Determining amount of contributor-derived nucleic acids of mixed sample of graft recipients (3 + genetically distinct genomic contributors)

Abstract

Disclosed herein are computer-implemented systems, kits, and methods for determining the amount of contributor-derived nucleic acids in a biological sample from a graft recipient, the biological sample comprising nucleic acids from two or more genetically distinct contributors. The determined amount of contributor-derived nucleic acid can be used to monitor the status of a graft, for example, to assess the risk of graft rejection. In some examples, the two or more genetically distinct contributors may include the graft recipient, a fetus, and a graft donor. In some examples, the two or more genetically distinct contributors may include the graft recipient, a first graft donor, and a second graft donor. For example, the systems and methods determine an estimated percentage of the contributor-derived nucleic acids and / or an estimated percentage of fetal-derived nucleic acids.

Description

Technical Field

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 435,153, filed on Dec. 23, 2022, the content of which is incorporated herein by reference in its entirety for all purposes. Technical Field

[0003] The present disclosure generally relates to computer-implemented systems, kits, and methods for determining the amount of nucleic acids of contributor origin in a biological sample from a graft recipient without prior genotype knowledge, the biological sample comprising nucleic acids from two or more genetically distinct genomic contributors. Background Art

[0004] Using polymorphic markers to monitor the health status of transplanted organs, tissues, and cells received by a graft recipient from a donor is particularly complex when the recipient carries additional genomic, donor-related, and / or genotype information from genetically distinct contributors (such as single nucleotide polymorphism (SNP) genotype information for identifying which allele belongs to which genomic contributor) that is not available. This can be the case in a multi-organ transplant scenario, where a graft recipient receives at least one organ, tissue, and / or cell graft from one or more donors simultaneously or sequentially, which then become genomic contributors, such that the genetically distinct contributors include (e.g., in the case where the graft recipient has received at least two grafts) the recipient genomic contributor, the first graft donor genomic contributor, and the second graft donor genomic contributor.

[0005] Alternatively or in addition, if a graft recipient becomes pregnant before or after receiving at least one organ, tissue, and / or cell graft, she may carry additional genomes from at least two genetically distinct contributors, where both the fetus and the graft are contributors, such that the genetically distinct contributors include, for example, the maternal genomic contributor, the fetal genomic contributor, and the graft donor genomic contributor.

[0006] Currently, there is an unmet need for monitoring the health of transplanted organs, tissues, and cells in graft recipients who carry additional genomes from at least two genetically distinct contributors due to pregnancy and receiving at least one organ, tissue, and / or cell graft, or due to receiving two or more simultaneous or sequential organ, tissue, and / or cell grafts. Summary of the Invention

[0007] A computer-implemented method for determining the amount of cell-free nucleic acids of contributor origin in a mixed sample obtained from a graft recipient, the mixed sample comprising cell-free nucleic acids from at least three genetically distinct contributors. The method includes: receiving, via a computer or input function, nucleic acid sequence data from a set of single nucleotide polymorphisms (SNPs) derived from the cell-free nucleic acids from the at least three genetically distinct contributors; receiving the genomic relationships among the at least three genetically distinct contributors; determining and grouping minor allele frequency (MAF) information from the set of SNPs; and determining the amount of cell-free nucleic acids of contributor origin based on the genomic relationships and the MAF grouping. Additionally or alternatively, in some embodiments, the graft recipient is a pregnant woman, and the at least three genetically distinct contributors include a maternal genomic contributor, a fetal genomic contributor, and a graft donor genomic contributor. Additionally or alternatively, in some embodiments, the graft recipient has received at least two grafts, and the at least three genetically distinct contributors include a recipient genomic contributor, a first graft donor genomic contributor, and a second graft donor genomic contributor. Additionally or alternatively, in some embodiments, the amount of cell-free nucleic acids of contributor origin is the percentage of the cell-free nucleic acids of contributor origin in the mixed sample. Additionally or alternatively, in some embodiments, determining and grouping the MAF information is based on a set of longitudinal samples. Additionally or alternatively, in some embodiments, the set of longitudinal samples has the same genotype. Additionally or alternatively, in some embodiments, the set of SNPs includes fewer than 500 SNPs. Additionally or alternatively, in some embodiments, the computer-implemented method further includes: determining the genotype of one or more of the graft recipient, fetus, or donor based on the MAF information grouping, wherein the graft recipient is a pregnant woman. Additionally or alternatively, in some embodiments, determining and grouping the MAF information includes: reordering the set of SNPs according to an average or median MAF value; determining the MAF information that includes summary statistics of MAF changes in the set of SNPs; and grouping the set of SNPs according to the summary statistics of MAF changes. Additionally or alternatively, in some embodiments, determining and grouping the MAF information includes: determining a separation point in the summary statistics of MAF changes by determining local minima or maxima in a window. Additionally or alternatively, in some embodiments, the separation point is used to group the SNPs into homozygous genotype groups and heterozygous genotype groups.Additionally or alternatively, in some embodiments, determining and grouping the MAF information includes: generating a waterfall plot of the MAF information; grouping the MAF information by dividing the waterfall plot into groups; and calculating an average MAF value for the group, wherein the amount of cell-free nucleic acid of the determined contributor source is based on the calculated average MAF value. Additionally or alternatively, in some embodiments, determining and grouping the MAF information includes: selecting a first sample having the highest average MAF value in a plurality of samples; selecting a second sample having the lowest correlation coefficient associated with the first sample; determining a MAF change summary statistic value by subtracting the MAF values of the selected first sample and the selected second sample; determining a separation point in the MAF change summary statistic value; and grouping the MAF information based on the separation point. Additionally or alternatively, in some embodiments, determining and grouping the MAF information includes: selecting an index sample having the highest average MAF value in a plurality of samples; determining a MAF difference between the index sample and each of the plurality of samples; determining a MAF change summary statistic value by combining the MAF differences; determining a separation point in the MAF change summary statistic value; and grouping the MAF information based on the separation point. Additionally or alternatively, in some embodiments, determining and grouping the MAF information includes: selecting a first index sample having the highest average MAF value in a set of highly re-ordered SNPs; selecting a second index sample having the highest average MAF in a set of lowly re-ordered SNPs; determining a MAF difference between the first index sample and each of the set of highly re-ordered SNPs; determining a MAF difference between the second index sample and each of the set of lowly re-ordered SNPs; determining a MAF change summary statistic value by combining the MAF differences; determining a separation point in the MAF change summary statistic value; and grouping the MAF information based on the separation point. Additionally or alternatively, in some embodiments, the computer-implemented method further includes: generating a waterfall plot for the mixed sample, wherein the waterfall plot includes one or more staircase levels having one or more steps, and the SNPs of the one or more steps have the same genotype. Additionally or alternatively, in some embodiments, the graft recipient receives a graft comprising one or more of the following: a kidney graft, a heart graft, a lung graft, a liver graft, a pancreas graft, a vascularized composite graft, an intestinal graft, a stomach graft, a testicular graft, a penile graft, an ovarian graft, a uterine graft, a thymus graft, a facial graft, a hand graft, a leg graft, a bone graft, a corneal graft, a skin graft, a heart valve graft, a vascular graft, or any combination thereof. Additionally or alternatively, in some embodiments, the mixed sample is a blood sample.

[0008] A kit for determining the amount of cell-free nucleic acids of contributor origin in a mixed sample obtained from a graft recipient, the mixed sample comprising cell-free nucleic acids from at least three genetically distinct contributors. The kit comprises instructions for: receiving, via a computer or input function, nucleic acid sequence data of a set of single nucleotide polymorphisms (SNPs) derived from the cell-free nucleic acids from the at least three genetically distinct contributors; receiving the genomic relationships between the at least three genetically distinct contributors; determining and grouping minor allele frequency (MAF) information from the set of SNPs; and determining the amount of cell-free nucleic acids of contributor origin based on the genomic relationships and the MAF grouping. Additionally or alternatively, in some embodiments, the graft recipient is a pregnant woman, and the at least three genetically distinct contributors include a maternal genomic contributor, a fetal genomic contributor, and a graft donor genomic contributor. Additionally or alternatively, in some embodiments, the graft recipient has received at least two grafts, and the at least three genetically distinct contributors include a recipient genomic contributor, a first graft donor genomic contributor, and a second graft donor genomic contributor. Additionally or alternatively, in some embodiments, the amount of cell-free nucleic acids of contributor origin is the percentage of cell-free nucleic acids of contributor origin in the mixed sample. Additionally or alternatively, in some embodiments, determining and grouping the MAF information is based on a set of longitudinal samples. Additionally or alternatively, in some embodiments, the set of longitudinal samples has the same genotype. Additionally or alternatively, in some embodiments, the set of SNPs comprises fewer than 500 SNPs. Additionally or alternatively, in some embodiments, the kit further comprises instructions for: determining the genotype of one or more of the graft recipient, fetus, or donor based on the MAF information grouping, wherein the graft recipient is a pregnant woman. Additionally or alternatively, in some embodiments, determining and grouping the MAF information includes: reordering the set of SNPs according to an average or median MAF value; determining the MAF information that includes summary statistics of MAF changes in the set of SNPs; and grouping the set of SNPs according to the summary statistics of MAF changes. Additionally or alternatively, in some embodiments, determining and grouping the MAF information includes: determining a separation point in the summary statistics of MAF changes by determining local minima or maxima in a window. Additionally or alternatively, in some embodiments, the separation point is used to group the SNPs into homozygous genotype groups and heterozygous genotype groups.Additionally or alternatively, in some embodiments, determining and grouping the MAF information includes: generating a waterfall plot of the MAF information; grouping the MAF information by dividing the waterfall plot into groups; and calculating an average MAF value for the group, wherein the amount of cell-free nucleic acid of the determined contributor source is based on the calculated average MAF value. Additionally or alternatively, in some embodiments, determining and grouping the MAF information includes: selecting a first sample having the highest average MAF value in a plurality of samples; selecting a second sample having the lowest correlation coefficient associated with the first sample; determining a MAF change summary statistic by subtracting the MAF values of the selected first sample and the selected second sample; determining a break point in the MAF change summary statistic; and grouping the MAF information based on the break point. Additionally or alternatively, in some embodiments, determining and grouping the MAF information includes: selecting an index sample having the highest average MAF value in a plurality of samples; determining the MAF difference between the index sample and each of the plurality of samples; determining a MAF change summary statistic by combining the MAF differences; determining a break point in the MAF change summary statistic; and grouping the MAF information based on the break point. Additionally or alternatively, in some embodiments, determining and grouping the MAF information includes: selecting a first index sample having the highest average MAF value in a set of highly reordered SNPs; selecting a second index sample having the highest average MAF in a set of lowly reordered SNPs; determining the MAF difference between the first index sample and each of the set of highly reordered SNPs; determining the MAF difference between the second index sample and each of the set of lowly reordered SNPs; determining a MAF change summary statistic by combining the MAF differences; determining a break point in the MAF change summary statistic; and grouping the MAF information based on the break point. Additionally or alternatively, in some embodiments, the kit further comprises instructions for: generating a waterfall plot for the mixed sample, wherein the waterfall plot comprises one or more ladder levels having one or more steps, and the SNPs of the one or more steps have the same genotype. Additionally or alternatively, in some embodiments, the graft recipient receives a graft comprising one or more of: a kidney graft, a heart graft, a lung graft, a liver graft, a pancreas graft, a vascularized composite graft, an intestinal graft, a gastric graft, a testicular graft, a penile graft, an ovarian graft, a uterine graft, a thymic graft, a facial graft, a hand graft, a leg graft, a bone graft, a corneal graft, a skin graft, a heart valve graft, a vascular graft, or any combination thereof. Additionally or alternatively, in some embodiments, the mixed sample is a blood sample.

[0009] A system for determining the amount of cell - free nucleic acids of contributor origin in a mixed sample obtained from a graft recipient, the mixed sample comprising cell - free nucleic acids from at least three genetically distinct contributors. The system includes: an interaction device configured to receive an input; a determination unit configured to receive nucleic acid sequence data from a set of single - nucleotide polymorphisms (SNPs) derived from the cell - free nucleic acids from the at least three genetically distinct contributors via the interaction device; receive the genomic relationships between the at least three genetically distinct contributors; determine and group minor allele frequency (MAF) information from the set of SNPs; and determine the amount of cell - free nucleic acids of contributor origin based on the genomic relationships and the MAF grouping. Additionally or alternatively, in some embodiments, the graft recipient is a pregnant woman, and the at least three genetically distinct contributors include a maternal genomics contributor, a fetal genomics contributor, and a graft donor genomics contributor. Additionally or alternatively, in some embodiments, the graft recipient has received at least two grafts, and the at least three genetically distinct contributors include a recipient genomics contributor, a first graft donor genomics contributor, and a second graft donor genomics contributor. Additionally or alternatively, in some embodiments, the amount of cell - free nucleic acids of contributor origin is the percentage of cell - free nucleic acids of contributor origin in the mixed sample. Additionally or alternatively, in some embodiments, determining and grouping the MAF information is based on a set of longitudinal samples. Additionally or alternatively, in some embodiments, the set of longitudinal samples has the same genotype. Additionally or alternatively, in some embodiments, the set of SNPs includes fewer than 500 SNPs. Additionally or alternatively, in some embodiments, the determination unit is further configured to determine the genotype of one or more of the following based on the MAF information grouping: the graft recipient, the fetus, or the donor, where the graft recipient is a pregnant woman. Additionally or alternatively, in some embodiments, the determination unit being configured to determine and group the MAF information includes the determination unit being configured to: re - order the set of SNPs according to an average or median MAF value; determine the MAF information including the summary statistics of MAF changes in the set of SNPs; and group the set of SNPs according to the summary statistics of MAF changes. Additionally or alternatively, in some embodiments, the determination unit being configured to determine and group the MAF information includes the determination unit being configured to: determine the separation points in the summary statistics of MAF changes by determining local minima or maxima in a window. Additionally or alternatively, in some embodiments, the separation points are used to group the SNPs into homozygous genotype groups and heterozygous genotype groups.Additionally or alternatively, in some embodiments, the determining unit is configured to determine and group the MAF information including the determining unit being configured to: generate a waterfall plot of the MAF information; group the MAF information by dividing the waterfall plot into groups; and calculate the average MAF value of the groups, wherein the amount of cell-free nucleic acid of the determined contributor source is based on the calculated average MAF value. Additionally or alternatively, in some embodiments, the determining unit is configured to determine and group the MAF information including the determining unit being configured to: select a first sample having the highest average MAF value among a plurality of samples; select a second sample having the lowest correlation coefficient associated with the first sample; determine a summary statistic value of the MAF change by subtracting the MAF values of the selected first sample and the selected second sample; determine a separation point in the summary statistic value of the MAF change; and group the MAF information based on the separation point. Additionally or alternatively, in some embodiments, the determining unit is configured to determine and group the MAF information including the determining unit being configured to: select an index sample having the highest average MAF value among a plurality of samples; determine the MAF difference between the index sample and each of the plurality of samples; determine a summary statistic value of the MAF change by combining the MAF differences; determine a separation point in the summary statistic value of the MAF change; and group the MAF information based on the separation point. Additionally or alternatively, in some embodiments, the determining unit is configured to determine and group the MAF information including the determining unit being configured to: select a first index sample having the highest average MAF value among a group of highly re-ordered SNPs; select a second index sample having the highest average MAF among a group of lowly re-ordered SNPs; determine the MAF difference between the first index sample and each of the group of highly re-ordered SNPs; determine the MAF difference between the second index sample and each of the group of lowly re-ordered SNPs; determine a summary statistic value of the MAF change by combining the MAF differences; determine a separation point in the summary statistic value of the MAF change; and group the MAF information based on the separation point. Additionally or alternatively, in some embodiments, the determining unit is further configured to: generate a waterfall plot for the mixed sample, wherein the waterfall plot includes one or more step levels having one or more steps, and the SNPs of the one or more steps have the same genotype. Additionally or alternatively, in some embodiments, the graft recipient receives a graft including one or more of the following: a kidney graft, a heart graft, a lung graft, a liver graft, a pancreas graft, a vascularized composite graft, an intestinal graft, a stomach graft, a testicular graft, a penile graft, an ovarian graft, a uterine graft, a thymus graft, a facial graft, a hand graft, a leg graft, a bone graft, a corneal graft, a skin graft, a heart valve graft, a vascular graft, or any combination thereof.Additionally or alternatively, in some embodiments, the mixed sample is a blood sample. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 FIG. 6 shows an example system for determining the amount of nucleic acid from a contributor source in a biological sample, according to some embodiments.

