WO2018144228A1 - Systèmes et procédés de détermination quantitative du nombre de copies d'un gène - Google Patents

Systèmes et procédés de détermination quantitative du nombre de copies d'un gène Download PDF

Info

Publication number
WO2018144228A1
WO2018144228A1 PCT/US2018/014163 US2018014163W WO2018144228A1 WO 2018144228 A1 WO2018144228 A1 WO 2018144228A1 US 2018014163 W US2018014163 W US 2018014163W WO 2018144228 A1 WO2018144228 A1 WO 2018144228A1
Authority
WO
WIPO (PCT)
Prior art keywords
cycle threshold
wells
gene
target gene
threshold value
Prior art date
Application number
PCT/US2018/014163
Other languages
English (en)
Inventor
Xin Wang
Daniel Davison
Kevin R. HAAS
Eric A. Evans
Original Assignee
Counsyl, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Counsyl, Inc. filed Critical Counsyl, Inc.
Publication of WO2018144228A1 publication Critical patent/WO2018144228A1/fr

Links

Classifications

    • 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
    • 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
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • 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
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/20—Polymerase chain reaction [PCR]; Primer or probe design; Probe optimisation
    • 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
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • 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
    • C12Q1/6844—Nucleic acid amplification reactions
    • C12Q1/686—Polymerase chain reaction [PCR]

Definitions

  • Genetic diseases are caused by an abnormality in a person's genome ranging from a discrete mutation in a single genomic deoxyribonucleic acid (DNA) base of a gene to gross chromosome abnormalities. Such genetic abnormalities may result in disease or increased risk of disease, such as increased risk of cancer, in the individual. In many cases individuals having a genetic abnormality and showing no symptoms may be a carrier for a genetic disease that may be passed to their offspring. Genetic screenings testing for a variety of genetic factors are increasingly available to individuals planning to have children. Genetic screenings may look at a variety of factors that individuals may consider as they are planning for their families.
  • Certain genetic disorders have been found to be associated with copy- number variations in sections of a person's genome.
  • the number of repeats of a particular gene may vary between individuals, with certain copy numbers of the gene in an individual genome being associated with a particular genetic disorder. For example, if an individual has an abnormal copy number of a specified gene in their genome, they may exhibit symptoms of a corresponding genetic disease, have an increased risk of a disease such as cancer, or they may be a carrier of the genetic disease with little or no observable symptoms of the disease. Screening for copy number variations in the specified gene may assist individuals by alerting them they have a genetic abnormality that may affect them or that they may be at an increased risk for passing the genetic disease to their offspring.
  • the autosomal recessive disorder proximal spinal muscular atrophy is a genetic disease that results from a low copy number of the survival of motor neuron 1 (SMN1) gene in the genome of an individual in comparison to the broader population.
  • SMA is a severe neuromuscular disease characterized by degeneration of alpha motor neurons in the spinal cord, which results in progressive proximal muscle weakness and paralysis.
  • SMA is the second most common fatal autosomal recessive disorder after cystic fibrosis, with an estimated prevalence of 1 in 10,000 live births and an estimated average carrier frequency of 1/40-1/60. The homozygous absence of SMN1 gene exon 7 is found in approximately 95% of affected patients.
  • SMA is traditionally categorized into various types. For children with SMA, SMA is categorized as: type I, severe; type II, intermediate; and type III, mild. For adults with mild symptoms of SMA, SMA is categorized as type IV. Additionally, for prenatal onset of very severe symptoms of SMA and early neonatal death due to SMA, SMA is categorized as type 0.
  • a method may include (i) performing real-time polymerase chain reactions (qPCRs) on a plurality of genomic DNA samples prepared in a plurality of wells of an assay plate, each of the plurality of genomic DNA samples having an unknown target gene copy number for a target gene sequence and a reference gene copy number of M for a reference gene sequence, each of the plurality of wells including one of the plurality of genomic DNA samples, a target probe specific to the target gene sequence, and a reference probe specific to the reference gene sequence, (ii) determining a cycle threshold value for the target gene sequence and a cycle threshold value for the reference gene sequence in each of the plurality of wells, (iii) calculating a delta cycle threshold value for each of the plurality of wells by determining the difference between the cycle threshold value for the target gene sequence and the cycle threshold value for the reference gene sequence in each respective well,
  • qPCRs real-time polymerase chain reactions
  • determining the target gene copy number in each of the plurality of genomic DNA samples based on the modified Gaussian mixture model may further include determining that the delta cycle threshold values for each of the plurality of wells falls within a modified range of the plurality of modified ranges of delta cycle threshold values in the modified Gaussian mixture model.
