Determination of the amount of contributor-derived nucleic acid in a mixed sample (3+ genetically distinct genomic contributors) of transplant recipients

Abstract

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

Description

[Technical Field]

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 435,153, filed December 23, 2022, the contents of which are incorporated herein by reference in their entirety for all purposes.

[0002] The present disclosure generally relates to computer-implemented systems, kits, and methods for determining the amount of contributor-derived nucleic acid in a biological sample from a transplant recipient containing nucleic acid from two or more genetically distinct genomic contributors, without prior genotype knowledge. [Background technology]

[0003] Monitoring the health of transplant organs, tissues, and cells received by a transplant recipient from a donor using polymorphic markers is particularly complicated when the recipient carries additional genomes from genetically distinct contributors, related to the donor, and / or when genotype information, such as single nucleotide polymorphism (SNP) genotype information, is not available to identify which alleles belong to which genome contributor. This may be the case in a multi-organ transplant scenario where the transplant recipient receives at least one organ, tissue, and / or cell transplant from one or more donors simultaneously or sequentially, which then become genome contributors, resulting in a genetically distinct contributor that includes, for example, a recipient genome contributor, a first transplant donor genome contributor, and a second transplant donor genome contributor if the transplant recipient has received at least two transplants.

[0004] Alternatively, or in addition, a transplant recipient may carry additional genomes from at least two genetically distinct contributors if conceived before or after receiving at least one organ, tissue, and / or cell transplant, where both the fetus and the graft are contributors, such that the genetically distinct contributors include, for example, a maternal genomic contributor, a fetal genomic contributor, and a transplant donor genomic contributor.

[0005] Currently, there is an unmet need to monitor the health of transplanted organs, tissues, and cells in transplant recipients who, due to pregnancy and receipt of at least one organ, tissue, and / or cell transplant, or due to receipt of two or more simultaneous or sequential organ, tissue, and / or cell transplants, possess additional genomes from at least two genetically distinct contributors. Summary of the Invention

[0006] Disclosed are computer-implemented methods for determining the amount of cell-free nucleic acid from contributors in a mixed sample obtained from a transplant recipient, the mixed sample containing cell-free nucleic acid from at least three genetically distinct contributors. The method includes receiving, via a computer or input function, nucleic acid sequence data from a panel of single nucleotide polymorphisms (SNPs) from the cell-free nucleic acid from the at least three genetically distinct contributors, receiving genomic relationships between the at least three genetically distinct contributors, determining and grouping minor allele frequency (MAF) information from the panel of SNPs, and determining the amount of cell-free nucleic acid from the contributors based on the genomic relationships and MAF grouping. Additionally or alternatively, in some embodiments, the transplant recipient is a pregnant woman, and the at least three genetically distinct contributors include a maternal genomic contributor, a fetal genomic contributor, and a transplant donor genomic contributor. Additionally or alternatively, in some embodiments, the transplant recipient has received at least two transplants, and the at least three genetically distinct contributors include a recipient genomic contributor, a first transplant donor genomic contributor, and a second transplant donor genomic contributor. Additionally or alternatively, in some embodiments, the amount of cell-free nucleic acid derived from the contributor is a percentage of cell-free nucleic acid derived from the contributor 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 have the same genotype. Additionally or alternatively, in some embodiments, the panel 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 transplant recipient, the fetus, or the donor based on the MAF information grouping, wherein the transplant recipient is a pregnant woman.Additionally or alternatively, in some embodiments, determining and grouping the MAF information comprises rearranging the panel of SNPs according to a mean or median MAF value, determining MAF information comprising a MAF variation summary statistic in the panel of SNPs, and grouping the panel of SNPs according to the MAF variation summary statistic. Additionally or alternatively, in some embodiments, determining and grouping the MAF information comprises determining a split point within the MAF variation summary statistic by determining a minimum or maximum within a window. Additionally or alternatively, in some embodiments, the split point is used to group the SNPs into homozygous and heterozygous genotype groups. Additionally or alternatively, in some embodiments, determining and grouping the MAF information comprises generating a waterfall plot of the MAF information, grouping the waterfall plot of the MAF information, grouping the MAF information by segmenting the waterfall plot into groups, and calculating an average MAF value for the groups, wherein the amount of cell-free nucleic acid from the determined contributor 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 including the highest average MAF value among the plurality of samples, selecting a second sample including the lowest correlation coefficient associated with the first sample, determining a MAF variation summary statistic by subtracting the MAF values ​​of the selected first sample and the selected second sample, determining a separation point in the MAF variation summary statistic, 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 including the highest average MAF value among the plurality of samples, determining a MAF difference between the index sample and each of the plurality of samples, determining a MAF variation summary statistic by merging the MAF differences, determining a separation point in the MAF variation summary statistic, and grouping the MAF information based on the separation point.Additionally or alternatively, in some embodiments, determining and grouping the MAF information comprises: selecting a first index sample having the highest average MAF value among the set of highly rearranged SNPs; selecting a second index sample having the highest average MAF among the set of low rearranged SNPs; determining a MAF difference between the first index sample and each of the set of highly rearranged SNPs; determining a MAF difference between the second index sample and each of the set of low rearranged SNPs; determining a MAF variation summary statistic by merging the MAF differences; determining a split point in the MAF variation summary statistic; and grouping the MAF information based on the split point. Additionally or alternatively, in some embodiments, the computer-implemented method further comprises generating a waterfall plot of the mixed sample, wherein the waterfall plot comprises one or more hierarchical steps 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 transplant recipient has received a transplant comprising one or more of a kidney transplant, a heart transplant, a lung transplant, a liver transplant, a pancreas transplant, a vascularized composite transplant, an intestine transplant, a stomach transplant, a testis transplant, a penis transplant, an ovary transplant, a uterus transplant, a thymus transplant, a face transplant, a hand transplant, a leg transplant, a bone transplant, a cornea transplant, a skin transplant, a heart valve transplant, a blood vessel transplant, or any combination thereof. Additionally or alternatively, in some embodiments, the mixed sample is a blood sample.

[0007] Disclosed is a kit for determining the amount of cell-free nucleic acid from contributors in a mixed sample obtained from a transplant recipient, the mixed sample containing cell-free nucleic acid from at least three genetically distinct contributors. The kit includes instructions for receiving, via a computer or input function, nucleic acid sequence data from a panel of single nucleotide polymorphisms (SNPs) from the cell-free nucleic acid from the at least three genetically distinct contributors, instructions for receiving genomic relationships between the at least three genetically distinct contributors, determining and grouping minor allele frequency (MAF) information from the panel of SNPs, and determining the amount of cell-free nucleic acid from the contributors based on the genomic relationships and MAF grouping. Additionally or alternatively, in some embodiments, the transplant recipient is a pregnant woman, and the at least three genetically distinct contributors include a maternal genomic contributor, a fetal genomic contributor, and a transplant donor genomic contributor. Additionally or alternatively, in some embodiments, the transplant recipient has received at least two transplants, and the at least three genetically distinct contributors include a recipient genomic contributor, a first transplant donor genomic contributor, and a second transplant donor genomic contributor. Additionally or alternatively, in some embodiments, the amount of cell-free nucleic acid from the contributors is a percentage of cell-free nucleic acid from the contributors 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 panel of SNPs includes fewer than 500 SNPs. Additionally or alternatively, in some embodiments, the kit further includes instructions for determining the genotype of one or more of the transplant recipient, fetus, or donor based on the MAF information grouping, and the transplant recipient is a pregnant woman.Additionally or alternatively, in some embodiments, determining and grouping the MAF information comprises rearranging the panel of SNPs according to a mean or median MAF value, determining MAF information comprising a MAF variation summary statistic in the panel of SNPs, and grouping the panel of SNPs according to the MAF variation summary statistic. Additionally or alternatively, in some embodiments, determining and grouping the MAF information comprises determining a split point within the MAF variation summary statistic by determining a minimum or maximum within a window. Additionally or alternatively, in some embodiments, the split point is used to group the SNPs into homozygous and heterozygous genotype groups. Additionally or alternatively, in some embodiments, determining and grouping the MAF information comprises generating a waterfall plot of the MAF information, grouping the waterfall plot of the MAF information, grouping the MAF information by segmenting the waterfall plot into groups, and calculating an average MAF value for the groups, wherein the amount of cell-free nucleic acid from the determined contributor 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 including the highest average MAF value among the plurality of samples, selecting a second sample including the lowest correlation coefficient associated with the first sample, determining a MAF variation summary statistic by subtracting the MAF values ​​of the selected first sample and the selected second sample, determining a separation point in the MAF variation summary statistic, 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 including the highest average MAF value among the plurality of samples, determining a MAF difference between the index sample and each of the plurality of samples, determining a MAF variation summary statistic by merging the MAF differences, determining a separation point in the MAF variation summary statistic, and grouping the MAF information based on the separation point.Additionally or alternatively, in some embodiments, determining and grouping the MAF information comprises: selecting a first index sample having the highest average MAF value among the set of highly rearranged SNPs; selecting a second index sample having the highest average MAF among the set of low rearranged SNPs; determining a MAF difference between the first index sample and each of the set of highly rearranged SNPs; determining a MAF difference between the second index sample and each of the set of low rearranged SNPs; determining a MAF variation summary statistic by merging the MAF differences; determining a split point in the MAF variation summary statistic; and grouping the MAF information based on the split point. Additionally or alternatively, in some embodiments, the kit further comprises instructions for generating a waterfall plot for the mixed sample, the waterfall plot comprising one or more hierarchical staircases having one or more steps, wherein the SNPs of one or more steps have the same genotype. Additionally or alternatively, in some embodiments, the transplant recipient has received a transplant comprising one or more of a kidney transplant, a heart transplant, a lung transplant, a liver transplant, a pancreas transplant, a vascularized composite transplant, an intestine transplant, a stomach transplant, a testis transplant, a penis transplant, an ovary transplant, a uterus transplant, a thymus transplant, a face transplant, a hand transplant, a leg transplant, a bone transplant, a cornea transplant, a skin transplant, a heart valve transplant, a blood vessel transplant, or any combination thereof. Additionally or alternatively, in some embodiments, the mixed sample is a blood sample.

[0008] A system for determining the amount of cell-free nucleic acid from contributors in a mixed sample obtained from a transplant recipient, the mixed sample containing cell-free nucleic acid from at least three genetically distinct contributors, is disclosed. The system includes an interface configured to receive an input; and a determination unit configured to: receive, via the interface, nucleic acid sequence data from a panel of single nucleotide polymorphisms (SNPs) from the cell-free nucleic acid from the at least three genetically distinct contributors; receive genomic relationships between the at least three genetically distinct contributors; determine and group minor allele frequency (MAF) information from the panel of SNPs; and determine the amount of cell-free nucleic acid from the contributors based on the genomic relationships and MAF grouping. Additionally or alternatively, in some embodiments, the transplant recipient is a pregnant woman, and the at least three genetically distinct contributors include a maternal genomic contributor, a fetal genomic contributor, and a transplant donor genomic contributor. Additionally or alternatively, in some embodiments, the transplant recipient has received at least two transplants, and the at least three genetically distinct contributors include a recipient genomic contributor, a first transplant donor genomic contributor, and a second transplant donor genomic contributor. Additionally or alternatively, in some embodiments, the amount of cell-free nucleic acid from the contributors is a percentage of cell-free nucleic acid from the contributors 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 panel 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 transplant recipient, the fetus, or the donor based on the MAF information grouping, and the transplant recipient is a pregnant woman.Additionally or alternatively, in some embodiments, the determining unit configured to determine and group the MAF information comprises a determining unit configured to rearrange the panel of SNPs according to a mean or median MAF value, determine MAF information comprising a MAF variation summary statistic in the panel of SNPs, and group the panel of SNPs according to the MAF variation summary statistic. Additionally or alternatively, in some embodiments, the determining unit configured to determine and group the MAF information comprises a determining unit configured to determine a split point in the MAF variation summary statistic by determining a minimum or maximum within a window. Additionally or alternatively, in some embodiments, the split point is used to group the SNPs into homozygous and heterozygous genotype groups. Additionally or alternatively, in some embodiments, the determination unit configured to determine and group the MAF information comprises a determination unit configured to: generate a waterfall plot of the MAF information, group the waterfall plot of the MAF information, group the MAF information by segmenting the waterfall plot into groups, and calculate an average MAF value for the group, wherein the determined amount of cell-free nucleic acid from the contributor is based on the calculated average MAF value. Additionally or alternatively, in some embodiments, the determination unit configured to determine and group the MAF information comprises a determination unit configured to: select a first sample having the highest average MAF value among the plurality of samples, select a second sample having the lowest correlation coefficient associated with the first sample, determine a MAF variation summary statistic by subtracting the MAF values ​​of the selected first sample and the selected second sample, determine a split point in the MAF variation summary statistic, and group the MAF information based on the split point.Additionally or alternatively, in some embodiments, the determination unit configured to determine and group the MAF information comprises a determination unit configured to: select an index sample including the highest average MAF value among the plurality of samples; determine MAF differences between the index sample and each of the plurality of samples; determine a MAF variation summary statistic by merging the MAF differences; determine separation points in the MAF variation summary statistic; and group the MAF information based on the separation points. Additionally or alternatively, in some embodiments, the determining unit configured to determine and group the MAF information comprises a determining unit configured to: select a first index sample having the highest average MAF value among the set of highly rearranged SNPs, select a second index sample having the highest average MAF among the set of low rearranged SNPs, determine a MAF difference between the first index sample and each of the set of highly rearranged SNPs, determine a MAF difference between the second index sample and each of the set of low rearranged SNPs, determine a MAF variation summary statistic by merging the MAF differences, determine a split point in the MAF variation summary statistic, and group the MAF information based on the split point. Additionally or alternatively, in some embodiments, the determining unit is further configured to generate a waterfall plot of the mixed sample, the waterfall plot including one or more hierarchical staircases having one or more steps, wherein the SNPs of one or more steps have the same genotype. Additionally or alternatively, in some embodiments, the transplant recipient has received a transplant comprising one or more of a kidney transplant, a heart transplant, a lung transplant, a liver transplant, a pancreas transplant, a vascularized composite transplant, an intestine transplant, a stomach transplant, a testis transplant, a penis transplant, an ovary transplant, a uterus transplant, a thymus transplant, a face transplant, a hand transplant, a leg transplant, a bone transplant, a cornea transplant, a skin transplant, a heart valve transplant, a blood vessel transplant, or any combination thereof. Additionally or alternatively, in some embodiments, the mixed sample is a blood sample. [Brief explanation of the drawings]

