Application of local pedigree inference and polygenic risk scores to predict complex disease risk in mixed-race individuals

Abstract

Systems, devices, methods, and computer program products for generating admixture PRSs for admixture subjects are disclosed. An exemplary method includes assigning ancestry labels to one or more phased genotype segments of interest and generating one or more ancestry-specific sets. For each ancestry-specific set, the method further includes applying a polygenic risk model to each phased genotype segment of interest in the ancestry-specific set to generate one or more ancestry-specific raw partial PRSs, applying the polygenic risk model to corresponding non-admixture genotype segments to generate one or more non-admixture ancestry-specific raw partial PRSs, determining a mean PRS and a standard deviation PRS for the non-admixture ancestry cohort, and normalizing the one or more ancestry-specific raw partial PRSs to generate normalized partial PRSs.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 63 / 379,395, filed October 13, 2022, which is incorporated herein by reference in its entirety.

[0002] The present disclosure relates generally to determining disease risk, and more particularly to methods for determining a mixed race individual's risk of developing a disease. [Background technology]

[0003] The present disclosure relates generally to determining disease risk, and more particularly to methods for determining a mixed race individual's risk of developing a disease. Summary of the Invention [Means for solving the problem]

[0004] Polygenic risk scores (PRS) have been used successfully to predict composite phenotypes such as coronary artery disease (CAD) or breast cancer (BC). However, their main limitation is their poor performance in non-European and more recently admixed individuals, due to the insufficient representation of non-Europeans in published training cohorts.

[0005] The proposed method / workflow is intended to improve the performance of PRS models in recently admixed individuals.

[0006] This method utilizes the best-performing PRS scores for a given lineage, their effect sizes in individuals of non-admixed lineage, and local lineage decomposition to calculate a single lineage- and effect-size-weighted PRS score, which can be used as a feature / predictor in downstream classification models to identify individuals at increased risk of disease.

[0007] input: - Query sample (step-by-step) VCF file - Known lineage reference (staged) VCF file(s) - PRS model weights for query sample scoring - Effect sizes of PRS models estimated in individuals of non-admixed descent

[0008] output: - Composite PRS score calculated according to one of the following methods: - Sum of partial PRS model scores weighted by overall phylogenetic proportions - Sum of partial PRS model scores weighted by overall ancestry proportion and estimated PRS effect size in individuals of non-admixed ancestry - Sum of partial PRS model scores weighted by overall ancestry proportion and partial PRS effect size estimated in individuals of non-admixed ancestry

[0009] Compared to existing methods using local lineage deconvolution of PRS, which only weight partial scores by the estimated lineage proportions and additional scaling factors from other previously used methods, our approach additionally includes weighting partial model scores by the effect size of the full or partial PRS model estimated in an independent training cohort of non-admixed lineages.

[0010] Having described in general terms certain exemplary embodiments, reference is now made to the accompanying drawings, which are not necessarily drawn to scale. Some embodiments may include fewer or more components than shown in the figures. [Brief explanation of the drawings]

[0011] [Figure 1]FIG. 1 shows a schematic block diagram of an exemplary method used to calculate partial lineage-specific PRS scores and their coefficients using two-way admixture as an example, according to certain exemplary embodiments described herein.

[0012] [Figure 2] 10 illustrates the performance of a method on a cohort of Latino or Hispanic mixed race individuals, according to certain exemplary embodiments described herein.

[0013] [Figure 3] 1 shows a schematic block diagram of an example circuit embodying a device that may perform various operations according to example embodiments described herein. DETAILED DESCRIPTION OF THE INVENTION

[0014] Certain exemplary embodiments will now be described in more detail with reference to the accompanying figures. The figures depict some, but not necessarily all, embodiments. Because the invention described herein may be embodied in many different forms, the invention should not be limited to only the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements.

