US20120185176A1 - Methods for Non-Invasive Prenatal Ploidy Calling - Google Patents

Methods for Non-Invasive Prenatal Ploidy Calling Download PDF

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US20120185176A1
US20120185176A1 US13/499,086 US201013499086A US2012185176A1 US 20120185176 A1 US20120185176 A1 US 20120185176A1 US 201013499086 A US201013499086 A US 201013499086A US 2012185176 A1 US2012185176 A1 US 2012185176A1
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chromosome
genetic
target individual
dna
snps
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Matthew Rabinowitz
Allison Ryan
George Gemelos
Milena Banjevic
Zachary Demko
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Natera Inc
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/10—Ploidy or copy number detection
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6813—Hybridisation assays
    • C12Q1/6827—Hybridisation assays for detection of mutation or polymorphism
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
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    • C12Q1/6874—Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
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    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • G—PHYSICS
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    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • C—CHEMISTRY; METALLURGY
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers
    • C—CHEMISTRY; METALLURGY
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/16—Primer sets for multiplex assays

Definitions

  • a human being normally has two sets of 23 chromosomes in every somatic cell, with one copy coming from each parent.
  • Aneuploidy a state where a cell has the wrong number of chromosomes, is responsible for a significant percentage of children born with genetic conditions.
  • Detection of chromosomal abnormalities can identify individuals, including fetuses or embryos, with conditions such as Down syndrome, Edwards syndrome, Klinefelters syndrome, and Turner syndrome, among others. Since chromosomal abnormalities are generally undesirable, the detection of such a chromosomal abnormality in a fetus may provide the basis for the decision to terminate a pregnancy.
  • Prenatal diagnosis can alert physicians and parents to abnormalities in growing fetuses.
  • Some currently available methods such as amniocentesis and chorionic villus sampling (CVS), are able to diagnose genetic defects with high accuracy; however, they may carry a risk of spontaneous abortion.
  • Other methods can indirectly estimate a risk of certain genetic defects non-invasively, for example from hormone levels in maternal blood and/or from ultrasound data, however their accuracies are much lower.
  • cell-free fetal DNA and intact fetal cells can enter maternal blood circulation. This provides an opportunity to directly measure genetic information about a fetus, specifically the aneuploidy state of the fetus, in a manner which is non-invasive, for example from a maternal blood draw.
  • Methods for non-invasive prenatal ploidy calling are disclosed herein.
  • methods are disclosed for the determination of the ploidy state of a target individual where the measured genetic material of the target is contaminated with genetic material of the mother, by using the knowledge of the maternal genetic data. This is in contrast to methods that are able to determine the ploidy state of a target individual from genetic data that is noisy due to poor measurements; the contamination in this data is random. This is also in contrast to methods that are able to determine the ploidy state of a target individual from genetic data that is difficult to interpret because of contamination by DNA from unrelated individuals; the contamination in that data is genetically random.
  • the methods disclosed herein are able to determine the ploidy state of a target individual when the difficulty of interpretation is due to contamination of DNA from a parent; the contamination in this data is at least half identical to the target data, and is therefore difficult to correct for.
  • a method of the present disclosure uses the knowledge of the contaminating maternal genotype to create a model of the expected genetic measurements given a mixture of the maternal and the target genetic material, wherein the target genetic data is not known a priori. This step is not necessary where the uncertainty in the genetic data is due to random noise.
  • the target individual is a fetus
  • the target individual's genetic data comprises free floating DNA found in maternal blood
  • the contaminating genetic material comprises free floating maternal DNA also found in maternal blood.
  • the target individual is a fetus
  • the target individual's genetic data comprises DNA found in fetal cells found in maternal blood
  • the contaminating genetic material comprises DNA found in maternal cells also found in maternal blood.
  • the target individual is a fetus
  • the determination of the ploidy state is done in the context of non-invasive prenatal diagnosis, and where a clinical decision is made based on the ploidy state determination.
  • genetic data from one or both parents of the target individual is used in the determination of the ploidy state of the target.
  • the chromosomes of interest include chromosomes 13, 18, 21, X and Y.
  • the determination is transformed into a report which may be sent to a relevant healthcare practitioner.
  • the series of steps outlined above result in a transformation of the genetic matter of a pregnant mother and the father into an actionable decision that results in a pregnancy being continued or terminated.
  • the ploidy state determination is used to make a clinical decision.
  • the clinical decision may be to terminate a pregnancy where the fetus is found to have a genetic abnormality.
  • the disclosure focuses on genetic data from human subjects, and more specifically on developing fetuses, as well as related individuals, it should be noted that the methods disclosed apply to the genetic data of a range of organisms, in a range of contexts.
  • the techniques described for making ploidy determination are most relevant in the context of prenatal diagnosis in conjunction with amniocentesis, chorion villus biopsy, fetal tissue sampling, and non-invasive prenatal diagnosis, where a small quantity of fetal genetic material is isolated from maternal blood, for example prenatal serum screens, the triple test, the quad test.
  • the use of this method may facilitate diagnoses focusing on inheritable diseases, chromosome copy number predictions, increased likelihoods of defects or abnormalities, as well as making predictions of susceptibility to various disease- and non-disease phenotypes for individuals to enhance clinical and lifestyle decisions.
  • the fetal or embryonic genomic data can be used to detect if the cell is aneuploid, that is, where the wrong number of one or more autosomal chromosomes are present in an individual, and/or if the wrong number of sexual chromosomes are present in the individual.
  • the genetic data can also be used to detect for uniparental disomy, a condition in which two of a given chromosome are present, both of which originate from one parent. This is done by creating a set of hypotheses about the potential states of the DNA, and testing to see which hypothesis has the highest probability of being true given the measured data.
  • the small amount of genetic material of a fetus may be transformed through amplification into a large amount of genetic material that encodes similar or identical genetic data.
  • the genetic data contained molecularly in the large amount of genetic material may be transformed into raw genetic data in the form of digital signals, optionally stored in computer memory, by way of a genotyping method.
  • the raw genetic data may be transformed, by way of the PARENTAL SUPPORTTM method, into copy number calls for one or a number of chromosomes, also optionally stored in computer memory.
  • the copy number call may be transformed into a report for a physician, who may then act on the information in the report.
  • the direct measurements of genetic material, amplified or unamplified, present at a plurality of loci can be used to detect for monosomy, uniparental disomy, matched trisomy, unmatched trisomy, tetrasomy, and other aneuploidy states.
  • One embodiment of the present disclosure takes advantage of the fact that under some conditions, the average level of amplification and measurement signal output is invariant across the chromosomes, and thus the average amount of genetic material measured at a set of neighboring loci will be proportional to the number of homologous chromosomes present, and the ploidy state may be called in a statistically significant fashion.
  • different alleles have a statistically different characteristic amplification profiles given a certain parent context and a certain ploidy state; these characteristic differences can be used to determine the ploidy state of the chromosome.
  • calculated, phased, reconstructed and/or determined genetic data from the target individual and/or from one or more related individuals may be used as input for a ploidy calling aspect of the present disclosure.
  • a method for determining a copy number of a chromosome of interest in a target individual, using genotypic measurements made on genetic material from the target individual, wherein the genetic material of the target individual is mixed with genetic material from the mother of the target individual comprises obtaining genotypic data for a set of SNPs of the parents of the target individual; making genotypic measurements for the set of SNPs on a mixed sample that comprises DNA from the target individual and also DNA from the mother of the target individual; creating, on a computer, a set of ploidy state hypothesis for the chromosome of interest of the target individual; determining, on the computer, the probability of each of the hypotheses given the genetic measurements of the mixed sample and the genetic data of the parents of the target individual; and using the determined probabilities of each hypothesis to determine the most likely copy number of the chromosome of interest in the target individual.
  • the target individual is a fetus.
  • the copy number determination is used to make a clinical decision.
  • the target individual is a fetus, and the clinical decision is to terminate a pregnancy where the fetus is found to have a genetic abnormality, or to not terminate the pregnancy where the fetus is not found to have a genetic abnormality.
  • the set of SNPs comprises a plurality of SNPs from the chromosome of interest, and a plurality of SNPs from at least one chromosome that is expected to be disomic on the target individual.
  • the step of determining, on the computer, the probability of each of the hypotheses comprises using the genotypic data of the parents to determine parental contexts for each of the SNPs; grouping the genotypic measurements of the mixed sample into the parental contexts; using the grouped genotypic measurements from at least one chromosome that is expected to be disomic to determine a platform response; using the grouped genotypic measurements from at least one chromosome that is expected to be disomic to determine a ratio of fetal to maternal DNA in the mixed sample; using the determined platform response and the determined ratio to predict an expected distribution of SNP measurements for each set of SNPs in each parental context under each hypothesis; and calculating the probabilities that each of the hypotheses is true given the platform response, and given the ratio, and given the grouped genotypic measurements of the mixed sample, and given the predicted expected distributions, for each parental context, for each hypothesis.
  • the chromosome of interest is selected from the group consisting of chromosome 13, chromosome 18, chromosome 21, the X chromosome, the Y chromosome, and combinations thereof.
  • the method is used to determine the copy number of a number of chromosomes in the target individual, where the number is selected from the group consisting of one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty one, twenty two, and twenty three.
  • the mixed sample is maternal blood, maternal plasma or some other substance taken from a pregnant mother.
  • the target individual's genetic material is free floating DNA found in maternal blood or serum.
  • the target individual's genetic material is nuclear DNA found in one or more cells from the target individual.
  • a confidence is computed for the chromosome copy number determination.
  • the ratio of fetal to maternal DNA in the mixed sample is determined for individual chromosomes.
  • the step of obtaining of genotypic data, and/or the step of making genetotypic measurements is done by measuring genetic material using techniques selected from the group consisting of padlock probes, circularizing probes, genotyping microarrays, SNP genotyping assays, chip based microarrays, bead based microarrays, other SNP microarrays, other genotyping methods, Sanger DNA sequencing, pyrosequencing, high throughput sequencing, reversible dye terminator sequencing, sequencing by ligation, sequencing by hybridization, other methods of DNA sequencing, other high throughput genotyping platforms, fluorescent in situ hybridization (FISH), comparative genomic hybridization (CGH), array CGH, and multiples or combinations thereof.
  • FISH fluorescent in situ hybridization
  • CGH comparative genomic hybridization
  • array CGH array CGH
  • the step of measuring genetic material is done on genetic material that is amplified, prior to being measured, using a technique that is selected from the group consisting of Polymerase Chain Reaction (PCR), ligand mediated PCR, degenerative oligonucleotide primer PCR, Multiple Displacement Amplification (MDA), allele-specific PCR, allele-specific amplification techniques, bridge amplification, padlock probes, circularizing probes, and combinations thereof.
  • PCR Polymerase Chain Reaction
  • MDA Multiple Displacement Amplification
  • allele-specific PCR allele-specific amplification techniques
  • bridge amplification padlock probes
  • padlock probes circularizing probes, and combinations thereof.
  • the step of determining the copy number of the chromosome of interest is performed for the purpose of screening for a chromosomal condition where the chromosomal condition is selected from the group consisting of nullsomy, monosomy, disomy, uniparental disomy, euploidy, trisomy, matched trisomy, unmatched trisomy, maternal trisomy, paternal trisomy, tetrasomy, matched tetrasomy, unmatched tetrasomy, other aneuploidy, unbalanced translocation, balanced translocation, recombination, deletion, insertion, mosaicism, and combinations thereof.
  • the chromosomal condition is selected from the group consisting of nullsomy, monosomy, disomy, uniparental disomy, euploidy, trisomy, matched trisomy, unmatched trisomy, maternal trisomy, paternal trisomy, tetrasomy, matched tetrasomy, un
  • the method is used for the purpose of paternity testing.
  • a method for determining a copy number of a chromosome of interest in a target individual, using genotypic measurements made on genetic material from the target individual, wherein the genetic material of the target individual is mixed with genetic material from the mother of the target individual comprises obtaining genotypic data for a set of SNPs of the mother of the target individual; making genotypic measurements for the set of SNPs on a mixed sample that comprises DNA from the target individual and also DNA from the mother of the target individual; creating, on a computer, a set of ploidy state hypothesis for the chromosome of interest of the target individual; determining, on the computer, the probability of each of the hypotheses given the genetic measurements of the mixed sample and the genetic data of the parents of the target individual; and using the determined probabilities of each hypothesis to determine the most likely copy number of the chromosome of interest in the target individual.