[0009] [Figure 1] 1 shows an exemplary system for determining the amount of nucleic acid from a contributor in a biological sample, according to some embodiments. [Figure 2] 1 shows a flowchart of an exemplary general computer-implemented method for determining the amount of contributor-derived nucleic acid in a mixed sample containing cell-free nucleic acid from at least three genomic and genetically distinct contributors, according to some embodiments. [Figure 3] 1 shows a flowchart of an exemplary computer-implemented method for determining the amount of contributor-derived nucleic acid in a mixed sample containing cell-free nucleic acid from at least three genomic and genetically distinct contributors, according to some embodiments. [Figure 4] 1 illustrates an exemplary method for grouping single nucleotide polymorphisms (SNPs) and inferring SNP genotypes for some or all genomic contributors, according to some embodiments. [Figure 5A] 1 shows an example of a waterfall plot of minor allele frequency (MAF) values ​​for a panel of single nucleotide polymorphisms (SNPs), according to some embodiments. [Figure 5B] 1 shows an example of a waterfall plot of minor allele frequency (MAF) values ​​for a panel of single nucleotide polymorphisms (SNPs), according to some embodiments. [Figure 6] 1 shows an example of a waterfall plot of minor allele frequency (MAF) values ​​for a panel of single nucleotide polymorphisms (SNPs), according to some embodiments. [Figure 7A] 1 shows an exemplary flowchart of a method for determining minor allele frequency (MAF) information in a panel of single nucleotide polymorphisms (SNPs), according to some embodiments. [Figure 7B] 1 shows an exemplary plot of minor allele frequency (MAF) variation summary statistics, according to some embodiments. [Figure 7C]1 shows an exemplary minor allele frequency (MAF) waterfall plot and a plot of the MAF variation summary statistic, including three groups of single nucleotide polymorphisms (SNPs) with distinct genotypes of one of the genomic contributors (Transplant Donor 1), according to some embodiments of the present disclosure. [Figure 8A] 1 shows an exemplary method for one-to-one minor allele frequency (MAF) variation summary statistics, according to some embodiments. [Figure 8B] 1 shows an example of a minor allele frequency (MAF) waterfall plot for five samples, according to some embodiments. [Figure 8C] 1 shows exemplary minor allele frequency (MAF) variation summary statistics, according to some embodiments. [Figure 9] 1 shows an exemplary method for one-versus-total minor allele frequency (MAF) variation summary statistics, according to some embodiments. [Figure 10A] 1 shows an exemplary method for all-to-all minor allele frequency (MAF) variation summary statistics, according to some embodiments. [Figure 10B] 1 shows an exemplary plot of minor allele frequency (MAF) values ​​for multiple samples, according to some embodiments. [Figure 11A] 1 shows an exemplary comparison of standard deviation simple minor allele frequency (MAF) variation summary statistics, one-to-one MAF variation summary statistics, one-to-one MAF variation summary statistics, and all-to-all MAF variation summary statistics, according to some embodiments. [Figure 11B] 1 shows an exemplary comparison of standard deviation simple minor allele frequency (MAF) variation summary statistics, one-to-one MAF variation summary statistics, one-to-one MAF variation summary statistics, and all-to-all MAF variation summary statistics, according to some embodiments. [Figure 11C] 1 shows an exemplary comparison of standard deviation simple minor allele frequency (MAF) variation summary statistics, one-to-one MAF variation summary statistics, one-to-one MAF variation summary statistics, and all-to-all MAF variation summary statistics, according to some embodiments. [Figure 11D]1 shows an exemplary comparison of standard deviation simple minor allele frequency (MAF) variation summary statistics, one-to-one MAF variation summary statistics, one-to-one MAF variation summary statistics, and all-to-all MAF variation summary statistics, according to some embodiments. [Figure 12] 1 shows an exemplary flowchart of a method for determining the amount of cell-free nucleic acid from a contributor, according to some embodiments. [Figure 13] 1 illustrates an exemplary device for implementing the disclosed systems, kits, and methods, according to some embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0010] The following description is presented to enable any person skilled in the art to make and use various embodiments. Descriptions of specific devices, techniques, and applications are provided only as examples. Various modifications to the examples described herein will be readily apparent to those skilled in the art, and the general principles defined herein may 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 are to be accorded the scope consistent with the claims. All references cited herein, including patent applications and publications, are incorporated herein by reference in their entirety.

[0011] definition The terminology used in the description of various embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It will also be understood that the term "and / or," as used herein, refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms "includes," "including," "comprises," and / or "comprising," when used herein, specify the presence of stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof.

[0012] As used herein, the term "sample" or "biological sample" refers to any sample obtained from a transplant recipient, including, but not limited to, whole blood, plasma, serum, peripheral blood mononuclear cells, lymph, buccal swab, saliva, sputum, tears, sweat, ear fluid, bone marrow suspension, urine, feces, lung lavage fluid, semen, vaginal fluid, cerebrospinal fluid, brain fluid, peritoneal fluid, milk, respiratory secretions, intestinal or urinary tract fluid, or tissue and / or cells from a biopsy.

[0013] The term "amount" as used herein refers to any quantitative value resulting from the analysis of nucleic acids and may be expressed as a relative or absolute value.

[0014] As used herein, the term "graft" refers to the transplantation of any cells, tissues, or organs (including combinations thereof) from a donor to a recipient. The term "graft" with respect to a tissue or organ can refer to the entire tissue or organ (e.g., the entire liver) or a portion thereof. "Graft" refers to any organ, tissue, or cell transplant that is transplanted alone or in combination with one or more other organ, tissue, or cell transplants.

[0015] The term "organ transplant" as used herein encompasses both solid and hollow organ transplants, including, but not limited to, kidney transplants, heart transplants, lung transplants, liver transplants, pancreas transplants, vascularized composite transplants, intestine transplants, stomach transplants, testis transplants, penis transplants, ovary transplants, uterus transplants, thymus transplants, face transplants, hand transplants, leg transplants, bone transplants, cornea transplants, skin transplants, heart valve transplants, blood vessel transplants, or any combination thereof.

[0016] The term "tissue transplant" includes tissue transplants such as skin tissue, and organ tissue transplants such as ovarian tissue transplants, kidney tissue transplants, lung tissue transplants, pancreatic tissue transplants, esophageal tissue transplants, spleen tissue transplants, or any combination thereof.

[0017] The term "cell graft" includes cellular grafts such as pluripotent stem cells, multipotent stem cells, blood-forming 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 harvested directly from a transplant donor for administration to the transplant recipient, cells harvested from a transplant donor and genetically engineered before administration to the transplant recipient, cells harvested from a transplant donor and cultured before administration to the transplant recipient, cells harvested from a transplant donor and subjected to a manufacturing process before administration to the transplant recipient, and any combination thereof. Cells may also be stored prior to administration to the transplant recipient (i.e., "off-the-shelf" cells).

[0018] As used herein, the term "donor" or "transplant donor" refers to a human or non-human subject that is genetically distinguishable from the transplant recipient, where the "donor" provides organs, tissues, and / or cells for transplantation into the transplant 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 transplant recipient. In some embodiments, a graft from a human or non-human donor is transplanted into a non-human transplant recipient. In some embodiments, the donor and transplant recipient belong to the same species. In some embodiments, the donor and transplant recipient belong to different species, e.g., the donor is a non-human subject, such as an animal, while the transplant recipient is a human subject.

[0019] As used herein, the term "nucleic acid" refers to RNA or DNA, which may be linear, circular, or branched, single-stranded, 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 of DNA, RNA, mRNA, miRNA, double-stranded DNA, single-stranded DNA, single-stranded DNA hairpins, DNA / RNA hybrids, RNA hairpins, 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.

[0020] As used herein, the term "cell-free nucleic acid" refers to nucleic acid that exists outside of cells and can circulate. In some embodiments, cell-free nucleic acid is nucleic acid that exists outside of cells and circulates in various bodily fluids of a transplant 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 hairpins, DNA / RNA hybrids, RNA hairpins, and fragments and combinations thereof that exist outside of cells. Cell-free DNA can originate from various locations within a cell, for example, nuclear DNA and mitochondrial DNA.

[0021] As used herein, the term "cellular nucleic acid" or "cellular nucleic acid" refers to nucleic acid present within a cell. In some embodiments, cellular nucleic acid or cellular nucleic acid is nucleic acid present within the cells and various bodily fluids (e.g., blood, plasma, serum, urine, etc.) of a transplant recipient. In some embodiments, cellular nucleic acid or cellular nucleic acid refers to any DNA, RNA, mRNA, miRNA, double-stranded DNA, single-stranded DNA, single-stranded DNA hairpin, DNA / RNA hybrid, or RNA hairpin present within a cell.

[0022] The term "polymorphic marker" as used herein refers to a polymorphic locus at which two or more alternative nucleic acid sequences or alleles exist due to, for example, one or more base changes, one or more insertions, one or more repeats, one or more deletions, and variations thereof. A polymorphic marker may also be a locus at which one or more bases are modified by methylation. Polymorphic markers may include single nucleotide polymorphisms (SNPs), short tandem repeats (STRs), restriction fragment length polymorphisms (RFLPs), variable number of tandem repeats (VNTRs), hypervariable regions, minisatellites, dinucleotide repeats, trinucleotide repeats, tetranucleotide repeats, simple sequence repeats, and insertion elements.

[0023] As used herein, the term "mixed sample" refers to a biological sample obtained from a transplant recipient that contains nucleic acids from multiple genetically distinct contributors. For example, a sample obtained from a pregnant transplant recipient of a single transplant may contain nucleic acids from three genetically distinct contributors, such as nucleic acids from the transplant recipient, e.g., "contributor 1," nucleic acids from the transplant, e.g., "contributor 2," and nucleic acids from the fetus, e.g., "contributor 3." As a further example, a sample obtained from a non-pregnant transplant recipient of two or more simultaneous or sequential transplants from different donors may contain nucleic acids from three or more genetically distinct contributors, such as nucleic acids from the transplant recipient, e.g., "contributor 1," nucleic acids from transplant "A," e.g., "contributor 2," and nucleic acids from transplant "B," e.g., "contributor 3." As a further example, a sample obtained from a pregnant transplant recipient of two or more simultaneous or sequential transplants from different donors may contain nucleic acids from four or more genetically distinct contributors, e.g., nucleic acids derived from the transplant recipient, e.g., "contributor 1," nucleic acids derived from two transplants, e.g., "contributor 2" and "contributor 3," and nucleic acids derived from the fetus, e.g., "contributor 4."

[0024] Overview The present disclosure is based, at least in part, on the development of applicant's computer-implemented systems, kits, and methods for determining (including estimating) the amount of contributor-derived nucleic acids in a biological sample from a transplant recipient that contains nucleic acids from two or more genetically distinct contributors, which may be useful for monitoring the status of the transplant, e.g., with respect to assessing the risk of transplant rejection. In some embodiments, the status of the transplant may be classified and monitored based on the determined amount of contributor-derived nucleic acids in a biological sample from the transplant recipient, which may contain nucleic acids from at least three genetically distinct contributors, e.g., the transplant recipient, the fetus, and the transplant donor. For example, the systems and methods determine an estimated percentage of contributor-derived nucleic acids and / or an estimated percentage of fetal-derived nucleic acids. In some embodiments, the status of the transplant may be classified and monitored based on the determined (e.g., estimated) amount of contributor-derived nucleic acids in a biological sample from the transplant recipient, which may contain nucleic acids from at least three genetically distinct contributors, e.g., the transplant recipient, the first transplant donor, and the second transplant donor.