[0015] Definitions of Certain Terms Unless otherwise defined, technical and scientific terms used herein have the meanings commonly understood by one of ordinary skill in the art to which this invention pertains. Materials to which reference is made in the following description and examples are available from commercial sources unless otherwise noted.

[0016] The terms "computer-readable medium" and "memory" refer to non-transitory storage hardware, non-transitory storage devices, or non-transitory computer system memory capable of storing computer-executable instructions or software programs that can be accessed by a controller, microcontroller, computing system, or module of a computing system. The non-transitory computer-readable medium can be accessed by the computing system or module of a computing system to retrieve and / or execute the computer-executable instructions or software programs stored on the medium. Exemplary non-transitory computer-readable media can include, but are not limited to, one or more types of hardware memory, non-transitory tangible media (e.g., one or more magnetic storage disks, one or more optical disks, one or more USB flash drives), computer system memory or random access memory (e.g., DRAM, SRAM, EDORAM), etc.

[0017] The term "computing device" may refer to any computer embodied in hardware, software, firmware, and / or any combination thereof. Non-limiting examples of computing devices include, but are not limited to, personal computers, servers, laptops, mobile devices, smartphones, fixed terminals, personal digital assistants ("PDAs"), kiosks, custom hardware devices, wearable devices, smart home devices, Internet of Things ("IoT") enabled devices, and network-connected computing devices.

[0018] Exemplary Implementation Device FIG. 3 illustrates an apparatus 300 that may include an example system that may implement the example embodiments described herein. The apparatus may include a processor 302, a memory 304, communications circuitry 306, and input / output circuitry 308, each of which is described in more detail below, along with any number of additional hardware components not explicitly shown in FIG. 3. While FIG. 3 illustrates various components as connected only to the processor 302, it will be understood that the apparatus 300 may further include a bus (not explicitly shown in FIG. 3) for passing information between any combination of the various components of the apparatus 300. The apparatus 300 may be configured to perform various operations described above and below in connection with FIG. 3.

[0019] The processor 302 (and / or coprocessors, or any other processors assisting or otherwise associated with the processor) may communicate with the memory 304 via a bus to pass information between components of the apparatus. The processor 302 may be embodied in several different ways, and may include, for example, one or more processing devices configured to execute independently. Additionally, the processor may include one or more processors configured in tandem via a bus to enable independent execution of software instructions, pipelining, and / or multithreading. Use of the term "processor" may be understood to include a single-core processor, a multi-core processor, multiple processors in the apparatus 300, a remote processor or a "cloud" processor, or any combination thereof.

[0020] Processor 302 may be configured to execute software instructions stored in memory 304 or otherwise accessible to the processor (e.g., software instructions stored on a separate storage device). In some cases, the processor may be configured to perform hard-coded functions. Thus, whether configured through hardware or software methods, or a combination of hardware and software, processor 302 represents an entity (e.g., physically embodied in circuitry) that, when appropriately configured, can perform operations in accordance with various embodiments of the present invention. Alternatively, as another example, if processor 302 is embodied as an executor of software instructions, the software instructions, when executed, may specifically configure processor 302 to perform the algorithms and / or operations described herein.

[0021] The memory 304 may be non-transitory and may include, for example, one or more volatile and / or non-volatile memories. In other words, for example, the memory 304 may be an electronic storage device (e.g., a computer-readable storage medium). The memory 304 may be configured to store information, data, content, applications, software instructions, etc. to enable the device to perform various functions in accordance with example embodiments contemplated herein.

[0022] Communications circuitry 306 may be any means, such as a device or circuit embodied in hardware or a combination of hardware and software, configured to receive and / or transmit data to and from a network and / or other devices, circuits, or modules in communication with apparatus 300. In this regard, communications circuitry 306 may include, for example, a network interface for enabling communication with a wired or wireless communications network. For example, communications circuitry 306 may include one or more network interface cards, antennas, buses, switches, routers, modems, and supporting hardware and / or software, or any other devices suitable for enabling communication over a network. Additionally, communications circuitry 306 may include processing circuitry for causing the transmission of such signals to a network or for processing the reception of signals received from the network.