  • the method may further include determining, for each of the plurality of wells, whether the delta cycle threshold value, the cycle threshold value for the target gene sequence, and the cycle threshold value for the reference gene sequence meet specified acceptability criteria.
  • the method may include (i) performing the qPCRs on a plurality of control DNA samples prepared in a set of control wells of the assay plate, each of the plurality of control DNA samples having a known target gene copy number of 0 to N, each of the plurality of control wells including one of the plurality of control DNA samples, the target probe, and the reference probe, (ii) determining a cycle threshold value for the target gene sequence and a cycle threshold value for the reference gene sequence in each of the plurality of control wells, (iii) calculating a delta cycle threshold value for each of the plurality of control wells by determining the difference between the cycle threshold value for the target gene sequence and the cycle threshold value for the reference gene sequence in the respective control well, and (iv) determining, for each of the plurality of control wells, whether the delta cycle threshold value, the cycle threshold value for the target gene sequence, and the cycle threshold value for the reference gene sequence meet specified acceptability criteria.
  • each of the plurality of wells may further include at least one primer.
  • the target probe may include a first fluorescent dye
  • the target probe may include a minor groove binder.
  • N may be equal to 3 and M may be equal to 2.
  • the target gene copy numbers of 1 to N may include copy numbers of 1, 2, and 3 copies of the target gene sequence.
  • the target probe may be an SMN1 probe including SEQ ID NO: 1 and the target gene sequence may be a survival of motor neuron 1 (SMN1) gene sequence.
  • Each of the plurality of wells may further include a survival of motor neuron 2 (SMN2) blocker including SEQ ID NO: 4.
  • the target gene sequence may be an SMN2 gene sequence and the target probe may be an SMN2 probe.
  • generating the modified Gaussian mixture model may further include defining at least one no-call range of delta cycle threshold values that are not correlated to any of the target gene copy numbers of 1 to N.
  • the method may further include (i) performing the qPCRs on the plurality of genomic DNA samples prepared in an additional plurality of wells of the assay plate, each of the plurality of genomic DNA samples having a reference gene copy number of M for an additional reference gene sequence, each of the additional plurality of wells including one of the plurality of genomic DNA samples, the target probe, and an additional reference probe specific to the additional reference gene sequence, (ii) determining a cycle threshold value for the target gene sequence and a cycle threshold value for the reference gene sequence in each of the additional plurality of wells, and (iii) calculating a delta cycle threshold value for each of the additional plurality of wells by determining the difference between the cycle threshold value for the target gene sequence and the cycle threshold value for the additional reference gene sequence in each respective well of the additional plurality of wells.
  • Fitting the Gaussian mixture model may further include fitting the Gaussian mixture model to the delta cycle threshold values for the plurality of wells and the delta cycle threshold values for the additional plurality of wells to correlate the separate ranges of delta cycle threshold values to each of the target gene copy numbers of 1 to N.
  • Generating the modified Gaussian mixture model may further include performing the iterative expectation-maximization routine on the Gaussian mixture model to further fit the Gaussian mixture model to the delta cycle threshold values for the plurality of wells and the delta cycle threshold values for the additional plurality of wells and correlate the separate modified ranges of delta cycle threshold values to each of the target gene copy numbers of 1 to N.
  • performing the iterative expectation-maximization routine on the Gaussian mixture model may further comprise calculating a mean delta cycle threshold value having a maximized likelihood for at least one of the target gene copy numbers of 1 to N, and calculating a qPCR efficiency parameter having a maximized likelihood.
  • the method may further include determining that an additional genomic DNA sample prepared in an additional plurality of wells of the assay plate has a target gene copy number of 0 based on calculated delta cycle threshold values for the additional plurality of wells being outside the separate modified ranges of delta cycle threshold values corresponding to each of the target gene copy numbers of 1 to N.
  • a system for quantitatively determining copy numbers of a gene of interest in genomic DNA samples may include (i) a PCR device that performs qPCRs on a plurality of genomic DNA samples prepared in a plurality of wells of an assay plate, each of the plurality of genomic DNA samples having an unknown target gene copy number for a target gene and a reference gene copy number of M for a reference gene, each of the plurality of wells including one of the plurality of genomic DNA samples, a target probe specific to a sequence of the target gene, and a reference probe specific to a sequence of the reference gene, (ii) a cycle threshold module, stored in memory, that determines a cycle threshold value for the target gene and a cycle threshold value for the reference gene in each of the plurality of wells and calculates a delta cycle threshold value for each of the plurality of wells by determining the difference between the cycle threshold value for the target gene and the cycle threshold value for the reference gene in each respective well, (iii) a copy number module,
  • FIG. 1 is an illustration of general amplification product amounts of SMN1 genes and reference genes in individuals identified as carriers and non-carriers.