  • FIG. 1 shows a model fit for both X (left plot) and Y (right plot) channels in a sample with 40 percent DNA from the target individual.
  • FIG. 2 shows components of the measurement vector compared against the predictions under three hypotheses. This plot is from chromosome 16 of a sample which is 40 percent target DNA and 60 percent mother DNA. The true hypothesis is H110.
  • FIG. 3 shows components of the measurement vector compared against the predictions under three hypotheses.
  • FIG. 3 is from chromosome 21 of the same sample as in FIG. 2 , and the correct hypothesis is H210.
  • the ploidy state of a target individual can be determined for one, some, or all chromosomes, in the individual.
  • the genetic material of the target individual is used to make the ploidy determination, and where the genetic material of the target individual is contaminated with genetic material of the mother of the target individual.
  • genetic data of one or both parents of the target individual, optionally including genetic data from other relatives of the target individual is used in the ploidy determination.
  • Copy number calling is the concept of determining the number and identity of chromosomes in an individual, either on a per cell basis, or in a bulk manner.
  • the amount of genetic material contained in a single cell, a small group of cells, or a sample of DNA may be used as a proxy for the number of chromosomes in the target individual.
  • the present disclosure allows the determination of aneuploidy from the genetic material contained in a small sample of cells, or a small sample of DNA, provided the genome of at least one or both parents are available.
  • Some aspects of the present disclosure use the concept of parental context, where the parental contexts describe, for a given SNP, the possible set of alleles that a child may have inherited from the parents.
  • a specific statistical distribution of SNP measurements is expected, and that distribution will vary depending on the parental context and on the ploidy state of the chromosome segment on which the SNP is found.
  • DNA measurements whether obtained by sequencing techniques, genotyping arrays, or any other technique, contain a degree of error.
  • the relative confidence in a given DNA measurement is affected by many factors, including the amplification method, the technology used to measure the DNA, the protocol used, the amount of DNA used, the integrity of the DNA used, the operator, and the freshness of the reagents, just to name a few.
  • One way to increase the accuracy of the measurements is to use informatics based techniques to infer the correct genetic state of the DNA in the target based on the knowledge of the genetic state of related individuals.
  • related individuals are expected to share certain aspect of their genetic state, when the genetic data from a plurality of related individuals is considered together, it is possible to identify likely errors and omissions in the measurements, and increase the accuracy of the knowledge of the genetic states of all the related individuals. In addition, a confidence may be computed for each call made.
  • a computer readable medium is a medium that stores computer data in machine readable form.
  • a computer readable medium can comprise computer storage media as well as communication media, methods or signals.
  • Computer storage media also called computer memory, includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data.
  • Computer storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology; CD-ROM, DVD, or other optical storage; cassettes, tape, disk, or other magnetic storage devices; or any other medium which can be used to tangibly store the desired information and which can be accessed by the computer.
  • Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application-specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof.
  • These various implementations can include one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
  • Such computer programs also known as programs, software, software applications or code
  • a computer program may be deployed in any form, including as a stand-alone program, or as a module, component, subroutine, or other unit suitable for use in a computing environment.
  • a computer program may be deployed to be executed or interpreted on one computer or on multiple computers at one site, or distributed across multiple sites and interconnected by a communication network.
  • the parental context refers to the genetic state of a given SNP, on each of the two relevant chromosomes for each of the two parents of the target. Note that in one embodiment, the parental context does not refer to the allelic state of the target, rather, it refers to the allelic state of the parents.
  • the parental context for a given SNP may consist of four base pairs, two paternal and two maternal; they may be the same or different from one another. In this disclosure, it may be written as “m 1 m 2
  • the parental context may be written as “f 1 f 2
  • subscripts “1” and “2” refer to the genotype, at the given allele, of the first and second chromosome; also note that the choice of which chromosome is labeled “1” and which is labeled “2” is arbitrary.
  • a and B are often used to generically represent base pair identities; A or B could equally well represent C (cytosine), G (guanine), A (adenine) or T (thymine).
  • C cytosine
  • G guanine
  • A adenine
  • T thymine
  • any of the four possible alleles could occur at a given allele, and thus it is possible, for example, for the mother to have a genotype of AT, and the father to have a genotype of GC at a given allele.
  • empirical data indicate that in most cases only two of the four possible base pairs are observed at a given allele.
  • the discussion assumes that only two possible base pairs will be observed at a given allele, although the embodiments disclosed herein could be modified to take into account the cases where this assumption does not hold.
  • a “parental context” may refer to a set or subset of target SNPs that have the same parental context. For example, if one were to measure 1000 alleles on a given chromosome on a target individual, then the context AA
  • the genetic data from one parent can be phased, while the genetic data from the other parent to be unphased, in which case there would be twelve parental contexts. Every SNP allele on a chromosome, excluding some SNPs on the sex chromosomes, has one of these parental contexts. Note that some of these contexts may behave the same was other contexts, and one could lump those context together; this could be functionally equivalent to using the full number of contexts. Alternately, one could choose to ignore certain contexts for the purposes of analysis.
  • the SNPs from each parental context may be grouped together, such that the SNP measurements from the target genetic sample may be treated statistically, as a group, and compared with expected behavior for various hypotheses.
  • Grouping the SNPs by context simply refers to creating subsets of SNPs that are differentiated by parental context, where each subset may be treated in a bulk manner. Grouping the SNPs is beneficial because the expected bulk behavior of a set of SNPs depends its parental context.
  • parental contexts may be useful in the context of copy ploidy determination.
  • SNPs within a first parental context may be expected to statistically behave similarly when measured for a given ploidy state.
  • some sets of SNPs from a second parental context may be expected to statistically behave differently from those in the first parental context in certain circumstances, such as for certain ploidy states, and the difference in behavior may be characteristic of one or a set of particular ploidy states.
  • a hypothesis may refer to a possible genetic state. It may refer to a possible ploidy state.
  • a set of hypotheses refers to a set of possible genetic states. In some embodiments, a set of hypotheses may be designed such that one hypothesis from the set will correspond to the actual genetic state of any given individual. In some embodiments, a set of hypotheses may be designed such that every reasonably possible genetic state may be described by at least one hypothesis from the set. In some embodiments of the present disclosure, one aspect of the method is to determine which hypothesis corresponds to the actual genetic state of the individual in question.
  • one step involves creating a hypothesis.
  • it may be a copy number hypothesis.
  • it may involve a hypothesis concerning which segments of a chromosome from each of the related individuals correspond genetically to which segments, if any, of the other related individuals.
  • Creating a hypothesis may refer to the act of setting the limits of the parameters such that the entire set of possible genetic states that are under consideration are encompassed by those parameters.
  • Creating a hypothesis may refer to the act of setting the limits of the parameters such that a limited set of possible genetic states that are under consideration are encompassed by those parameters.
  • Creating a set of hypotheses may refer to estimating and/or describing the statistically expected bounds of measured values that correspond to each of the hypotheses.
  • Creating a set of hypotheses may refer to a knowledgeable person listing those possible ploidy states that may be reasonably likely under the circumstances. In one embodiment, it may refer to estimating the profile of SNP measurements of a target individual as measured on a high throughput SNP array for a set of parental contexts.
  • a ‘copy number hypothesis’ also called a ‘ploidy hypothesis’, or a ‘ploidy state hypothesis’, may refer to a hypothesis concerning a possible ploidy state for a given chromosome, or section of a chromosome, in the target individual. It may also refer to the ploidy state at more than one of the chromosomes in the individual.
  • a set of copy number hypotheses may refer to a set of hypotheses where each hypothesis corresponds to a different possible ploidy state in an individual over one chromosome, or it may refer to a combination of single-chromosome hypotheses over more than one chromosomes, where the number of different chromosomes could vary, in humans, from 2 to 23.
  • a normal individual contains one of each chromosome from each parent. However, due to errors in meiosis and mitosis, it is possible for an individual to have 0, 1, 2, or more of a given chromosome from each parent. In practice, it is rare to see more that two of a given chromosomes from a parent. In this disclosure, the embodiments only consider the possible hypotheses where 0, 1, or 2 copies of a given chromosome come from a parent. In some embodiments, for a given chromosome, there are nine possible hypotheses: the three possible hypothesis concerning 0, 1, or 2 chromosomes of maternal origin, multiplied by the three possible hypotheses concerning 0, 1, or 2 chromosomes of paternal origin.
  • (m,f) refer to the hypothesis where m is the number of a given chromosome inherited from the mother, and f is the number of a given chromosome inherited from the father. Therefore, the nine hypotheses are (0,0), (0,1), (0,2), (1,0), (1,1), (1,2), (2,0), (2,1), and (2,2), and these may also be written H00, H01, H02, H10, H11, H12, H20, H21, H22.
  • the different hypotheses correspond to different ploidy states. For example, (1,1) refers to a normal disomic chromosome; (2,1) refers to a maternal trisomy, and (0,1) refers to a monosomy.
  • the hypothesis may be written as (m,f x ,f y ), to take into account the sex chromosome, where f x refers to an X-chromosome or autosomal chromosome inherited from the father, and f y refers to a Y-chromosome inherited from the father.
  • f x refers to an X-chromosome or autosomal chromosome inherited from the father
  • f y refers to a Y-chromosome inherited from the father.
  • the f y may simply act as a placeholder.
  • H000 represents the nullsomy hypothesis
  • H100, H010 and H001 represent the monosomy hypotheses
  • H110 and H101 represent the normal disomy hypotheses
  • H200, H020, H002, and H011 represent uniparental disomy hypotheses
  • H210, H120, and H111 represent the trisomy hypotheses
  • H220, H211, and H202 represent some of the possible tetrasomy hypotheses.
  • the trisomy case where two chromosomes are inherited from one parent and one chromosome is inherited from the other parent may be further differentiated into two cases: one where the two chromosomes are identical (matched copy error), and one where the two chromosomes are homologous but not identical (unmatched copy error).
  • each allele may be specified as being part of either of two haplotypes
  • the ploidy hypothesis may refer to a hypothesis concerning which chromosome from other related individuals correspond to a chromosome found in the target individual's genome.
  • the method uses the knowledge that related individuals can be expected to share haplotype blocks, and using measured genetic data from related individuals, along with a knowledge of which haplotype blocks match between the target individual and the related individual, it is possible to infer the correct genetic data for a target individual with higher confidence than using the target individual's genetic measurements alone.
  • the ploidy hypothesis may concern not only the number of chromosomes, but also which chromosomes in related individuals are identical, or nearly identical, with one or more chromosomes in the target individual.
  • the algorithms when the algorithms operate on the input genetic data, they may output a determined statistical probability for each of the hypotheses under consideration.
  • the probabilities of the various hypotheses may be determined by mathematically calculating, for each of the various hypotheses, the value of the probability, as stated by one or more of the expert techniques, algorithms, and/or methods described elsewhere in this disclosure, related disclosures, and/or encompassed by the PARENTAL SUPPORTTM technique, using the relevant genetic data as input.
  • the calculation may produce an exact value, it may give an estimate, it may include an error term, it may include a confidence, and it may represent a statistical likelihood.
  • the probabilities of the different hypotheses may be combined. This may entail, for each hypothesis, multiplying the probabilities as determined by each technique. The product of the probabilities of the hypotheses may be normalized. Note that one ploidy hypothesis refers to one possible ploidy state for a chromosome.
  • the probability for a given hypothesis is greater than the probability for any of the other hypotheses, then that hypothesis may be determined to be the most likely.
  • a hypothesis may be determined to be the most likely, and the ploidy state, or other genetic state, may be called if the normalized probability is treater than a threshold.
  • this may mean that the number and identity of the chromosomes that are associated with that hypothesis may be called as the ploidy state.
  • this may mean that the identity of the alleles that are associated with that hypothesis may be called as the allelic state, and/or the genetic state.
  • the threshold may be between about 50% and about 80%.