[0025] In some embodiments, the nucleic acid from the contributor may be cell-free nucleic acid from the transplant donor, e.g., cell-free DNA from the contributor. In some embodiments, the nucleic acid from the contributor may be cellular nucleic acid (cellular nucleic acid) from the transplant donor, e.g., DNA from cells (cellular DNA) from the contributor. In some embodiments, the nucleic acid from the contributor may be cell-free nucleic acid from a fetus, e.g., DNA from a fetus or fetal cell-free DNA. In some embodiments, the nucleic acid from the contributor may be cell-free nucleic acid from a transplant recipient, e.g., cell-free DNA from a transplant recipient.

[0026] In some embodiments, the transplant recipient has received an organ transplant and is pregnant. In some embodiments, the transplant recipient has received an organ transplant and is recently pregnant, e.g., within the past 1, 3, 6, or 12 months. In some embodiments, the transplant recipient has received a tissue transplant and is pregnant. In some embodiments, the transplant recipient has received a tissue transplant and is recently pregnant, e.g., within the past 1, 3, 6, or 12 months. In some embodiments, the transplant recipient has received a cell transplant and is pregnant. In some embodiments, the transplant recipient has received a cell transplant and is recently pregnant, e.g., within the past 1, 3, 6, or 12 months.

[0027] In some embodiments, the transplant recipient has received multiple simultaneous or sequential organ transplants from multiple genetically distinct transplant donors and is pregnant, hi some embodiments, the transplant recipient has received multiple simultaneous or sequential organ transplants from multiple genetically distinct transplant donors and is recently pregnant, e.g., within the past 1, 3, 6, or 12 months.

[0028] In some embodiments, the transplant recipient has received multiple simultaneous or sequential tissue transplants from multiple genetically distinct transplant donors and is pregnant, hi some embodiments, the transplant recipient has received multiple simultaneous or sequential tissue transplants from multiple genetically distinct transplant donors and has recently become pregnant, e.g., within the past 1, 3, 6, or 12 months.

[0029] In some embodiments, the transplant recipient may not be pregnant or may not have been recently pregnant. In some embodiments, the transplant recipient has received multiple simultaneous or sequential organ transplants from multiple genetically distinguishable transplant donors. In some embodiments, the transplant recipient has received multiple simultaneous or sequential tissue transplants from multiple genetically distinguishable transplant donors. In some embodiments, the transplant recipient has received multiple simultaneous or sequential cell transplants from multiple genetically distinguishable transplant donors. In some embodiments, the transplant recipient has received multiple simultaneous or sequential organ, tissue, and / or cell transplants from multiple genetically distinguishable transplant donors.

[0030] Various embodiments described herein can be performed without prior genotype knowledge (predetermined genotype) from any genomic contributor, such as SNP genotype information for identifying which allele belongs to which genomic contributor for a particular SNP. Thus, the amount of cellular or cell-free nucleic acid from a contributor in a biological sample from a transplant recipient containing nucleic acids from two or more genetically distinct contributors can be determined without obtaining, considering, or using previous or predetermined genotype information from the transplant recipient, any transplant donor, or any other genotype information from any source. Such previous or predetermined genotype information can include, for example, genotype information from the transplant recipient, or genotype information from any transplant donor across the entire genome or a portion thereof, and / or genotype information (SNP genotype) at the particular polymorphic marker being analyzed, e.g., selected SNPs. In some embodiments, individual genotyping of the transplant recipient may not be performed. In some embodiments, individual genotyping of any genetically distinct contributor, e.g., any transplant donor, may not be performed. In some embodiments, neither the transplant recipient nor any transplant donor may be individually genotyped. In some embodiments, when determining the amount of cellular or cell-free nucleic acid derived from a contributor in a biological sample from a transplant recipient that contains nucleic acids from two or more genetically distinct contributors, previous or predetermined genotype information from the transplant recipient may not be taken into account. In some embodiments, the amount of cellular or cell-free nucleic acid derived from a contributor in a biological sample from a transplant recipient that contains nucleic acids from two or more genetically distinct contributors may be determined without taking into account previous or predetermined genotype information (SNP genotypes) from the transplant recipient and without taking into account previous or predetermined genotype information from any transplant donor. In some embodiments, the amount of cellular or cell-free nucleic acid derived from a contributor in a biological sample from a transplant recipient that contains nucleic acids from two or more genetically distinct contributors may be determined without using any previous or predetermined genotype information from any genetically distinct contributors.

[0031] According to various embodiments described herein, the amount of cellular or cell-free nucleic acid from contributors in a biological sample from a transplant recipient, which comprises nucleic acids from two or more genetically distinct contributors, can be determined according to an experimental (or laboratory) workflow that includes extraction of cell-free or cellular nucleic acid from a biological sample obtained from the transplant recipient, targeted amplification of selected polymorphic loci, e.g., selected SNPs, and targeted high-throughput sequencing, as described in U.S. patent application Ser. No. 14 / 658,061, filed March 13, 2021, and U.S. patent application Ser. No. 17 / 351,040, filed June 17, 2015, respectively, both of which are incorporated herein by reference in their entireties.

[0032] In some embodiments, the amount of cellular or cell-free nucleic acid from contributors in a biological sample from a transplant recipient, which contains nucleic acid from two or more genetically distinct contributors, can be determined after receiving experimental data, e.g., 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.

[0033] In some embodiments, the amount of cellular or cell-free nucleic acid derived from a contributor in a biological sample from a transplant recipient can be a relative value expressed as a ratio or percentage of cellular or cell-free nucleic acid derived from a contributor to total cellular or cell-free nucleic acid. In some embodiments, for example, when a pregnant transplant recipient receives an organ transplant from a transplant donor, the amount of cell-free nucleic acid derived from a contributor, e.g., cell-free DNA derived from the transplant donor, can be a relative value expressed as a ratio or percentage of cell-free DNA derived from a contributor to total cell-free DNA, e.g., cell-free DNA derived from a contributor + cell-free DNA derived from the transplant recipient + cell-free DNA derived from the fetus. In some embodiments, for example, if a non-pregnant transplant recipient receives two simultaneous organ transplants from two different transplant donors, the amount of cell-free nucleic acid from contributor 1, e.g., cell-free DNA from transplant donor 1, can be a relative value expressed as a ratio or percentage of cell-free DNA from contributor 1 to total cell-free DNA, e.g., cell-free DNA including cell-free DNA from transplant donor 1 + cell-free DNA from transplant donor 2 + cell-free DNA from the transplant recipient.

[0034] In some embodiments, the amount of cellular or cell-free nucleic acid from a contributor can be a relative value 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 genetically distinct contributor "1" to cell-free DNA from genetically distinct contributor "2" and / or genetically distinct contributor "3." In some embodiments, for example, if a pregnant transplant recipient receives an organ transplant from a transplant donor, the amount of cell-free nucleic acid from contributor 1, e.g., cell-free DNA from the transplant donor, can be a relative value expressed as the ratio or percentage of cell-free nucleic acid from contributor 1 to cell-free nucleic acid from contributor 2, e.g., cell-free DNA from the transplant donor to cell-free DNA from the fetus, or vice versa. In some embodiments, for example, if a non-pregnant transplant recipient has received two sequential organ transplants from two different transplant donors, the amount of cell-free nucleic acid from contributor 1, e.g., cell-free DNA from transplant donor 1, can be a relative value expressed as a ratio or percentage of cell-free nucleic acid from contributor 1 to cell-free nucleic acid from contributor 2, e.g., cell-free DNA from transplant donor 2 to cell-free DNA from contributor 1, or vice versa.

[0035] In some embodiments, the amount of cellular or cell-free nucleic acid from a contributor in a biological sample from a transplant recipient can be an absolute value resulting from comparison to an internal or reference standard, or adjustment based on the use of an internal or reference standard. In some embodiments, for example, the amount of cell-free DNA from a contributor can be an absolute value resulting from comparison to an internal or reference standard, or adjustment based on the use of an internal or reference standard, added before or after extraction of cell-free DNA from the biological sample from the transplant recipient.

[0036] Thus, in some embodiments, samples obtained from pregnant transplant recipients of single organ, tissue, or cell transplants can be analyzed to determine the amount of recipient-derived (e.g., from contributor 1), transplant donor-derived (e.g., from contributor 2), and / or fetal-derived (e.g., from contributor 3) cell-free DNA as a ratio or percentage of total (recipient-derived, transplant donor-derived, and fetal-derived) cell-free DNA. In other embodiments, samples obtained from pregnant transplant recipients of single organ, tissue, or cell transplants can be analyzed to determine the absolute amount of recipient-derived, transplant donor-derived, and / or fetal-derived cell-free DNA using an internal or reference standard.

[0037] In some embodiments, samples obtained from non-pregnant transplant recipients of two simultaneous or sequential organ, tissue, or cell transplants from different donors can be analyzed to determine the amount of recipient-derived cell-free DNA (e.g., from contributor 1, transplant donor 1-derived cell-free DNA (e.g., from contributor 2), and / or transplant donor 2-derived cell-free DNA (e.g., from contributor 3)) as a ratio or percentage of total (recipient-derived cell-free DNA, transplant donor 1-derived cell-free DNA, and transplant donor 2-derived cell-free DNA). In other embodiments, samples obtained from non-pregnant transplant recipients of two simultaneous or sequential organ, tissue, or cell transplants from different donors can be analyzed to determine the absolute amount of recipient-derived, transplant donor 1-derived, and / or transplant donor 2-derived cell-free DNA using an internal or reference standard.

[0038] In some embodiments, samples obtained from pregnant transplant recipients of two or more simultaneous or sequential organ, tissue, or cell transplants from different donors can be analyzed to determine the amount of recipient-derived (e.g., from contributor 1), transplant donor 1-derived (e.g., from contributor 2), transplant donor 2-derived (e.g., from contributor 3), and / or fetal-derived (e.g., from contributor 4) cell-free DNA as a ratio or percentage of total (recipient-derived, transplant donor 1-derived, transplant donor 2-derived, and fetal-derived) cell-free DNA. In other embodiments, samples obtained from pregnant transplant recipients of two or more simultaneous or sequential organ, tissue, or cell transplants from different donors can be analyzed to determine the absolute amount of recipient-derived, transplant donor 1-derived, transplant donor 2-derived, and / or fetal-derived cell-free DNA using an internal or reference standard.

[0039] Changes in the relative or absolute amounts of cell-free DNA from contributing factors, particularly changes over time in the relative or absolute amounts of cell-free DNA from the transplant donor, can be useful for informing the status of the transplant in the transplant recipient (or the status of each transplant in the case of multiple transplants) and / or for informing the status of the fetus (in the case of a pregnant transplant recipient), as well as for informing the need to adjust, e.g., reduce or maintain, the immunosuppressive therapy being administered to the transplant recipient. Changes in the relative or absolute amounts of cell-free DNA from contributing factors, particularly changes over time in the relative or absolute amounts of cell-free DNA from the transplant donor, can also be useful in determining the risk of transplant rejection.

[0040] In some embodiments, the amount of cellular or cell-free nucleic acid from a contributor in a biological sample from a transplant recipient can be compared to a suitable threshold or threshold range to obtain information about the status of one or more organs, tissues, and / or cell grafts, or the fetus. The threshold or threshold range can be a predetermined value or range indicating the presence or absence of a condition, or the presence or absence of risk. The threshold can be a single cutoff value, such as a median or mean, or can be determined from a baseline value before the presence or onset of a condition, or the presence of risk, or after a course of treatment. The baseline value can be the amount of nucleic acid from a contributor in a pre-transplant sample from the transplant recipient, such as cell-free DNA from the transplant donor, which may be zero or negligible, but may also indicate a baseline error in the system. The baseline value can also be the amount of nucleic acid from a contributor in a pre-pregnancy sample from the transplant recipient, such as cell-free DNA from the fetus, which may be zero or negligible, but may also indicate a baseline error in the system. After appropriate analytical parameters are selected, the amount of nucleic acid from a contributor, such as the amount of cell-free DNA from a contributor in a pregnant transplant recipient, can be determined by comparing it to a suitable threshold to indicate the status of the transplant. Similarly, determining changes in the amount of nucleic acid derived from a contributor, such as changes in the amount of cell-free DNA derived from a contributor, over a period of time in a pregnant transplant recipient can inform the status of the transplant.

[0041] In some embodiments, the amount of cellular or cell-free nucleic acid derived from a contributor can be compared to previous, i.e., previously determined, amounts derived from the same genomic contributor to provide longitudinal data and information regarding the status of one or more organ, tissue, and / or cell transplants in a non-pregnant recipient or fetus of, for example, multiple simultaneous or sequential transplants. In some embodiments, the amount of cellular or cell-free nucleic acid derived from a contributor can be compared to previous, i.e., previously determined, amounts derived from the same genomic contributor to provide longitudinal data and information regarding the status of the organ, tissue, and / or cell transplant and fetus in, for example, a pregnant transplant recipient of a single donor transplant.