  • FIG. 2 is a block diagram of an exemplary system for quantitatively determining copy numbers of a gene of interest in genomic DNA samples.
  • FIG. 3 is a flow diagram of an exemplary method for quantitatively determining copy numbers of a gene of interest in genomic DNA samples.
  • FIG. 4 is a block diagram of an exemplary computing system capable of implementing at least a portion of one or more of the embodiments described and/or illustrated herein.
  • FIG. 5 is a diagram showing an exemplary assay plate layout.
  • FIG. 6 is an exemplary amplification plot for a genomic DNA sample replicate.
  • FIG. 7 is a plot showing ACt values for a plurality of genomic DNA samples and controls.
  • the instant disclosure is directed to methods and systems for quantitatively determining a copy number of a gene of interest in a genomic DNA sample.
  • the disclosed systems and methods may be utilized to accurately and efficiently determine gene copy numbers for target genes.
  • the systems and methods may be used in disease identification and management to, for example, determine whether a subject has a concerning genetic abnormality or is predisposed to acquiring a disease and/or to determine whether an individual is a carrier of a genetic disease that may be passed to offspring.
  • the systems and methods may quantitatively determine copy numbers of genes associated with spinal muscular atrophy (SMA).
  • SMA is caused by mutations in the survival motor neuron (SMN) gene.
  • the SMN gene includes nine exons and gives rise to the 38-kD SMN protein.
  • the SMN protein plays a critical role in assembly and regeneration of small nuclear ribonuclear proteins.
  • the SMN protein also functions in axonal RNA transport and mRNA splicing.
  • the SMN gene is located in an inverted, duplicated region of chromosome five, which includes two highly homologous copies of the SMN gene, namely SMNl, which is a telomeric copy of the gene, and SMN2, which is a centromeric copy.
  • a single point mutation in exon 7 distinguishes the SMNl gene from the SMN2 gene, which contains the point mutation in exon 7. This point mutation affects splicing, so most transcripts arising from the SMN2 gene lack exon 7.
  • the SMNl gene transcribes full-length mRNA, while the SMN2 gene primarily transcribes a shortened mRNA species lacking exon 7. Because SMN protein without exon 7 has a lower oligomerization efficiency, it is much more prone to degradation. Hence, the point mutation in exon 7 of the SMN2 gene results in lower overall generation of the SMN protein from the SMN2 gene in comparison to the SMNl gene.
  • SMA affected individuals have a homozygous deletion involving the SMNl gene. Although affected individuals retain at least one copy of SMN2, the SMN2 gene only partially compensates for the homozygous loss of the SMNl gene due to the lower oligomerization efficiency of the SMN2 gene.
  • the disclosed systems and methods allow for highly reliable real-time determination of the SMNl gene and/or SMN2 gene in the genome of an individual.
  • the copy number of the target SMNl gene and/or SMN2 gene may be determined using a multiplex real-time PCR (qPCR) procedure in which reference genes are amplified in the same reaction mixtures as the target genes.
  • qPCR multiplex real-time PCR
  • the accuracy of the copy number determination may be further increased through the use of analytical modeling of the qPCR results, allowing for robust copy number determinations using minimal sample amounts and few sample replicates.
  • the systems and methods are not limited to these genes and may be utilized in the determination of copy numbers of any suitable genes, without limitation.
  • the disclosed systems and methods may allow for determination of SMNl gene copy numbers for genomic DNA samples in order to screen for various types of SMA.
  • the systems and methods may be used to screen for SMA types I, II, III, and IV, which are shown in Table 1 below.
  • extracted genomic DNA may be subjected to qPCR to quantify the copy number of the SMNl exon 7 at the +6 nucleotide that distinguishes the SMNl gene from the SMN2 gene.
  • a multiplex qPCR utilizing target gene and reference gene assays may be performed on genomic DNA samples, with the reference gene assays targeting endogenous housekeeping genes having an invariant copy number. The amount of SMNl gene in a sample may be measured relative to the reference gene corresponding to each respective reference gene assay.
  • a plurality of replicates of each genomic DNA sample may be tested. For example, a total of four replicates of each genomic DNA sample may be tested, with two replicates including a first reference assay targeting a first reference gene and two replicates including a second reference assay targeting a second reference gene.