  • the threshold may be between about 80% and about 90%. In some embodiments the threshold may be between about 90% and about 95%. In some embodiments the threshold may be between about 95% and about 99%. In some embodiments the threshold may be between about 99% and about 99.9%. In some embodiments the threshold may be above about 99.9%.
  • the PARENTAL SUPPORTTM method is a collection of methods that may be used to determine the genetic data, with high accuracy, of one or a small number of cells, specifically to determine disease-related alleles, other alleles of interest, and/or the ploidy state of one or more chromosomes on the cell(s). PARENTAL SUPPORTTM may refer to any of these methods. PARENTAL SUPPORTTM is an example of an informatics based method.
  • the PARENTAL SUPPORTTM method makes use of known parental genetic data, i.e. haplotypic and/or diploid genetic data of the mother and/or the lather, together with the knowledge of the mechanism of meiosis and the imperfect measurement of the target DNA, and possibly of one or more related individuals, in order to reconstruct, in silico, on a computer, the genotype at a plurality of alleles, and/or the ploidy state of an embryo or of any target cell(s), and the target DNA at the location of key loci with a high degree of confidence.
  • the PARENTAL SUPPORTTM method can reconstruct not only single-nucleotide polymorphisms that were measured poorly, but also insertions and deletions, and SNPs or whole regions of DNA that were not measured at all. Furthermore, the PARENTAL SUPPORTTM method can both measure multiple disease-linked loci as well as screen for aneuploidy, from a single cell, or from the same small amount of DNA. In some embodiments, the PARENTAL SUPPORTTM method may be used to characterize one or more cells from embryos biopsied during an IVF cycle to determine the genetic condition of the one or more cells. In some embodiments, the PARENTAL SUPPORTTM method may be used to determine the ploidy state of a fetus from free floating fetal DNA and/or fetal cells that may be found in maternal blood, or from some other source.
  • the PARENTAL SUPPORTTM method allows the cleaning of noisy genetic data. This may be done by inferring the correct genetic alleles in the target genome (embryo or fetus) using the genotype of related individuals (parents) as a reference. PARENTAL SUPPORTTM may be particularly relevant where only a small quantity of genetic material is available (e.g. PGD or NIPGD) and where direct measurements of the genotypes are inherently noisy due to the limited amounts of genetic material.
  • the PARENTAL SUPPORTTM method is able to reconstruct highly accurate ordered diploid allele sequences on the embryo, together with copy number of chromosomes segments, even though the conventional, unordered diploid measurements may be characterized by high rates of allele dropouts, drop-ins, variable amplification biases and other errors.
  • the method may employ both an underlying genetic model and an underlying model of measurement error.
  • the genetic model may determine both allele probabilities at each SNP and crossover probabilities between SNPs. Allele probabilities may be modeled at each SNP based on data obtained from the parents and model crossover probabilities between SNPs based on data obtained from the HapMap database, as developed by the International HapMap Project. Given the proper underlying genetic model and measurement error model, maximum a posteriori (MAP) estimation may be used, with modifications for computationally efficiency, to estimate the correct, ordered allele values at each SNP in the embryo.
  • MAP maximum a posteriori
  • One aspect of the PARENTAL SUPPORTTM technology is a chromosome copy number calling algorithm that in some embodiments uses parental genotype contexts.
  • the algorithm may use the phenomenon of locus dropout (LDO) combined with distributions of expected embryonic genotypes. During whole genome amplification, LDO necessarily occurs. LDO rate is concordant with the copy number of the genetic material from which it is derived, i.e., fewer chromosome copies result in higher LDO, and vice versa. As such, it follows that loci with certain contexts of parental genotypes behave in a characteristic fashion in the embryo, related to the probability of allelic contributions to the embryo.
  • LDO locus dropout
  • the embryo should never have AB or AA states. In this case, measurements on the A detection channel are expected to have a distribution determined by background noise and various interference signals, but no valid genotypes. Conversely, if both parents have homozygous AA states, then the embryo should never have AB or BB states, and measurements on the A channel are expected to have the maximum intensity possible given the rate of LDO in a particular whole genome amplification reaction.
  • loci corresponding to the specific parental contexts behave in a predictable fashion, based on the additional allelic content that is contributed by, or is missing from, one of the parents. This allows the ploidy state at each chromosome, or chromosome segment, to be determined. The details of embodiments of this method are described elsewhere in this disclosure.
  • the measurements may differ in ways that correlate with the number of cells used. In some cases, the measurements may differ based on the measurement technique, for example, which sequencing technique or array genotyping technique is used. In some cases different chromosomes may amplify to different extents. In some cases, certain alleles may be more or less likely to amplify. In some cases, the error, bias, or differential response may be due to a combination of factors. In many or all of these cases, the statistical predictability of these measurement differences, termed the ‘platform response’, may be used to correct for these factors, and can result in data for which the accuracy is maximized, and where each measurement is associated with an appropriate confidence.
  • the platform response the statistical predictability of these measurement differences
  • the platform response may be described as a mathematical characterization of the input/output characteristics of a genetic measurement platform, such as TAQMAN, the AFFYMETRIX GENECHIP or the ILLUMINA INFINIUM BEADARRAY.
  • the platform response may be specific to a particular platform, to a particular model of genotyping machine, to a particular genotyping machine, or even to a particular scientist using a particular genotyping machine.
  • the input to the channel is the amplified genetic material with any annealed, fluorescently tagged genetic material.
  • the channel output could be allele calls (qualitative) or raw numerical measurements (quantitative), depending on the context.
  • the platform response may consist of an error transition matrix that describes the conditional probability of seeing a particular output genotype call given a particular true genotype input.
  • the platform response in which the platform's output is left as raw numeric measurements, the platform response may be a conditional probability density function that describes the probability of the numerical outputs given a particular true genotype input.
  • the knowledge of the platform response may be used to statistically correct for the bias. In some embodiments of the present disclosure, the knowledge of the platform response may be used to increase the accuracy of the genetic data. This may be done by performing a statistical operation on the data that acts in the opposite manner as the biasing tendency of the measuring process. It may involve attaching the appropriate confidence to a given datum, such that when combined with other data, the hypothesis found to be most likely is indeed most likely to correspond to the actual genetic state of the individual in question.
  • a statistical method may be used to remove the bias in the data due to the tendency for certain maternal or paternal alleles to amplify in a disproportionate manner to the other alleles. In some embodiments of the present disclosure, a statistical method may be used to remove the bias in the data due to the tendency for certain probes to amplify certain SNPs in a manner that is disproportionate to other SNPs.
  • the context means should fall on the line defined by the means for contexts BB
  • the average contexts means do not fall on this line, but are biased in a statistical manner; this may be termed “off line bias”.
  • a statistical method may be used to correct for the off line bias in the data.
  • splayed dots on the context means plot could be caused by translocation. If a translocation occurs, then one may expect to see abnormalities on the endpoints of the chromosome only. Therefore, if the chromosome is broken up into segments, and the context mean plots of each segment are plotted, then those segments that lie on the of a translocation may be expected to respond like a true trisomy or monosomy, while the remaining segments look disomic. In some embodiments of the present disclosure, a statistical method may be used to determine if translocation has occurred on a given chromosome by looking at the context means of different segments of the chromosome.
  • the method it is possible to determine the ploidy state of a fetus in a non-invasive manner by measuring fetal DNA contained in maternal blood. Note that this may be complicated considerably by the fact that the amount of fetal DNA available in maternal blood may be small. The amount of fetal free floating DNA found in serum is typically less than 50%, and often less than 20%, and the background maternal free floating DNA makes measurements on the fetal DNA very noisy and difficult to interpret. The number of fetal cells in maternal blood is often less than 1 cell in 100,000, and can be as low as 1 cell in a million, or lower.
  • This method overcomes the difficulties described here, as well as other difficulties known in the art. The method may be applicable in cases where the amount of target DNA is in any proportion with the non-target DNA; for example, the target DNA could make up anywhere between 0.01% and 99.99% of the DNA present.
  • the first step of the method is to make genomic measurements on the mother and optionally the father, such that the diploid genetic data is known at a large number of alleles for one or both parents.
  • the number of alleles may range from 100 to 100,000,000. In an embodiment, the number of alleles ranges from 500 to 100,000 per chromosome targeted. In an embodiment, the number of alleles ranges from 1,000 to 20,000 per chromosome targeted.
  • the alleles are SNPs known to be polymorphic in the human population. Once the parental genotypes are known at a set of SNPs, the SNPs may be subdivided into a number of sets of SNPs where each set corresponds to the set of SNPs in a particular parental context.
  • the next step is to create a number of hypotheses, one for each hypothetical ploidy state of interest on a chromosome of interest, and determine the expected statistical distribution of genotypic measurements for that hypothetical child, given expected ADO rates, and given the expected platform response.
  • a number of hypotheses one for each hypothetical ploidy state of interest on a chromosome of interest
  • chromosome 21 several hypothetical child genotypes may be envisioned, for example, one for a child that is disomic at chromosome 21 (H110), and a one for a child that has maternal trisomy at chromosome 21 (H210).
  • (H ⁇ ) denotes the hypothesis where ⁇ copies of a maternally derived chromosome are present, ⁇ copies of a paternally derived chromosome are present, and ⁇ is placeholder set to 0; in the case of the sex chromosome, (H ⁇ ) denotes the hypothesis where ⁇ copies of a maternally derived chromosome are present, ⁇ indicates the number of paternally derived X chromosomes that are present, and ⁇ indicates the number of paternally derived Y chromosomes that are present.
  • the hypothetical genotypes are not necessarily SNP-by-SNP genotypes, rather they may be expected statistical distributions of SNPs within a given parental context. For example, imagine looking only at the parental context AA
  • the H210 child is expected to have an equal chance of being AAA or AAB within that parental context, and thus, one would expect to see approximately, a 5:1 A:B ratio for the SNPs that are in the AA
  • y the average measured intensity from SNPs in a given context on a particular chromosome, on a particular channel
  • x the statistically expected number of allele copies present per locus, for the channel being measured, for SNPs in the context.
  • ⁇ the fraction of fetal. DNA in the sample
  • n the fraction of SNPs that are A for a given genotype
  • ⁇ a term denoting observational noise, which is a random variable with an unknown distribution.
  • One may state that x ⁇ (1 ⁇ )n mother + ⁇ n fetus , and also that y f(x)+ ⁇ , that is, the distribution of the measurements of a set of SNPs within a given parental context will be some function of the number of expected alleles in the sample and the platform response, plus a noise factor.
  • f(x) may be assumed to be a second order polynomial, that is, f(x) ⁇ f 1 x 2 +f 2 x+f 3 .
  • f(x) may be assumed to be a first order polynomial, that is, f(x) ⁇ f 1 x+f 2 .
  • f(x) could be assumed to be any number of functions such as a first order polynomial, a third order polynomial, any other polynomial, any exponential, or any other algebraic or other relationship between x and y.
  • ⁇ could be any number of distributions, including a Gaussian, a Rayleigh distribution, a Pearson distribution, or a Bernoulli distribution.
  • x is known in terms of ⁇ and n, and f 1 , f 2 , f 3 , ⁇ , and the distribution of ⁇ , parameterized by V, is unknown.
  • a genotypic measurement, y is made of the sample for a number of SNPs, for each context, for each channel, over a number of chromosomes, including the chromosome(s) of interest, whose ploidy state is to be determined, as well as at least one chromosome that may be expected to be disomic.
  • Each set of y's are then combined into a vector. Note that the set of chromosomes whose ploidy state is to be determined and the at least one chromosome that may be expected to be disomic may overlap.
  • chromosomes that can result in a live birth even when aneuploid, most commonly, chromosome 13, 18, 21, X and Y. It is also known for live children to be born with aneuploidy at chromosomes 4, 5, 7, 8, 9, 11, 15, 16, 22. Note that other aneuploidy states, such as translocations and uniparental disomy, at any chromosome may give rise to born children with chromosomal abnormalities.
  • One of the chromosomes which is infrequently found to be aneuploid in gestating fetuses with a heartbeat, such as 1, 2, or 3 may be used as a reference diploid chromosome.
  • one of the chromosomes that is targeted for aneuploidy testing may be used as a reference, since it is unlikely that more than one gross chromosomal abnormality exists in a gestating fetus.