[0023] The device 300 may include input / output circuitry 308 configured to provide output to a user and, in some embodiments, to receive indications of user input. Note that in some embodiments, the input / output circuitry 308 is not included, in which case user input may be received via a separate device. The input / output circuitry 308 may include a user interface, such as a display, and may further include components that manage use of the user interface, such as a web browser, a mobile application, or a dedicated client device. In some embodiments, the input / output circuitry 308 may include a keyboard, a mouse, a touchscreen, a touch area, soft keys, a microphone, a speaker, and / or other input / output mechanisms. The input / output circuitry 308, with the aid of the processor 302, may control one or more functions of one or more of these user interface elements through software instructions (e.g., application software and / or system software such as firmware) stored in memory accessible to the processor 302 (e.g., memory 304).

[0024] In some embodiments, various components of device 300 may be hosted remotely (e.g., by one or more cloud servers), and thus not all components need to be present in one physical location. Furthermore, some of the functionality described herein may be provided by third-party circuitry. For example, device 300 may access one or more third-party circuitry via any type of network connection that facilitates the transmission of data and electronic information between device 300 and the third-party circuitry. Device 300 may also remotely communicate with one or more of the components described above as comprising device 300.

[0025] As will be appreciated based on this disclosure, some exemplary embodiments may take the form of a computer program product that includes software instructions stored on at least one non-transitory computer-readable storage medium (e.g., memory 304). In such embodiments, any suitable non-transitory computer-readable storage medium may be utilized, some examples of which are non-transitory hard disks, CD-ROMs, flash memory, optical storage devices, and magnetic storage devices. With respect to the particular device embodied by apparatus 300 depicted in FIG. 3 , it should be understood that these software instructions, when loaded into a computing device or apparatus, produce a special-purpose machine that includes means for performing the various functions described herein.

[0026] Having described the specific components of the device 300, an exemplary embodiment is described below.

[0027] Example Operation 1 illustrates an exemplary method for calculating partial lineage-specific PRS scores and their coefficients using a two-way admixture as an example. As noted above, the steps illustrated in FIG. 1 may be performed by a computing device such as apparatus 300 described above.

[0028] Step 0. Using a non-admixed training cohort (e.g., the UKBB cohort, or other cohort with available genotype and phenotype labels), the performance of candidate PRS models for each continental lineage is evaluated, and the best-performing model for each continental lineage is identified.

[0029] Step 1. A patient's DNA sample is collected and subjected to whole-genome sequencing (WGS), genotyping, and phasing. This analysis can be accomplished using long-read sequencing technologies (i.e., read lengths of at least about 5 kb or greater, including about 20 kb or greater, and ultra-long-read sequencing read lengths of about 100 kb or greater). These services are provided by established vendors such as Pacific Biosciences, Oxford Nanopore Technologies, and Illumina.

[0030] Step 2. The local lineage of the patient sample is inferred using a reference cohort of samples of known lineage, such as the 1000 Genomes Project and one of the methods previously described.

[0031] Following ancestry inference, each marker in the patient sample is labeled with the inferred ancestry, and the haplotype is divided into regions corresponding to each inferred ancestry.

[0032] Step 3. Lineage-specific regions of interest are scored using the best-performing PRS model for a given lineage (identified in step 0) to obtain raw partial PRS scores. Simultaneously, the same segments are scored within a non-admixed ancestry reference cohort (e.g., 1000 Genomes Project samples).

[0033] Furthermore, in one variation of this method, the same region is scored in non-admixed lineages of a training cohort for which phenotypic information is available (e.g., UKBB or other biobank data).

[0034] Step 4. The mean and standard deviation of the partial PRS scores in the reference cohort are calculated and used to center and scale each partial PRS score for the patient. Similarly, the same mean and standard deviation are used to center and scale the partial scores for the training cohort.