[0011] Figure 2 FIG. 10 shows a flow chart of an example general computer-implemented method for determining the amount of nucleic acid from a contributor source in a mixed sample that includes cell-free nucleic acid from at least three genomically and genetically distinct contributors, according to some embodiments.

[0012] Figure 3 FIG. 14 shows a flow chart of an example computer-implemented method for determining the amount of nucleic acid from a contributor source in a mixed sample that includes cell-free nucleic acid from at least three genomically and genetically distinct contributors, according to some embodiments.

[0013] Figure 4 FIG. 18 shows an example method for grouping single nucleotide polymorphisms (SNPs) and inferring the SNP genotypes of some or all of the genomic contributors, according to some embodiments.

[0014] Figure 5A and Figure 5B FIG. 24 shows an example waterfall plot of minor allele frequency (MAF) values for a set of single nucleotide polymorphisms (SNPs), according to some embodiments.

[0015] Figure 6 FIG. 28 shows an example waterfall plot of minor allele frequency (MAF) values for a set of single nucleotide polymorphisms (SNPs), according to some embodiments.

[0016] Figure 7A FIG. 32 shows an example flow chart of a method for determining minor allele frequency (MAF) information for a set of single nucleotide polymorphisms (SNPs), according to some embodiments.

[0017] Figure 7B FIG. 36 shows an example plot of minor allele frequency (MAF) change summary statistics, according to some embodiments.

[0018] Figure 7C FIG. 40 shows an example minor allele frequency (MAF) waterfall plot, according to some embodiments of the present disclosure, and a plot of MAF change summary statistics for three sets of single nucleotide polymorphisms (SNPs) that include different genotypes of one of the genomic contributors (graft donor-1) among the genomic contributors.

[0019] Figure 8A Illustrates an example method for a pair of summary statistics of minor allele frequency (MAF) changes according to some embodiments.

[0020] Figure 8B Illustrates an example minor allele frequency (MAF) waterfall plot for five samples according to some embodiments.

[0021] Figure 8C Illustrates example summary statistics of minor allele frequency (MAF) changes according to some embodiments.

[0022] Figure 9 Illustrates an example method for a pair of summary statistics of multiple minor allele frequency (MAF) changes according to some embodiments.

[0023] Figure 10A Illustrates an example method for a pair of multiple summary statistics of multiple minor allele frequency (MAF) changes according to some embodiments.

[0024] Figure 10B Illustrates an example plot of minor allele frequency (MAF) values for multiple samples according to some embodiments.

[0025] Figures 11A to 11D Illustrates an example comparison of standard deviation simple minor allele frequency (MAF) change summary statistics, one-to-one MAF change summary statistics, one-to-one MAF change summary statistics, and many-to-many MAF change summary statistics according to some embodiments.

[0026] Figure 12 Illustrates an example flowchart of a method for determining the amount of cell-free nucleic acid for a contributor source according to some embodiments.

[0027] Figure 13 Illustrates an example device for implementing the disclosed systems, kits, and methods according to some embodiments. Detailed Description

[0028] The following description is presented to enable a person skilled in the art to make and use various embodiments. The description of specific devices, techniques, and applications is provided only as an example. Various modifications to the examples described herein will be apparent to those of ordinary skill in the art, and the general principles defined herein can be applied to other examples and applications without departing from the spirit and scope of the various embodiments. The various embodiments are not limited to the examples described herein, but should be accorded a scope consistent with the claims. All references cited herein (including patent applications and publications) are hereby incorporated by reference in their entirety.

[0029] Definitions

[0030] The terms used in the description of the various embodiments herein are for the purpose of describing particular embodiments only and are not intended to be limiting. As used in the specification and the appended claims, unless the context clearly dictates otherwise, the singular forms "a," "an," and "the" include plural referents. It should also be understood that as used herein, the term "and / or" refers to and encompasses any and all possible combinations of one or more of the associated listed items. It should also be understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of the stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof.

[0031] As used herein, the term "sample" or "biological sample" refers to any sample obtained from a graft recipient, including but not limited to whole blood, plasma, serum, peripheral blood mononuclear cells, lymph fluid, buccal swabs, saliva, sputum, tears, sweat, ear fluid, bone marrow suspension, urine, feces, lung lavage fluid, semen, vaginal fluid, cerebrospinal fluid, cerebral fluid, ascites, milk, secretions of the respiratory, intestinal, or urinary tract, or tissue and / or cells from a biopsy.

[0032] As used herein, the term "quantity" refers to any quantitative value generated by nucleic acid analysis and may represent a relative or absolute value.

[0033] As used herein, the term "graft" refers to any graft of cells, tissues, or organs from a donor to a recipient, including combinations thereof. The term "graft" with respect to a tissue or organ may refer to the whole tissue or organ (e.g., the whole liver) or a part thereof. A "graft" is any organ, tissue, or cell graft transplanted alone or in combination with one or more other organ, tissue, or cell grafts.

[0034] As used herein, the term "organ graft" encompasses both solid organ grafts and hollow organ grafts and includes but is not limited to kidney grafts, heart grafts, lung grafts, liver grafts, pancreas grafts, vascularized composite grafts, intestinal grafts, stomach grafts, testicular grafts, penile grafts, ovarian grafts, uterine grafts, thymic grafts, facial grafts, hand grafts, leg grafts, bone grafts, corneal grafts, skin grafts, heart valve grafts, vascular grafts, or any combination thereof.

[0035] The term "tissue graft" includes tissue grafts (such as skin tissue) and organ tissue grafts (such as ovarian tissue grafts, kidney tissue grafts, lung tissue grafts, pancreas tissue grafts, esophageal tissue grafts, spleen tissue grafts, or any combination thereof).

[0036] The term "cell graft" includes cell grafts such as pluripotent stem cells, multipotent stem cells, hematopoietic stem cells (e.g., hematopoietic stem cells), blood cells (e.g., peripheral blood mononuclear cells), umbilical cord blood cells, pancreatic islet cells, skin cells, cardiomyocytes, neurons, dendritic cells, macrophages, lymphocytes, NK cells, NKT cells, B cells, T cells, regulatory T cells or genetically engineered T cells (e.g., chimeric antigen receptor (CAR) T cells). These include, but are not limited to, cells taken directly from a graft donor for administration to a graft recipient, cells taken from a graft donor and genetically engineered prior to administration to a graft recipient, cells taken from a graft donor and cultured prior to administration to a graft recipient, cells taken from a graft donor and subjected to a manufacturing process prior to administration to a graft recipient, and any combination thereof. The cells can also be stored prior to administration to the graft recipient (i.e., "off-the-shelf" cells).

[0037] As used herein, the term "donor" or "graft donor" refers to a human or non-human subject that is genetically distinguishable from the graft recipient, where the "donor" supplies an organ, tissue, and / or cells for transplantation into the graft recipient. In some embodiments, the donor is a human subject. In other embodiments, the donor is a non-human subject, such as an animal, e.g., a pig. In some embodiments, a graft from a human or non-human donor is transplanted into a human graft recipient. In some embodiments, a graft from a human or non-human donor is transplanted into a non-human graft recipient. In some embodiments, the donor and the graft recipient are of the same species. In some embodiments, the donor and the graft recipient are of different species, e.g., the donor is a non-human subject, such as an animal, and the graft recipient is a human subject.

[0038] As used herein, the term "nucleic acid" refers to RNA or DNA and can be linear, circular, or branched, single-stranded or double-stranded, or a hybrid thereof. The term "nucleic acid" also encompasses RNA / DNA hybrids. In some embodiments, the term "nucleic acid" refers to any one of DNA, RNA, mRNA, miRNA, double-stranded DNA, single-stranded DNA, single-stranded DNA hairpin, DNA / RNA hybrid, RNA hairpin, and fragments and combinations thereof. In some embodiments, the term "nucleic acid" refers to mitochondrial DNA, cell-free mitochondrial DNA, cellular DNA, or cell-free DNA.

[0039] As used herein, the term "cell-free nucleic acid" refers to nucleic acids that exist outside of cells and can circulate. In some embodiments, cell-free nucleic acids are nucleic acids that exist outside of cells and circulate in various body fluids of a graft recipient (e.g., blood, plasma, serum, urine, etc.). In some embodiments, "cell-free nucleic acid" refers to any DNA ("cell-free DNA"), RNA ("cell-free RNA"), mRNA, miRNA, double-stranded DNA, single-stranded DNA, single-stranded DNA hairpin, DNA / RNA hybrid, RNA hairpin, and fragments and combinations thereof that exist outside of cells. Cell-free DNA can be derived from various locations within a cell, e.g., from nuclear DNA and mitochondrial DNA.

[0040] As used herein, the term "cellular nucleic acid" or "cell-based nucleic acid" refers to nucleic acids that exist within cells. In some embodiments, cellular nucleic acids or cell-based nucleic acids are nucleic acids that exist within cells and within various body fluids of a graft recipient (e.g., blood, plasma, serum, urine, etc.). In some embodiments, cellular nucleic acids or cell-based nucleic acids refer to any DNA, RNA, mRNA, miRNA, double-stranded DNA, single-stranded DNA, single-stranded DNA hairpin, DNA / RNA hybrid, or RNA hairpin that exists inside a cell.

[0041] As used herein, the term "polymorphism marker" refers to a polymorphic locus, e.g., where two or more alternative nucleic acid sequences or alleles occur due to a change in one or more bases, one or more insertions, one or more duplications, one or more deletions, and variant forms thereof. A polymorphism marker can also be a locus where one or more bases are modified by methylation. Polymorphism markers can include single nucleotide polymorphisms (SNPs), short tandem repeats (STRs), restriction fragment length polymorphisms (RFLPs), variable number tandem repeats (VNTRs), hypervariable regions, minisatellites, dinucleotide repeats, trinucleotide repeats, tetranucleotide repeats, simple sequence repeats, and insertion elements.

[0042] As used herein, the term "mixed sample" refers to a biological sample obtained from a graft recipient that contains nucleic acids from multiple genetically distinct contributors. For example, a sample obtained from a pregnant graft recipient of a single graft can contain nucleic acids from three genetically distinct contributors, such as nucleic acids from the graft recipient (e.g., "contributor 1"), nucleic acids from the graft (e.g., "contributor 2"), and nucleic acids from the fetus (e.g., "contributor 3"). As another example, a sample obtained from a non-pregnant graft recipient of two or more simultaneous or sequential grafts from different donors can contain nucleic acids from three or more genetically distinct contributors, such as nucleic acids from the graft recipient (e.g., "contributor 1"), nucleic acids from graft "A" (e.g., "contributor 2"), and nucleic acids from graft "B" (e.g., "contributor 3"), and so on. As another example, a sample obtained from a pregnant graft recipient of two or more simultaneous or sequential grafts from different donors can contain nucleic acids from four or more genetically distinct contributors, such as nucleic acids from the graft recipient (e.g., "contributor 1"), nucleic acids from two grafts (e.g., "contributor 2" and "contributor 3"), and nucleic acids from the fetus (e.g., "contributor 4"), and so on.

[0043] Overview

[0044] The present disclosure is at least in part based on the applicant's development of computer-implemented systems, kits, and methods for determining (including estimating) the amount of nucleic acids of contributor origin in a biological sample from a graft recipient that contains nucleic acids from two or more genetically distinct contributors, which can be used, for example, to monitor the status of a graft with respect to assessing the risk of graft rejection. In some embodiments, the status of a graft can be classified and monitored, for example, based on the amount of nucleic acids of contributor origin determined in a biological sample from a graft recipient that can contain nucleic acids from at least three genetically distinct contributors (such as the graft recipient, the fetus, and the graft donor). For example, the system and method determine the estimated percentage of nucleic acids of contributor origin and / or the estimated percentage of nucleic acids of fetal origin. In some embodiments, the status of a graft can be classified and monitored, for example, based on the determined (e.g., estimated) amount of nucleic acids of contributor origin in a biological sample from a graft recipient that can contain nucleic acids from at least three genetically distinct contributors (such as the graft recipient, a first graft donor, and a second graft donor).

[0045] In some embodiments, the contributor-derived nucleic acid can be cell-free nucleic acid derived from a graft donor, e.g., contributor-derived cell-free DNA. In some embodiments, the contributor-derived nucleic acid can be cellular (cellular) nucleic acid derived from a graft donor, e.g., contributor-derived cellular (cellular) DNA. In some embodiments, the contributor-derived nucleic acid can be cell-free nucleic acid derived from a fetus, e.g., fetus-derived or fetal cell-free DNA. In some embodiments, the contributor-derived nucleic acid can be cell-free nucleic acid derived from a graft recipient, e.g., graft recipient-derived cell-free DNA.