  • the chromosomes targeted for aneuploidy detection include 13, 18, 21, X and Y.
  • the observed y m can be compared against the distributions for y p and the likelihood of each hypothesis can be determined, which is the probability of observing y m according to the predicted model.
  • the hypothesis with the highest likelihood corresponds to the most likely ploidy state of the fetus.
  • a confidence in the ploidy call may be calculated from the different likelihoods of the various hypotheses.
  • H120) have been calculated.
  • the prior probability of each hypothesis is known from statistical population study. For example, p(H110) is the overall probability of disomy on this chromosome for the population of interest. If p(y m
  • H110) is the highest likelihood, then the confidence on disomy is calculated using Bayes rule as confidence p(y m
  • the present disclosure presents a method by which one may determine the ploidy state of a gestating fetus, at one or more chromosome, in a non-invasive manner, using genetic information determined from fetal DNA found in maternal blood, and genetic data from the mother and the father.
  • the fetal DNA may be purified, partially purified, or not purified; genetic measurements may be made on DNA that originated from more than one individual.
  • Informatics type methods can infer genetic information of the target individual, such as the ploidy state, from the bulk genotypic measurements at a set of alleles.
  • the set of alleles may contain various subsets of alleles, wherein one or more subsets may correspond to alleles that are found on the target individual but not found on the non-target individuals, and one or more other subsets may correspond to alleles that are found on the non-target individual and are not found on the target individual.
  • the set of alleles may also contain subsets of alleles where the allele is found on the target and the non-target in differing expected ratios.
  • the method may involve comparing ratios of measured output intensities for various subsets of alleles to expected ratios given various potential ploidy states.
  • the platform response may be determined, and a correction for the bias of the system may be incorporated into the method.
  • the ploidy determination may be made with a computed confidence.
  • the ploidy determination may be linked to a clinical action. That clinical action may be to terminate or not terminate a pregnancy.
  • An embodiment of the invention involves the case where the target individual is a fetus, and the non-target individual is the biological mother of the fetus.
  • the method works as follows.
  • a simple version of idea is to attempt to quantify the amount of fetal DNA at SNPs where the fetus has an allele that the mother does not.
  • the genotypic data of the parents are measured using a method that produces data for a set of SNPs.
  • the SNPs are sorted into parental contexts.
  • the SNPs found in contexts where the mother is heterozygous, AB, are considered to be less informative, since the contaminating DNA in maternal blood will have a large amount of both alleles.
  • the SNPs found in contexts where the mother and father have the same set of alleles are also considered to be less informative, since the background and the fetal signal are the same.
  • the simple method focuses on the contexts where the father has an allele that the mother does not, for example: AA
  • the fetus is expected to be AB, and therefore the B allele should appear in fetal DNA.
  • the fetus is expected to be AA half the time, and AB half the time, meaning the B allele should appear in fetal DNA half the time.
  • the appropriate SNPs are identified where the B SNP has been measured, indicating that the fetus is AB, along with the quantities of DNA measured for each of those SNPs.
  • the intensities of the measurements of the SNPs for a chromosome assumed to be disomic are compared to the intensities of the measurements of the SNPs for the chromosome of interest are compared, adjusted appropriately for platform response.
  • the chromosome of interest is considered to be disomic, and if the intensities on the chromosome is about 50% greater than the intensities on the assumed disomic chromosome, then the chromosome of interest is considered to be paternal trisomic.
  • the contexts may be used, including those of greater and lesser informativeness.
  • some or all of the SNPs may be used. For those contexts and SNPs that are more informative, for example, the SNPs in the AA
  • the explanation above focuses on measuring the number of paternal chromosomes. A similar method may be used to determine the number of maternal chromosomes, with appropriate adjustments made.
  • the expected ratios of SNP intensities for the disomy and trisomy hypothesis will be different, because the background maternal genotypic data and the fetal genotypic data will be similar or identical.
  • the mixed sample contains 20% fetal DNA and 80% maternal DNA
  • BB context for a disomy, one would expect a ratio of 90:10 for the A:B measurements (80% A plus 20% 1:1 A:B), for a maternal trisomy one would expect a ratio closer to 93.3:6.7 (80% A plus 20% 2:1 A:B), and for a paternal trisomy one would expect a ratio closer to 86.7:13.3 (80% A plus 20% 1:2 A:B).
  • this method may be used equally well with more or less genotypic information from the parents. For example, if the father's genotype is unknown, the method may consider all contexts where the mother is homozygous (AA) to be more informative, and the chance of the fetus having a B SNPs may be calculated roughly from known SNP heterozygosities in the population. At the same time, if the father's genotype is phased, that is, the haplotypes are known, copy number accuracies may be increased, since there will be strong correlations between expected contexts.
  • AA homozygous
  • each of the parent contexts, and chromosomes known to be euploid it is possible to estimate, by a set of simultaneous equations, the amount of DNA in the maternal blood from the mother and the amount of DNA in the maternal blood from the fetus.
  • These simultaneous equations are made possible by the knowledge of the alleles present on the mother, and optionally, the father.
  • the genetic data from both the mother and the father is used.
  • alleles present on the father and not present on the mother provide a direct measurement of fetal DNA.
  • H mp where m represents the number of maternal chromosomes and p represents the number of paternal chromosomes e.g. H 11 representing euploid, or H 21 and H 12 representing maternal and paternal trisomy respectively.
  • the method may be employed with knowledge of the maternal genotype, and without knowledge of the paternal genotype.
  • father contexts by looking at the SNP data for those measurements on the mixed sample that cannot be explained by mother data.
  • AA homozygous
  • B alleles For those SNPs it is possible to infer that the father was AB or BB, and the fetus is AB.
  • the fetus is AA with a certain probability, where the probability is correlated to the ADO and LDO rates. It is also possible to use parental data with a certain degree of uncertainty attached to the measurements.
  • the methods described herein can be adapted to determine the ploidy state of the fetus given greater or lesser amounts of genetic information from the parents.
  • the expected amount of genetic material in the maternal blood from the mother is constant across all loci.
  • the expected amount of genetic material present in the maternal blood from the fetus is constant across all loci assuming the chromosomes are euploid.
  • the chromosomes that are non-viable are all euploid in the fetus. In one embodiment, only some of the non-viable chromosomes on the fetus need be euploid.
  • y ijk g ijk (x ijk )+ ⁇ ijk
  • g ijk is platform response for particular locus and allele ijk
  • ⁇ ijk is independent noise on the measurement for that locus and allele.
  • x ijk am ijk + ⁇ c ijk
  • a is the amplification factor (or net effect of leakage, diffusion, amplification etc.) of the genetic material present on each of the maternal chromosomes
  • m ijk is the copy number of the particular allele on the maternal chromosomes
  • ⁇ is the amplification factor of the genetic material present on each of the child chromosomes
  • c ijk is the copy number (either 0, 1, 2, 3) of the particular allele on the child chromosomes.
  • amplification factors a and ⁇ are used without loss of generality, and a y-axis intercept b is added which defines the noise level when there is no genetic material.
  • the goal is to estimate a and ⁇ . It is also possible to estimate b independently, but in this section, the noise level is assumed to be roughly constant across loci, and only the set of equations based on parent contexts are used to estimate a and ⁇ . The measurement at each locus is given by
  • the parent contexts are represented in terms of alleles A and B, where the first two alleles represent the mother and the second two alleles represent the father: T ⁇ AA
  • T there is a set of loci i,j where the parent DNA conforms to that context, represented i,j ⁇ T.
  • m k,T , c k,T and ⁇ k,T represent the means of the respective values over all the loci conforming to the parent context T, or over all i,j ⁇ T.
  • the mean or expected values c k,T will depend on the ploidy status of the child.
  • the expected values are calculated assuming different hypotheses on the child, for example: euploidy and maternal trisomy.
  • hypotheses are denoted by the notation H mf , where m refers to the number of chromosomes from the mother and f refers to the number of chromosomes from the father e.g. H 11 is euploid, H 21 is maternal trisomy. Note that there is symmetry between some of the states by switching A and B, but all states are included for clarity:
  • Y [Y AA
  • Y′ Y measured over all the euploid chromosomes
  • Y′′ Y measured over a particular chromosome under test, such as chromosome 21, which may be aneuploid.
  • H * argmin H ⁇ C′′ ⁇ 1/2 ( Y′′ ⁇ B ⁇ A H ⁇ circumflex over (P) ⁇ ) ⁇ 2
  • H 11 euploid
  • H 21 eternal trisomy
  • the confidence that the chromosome is in fact euploid is given by p H 11 .
  • bias matrix B is redefined as follows:
  • ⁇ right arrow over (1) ⁇ is a 9 ⁇ 1 matrix of ones.
  • the parameters b A and b B can either be assumed based on a-priori measurements, or can be included in the matrix P and actively estimated (i.e. there is sufficient rank in the equations over all the contexts to do so).
  • y ijk g ijk (am ijk + ⁇ c ijk )+ ⁇ ijk
  • y ijk g ijk (am ijk + ⁇ c ijk )+ ⁇ ijk
  • the function which is monotonic for the vast majority of genotyping platforms
  • This approach is particularly good when g ijk is an affine function so that the inversion does not produce amplification or biasing of the noise in ⁇ ′ ijk .
  • Another embodiment which may be more optimal from a noise perspective is to linearize the measurements around an operating point i.e.:
  • a regression function linking each chromosome's mean signal level to every other chromosomes mean signal level, combine the signal from all chromosome by weighting based on variance of the regression fit, and look to see whether the test chromosome of interest is within the acceptable range as defined by the other chromosomes.
  • this method may be used in conjunction with other methods previously disclosed by Gene Security Network, especially those methods that are part of PARENTAL SUPPORTTM, and are mentioned elsewhere in this disclosure, such that one may phase the parents so that it is known what is contained on each individual maternal and paternal chromosome.
  • Gene Security Network especially those methods that are part of PARENTAL SUPPORTTM, and are mentioned elsewhere in this disclosure, such that one may phase the parents so that it is known what is contained on each individual maternal and paternal chromosome.
  • the odds ratio of each of the alleles at heterozygous loci one may determine which haplotype of the mother is present on the child. Then one can compare the signal level of the measurable maternal haplotype to the paternal haplotype that is present (without background noise from the mother) and see when that ratio of 1:1 is not satisfied due to aneuploidy which causes an imbalance between maternal and paternal alleles.
  • the raw data may be produced by a microarray which measures the response from each possible allele on a selection of SNPs.
  • the microarray may be an ILLUMINA SNP microarray, or an AFFYMETRIX SNP microarray.
  • other sources of data may also be used, such as a sufficiently large number of TAQMAN probes or a non-SNP based array.
  • the raw genetic data may from other sources as well, such as DNA sequencing.
  • a SNP is typically expected to be one of two nucleotides. For example, it may be expected to be either a G or C, and may be measured for the G or C response; alternately, at a SNP which could have A or T it may be measured for the A and T response. Since only two alleles are possible at each SNP, the measurements may be aggregated without regard for whether the SNP is A/T or C/G. Instead, this disclosure may refer to responses on the x and y channels, and generic alleles A or B. Thus the possible genotypes in this example are AA, AB and BB for all SNPs. There are other ways of grouping the allele calls that will not affect the essence of the invention.
  • Measurements may be initially aggregated over SNPs from the same parent context based on unordered parent genotypes.
  • all SNPs where the mother's genotype is AA and the father's genotype is BB are members of the AA
  • the combination of 3 possible genotypes over 2 parents means that the measurements from a single chromosome would consist of 18 context means, 9 on each channel.
  • the amount of DNA measured at a SNP will depend on the number of alleles present at that SNP in the maternal and fetal chromosomes, and the overall concentrations of DNA present in the sample from the mother and fetus.
  • the factor ⁇ reflects the overall concentration of DNA in the sample, and the ratio of mother to child is 1 to ⁇ .
  • the genotypes of a disomic child is known. For example, if one parent's genotype is AA and the other's is BB, the child genotype must be AB. In contrast, SNPs where a parent is heterozygous will have unknown child genotype.