[0042] Determining the amount of nucleic acid derived from a contributor, such as cell-free DNA derived from a contributor, in a biological sample from a pregnant transplant recipient of a single-donor organ, tissue, or cell transplant can be useful for classifying, determining, and / or monitoring the status of the transplant. Classifying, determining, and / or monitoring the status of the transplant can be valuable and informative with regard to clinical decisions by the treating physician or medical professional involved in the transplant recipient's treatment, for example, regarding the need to adjust, e.g., increase, decrease, change, or initiate the transplant recipient's immunosuppressive or anti-rejection treatment. The disclosed methods can be useful for classifying, determining, and / or monitoring the status of the transplant in the case of a pregnant transplant recipient of a single-donor organ, tissue, or cell transplant, based on the determined (including estimated) amount of nucleic acid derived from a contributor, such as cell-free DNA derived from a contributor.

[0043] Determining the amount of nucleic acid from contributor 1, such as cell-free DNA from transplant donor 1, and the amount of nucleic acid from contributor 2, such as cell-free DNA from transplant donor 2, in a biological sample from a transplant recipient of two simultaneous or sequential organ, tissue, and / or cell transplants from different donors can be useful for classifying, determining, and / or monitoring the status of one or both transplants. The status of the transplants can be valuable and informative with regard to clinical decisions by the treating physician or medical professional involved in the transplant recipient's treatment, for example, with regard to the need to adjust, e.g., increase, decrease, change, or initiate the transplant recipient's immunosuppressive or anti-rejection treatment. The methods of the present disclosure can be used to classify, determine, or monitor the status of one or more transplants based on the determined (including estimated) amounts of nucleic acid from contributors, such as cell-free DNA from transplant donor 1 and cell-free DNA from transplant donor 2.

[0044] Nucleic acids derived from contributing factors The disclosed methods include analyzing nucleic acids in a biological sample from a transplant recipient to determine the amount of nucleic acid from one or more genetically distinct contributors, e.g., contributors such as cell-free DNA from the transplant donor, which is useful for informing the status of the transplant and / or informing the need to adjust the immunosuppressive therapy being administered to the transplant recipient. Similarly, determining changes over time in the amount of nucleic acid from contributors, such as cell-free DNA from one or more grafts, in a transplant recipient according to the disclosed methods can be useful for informing the status of the transplant and / or informing the need to adjust the immunosuppressive therapy being administered to the transplant recipient.

[0045] According to various embodiments described herein, the amount of cellular or cell-free nucleic acid from contributors in a biological sample from a transplant recipient, which comprises nucleic acids from two or more genetically distinct contributors, can be determined according to an experimental (or laboratory) workflow including extraction of cell-free or cellular nucleic acid from a biological sample obtained from the transplant recipient, targeted amplification and targeted high-throughput sequencing of selected polymorphic loci, e.g., a panel of single nucleotide polymorphisms (SNPs), which can be selected as described in U.S. patent application Ser. No. 14 / 658,061, filed March 13, 2021, and U.S. patent application Ser. No. 17 / 351,040, filed June 17, 2015, respectively, both of which are incorporated herein by reference in their entireties.

[0046] In some embodiments, the amount of cellular or cell-free nucleic acid from contributors in a biological sample from a transplant recipient, which contains nucleic acid from two or more genetically distinct contributors, can be determined after receiving experimental data, e.g., 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.

[0047] Transplant recipients and samples The disclosed methods include determining the amount of nucleic acid from a contributor, e.g., cell-free DNA from the transplant donor, from a biological sample obtained from a recipient of an organ, tissue, and / or cell transplant from a transplant donor. The transplant recipient may have received one or more transplants simultaneously or sequentially. Organ transplants may include, for example, kidney transplants, heart transplants, lung transplants, liver transplants, pancreas transplants, vascularized combined transplants, intestine transplants, stomach transplants, testis transplants, penis transplants, ovary transplants, uterus transplants, thymus transplants, face transplants, hand transplants, leg transplants, bone transplants, cornea transplants, skin transplants, heart valve transplants, blood vessel transplants, or any combination thereof, such as heart-lung or pancreas-kidney transplants. The graft received by the transplant recipient from the donor may also include a tissue graft, e.g., skin tissue, or a cellular graft, such as pluripotent stem cells, multipotent stem cells, blood-forming 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).

[0048] Biological samples from transplant recipients may include, but are not limited to, whole blood, plasma, serum, peripheral blood mononuclear cells, lymph, buccal swab, saliva, sputum, tears, sweat, ear fluid, bone marrow suspension, urine, feces, lung lavage fluid, semen, vaginal fluid, cerebrospinal fluid, brain fluid, ascites, milk, respiratory secretions, and intestinal or urinary tract fluid, or tissue and / or cells from a biopsy.

[0049] Some samples obtained from transplant recipients may contain cell-free DNA, and the total cell-free DNA present in the sample may be entirely cell-free DNA from the recipient, or the total cell-free DNA present in the sample may comprise a mixture of cell-free DNA from the recipient and cell-free DNA from the graft. Some samples obtained from pregnant transplant recipients may contain cell-free DNA, and the total cell-free DNA present in the sample may be entirely cell-free DNA from the recipient, or the total cell-free DNA present in the sample may comprise a mixture of cell-free DNA from the recipient, cell-free DNA from the fetus, and cell-free DNA from the transplant donor.

[0050] After a sample is obtained, it can be used directly, frozen, or otherwise stored under conditions that maintain the integrity of the cell-free DNA for a period of time by preventing degradation and / or contamination by genomic DNA or other nucleic acids. Samples can be collected from the transplant recipient over a period of time. Samples can be collected from the transplant recipient at various times and for various periods, before and after transplantation, or, in the case of pregnant transplant recipients, before and after pregnancy, for use in determining the amount of nucleic acid derived from a contributing factor according to the methods of the present disclosure. For example, samples can be collected from the transplant recipient within days, weeks, and / or months after transplantation, as well as at daily, weekly, monthly, and / or yearly intervals. In the case of pregnant transplant recipients, samples can be collected from the transplant recipient within days, weeks, and / or months after conception, during pregnancy, and at daily, weekly, and / or monthly intervals. Samples can be collected from the transplant recipient at various alternative time points as clinically useful and / or indicated. In some embodiments, the time period for obtaining a sample from a transplant recipient may be within the first few days after transplant, for example, to monitor induction therapy. In some embodiments, the time period for obtaining a sample from a transplant recipient may be during tapering of the immunosuppressive regimen, occurring during the first 12 months after transplant. In some embodiments, the time period for obtaining a sample from a transplant recipient may be during the initial long-term immunosuppression maintenance phase beginning approximately 12-14 months after transplant. In some embodiments, the time period for obtaining a sample from a transplant recipient may be any time during the entire long-term maintenance of the immunosuppressive regimen, for example, greater than 12 months after transplant.

[0051] In some embodiments, samples from a transplant recipient (pregnant or non-pregnant) may be obtained about once a week, about once every two weeks, about once every three weeks, about once a month, about once every two months, about once every three months, about once every four months, about once every five months, about once every six months, about once a year, or about once every two years, or more, after the initial sampling event. The appropriate timing and frequency of sampling can be determined for a given transplant recipient by one of skill in the art.

[0052] Analysis of cell-free nucleic acid (DNA) in transplant recipients The computer-implemented systems, kits, and methods of the present disclosure include analysis of nucleic acids from contributors, such as cell-free DNA, in a biological sample from a transplant recipient, the nucleic acids comprising nucleic acids from two or more genetically distinct contributors. In some embodiments, the amount of nucleic acids from contributors, e.g., cell-free DNA, in a biological sample from a transplant recipient can be determined after receiving relevant experimental data, e.g., sequencing reads, or other relevant data-related information, e.g., quality control-related data, results, genotype information, SNP mutation rates, etc., from a database or other non-experimental source. In some embodiments, the amount of nucleic acids from contributors, such as cell-free DNA, in a biological sample from a transplant recipient can be determined according to an experimental (or laboratory) workflow including extraction of cell-free nucleic acids from a biological sample obtained from the transplant recipient, targeted amplification and targeted high-throughput sequencing of selected polymorphic loci, e.g., a panel of single nucleotide polymorphisms (SNPs), which can be selected as described in U.S. Patent Application No. 14 / 658,061, filed March 13, 2015. After cell-free DNA is extracted or otherwise obtained from a biological sample from a transplant recipient, a panel of polymorphic markers suitable for distinguishing cell-free DNA from various genetically different contributors in the cell-free DNA, for example, for distinguishing cell-free DNA from the transplant donor from cell-free DNA from the recipient, can be analyzed to determine the amount of cell-free DNA from the contributors. Various polymorphic markers can be selected for inclusion in the analyzed panel, as long as the polymorphic marker panel as a whole is suitable for distinguishing cell-free DNA from various genetically different contributors, for example, for distinguishing cell-free DNA from the transplant donor from cell-free DNA from the recipient in the cell-free DNA. Polymorphic markers can include, for example, single nucleotide polymorphisms (SNPs), restriction fragment length polymorphisms (RFLPs), short tandem repeats (STRs), variable number of 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 panel can be used for each transplant recipient, and there is no need to customize the polymorphic marker panel to individualize the panel for different transplant recipients. In some embodiments, the polymorphic markers can be SNPs. SNPs can be selected based on, for example, a population-wide minor allele frequency of >0.4, a target population minor allele frequency of >0.4, the lowest polymerase error rate (in the test system) for six potential allele transitions or transversions, and low genomic linkage, for example, a distance of >500 kb between SNPs. The SNPs included in the SNP panel, or any other polymorphic marker panel, can be those previously identified as suitable for distinguishing between any two unrelated individuals (Pakstis, AJ, Speed, WC, Fang, R. et al., "SNPs for a universal individual identification panel," Hum Genet 127, 315-324 (2010)). A SNP panel may include at least 10, at least 20, at least 50, at least 100, at least 200, at least 500, at least 1,000, or more SNPs.

[0053] Amplification and sequencing Once extracted from a biological sample from a transplant recipient, nucleic acids such as cell-free DNA can be amplified and sequenced for downstream analysis, such as analysis of a panel of polymorphic markers, e.g., SNPs, from the 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 March 13, 2015, which is incorporated herein by reference in its entirety.

[0054] Summarizing and grouping single nucleotide polymorphism (SNP) minor allele frequency (MAF) signals to infer SNP genotypes for some contributing factors The disclosed methods allow for inferring SNP genotypes for each genomic contributor from MAF signals, including processes of summarization and grouping (including clustering), for biallelic SNPs. According to various embodiments of the present invention, a biological sample from a transplant recipient may contain cell-free nucleic acid (e.g., cell-free DNA) from two or more genomic and genetically distinct contributors, and therefore, the genotype at a particular SNP locus may be determined by a GT gene expression analysis. i and represents (consists of) the set of genotypes of all contributing factors at this SNP. For example, for a pregnant transplant recipient, the genotype (GT_rp) at a particular SNP is i ) is a pregnant transplant recipient (GT_rp i ), transplant donor (GT_dn i ), and the SNP genotypes of the fetuses of the transplant recipients (GT_ft i ) represents the set of SNP genotypes, and GT i :=(GT_rp i ,GT_dn i ,GT_ft i ) In some embodiments, the methods of the present invention can be performed without prior or predetermined knowledge of any SNP genotypes from any contributing factors, so that SNP genotypes are latent variables that can be inferred in the grouping process.

[0055] There are different types and levels of SNP signals that can be summarized 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 and alternative allele frequencies (AAF), major and minor allele frequencies (MAF), etc., and utilized to infer SNP genotypes for any genomic contributors. Such summarized signals for each SNP can be summarized read counts, summarized AAF values, summarized MAF values, etc. for the amplicon.

[0056] In some embodiments, minor allele frequency (MAF) values ​​can be summarized as representative SNP signals and grouped (clustered) to infer the SNP genotype of one or more contributing factors. For example, for the i-th SNP (S i ) MAF value is x i The MAF of a panel of selected SNPs sequenced from cell-free DNA obtained from one sample from a transplant recipient after transplantation can be expressed as X:=(x1, x2,..., x n ), where n is the total number of SNPs selected. In embodiments where multiple longitudinal samples from the same transplant recipient are analyzed, the i-th SNP (S i ) MAF value is

[0057]

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[0059]

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[0060] In some embodiments, MAF values ​​can be summarized from nucleic acid sequence reads and grouped (clustered) to infer the SNP genotypes of one or more contributing factors. For example, in the case of pregnant transplant recipients, the SNP genotypes of three contributing factors, i.e., pregnant transplant recipient (GT_rp), i ), transplant donor (GT_dn i), or the fetus of the transplant recipient (GT_ft i ) are unknown, at least two of the SNP genotypes of the three contributing factors can be inferred through the following signals at some of the sequenced SNPs:

[0061]

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[0062] In some embodiments,

[0063]

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[0065]

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[0069] In embodiments where the waterfall plot includes any number of samples from a transplant recipient after transplant,

[0070]

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[0071]

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[0072] Grouping of SNP signals In some embodiments, the SNP genotype of a contributor, or a portion thereof, is the signal S of the SNP. i In embodiments where a waterfall plot is used for visual representation, the grouping (clustering) process can be similar to "segmenting" the data curve or cascade into groups. iFor grouping (clustering), a group label can be assigned 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. Then, for example, within the recipient_homo group, SNPs can be further assigned to subgroups, labeled, for example, fetal_homo / fetal_hetero, to indicate the inferred fetal homozygous / heterozygous genotype.

