can robustly improve PRS prediction accuracy due to genetic correlation between populations (pg. 633, r. column). In this way, Cai advantageously teaches performance of cross-population PRS analysis wherein auxiliary populations have a larger sample size than a target population.
Cai presents findings that XPASS outperforms existing summary-level PRS models and, when auxiliary data is available and genetic correlation is non-zero, achieves the highest prediction R2 among all compared methods (pg. 642, l. column). Cai also teaches that XPASS can be applied to a wide class of phenotypes, including complex diseases and molecular phenotypes (pg. 647, r. column).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented estimation of pairwise genetic correlation among a plurality of phenotypes using the claimed particular formula and filtering phenotypes for inclusion in a predictive model, as taught by Bulik-Sullivan, in combination the phenotypic prediction method disclosed by Zhang, because Bulik-Sullivan teaches that these processes can be accomplished using a form of genetic data (GWAS summary statistics) tabulated and employed by the method of Zhang (pg. 1236, Abstract), are robust to oversampling, are computationally fast, and scale easily (pp. 1239-40, Discussion). Bulik-Sullivan thereby indicates that these techniques can be readily combined with the method of Zhang and would confer analytical advantages. Said practitioner would have had a reasonable expectation of success because Zhang and Bulik-Stewart both discuss regression analysis based on GWAS summary statistics (i.e., concern similar fields of endeavor).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have combined a multi-polygenic approach, as taught by Krapohl, with the phenotypic prediction method disclosed by Zhang, because Krapohl teaches that this approach yields better individual-level phenotype prediction than single-score predictor models (i.e., PRS models as generated by the method of Zhang) for independent test data (pg. 1372, Discussion).
Additionally, said practitioner would have implemented elastic net regularization, as taught by Krapohl, because Krapohl teaches that multiple linear regression models (e.g., multi-polygenic models) are subject to overfitting while elastic net regularization prevents overfitting (pg. 1369, Models). Krapohl thus teaches that implementation of elastic net regularization is a favorable complement to implementation of a multi-polygenic approach, as this technique prevents a statistical issue associated with such models.
Said practitioner would have had a reasonable expectation of success because Zhang and Krapohl both discuss phenotypic risk prediction from GWAS summary statistics.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented selection of auxiliary populations (i.e., candidate phenotypes) wherein each auxiliary population has a larger sample size than a target population (i.e., phenotype of interest), within the phenotypic prediction method disclosed by Zhang, in view of Bulik-Sullivan and Krapohl, because Cai teaches that leveraging genetic correlations of a target population to auxiliary populations having larger-scale GWAS data available, to construct a cross-traits PRS, can improve predictive accuracy for under-represented target populations with respect to a wide range of phenotypes and outperforms numerous comparable methods (pg. 642, l. column; pg. 647, l-r. columns). Said practitioner would have had a reasonable expectation of success because Zhang and Cai both discuss phenotypic risk prediction from GWAS summary statistics.
In this way the disclosure of Zhang, in view of Bulik-Sullivan, Krapohl and Cai, makes obvious the limitations of claims 1-8, 10-17, 19 and 21-22. Thus, the invention is prima facie obvious.
Claim 9 is rejected under 35 USC § 103 as being unpatentable over Zhang, in view of Bulik-Sullivan, Krapohl and Cai, as applied to claim 1 above, and further in view of Zheng et al (previously cited). The new grounds of rejection presented herein were necessitated by Applicant’s amendment of the claims (filed 12/5/2025).
With respect to claim 9, Zhang discloses embodiments wherein the considered phenotypic trait comprises a plurality of labels (para. 61), i.e., a plurality of phenotypes. Zhang does not particularly disclose analysis of more than 100 phenotypes.
Bulik-Sullivan exemplifies estimating genetic correlations among 24 phenotypes (pg. 1238, l. column). Bulik-Sullivan does not teach analysis of more than 100 phenotypes.
Krapohl exemplifies selecting 81 GWAS summary statistics (corresponding to traits) from LD Hub, a centralized repository for summary statistics (pg. 1369, Sample). Krapohl does not teach analysis of more than 100 phenotypes.
Cai teaches application of XPASS to jointly model a wide spectrum of phenotypes (pg. 648, l. column), but does not teach analysis of more than 100 phenotypes.