  • Cycle threshold (Ct) values for the target SMNl gene and the reference gene may be determined for each of the sample replicates following qPCR measurement. The determined Ct values may then be converted to copy number estimates using a relative Ct threshold (ACt) determination for each sample replicate.
  • Clusters of ACt values corresponding to SMNl gene copy numbers of 1, 2, and 3 may be located by fitting a Gaussian mixture model to the ACt values.
  • the Gaussian mixture model may be further fit to the ACt values by performing an iterative expectation- maximization routine to find maximum likelihood parameters of the Gaussian mixture model, resulting in a modified Gaussian mixture model.
  • the copy number for each of the replicates of each genomic DNA sample may then be determined based on the modified Gaussian mixture model. More particularly, the ACt value for each of the replicates may be associated with a cluster range of ACt values corresponding to one of the SMN1 gene copy numbers of 1, 2, and 3. If discordance exists among copy number assignments for replicates of a genomic DNA sample and/or if copy number assignments for replicates of the sample are ambiguous, then the copy call of the sample may be deemed inconclusive and no copy number may be assigned for the sample.
  • the copy number assigned to the genomic DNA sample may be the copy number assigned in each of the replicates for the genomic DNA sample, or a majority of the replicates (e.g., 3 out of 4 total replicates) for the genomic DNA sample.
  • an additional qPCR may be performed on the genomic DNA sample with a third reference assay targeting a third standard reference gene and a new Gaussian mixture model may be fitted to the additional data in an attempt to generate a concordant copy number assignment for the replicates of the genomic DNA sample.
  • FIG. 1 is an illustration of general amplification product amounts of SMN1 genes and reference genes in individuals identified as SMA carriers and SMA non-carriers.
  • Individuals having genomic DNA samples that are determined to have half the quantity of SMN1 genes relative to the endogenous reference genes i.e., genomic DNA samples having an SMN1 gene copy number of 1 in comparison to a reference gene copy number of 2 may be identified as SMA carriers.
  • SMA carriers For example, a diploid genome of an SMA carrier includes a single copy of the SMN1 gene and 2 copies of an endogenous reference gene, such as the hTERT gene. Accordingly, as illustrated in FIG.
  • SMA non-carriers individuals having genomic DNA samples that are determined to have an SMN1 copy number of 2 or 3 may be identified as SMA non-carriers.
  • a diploid genome of an SMA non-carrier may include 2 copies of the SMN1 gene and 2 copies of the hTERT gene. As illustrated in FIG.
  • FIG. 2 is a block diagram of an exemplary system 200 for quantitatively determining copy numbers of a gene of interest in genomic DNA samples.
  • exemplary system 200 may include a PCR device 210, an assay plate reader 220, and one or more modules 232 for performing one or more tasks.
  • PCR device 210 may include any suitable device for performing PCRs simultaneously on a plurality of samples for purposes of amplifying desired DNA segments.
  • PCR device 210 may include a manual, automated, or semi-automated qPCR device having a thermal cycler for discretely raising and lowering temperature.
  • PCR device 210 may be configured to perform PCRs on samples prepared in an assay plate, such as a 96-well or a 384-well assay plate.
  • Assay plate reader 220 may include any suitable device for determining amounts of amplification products produced during PCRs performed by PCR device 210.
  • assay plate reader 220 may include a fluorescence or luminescence plate reader that detects fluorescence (e.g., fluorescence intensity) and/or luminescence, in individual wells of an assay plate during or between PCR cycles. Assay plate reader 220 may be integrated with PCR device 210.
  • modules 232 may include a cycle threshold module 234 that determines cycle threshold (Ct) values and calculates delta cycle threshold (ACt) values for target and reference genes.
  • Exemplary system 200 may additionally include a copy number module 236 that fits a suitable model (e.g., a Gaussian mixture model) to sample ACt values to determine target gene copy numbers corresponding to ranges of ACt values.
  • a suitable model e.g., a Gaussian mixture model
  • one or more of modules 232 in FIG. 2 may represent one or more software applications or programs that, when executed by a computing device, may cause the computing device to perform one or more tasks.
  • one or more of modules 232 may represent modules stored and configured to run on one or more computing devices.
  • One or more of modules 232 in FIG. 2 may also represent all or portions of one or more special-purpose computers configured to perform one or more tasks.
  • example system 200 may also include one or more memory devices, such as memory 230.
  • Memory 230 generally represents any type or form of volatile or non-volatile storage device or medium capable of storing data and/or computer- readable instructions.