[0073] Various grouping methods can be used in embodiments of the present invention. In some embodiments, thresholding can be used as a grouping method when a binary result is desired, such that for a SNP with a summary statistic of y, if y is above a predetermined threshold T, it belongs to one (first) group, and if y is below T, it belongs to another (second) group. Thresholding can be used, for example, to group a SNP summary statistic Y=(y,...,y n )y i’ where i' is the new position index of the SNP after rearrangement and reshuffling.

[0074]

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[0075]

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[0076] For example, as visually represented in a waterfall plot, the genotype of the transplant recipient can be identified in the "first stage" of the plot (eg, as shown in Figure 5A).

[0077] In some embodiments, the grouping process may be based on longitudinal MAF variation, e.g., using MAF data or information obtained after analysis of multiple longitudinal samples from the same transplant recipient to infer the fetal genotype, pregnant transplant recipient genotype, within the transplant recipient homozygous SNP genotype (homozygous recipient) group. For example, as visually represented in a waterfall plot, the fetal genotype may be identified in the "second stage" of the plot (e.g., as shown in FIG. 5B).

[0078] In some embodiments, y i’ The MAF variation summary statistic of SNPs, denoted as , can be selected via one of the processes ("one-to-one," "one-to-all," "all-to-all") to summarize the MAF variation and used in grouping the SNPs.

[0079]

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[0080] In some embodiments, the MAF variation summary statistic y of the SNPs i’ can be used to determine a "separation point" P on the rearranged position index i',

[0081]

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[0083] P is

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[0085] Encoding Implementation of various features of the present disclosure may use at least one non-transitory computer-readable storage medium (e.g., computer memory, floppy disk, compact disk, 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 may 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. Reference to a computer program that, when executed, performs the functions discussed above is not limited to an application program running on a host computer. Rather, the term computer program is used generically herein 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.

[0086] The program can provide a method for determining the amount of cellular or cell-free nucleic acid from a contributor in a biological sample from a transplant recipient that contains nucleic acid from two or more genetically distinct contributors by accessing experimental data such as sequencing reads from a database or other non-experimental source, or other data-related information such as quality control related data, results, genotype information, SNP mutation rates, etc.

[0087] Determining transplant status The method of the present disclosure for determining the amount of cell-free nucleic acid derived from contributors, such as cell-free DNA, in biological samples from transplant recipients can be used to determine the transplant status of transplant recipients.The amount of cell-free DNA derived from contributors that exceeds a suitable threshold in transplant recipients, as well as the change in the amount of cell-free DNA derived from contributors over time, can be informative regarding the transplant status.

[0088] In some embodiments, an amount of cell-free nucleic acid from a contributor in a transplant recipient above a suitable threshold, or an increase in the amount of cell-free nucleic acid from a contributor in a transplant recipient over time, may indicate transplant rejection, immunosuppressive therapy, nephrotoxicity of immunosuppressive treatment, a need to adjust for infection, and / or a need for further investigation of the transplant status.

[0089] In some embodiments, the amount of cell-free nucleic acid derived from a contributing factor in the transplant recipient below a suitable threshold, or a decrease in the amount of cell-free nucleic acid derived from a contributing factor in the transplant recipient over time, may indicate graft tolerance, the need to adjust immunosuppressive therapy, and / or the need for further investigation of the transplant status.

[0090] In some embodiments, the lack of change in the amount of cell-free nucleic acid from contributors over time in a transplant recipient may indicate a stable transplant state and / or an opportunity to adjust, e.g., reduce or discontinue, immunosuppressive therapy.

[0091] Adjustment of immunosuppressive therapy The disclosed methods for determining the amount of cell-free nucleic acid from a contributing factor in a biological sample from a transplant recipient can be used to inform the need to adjust the immunosuppressive therapy administered to the transplant recipient, which generally refers to the administration of immunosuppressants or other therapeutic agents that suppress the immune response in a subject. Exemplary immunosuppressants include, for example, anticoagulants, antimalarials, cardiac medications, nonsteroidal anti-inflammatory drugs (NSAIDs) and steroids, such as Ace inhibitors, aspirin, azathioprine, B7RP-1-fc, beta-blockers, brequinar sodium, Campath-1H, celecoxib, chloroquine, corticosteroids, coumadin, cyclophosphamide, cyclosporin A, DHEA, deoxypergualin, dexamethasone, diclofenac, dolobid, etodolac, everolimus, FK778, feldene, fenoprofen, flurbiprofen, heparin, hydralazine, hydroxychloroquine, CTLA-4 or LFA3 immunoglobulin, ibuprofen, indomethacin, ISAtx-247, ketoprofen, Examples of such anti-cancer drugs include ketorolac, leonomide, meclofenamate, mefenamic acid, mepacrine, 6-mercaptopurine, meloxicam, methotrexate, mizoribine, mycophenolate mofetil, naproxen, oxaprozin, plaquenil, NOX-100, prednisone, methiprenison, rapamycin (sirolimus), sulindac, tacrolimus (FK506), thymoglobulin, tolmetin, tresperimus, U0126, and the like, as well as antibodies such as alpha lymphocyte antibodies, adalimumab, anti-CD3 antibodies, anti-CD25 antibodies, anti-CD52 antibodies, anti-IL2R antibodies, and anti-TAC antibodies, basiliximab, daclizumab, etanercept, hu5C8, infliximab, OKT4, and natalizumab.

[0092] In some embodiments, adjusting immunosuppressive therapy can include changing the type or form of immunosuppressant or other immunosuppressive therapy administered to the transplant recipient. In some embodiments, if the transplant recipient is not receiving immunosuppressive therapy, the disclosed methods can indicate the need to initiate administration of immunosuppressive therapy to the transplant recipient.

[0093] Additional analysis The disclosed methods may be performed in addition to or in conjunction with other analyses of samples from transplant recipients to determine the amount of cell-free nucleic acid from contributors, such as cell-free DNA, to determine the status of the transplant in the transplant recipient, and / or to inform the need for adjustments to immunosuppressive therapy being administered to the transplant recipient.

[0094] In some embodiments, nucleic acid extracted from a biological sample of a transplant recipient may also be tested for the presence and / or amount of infectious agents, such as viruses, bacteria, fungi, parasites, etc. In some embodiments, nucleic acid extracted from a biological sample of a transplant recipient may be tested for the presence and / or amount of infectious agents commonly encountered after organ transplantation. Infectious agents that may be tested for 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, and Pneumocystis carinii; or parasites such as Toxoplasma gondii.

[0095] In some embodiments, the results of testing for the presence and / or amount of an infectious agent can be used to determine the infectious status of the transplant recipient. In some embodiments, the results of testing for the presence and / or amount of an infectious agent can be used to inform the need to adjust the immunosuppressive therapy being administered to the transplant recipient, such as reducing or changing the immunosuppressive therapy after the presence and / or a particular amount of an infectious agent is identified.

[0096] The computer-determined status of the graft may be provided by a medical analysis tool that is easily accessible to a physician or medical professional. The medical analysis tool may display the status of the graft, for example, on a user interface, a report printout, etc. The physician or medical professional may use the computer-determined status in addition to, or instead of, the physician's or medical professional's assessment of the status of the graft. The computer-determined status may be provided to the physician or medical professional in the form of a determined (estimated) amount of donor-derived cell-free DNA and / or a risk of graft rejection. For example, the medical analysis tool may output a 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 may be used by the physician or medical professional as a guide for treatment options, monitoring protocols, and / or clinical diagnosis.

[0097] Various techniques and process flow steps are 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 referenced herein. However, it will be apparent to one skilled in the art that one or more aspects and / or features described or referenced 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 some of the aspects and / or features described or referenced herein.

[0098] In the following description of examples, reference is made to the accompanying drawings that form a part hereof, and which show, by way of illustration, specific examples that may be practiced. It is to be understood that other examples may be utilized and structural changes may be made without departing from the scope of the disclosed examples.

[0099] Exemplary systems and methods for determining the amount of nucleic acids from contributors in a biological sample from a pregnant transplant recipient FIG. 1 shows an exemplary system 100 for determining the amount of nucleic acid from contributors in a biological sample obtained from a pregnant recipient of an organ transplant (or from a transplant recipient who has received multiple transplants), which contains a mixture of cell-free DNA from genetically different contributors, e.g., cell-free DNA from the recipient, cell-free DNA from the fetus, and cell-free DNA from the transplant donor.

[0100] System 100 may include an interface 160 and a decision unit 170. Various embodiments of the system may include some or all of the components shown in the figures, or other components not shown. System 100 may be, for example, a medical analysis tool. A treating physician or medical professional may use the medical analysis tool to help monitor the status of the transplant in the transplant recipient, as well as to monitor or evaluate adjustments to immunosuppressive therapy administered or to be administered to the transplant recipient. The disclosed methods may be used to classify, determine, or monitor the status of the transplant based on the determined amount of nucleic acids derived from contributors in a biological sample obtained from the transplant recipient, the biological sample containing a mixture of cell-free DNA derived from genetically distinct contributors.

[0101] In some embodiments, interface 160 can be used to receive information and / or data obtained after analysis of a mixed sample, such as nucleic acid sequence data 140, or generally input data, and / or provide one or more outputs to a treating physician or medical professional. In some embodiments, interface 160 can receive nucleic acid sequence data 140 or input data via a computer or input function. In some embodiments, nucleic acid sequence data 140 or input data can be information from laboratory tests obtained, for example, from blood and / or urine samples of the transplant recipient. Additionally, in some embodiments, the nucleic acid sequence data can include relationship information between genetically distinct contributors. In some embodiments, interface 160 can output a determined amount of contributor-derived nucleic acids 150 in the sample and / or the status of the transplant. The determined amount of contributor-derived nucleic acids 150 provided by the medical analysis tool can be the percentage of contributor-derived nucleic acids in a biological sample obtained from the transplant recipient, the biological sample including a mixture of cell-free DNA derived from genetically distinct contributors. Additionally or alternatively, interface 160 can provide an output indicating the status of the transplant. For example, interface 160 may output a numerical value indicating risk of transplant rejection (eg, high risk, low risk, no risk, etc.) based on the amount of nucleic acid 150 from the determined contributor.

[0102] The determination unit 170 can analyze the nucleic acid sequence data 140 and determine the amount of nucleic acid derived from the contributors and / or the transplant status. The determination unit 170 can infer SNP genotypes for all genome contributors to determine the amount of nucleic acid derived from the contributors, or can infer SNP genotypes for only some contributors and summarize and group (cluster) them to determine the amount of nucleic acid derived from the contributors. SNP minor allele frequency (MAF) signals can be obtained from analyzing a panel of selected SNPs on cell-free nucleic acid from a single sample from the transplant recipient after transplantation, or from multiple longitudinal samples taken from the same transplant recipient over a period of time (days, weeks, months) after transplantation. In some embodiments, the determination unit 170 can determine the risk of transplant rejection based on the determined amount of nucleic acid 150 derived from the contributors.

[0103] In some embodiments, system 100 may be a kit that can be used according to one or more methods disclosed herein, for example, for post-transplant monitoring. In some embodiments, the kit may include reagents, controls, instructions for use, and / or software instructions for analyzing nucleic acids from contributors, such as cell-free DNA from a transplant donor. In some embodiments, the kit may include sufficient reagents to analyze a single sample. In some embodiments, the kit may include sufficient reagents to analyze several samples. In some embodiments, the kit may include instructions for specifying target values. In some embodiments, the kit may include instructions for specifying control materials that can be used in conjunction with the reagents and instructions provided in the kit. In some embodiments, the kit may include instructions for use according to one or more methods disclosed herein. In some embodiments, the kit may include instructions for accessing a computer, such as a database, to retrieve data, such as nucleic acid sequence data. In some embodiments, the kit may include reference information, such as scientific literature references, comparative references, package insert materials, clinical trial results, etc. In some embodiments, the kit may include instructions and specifications for input material quality or input preparation methods.

[0104] 2 shows a flowchart of an exemplary general computer-implemented method for determining the amount of contributor-derived nucleic acids in a mixed sample containing cell-free nucleic acids from at least three genomic and genetically distinct contributors, according to some embodiments. Method 200 may include step 202, in which system 100 (including its determination unit 170) may receive information and / or data obtained after analysis of a mixed sample obtained from a transplant recipient after transplantation. In some embodiments, the information and / or data may be nucleic acid sequence data 140 (reads) obtained after sequencing cell-free nucleic acids from the mixed sample, or general input data.