[0046] In some embodiments, the graft recipient has received an organ graft and has become pregnant. In some embodiments, the graft recipient has received an organ graft and has recently (e.g., within the past 1 month, 3 months, 6 months, or 12 months) become pregnant. In some embodiments, the graft recipient has received a tissue graft and has become pregnant. In some embodiments, the graft recipient has received a tissue graft and has recently (e.g., within the past 1 month, 3 months, 6 months, or 12 months) become pregnant. In some embodiments, the graft recipient has received a cell graft and has become pregnant. In some embodiments, the graft recipient has received a cell graft and has recently (e.g., within the past 1 month, 3 months, 6 months, or 12 months) become pregnant.

[0047] In some embodiments, the graft recipient has received multiple simultaneous or sequential organ grafts from multiple genetically distinguishable graft donors and has become pregnant. In some embodiments, the graft recipient has received multiple simultaneous or sequential organ grafts from multiple genetically distinguishable graft donors and has recently (e.g., within the past 1 month, 3 months, 6 months, or 12 months) become pregnant.

[0048] In some embodiments, the graft recipient has received multiple simultaneous or sequential tissue grafts from multiple genetically distinguishable graft donors and has become pregnant. In some embodiments, the graft recipient has received multiple simultaneous or sequential tissue grafts from multiple genetically distinguishable graft donors and has recently (e.g., within the past 1 month, 3 months, 6 months, or 12 months) become pregnant.

[0049] In some embodiments, the graft recipient may not be pregnant or may not have been recently pregnant. In some embodiments, the graft recipient has received multiple simultaneous or sequential organ grafts from multiple genetically distinguishable graft donors. In some embodiments, the graft recipient has received multiple simultaneous or sequential tissue grafts from multiple genetically distinguishable graft donors. In some embodiments, the graft recipient has received multiple simultaneous or sequential cell grafts from multiple genetically distinguishable graft donors. In some embodiments, the graft recipient has received multiple simultaneous or sequential organ, tissue, and / or cell grafts from multiple genetically distinguishable graft donors.

[0050] The various embodiments described herein can be carried out without prior knowledge of the genotypes of any genomic contributors (pre-determined genotypes) (such as SNP genotype information for identifying which allele belongs to which genomic contributor for a particular SNP). Thus, the amount of contributor-derived cellular or acellular nucleic acid in a biological sample from a graft recipient (the biological sample containing nucleic acids from two or more genetically distinct contributors) can be determined without obtaining, considering, or using prior or pre-determined genotype information from the graft recipient, from any graft donor, or any other genotype information from any source. Such prior or pre-determined genotype information can include, for example, genotype information across the entire genome or portions thereof from the graft recipient or from any graft donor and / or genotype information (SNP genotype) at a particular polymorphic marker being analyzed (e.g., a selected SNP). In some embodiments, separate genotyping of the graft recipient may not be performed. In some embodiments, separate genotyping of any genetically distinct contributor (e.g., any graft donor) may not be performed. In some embodiments, neither the graft recipient nor any graft donor may be separately genotyped. In some embodiments, when determining the amount of contributor-derived cellular or acellular nucleic acid in a biological sample from a graft recipient (the biological sample containing nucleic acids from two or more genetically distinct contributors), prior or pre-determined genotype information from the graft recipient may not be considered. In some embodiments, the amount of contributor-derived cellular or acellular nucleic acid in a biological sample from a graft recipient (the biological sample containing nucleic acids from two or more genetically distinct contributors) can be determined without considering prior or pre-determined genotype information (SNP genotype) from the graft recipient and without considering prior or pre-determined genotype information from any graft donor. In some embodiments, the amount of contributor-derived cellular or acellular nucleic acid in a biological sample from a graft recipient (the biological sample containing nucleic acids from two or more genetically distinct contributors) can be determined without using any prior or pre-determined genotype information from any genetically distinct contributor.

[0051] According to various embodiments described herein, an experimental (or laboratory) workflow can be followed to determine the amount of cell-free or cellular nucleic acid of donor origin in a biological sample from a graft recipient, the biological sample containing nucleic acids from two or more genetically distinct donors, the workflow involving extracting cell-free nucleic acid or cellular nucleic acid from a biological sample obtained from a graft recipient, targeted amplification and targeted high-throughput sequencing of selected polymorphic loci (e.g., selected SNPs, as described respectively in U.S. Patent Application No. 14 / 658,061, filed Mar. 13, 2015, and U.S. Patent Application No. 17 / 351,040, filed Jun. 17, 2021), both of which are incorporated herein by reference in their entireties.

[0052] In some embodiments, the amount of cell-free or cellular nucleic acid of donor origin in a biological sample from a graft recipient, the biological sample containing nucleic acids from two or more genetically distinct donors, can be determined after receiving experimental data (such as sequencing reads) or other data-related information (such as quality control-related data, results, genotype information, SNP mutation rates, etc.) from a database or other non-experimental source.

[0053] In some embodiments, the amount of cell-free or cellular nucleic acid of donor origin in a biological sample from a graft recipient can be a relative value, which is expressed as a ratio or percentage of the cell-free or cellular nucleic acid of donor origin relative to the total cell-free or cellular nucleic acid. In some embodiments, for example, in the case where a pregnant graft recipient has received an organ graft from a graft donor, the amount of cell-free nucleic acid of donor origin (e.g., cell-free DNA of graft donor origin) can be a relative value, which is expressed as a ratio or percentage of the cell-free DNA of donor origin relative to the total cell-free DNA (e.g., cell-free DNA including cell-free DNA of graft donor origin plus cell-free DNA of graft recipient origin plus cell-free DNA of fetal origin). In some embodiments, for example, in the case where a non-pregnant graft recipient has received two simultaneous organ grafts from two different graft donors, the amount of cell-free nucleic acid of donor-1 origin (e.g., cell-free DNA of graft donor-1 origin) can be a relative value, which is expressed as a ratio or percentage of the cell-free DNA of donor-1 origin relative to the total cell-free DNA (e.g., cell-free DNA including cell-free DNA of graft donor-1 origin plus cell-free DNA of graft donor-2 origin plus cell-free DNA of graft recipient origin).

[0054] In some embodiments, the amount of contributor-derived cellular or cell-free nucleic acid can be a relative value that is expressed as a ratio or percentage of nucleic acid from another genetically distinct contributor, such as the ratio or percentage of cell-free DNA from a genetically distinct contributor "1" relative to cell-free DNA from genetically distinct contributor "2" and / or genetically distinct contributor "3", and so on. In some embodiments, for example, in the case where a pregnant graft recipient has received an organ graft from a graft donor, the amount of contributor-1-derived cell-free nucleic acid (e.g., cell-free DNA from the graft donor) can be a relative value that is expressed as the ratio or percentage of contributor-1-derived cell-free nucleic acid relative to contributor-2-derived cell-free nucleic acid (e.g., cell-free DNA from the graft donor relative to cell-free DNA from the fetus), or vice versa. In some embodiments, for example, in the case where a non-pregnant graft recipient has received two successive organ grafts from two different graft donors, the amount of contributor-1-derived cell-free nucleic acid (e.g., cell-free DNA from graft donor-1) can be a relative value that is expressed as the ratio or percentage of contributor-1-derived cell-free nucleic acid relative to contributor-2-derived cell-free nucleic acid (e.g., cell-free DNA from graft donor-1 relative to cell-free DNA from graft donor-2), or vice versa.

[0055] In some embodiments, the amount of contributor-derived cellular or cell-free nucleic acid in a biological sample from a graft recipient can be an absolute value that results from a comparison to an internal standard or reference standard or an adjustment based on the use of an internal standard or reference standard. In some embodiments, for example, the amount of contributor-derived cell-free DNA can be an absolute value that results from a comparison to an internal standard or reference standard or an adjustment based on the use of an internal standard or reference standard, which is added before or after extraction of cell-free DNA from the biological sample of the graft recipient.

[0056] Thus, in some embodiments, samples obtained from a pregnant graft recipient of a single organ, tissue, or cell graft can thus be analyzed to determine the amount of recipient-derived (e.g., contributor-1 sourced), graft donor-sourced (e.g., contributor-2 sourced), and / or fetal-sourced (e.g., contributor-3 sourced) cell-free DNA as a ratio or percentage of the total (recipient-sourced, graft donor-sourced, and fetal-sourced) cell-free DNA. In other embodiments, using an internal standard or a reference standard, samples obtained from a pregnant graft recipient of a single organ, tissue, or cell graft can be analyzed to determine the absolute amount of recipient-sourced, graft donor-sourced, and / or fetal-sourced cell-free DNA.

[0057] In some embodiments, samples obtained from a non-pregnant graft recipient of two simultaneous or successive organ, tissue, or cell grafts from different donors can be analyzed to determine the amount of recipient-sourced (e.g., contributor-1 sourced), graft donor-1-sourced (e.g., contributor-2 sourced), and / or graft donor-2-sourced (e.g., contributor-3 sourced) cell-free DNA as a ratio or percentage of the total (recipient-sourced, graft donor-1-sourced, and graft donor-2-sourced) cell-free DNA. In other embodiments, using an internal standard or a reference standard, samples obtained from a non-pregnant graft recipient of two simultaneous or successive organ, tissue, or cell grafts from different donors can be analyzed to determine the absolute amount of recipient-sourced, graft donor-1-sourced, and / or graft donor-2-sourced cell-free DNA.

[0058] In some embodiments, samples obtained from a pregnant graft recipient of two or more simultaneous or successive organ, tissue, or cell grafts from different donors can be analyzed to determine the amount of recipient-sourced (e.g., contributor-1 sourced), graft donor-1-sourced (e.g., contributor-2 sourced), graft donor-2-sourced (e.g., contributor-3 sourced), and / or fetal-sourced (e.g., contributor-4 sourced) cell-free DNA as a ratio or percentage of the total (recipient-sourced, graft donor-1-sourced, graft donor-2-sourced, and fetal-sourced) cell-free DNA. In other embodiments, using an internal standard or a reference standard, samples obtained from a pregnant graft recipient of two or more simultaneous or successive organ, tissue, or cell grafts from different donors can be analyzed to determine the absolute amount of recipient-sourced, graft donor-1-sourced, graft donor-2-sourced, and / or fetal-sourced cell-free DNA.

[0059] Changes in the relative or absolute amount of cell-free DNA of contributor origin (particularly changes in the relative or absolute amount of cell-free DNA of graft donor origin over time) can be used to inform the status of a graft in a graft recipient (or the status of each graft in the case of multiple grafts) and / or to inform the status of a fetus (in the case of a pregnant graft recipient), as well as to inform the need to adjust (e.g., reduce or maintain) immunosuppressive therapy being administered to the graft recipient. Changes in the relative or absolute amount of cell-free DNA of contributor origin (particularly changes in the relative or absolute amount of cell-free DNA of graft donor origin over time) can also be used to determine the risk of graft rejection.

[0060] In some embodiments, the amount of contributor-origin cellular or cell-free nucleic acid in a biological sample from a graft recipient can be compared to a suitable threshold or range of thresholds to obtain information about the status of one or more organ, tissue, and / or cell grafts or a fetus. The threshold or range of thresholds can be a pre-determined value or pre-determined range indicating the presence or absence of a condition or the presence or absence of a risk. The threshold can be a single cut-off value (such as a median or mean value), and can be determined from a baseline value before the presence or onset of a disease or risk or after a course of treatment. The baseline value can be the amount of contributor-origin nucleic acid (such as cell-free DNA of graft donor origin) in a pre-transplant sample from the graft recipient, which will generally be zero or negligible, but can also indicate baseline error in the system. The baseline value can also be the amount of contributor-origin nucleic acid (such as cell-free DNA of fetal origin) in a pre-pregnancy sample from the graft recipient, which will generally be zero or negligible, but can also indicate baseline error in the system. Once appropriate analysis parameters are selected, determining the amount of contributor-origin nucleic acid (such as the amount of cell-free DNA of contributor origin in a pregnant graft recipient) compared to a suitable threshold can inform the status of the graft. Similarly, determining the change in the amount of contributor-origin nucleic acid in a pregnant graft recipient over a period of time (such as the change in the amount of cell-free DNA of contributor origin) can inform the status of the graft.

[0061] In some embodiments, the amount of contributor-derived cellular or cell-free nucleic acid can be compared to the amount of a prior (i.e., previously determined) nucleic acid from the same genomic contributor to obtain longitudinal data and information about the status of one or more organ, tissue, and / or cell grafts (e.g., in a non-pregnant recipient or fetus of multiple simultaneous or sequential grafts). In some embodiments, the amount of contributor-derived cellular or cell-free nucleic acid can be compared to the amount of a prior (i.e., previously determined) nucleic acid from the same genomic contributor to obtain longitudinal data and information about the status of an organ, tissue, and / or cell graft and a fetus (e.g., in a pregnant graft recipient of a single donor graft).

[0062] Determining the amount of contributor-derived nucleic acid, such as contributor-derived cell-free DNA, in a biological sample from a pregnant graft recipient of a single donor organ, tissue, or cell graft can be used to classify, determine, and / or monitor the status of the graft. The status of the graft is of significant value and informational significance for clinical decisions made by a treating physician or medical expert regarding the treatment of the graft recipient (e.g., regarding the need to adjust (e.g., increase, decrease, change, or initiate) immunosuppressive or anti-rejection therapy in the graft recipient). In the case of a pregnant graft recipient of a single donor organ, tissue, or cell graft, the methods of the present disclosure can be used to classify, determine, and / or monitor the status of the graft based on the determined (including estimated) amount of contributor-derived nucleic acid, such as contributor-derived cell-free DNA.

[0063] Determining the amount of contributor-1-derived nucleic acid, such as graft donor-1-derived cell-free DNA, and the amount of contributor-2-derived nucleic acid, such as graft donor-2-derived cell-free DNA, in a biological sample from a graft recipient of two simultaneous or sequential organ, tissue, and / or cell grafts from different donors can be used to classify, determine, and / or monitor the status of one or both grafts. The status of the graft is of significant value and informational significance for clinical decisions made by a treating physician or medical expert regarding the treatment of the graft recipient (e.g., regarding the need to adjust (e.g., increase, decrease, change, or initiate) immunosuppressive or anti-rejection therapy in the graft recipient). The methods of the present disclosure can be used to classify, determine, or monitor the status of one or more grafts based on the determined (including estimated values) amounts of contributor-derived nucleic acids, such as graft donor-1-derived cell-free DNA and graft donor-2-derived cell-free DNA.