  • memory 230 may store, load, and/or maintain one or more of modules 232.
  • Examples of memory 230 include, without limitation, Random Access Memory (RAM), Read Only Memory (ROM), flash memory, Hard Disk Drives (HDDs), Solid- State Drives (SSDs), optical disk drives, caches, variations or combinations of one or more of the same, and/or any other suitable storage memory.
  • example system 200 may also include one or more physical processors, such as physical processor 240.
  • Physical processor 240 generally represents any type or form of hardware-implemented processing unit capable of interpreting and/or executing computer-readable instructions.
  • physical processor 240 may access and/or modify one or more of modules 232 stored in memory 230. Additionally or alternatively, physical processor 240 may execute one or more of modules 232 to facilitate identifying message payload bit fields in electronic communications.
  • Examples of physical processor 240 include, without limitation, microprocessors, microcontrollers, Central Processing Units (CPUs), Field-Programmable Gate Arrays (FPGAs) that implement softcore processors, Application-Specific Integrated Circuits (ASICs), portions of one or more of the same, variations or combinations of one or more of the same, and/or any other suitable physical processor.
  • CPUs Central Processing Units
  • FPGAs Field-Programmable Gate Arrays
  • ASICs Application-Specific Integrated Circuits
  • FIG. 3 is a flow diagram of an exemplary method 200 for quantitatively determining a copy number of a gene of interest in a genomic DNA sample.
  • Some of the steps shown in FIG. 3 may be performed by any suitable computer-executable code and/or computing system, including system 200 in FIG. 2.
  • some of the steps shown in FIG. 3 may represent an algorithm whose structure includes and/or is represented by multiple sub-steps, examples of which will be provided in greater detail below.
  • one or more of the systems described herein may perform qPCRs on a plurality of genomic DNA samples prepared in a plurality of wells of an assay plate, each of the plurality of genomic DNA samples having an unknown target gene copy number for a target gene and a reference gene copy number of M for a reference gene.
  • PCR device 210 in FIG. 2 may perform qPCRs on the plurality of genomic DNA samples by discretely raising and lowering the temperature of the genomic DNA samples over a plurality of qPCR cycles.
  • Each of the plurality of wells may include one of the plurality of genomic DNA samples, a target probe specific to a sequence of the target gene, and a reference probe specific to a sequence of the reference gene.
  • Performing the qPCRs may include running an assay plate including the plurality of genomic DNA samples and a plurality of control DNA samples through a plurality of qPCR cycles.
  • the assay plate may comprise a multi-well assay plate, such as a 96-well or 384-well optical PCR plate.
  • the genomic DNA samples may include a plurality of genomic samples from different individuals, such as DNA samples obtained from blood, saliva, and/or any other suitable DNA source and prepared in any suitable manner for testing and analysis.
  • the genomic DNA samples may include an unknown copy number for a target gene.
  • the target gene may be, for example, an SMN1 gene or an SMN2 gene.
  • the target gene may also be any other suitable gene having an unknown copy number, without limitation.
  • a reference gene having a known copy number that is consistent between samples may be present in each of the genomic DNA samples.
  • the reference gene may include, for example, a telomerase reverse transcriptase (TERT) gene, a ribonuclease P (RNase P) gene, or an albumin (ALB) gene, each of which is expected to be present at a copy number of 2 in genomic DNA.
  • TERT telomerase reverse transcriptase
  • RNase P ribonuclease P
  • ALB albumin
  • Each of the plurality of wells of the assay plate may include one of the plurality of genomic DNA samples.
  • each of the genomic DNA samples may be loaded into two or more wells of the assay plate.
  • each of the genomic DNA samples may be loaded into a total of four replicate wells of the assay plate.
  • Each of the plurality of wells of the assay plate may include a target probe, such as an SMN1 probe that targets the SMN1 gene or an SMN2 probe that targets the SMN2 gene.
  • the target probe may be an SMN1 probe including SEQ ID NO: 1.
  • the target probe may additionally include a first fluorescent dye, such as, for example, a FAM, VIC, HEX, TET, TAMRA, JOE, ROX, PET, NED, Cy3, Cy5, SyBr Green, GFP, EGFP, Texas Red, or any other suitable fluorescent dye, without limitation.
  • a suitable quencher such as a nonfluorescent quencher for quenching the first fluorescent dye.
  • Example quenchers may include aNFQ, TAMRA, QSY, BHQ, or any other suitable quencher, without limitation.
  • the target probe may also include a minor groove binder to increase target sequence specificity of the target probe.
  • Each of the plurality of wells of the assay plate may also include a reference probe specific to a sequence of the reference gene.