[0105] In some embodiments, analysis of the mixed sample may include an experimental (or laboratory) workflow including extraction of cell-free nucleic acids from a biological sample obtained from the transplant recipient, including 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 panel of single nucleotide polymorphisms (SNPs), which may be selected as described in U.S. Patent Application No. 14 / 658,061. In some embodiments, analysis of the mixed sample may include receiving 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, etc., from a database or other non-experimental source. 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, etc., may be received via a computer or input function. For example, a treating physician or medical professional may provide general input data as input using interface 160, including, but not limited to, experimental data such as nucleic acid sequence data 140 (reads) for cell-free nucleic acids from a mixed sample, other data-related information such as quality control-related data, results, genotype information, etc. Additionally or alternatively, a computer (e.g., a database) may provide general input data, including, but not limited to, experimental data such as nucleic acid sequence data 140 (reads) for cell-free nucleic acids from a mixed sample, quality control-related data, results, other data-related information such as genotype information, etc.

[0106] In step 204, the system's determination unit 170 may receive the genomic relationships between (genetically distinct) genomic contributors. For example, the mixed sample may be from a gestational transplant recipient who received a transplant from the fetus's father, and thus the relationship includes a maternal-child-paternal relationship. As another example, if the mixed sample is from a gestational transplant recipient who received a transplant from the fetus's paternal aunt, the relationship includes a maternal-child-paternal aunt relationship. Other non-limiting exemplary relationships include maternal-child maternal grandmother, maternal-child maternal grandfather, maternal-child paternal grandmother, maternal-child paternal grandfather, maternal-child paternal uncle, maternal-child sibling, maternal-child unrelated donor, etc.

[0107] In step 206, the determination unit 170 of the system may determine (and group) minor allele frequency (MAF) information (MAF values ​​or MAF variations) from the panel of single nucleotide polymorphisms (SNPs). Step 208 may include determining SNP rearrangement MAF information, and step 210 may include determining the recipient genotype for each SNP.

[0108] In step 212, the system 100 can determine a MAF variation summary statistic for each SNP with a recipient homozygous genotype. In step 214, the system 100 can determine a group label for each SNP according to the MAF variation summary statistic.

[0109] Step 216 of method 200 may include determining an average MAF value for each group of SNPs. Then, in step 218, the amount of nucleic acid from the contributor is determined. The amount of nucleic acid from the contributor may be determined based on the genomic contributor relationships (e.g., received in step 204) and the average MAF information.

[0110] 3 shows a flowchart of an exemplary computer-implemented method for determining the amount of contributor-derived nucleic acid in a mixed sample containing cell-free nucleic acid from at least three genomic and genetically distinct contributors, according to some embodiments. Method 300 may include steps 302, 304, and 306, which may be similar to steps 202, 204, and 206 of FIG. 2.

[0111] In step 307, the determination unit 170 can infer a SNP genotype for each genomic contributor or portion of genomic contributors by summarizing and grouping the panel of sequenced SNPs from the cell-free nucleic acids from the mixed sample (discussed in more detail above).

[0112] In step 308, the determination unit 170 of the system can determine the amount of nucleic acid derived from the contributor based on the genomic contributor relationships and the inferred SNP genotypes of the genomic contributors. For example, the determined amount of nucleic acid derived from the contributor can be the percentage of nucleic acid derived from the transplant donor, such as cell-free DNA derived from the transplant donor.

[0113] In some embodiments, based on the determined amount of nucleic acid derived from the contributor, the system may determine the risk of transplant rejection in the transplant recipient. The determined amount of nucleic acid derived from the contributor (from step 308) or the risk of transplant rejection may be displayed, for example, on a computer screen (e.g., interface 160) or included in a report. Additionally or alternatively, in some embodiments, the system may use the determined amount of nucleic acid derived from the contributor to assess the health of the fetus. At a given stage of pregnancy, the fetal cell-free DNA fraction is expected to fall within a target range. If the determined amount of nucleic acid derived from the contributor corresponds to a fetal cell-free DNA fraction outside this range, system 100 may display this information or corresponding information, for example, on a user interface, a report printout, etc. For example, system 100 may suggest to a physician or medical professional that the fetus may have a potential health problem that should be further investigated.

[0114] Although the description and figures show certain steps of the method being performed in a particular order, the method steps may be performed in other orders not described or shown. Additionally or alternatively, embodiments of the present disclosure may include performing all, some, or none of the steps of method 300, where appropriate. Furthermore, although certain components, devices, or systems are described as performing the steps of method 300, any suitable combination of components, devices, or systems (including those not expressly disclosed) may be used to perform the steps.

[0115] Exemplary SNP genotype inference for some or all genomic contributors In some embodiments, the system may determine the amount of nucleic acid from the contributors by summarizing and grouping SNP minor allele frequency (MAF) signals obtained from analyzing a panel of selected SNPs on cell-free nucleic acid from a single sample from the transplant recipient post-transplant or from multiple longitudinal samples taken from the same transplant recipient over a period of time (days, weeks, months) post-transplant, thereby inferring SNP genotypes for some or all of the contributors.

[0116] 4 shows an exemplary method for grouping SNPs and inferring SNP genotypes for some or all genomic contributors by summarizing and grouping SNP minor allele frequency (MAF) signals obtained from analyzing a panel of selected SNPs on cell-free nucleic acid from multiple longitudinal samples taken from the same transplant recipient over a period of time (days, weeks, months) post-transplant, according to some embodiments. Method 400 can include determining MAF variation summary statistics, such as a simple MAF variation summary statistic of standard deviation 402 (or minimum-maximum range), a "one-to-one" MAF variation summary statistic 412, a "one-to-all" MAF variation summary statistic 422, and / or an "all-to-all" MAF variation summary statistic 432. In some embodiments, one or more of the processes are compared in step 442 to determine a MAF variation summary statistic 452 for use in determining the amount of nucleic acid from the contributors. In some embodiments, the process may include generating one or more MAF variation summary statistics, and in some embodiments, the comparison of step 442 may include selecting the MAF variation summary statistic that represents the greatest difference between the groups among the one or more MAF variation summary statistics.

[0117] Simple MAF Variation Summary Statistics of Standard Deviation 402: In some embodiments, the simple MAF variation summary statistics of standard deviation 402 (or minimum-maximum range) may include sorting the MAF values ​​and creating a scatter plot. FIG. 5A shows an example of a waterfall plot of MAF values ​​for a panel of SNPs for the simple MAF variation summary statistics of standard deviation, according to some embodiments. The waterfall plot may include multiple groups, such as a recipient heterozygous group 502 and a recipient homozygous group 504. The recipient heterozygous group 502 may include SNPs with MAF values ​​that may be greater than the MAF values ​​of the SNPs in the recipient homozygous group 504. As shown in the exemplary waterfall plot, the recipient heterozygous group 502 may include a first set of SNPs (e.g., 160 SNPs) with MAF values ​​equal to or greater than a threshold (e.g., 0.24), and the recipient homozygous group 504 may include the remaining SNPs with MAF values ​​less than the threshold (e.g., 0.24).

[0118] The waterfall plot may show one or more hierarchical "staircases" (second hierarchical stairs) containing one or more "steps." The stairs and steps represent groups of SNPs that share the same genotype of one or more genomic contributors. By identifying such stairs or steps by grouping, SNPs can be used to determine the genotype of one or more genomic contributors. For example, as shown in FIG. 5A, the first hierarchical stairs can be used to distinguish between recipient heterozygote group 502 and recipient homozygote group 504. In some embodiments, the first hierarchical stairs of group 502 and group 504 can correspond to the SNP genotype of the major genomic contributor (transplant recipient), and second hierarchical stairs ("steps") can be further identified within group 504.

[0119] In some embodiments, the waterfall plot may show a second hierarchical staircase ("step") in the recipient homozygous group 504, as shown in the plot of FIG. 5B, and the SNP genotypes of one or more minor contributors may be determined from the steps. In one example, the genotypes of the contributors may be determined based on the steps compared to a threshold. The determined amounts of nucleic acids from the contributors may be determined from the MAF values ​​grouped by the genotypes of the contributors.

[0120] The second hierarchical steps may not be distinguishable in the waterfall plot, such as when the SNPs are mixed. An exemplary waterfall plot in which the second hierarchical steps are not distinguishable is shown in FIG. 6. For example, the first hierarchical steps are distinguishable, allowing the system to separate the SNPs into a recipient heterozygous group 602 and a recipient homozygous group 604. In the recipient homozygous group 604, the second hierarchical groups are mixed and therefore cannot be easily separated from each other, resulting in the inability to determine the SNP genotype from the waterfall plot. The lack of a distinguishable second hierarchical step in the waterfall plot can make it difficult to accurately determine the amount of nucleic acid from the contributing factors.

[0121] In some embodiments, the waterfall plot may include multiple hierarchical steps. For example, as shown in Figures 5A and 5B, the multiple hierarchical steps may include a first hierarchical step (Figure 5A) and a second hierarchical step (Figure 5B). The first hierarchical step may be determined by separating the plot into groups based on thresholding (described above). The second hierarchical step may be determined by separating one or more first hierarchical steps. In the example shown in Figure 5B, the second hierarchical step may include three steps. Each step may include SNPs grouped together based on similar MAF values.

[0122] Embodiments of the present disclosure may include methods for determining one or more genotypes from MAF values. In some instances, a waterfall plot of MAF values ​​may not show a second hierarchical step. One non-limiting example is when the transplant recipient is pregnant, in which case the sample was collected in the early weeks of pregnancy (e.g., before 10 weeks of pregnancy), before conception, or after conception. In some embodiments, the determined genotype may include a genotype for the transplant recipient, fetus, or donor (including a previous or current donor) based on MAF variation that can be determined from the MAF value.

[0123] MAF variation can be determined, for example, based on a set of longitudinal samples from the same transplant recipient. The set of longitudinal samples can have the same SNP genotype. In some embodiments, if the transplant recipient is pregnant, the set of longitudinal samples can include one or more samples from the transplant recipient taken before conception, one or more samples from the transplant recipient taken before 10 weeks after conception, one or more samples from the transplant recipient taken at least 10 weeks after conception, or a combination thereof. In some embodiments, if the transplant recipient has received multiple transplants, the set of longitudinal samples can include one or more samples from the transplant recipient taken before one or more previous transplants, one or more samples from the transplant recipient taken before the current transplant, one or more samples from the transplant recipient after previous and current transplants, or a combination thereof.

[0124] The SNP genotype of the fetus or current donor can be determined from the MAF variation over time. Additionally or alternatively, a plot of the MAF variation summary statistic can be used to determine the amount of nucleic acid from the contributing factors.

[0125] 7A shows an exemplary method for determining MAF information for a panel of SNPs, according to some embodiments. As described above, system 100 may receive nucleic acid sequence data 140. The nucleic acid sequence data 140 may include longitudinal MAF data (e.g., MAF values) for each SNP. Method 750 may include, in step 752, sorting the samples according to MAF values. In some embodiments, step 752 may include rearranging the SNPs according to the mean or median MAF value of the SNPs across the samples. In some embodiments, the SNPs may be grouped according to the homozygous or heterozygous genotype of the transplant recipient. Grouping may be performed by using a threshold parameter, as described above.

[0126] In step 754, the system 100 can determine a MAF variation summary statistic for the panel of SNPs. The MAF variation summary statistic can include MAF variation calculated in simple forms such as standard deviation or dynamic range, or in more complex forms. In some embodiments, smoothing and differentiation functions can be applied to the MAF variation summary statistic to produce a MAF variation difference plot. FIG. 7B shows an exemplary plot of the MAF variation summary statistic. The smoothing and differentiation function can be any type of function that smooths and measures the local slope of the MAF variation summary statistic. A minimum or maximum of the smoothing and differentiation function can help determine a "split point" P in the MAF variation summary statistic. Exemplary 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 delayed differencing of the array), etc.

[0127] In step 758 (of FIG. 7A ), the system can group SNPs according to the MAF variation summary statistic, e.g., groups 502 and 504 of FIG. 5A . This step can include determining a split point by determining a local minimum or maximum of a smoothing and differentiation function applied to the MAF variation summary statistic within a particular window of SNP position indices, such as window 720 shown in FIG. 7B . The location of window 720 can be determined based on relationship information between genomic contributors. Split point 722 (also referred to as split point P throughout this disclosure) can be used to group (cluster) SNPs into homozygous and heterozygous groups. After the SNPs are grouped, in some embodiments, the grouping can undergo a final quality control check.

[0128] In some embodiments, the system may group SNPs into more than two groups in the MAF variation summary statistic. The MAF variation summary statistic may be generated for a mixed sample of transplant recipients who have received multiple transplants. Figure 7C shows an exemplary MAF waterfall plot and a plot of the MAF variation summary statistic including three groups of SNPs with distinct genotypes of one of the genomic contributors (Transplant Donor 1), according to some embodiments of the present disclosure. As shown in the figure, there may be three groups 702, 704, and 706.