[0064] Nucleic acids of contributor origin

[0065] The methods of the present disclosure relate to analyzing nucleic acids in a biological sample from a graft recipient to determine the amount of contributor-derived nucleic acids (such as cell-free DNA) from one or more genetically distinct contributors (e.g., the graft donor), which amount can be used to inform the status of the graft and / or to inform the need to adjust the immunosuppressive therapy being administered to the graft recipient. Similarly, determining the change over time in the amount of contributor-derived nucleic acids (such as cell-free DNA) from one or more grafts in a graft recipient according to the methods of the present disclosure can be used to inform the status of the graft and / or to inform the need to adjust the immunosuppressive therapy being administered to the graft recipient.

[0066] According to various embodiments described herein, an experimental (or laboratory) workflow can be followed to determine the amount of contributor-derived cellular or cell-free nucleic acids in a biological sample from a graft recipient (the biological sample containing nucleic acids from two or more genetically distinct contributors), the workflow involving extracting cell-free nucleic acids or cellular nucleic acids from the biological sample obtained from the graft recipient, targeted amplification and targeted high-throughput sequencing of selected polymorphic loci (e.g., a set of single nucleotide polymorphisms (SNPs), which can be selected as described, respectively, in U.S. Patent Application No. 14 / 658,061, filed on March 13, 2015, and U.S. Patent Application No. 17 / 351,040, filed on June 17, 2021), both of which U.S. patent applications are hereby incorporated by reference in their entireties.

[0067] In some embodiments, the amount of contributor-derived cellular or cell-free nucleic acids in a biological sample from a graft recipient (the biological sample containing nucleic acids from two or more genetically distinct contributors) can be determined after receiving experimental data (such as sequencing reads) or other data-related information (such as quality control-related data, results, genotype information, SNP mutation rates, etc.) from a database or other non-experimental source.

[0068] Graft recipients and samples

[0069] The methods of the present disclosure relate to determining the amount of nucleic acid (e.g., cell-free DNA of graft donor origin) that contributes to the source of a biological sample obtained from a recipient of an organ, tissue, and / or cell graft from a graft donor. The graft recipient may have received one or more grafts simultaneously or sequentially. Organ grafts can include, for example, kidney grafts, heart grafts, lung grafts, liver grafts, pancreas grafts, vascularized composite grafts, intestinal grafts, gastric grafts, testicular grafts, penile grafts, ovarian grafts, uterine grafts, thymic grafts, facial grafts, hand grafts, leg grafts, bone grafts, corneal grafts, skin grafts, heart valve grafts, vascular grafts, or any combination thereof (such as a cardio-pulmonary or pancreas-kidney graft). The graft received by the graft recipient from the donor can also include a tissue graft, such as skin tissue, or a cell graft, such as pluripotent stem cells, multipotent stem cells, hematopoietic stem cells (e.g., hematopoietic stem cells), blood cells (e.g., peripheral blood mononuclear cells), cord blood cells, islet cells, skin cells, cardiomyocytes, neurons, dendritic cells, macrophages, lymphocytes, NK cells, NKT cells, B cells, T cells, regulatory T cells, or genetically engineered T cells (e.g., chimeric antigen receptor (CAR) T cells).

[0070] Biological samples from the graft recipient can include, but are not limited to, whole blood, plasma, serum, peripheral blood mononuclear cells, lymph fluid, buccal swabs, saliva, sputum, tears, sweat, ear fluid, bone marrow suspension, urine, feces, lung lavage fluid, semen, vaginal fluid, cerebrospinal fluid, brain fluid, ascites, milk, secretions of the respiratory and intestinal or urinary tracts, or tissues and / or cells from a biopsy.

[0071] Some samples obtained from the graft recipient can contain cell-free DNA, and the total cell-free DNA present in the sample can be entirely recipient-derived cell-free DNA, or the total cell-free DNA present in the sample can include a mixture of recipient-derived cell-free DNA and graft-derived cell-free DNA. Some samples obtained from a pregnant graft recipient can contain cell-free DNA, and the total cell-free DNA present in the sample can be entirely recipient-derived cell-free DNA, or the total cell-free DNA present in the sample can include a mixture of recipient-derived cell-free DNA, fetal-derived cell-free DNA, and graft donor-derived cell-free DNA.

[0072] Once a sample is obtained, the sample can be used directly, frozen, or otherwise stored under conditions that maintain the integrity of cell-free DNA for a period of time by preventing degradation and / or contamination by genomic DNA or other nucleic acids. Samples can be obtained from the graft recipient over a period of time. According to the methods of the present disclosure, samples can be obtained from the graft recipient at various times and over various time periods before and after transplantation, and in the case of a pregnant graft recipient, before and after pregnancy, to determine the amount of nucleic acid of donor origin. For example, samples can be obtained from the graft recipient days, weeks, and / or months after transplantation and at daily, weekly, monthly, and / or yearly intervals. In the case of a pregnant graft recipient, samples can be obtained from the graft recipient days, weeks, and / or months after conception, during pregnancy, and after pregnancy at daily, weekly, and / or monthly intervals. As clinically available and / or indicated, samples can be obtained from the graft recipient at various alternative times. In some embodiments, the time period for obtaining a sample from the graft recipient can be within the first few days after transplantation, e.g., to monitor induction therapy. In some embodiments, the time period for obtaining a sample from the graft recipient can be during a tapering immunosuppression regimen, i.e., a period that occurs during the first 12 months after transplantation. In some embodiments, the time period for obtaining a sample from the graft recipient can be during an initial long-term immunosuppression maintenance phase that begins at about 12 to 14 months after transplantation. In some embodiments, the time period for obtaining a sample from the graft recipient can be during the entire long-term maintenance of the immunosuppression regimen, e.g., at any time more than 12 months after transplantation.

[0073] In some embodiments, samples can be obtained from the (pregnant or non-pregnant) graft recipient at approximately once a week, approximately once every 2 weeks, approximately once every 3 weeks, approximately once a month, approximately once every two months, approximately once every three months, approximately once every four months, approximately once every five months, approximately once every six months, approximately once a year, or approximately once every two years or longer after an initial sampling event. For a given graft recipient, one of ordinary skill in the art can determine the appropriate sampling timing and frequency.

[0074] Analysis of cell-free nucleic acids (DNA) in graft recipients

[0075] The computer-implemented systems, kits, and methods of the present disclosure relate to the analysis of contributor-derived nucleic acids, such as cell-free DNA, in a biological sample from a graft recipient, the biological sample containing nucleic acids from two or more genetically distinct contributors. In some embodiments, the amount of contributor-derived nucleic acids, such as cell-free DNA, in a biological sample from a graft recipient can be determined after receiving relevant experimental data (such as sequencing reads) or other relevant data-related information (such as quality control-related data, results, genotype information, SNP mutation rates, etc.) from a database or other non-experimental sources. In some embodiments, an experimental (or laboratory) workflow can be followed to determine the amount of contributor-derived nucleic acids, such as cell-free DNA, in a biological sample from a graft recipient, the workflow involving extraction of cell-free nucleic acids from the biological sample obtained from the graft recipient, targeted amplification of selected polymorphic loci (e.g., a set of single nucleotide polymorphisms (SNPs), which can be selected as described in U.S. Patent Application No. 14 / 658,061, filed on March 13, 2015), and targeted high-throughput sequencing. After cell-free DNA has been extracted or otherwise obtained from the biological sample from the graft recipient, a set of polymorphic markers applicable to distinguishing cell-free DNA derived from various genetically distinct contributors (e.g., for distinguishing graft donor-derived cell-free DNA from recipient-derived cell-free DNA) in the cell-free DNA can be analyzed to determine the amount of contributor-derived cell-free DNA. Various polymorphic markers can be selected to be included in the batch to be analyzed, provided that the polymorphic marker batch as a whole is applicable to distinguishing cell-free DNA derived from various genetically distinct donors (e.g., distinguishing graft donor-derived cell-free DNA from recipient-derived cell-free DNA) in cell-free DNA. Polymorphic markers can include, for example, single nucleotide polymorphisms (SNPs), restriction fragment length polymorphisms (RFLPs), short tandem repeats (STRs), variable number tandem repeats (VNTRs), hypervariable regions, minisatellites, dinucleotide repeats, trinucleotide repeats, tetranucleotide repeats, simple sequence repeats, and insertion elements. Polymorphic markers can include one or more bases modified by methylation. The same polymorphic marker batch can be used for each graft recipient; there is no need to customize the polymorphic marker batch for different graft recipients to individualize the batch. In some embodiments, the polymorphic marker can be an SNP. The SNPs can be selected based on, for example, having an overall population minor allele frequency >0.4, a target population minor allele frequency >0.4, a minimum polymerase error rate of 6 potential allele transitions or transversions (in the test system), and low linkage on the genome, such as, for example, a distance >500 kb between SNPs.SNPs to be included in an SNP batch or any other polymorphism marker batch can be those previously identified as suitable for distinguishing any two unrelated individuals (Pakstis, A.J., Speed, W.C., Fang, R. et al. SNPs for a universal individual identification panel; Hum Genet 127, 315–324 (2010)). An SNP batch can include at least 10, at least 20, at least 50, at least 100, at least 200, at least 500, at least 1000 or more SNPs.

[0076] Amplification and sequencing

[0077] Upon extraction from a biological sample from a graft recipient, nucleic acids such as cell-free DNA can be amplified and sequenced for downstream analysis such as for analysis of a set of polymorphism markers (e.g., SNPs) from cell-free DNA. Various protocols for cell-free DNA extraction, amplification and sequencing using high-throughput sequencing methods including next-generation sequencing are known in the art and are described in U.S. Patent Application No. 14 / 658,061, filed Mar. 13, 2015, which is hereby incorporated by reference in its entirety.

[0078] Performing SNP genotype inference for some contributors by aggregating and grouping single nucleotide polymorphism (SNP) minor allele frequency (MAF) signals Performing SNP genotype inference for some contributors

[0079] The methods of the present disclosure involve the process of aggregating and grouping (including clustering) MAF signals from which, for a biallelic SNP, the SNP genotype of each genomic contributor can be inferred. According to various embodiments of the present invention, since a biological sample from a graft recipient can contain cell-free nucleic acids such as cell-free DNA from two or more genomically and genetically distinct contributors, the genotype at a particular SNP locus (herein represented as GT i ) represents the set (composed of the set of genotypes of all contributors) of genotypes of all contributors at that SNP. For example, for a pregnant graft recipient (GT_rp i ), the genotype of a particular SNP represents the set of SNP genotypes of the pregnant graft recipient (GT_rp i ), the graft donor (GT_dn i ) and the graft recipient fetus (GT_ft i ), and can be expressed as GT i :=(GT_rp i ,GT_dn i ,GT_ft i)。In some embodiments, since the methods of the present invention can be performed without prior or pre - determined knowledge of any SNP genotypes from any contributor, SNP genotypes are latent variables that can be inferred during the grouping process.

[0080] There are different types and levels of SNP signals that can be aggregated from various signals and information such as nucleic acid sequence reads (nucleic acid sequence data or nucleic acid sequence read counts), SNP allele frequency information (in the form of counts, percentages, or ratios) (e.g., reference allele frequency and alternative allele frequency (AAF), major allele frequency and minor allele frequency (MAF)), and used to infer the SNP genotypes of any genomic contributor. For each SNP, such aggregated signals can be aggregated read counts for amplicons, aggregated AAF values, aggregated MAF values, etc.

[0081] In some embodiments, minor allele frequency (MAF) values can be aggregated as representative SNP signals and grouped (clustered) to infer the SNP genotypes of one or more contributors. For example, the MAF value of the i - th SNP (S i ) can be denoted as xi, and the MAFs of a selected set of SNPs from cell - free DNA sequencing of a sample from a graft recipient after transplantation can be denoted as X := (x1, x2, …, xn), where n is the total number of selected SNPs. In embodiments where multiple longitudinal samples from the same graft recipient are analyzed, the MAF value of the i - th SNP in the j - th sample (S i ) can be denoted as Xj is all the MAF values of the j - th sample, Si is the set of MAF values of all samples at the i - th SNP, and the m×n matrix is the set of MAF values of all samples at all SNPs, where m is the number of samples:

[0082]

[0083]

[0084] In some embodiments, MAF values can be aggregated from nucleic acid sequence reads and grouped (clustered) to infer the SNP genotypes of one or more contributors. For example, in the case of a pregnant graft recipient, where the SNP genotypes of three contributors: the pregnant graft recipient (GT_rp i ), the graft donor (GT_dn i ), or the fetus of the graft recipient (GT_ft i ) are not known, can be determined by The signals infer at least two genotypes among the SNP genotypes of three contributors for some of the SNPs being sequenced. Once at least partially inferring the SNP genotypes of the contributors (e.g., the SNP genotypes of two out of three contributors, the SNP genotypes of three out of four contributors, etc.), the amount of nucleic acid from one or more specific contributor sources can be determined, such as, for example, the amount of cell-free DNA from the graft donor source and / or the amount of cell-free DNA from the fetal source, or the amount of cell-free DNA from graft donor - 1 source and / or the amount of cell-free DNA from graft donor - 2 source.

[0085] In some embodiments, the variables of the initial signal matrix (represented as ) can be used to infer the SNP genotypes of the contributors. In some embodiments, a "waterfall plot" can be used to visually represent as an m×n matrix, with the re-ordered SNP signals from the initial signal matrix :

[0086]

[0087] {S′1,…,S′ n} = {S1,…,Sn}

[0088] In embodiments where the waterfall plot contains any number of samples from a graft recipient after transplantation, can be a shuffled set of the SNP signals Si from the initial matrix reordered (e.g., in descending or ascending order) with some summary statistics of the SNP signals.

[0089] Grouping SNP signals

[0090] In some embodiments, the SNP genotypes of the contributors or portions thereof can be inferred by a grouping (including clustering) process of the signals S i of the SNPs. In embodiments where the waterfall plot can be used for visual representation, the grouping (clustering) process can be similar to "segmenting" the data curves or cascades into groups. For the SNP signal S iGrouping (clustering) can assign a group label to each SNP in the group to indicate the inferred common contributor genotype shared in that group. For example, SNPs with recipient homozygous / heterozygous genotypes after grouping (clustering) can be labeled as recipient_homo / recipient_hetero. Subsequently, within the recipient_homo group, for example, SNPs can be further assigned to subgroups and labeled as, for example, fetus_homo / fetus_hetero, to indicate the inferred fetal homozygous / heterozygous genotype.