  • each of the wells may include a TERT reference probe that targets the TERT gene, an RNase P reference probe that targets the RNase P gene, an ALB reference probe that targets the ALB gene, or any other suitable reference probe.
  • an ALB reference probe may be an ALB exonl2 probe.
  • the reference probe may include a second fluorescent dye that is different than the first fluorescent dye attached to the target probe.
  • the second fluorescent dye may include, for example, a FAM, VIC, HEX, TET, TAMRA, JOE, ROX, PET, NED, Cy3, Cy5, SyBr Green, GFP, EGFP, Texas Red, or any other suitable fluorescent dye having a wavelength that differs from the first fluorescent dye, without limitation.
  • the reference probe may include a suitable quencher, such as a nonfluorescent quencher for quenching the second fluorescent dye.
  • the quencher may include, for example, a NFQ, TAMRA, QSY, BHQ, or any other suitable quencher, without limitation.
  • the target probe may also include a minor groove binder to increase target sequence specificity of the target probe.
  • the plurality of wells of the assay plate may additionally include at least one primer.
  • each of the wells may include a first primer pair directed to the target gene and a second primer pair directed to the corresponding reference gene in the well.
  • Examples of a first primer pair directed to a target SMN1 gene may include, for example, a forward primer including SEQ ID NO: 2 and a reverse primer including SEQ ID NO: 3.
  • Each of the wells may additionally include a second primer pair directed to the corresponding reference gene targeted by the reference probe in the respective well.
  • a well including an ALB reference probe may include a second primer pair directed to the ALB gene, the second primer pair having a forward primer and a reverse primer.
  • each of the plurality of wells of the assay plate may further include a blocker that blocks at least one gene sequence.
  • each of the plurality of wells may include an SMN2 blocker that binds to and blocks at least a portion of the SMN2 gene, preventing amplification of the SMN2 gene.
  • the SMN2 blocker may include SEQ ID NO: 4.
  • an assay plate may be prepared with two separate reference gene assays, each of which is run with the target gene assay.
  • a first set of wells may be prepared to include the target probe and a first reference probe and a second set of wells may be prepared to include the target probe and a second reference probe.
  • the first set of wells may include, for example, the SMN1 target probe and the RNase P reference probe and a second set of wells may include, for example, the SMN1 target probe and the TERT reference probe.
  • the first or second reference probe may include, for example, the ALB reference probe.
  • each sample may be prepared in four replicate wells of the assay plate, with two replicates including the first reference probe and the other two replicates including the second reference probe.
  • the samples may be prepared with any suitable number of replicates and any suitable number of different reference probes, without limitation.
  • An assay plate may additionally be prepared to include control wells including control DNA samples having genomic DNA samples with known target gene copy numbers.
  • sets of control wells may be prepared with control DNA samples having known target gene copy numbers corresponding to potential copy numbers for the target gene.
  • sets of control wells may include SMN1 gene copy numbers of 0, 1, 2, and 3.
  • Sets of control wells may include control DNA samples including any other suitable target gene copy numbers, without limitation.
  • An assay plate may include an additional set of control wells that include no-template controls (NTC) to confirm the absence of background contamination.
  • NTC no-template controls
  • each of the control wells may be prepared in four replicate wells of the assay plate, with two replicates including the respective control DNA sample or NTC control and the first reference probe and the other two replicates including the respective control DNA sample or NTC control and the second reference probe.
  • one or more of the systems described herein may determine a cycle threshold value for the target gene and a cycle threshold value for the reference gene in each of the plurality of wells.
  • assay plate reader 220 in FIG. 2 may determine an amplification product amount in each of the plurality of wells and cycle threshold module 234 may determine a cycle threshold value for the target gene and a cycle threshold value for the reference gene in each of the plurality of wells.
  • the cycle threshold value Ct for each of the sample wells and control wells is the qPCR cycle at which a certain amplification product reaches a specified detection threshold.
  • the specified detection threshold may be a fluorescence detection threshold at which a specified amount or proportion of fluorescence is detected, the fluorescence being correlated to the breakdown of the respective target and reference probes during the qPCR cycles.
  • the amount of a first fluorescent dye of a target probe may be distinguished from a second fluorescent dye of a reference probe based on a difference in wavelength between the first fluorescent dye and the second fluorescent dye.
  • one or more of the systems described herein may calculate a delta cycle threshold value (ACt) for each of the plurality of wells by determining the difference between the cycle threshold value for the target gene and the cycle threshold value for the reference gene in each respective well.