Zheng presents an integrated R toolkit (PhenoSpD) for estimating phenotypic correlations using GWAS summary statistics, and exemplifies application to 487 traits from UK Biobank data (pg. 1, Abstract). Zheng also mentions public provision of precalculated matrices of pairwise genetic correlations for traits from publicly-available GWAS datasets, including a matrix of 221 traits from LD Hub (pg. 8, l. column), and provides evidence that large-scale genetic association databases, including MR-Base and LD Hub, had harmonized GWAS summary-level results for roughly 1,700 traits at time of the reference’s publication (pg. 2, Introduction).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented analysis of more than 100 phenotypes, as taught by Zheng, to enhance the phenotypic prediction method disclosed by Zhang, in view of Bulik-Sullivan, Krapohl and Cai, because Zheng teaches that GWAS data pertaining to such a range of phenotypes is publicly-available (pg. 1, Abstract; pg. 2, Introduction) and even presents precalculated genetic correlation matrices of such a range of phenotypes (pg. 8, l. column). Zhang thereby indicates that the method of Zhang, in view of Bulik-Sullivan, Krapohl and Cai, can be readily applied to analysis of such a range of phenotypes. Said practitioner would have had a reasonable expectation of success because Zhang and Zheng both discuss regression analysis based on GWAS summary statistics (i.e., concern similar fields of endeavor).
In this way the disclosure of Zhang, in view of Bulik-Sullivan, Krapohl, Cai and Zheng, makes obvious the limitations of claim 9. Thus, the invention is prima facie obvious.
Claim 18 is rejected under 35 USC § 103 as being unpatentable over Zhang, in view of Bulik-Sullivan, Krapohl and Cai, as applied to claims 1 and 17 above, and further in view of Privé et al (previously cited). The new grounds of rejection presented herein were necessitated by Applicant’s amendment of the claims (filed 12/5/2025).
With respect to claim 18, Zhang discloses calculating a PRS for each training individual based on the individual’s genotypes at an identified subset of SNP loci, wherein the subset of SNPs is identified based on p-values being below a threshold (para. 4; Fig. 6B, label 670). In this way, Zhang discloses that generating the PRS models is accomplished by a thresholding method. Zhang does not specifically disclose a stacked clumping and thresholding method.
Bulik-Sullivan teaches removing SNPs based on chi-squared values being above a threshold (pg. 1242, Two step estimator). Bulik-Sullivan does not teach a stacked clumping and thresholding method.
Krapohl teaches selecting dropping predictor variables based on their correlation (pg. 1369, Models). Krapohl does not teach a stacked clumping and thresholding method.
Cai discusses selection of SNPs based on p-value thresholds (pg. 637, r. column; pg. 638, r. column – pg. 639, l. column). Cai does not teach a stacked clumping and thresholding method.
Privé discusses calculation of polygenic scores, based on GWAS summary statistics, via a stacked clumping and thresholding (SCT) method and teaches that SCT substantially improves prediction accuracy of calculated polygenic scores relative to prior comparable techniques (pg. 1213, Abstract; pg. 1217, Discussion).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented SCT, as taught by Privé, to enhance the phenotypic prediction method disclosed by Zhang, in view of Bulik-Sullivan Krapohl and Cai, because Privé teaches that SCT generates PRS models having improved predictive accuracy (pg. 1213, Abstract; pg. 1217, Discussion). Said practitioner would have had a reasonable expectation of success because Zhang and Privé both discuss generation of PRS models based on GWAS summary statistics (i.e., concern the same field of endeavor).
In this way the disclosure of Zhang, in view of Bulik-Sullivan, Krapohl, Cai and Privé, makes obvious the limitations of claim 18. Thus, the invention is prima facie obvious.
Conclusion
At this point in prosecution, no claim is allowed.
The following prior art, made of record and not relied upon, is considered pertinent to applicant's disclosure:
Turley et al (Nature Genetics 50: 229-237; published 1/1/2018) discusses multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from GWAS of different traits which exploits the genetic correlation (p-values) among traits to construct polygenic scores with improved predictive power for each trait (pg. 229, Abstract and r. column).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.C.S./Examiner, Art Unit 1685
/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 March 18, 2026