[0091] In embodiments of the present invention, various grouping methods can be used. In some embodiments, if a binary outcome is desired, thresholding can be used as a grouping method: for SNPs with summary statistic value y, if y is higher than a given threshold T, it belongs to one (first) group, and if y is lower than T, it belongs to another (second) group. Thresholding can be used, for example, to infer the graft recipient SNP genotype using the SNP summary statistic Y = (y1,…,yn). yi ′ For example, using the mean or median of the MAF values of the SNPs, where i′ is the new position index for the SNP after reordering, as in the shuffling same as:

[0092]

[0093] yi ′ = mean(Si ′ ), or yi ′ = median(Si ′ )

[0094] The genotype of the graft recipient, visually represented (e.g., in a waterfall plot), can be identified in the "first-tier stage" of the plot (e.g., as Figure 5A shown).

[0095] In some embodiments, the grouping process can use MAF data or information obtained after analyzing multiple longitudinal samples from the same graft recipient based on longitudinal MAF changes, for example, to infer the genotype of the fetus of a pregnant graft recipient within the group of graft recipient homozygous SNP genotypes (recipient homo). The genotype of the fetus, visually represented (e.g., in a waterfall plot), can be identified in the "second-tier stage" of the plot (e.g., as Figure 5B shown).

[0096] In some embodiments, the summary statistic of the MAF change of the SNP (denoted as yi ′)One of the following processes ("one-to-one", "one-to-many", "many-to-many") can be used to select for summarizing MAF changes and for grouping SNPs.

[0097]

[0098] In some embodiments, the summary statistic yi of the MAF change of the SNPs ′ can be used to determine a "separation point" P at the reordering position index i′ for grouping the SNPs by comparing their indices i′ in the shuffling where P:

[0099]

[0100] P can be selected as: where gh can be selected as a function reflecting the local slope of Y, e.g., based on successive differences or rolling differences (smoothed over a window), as described with respect to Figure 7B The above embodiments of the present disclosure can be implemented in a variety of ways. For example, some aspects of the embodiments can be implemented using hardware, software, or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or set of processors, whether provided in a single computer or distributed among multiple computers. Any component or collection of components that performs the above functions can generally be considered one or more controllers that control the functions discussed above. The one or more controllers can be implemented in a variety of ways, such as using dedicated hardware, or using general-purpose hardware (e.g., one or more processors) programmed with microcode or software to perform the above functions.

[0101] The specific implementation of the various features of the present disclosure can use at least one non-transitory computer-readable storage medium (e.g., computer memory, floppy disk, compact disc, magnetic tape, etc.) encoded with a computer program (i.e., a plurality of instructions) that, when executed on a processor, performs the functions discussed above. The computer-readable storage medium can be portable such that the program stored thereon can be loaded onto any computer resource to implement certain aspects of the present disclosure discussed herein. The reference to a computer program that, when executed, performs the functions discussed above is not limited to an application running on a main computer. Instead, the term computer program is used herein in a general sense to refer to any type of computer code (e.g., software or microcode) that can be used to program a processor to implement certain aspects of the present disclosure.

[0102] The program can provide a method for determining the amount of cell-free or cell-based nucleic acids of donor origin in a biological sample from a graft recipient, the biological sample containing nucleic acids from two or more genetically distinct donors, by evaluating experimental data (such as sequencing reads) or other data-related information (such as quality control-related data, results, genotype information, SNP mutation rates, etc.) from a database or other non-experimental sources.

[0103] Determining the status of the graft

[0104] The methods of the present disclosure for determining the amount of cell-free nucleic acids (such as cell-free DNA) of donor origin in a biological sample from a graft recipient can be used to determine the status of a graft in the graft recipient. An amount of cell-free DNA of donor origin above a suitable threshold in the graft recipient and a change in the amount of cell-free DNA of donor origin over time can provide information about the status of the graft.

[0105] In some embodiments, an amount of cell-free nucleic acids of donor origin above a suitable threshold in the graft recipient or an increase in the amount of cell-free nucleic acids of donor origin in the graft recipient over time can indicate graft rejection, a need to adjust immunosuppressive therapy, immunosuppressive therapy nephrotoxicity, infection, and / or a need for further investigation of the graft status.

[0106] In some embodiments, an amount of cell-free nucleic acids of donor origin below a suitable threshold in the graft recipient or a decrease in the amount of cell-free nucleic acids of donor origin in the graft recipient over time can indicate graft tolerance, a need to adjust immunosuppressive therapy, and / or a need for further investigation of the graft status.

[0107] In some embodiments, no change in the amount of cell-free nucleic acids of donor origin in the graft recipient over time can indicate a stable graft status and / or an opportunity to adjust (e.g., reduce or discontinue) immunosuppressive therapy.

[0108] Adjustment of immunosuppressive therapy

[0109] The methods of the present disclosure for determining the amount of cell-free nucleic acids of donor origin in a biological sample from a graft recipient can be used to inform the need to adjust an immunosuppressive therapy being administered to the graft recipient. Immunosuppressive therapy generally refers to the administration of an immunosuppressive agent or other therapeutic agent that suppresses the immune response to a subject. Exemplary immunosuppressive agents can include, for example, anticoagulants, antimalarials, cardiac medications, non-steroidal anti-inflammatory drugs (NSAIDs), and steroids, including, for example, Ace inhibitors, aspirin, azathioprine, B7RP-1-fc, β-blockers, brequinar sodium, campath-1H, celecoxib, chloroquine, corticosteroids, warfarin, cyclophosphamide, cyclosporin A, DHEA, deoxyspergualin, dexamethasone, diclofenac, diflunisal, etodolac, everolimus, FK778, piroxicam, fenoprofen, flurbiprofen, heparin, hydralazine, hydroxychloroquine, CTLA-4 or LFA3 immunoglobulins, ibuprofen, indomethacin, ISAtx-247, ketoprofen, ketorolac, leflunomide, meclofenamic acid, mefenamic acid, mepacrine, 6-mercaptopurine, meloxicam, methotrexate, mizoribine, mycophenolate mofetil, naproxen, oxaprozin, hydroxychloroquine, NOX-100, prednisone, methylprednisolone, rapamycin (sirolimus), sulindac, tacrolimus (FK506), thymoglobulin, tolmetin, teripamulin, U0126, and antibodies, including, for example, α-lymphocyte antibodies, adalimumab, anti-CD3, anti-CD25, anti-CD52, anti-IL2R, and anti-TAC antibodies, basiliximab, daclizumab, etanercept, hu5C8, infliximab, OKT4, and natalizumab.

[0110] In some embodiments, the adjustment of the immunosuppressive therapy can include changing the type or form of the immunosuppressive agent or other immunosuppressive therapy being administered to the graft recipient. In some embodiments, when the graft recipient is not receiving immunosuppressive therapy, the methods of the present disclosure may indicate the need to initiate immunosuppressive therapy in the graft recipient.

[0111] Additional analysis

[0112] The methods of the present disclosure can be performed as a supplement to or in combination with other analyses of a sample from a graft recipient to determine the amount of cell-free nucleic acids of donor origin (such as cell-free DNA), determine the status of the graft in the graft recipient, and / or inform the need to adjust an immunosuppressive therapy being administered to the graft recipient.

[0113] In some embodiments, it is also possible to test for the presence and / or amount of infective agents (such as viruses, bacteria, fungi, parasites, etc.) in nucleic acids extracted from biological samples of a graft recipient. In some embodiments, it is possible to test for the presence and / or amount of infective agents that are frequently encountered after organ transplantation in nucleic acids extracted from biological samples of a graft recipient. Infective agents that can be tested include, but are not limited to: viruses, such as cytomegalovirus, Epstein - Barr virus, Anelloviridae, and BK virus; bacteria, such as Pseudomonas aeruginosa, Enterobacteriaceae, Nocardia, Streptococcus pneumonia, Staphylococcus aureus, and Legionella; fungi, such as Candida, Aspergillus, Cryptococcus, Pneumocystis carinii; or parasites, such as Toxoplasma gondii.

[0114] In some embodiments, the test results for the presence and / or amount of an infectious agent can be used to determine the infection status in a graft recipient. In some embodiments, the test results for the presence and / or amount of an infective agent can be used to inform the need to adjust the immunosuppressive therapy being administered to the graft recipient, such as to reduce or alter the immunosuppressive therapy once the presence and / or a specific amount of the infective agent is confirmed.

[0115] The computer - determined results of the status of the transplantation can be provided via a medical analysis tool that is accessible to a physician or medical expert. The medical analysis tool can display the status of the transplantation, for example, in a user interface, report printing, etc. The physician or medical expert can use the computer - determined status as a supplement to, or in place of, the physician's or medical expert's assessment of the status of the transplantation. The computer - determined status can be provided to the physician or medical expert in the form of the determined (estimated) amount of donor - derived cell - free DNA and / or the risk of graft rejection. For example, the medical analysis tool can output the determined (estimated) amount of donor - derived cell - free DNA (0.3%) or a numerical value indicating, for example, the risk of graft rejection. The computer - determined status can serve as a guide for the physician or medical expert in treatment regimens, monitoring protocols, and / or clinical diagnoses.

[0116] Various techniques and process steps will be described in detail, with reference to examples illustrated in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects and / or features described or mentioned herein. However, it will be apparent to one of ordinary skill in the art that one or more aspects and / or features described or mentioned herein may be practiced without some or all of these specific details. In other instances, well-known process steps and / or structures have not been described in detail in order to avoid obscuring certain aspects and / or features described or mentioned herein.

[0117] In the following description of the examples, reference is made to the accompanying drawings, which are a part of this disclosure and illustrate specific examples that can be implemented. It should be understood that other examples may be used and structural changes may be made without departing from the scope of the disclosed examples.

[0118] Example systems and methods for determining the amount of nucleic acids of contributor origin in biological samples from pregnant graft recipients Example systems and methods

[0119] Figure 1 An example system 100 is shown for determining the amount of contributor-derived nucleic acids in a biological sample obtained from a pregnant recipient of an organ graft (or a graft recipient who has received multiple grafts), the biological sample comprising a mixture of cell-free DNA from genetically distinct contributors (e.g., recipient-derived cell-free DNA, fetus-derived cell-free DNA, and graft donor-derived cell-free DNA).

[0120] System 100 may include an interaction device 160 and a determination unit 170. Various embodiments of the system may include some or all of the components shown in the figure or other components not shown in the figure. The system 100 may be, for example, a medical analysis tool. A treating physician or medical expert may use the medical analysis tool to assist in monitoring the status of the graft in the graft recipient and in monitoring or evaluating adjustments to immunosuppressive therapy administered or to be administered to the graft recipient. The methods of the present disclosure may be used to classify, determine, or monitor the status of a graft based on the amount of contributor-derived nucleic acids determined in a biological sample obtained from the graft recipient, the biological sample comprising a mixture of cell-free DNA from genetically distinct contributors.

[0121] In some embodiments, the interaction device 160 can be used to receive information and / or data (such as nucleic acid sequence data 140) obtained after the analysis of a mixed sample, or generally input data, and / or to provide one or more outputs to a treating physician or medical expert. In some embodiments, the interaction device 160 can receive the nucleic acid sequence data 140 or input data via a computer or input function. In some embodiments, the nucleic acid sequence data 140 or input data can be, for example, information from laboratory tests obtained from a blood and / or urine sample of a graft recipient. Additionally, in some embodiments, the nucleic acid sequence data can include relationship information between genetically distinct contributors. In some embodiments, the interaction device 160 can output the amount 150 of nucleic acid of the determined contributor source of the sample and / or the status of the graft. The determined amount 150 of nucleic acid of the contributor source provided by the medical analysis tool can be the percentage of nucleic acid of the contributor source in a biological sample obtained from a graft recipient (the biological sample containing a mixture of cell-free DNA from genetically distinct contributors). Additionally or alternatively, the interaction device 160 can provide an output indicating the status of the graft. For example, the interaction device 160 can output a numerical value indicating the risk of graft rejection (e.g., high risk, low risk, no risk, etc.) based on the determined amount 150 of nucleic acid of the contributor source.

[0122] The determination unit 170 can analyze the nucleic acid sequence data 140 and determine the amount of nucleic acid of the contributor source and / or the status of the graft. The determination unit 170 can determine the amount of nucleic acid of the contributor source by inferring the SNP genotypes of all genomic contributors or only some contributors by aggregating and grouping (clustering) SNP minor allele frequency (MAF) signals (the signals obtained from analyzing a selected set of SNPs on cell-free nucleic acids from a single sample from a graft recipient after transplantation or from multiple longitudinal samples taken from the same graft recipient over a period of time (days, weeks, months) after transplantation). In some embodiments, the determination unit 170 can determine the risk of graft rejection based on the determined amount 150 of nucleic acid of the contributor source.

[0123] In some embodiments, system 100 can be a kit that can be used, for example, for post-transplantation monitoring according to one or more methods disclosed herein. In some embodiments, the kit can include reagents, controls, instructions for use, and / or software instructions for analyzing nucleic acids of contributor origin (such as cell-free DNA of graft donor origin). In some embodiments, the kit can include reagents sufficient to analyze a single sample. In some embodiments, the kit can include reagents sufficient to analyze a number of samples. In some embodiments, the kit can include instructions specifying target values. In some embodiments, the kit can include control materials that can be used in combination with the reagents and instructions provided in the kit. In some embodiments, the kit can include instructions for use according to one or more methods disclosed herein. In some embodiments, the kit can include instructions for accessing a computer (such as a database) to retrieve data (such as, for example, nucleic acid sequence data). In some embodiments, the kit can include reference information, such as scientific literature references, comparative references, package insert materials, clinical trial results. In some embodiments, the kit can include instructions and specifications for the quality of the input material or the input preparation method.

[0124] Figure 2 A flowchart of an example general computer-implemented method for determining the amount of nucleic acids of contributor origin in a mixed sample is shown, the mixed sample comprising cell-free nucleic acids from at least three genomically and genetically distinct contributors. Method 200 can include step 202, where system 100 (including its determination unit 170) can receive information and / or data obtained after analyzing a mixed sample obtained from a graft recipient after transplantation. In some embodiments, the information and / or data can be nucleic acid sequence data 140 (reads) obtained after sequencing cell-free nucleic acids from the mixed sample, or general input data.