  • cycle threshold module 234 in FIG. 2 may calculate a delta cycle threshold value for each of the plurality of wells.
  • the ACt value for each of the sample wells and each of the control wells may be determined by subtracting the Ct value for the reference sequence from the Ct value for the target sequence in each respective well of the assay plate.
  • ACt may decrease as the Ct value for the target gene increases.
  • one or more of the systems described herein may fit a Gaussian mixture model to the delta cycle threshold values for the plurality of wells to correlate separate ranges of delta cycle threshold values to each of target gene copy numbers of 1 to N (where N is a natural number).
  • copy number module 236 in FIG. 2 may fit the Gaussian mixture model to the ACt values.
  • N may be any suitable copy number for the target gene.
  • the target gene is the SMN1 gene
  • the target gene copy numbers may be 1, 2, and 3 respectively.
  • the target gene copy number may include copy numbers of 4 or more.
  • the Gaussian mixture model may be fitted to the non-zero copy numbers (i.e., copy numbers of 1, 2, and 3).
  • genomic DNA samples having a copy number of 0 may be correlated to ACt values that are outside of ACt value ranges modeled by the Gaussian mixture model.
  • the Gaussian mixture model may be fitted to the calculated ACt values according to any suitable methodology, without limitation.
  • the Gaussian mixture model may be developed as follows. Letting Xt (T) be the amount of target gene amplification product detected at qPCR cycle t, a deterministic model for Xt (T) is represented according to Equation (1):
  • d (T) is a factor modeling the detectability of the target amplification product by the qPCR assay
  • the model differs only in that the copy number is known (assuming two copies of the reference sequence in a diploid genome for the RNase P, TERT, and ALB genes), and Xt (R) (the amount of reference gene amplification product detected at qPCR cycle t) and the detectability factor for the reference amplification product are represented according to Equation (2): [0051] Letting A be the threshold amount of amplification product at the critical detection threshold, then Ct (T) for the target gene sequence and for the reference gene sequence are respectively represented according to Equations (3) and (4):
  • the Gaussian distribution model is fitted to the ACt values for each of the sample wells of an assay plate as follows.
  • Ai is the ACt value for well i
  • Zi is the sample copy number for well i
  • ⁇ ⁇ is the mean ACt value for a z-copy cluster (z being, for example, 1, 2, or 3 for the SMN1 gene)
  • ⁇ is the precision (variance -1 ) of the Gaussian clusters.
  • ACt values for samples with the same copy number are assumed to have a Gaussian distribution with variance T 1 around the mean value given by the deterministic model.
  • a ID Gaussian mixture model having three clusters corresponding to copy numbers 1, 2, and 3 is fitted to the ACt values.
  • the mean of the 1-copy cluster is parameter ⁇ .
  • the means of the 2- and 3-copy clusters ( ⁇ ⁇ ) are determined by the PCR efficiency parameter a according to Equation (6):
  • each genomic DNA sample may be included in 4 sample wells of an assay plate, with 2 replicate wells for each of 2 different reference genes (e.g., RNase P, TERT, and/or ALB reference genes).
  • the calculated mean ⁇ ⁇ values for each of the 1-, 2-, and 3-copy clusters may used in conjunction with the calculated precision x value to determine ranges of ACt values corresponding to each copy number of 1, 2, and 3 for the target gene.
  • the Gaussian mixture model may be determined separately for each individual qPCR assay plate subjected to the qPCR to ensure results are not skewed due to variations between qPCR assay runs.
  • the calculated mean ⁇ ⁇ values for each of the 1-, 2, and 3-copy clusters may each be determined even when the corresponding assay plate does not include genomic DNA samples having one or two of the modeled copy numbers.
  • an assay plate may include genomic DNA samples having 2 copies of the SMN1 gene, but may not include genomic DNA samples (aside from the corresponding copy control samples) having only 1 copy of the SMN1 gene and/or may not include any samples having 3 copies of the SMN1 gene.
  • the mean ⁇ ⁇ of each of the 1- and 3- copy clusters may also be determined based on the mean ⁇ 2 of the 2-copy cluster.
  • one or more of the systems described herein may generate a modified Gaussian mixture model by performing an iterative expectation-maximization routine on the Gaussian mixture model to further fit the Gaussian mixture model to the delta cycle threshold values for the plurality of wells and correlate separate modified ranges of delta cycle threshold values to each of the target gene copy numbers of 1 to N.
  • copy number module 236 in FIG. 2 may generate the modified Gaussian mixture model.