  • BB where the child may inherit either an A or a B from the mother. The most general assumption is that the child will inherit the A and the B with equal probability, and so approximately half of the child genotypes in this context will be AB and half will be BB. Other assumptions may be made regarding the likelihood of a child inheriting a given allele from a given parent.
  • the genotype of each child SNP is not known, the average values of k x and k y are thus known for SNPs in each context, and so the equations below refers to these averages.
  • the average number of As in the child SNPs is 0.5 and the average number of Bs is 1.5.
  • the quantities x x and x y refer to the average amount of DNA present for SNPs in a particular context, where x x is the DNA that will be measured on the x channel (allele A) and x y is the DNA that will be measured on the y channel (allele B).
  • the quantity of DNA may be measured through the platform responses on the x and y channels.
  • SNPs in the same context may be aggregated to produce measurements y x , y y which are the context mean responses on the x and y channels. Assume that SNPs are i.i.d.
  • y c and y 1 be the means from the same context and same sample, from chromosome c and chromosome 1 respectively.
  • the expected value of y c /y 1 is defined as ⁇ c and may be calculated from a large set of training data.
  • the training data consists of hundreds of blastomeres which have been analyzed under a consistent laboratory protocol.
  • the chromosome weights ⁇ depend on microarray type (because different arrays measure different SNPs) and the type of lysis buffer used, but otherwise may be consistent between samples.
  • the expected number of As or Bs may be weighted by ⁇ to account for this effect, resulting in a chromosome-weighted number of alleles ⁇ circumflex over (m) ⁇ or ⁇ circumflex over (k) ⁇ .
  • the platform response model f x (x x ), f y (x y ) may be considered consistent across chromosomes.
  • the bias b may be observed to vary by chromosome and channel and the measurement noise v will vary on each measurement.
  • the bias of a particular chromosome and channel is the mean of the noise-only context, and is therefore a known (directly measured) quantity.
  • the noise-only contexts are AA
  • the measurement gives a baseline for the platform response in the absence of the signal which it measures.
  • the scalar noise covariance associated with each context mean measurement may be assumed to be proportional to 1 n where n is the number of SNPs included. This corresponds to the assumption of i.i.d. SNPs within each context.
  • the noise components may be assumed independent and normally distributed.
  • a linear platform response model (affine relationship between amount of DNA and measured signal) may be used.
  • a quadratic platform response may be used where f 1 and f 2 are specific to each sample and measurement channel and x is the quantity of DNA.
  • Other platform response models may be employed, including higher order algorithmic or exponential relationships. Substituting from (2) for the quantity of DNA results in the following model for the x and y channel responses on chromosome c from context i.
  • y xci f 1x ⁇ 2 ⁇ circumflex over (m) ⁇ 2 xci +f 1x ⁇ 2 ⁇ 2 ⁇ circumflex over (k) ⁇ 2 xci +2 f 1x ⁇ 2 ⁇ circumflex over (m) ⁇ xci ⁇ tilde over (k) ⁇ xci +f 2x ⁇ circumflex over (m) ⁇ xci +f 2x ⁇ circumflex over (k) ⁇ xci +b xc +v xci
  • y yci f 1y ⁇ 2 ⁇ circumflex over (m) ⁇ 2 yci +f 1y ⁇ 2 ⁇ 2 ⁇ circumflex over (k) ⁇ 2 yci +2 f 1y ⁇ 2 ⁇ circumflex over (m) ⁇ yci ⁇ tilde over (k) ⁇ yci +f 2y ⁇ circumflex over (m) ⁇ yci +f 2y ⁇ circumflex over (k) ⁇ yci +b yc +v yci (3)
  • the DNA concentration ⁇ and platform responses f 1x , f 2x , f 1y , f 2y may be combined to form the set of 5 parameters for the sample. Note that when the model includes terms of the form p 1 x ⁇ 2 , p 1 x ⁇ and p 2 x ⁇ , and so the parameter estimate cannot be solved exactly using linear methods.
  • this set of parameters p may be assumed to be common to all chromosomes and parent genotype contexts for a single sample, and so the model for a single chromosome c and context i can be written in the following condensed form based on the non-linear platform response function g.
  • the set of N measurements from a sample can be combined to form a vector equation in p.
  • the parameters may be different for different chromosomes, or for different samples.
  • y xci f 1x ⁇ 2 ⁇ circumflex over (m) ⁇ 2 xci +2 f 1x ⁇ 2 ⁇ circumflex over (m) ⁇ xci ⁇ circumflex over (k) ⁇ xci +f 2x ⁇ circumflex over (m) ⁇ xci +f 2x ⁇ circumflex over (k) ⁇ xci
  • y yci f 1y ⁇ 2 ⁇ circumflex over (m) ⁇ 2 yci +2 f 1y ⁇ 2 ⁇ circumflex over (m) ⁇ yci ⁇ circumflex over (k) ⁇ yci +f 2y ⁇ circumflex over (m) ⁇ yci +f 2y ⁇ circumflex over (k) ⁇ yci
  • a linear estimation method can be implemented by constructing an augmented parameter set which eliminates the non-linear terms by adding extra degrees of freedom.
  • This augmented parameter set has 8 degrees of freedom.
  • the linearized model for a chromosome c and context i can be written in matrix form.
  • a xci [ m ⁇ xci 2 ⁇ ⁇ m ⁇ xci ⁇ ⁇ 2 ⁇ ⁇ m ⁇ xci ⁇ k ⁇ xci ⁇ ⁇ k ⁇ xci ]
  • a yci [ m ⁇ yci 2 ⁇ ⁇ m ⁇ yci ⁇ ⁇ 2 ⁇ ⁇ m ⁇ yci ⁇ k ⁇ yci ⁇ ⁇ k ⁇ yci ]
  • y are the context mean measurements
  • A is the set of known coefficients
  • q is the set of parameters to be estimated
  • b is the known bias vector
  • v is assumed zero-mean Gaussian noise.
  • the strategy for parameter estimation is to assume a subset of the child's chromosomes are disomic (having one copy from each parent) and use these to learn the model parameters for the child sample. These sample model parameters are then used to classify the remaining chromosomes, determining how many copies are present from each parent.
  • the child allele contributions ⁇ circumflex over (m) ⁇ xci , ⁇ circumflex over (m) ⁇ yci may be calculated from (1) at the parameter estimation step under the assumption that the mother and father copy number contributions n m and n f are both one. If D is the number of assumed disomic chromosomes, then the measurement vector y for parameter estimation has size 18D (from nine context means measured on two channels).
  • the linearized quadratic model (7) leads to straightforward least-squares (LS) or maximum likelihood (ML) solutions for the best estimate of q.
  • the maximum likelihood solution depends on the number of SNPs incorporated in each measurement, given in the diagonal matrix N.
  • the maximum likelihood solution is used because the informativeness of the different measurement components varies widely, and the matrix N which determines this variation is known.
  • the quadratic sensor model may not lead to closed form solutions for the parameter estimate p which best fits the measurements.
  • a gradient descent optimization method may be applied which iteratively improves on an initial guess for p in order to minimize a cost function.
  • a non-linear least squares formulation for p minimizes the mean square difference between the measured data and the values predicted by the model.
  • the parameter estimate q* based on the linearized model may provide a convenient initial condition for the non-linear optimization because it solves an approximation of the same problem but can be calculated in closed form at little computational cost.
  • Comparison of the linearized (q) and non-linear (p) parameters below shows that the mapping from p to q is not invertible.
  • An estimate of the distribution of the noise vector v may be used in the calculation of observation likelihoods.
  • the assumption of i.i.d. SNPs implies that the context means will have variance proportional to the included number of SNPs.
  • the covariance V of v has the form ⁇ N ⁇ 1 where ⁇ is scalar and N is the diagonal matrix defining the number of SNPs measured in each context mean.
  • the matrix N is known, and ⁇ is estimated as the variance of the components of N 0.5 e.
  • the task is to estimate the copy number for the chromosome of interest, or for the remainder of the chromosomes.
  • a child copy number hypothesis has the form Hn m n f where (n m , n f ) represent the number of copies contributed by the mother and father, respectively.
  • the focus is placed on detection of trisomies, where one parent contributes an extra copy, because these errors may result in a viable fetus, and conditions such as Down Syndrome.
  • the copy number hypothesis predicts the expected number of child alleles present at a SNP with a particular parent context, according to (1).
  • the child's genotype will be AB, but under the maternal trisomy hypothesis H21 the child's genotypes will be AAB, and a higher signal on the x channel can be detected due to the extra A.
  • the number of child alleles present appears in the matrix A in the linearized model and in the function g(p) in the quadratic model, and depends on the assumed hypothesis in this manner.
  • the assumption of a particular copy number hypothesis h results in a corresponding model A h q or g h (p).
  • the various hypotheses will be evaluated by considering the likelihood of the observed data under the different models.
  • a high-confidence call between hypotheses h i and h j can be expected when d ij is large compared to the sensor noise variance.
  • phased father genotype data may be used.
  • this section is described an embodiment that takes advantage of the phased parental data.
  • This section discloses an extension of an embodiment described earlier; it is designed for the case where phased father genotypic data is available, and allows for more accurate parameter estimation and hypothesis fitting.
  • the AB genotype can be distinguished from the BA genotype. Therefore, in the AB genotype, the first haplotype has the A allele at a given locus, and the second haplotype has the B allele at the locus, whereas, in the BA genotype, the first haplotype has the B allele at the locus, and the second haplotype has the A allele at the locus.
  • genotype is unphased, or unordered no distinction is made between AB and BA, and it is typically referred to as AB.
  • Phasing of father genotype may be done by various methods, including several that may be found in the three patent applications Rabinowitz 2006, 2008 and 2009 that are incorporated by reference. It is assumed, in this section, that phased father genotypic data is available, meaning, on all chromosomes, the ordered father genotype is known on all SNPs, i.e. one can distinguish between first and second haplotype of the father's genotype. If the father's genotypic data is phased, and thus AB ⁇ BA for father, while mother data is not phased, i.e.
  • AB BA for mother, then there are twelve different possible parental contexts: AA
  • Measurements may be initially aggregated over SNPs from the same parental context based on phased father genotypes.
  • the combination of 3 possible mother genotypes and 4 possible father genotypes means that the measurements from a single chromosome will consist of 24 context means, 12 on each channel.
  • Maternal trisomy previously written in form (2,1), can be extended into sub-hypotheses (2,1,0) and (2,0,1).
  • Paternal trisomy can be extended into sub-hypotheses including paternal mitotic trisomies (1,2,0), (1,0,2) and paternal meiotic trisomy (1,1,1).
  • the child hypothesis written in the form (n m , n f1 , n f2 ), does not have to stay the same throughout the chromosome. For example suppose that a chromosome has normal disomy with first paternal strand (1,1,0), on a set of adjacent SNPs. If there is a crossover of paternal strands on the following SNP, the copy number hypothesis of the child changes to (1,0,1), now involving second father strand.
  • chromosome In order to keep a hypothesis constant over a given set of SNPs for the purpose of calculation, divide the chromosome into N segments of adjacent SNPs. One may divide the chromosomes into segments in a number of ways, for example, to keep the number of SNPs per segment constant, or to keep number of segments per chromosomes constant. Assume here that the copy number hypothesis is constant throughout the segment, with no crossovers present. Ambiguous segments with possible paternal crossovers are omitted in this explanation for clarity.
  • the measurements from a single chromosome will consist of 24*N context means, 12*N on each channel (for each of N segments on a chromosome).
  • the expected number of As (averaged over SNPs) in the child be k x and the expected number of Bs be k y (for a particular context, conditioned on a hypothesis).
  • the expected number of alleles depends on the context and the hypothesis.
  • k x 0.5 a m n m +a f1 n f1 +a f2 n f2
  • the model is similar to the model for unordered parental contexts:
  • the noise level for x channel response by looking at the x Channel response for parental context BB
  • BA should only be noise, with no signal, and have the same behavior as the responses for context AA
  • AB should only be noise, with no signal, and have the same behavior as the responses for context BB
  • the focus is placed on detection of trisomies, where one parent contributes an extra copy.
  • trisomies where one parent contributes an extra copy.