[0129] "One-to-One" MAF Variation Summary Statistics 412: A process for generating the "one-to-one" MAF variation summary statistics 412 may include determining the sample difference in MAF values ​​between two samples with the greatest variation in MAF values. FIG. 8A shows an exemplary method for one-to-one MAF variation summary statistics, according to some embodiments. This process may include rearranging a panel of SNPs according to mean or median MAF values ​​(step 802). FIG. 8B shows an exemplary MAF waterfall plot for five samples. Sample 852 with the highest mean MAF value (among the multiple samples) and sample 854 with the lowest correlation coefficient value are selected as the first and second samples, respectively (step 806 in FIG. 8A). Then, in step 808, the system may subtract the MAF values ​​of the first selected sample 852 and the second selected sample 854, resulting in a MAF variation summary statistic similar to that shown in FIG. 8C. Using this MAF variation summary statistic, in step 810 of FIG. 8A, the system can determine (through a process similar to that described in FIG. 7B) separation points 862 (in FIG. 8C) on the MAF variation summary statistic, such as when there are large differences (sharp edges).

[0130] "One-vs-All" MAF Variation Summary Statistics 422: In some embodiments, the process for generating the "one-vs-all" MAF variation summary statistics 422 may include determining the difference in MAF values ​​for each SNP between a selected index sample and the remainder of the plurality of samples. FIG. 9 shows an exemplary method for one-vs-all MAF variation summary statistics, according to some embodiments. Step 902 may be similar to step 802 (of process 412 in FIG. 8A). In step 906, an index sample (e.g., first sample 852 in FIG. 8B) may be selected as the sample with the highest average MAF value. In step 908, the MAF values ​​of the index sample and each of the plurality of samples may be subtracted for each SNP. In some embodiments, the subtraction may include subtracting from a cumulative minimum or cumulative maximum of the MAF values ​​of the plurality of samples. Then, as part of step 908, the results (from subtracting the cumulative minimum and subtracting the cumulative maximum) may be merged via a series of functions to generate the MAF variation summary statistics. In step 910, separation points may be determined on the MAF variance summary statistics (through a process similar to that described with respect to FIG. 7B, such as when there are large differences (sharp edges)).

[0131] "All-to-All" MAF Variation Summary Statistics 432: The process of generating the "all-to-all" MAF variation summary statistics 432 involves determining the difference in MAF values ​​for each SNP between two selected index samples and the plurality of samples. Step 1002 (of FIG. 10A) may be similar to steps 802 / 902 of FIGS. 8A and 9, respectively. In step 1006 of process 432, a first index sample, index 1 sample 1052 (shown in FIG. 10B), may be selected as the sample with the highest average MAF value for the top-end SNPs (the first set of samples with higher average MAF values), and a second index sample, index 2 sample 1056, may be selected as the sample with the highest average MAF value for the bottom-end SNPs (the second set of samples with lower average MAF values). Then, for each pair of the sample at index 1 and the first set of samples, and for each pair of the sample at index 2 and the second set of samples, the system can subtract the MAF values ​​and merge them via a series of functions to generate a MAF variation summary statistic in step 1007. In step 1008, separation points can be determined on the MAF variation summary statistic, such as when there are large differences (sharp edges), through a process similar to that described with respect to FIG.

[0132] In some embodiments, results from one or more processes can be compared (e.g., step 442 of FIG. 4 ) to determine a MAF variation summary statistic 452. FIGS. 11A-11D show exemplary comparisons of a simple MAF variation summary statistic of standard deviation 402 ( FIG. 11A ), a MAF variation summary statistic (“one-to-one”) 412 ( FIG. 11B ), a MAF variation summary statistic (“one-to-all”) 422 ( FIG. 11C ), and a MAF variation summary statistic (“all-to-all”) 432 ( FIG. 11D ). The gap in the MAF variation summary statistic between two groups of SNPs (502 and 504 shown in FIGS. 11B-11D ) can be, for example, approximately 0, 4, 5, and 6 for the standard deviation, the “one-to-one” statistic, the “one-to-all” statistic, and the “all-to-all” statistic, respectively. The gap is the difference in the mean MAF values ​​between SNP group 502 and SNP group 504. In some embodiments, the MAF variance summary statistic 452 may be selected as the "all-to-all" statistic 432 due to having the highest gap (out of 6). In some embodiments, the waterfall plot may not have major and minor groups, as shown in the plot of FIG.

[0133] Example of determining the amount of cell-free nucleic acid from contributing factors and determining the risk of transplant rejection The system can use the MAF variation summary statistic to determine one or more amounts of cell-free nucleic acids, such as cell-free DNA, in a mixed sample. Figure 12 shows an exemplary flowchart of a method for determining the amount of cell-free nucleic acids from a contributor, such as cell-free DNA from a transplant donor, according to some embodiments. Method 1200 can include the system calculating average MAF information for the panel of SNPs in each group (step 1202). For example, the system can calculate a first average MAF information for the recipient heterozygous group and a second average MAF information for the recipient homozygous group.

[0134] In step 1204, the system may determine one or more multipliers based on the relationships between genomic contributors. In some embodiments, the multipliers may function to convert the calculated average MAF values ​​of the grouped SNPs into an output of the amount of nucleic acid derived from the contributors. For example, if the transplant recipient is pregnant and the donor is unrelated to the transplant recipient, for a group of SNPs with a recipient homozygous genotype and a fetal homozygous genotype, the multiplier may be 2, resulting in a contributor-derived fraction.

[0135] In step 1206, the system may determine the amount of nucleic acid from the contributor. In some embodiments, the amount of nucleic acid from the contributor may be determined based on the average MAF value and the determined multiplier. For example, each group of SNPs in the MAF variation summary statistic and the corresponding average MAF value may be associated with one or more genomic contributors. The product of the MAF value and the multiplier may result in a determined amount of cell-free DNA from the corresponding genomic contributor.

[0136]

[0010] Embodiments of the present disclosure may include determining a risk of transplant rejection in a transplant recipient based on the determined amount of nucleic acid derived from a contributor. In some embodiments, the risk of transplant rejection is determined to be low if the determined amount of nucleic acid derived from a contributor is below a predetermined threshold, and the risk of transplant rejection is determined to be high if the estimated amount of nucleic acid derived from a contributor is above a predetermined threshold.

[0137] Exemplary Systems for Determining the Amount of Nucleic Acids from Contributing Factors in a Mixed Sample The systems, kits, and methods discussed herein may be implemented by a device. FIG. 13 illustrates an exemplary device implementing the disclosed systems, kits, and methods, according to some embodiments. In some embodiments, one or more computing devices 1300 may perform one or more steps of one or more methods described or illustrated herein. In particular embodiments, one or more computing devices 1300 may provide functionality described or illustrated herein. In particular embodiments, software executing on one or more computing devices 1300 may perform one or more steps of one or more methods described or illustrated herein or provide functionality described or illustrated herein. Particular embodiments may include one or more portions of one or more computing devices 1300.

[0138] The present disclosure contemplates any suitable number of computing systems 1300. The present disclosure contemplates one or more computing devices 1300 taking 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 system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, a kiosk, a mainframe, a mesh of computer systems, 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 integrated or distributed, may span multiple locations, may span multiple machines, may span multiple data centers, or may reside in a cloud, which may include one or more cloud components within one or more networks.

[0139] Where appropriate, one or more computing devices 1300 may perform one or more steps of one or more methods described or illustrated herein without substantial spatial or temporal limitations. By way of example and not limitation, one or more computing devices 1300 may perform one or more steps of one or more methods described or illustrated herein in real time or in batch mode. One or more computing devices 1300 may, where appropriate, perform one or more steps of one or more methods described or illustrated herein at different times or in different locations.

[0140] In particular embodiments, one or more computing devices 1300 may include a processor 1302, memory 1304, a database 1306, an input / output (I / O) interface 1308, a communications interface 1310, and a bus 1312. While 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 particular embodiments, processor 1302 may include hardware for executing instructions, such as those making up a computer program. By way of example and not limitation, to execute instructions, processor 1302 may retrieve (or fetch) instructions from an internal register, an internal cache, memory 1304, or database 1306, decode and execute them, and then write one or more results to an internal register, an internal cache, memory 1304, or database 1306. In particular embodiments, processor 1302 may include one or more internal caches for data, instructions, or addresses. This disclosure contemplates processor 1302 including any suitable number of any suitable internal caches, where appropriate. By way of example and not limitation, processor 1302 may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). Instructions in an instruction cache may be copies of instructions in memory 1304 or database 1306, and the instruction cache may speed up retrieval of those instructions by processor 1302.

[0141] Data in a data cache may be a copy of data in memory 1304 or database 1306 on which instructions executing on processor 1302 operate, may be the results of previous instructions executed by processor 1302 for access by subsequent instructions executing on processor 1302 or for writing to memory 1304 or database 1306, or may be other suitable data. A data cache may speed up read or write operations by processor 1302. A TLB may speed up virtual address translation for processor 1302. In particular embodiments, processor 1302 may include one or more internal registers for data, instructions, or addresses. This disclosure contemplates processor 1302 including any suitable number of any suitable internal registers, where appropriate. Where appropriate, processor 1302 may include one or more arithmetic logic units (ALUs), may be a multi-core processor, or may include one or more processors 1302. While this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor.

[0142] In particular embodiments, memory 1304 includes main memory for storing data for processor 1302 to operate on and instructions for processor 1302 to execute. By way of example and not limitation, one or more computing devices 1300 may load instructions into memory 1304 from database 1306 or another source (e.g., another one or more computing devices 1300, etc.). Processor 1302 may then load the instructions from memory 1304 into an internal register or cache. To execute the instructions, processor 1302 may retrieve the instructions from the internal register or cache and decode them. During or after executing the instructions, processor 1302 may write one or more results (which may be intermediate results or final results) to an internal register or cache. Processor 1302 may then write one or more of those results to memory 1304.

[0143] In particular embodiments, processor 1302 executes only instructions in one or more internal registers or internal caches or in memory 1304 (as opposed to database 1306 or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory 1304 (as opposed to database 1306 or elsewhere). One or more memory buses (which may each include an address bus and a data bus) may couple processor 1302 to memory 1304. Bus 1312 may include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between processor 1302 and memory 1304 to facilitate accesses to memory 1304 requested by processor 1302. In particular embodiments, memory 1304 includes random access memory (RAM). This RAM may be volatile memory, where appropriate. Where appropriate, this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Further, where appropriate, this RAM may be single-ported or multi-ported RAM. This disclosure contemplates any suitable RAM. Memory 1304 may include one or more memory devices, where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory.

[0144] In particular embodiments, database 1306 includes mass storage for data or instructions. By way of example and not limitation, database 1306 may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk, a magneto-optical disk, magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Database 1306 may include removable or non-removable (or fixed) media, where appropriate. Database 1306 may be internal or external to one or more computing devices 1300, where appropriate. In particular embodiments, database 1306 is non-volatile solid-state memory. In particular embodiments, database 1306 includes read-only memory (ROM). Where appropriate, this ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these. The present disclosure contemplates mass database 1306 taking any suitable physical form. Database 1306 may include, where appropriate, one or more storage control units that facilitate communication between processor 1302 and database 1306. Where appropriate, database 1306 may include one or more databases 1306. Although this disclosure describes and illustrates particular storage devices, this disclosure contemplates any suitable storage device.

[0145] In particular embodiments, I / O interface 1308 includes hardware, software, or both that provide one or more interfaces for communication between one or more computing devices 1300 and one or more I / O devices. One or more computing devices 1300 may include one or more of these I / O devices, where appropriate. One or more of these I / O devices may enable communication between a person and one or more computing devices 1300. By way of example and not limitation, an I / O device may include a keyboard, keypad, microphone, monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet, touch screen, trackball, video camera, another suitable I / O device, or a combination of two or more of these. An I / O device may include one or more sensors. This disclosure contemplates any suitable I / O devices and any suitable I / O interface 1308 therefor. Where appropriate, I / O interface 1308 may include one or more device or software drivers that enable processor 1302 to drive one or more of these I / O devices. I / O interface 1308 may include, where appropriate, one or more I / O interfaces 1308. Although this disclosure describes and illustrates particular I / O interfaces, this disclosure contemplates any suitable I / O interfaces.

[0146] In particular embodiments, communications interface 1310 includes hardware, software, or both that provide one or more interfaces for communications (e.g., packet-based communications, etc.) between 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, communications interface 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. This disclosure contemplates any suitable network and any suitable communications interface 1310 therefor.

[0147] By way of example, and not limitation, one or more computing devices 1300 may communicate with one or more portions 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 portions of one or more of these networks may be wired or wireless. By way of example, one or more computing devices 1300 may communicate with a wireless PAN (WPAN) (e.g., a Bluetooth® WPAN, etc.), a Wi-Fi network, a Wi-MAX network, a cellular network (e.g., a Global System for Mobile Communications (GSM) network, etc.), or other suitable wireless network, or a combination of two or more of these. One or more computing devices 1300 may include any suitable communication interface 1310 for any of these networks, where appropriate. The communication interface 1310 may include one or more communication interfaces 1310, where appropriate. Although this disclosure describes and illustrates a particular communication interface, this disclosure contemplates any suitable communication interface.