[0035] Step 5. In embodiments of the method utilizing a non-admixed strain training cohort, strain-specific partial PRS scores (partial_β in Equation 3) are calculated for the phenotype of interest. i An additional step is performed to estimate the effect size of the PRS. This is achieved by fitting a linear / logistic regression model for each lineage using the corresponding partial PRS scores as predictors.

[0036] An alternative method (not shown in Figure 1) for estimating the effect size of a lineage-specific partial PRS score is to use the effect size of the corresponding full PRS score (β in Equation 2). i , calculated using the complete genomes of the training cohort samples), which is also achieved by fitting a linear / logistic regression.

[0037] Step 6. The mixed race PRS score for the mixed race sample is calculated as a weighted sum of the partial PRS scores using one of the following three formulas:

[0038] Equation 1: Composite PRS score with partial scores weighted by overall phylogenetic proportion:

number

[0039] Equation 2: Composite PRS score including partial scores weighted by overall lineage proportions and full PRS model effect size estimated in an independent non-admixed lineage (training) cohort.

number

[0040] Equation 3: Composite PRS score including partial scores weighted by overall lineage proportions and partial PRS model effect sizes estimated in an independent non-admixed lineage (training) cohort.

number

[0041] Figure 2 shows the performance of the method in a cohort of mixed-race Latino / Hispanic individuals. PGS000008 is a single PRS model that does not utilize pedigree inference and was incorporated as a performance baseline. score_gw and score_bw are the composite scores calculated according to Equations 1 and 2, respectively. Values ​​on the x-axis are odds ratios (expressed in standard deviation units of the control sample) from a logistic regression model using breast cancer as the outcome. Error bars correspond to the standard deviation from 10 replicates of 10-fold cross-validation.

[0042] conclusion Many modifications and other embodiments of the inventions described herein will come to mind to one skilled in the art to which this invention pertains having the benefit of the teachings presented in the foregoing description and the associated drawings. It is, therefore, to be understood that the invention is not to be limited to the particular embodiments disclosed, and that modifications and other embodiments are intended to be included within the scope of the appended claims. Furthermore, while the foregoing description and the associated drawings describe exemplary embodiments in the context of certain exemplary combinations of elements and / or functions, it is to be understood that different combinations of elements and / or functions may be provided in alternative embodiments without departing from the scope of the appended claims. In this regard, combinations of elements and / or functions other than those expressly described above are also contemplated, for example, as may be recited in some of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

Claims

1. 1. A method for determining an admixture polygenic risk score (PRS) of an admixture subject, comprising: assigning a lineage label to one or more phased genotype segments of interest; generating one or more lineage-specific groupings, each lineage-specific grouping including the one or more phased genotype segments of interest corresponding to a particular lineage label; For each lineage-specific grouping, applying a polygenic risk model corresponding to the ancestry label of the ancestry-specific grouping to each phased genotype segment of interest of the ancestry-specific grouping to generate one or more ancestry-specific raw partial PRSs; applying the polygenic risk model to corresponding non-admixed genotype segments of a non-admixed ancestry reference cohort corresponding to the ancestry-specific groupings and the same ancestry labels to generate raw partial PRSs of one or more non-admixed ancestry; determining a mean PRS and a standard deviation PRS for the non-admixed lineage reference cohort based on the raw partial PRS of the one or more non-admixed lineages; normalizing the one or more lineage-specific raw partial PRS based on the mean PRS and the standard deviation PRS to generate a normalized partial PRS; generating the one or more lineage-specific groupings, generating the mixed-race PRS for the mixed-race subject based on a weighted sum of the normalized partial PRS for each lineage-specific grouping; The method comprising:

2. the phased genotype segments of interest are markers or haplotypes; Assigning the pedigree label to the one or more phased genotype segments of interest further comprises: assigning said pedigree label to each marker of the phased genotype of interest based on a reference cohort of known pedigree samples; assigning the pedigree label to each haplotype of the phased genotype of interest; The method of claim 1 , comprising at least one of:

3. Normalizing the one or more lineage-specific raw partial PRSs may further comprise: centering the one or more lineage-specific raw PRS based on the mean PRS and the standard deviation PRS; scaling the one or more lineage-specific raw PRS based on the mean PRS and the standard deviation PRS; The method of claim 1 , comprising:

4. obtaining a mixed race genotype from the mixed race subject; phasing the genotype of interest to generate the one or more phased genotype segments of interest; The method of claim 1 further comprising:

5. 5. The method of claim 4, wherein the phasing of the admixture genotype is performed using one or more of a population-based method or a molecular-based method.

6. 5. The method of claim 4, further comprising performing whole genome sequencing on a biological sample obtained from the mixed-race subject to determine the mixed-race genotype.

7. Producing the hybrid PRS further comprises: determining a lineage-specific sum for each lineage-specific grouping based on the corresponding normalized partial PRS and overall lineage proportions; determining the mixed PRS based on each lineage-specific sum for the one or more lineage-specific groupings; The method of claim 1 , comprising:

8. Producing the hybrid PRS further comprises: determining a lineage-specific sum for each lineage-specific grouping based on the corresponding normalized partial PRS, overall lineage proportion, and full PRS model effect size parameters; determining the mixed PRS based on each lineage-specific sum for the one or more lineage-specific groupings; The method of claim 1 , comprising:

9. Producing the hybrid PRS further comprises: determining a lineage-specific sum for each lineage-specific grouping based on the corresponding normalized partial PRS, overall lineage proportion, and partial PRS model effect size parameters; determining the mixed PRS based on each lineage-specific sum for the one or more lineage-specific groupings; The method of claim 1 , comprising:

10. identifying one or more non-admixed lineage training sets corresponding to each non-admixed lineage cohort, each non-admixed lineage training set comprising one or more non-admixed lineage training genotype segments; For each non-admixed lineage training set, applying a polygenic risk model corresponding to the ancestry labels of the non-admixed ancestry training set to each non-admixed ancestry training genotype segment of the non-admixed ancestry training set to generate one or more non-admixed ancestry training portions PRS; normalizing the one or more non-admixed lineage training partial PRS based on the mean PRS and the standard deviation PRS to generate a normalized non-admixed lineage training partial PRS; performing the identifying step. The method of claim 1 further comprising:

11. 11. The method of claim 10, further comprising determining partial PRS model effect size parameters using a regression model based on each non-admixed lineage training partial PRS.

12. identifying one or more non-admixed lineage training sets corresponding to each non-admixed lineage cohort, each non-admixed lineage training set comprising one or more non-admixed lineage training genotype segments, each non-admixed lineage training genotype segment corresponding to the complete genotype of a corresponding non-admixed lineage individual; For each non-admixed lineage training set, applying a polygenic risk model corresponding to the ancestry labels of the non-admixed ancestry training set to each non-admixed ancestry training genotype segment of the non-admixed ancestry training set to generate one or more non-admixed ancestry training complete PRSs; normalizing the one or more unadulterated lineage training complete PRS based on the mean PRS and the standard deviation PRS to generate a normalized unadulterated lineage training complete PRS; performing the identifying step. The method of claim 1 further comprising:

13. 13. The method of claim 12, further comprising determining full PRS model effect size parameters using a regression model based on each non-admixed lineage training full PRS.

14. An apparatus for generating a mixed-race PRS for a mixed-race subject, the apparatus comprising: a processor; and a memory storing software instructions that, when executed by the processor, cause the apparatus to perform the steps of any of claims 1 to 13.

15. 14. A computer program product for generating a mixed-race PRS for a mixed-race subject, the computer program product comprising at least one non-transitory computer-readable storage medium storing software instructions that, when executed by said device, cause said device to perform the steps of any of claims 1 to 13.

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