[0125] In some embodiments, the analysis of a mixed sample can include an experimental (or laboratory) workflow that involves: extracting cell-free nucleic acids from a biological sample obtained from a graft recipient, the biological sample containing cell-free nucleic acids (such as cell-free DNA) from at least three genetically distinct contributors; targeted amplification and targeted high-throughput sequencing of selected polymorphic loci (e.g., a set of single nucleotide polymorphisms (SNPs) that can be selected as described in U.S. Patent Application No. 14 / 658,061). In some embodiments, the analysis of a mixed sample can include receiving experimental data from a database or other non-experimental source, such as nucleic acid sequence data 140 (reads), and / or other data-related information, such as quality control-related data, results, genotype information, etc. In some embodiments, experimental data, such as nucleic acid sequence data 140 (reads), and / or other data-related information, such as quality control-related data, results, genotype information, can be received via a computer or an input function. For example, a treating physician or medical expert can provide general input data (including but not limited to experimental data, such as nucleic acid sequence data 140 (reads) on cell-free nucleic acids from a mixed sample, other data-related information, such as quality control-related data, results, genotype information) as an input using an interaction device 160. Additionally or alternatively, a computer (e.g., a database) can provide general input data, including but not limited to experimental data, such as nucleic acid sequence data 140 (reads) on cell-free nucleic acids from a mixed sample, other data-related information, such as quality control-related data, results, genotype information, etc.

[0126] In step 204, the determination unit 170 of the system can receive the genomic relationships between (genetically distinct) genomic contributors. For example, the mixed sample can be from a pregnant graft recipient who received a graft from the fetus's father, and thus the relationship includes a mother-child-father relationship. Another example is that if the mixed sample is from a pregnant graft recipient who received a graft from the fetus's aunt, then the relationship includes a mother-child-aunt relationship. Other non-limiting example relationships include mother-child-grandmother, mother-child-grandfather, mother-child-grandmother, mother-child-grandfather, mother-child-uncle, mother-child-sibling, mother-child-unrelated donor, etc.

[0127] In step 206, the determination unit 170 of the system can determine (and group) the minor allele frequency (MAF) information (MAF values or MAF changes) from a set of single nucleotide polymorphisms (SNPs). Step 208 can include determining the SNP re-ordered MAF information, and step 210 can include determining the recipient genotype for each SNP.

[0128] In step 212, system 100 may determine summary statistics of MAF changes for each SNP with a homozygous genotype of the recipient. In step 214, system 100 may determine a group label for each SNP based on the summary statistics of MAF changes.

[0129] Step 216 of method 200 may include determining the average MAF value for each group of SNPs. Then, in step 218, the amount of nucleic acid of the contributor source is determined. The amount of nucleic acid of the contributor source may be determined based on genomic contributor relationships (e.g., received in step 204) and average MAF information.

[0130] Figure 3 A flowchart of an example computer-implemented method for determining the amount of nucleic acid of a contributor source in a mixed sample is shown, the mixed sample containing cell-free nucleic acids from at least three genomically and genetically distinct contributors. Method 300 may include steps 302, 304, and 306 that may be similar to Figure 2 steps 202, 204, and 206.

[0131] In step 307, determination unit 170 may infer the SNP genotypes of each genomic contributor or some of the genomic contributors among the genomic contributors by summarizing and grouping a set of sequenced SNPs from cell-free nucleic acids derived from the mixed sample (discussed in more detail above).

[0132] In step 308, determination unit 170 of the system may determine the amount of nucleic acid of the contributor source based on genomic contributor relationships and the inferred SNP genotypes of the genomic contributors. For example, the determined amount of nucleic acid of the contributor source may be the percentage of nucleic acid derived from the graft donor (such as cell-free DNA from the graft donor source).

[0133] In some embodiments, based on the amount of nucleic acid from the determined contributor source, the system can determine the risk of graft rejection in the graft recipient. For example, the amount of nucleic acid from the determined contributor source (from step 308) or the risk of graft rejection can be displayed on a computer screen (e.g., interactive device 160) or described in a report. Additionally or alternatively, in some embodiments, the system can use the amount of nucleic acid from the determined contributor source to assess the health status of a fetus. At a given stage of pregnancy, the fetal cell-free DNA fraction is expected to fall within a target range. If the amount of nucleic acid from the determined contributor source corresponds to a fetal cell-free DNA fraction outside of this range, the system 100 can display this information or corresponding information on, for example, a user interaction device, a report printout, etc. For example, the system 100 can alert a physician or medical expert that the fetus may have a potential health problem that should be further investigated.

[0134] Although the description and illustration show the specific steps of the method in a particular order, the steps of the method can be performed in other orders that are not described or shown. Additionally or alternatively, embodiments of the present disclosure can include performing all, some, or none of the steps of method 300, as appropriate. Further, although certain components, devices, or systems are described as performing the steps of method 300, any suitable combination of components, devices, or systems (including combinations not explicitly disclosed) can be used to perform the steps.

[0135] Example SNP genotype inference for some or all genomic contributors

[0136] In some embodiments, the system can determine the amount of nucleic acid from the contributor source by inferring the SNP genotypes of some or all of the contributors by aggregating and grouping SNP minor allele frequency (MAF) signals obtained from analyzing a selected set of SNPs on cell-free nucleic acids from a single sample taken from a graft recipient after transplantation or from multiple longitudinal samples taken from the same graft recipient over a period of time (days, weeks, months) after transplantation.

[0137] Figure 4Illustrated is an example method according to some embodiments for grouping SNPs and inferring SNP genotypes of some or all genomic contributors by aggregating and grouping SNP minor allele frequency (MAF) signals obtained from a selected set of SNPs on cell-free nucleic acids analyzed from multiple longitudinal samples taken from the same graft recipient over a period of time (days, weeks, months) after transplantation. Method 400 may include determining MAF change summary statistics such as standard deviation (or minimum - maximum range) simple MAF change summary statistic 402, "one - to - one" MAF change summary statistic 412, "one - to - many" MAF change summary statistic 422, and / or "many - to - many" MAF change summary statistic 432. In some embodiments, one or more processes in the process are compared in step 442 to determine the MAF change summary statistic 452 used in determining the amount of nucleic acid of the contributor source. In some embodiments, the process may include generating one or more MAF change summary statistics, and in some embodiments, the step 442 comparison may involve selecting the MAF change summary statistic representing the maximum dissimilarity between groups among the one or more MAF change summary statistics.

[0138] Standard deviation simple MAF change summary statistic 402 : In some embodiments, the standard deviation (or minimum - maximum range) simple MAF change summary statistic 402 may include sorting the MAF values and forming a scatter plot. Figure 5A Illustrated is an example waterfall plot of MAF values for a set of SNPs for the standard deviation simple MAF change summary statistic according to some embodiments. The waterfall plot may include multiple groups such as recipient - heterozygous group 502 and recipient - homozygous group 504. The recipient - heterozygous group 502 may contain SNPs having MAF values that can be greater than the MAF values of SNPs in the recipient - homozygous group 504. As shown in the example waterfall plot, the recipient - heterozygous group 502 may contain a first set of SNPs (e.g., 160 SNPs) having MAF values greater than or equal to a threshold (e.g., 0.24), and the recipient - homozygous group 504 may contain the remaining SNPs having MAF values less than the threshold (e.g., 0.24).

[0139] The waterfall plot may exhibit one or more "tiers" (second - tier tiers) including one or more "steps". The tiers and steps represent groups of SNPs sharing the same genotype of one or more genomic contributors. By grouping and identifying such tiers or steps, the SNPs can be used to determine the genotypes of one or more genomic contributors. For example, as Figure 5AAs shown, the first-level ladder can be used to distinguish the recipient-heterozygous group 502 and the recipient-homozygous group 504. In some embodiments, the first-level ladders of groups 502 and 504 can correspond to the SNP genotypes of the major genomic contributor (graft recipient), and the second-level ladders ("steps") can be further identified within group 504.

[0140] In some embodiments, a waterfall plot can show the second ladder level ("steps") in the recipient-homozygous group 504, such as Figure 5B as shown in the figure, and the SNP genotypes of one or more minor contributors can be determined from the steps. In one example, the genotype of a contributor can be determined based on a ladder compared to a threshold. The amount of nucleic acid from the determined contributor source can be determined from the MAF values grouped by the genotype of the contributor.

[0141] The second-level ladder may not be recognizable in the waterfall plot, such as when SNPs are mixed. Figure 6 An example waterfall plot is shown in which the second-level ladder is not recognizable. For example, the first-level ladder is recognizable, allowing the system to separate SNPs into the recipient-heterozygous group 602 and the recipient-homozygous group 604. In the recipient-homozygous group 604, the second-level groups are mixed and thus may not be easily separated from each other, and thus the SNP genotypes cannot be determined from the waterfall plot. The lack of a recognizable second-level ladder in the waterfall plot may make it difficult to accurately determine the amount of nucleic acid from the contributor source.

[0142] In some embodiments, a waterfall plot can include multiple ladder levels. For example, as Figure 5A and Figure 5B shown, the multiple ladder levels can include a first-level ladder ( Figure 5A ) and a second-level ladder ( Figure 5B ). The first-level ladder can be determined by separating the plot into groups based on threshold processing (as described above). The second-level ladder can be determined by separating one or more of the first-level ladders. In the example shown in Figure 5B , the second-level ladder can include three steps. Each step can contain SNPs grouped together based on similar MAF values.

[0143] Embodiments of the present disclosure may include a method for determining one or more genotypes from MAF values. In some cases, the waterfall plot of MAF values may not exhibit a second-tier step. A non-limiting example is when the graft recipient is a pregnant woman, where the sample is taken in the first few weeks of pregnancy (e.g., before 10 weeks since conception), before conception, or after conception. In some embodiments, the determined genotype may include the genotype of the graft recipient, the fetus, or the donor (including a previous donor or a current donor) based on MAF changes that can be determined from the MAF values.

[0144] For example, MAF changes can be determined based on a set of longitudinal samples from the same graft recipient. The set of longitudinal samples can have the same SNP genotype. In some embodiments, when the graft recipient is a pregnant woman, the set of longitudinal samples can include one or more samples from the graft recipient taken before conception, one or more samples from the graft recipient taken before 10 weeks since conception, one or more samples from the graft recipient taken at least 10 weeks after conception, or a combination thereof. In some embodiments, when the graft recipient has received multiple grafts, the set of longitudinal samples can include one or more samples from the graft recipient taken before receiving one or more previous grafts, one or more samples from the graft recipient taken before receiving the current graft, one or more samples from the graft recipient after receiving the previous and current grafts, or a combination thereof.

[0145] Fetal or current donor SNP genotypes can be determined from longitudinal MAF changes. Additionally or alternatively, a plot of MAF change summary statistics can be used to determine the amount of nucleic acid from the contributor source.

[0146] Figure 7A An example method for determining MAF information in a set of SNPs according to some embodiments is shown. As discussed above, system 100 can receive nucleic acid sequence data 140. The nucleic acid sequence data 140 can include longitudinal MAF data (e.g., MAF values) for each SNP. Method 750 can include sorting the samples according to the MAF values in step 752. In some embodiments, step 752 can include re-sorting the SNPs according to the average or median MAF value of the SNP across the samples. In some embodiments, the SNPs can be grouped according to the homozygous or heterozygous genotype of the graft recipient. The grouping can be performed by using a threshold parameter, as discussed above.

[0147] In step 754, system 100 may determine a summary statistic of the MAF change in the set of SNPs. The summary statistic of the MAF change includes MAF changes calculated in a simple form (such as standard deviation or dynamic range) or in a more complex form. In some embodiments, smoothing and differentiation functions may be applied to the summary statistic of the MAF change, thereby generating a MAF change difference plot.

[0148] Figure 7B An example plot of the summary statistic of the MAF change is shown. The smoothing and differentiation functions can be any type of function that smooths and measures the local slope of the summary statistic of the MAF change. The local minimum or maximum of the smoothing and differentiation functions can help determine the "separation point" P in the summary statistic of the MAF change. Example smoothing and differentiation functions can include, but are not limited to, the first derivative of a Gaussian function (using selected parameters), a linear smoothing and differentiation function (using lag differences of a sequence), etc.

[0149] In ( Figure 7A ) step 758, the system may group the SNPs according to the summary statistic of the MAF change, e.g., Figure 5A groups 502 and 504. This step may involve determining the separation point by identifying the local minimum or maximum of the smoothing and differentiation functions applied to the summary statistic of the MAF change within a certain window (such as Figure 7B window 720 shown) indexed at the SNP positions. The position of window 720 may be determined based on relationship information among genomics contributors. The separation point 722 (also referred to as separation point P throughout this disclosure) may be used to group (cluster) the SNPs into homozygous and heterozygous groups. After the SNPs have been grouped, in some embodiments, the grouping may undergo a final quality control check.

[0150] In some embodiments, the system may group the SNPs into more than two groups in the summary statistic of the MAF change. The summary statistic of the MAF change may be generated for a pooled sample of graft recipients who have received multiple grafts. Figure 7C An example MAF waterfall plot according to some embodiments of the present disclosure, and a plot of the summary statistic of the MAF change for three groups of SNPs containing different genotypes of one of the genomics contributors (graft donor-1) among the genomics contributors are shown. As shown, there may be three groups 702, 704, and 706.

[0151] "One-to-one" MAF change summary statistic 412 : The process of generating the "one-to-one" summary statistic of the MAF change 412 may include determining the sample difference of the MAF values between two samples having the largest change in MAF values. Figure 8AShows an example method for one-to-one MAF change summary statistics according to some embodiments. The process may include reordering a set of SNPs according to an average or median MAF value (step 802). Figure 8B Shows an example MAF waterfall plot for five samples. The sample 852 with the highest average MAF value among the multiple samples and the sample 854 with the lowest correlation coefficient value with sample 852 are respectively selected as the first sample and the second sample ( Figure 8A step 806). Then, in step 808, the system may subtract the MAF values of the first selected sample 852 and the second selected sample 854, thereby generating a MAF change summary statistic similar to that Figure 8C shown. Using this MAF change summary statistic, in Figure 8A step 810, the system may determine the ( Figure 8C of) separation point 862 on the MAF change summary statistic (by a process similar to the process Figure 7B described), such as a case where there is a large difference (sharp edge).

[0152] "One-to-many" MAF change summary statistic 422 : In some embodiments, the process of generating the "one-to-many" MAF change summary statistic 422 may include determining the MAF value difference for each SNP between a selected index sample and the remaining samples in the multiple samples. Figure 9 Shows an example method for one-to-many MAF change summary statistics according to some embodiments. Step 902 may be similar to step 802 of the process 412 ( Figure 8A ). In step 906, an index sample (e.g., Figure 8B the first sample 852) may be selected as the sample with the highest average MAF value. In step 908, the MAF values of the index sample and each sample in the multiple samples may be subtracted at each SNP. In some embodiments, the subtraction may include subtracting from the cumulative minimum or cumulative maximum of the MAF values of the multiple samples. Then, as part of step 908, the results (from subtracting the cumulative minimum and subtracting the cumulative maximum) may be combined via a series of functions to generate the MAF change summary statistic. In step 910, a separation point may be determined on the MAF change summary statistic (by a process similar to the process Figure 7B described, such as a case where there is a large difference (sharp edge)).