[0148] In particular embodiments, bus 1312 includes hardware, software, or both that couple one or more components of computing device 1300 together. By way of example, and not limitation, bus 1312 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, 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 thereof. Bus 1312 may include one or more buses 1312, where appropriate. Although this disclosure describes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect.

[0149] As used herein, a computer-readable non-transitory storage medium may include, where appropriate, one or more semiconductor-based or other integrated circuits (ICs) (such as field programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid (HHDs), optical disks, optical disk drives (ODDs), magneto-optical disks, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM drives, Secure Digital cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more of these. A computer-readable non-transitory storage medium may, where appropriate, be volatile, non-volatile, or a combination of volatile and non-volatile.

[0150] The term computer-readable non-transitory storage medium may include a single medium or multiple media (e.g., centralized or distributed databases 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 interpreted to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by a device, causing 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 interpreted to include, but is not limited to, solid-state memory, optical and magnetic media, and carrier wave signals.

[0151] Although examples of the present disclosure have been fully described with reference to the accompanying drawings, it should be noted that various changes and modifications will become apparent to those skilled in the art, and such changes and modifications are to be understood as being included within the scope of the examples of the present disclosure as defined by the appended claims.

Claims

1. 1. A computer-implemented method for determining the amount of cell-free nucleic acid from contributors in a mixed sample obtained from a transplant recipient, the mixed sample comprising cell-free nucleic acid from at least three genetically distinct contributors, the method comprising: receiving, via a computer or input function, nucleic acid sequence data from a panel of single nucleotide polymorphisms (SNPs) from cell-free nucleic acid from the at least three genetically distinct contributors; receiving a genomic relationship between the at least three genetically distinct contributors; determining and grouping minor allele frequency (MAF) information from said panel of SNPs; and determining the amount of cell-free nucleic acid from the contributor based on the genomic relationship and the MAF grouping.

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

3. 2. The computer-implemented method of claim 1, wherein the transplant recipient has received at least two transplants and the at least three genetically distinct contributors include a recipient genomic contributor, a first transplant donor genomic contributor, and a second transplant donor genomic contributor.

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

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

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

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

8. 8. The computer-implemented method of claim 1, further comprising determining a genotype of one or more of the transplant recipient, fetus, or donor based on the MAF information grouping, wherein the transplant recipient is a pregnant woman.

9. Determining and grouping MAF information rearranging the panel of SNPs according to mean or median MAF value; determining the MAF information comprising a summary statistic of MAF variation in the panel of SNPs; and grouping the panel of SNPs according to the MAF variation summary statistic.

10. Determining and grouping MAF information The computer-implemented method of claim 9 , comprising determining separation points in the MAF variability summary statistic by determining local minima or maxima within a window.

11. The computer-implemented method of claim 10 , wherein the split points are used to group the SNPs into homozygous and heterozygous genotype groups.

12. Determining and grouping MAF information generating a waterfall plot of the MAF information; grouping the MAF information by segmenting the waterfall plot into groups; and calculating a mean MAF value for the group, wherein the amount of cell-free nucleic acid from the determined contributing factors is based on the calculated mean MAF value.

13. Determining and grouping MAF information selecting a first sample having the highest average MAF value among the plurality of samples; selecting a second sample having the lowest correlation coefficient associated with the first sample; determining a MAF variance summary statistic by subtracting the MAF values ​​of the selected first sample and the selected second sample; determining a separation point in the MAF variance summary statistic; and grouping the MAF information based on the separation points.

14. Determining and grouping MAF information selecting an index sample having the highest average MAF value among the plurality of samples; determining a MAF difference between the index sample and each of the plurality of samples; determining a MAF variance summary statistic by merging the MAF differences; determining a separation point in the MAF variance summary statistic; and grouping the MAF information based on the separation points.

15. Determining and grouping MAF information selecting a first index sample containing the highest average MAF value among the set of highly rearranged SNPs; selecting a second index sample that contains the highest average MAF among the set of low rearrangement SNPs; determining a MAF difference between the first index sample and each of the set of highly rearranged SNPs; determining a MAF difference between the second index sample and each of the set of low rearrangement SNPs; determining a MAF variance summary statistic by merging the MAF differences; determining a separation point in the MAF variance summary statistic; and grouping the MAF information based on the separation points.

16. 16. The computer-implemented method of claim 1, further comprising generating a waterfall plot for the mixed sample, wherein the waterfall plot comprises one or more hierarchical steps having one or more steps, and the SNPs in the one or more steps have the same genotype.

17. 17. The computer-implemented method of any of claims 1-16, wherein the transplant recipient has received a transplant comprising one or more of a kidney transplant, a heart transplant, a lung transplant, a liver transplant, a pancreas transplant, a vascularized composite transplant, an intestine transplant, a stomach transplant, a testis transplant, a penis transplant, an ovary transplant, a uterus transplant, a thymus transplant, a face transplant, a hand transplant, a leg transplant, a bone transplant, a cornea transplant, a skin transplant, a heart valve transplant, a blood vessel transplant, or any combination thereof.

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

19. 1. A kit for determining the amount of cell-free nucleic acid from contributors in a mixed sample obtained from a transplant 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 from a panel of single nucleotide polymorphisms (SNPs) from cell-free nucleic acid from the at least three genetically distinct contributors; instructions to receive a genomic relationship between the at least three genetically distinct contributors; instructions for determining and grouping minor allele frequency (MAF) information from said panel of SNPs; and instructions for determining the amount of cell-free nucleic acid from said contributor based on said genomic relationship and said MAF grouping.

20. 20. The kit of claim 19, wherein the transplant recipient is a pregnant woman and the at least three genetically distinct contributors include a maternal genomic contributor, a fetal genomic contributor, and a transplant donor genomic contributor.

21. 20. The kit of claim 19, wherein the transplant recipient has received at least two transplants and the at least three genetically distinct contributors include a recipient genomic contributor, a first transplant donor genomic contributor, and a second transplant donor genomic contributor.

22. The kit according to any one of claims 19 to 21, wherein the amount of cell-free nucleic acid derived from the contributing factor is a percentage of the cell-free nucleic acid derived from the contributing factor in the mixed sample.

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

24. 24. The kit of claim 23, wherein the set of longitudinal samples have the same genotype.

25. The kit of any one of claims 19 to 24, wherein said panel of SNPs comprises less than 500 SNPs.

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

27. Determining and grouping MAF information rearranging the panel of SNPs according to mean or median MAF value; determining the MAF information comprising a summary statistic of MAF variation in the panel of SNPs; and grouping the panel of SNPs according to the MAF variation summary statistic.

28. Determining and grouping MAF information 28. The kit of claim 27, comprising determining a separation point in the MAF variability summary statistic by determining a local minimum or maximum within a window.

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

30. Determining and grouping MAF information generating a waterfall plot of the MAF information; grouping the MAF information by segmenting the waterfall plot into groups; 30. The kit of any one of claims 19 to 29, comprising: calculating a mean MAF value for the group, wherein the amount of cell-free nucleic acid from the determined contributor is based on the calculated mean MAF value.

31. Determining and grouping MAF information selecting a first sample having the highest average MAF value among the plurality of samples; selecting a second sample having the lowest correlation coefficient associated with the first sample; determining a MAF variance summary statistic by subtracting the MAF values ​​of the selected first sample and the selected second sample; determining a separation point in the MAF variance summary statistic; and grouping the MAF information based on the separation points.

32. Determining and grouping MAF information selecting an index sample having the highest average MAF value among the plurality of samples; determining a MAF difference between the index sample and each of the plurality of samples; determining a MAF variance summary statistic by merging the MAF differences; determining a separation point in the MAF variance summary statistic; and grouping the MAF information based on the separation points.

33. Determining and grouping MAF information selecting a first index sample containing the highest average MAF value among the set of highly rearranged SNPs; selecting a second index sample that contains the highest average MAF among the set of low rearrangement SNPs; determining a MAF difference between the first index sample and each of the set of highly rearranged SNPs; determining a MAF difference between the second index sample and each of the set of low rearrangement SNPs; determining a MAF variance summary statistic by merging the MAF differences; determining a separation point in the MAF variance summary statistic; and grouping the MAF information based on the separation points.

34. The kit comprises:

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

35. 35. The kit of any of claims 19-34, wherein the transplant recipient has received a transplant comprising one or more of a kidney transplant, a heart transplant, a lung transplant, a liver transplant, a pancreas transplant, a vascularized composite transplant, an intestine transplant, a stomach transplant, a testis transplant, a penis transplant, an ovary transplant, a uterus transplant, a thymus transplant, a face transplant, a hand transplant, a leg transplant, a bone transplant, a cornea transplant, a skin transplant, a heart valve transplant, a blood vessel transplant, 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. 1. A system for determining the amount of cell-free nucleic acid from contributors in a mixed sample obtained from a transplant recipient, the mixed sample comprising cell-free nucleic acid from at least three genetically distinct contributors, the system comprising: an interface configured to receive input; a decision unit, receiving, via the interface, nucleic acid sequence data from a panel of single nucleotide polymorphisms (SNPs) from cell-free nucleic acid from the at least three genetically distinct contributors; receiving a genomic relationship between the at least three genetically distinct contributors; Determining and grouping minor allele frequency (MAF) information from the panel of SNPs; and determining an amount of cell-free nucleic acid from the contributor based on the genomic relationship and the MAF grouping.

38. 38. The system of claim 37, wherein the transplant recipient is a pregnant woman and the at least three genetically distinct contributors include a maternal genomic contributor, a fetal genomic contributor, and a transplant donor genomic contributor.

39. 38. The system of claim 37, wherein the transplant recipient has received at least two transplants and the at least three genetically distinct contributors include a recipient genomic contributor, a first transplant donor genomic contributor, and a second transplant donor genomic contributor.

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

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

42. 42. The system of claim 41, wherein the set of longitudinal samples have the same genotype.

43. 43. The system of any of claims 37 to 42, wherein the panel of SNPs comprises less than 500 SNPs.

44. The decision unit: The system of any one of claims 37 to 43, further configured to determine the genotype of one or more of the transplant recipient, fetus, or donor based on the MAF information grouping, and wherein the transplant recipient is a pregnant woman.

45. The determining unit configured to determine and group MAF information, rearranging the panel of SNPs according to mean or median MAF value; determining the MAF information comprising a summary statistic of MAF variation in the panel of SNPs; and grouping the panel of SNPs according to the MAF variation summary statistic.

46. The determining unit configured to determine and group MAF information, 46. ​​The system of claim 45, comprising the determining unit configured to determine separation points in the MAF variability summary statistic by determining local minima or maxima within a window.

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

48. The determining unit configured to determine and group MAF information, generating a waterfall plot of the MAF information; grouping the MAF information by segmenting the waterfall plot into groups; 48. The system of any one of claims 37 to 47, comprising the determining unit configured to: calculate an average MAF value for the group, wherein the determined amount of cell-free nucleic acid from a contributing factor is based on the calculated average MAF value.

49. The determining unit configured to determine and group MAF information, selecting a first sample having the highest average MAF value among the plurality of samples; selecting a second sample having the lowest correlation coefficient associated with the first sample; determining a MAF variance summary statistic by subtracting the MAF values ​​of the selected first sample and the selected second sample; determining a separation point in the MAF variance summary statistic; and grouping the MAF information based on the separation points.

50. The determining unit configured to determine and group MAF information, selecting an index sample having the highest average MAF value among the plurality of samples; determining a MAF difference between the index sample and each of the plurality of samples; determining a MAF variance summary statistic by merging the MAF differences; determining a separation point in the MAF variance summary statistic; and grouping the MAF information based on the separation points.

51. The determining unit configured to determine and group MAF information, selecting a first index sample containing the highest average MAF value among the set of highly rearranged SNPs; selecting a second index sample that contains the highest average MAF among the set of low rearrangement SNPs; determining a MAF difference between the first index sample and each of the set of highly rearranged SNPs; determining a MAF difference between the second index sample and each of the set of low rearrangement SNPs; determining a MAF variance summary statistic by merging the MAF differences; determining a separation point in the MAF variance summary statistic; and grouping the MAF information based on the separation points.

52. The decision unit:

52. The system of any one of claims 37 to 51, further configured to generate a waterfall plot for the mixed sample, wherein the waterfall plot comprises one or more hierarchical steps having one or more steps, and the SNPs of the one or more steps have the same genotype.

53. 53. The system of any of claims 37-52, wherein the transplant recipient has received a transplant comprising one or more of a kidney transplant, a heart transplant, a lung transplant, a liver transplant, a pancreas transplant, a vascularized composite transplant, an intestine transplant, a stomach transplant, a testis transplant, a penis transplant, an ovary transplant, a uterus transplant, a thymus transplant, a face transplant, a hand transplant, a leg transplant, a bone transplant, a cornea transplant, a skin transplant, a heart valve transplant, a blood vessel transplant, or any combination thereof.

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

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