  • Hypothesis fitting on ‘test’ chromosomes may be done similarly as for unordered genotypes, except that each trisomy sub-hypothesis (for example (101) vs. (110)) may be fit separately for each segment, and the hypothesis for the ploidy state of the segments may be aggregated, only focusing on the overall ploidy state (now considering (101) and (110) to be the same, both disomy; focusing on, for example, disomy vs. maternal trisomy vs. paternal trisomy) and statistics may be calculated for whole chromosomes.
  • the probability of a particular sub-hypothesis in ordered hypothesis format is P i (n m ,n f1 ,n f2 ).
  • P i (2,1) P i (2,1,0)+P i (2,0,1).
  • Genomic samples were prepared from a maternal (AG16778, CORIELL) and an offspring (AG16777; CORIELL) tissue culture cell line. Cells were grown under standard conditions (1x RPMI Medium 1640, 15% Fetal Bovine Serum (FBS), 0.85% Streptomycin), and genomic DNA was purified using a QIAAMP DNA Micro Kit (QIAGEN) according to manufacturer's recommendations. Purified DNA was quantified using a NANODROP instrument (THERMO SCIENTIFIC) and diluted to appropriate concentrations in 1 ⁇ Tris-EDTA buffer.
  • a series of three mixed genomic samples were prepared by combining (a) 59.4 ng AG16777 DNA with 132.6 ng AG16778 DNA (30% AG16777), (b) 76.8 ng AG16777 DNA with 115.2 ng AG16778 DNA (40% AG16777), and (c) 115.2 ng AG16777 DNA with 76.8 ng AG16778 DNA (60% AG16777).
  • the three, samples were diluted in H2O for a total DNA concentration of 3 ng/ul. Samples were stored at ⁇ 20 C, and then analyzed on the INFINIUM array platform (ILLUMINA), which was performed according to manufacturer's recommendations.
  • ILLUMINA INFINIUM array platform
  • This method is appropriate for any nucleic acids which may be used for the ILLUMINA INFINIUM array platform, or any other SNP based genotyping method, for example isolated free-floating DNA from plasma or amplifications (e.g. whole genome amplification, PCR) of the same, isolated genomic DNA from other cell types (e.g. lymphocytes) or amplifications of the same. Any method that generates genomic DNA (e.g. extraction of DNA, purification) may be used for sample preparation.
  • any method that generates genomic DNA e.g. extraction of DNA, purification
  • the genomic DNA used here was premixed to simulate a mix of fetal and maternal DNA, however, the method is also applicable to DNA (or amplifications thereof) as such (i.e. not premixed).
  • Three samples were prepared from these cell lines, having 30, 40 and 60 percent of offspring DNA (relative to the mother).
  • the offspring cell line has trisomy on chromosome 21.
  • FIG. 1 shows the model parameter fit for (b), the 40 percent sample.
  • the x-axis shows the total number of alleles on the channel of interest, ⁇ circumflex over (m) ⁇ + ⁇ circumflex over (k) ⁇ . These values range from zero to four. Considering the x channel, there are no expected alleles in the BB
  • the y-axis measures platform response as a function of the number of alleles.
  • Circles are the measured context means (9 on each channel from each of the assumed disomic chromosomes) and the line shows the corresponding value predicted by the model parameters p*, for the same number of alleles. Note that the y-axis values on the two plots are quite different, showing that the x and y channel responses must be modeled separately.
  • FIG. 2 shows the 18 components of the measurement y 16 from chromosome 16 on the sample with 40 percent fetal DNA.
  • the first nine measurements are from the x channel and the next nine measurements are from the y channel.
  • the contexts are ordered as follows: AA
  • the 18 measurements are compared to the predicted values for the three hypotheses H11, H12 and H21. It is clear that the data most closely matches the H11 hypothesis (disomy).
  • the correct call was produced by the algorithm, with assigned probability of 1.0 based on a uniform prior distribution.
  • FIG. 3 shows chromosome 21, which has a truth of H21. The correct call was also made with assigned probability 1.0. The complete set of hypothesis calls and assigned probabilities is shown in Table 1.
  • the context mean measurements for the classified chromosomes for samples (a), (b), and (c), are shown in Tables 2, 3 and 4, respectively. In these tables, columns correspond to the chromosomes and rows correspond to the context mean measurements, ordered as described for FIG. 2 by channel and then by context.
  • identification of parent haplotypes may be used to estimate the recombination locations that determine which haplotypes are present in the child. Identification of which parent haplotype is present at each position in the child determines the child genotype. This may result in lower model variances because positions with different child genotypes will no longer be averaged. Certain methods disclosed herein can be modified to detect meiotic trisomies when both of a parent's haplotypes are present.
  • a method for determining the ploidy state of one or more chromosome in a target individual may include any of the following steps, and combinations thereof:
  • genetic data from the target individual and from one or more related individuals may be obtained.
  • the related individuals include both parents of the target individual.
  • the related individuals include siblings of the target individual.
  • the related individuals may include the parents and one or more grandparents.
  • This genetic data for individuals may be obtained from data in silico; it may be output data from an informatics method designed to clean genetic data, or it may be from other sources.
  • the genotypic data of the parents can be obtained and optionally phased using methods found in the three patent applications, Rabinowtiz 2006, 2008 and 2009, referenced elsewhere in this application. Any number of methods may be used to obtain the parental genotypic data provided that the set of SNPs measured on the mixed sample of fetal and maternal DNA is sufficiently overlapping with the set of SNPs for which that parental genotype is known.
  • Amplification of the DNA a process which transforms a small amount of genetic material to a larger amount of genetic material that contains a similar set of genetic data, can be done by a wide variety of methods, including, but not limited to, Polymerase Chain Reaction (PCR), ligand mediated PCR, degenerative oligonucleotide primer PCR, Multiple Displacement Amplification, allele-specific amplification techniques, Molecular Inversion Probes (MIP), padlock probes, other circularizing probes, and combination thereof. Many variants of the standard protocol may be used, for example increasing or decreasing the times of certain steps in the protocol, increasing or decreasing the temperature of certain steps, increasing or decreasing the amounts of various reagents, etc.
  • the DNA amplification transforms the initial sample of DNA into a sample of DNA that is similar in the set of sequences, but of much greater quantity. In some cases, amplification may not be required.
  • the genetic data of the target individual and/or of the related individual can be transformed from a molecular state to an electronic state by measuring the appropriate genetic material using tools and or techniques taken from a group including, but not limited to: genotyping microarrays, APPLIED BIOSCIENCE'S TAQMAN SNP genotyping assay, the ILLUMINA genotyping system, for example the ILLUMINA BEADARRAY platform using, for example, the 1M-DUO chip, an AFFYMETRIX GENOTYPING PLATFORM, such as the AFFYMETRIX 6.0 GENECHIP, AFFYMETRIX'S GENFLEX TAG array, other genotyping microarrays.
  • genotyping microarrays APPLIED BIOSCIENCE'S TAQMAN SNP genotyping assay
  • the ILLUMINA genotyping system for example the ILLUMINA BEADARRAY platform using, for example, the 1M-DUO chip
  • an AFFYMETRIX GENOTYPING PLATFORM such as the AFF
  • a high throughput sequencing method may be used, such as Sanger DNA sequencing, pyrosequencing, the ILLUMINA SOLEXA platform, ILLUMINA's GENOME ANALYZER, or APPLIED BIOSYSTEM's 454 sequencing platform, HELICOS's TRUE SINGLE MOLECULE SEQUENCING platform, or any other sequencing method, fluorescent in-situ hybridization (FISH), array comparative genomic hybridization (CGH), other high through-put genotyping platforms, and combinations thereof. All of these methods physically transform the genetic data stored in a sample of DNA into a set of genetic data that is typically stored in a memory device en route to being processed.
  • FISH fluorescent in-situ hybridization
  • CGH array comparative genomic hybridization
  • Any relevant individual's genetic data can be measured by analyzing substances taken from a group including, but not limited to: the individual's bulk diploid tissue, one or more diploid cells from the individual, one or more haploid cells from the individual, one or more blastomeres from the target individual, extra-cellular genetic material found on the individual, extra-cellular genetic material from the individual found in maternal blood, cells from the individual found in maternal blood, one or more embryos created from (a) gamete(s) from the related individual, one or more blastomeres taken from such an embryo, extra-cellular genetic material found on the related individual, genetic material known to have originated from the related individual, and combinations thereof.
  • a set of at least one ploidy state hypothesis may be created for each of the chromosomes of interest of the target individual.
  • Each of the ploidy state hypotheses may refer to one possible ploidy state of the chromosome or chromosome segment of the target individual.
  • the set of hypotheses may include some or all of the possible ploidy states that the chromosome of the target individual may be expected to have.
  • Some of the possible ploidy states may include nullsomy, monosomy, disomy, uniparental disomy, euploidy, trisomy, matching trisomy, unmatching trisomy, maternal trisomy, paternal trisomy, tetrasomy, balanced (2:2) tetrasomy, unbalanced (3:1) tetrasomy, other aneuploidy, and they may additionally involve unbalanced translocations, balanced translocations, Robertsonian translocations, recombinations, deletions, insertions, crossovers, and combinations thereof.
  • the set of determined probabilities may then be combined. This may entail, for each hypothesis, averaging or multiplying the probabilities as determined by each technique, and it also may involve normalizing the hypotheses. In some embodiments, the probabilities may be combined under the assumption that they are independent. The set of the products of the probabilities for each hypothesis in the set of hypotheses is then output as the combined probabilities of the hypotheses.
  • the determined probabilities as determined by the method disclosed herein may be combined with probabilities of other hypotheses that are calculated by diagnostic methods that work on entirely different precepts. For example, some methods used for prenatal diagnosis involve measuring the levels of certain hormones in maternal blood, where those hormones are correlated with various genetic abnormalities. Some examples of this are the first trimester serum screen, the triple test, and the quad test. Some methods involve measuring dimensions and other qualities of the fetus that can be measured using ultrasound, for example, the nuchal translucency. Some of these methods can calculate a probability that the fetus is euploid, or is afflicted with trisomy, especially trisomy 18 and/or trisomy 21.
  • the method involves measuring maternal blood levels of alpha-fetoprotein (AFP).
  • the method may involve measuring maternal blood levels of unconjugated estriol (UE 3 ).
  • the method may involve measuring maternal blood levels of beta human chorionic gonadotropin ( ⁇ -hCG).
  • the method may involve measuring maternal blood levels of invasive trophoblast antigen (ITA). In some embodiments, the method may involve measuring maternal blood levels of inhibin-A. In some embodiments, the method may involve measuring maternal blood levels of pregnancy-associated plasma protein A (PAPP-A). In some embodiments, the method may involve measuring maternal blood levels of other hormones or maternal serum markers. In some embodiments, some of the predictions may have been made using other methods. In some embodiments, some of the predictions may have been made using a fully integrated test such as one that combines ultrasound and blood test at about 10-14 weeks of pregnancy and a second blood test at about 15-20 weeks. In some embodiments, the method involves measuring the fetal nuchal translucency (NT) as measured by ultrasound. In some embodiments, the method involves using the measured levels of the aforementioned hormones for making predictions. In some embodiments the method involves a combination of the aforementioned methods.
  • ITA invasive trophoblast antigen
  • PAPP-A pregnancy-associated plasma protein A
  • the method may involve measuring maternal blood levels of other
  • the output of the method described herein can be combined with the output of one or a plurality of other methods.
  • There are many ways to combine the predictions for example, one could convert the hormone measurements into a multiple of the median (MoM) and then into likelihood ratios (LR).
  • LR likelihood ratios
  • other measurements could be transformed into LRs using the mixture model of NT distributions.
  • the LRs for NT and the biochemical markers could be multiplied by the age and gestation-related risk to derive the risk for various conditions, such as trisomy 21. Detection rates (DRs) and false-positive rates (FPRs) could be calculated by taking the proportions with risks above a given risk threshold.
  • DRs Detection rates
  • FPRs false-positive rates
  • One embodiment may involve a situation with four measured hormone levels, where the probability distribution around those hormones is known: p(x 1 , x 2 , x 3 , x 4
  • the ploidy state for the target individual is determined to be the ploidy state that is associated with the hypothesis whose probability is the greatest.
  • one hypothesis will have a normalized, combined probability greater than 90%.