[0153] "Many-to-many" MAF change summary statistic 432 : The process of generating the "many-to-many" MAF change summary statistic 432 includes determining the MAF value difference for each SNP between two selected index samples and the multiple samples. ( Figure 10A Step 1002) may be respectively similar toFigure 8A and Figure 9 is similar to step 802 / 902. In step 1006 of process 432, the first index sample (index-1 sample 1052 ( Figure 10B as shown)) can be selected as the sample with the highest average MAF value having an SNP in the higher end (the first set of multiple samples with a higher average MAF value), and the second index sample (index-2 sample 1056) can be selected as the sample with the highest average MAF value having an SNP in the lower end (the second set of multiple samples with a lower average MAF value). Then, for each pair of index-1 samples and the first set of multiple samples, and for each pair of index-2 samples and the second set of multiple samples, in step 1007, the system can subtract the MAF values and can combine them via a series of functions to generate a MAF change summary statistic value. In step 1008, a separation point can be determined on the MAF change summary statistic value by a process similar to the process [[ID=⑥]] Figure 7B [[ID=⑦]]described (such as in the case of a large difference (sharp edge)). [[ID=⑧]] [[ID=⑨]]

[0154] [[ID=⑩]]In some embodiments, the results from one or more processes can be compared (e.g., [[ID=⑪]] Figure 4 [[ID=⑫]]step 442) to determine the MAF change summary statistic value 452. [[ID=⑬]] Figures 11A to 11D [[ID=⑭]]shows an example comparison of the standard deviation simple MAF change summary statistic value 402 ([[ID=⑮]] Figure 11A [[ID=⑯]]), the MAF change summary statistic value ("one-to-one") 412 ([[ID=⑰]] Figure 11B [[ID=⑱]]), the MAF change summary statistic value ("one-to-many") 422 ([[ID=⑲]] Figure 11C [[ID=⑳]]), and the MAF change summary statistic value ("many-to-many") 432 ([[ID=㉑]] Figure 11D [[ID=㉒]]). The gap in the MAF change summary statistic value between two sets of SNPs ([[ID=㉓]] Figures 11B to 11D [[ID=㉔]]502 and 504 as shown) can be about 0, 4, 5, and 6 for the standard deviation, "one-to-one" statistic, "one-to-many" statistic, and "many-to-many" statistic, respectively. The gap is the difference between the average MAF values of SNP group 502 and SNP group 504. In some embodiments, due to having the highest gap (6), the MAF change summary statistic value 452 can be selected as the "many-to-many" statistic value 432. In some embodiments, the waterfall plot may not have a major group and a minor group, as [[ID=㉕]] Figure 11A [[ID=㉖]]shown in the figure. [[ID=㉗]] [[ID=㉘]]

[0155] [[ID=㉙]] Example determination of the amount of cell-free nucleic acids of contributor origin and determination of the risk of graft rejection [[ID=㉚]] [[ID=㉛]]

[0156] [[ID=㉜]]The system can use the MAF change summary statistic value to determine one or more amounts of cell-free nucleic acids (such as cell-free DNA) in a mixed sample. [[ID=㉝]] Figure 12An example flowchart of a method for determining the amount of cell-free nucleic acid (such as cell-free DNA of graft donor origin) for determining the contributor source according to some embodiments is shown. Method 1200 may include the system calculating the average MAF information of a set of SNPs in each group (step 1202). For example, the system may calculate a first average MAF information for the recipient-heterozygous group and a second average MAF information for the recipient-homozygous group.

[0157] In step 1204, the system may determine one or more multipliers based on the relationship between genomic contributors. In some embodiments, the multiplier may be used as an output for converting the calculated average MAF value of the grouped SNPs into the amount of nucleic acid of contributor origin. For example, when the graft recipient is a pregnant woman and the donor is unrelated to the graft recipient, for a group of SNPs with recipient-homozygous and fetal-homozygous genotypes, the multiplier may be 2 to generate a fraction of contributor origin.

[0158] In step 1206, the system may determine the amount of nucleic acid of contributor origin. In some embodiments, the amount of nucleic acid of contributor origin may be determined based on the average MAF value and the determined multiplier. For example, the summary statistics of MAF changes for each group of SNPs and the corresponding average MAF values may be associated with one or more genomic contributors. The product of the MAF value and the multiplier may yield the determined amount of cell-free DNA from the corresponding genomic contributor.

[0159] Embodiments of the present disclosure may include determining the risk of graft rejection in a graft recipient based on the determined amount of nucleic acid of contributor origin. In some embodiments, when the determined amount of nucleic acid of contributor origin is below a pre-determined threshold, the risk of graft rejection is determined to be low, and when the estimated amount of nucleic acid of contributor origin is greater than the pre-determined threshold, the risk of graft rejection is determined to be high.

[0160] Example system for determining the amount of nucleic acids of contributor origin in a mixed sample

[0161] The systems, kits, and methods discussed herein may be implemented by a device. Figure 13An example device implementing the disclosed systems, kits, and methods according to some embodiments is shown. In some embodiments, the one or more computing devices 1300 may perform one or more steps of one or more methods described or shown herein. In certain embodiments, the one or more computing devices 1300 may provide the functionality described or shown herein. In certain embodiments, software running on the one or more computing devices 1300 may perform one or more steps of one or more methods described or shown herein, or may provide the functionality described or shown herein. Certain embodiments may include one or more portions of the one or more computing devices 1300.

[0162] The present disclosure contemplates any suitable number of computing systems 1300. The present disclosure contemplates one or more computing devices 1300 in any suitable physical form. By way of example and not limitation, the one or more computing devices 1300 may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (e.g., a computer-on-module (COM) or a system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a computer system network, a mobile phone, a personal digital assistant (PDA), a server, a tablet computer system, an augmented / virtual reality device, or a combination of two or more of these. Where appropriate, the one or more computing devices 1300 may be single or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which may include one or more cloud components in one or more networks.

[0163] Where appropriate, the one or more computing devices 1300 may perform one or more steps of one or more methods described or shown herein without substantial spatial or temporal limitations. By way of example and not limitation, the one or more computing devices 1300 may perform one or more steps of one or more methods described or shown herein in real time or in batch mode. Where appropriate, the one or more computing devices 1300 may perform one or more steps of one or more methods described or shown herein at different times or at different locations.

[0164] In some embodiments, the one or more computing devices 1300 may include a processor 1302, a memory 1304, a database 1306, an input / output (I / O) interface device 1308, a communication interface device 1310, and a bus 1312. Although this disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, this disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement. In some embodiments, the processor 1302 may include hardware for executing instructions, such as those that make up a computer program. By way of example and not limitation, to execute instructions, the processor 1302 may retrieve (or fetch) the instructions from an internal register, an internal cache, the memory 1304, or the database 1306; decode and execute them; and then write one or more results to an internal register, an internal cache, the memory 1304, or the database 1306. In some embodiments, the processor 1302 may include one or more internal caches for data, instructions, or addresses. In appropriate instances, this disclosure contemplates a processor 1302 that includes any suitable number of any suitable internal caches. By way of example and not limitation, the processor 1302 may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). The instructions in the instruction cache may be copies of the instructions in the memory 1304 or the database 1306, and the instruction cache may speed up the retrieval of those instructions by the processor 1302.

[0165] The data in the data cache may be: a copy of the data in the memory 1304 or the database 1306 that causes an instruction to be executed at the processor 1302 to perform an operation; the result of a previous instruction that is executed at the processor 1302 and is accessed by a subsequent instruction that is executed at the processor 1302 or is written to the memory 1304 or the database 1306; or other suitable data. The data cache may speed up the read or write operations of the processor 1302. The TLB may speed up the virtual address translation of the processor 1302. In some embodiments, the processor 1302 may include one or more internal registers for data, instructions, or addresses. In appropriate instances, this disclosure contemplates a processor 1302 that includes any suitable number of any suitable internal registers. In appropriate instances, the processor 1302 may include one or more arithmetic logic units (ALUs); be a multi-core processor; or include one or more processors 1302. Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor.

[0166] In some embodiments, the memory 1304 includes a main memory for storing instructions for the processor 1302 to execute data for the operation of the processor 1302. By way of example and not limitation, the one or more computing devices 1300 may load instructions from the database 1306 or another source (such as, for example, another one or more computing devices 1300) into the memory 1304. The processor 1302 may then load the instructions from the memory 1304 into internal registers or an internal cache. To execute the instructions, the processor 1302 may retrieve the instructions from the internal registers or the internal cache and decode them. During or after the execution of the instructions, the processor 1302 may write one or more results (which may be intermediate results or final results) to the internal registers or the internal cache. The processor 1302 may then write one or more of those results to the memory 1304.

[0167] In some embodiments, the processor 1302 only executes instructions in one or more internal registers or the internal cache or in the memory 1304 (as opposed to in the database 1306 or elsewhere), and only operates on data in one or more internal registers or the internal cache or in the memory 1304 (as opposed to in the database 1306 or elsewhere). One or more memory buses (which may each include an address bus and a data bus) may couple the processor 1302 to the memory 1304. The bus 1312 may include one or more memory buses, as described below. In some embodiments, one or more memory management units (MMUs) reside between the processor 1302 and the memory 1304 and facilitate access to the memory 1304 requested by the processor 1302. In some embodiments, the memory 1304 includes random access memory (RAM). In appropriate cases, the RAM may be volatile memory. In appropriate cases, the RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Additionally, in appropriate cases, the RAM may be single-port or multi-port RAM. The present disclosure contemplates any suitable RAM. In appropriate cases, the memory 1304 may include one or more memory devices. Although the present disclosure describes and illustrates particular memories, the present disclosure contemplates any suitable memory.

[0168] In some embodiments, database 1306 includes a mass storage device for data or instructions. By way of example and not limitation, database 1306 can include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, database 1306 can include removable or non-removable (or fixed) media. In appropriate cases, database 1306 can be internal or external to the one or more computing devices 1300. In some embodiments, database 1306 is non-volatile solid state memory. In some embodiments, database 1306 includes read only memory (ROM). In appropriate cases, the ROM can be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these. The present disclosure contemplates a mass database 1306 in any suitable physical form. In appropriate cases, database 1306 can include one or more storage control units facilitating communication between processor 1302 and database 1306. In appropriate cases, database 1306 can include one or more databases 1306. Although the present disclosure describes and illustrates particular storage devices, the present disclosure contemplates any suitable storage device.

[0169] In some embodiments, I / O interaction device 1308 includes hardware, software, or both that provide one or more interaction devices for communication between the one or more computing devices 1300 and one or more I / O devices. In appropriate cases, the one or more computing devices 1300 can include one or more of these I / O devices. One or more of these I / O devices can enable communication between a person and the one or more computing devices 1300. By way of example and not limitation, I / O devices can include a keyboard, a keypad, a microphone, a monitor, a mouse, a printer, a scanner, a speaker, a still camera, a stylus, a tablet, a touch screen, a trackball, a video camera, another suitable I / O device, or a combination of two or more of these. The I / O devices can include one or more sensors. The present disclosure contemplates any suitable I / O devices and any suitable I / O interaction device 1308 therefor. In appropriate cases, I / O interaction device 1308 can include one or more devices or software drivers enabling processor 1302 to drive one or more of these I / O devices. In appropriate cases, I / O interaction device 1308 can include one or more I / O interaction devices 1308. Although the present disclosure describes and illustrates particular I / O interaction devices, the present disclosure contemplates any suitable I / O interaction device.

[0170] In some embodiments, the communication interaction device 1310 includes hardware, software, or both that provide one or more interaction devices for communication (such as, for example, packet-based communication) between the one or more computing devices 1300 and one or more other computing devices 1300 or one or more networks. By way of example and not limitation, the communication interaction device 1310 may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wired-based network, or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network (such as a WI-FI network). The present disclosure contemplates any suitable network and any suitable communication interaction device 1310 therefor.

[0171] By way of example and not limitation, the one or more computing devices 1300 may communicate with one or more parts of an ad hoc network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or the Internet, or a combination of two or more of these. One or more parts of one or more of these networks may be wired or wireless. As an example, the one or more computing devices 1300 may communicate with a wireless PAN (WPAN) (such as, for example, a Bluetooth WPAN), a WI-FI network, a WI-MAX network, a cellular telephone network (such as, for example, a Global System for Mobile Communications (GSM) network), or other suitable wireless networks, or a combination of two or more of these. In appropriate instances, the one or more computing devices 1300 may include any suitable communication interaction device 1310 for any of these networks. In appropriate instances, the communication interaction device 1310 may include one or more communication interaction devices 1310. Although the present disclosure describes and illustrates particular communication interaction devices, the present disclosure contemplates any suitable communication interaction device.

[0172] In some embodiments, bus 1312 includes hardware, software, or both that couple components of the one or more computing devices 1300 to each other. By way of example and not limitation, bus 1312 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HYPERTRANSPORT (HT) interconnector, an Industry Standard Architecture (ISA) bus, an INFINIBAND interconnector, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or another suitable bus or a combination of two or more of these. Where appropriate, bus 1312 may include one or more buses 1312. Although the present disclosure describes and shows particular buses, the present disclosure contemplates any suitable bus or interconnector.

[0173] As used herein, where appropriate, one or more computer-readable non-transitory storage media may include one or more semiconductor-based or other integrated circuits (ICs) (such as, for example, a Field Programmable Gate Array (FPGA) or an Application Specific IC (ASIC)), a hard disk drive (HDD), a hybrid hard disk drive (HHD), an optical disc, an optical disc drive (ODD), a magneto-optical disc, a magneto-optical drive, a floppy disk, a floppy disk drive (FDD), a magnetic tape, a solid state drive (SSD), a RAM drive, a SECURE DIGITAL card or drive, any other suitable computer-readable non-transitory storage medium, or any suitable combination of two or more of these. Where appropriate, the computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile.

[0174] The term one or more computer-readable non-transitory storage media may include a single medium or multiple media (such as, for example, a centralized or distributed database and / or associated caches and servers) that store one or more sets of instructions. The term "computer-readable non-transitory storage medium" should also be understood to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by a device and that causes the device to perform any one or more of the methods of the present invention. Thus, the term "computer-readable non-transitory storage medium" should be understood to include, without limitation, solid state memories, optical and magnetic media, and carrier signals.

[0175] Although the examples of the present disclosure have been described fully with reference to the accompanying drawings, it is noted that various changes and modifications will be apparent to those skilled in the art. Such changes and modifications should be understood to be included within the scope of the examples of the present disclosure as defined by the appended claims.