  • Each hypothesis is associated with one, or a set of, ploidy states, and the ploidy state associated with the hypothesis whose normalized, combined probability is greater than 90%, or some other threshold value, such as 50%, 80%, 95%, 98%, 99%, or 99.9%, may be chosen as the threshold required for a hypothesis to be called as the determined ploidy state.
  • the knowledge of the determined ploidy state may be used to make a clinical decision.
  • This knowledge typically stored as a physical arrangement of matter in a memory device, may then be transformed into a report. The report may then be acted upon.
  • the clinical decision may be to terminate the pregnancy; alternately, the clinical decision may be to continue the pregnancy.
  • the clinical decision may involve an intervention designed to decrease the severity of the phenotypic presentation of a genetic disorder.
  • the algorithm may not be feasible due to limits of computational power or time.
  • the computing power needed to calculate the most likely allele values for the target may increase exponentially with the number of sperm, blastomeres, and other input genotypes from related individuals.
  • these problems may be overcome by using a method termed “subsetting”, where the computations may be divided into smaller sets, run separately, and then combined.
  • one may have the genetic data of the parents along with that of ten embryos and ten sperm.
  • the number of sibling embryos used in the determination may be from one to three, from three to five, from five to ten, from ten to twenty, or more than twenty.
  • the number of sperm whose genetic data is known may be from one to three, from three to five, from five to ten, from ten to twenty, or more than twenty.
  • each chromosome may be divided into two to five, five to ten, ten to twenty, or more than twenty subsets.
  • any of the methods described herein may be modified to allow for multiple targets to come from same target individual, for example, multiple blood draws from the same pregnant mother. This may improve the accuracy of the model, as multiple genetic measurements may provide more data with which the target genotype may be determined.
  • one set of target genetic data served as the primary data which was reported, and the other served as data to double-check the primary target genetic data.
  • a plurality of sets of genetic data, each measured from genetic material taken from the target individual are considered in parallel, and thus both sets of target genetic data serve to help determine which sections of parental genetic data, measured with high accuracy, composes the fetal genome.
  • the source of the genetic material to be used in determining the genetic state of the fetus may be fetal cells, such as nucleated fetal red blood cells, isolated from the maternal blood.
  • the method may involve obtaining a blood sample from the pregnant mother.
  • the method may involve isolating a fetal red blood cell using visual techniques, based on the idea that a certain combination of colors are uniquely associated with nucleated red blood cell, and a similar combination of colors is not associated with any other present cell in the maternal blood.
  • the combination of colors associated with the nucleated red blood cells may include the red color of the hemoglobin around the nucleus, which color may be made more distinct by staining, and the color of the nuclear material which can be stained, for example, blue.
  • nucleated red blood cells By isolating the cells from maternal blood and spreading them over a slide, and then identifying those points at which one sees both red (from the Hemoglobin) and blue (from the nuclear material) one may be able to identify the location of nucleated red blood cells. One may then extract those nucleated red blood cells using a micromanipulator, use genotyping and/or sequencing techniques to measure aspects of the genotype of the genetic material in those cells.
  • one may stain the nucleated red blood cell with a die that only fluoresces in the presence of fetal hemoglobin and not maternal hemoglobin, and so remove the ambiguity between whether a nucleated red blood cell is derived from the mother or the fetus.
  • Some embodiments of the present disclosure may involve staining or otherwise marking nuclear material.
  • Some embodiments of the present disclosure may involve specifically marking fetal nuclear material using fetal cell specific antibodies.
  • the target individual is a fetus
  • the different genotype measurements are made on a plurality of DNA samples from the fetus.
  • the fetal DNA samples are from isolated fetal cells where the fetal cells may be mixed with maternal cells.
  • the fetal DNA samples are from free floating fetal DNA, where the fetal DNA may be mixed with free floating maternal DNA.
  • the fetal DNA may be mixed with maternal DNA in ratios ranging from 99.9:0.1% to 90:10%; 90:10% to 50:50%; 50:50% to 10:90%; or 10:90% to 0.1:99.9%.
  • PARENTAL SUPPORTTM When applied to the genetic data of the cell, PARENTAL SUPPORTTM could indicate whether or not a nucleated red blood cell is fetal or maternal in origin by identifying whether the cell contains one chromosome from the mother and one from the father, which would indicate that it is fetal, or two chromosomes from the mother, which would indicate that it is maternal.
  • the method may be used for the purpose of paternity testing. For example, given the SNP-based genotypic information from the mother, and from a man who may or may not be the genetic father, and the measured genotypic information from the mixed sample, it is possible to determine if the genotypic information of the male indeed represents that actual genetic father of the gestating fetus. A simple way to do this is to simply look at the contexts where the mother is AA, and the possible father is AB or BB. In these cases, one may expect to see the father contribution half (AA
  • One embodiment of the present disclosure could be as follows: a pregnant woman wants to know if her fetus is afflicted with Down Syndrome, and/or if it will suffer from Cystic Fibrosis, and she does not wish to bear a child that is afflicted with either of these conditions. A doctor takes her blood, and stains the hemoglobin with one marker so that it appears clearly red, and stains nuclear material with another marker so that it appears clearly blue. Knowing that maternal red blood cells are typically anuclear, while a high proportion of fetal cells contain a nucleus, he is able to visually isolate a number of nucleated red blood cells by identifying those cells that show both a red and blue color.
  • the PARENTAL SUPPORTTM method is able to determine that six of the ten cells are maternal blood cells, and four of the ten cells are fetal cells. If a child has already been born to a pregnant mother, PARENTAL SUPPORTTM can also be used to determine that the fetal cells are distinct from the cells of the born child by making reliable allele calls on the fetal cells and showing that they are dissimilar to those of the born child. Note that this method is similar in concept to the paternal testing embodiment of the invention.
  • the genetic data measured from the fetal cells may be of very poor quality, containing many allele drop outs, due to the difficulty of genotyping single cells.
  • the clinician is able to use the measured fetal DNA along with the reliable DNA measurements of the parents to infer aspects of the genome of the fetus with high accuracy using PARENTAL SUPPORTTM, thereby transforming the genetic data contained on genetic material from the fetus into the predicted genetic state of the fetus, stored on a computer.
  • the clinician is able to determine both the ploidy state of the fetus, and the presence or absence of a plurality of disease-linked genes of interest. It turns out that the fetus is euploidy, and is not a carrier for cystic fibrosis, and the mother decides to continue the pregnancy.
  • a couple where the mother, who is pregnant, and is of advanced maternal age wants to know whether the gestating fetus has Down syndrome or some other chromosomal abnormality.
  • the obstetrician takes a blood draw from the mother and father.
  • a technician centrifuges the maternal sample to isolate the plasma and the buffy coat.
  • the DNA in the buffy coat and the paternal blood sample are transformed through amplification and the genetic data encoded in the amplified genetic material is further transformed from molecularly stored genetic data into electronically stored genetic data by running the genetic material on a SNP array to measure the parental genotypes.
  • the plasma sample is may be further processed by a method such as running a gel, or using a size exclusion column, to isolate specific size fractions of DNA.
  • An informatics based technique that includes the invention described herein, such as PARENTAL SUPPORTTM, may be used to determine the ploidy state of the fetus. It is determined that the fetus has Down syndrome. A report is printed out, or sent electronically to the pregnant woman's obstetrician, who transmits the diagnosis to the woman. The woman, her husband, and the doctor sit down and discuss the options. The couple decides to terminate the pregnancy based on the knowledge that the fetus is afflicted with a trisomic condition.
  • a pregnant woman hereafter referred to as ‘the mother’ may decide that she wants to know whether or not her fetus(es) are carrying any genetic abnormalities or other conditions. She may want to ensure that there are not any gross abnormalities before she is confident to continue the pregnancy. She may go to her obstetrics doctor, who may take a sample of her blood. He may also take a genetic sample, such as a buccal swab, from her cheek. He may also take a genetic sample from the father of the fetus, such as a buccal swab, a sperm sample, or a blood sample. The doctor may enrich the fraction of free floating fetal DNA in the maternal blood sample.
  • the doctor may enrich the fraction of enucleated fetal blood cells in the maternal blood sample.
  • the doctor may use various aspects of the method described herein to determine genotypic data of the fetus. That genotypic data may include the ploidy state of the fetus, and/or the identity of one or a number of alleles in the fetus.
  • a report may be generated summarizing the results of the prenatal diagnosis.
  • the doctor may tell the mother the genetic state of the fetus. The mother may decide to discontinue the pregnancy based on the fact that the fetus has one or more chromosomal, or genetic abnormalities, or undesirable conditions. She may also decide to continue the pregnancy based on the fact that the fetus does not have any gross chromosomal or genetic abnormalities, or any genetic conditions of interest.
  • Another example may involve a pregnant woman who has been artificially inseminated by a sperm donor, and is pregnant. She is wants to minimize the risk that the fetus she is carrying has a genetic disease. She has blood drawn at a phlebotomist, and techniques described in this disclosure are used to isolate three nucleated fetal red blood cells, and a tissue sample is also collected from the mother and genetic father. The genetic material from the fetus and from the mother and father are amplified as appropriate and genotyped using the ILLUMINA INFINIUM BEADARRAY, and the methods described herein clean and phase the parental and fetal genotype with high accuracy, as well as to make ploidy calls for the fetus.
  • the fetus is found to be euploid, and phenotypic susceptibilities are predicted from the reconstructed fetal genotype, and a report is generated and sent to the mother's physician so that they can decide what clinical decisions may be best.
  • Another example may involve a woman who is pregnant but, owing to having had more than one sexual partner, is not certain of the paternity of her fetus.
  • the woman wants to know who is the genetic father of the fetus she is carrying. She and one of her sexual partners go to the hospital and both donate a blood sample.
  • the clinician using the methods described in this disclosure, is able to determine the paternity of the fetus. It turns out that the biological father of the fetus is not her favored partner, and based on this information, the woman decides to terminate her pregnancy.
  • a plurality of parameters may be changed without changing the essence of the present disclosure.
  • the genetic data may be obtained using any high throughput genotyping platform, or it may be obtained from any genotyping method, or it may be simulated, inferred or otherwise known.
  • a variety of computational languages could be used to encode the algorithms described in this disclosure, and a variety of computational platforms could be used to execute the calculations.
  • the calculations could be executed using personal computers, supercomputers, and parallel computers.
  • the method may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations thereof.
  • Apparatus of the invention can be implemented in a computer program product tangibly embodied in a machine-readable storage device for execution by a programmable processor; and method steps of the invention can be performed by a programmable processor executing a program of instructions to perform functions of the invention by operating on input data and generating output.
  • the invention can be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device.
  • Each computer program can be implemented in a high-level procedural or object-oriented programming language, or in assembly or machine language if desired; and in any case, the language can be a compiled or interpreted language.
  • Suitable processors include, by way of example, both general and special purpose microprocessors.
  • a processor will receive instructions and data from a read-only memory and/or a random access memory.
  • a computer will include one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks.
  • Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM disks. Any of the foregoing can be supplemented by, or incorporated in, ASICs (application-specific integrated circuits).
  • the results may be output in the form of a printed report, a display on a screen, or may be saved by way of a memory device that involves storage of information by way of a physical change in the substrate of the memory device, such as those listed above.
  • a report describing the determination of the ploidy state of the fetus may be generated that transmits the information to a heath care practitioner, and/or the parent.
  • a clinical decision may be made based on the determination.
  • the clinical decision to terminate a pregnancy may be made contingent upon that the fetus is aneuploid; the undesirability of the condition of aneuploidy in the fetus provides the basis for the decision to terminate the pregnancy.
  • the method includes the decision to terminate or to not terminate a pregnancy, and may also include the execution of that decision.
  • the raw genetic material of the mother and father is transformed by way of amplification to an amount of DNA that is similar in sequence, but larger in quantity. Then, by way of a genotyping method the genotypic data that is encoded by nucleic acids is transformed into genetic measurements that may be stored physically and/or electronically on a memory device, such as those described above.
  • the relevant algorithms that makeup the PARENTAL SUPPORTTM algorithm, relevant parts of which are discussed in detail in this disclosure, are translated into a computer program, using a programming language.
  • the computer program on the computer hardware instead of being physically encoded bits and bytes, arranged in a pattern that represents raw measurement data, they become transformed into a pattern that represents a high confidence determination of the ploidy state of the fetus.
  • the details of this transformation will rely on the data itself and the computer language and hardware system used to execute the method described herein, but is predictable if those contexts are known.
  • the data that is physically configured to represent a high quality ploidy determination of the fetus is transformed into a report which may be sent to a health care practitioner. This transformation may be carried out using a printer or a computer display.
  • the report may be a printed copy, on paper or other suitable medium, or else it may be electronic.
  • an electronic report it may be transmitted, it may be physically stored on a memory device at a location on the computer accessible by the health care practitioner; it also may be displayed on a screen so that it may be read.
  • the data may be transformed to a readable format by causing the physical transformation of pixels on the display device. The transformation may be accomplished by way of physically firing electrons at a phosphorescent screen, by way of altering an electric charge that physically changes the transparency of a specific set of pixels on a screen that may lie in front of a substrate that emits or absorbs photons.
  • This transformation may be accomplished by way of changing the nanoscale orientation of the molecules in a liquid crystal, for example, from nematic to cholesteric or smectic phase, at a specific set of pixels.
  • This transformation may be accomplished by way of an electric current causing photons to be emitted from a specific set of pixels made from a plurality of light emitting diodes arranged in a meaningful pattern.
  • This transformation may be accomplished by any other way used to display information, such as a computer screen, or some other output device or way of transmitting information.
  • the health care practitioner may then act on the report, such that the data in the report is transformed into an action.
  • the action may be to continue or discontinue the pregnancy, in which case a gestating fetus with a genetic abnormality is transformed into non-living fetus.
  • the transformations listed herein may be aggregated, such that, for example, one may transform the genetic material of a pregnant mother and the father, through a number of steps outlined in this disclosure, into a medical decision consisting of aborting a fetus with genetic abnormalities, or consisting of continuing the pregnancy. Alternately, one may transform a set of genotypic measurements into a report that helps a physician treat his pregnant patient.
  • the method described herein can be used to determine the ploidy state of a fetus even when the host mother, i.e. the woman who is pregnant, is not the biological mother of the fetus she is carrying.
  • Some of the math in this disclosure makes hypotheses concerning a limited number of states of aneuploidy. In some cases, for example, only zero, one or two chromosomes are expected to originate from each parent. In some embodiments of the present disclosure, the mathematical derivations can be expanded to take into account other forms of aneuploidy, such as quadrosomy, where three chromosomes originate from one parent, pentasomy, hexasomy etc., without changing the fundamental concepts of the present disclosure. At the same time, it is possible to focus on a smaller number of ploidy states, for example, only trisomy and disomy. Note that ploidy determinations that indicate a non-whole number of chromosomes may indicate mosaicism in a sample of genetic material.
  • a related individual may refer to any individual who is genetically related, and thus shares haplotype blocks with the target individual.
  • related individuals include: biological father, biological mother, son, daughter, brother, sister, half-brother, half-sister, grandfather, grandmother, uncle, aunt, nephew, niece, grandson, granddaughter, cousin, clone, the target individual himself/herself/itself, and other individuals with known genetic relationship to the target.
  • the term ‘related individual’ also encompasses any embryo, fetus, sperm, egg, blastomere, blastocyst, or polar body derived from a related individual.
  • the target individual may refer to an adult, a juvenile, a fetus, an embryo, a blastocyst, a blastomere, a cell or set of cells from an individual, or from a cell line, or any set of genetic material.
  • the target individual may be alive, dead, frozen, or in stasis.
  • the methods are equally applicable to any live or dead human, animal, or plant that inherits or inherited chromosomes from other individuals.
  • the fetal genetic data that can be generated by measuring the amplified DNA from a small sample of fetal DNA can be used for multiple purposes. For example, it can be used for detecting aneuploidy, uniparental disomy, unbalanced translocations, sexing the individual, as well as for making a plurality of phenotypic predictions based on phenotype-associated alleles.
  • particular genetic conditions may be focused on before screening, and if certain loci are especially relevant to those genetic conditions, then a more appropriate set of SNPs which are more likely to co-segregate with the locus of interest, can be selected, thus increasing the confidence of the allele calls of interest.
  • the genetic abnormality is a type of aneuploidy, such as Down syndrome (or trisomy 21), Edwards syndrome (trisomy 18), Patau syndrome (trisomy 13), Turner Syndrome (45X0) and Klinefelter's syndrome (a male with 2X chromosomes).
  • Congenital disorders such as those listed in the prior sentence, are commonly undesirable, and the knowledge that a fetus is afflicted with one or more phenotypic abnormalities may provide the basis for a decision to terminate the pregnancy.
  • the phenotypic abnormality may be a limb malformation, or a neural tube defect.
  • Limb malformations may include, but are not limited to, amelia, ectrodactyly, phocomelia, polymelia, polydactyly, syndactyly, polysyndactyly, oligodactyly, brachydactyly, achondroplasia, congenital aplasia or hypoplasia, amniotic band syndrome, and cleidocranial dysostos is.
  • the phenotypic abnormality may be a congenital malformation of the heart.
  • Congenital malformations of the heart may include, but are not limited to, patent ductus arteriosus, atrial septal defect, ventricular septal defect, and tetralogy of fallot.
  • the phenotypic abnormality may be a congenital malformation of the nervous system.
  • Congenital malformations of the nervous system include, but are not limited to, neural tube defects (e.g., spina bifida, meningocele, meningomyelocele, encephalocele and anencephaly), Arnold-Chiari malformation, the Dandy-Walker malformation, hydrocephalus, microencephaly, megencephaly, lissencephaly, polymicrogyria, holoprosencephaly, and agenesis of the corpus callosum.
  • neural tube defects e.g., spina bifida, meningocele, meningomyelocele, encephalocele and anencephaly
  • Arnold-Chiari malformation e.g., the Dandy-Walker malformation
  • hydrocephalus e.g., microencephaly, megencephaly, lissencephaly, poly
  • the phenotypic abnormality may be a congenital malformation of the gastrointestinal system.
  • Congenital malformations of the gastrointestinal system include, but are not limited to, stenosis, atresia, and imperforate anus.
  • the genetic abnormality is either monogenic or multigenic.
  • Genetic diseases include, but are not limited to, Bloom Syndrome, Canavan Disease, Cystic fibrosis, Familial Dysautonomia, Riley-Day syndrome, Fanconi Anemia (Group C), Gaucher Disease, Glycogen storage disease la, Maple syrup urine disease, Mucolipidosis IV, Niemann-Pick Disease, Tay-Sachs disease, Beta thalessemia, Sickle cell anemia, Alpha thalessemia, Beta thalessemia, Factor XI Deficiency, Friedreich's Ataxia, MCAD, Parkinson disease—juvenile, Connexin26, SMA, Rett syndrome, Phenylketonuria, Becker Muscular Dystrophy, Duchennes Muscular Dystrophy, Fragile X syndrome, Hemophilia A, Alzheimer dementia—early onset, Breast/Ovarian cancer, Colon cancer, Diabetes/MODY, Huntington disease, Myotonic Muscular Dystrophy
  • the systems, methods, and techniques of the present disclosure are used in methods to increase the probability of implanting an embryo obtained by in vitro fertilization that is at a reduced risk of carrying a predisposition for a genetic disease.
  • methods are disclosed for the determination of the ploidy state of a target individual where the measured genetic material of the target is contaminated with genetic material of the mother, by using the knowledge of the maternal genetic data. This is in contrast to methods that are able to determine the ploidy state of a target individual from genetic data that is noisy due to poor measurements; the contamination in this data is random. This is also in contrast to methods that are able to determine the ploidy state of a target individual from genetic data that is difficult to interpret because of contamination by DNA from unrelated individuals; the contamination in that data is genetically random.
  • the methods disclosed herein are able to determine the ploidy state of a target individual when the difficulty of interpretation is due to contamination of DNA from a parent; the contamination in this data is at least half identical to the target data, and is therefore difficult to correct for.
  • a method of the present disclosure uses the knowledge of the contaminating maternal genotype to create a model of the expected genetic measurements given a mixture of the maternal and the target genetic material, wherein the target genetic data is not known a priori. This step is not necessary where the uncertainty in the genetic data is due to random noise.
  • a method for determining the copy number of a chromosome of interest in a target individual, using genotypic measurements made on genetic material from the target individual, wherein the genetic material of the target individual is mixed with genetic material from the mother of the target individual comprises obtaining genotypic data for a set of SNPs of the parents of the target individual; making genotypic measurements for the set of SNPs on a mixed sample that comprises DNA from the target individual and also DNA from the mother of the target individual; creating, on a computer, a set of ploidy state hypothesis for the chromosome of interest of the target individual; determining, on the computer, the probability of each of the hypotheses given the genetic measurements of the mixed sample and of the genetic data of the parents of the target individual; and using the determined probabilities of each hypothesis to determine the most likely copy number of the chromosome of interest in the target individual.
  • the target individual and the parents of the target individual are human test subjects.
  • a computer implemented method for determining the copy number of a chromosome of interest in a target individual, using genotypic measurements made on genetic material from the target individual, where the genetic material of the target individual is mixed with genetic material from the mother of the target individual comprises obtaining genotypic data for a set of SNPs of the parents of the target individual; making genotypic measurements for the set of SNPs on a mixed sample that comprises DNA from the target individual and also DNA from the mother of the target individual; creating, on a computer, a set of ploidy state hypothesis for the chromosome of interest of the target individual; determining, on the computer, the probability of each of the hypotheses given the genetic measurements of the mixed sample and of the genetic data of the parents of the target individual; and using the determined probabilities of each hypothesis to determine the most likely copy number of the chromosome of interest in the target individual.
  • a method for determining the copy number of a chromosome of interest in a target individual, using genotypic measurements made on genetic material from the target individual, wherein the genetic material of the target individual is mixed with genetic material from the mother of the target individual comprises obtaining genotypic data for a set of SNPs of the mother of the target individual; making genotypic measurements for the set of SNPs on a mixed sample that comprises DNA from the target individual and also DNA from the mother of the target individual; creating, on a computer, a set of ploidy state hypothesis for the chromosome of interest of the target individual; determining, on the computer, the probability of each of the hypotheses given the genetic measurements of the mixed sample and of the genetic data of the mother of the target individual; and using the determined probabilities of each hypothesis to determine the most likely copy number of the chromosome of interest in the target individual.
  • a computer implemented method for determining the copy number of a chromosome of interest in a target individual, using genotypic measurements made on genetic material from the target individual, where the genetic material of the target individual is mixed with genetic material from the mother of the target individual comprises obtaining genotypic data for a set of SNPs of the mother of the target individual; making genotypic measurements for the set of SNPs on a mixed sample that comprises DNA from the target individual and also DNA from the mother of the target individual; creating, on a computer, a set of ploidy state hypothesis for the chromosome of interest of the target individual; determining, on the computer, the probability of each of the hypotheses given the genetic measurements of the mixed sample and of the genetic data of the mother of the target individual; and using the determined probabilities of each hypothesis to determine the most likely copy number of the chromosome of interest in the target individual.

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US20180032670A1 (en) 2018-02-01
US20160369346A1 (en) 2016-12-22
US20160357904A1 (en) 2016-12-08
EP2854056A3 (fr) 2015-06-03
US20160024564A1 (en) 2016-01-28
CN102597266A (zh) 2012-07-18
US20160171152A1 (en) 2016-06-16
US20170011166A1 (en) 2017-01-12
US9228234B2 (en) 2016-01-05
EP2854056A2 (fr) 2015-04-01
CA2774252A1 (fr) 2011-04-07
US10061890B2 (en) 2018-08-28
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US10216896B2 (en) 2019-02-26
EP2473638A1 (fr) 2012-07-11
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CA2774252C (fr) 2020-04-14
US10061889B2 (en) 2018-08-28
US20170076038A1 (en) 2017-03-16
US20130274116A1 (en) 2013-10-17
ES2640776T3 (es) 2017-11-06
US10522242B2 (en) 2019-12-31
US20140154682A1 (en) 2014-06-05
WO2011041485A1 (fr) 2011-04-07

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