Claims

1. A computer-implemented method for determining the amount of cell-free nucleic acids of contributor origin in a mixed sample obtained from a graft recipient, the mixed sample comprising cell-free nucleic acids from at least three genetically distinct contributors, the method comprising: Receiving, via a computer or an input function, nucleic acid sequence data from a set of single nucleotide polymorphisms (SNPs) derived from the cell-free nucleic acids from the at least three genetically distinct contributors; Receiving the genomic relationships between the at least three genetically distinct contributors; Determining and grouping minor allele frequency (MAF) information from the set of SNPs; And Determining the amount of cell-free nucleic acids of contributor origin based on the genomic relationships and the MAF grouping.

2. The computer-implemented method according to claim 1, wherein the graft recipient is a pregnant woman, and the at least three genetically distinct contributors include a maternal genomic contributor, a fetal genomic contributor, and a graft donor genomic contributor.

3. The computer-implemented method according to claim 1, wherein the graft recipient has received at least two grafts, and the at least three genetically distinct contributors include a recipient genomic contributor, a first graft donor genomic contributor, and a second graft donor genomic contributor.

4. The computer-implemented method according to any one of claims 1 to 3, wherein the amount of cell-free nucleic acids of contributor origin is the percentage of the cell-free nucleic acids of contributor origin in the mixed sample.

5. The computer-implemented method according to any one of claims 1 to 4, wherein determining and grouping the MAF information is based on a set of longitudinal samples.

6. The computer-implemented method according to claim 5, wherein the set of longitudinal samples has the same genotype.

7. The computer-implemented method according to any one of claims 1 to 6, wherein the set of SNPs comprises fewer than 500 SNPs.

8. The computer-implemented method according to any one of claims 1 to 7, the method further comprising: Determining the genotype of one or more of the graft recipient, fetus, or donor based on the MAF information grouping, wherein the graft recipient is a pregnant woman.

9. The computer-implemented method according to any one of claims 1 to 8, wherein determining and grouping the MAF information comprises: Reordering the set of SNPs according to an average or median MAF value; Determining the MAF information that includes summary statistics of MAF changes in the set of SNPs; And Grouping the set of SNPs according to the summary statistics of MAF changes.

10. The computer-implemented method according to claim 9, wherein determining and grouping the MAF information comprises: Determining separation points in the summary statistics of MAF changes by determining local minima or maxima in a window.

11. The computer-implemented method according to claim 10, wherein the separation point is used to group the SNPs into homozygous genotype groups and heterozygous genotype groups.

12. The computer-implemented method according to any one of claims 1 to 11, wherein determining and grouping the MAF information comprises: generating a waterfall plot of the MAF information; grouping the MAF information by dividing the waterfall plot into groups; and calculating the average MAF value of the groups, wherein the amount of cell-free nucleic acid of the determined contributor source is based on the calculated average MAF value.

13. The computer-implemented method according to any one of claims 1 to 12, wherein determining and grouping the MAF information comprises: selecting a first sample having the highest average MAF value among a plurality of samples; selecting a second sample having the lowest correlation coefficient associated with the first sample; determining a summary statistic of MAF change by subtracting the MAF values of the selected first sample and the selected second sample; determining a separation point in the summary statistic of MAF change; and grouping the MAF information based on the separation point.

14. The computer-implemented method according to any one of claims 1 to 13, wherein determining and grouping the MAF information comprises: selecting an index sample having the highest average MAF value among a plurality of samples; determining the MAF difference between the index sample and each of the plurality of samples; determining a summary statistic of MAF change by combining the MAF differences; determining a separation point in the summary statistic of MAF change; and grouping the MAF information based on the separation point.

15. The computer-implemented method according to any one of claims 1 to 14, wherein determining and grouping the MAF information comprises: selecting a first index sample having the highest average MAF value among a group of highly re-ordered SNPs; selecting a second index sample having the highest average MAF among a group of lowly re-ordered SNPs; determining the MAF difference between the first index sample and each of the group of highly re-ordered SNPs; determining the MAF difference between the second index sample and each of the group of lowly re-ordered SNPs; determining a summary statistic of MAF change by combining the MAF differences; determining a separation point in the summary statistic of MAF change; and grouping the MAF information based on the separation point.

16. The computer-implemented method according to any one of claims 1 to 15, the method further comprising: generating a waterfall plot for the mixed sample, wherein the waterfall plot comprises one or more stepped levels having one or more steps, and the SNPs of the one or more steps have the same genotype.

17. The computer-implemented method according to any one of claims 1 to 16, wherein the graft recipient receives a graft comprising one or more of the following: kidney graft, heart graft, lung graft, liver graft, pancreas graft, vascularized composite graft, intestinal graft, gastric graft, testicular graft, penile graft, ovarian graft, uterine graft, thymic graft, facial graft, hand graft, leg graft, bone graft, corneal graft, skin graft, heart valve graft, vascular graft, or any combination thereof.

18. The computer-implemented method according to any one of claims 1 to 17, wherein the mixed sample is a blood sample.

19. A kit for determining the amount of cell-free nucleic acid of contributor origin in a mixed sample obtained from a graft recipient, the mixed sample comprising cell-free nucleic acid from at least three genetically distinct contributors, the kit comprising instructions for: Receiving, via a computer or input function, nucleic acid sequence data of a set of single nucleotide polymorphisms (SNPs) from the cell-free nucleic acid from the at least three genetically distinct contributors; Receiving the genomic relationships between the at least three genetically distinct contributors; Determining and grouping the minor allele frequency (MAF) information from the set of SNPs; And Determining the amount of cell-free nucleic acid of contributor origin based on the genomic relationships and the MAF grouping.

20. The kit according to claim 19, wherein the graft recipient is a pregnant woman, and the at least three genetically distinct contributors include a maternal genomics contributor, a fetal genomics contributor, and a graft donor genomics contributor.

21. The kit according to claim 19, wherein the graft recipient has received at least two grafts, and the at least three genetically distinct contributors include a recipient genomics contributor, a first graft donor genomics contributor, and a second graft donor genomics contributor.

22. The kit according to any one of claims 19 to 21, wherein the amount of cell-free nucleic acid of contributor origin is the percentage of the cell-free nucleic acid of contributor origin in the mixed sample.

23. The kit according to any one of claims 19 to 22, wherein determining and grouping the MAF information is based on a set of longitudinal samples.

24. The kit according to claim 23, wherein the set of longitudinal samples has the same genotype.

25. The kit according to any one of claims 19 to 24, wherein the set of SNPs comprises fewer than 500 SNPs.

26. The kit according to any one of claims 19 to 25, wherein the kit further comprises instructions for: Determining the genotype of one or more of the following based on the MAF information grouping: the graft recipient, the fetus, or the donor, wherein the graft recipient is a pregnant woman.

27. The kit according to any one of claims 19 to 26, wherein determining and grouping the MAF information includes: Re - ordering the set of SNPs according to the average or median MAF value; Determining the MAF information including the summary statistics of the MAF variation in the set of SNPs; And Grouping the set of SNPs according to the summary statistics of the MAF variation.

28. The kit according to claim 27, wherein determining and grouping the MAF information includes: Determining the separation points in the summary statistics of the MAF variation by determining local minima or maxima in a window.

29. The kit according to claim 28, wherein the separation points are used to group the SNPs into homozygous genotype groups and heterozygous genotype groups.

30. The kit according to any one of claims 19 to 29, wherein determining and grouping the MAF information includes: Generating a waterfall plot of the MAF information; Grouping the MAF information by splitting the waterfall plot into groups; And Calculating the average MAF value of the groups, wherein the amount of cell - free nucleic acid from the determined contributor source is based on the calculated average MAF value.

31. The kit according to any one of claims 19 to 30, wherein determining and grouping the MAF information includes: Selecting a first sample with the highest average MAF value among multiple samples; Selecting a second sample with the lowest correlation coefficient associated with the first sample; Determining the summary statistics of the MAF variation by subtracting the MAF values of the selected first sample and the selected second sample; Determining the separation points in the summary statistics of the MAF variation; And Grouping the MAF information based on the separation points.

32. The kit according to any one of claims 19 to 31, wherein determining and grouping the MAF information includes: Selecting an index sample with the highest average MAF value among multiple samples; Determining the MAF difference between the index sample and each of the multiple samples; Determining the summary statistics of the MAF variation by combining the MAF differences; Determining the separation points in the summary statistics of the MAF variation; And Grouping the MAF information based on the separation points.

33. The kit according to any one of claims 19 to 32, wherein determining and grouping the MAF information includes: Selecting a first index sample with the highest average MAF value in a set of highly re - ordered SNPs; Selecting a second index sample with the highest average MAF in a set of lowly re - ordered SNPs; Determining the MAF difference between the first index sample and each of the set of highly re - ordered SNPs; Determining the MAF difference between the second index sample and each of the set of lowly re - ordered SNPs; Determining the summary statistics of the MAF variation by combining the MAF differences; Determining the separation points in the summary statistics of the MAF variation; And Grouping the MAF information based on the separation points.

34. The kit according to any one of claims 19 to 33, wherein the kit further comprises instructions for: generating a waterfall plot for the mixed sample, wherein the waterfall plot comprises one or more step levels having one or more steps, and the SNPs of the one or more steps have the same genotype.

35. The kit according to any one of claims 19 to 34, wherein the graft recipient receives a graft comprising one or more of the following: kidney graft, heart graft, lung graft, liver graft, pancreas graft, vascularized composite graft, intestine graft, stomach graft, testicle graft, penis graft, ovary graft, uterus graft, thymus graft, face graft, hand graft, leg graft, bone graft, corneal graft, skin graft, heart valve graft, vascular graft, or any combination thereof.

36. The kit according to any one of claims 19 to 35, wherein the mixed sample is a blood sample.

37. A system for determining the amount of cell-free nucleic acid of contributor origin in a mixed sample obtained from a graft recipient, the mixed sample comprising cell-free nucleic acid from at least three genetically distinct contributors, the system comprising: an interaction device configured to: receive an input; a determination unit configured to: receive, via the interaction device, nucleic acid sequence data from a set of single nucleotide polymorphisms (SNPs) from the cell-free nucleic acid derived from the at least three genetically distinct contributors; receive the genomic relationship between the at least three genetically distinct contributors; determine and group the minor allele frequency (MAF) information from the set of SNPs; and determine the amount of cell-free nucleic acid of contributor origin based on the genomic relationship and the MAF grouping.

38. The system according to claim 37, wherein the graft recipient is a pregnant woman, and the at least three genetically distinct contributors comprise a maternal genomics contributor, a fetal genomics contributor, and a graft donor genomics contributor.

39. The system according to claim 37, wherein the graft recipient has received at least two grafts, and the at least three genetically distinct contributors comprise a recipient genomics contributor, a first graft donor genomics contributor, and a second graft donor genomics contributor.

40. The system according to any one of claims 37 to 39, wherein the amount of cell-free nucleic acid of contributor origin is the percentage of cell-free nucleic acid of contributor origin in the mixed sample.

41. The system according to any one of claims 37 to 40, wherein the determination and grouping of the MAF information is based on a set of longitudinal samples.

42. The system according to claim 41, wherein the set of longitudinal samples has the same genotype.

43. The system according to any one of claims 37 to 42, wherein the set of SNPs comprises fewer than 500 SNPs.

44. The system according to any one of claims 37 to 43, wherein the determining unit is further configured to: Determine the genotype of one or more of the following based on the MAF information groups: the graft recipient, the fetus, or the donor, wherein the graft recipient is a pregnant woman.

45. The system according to any one of claims 37 to 44, wherein the determining unit configured to determine and group the MAF information comprises the determining unit configured to perform the following operations: Reorder the set of SNPs according to the average or median MAF value; Determine the MAF information including the summary statistics of the MAF changes in the set of SNPs; and Group the set of SNPs according to the summary statistics of the MAF changes.

46. The system according to claim 45, wherein the determining unit configured to determine and group the MAF information comprises the determining unit configured to perform the following operations: Determine the separation points in the summary statistics of the MAF changes by determining the local minima or maxima in the window.

47. The system according to claim 46, wherein the separation points are used to group the SNPs into homozygous genotype groups and heterozygous genotype groups.

48. The system according to any one of claims 37 to 47, wherein the determining unit configured to determine and group the MAF information comprises the determining unit configured to perform the following operations: Generate a waterfall plot of the MAF information; Group the MAF information by dividing the waterfall plot into groups; and Calculate the average MAF value of the groups, wherein the amount of cell-free nucleic acid of the determined contributor source is based on the calculated average MAF value.

49. The system according to any one of claims 37 to 48, wherein the determining unit configured to determine and group the MAF information comprises the determining unit configured to perform the following operations: Select a first sample with the highest average MAF value among multiple samples; Select a second sample with the lowest correlation coefficient associated with the first sample; Determine the summary statistics of the MAF changes by subtracting the MAF values of the selected first sample and the selected second sample; Determine the separation points in the summary statistics of the MAF changes; And Group the MAF information based on the separation points.

50. The system according to any one of claims 37 to 49, wherein the determining unit configured to determine and group the MAF information comprises the determining unit configured to perform the following operations: Select an index sample with the highest average MAF value among multiple samples; Determine the MAF difference between the index sample and each sample in the multiple samples; Determine the summary statistics of the MAF changes by combining the MAF differences; Determine the separation points in the summary statistics of the MAF changes; And Group the MAF information based on the separation points.

51. The system according to any one of claims 37 to 50, wherein the determining unit configured to determine and group MAF information comprises the determining unit configured to perform the following operations: Select a first index sample that includes the highest average MAF value among a set of highly re-ranked SNPs; Select a second index sample that includes the highest average MAF among a set of lowly re-ranked SNPs; Determine the MAF difference between the first index sample and each of the set of highly re-ranked SNPs; Determine the MAF difference between the second index sample and each of the set of lowly re-ranked SNPs; Determine a summary statistic of MAF change by combining the MAF differences; Determine a break point in the summary statistic of MAF change; And Group the MAF information based on the break point.

52. The system according to any one of claims 37 to 51, wherein the determining unit is further configured to: Generate a waterfall plot for the mixed sample, wherein the waterfall plot includes one or more staircase levels having one or more steps, and the SNPs of the one or more steps have the same genotype.

53. The system according to any one of claims 37 to 52, wherein the graft recipient receives a graft including one or more of the following: kidney graft, heart graft, lung graft, liver graft, pancreas graft, vascularized composite graft, intestine graft, stomach graft, testicular graft, penile graft, ovarian graft, uterine graft, thymus graft, facial graft, hand graft, leg graft, bone graft, corneal graft, skin graft, heart valve graft, vascular graft, or any combination thereof.

54. The system according to any one of claims 37 to 53, wherein the mixed sample is a blood sample.

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