  • the iterative expectation-maximization routine may be performed on the Gaussian mixture model to generate the modified Gaussian mixture model according to any suitable methodology, without limitation.
  • the iterative expectation-maximization routine may be performed on the Gaussian mixture model as follows.
  • the full-data likelihood L is a product of Gaussian densities according to Equation
  • Equation (9) The full data log-likelihood L of ⁇ is represented according to Equation (9):
  • Equation (12)
  • Equation (13) For the M-step of a, the full data log-likelihood L of a is represented according to Equation (13):
  • Equation (14) The expectation E with respect to the cluster labels Zi is represented according to Equation (14):
  • the Gaussian mixture model may then be updated with the updated mean ⁇ ' value for the 1-copy cluster and PCR efficiency parameter a' to generate the modified Gaussian mixture model having updated mean ⁇ ' value for the 1-copy cluster and mean ⁇ ⁇ ' values for each of the 2- and 3- copy clusters according to the relationship between means ⁇ ⁇ and PCR efficiency parameter a shown in Equation (6) above.
  • Separate modified ranges of ACt values may then be correlated to each of the target gene copy numbers of 1, 2, and 3 based on the modified Gaussian mixture model (see, e.g., FIG. 7).
  • the modified ranges of ACt values based on the modified Gaussian mixture model may be more precisely correlated to the respective target gene copy numbers of 1, 2, and 3 than the ranges of ACt values based on the Gaussian mixture model prior to performing the EM routine.
  • the copy number of the genomic DNA samples may therefore be more accurately and consistently identified based on the modified Gaussian mixture model.
  • one or more of the systems described herein may determine the target gene copy number in each of the plurality of genomic DNA samples based on the modified Gaussian mixture model.
  • copy number module 236 in FIG. 2 may determine the target gene copy number in each of the plurality of genomic DNA samples based on the modified Gaussian mixture model.
  • the ACt value for each well of the assay plate may be determined to fall within one of the modified ranges of ACt values determined in accordance with the modified Gaussian mixture model.
  • a threshold number of replicates for each genomic DNA sample must have a ACt value falling within the same modified range of ACt values in order for the genomic DNA sample to be determined to have a particular copy number.
  • At least 3 of 4 replicates for a particular genomic DNA sample must fall within the same modified range of ACt values corresponding to the same target copy number in order for the genomic DNA sample to be identified as having the target copy number.
  • all of the replicates (e.g., 4 replicates) for a particular genomic DNA sample must fall within the same modified range of ACt values corresponding to the same target copy number in order for the genomic DNA sample to be identified as having the target copy number. If a genomic DNA sample is not conclusively determined to have a particular copy number, then the genomic DNA sample may be subsequently retested.
  • At least one no-call zone may be identified between modified ranges of ACt values determined in accordance with the modified Gaussian mixture model. If one or more replicates of a genomic DNA sample have ACt values falling within a defined no-call zone, then the target copy number for genomic DNA sample may be determined to be inconclusive and the genomic DNA sample may be subsequently retested.
  • genomic DNA samples that are subject to retesting may be retested with reference probe assays that target one or more reference genes that differ from reference genes used in the initial testing assay. For example, a genomic DNA sample that is determined to have an inconclusive target copy number following an initial qPCR testing procedure using RNaseP and TERT reference assays may be retested using an ALB reference assay.
  • the sample may be determined to have a copy number of zero for the target gene.
  • a genomic DNA sample having ACt values that are larger than any of the modified ranges of ACt values may be determined to have no copies of the target gene.
  • each assay plate and sample replicate may be subject to various quality control criteria. If at least a threshold number of control wells of an assay plate fail one or more quality control criteria, the results of the assay plate may be disregarded and the genomic DNA samples in the assay plate may be subject to retesting on a new assay plate. In contrast, if at least a threshold number of replicates of a particular genomic DNA sample fail one or more quality control criteria, then the results for the particular genomic DNA sample may be deemed inconclusive and the genomic DNA sample may be retested on a new assay plate.
  • a genomic DNA sample or a control DNA sample having 1 or more copies of the target gene may fail quality acceptability criteria if a target Ct value or a reference Ct value for a well is greater than or equal to a specified threshold (e.g., 32.0), if a ACt value for a well is less not within a specified range (e.g., a range of -3.5 to 2.0), or if the copy number result is "no call" (e.g., the ACt value for the well is not within a modified range of ACt values determined by the modified Gaussian mixture model).
  • a specified threshold e.g. 32.0
  • a ACt value for a well is less not within a specified range (e.g., a range of -3.5 to 2.0)
  • the copy number result is "no call" (e.g., the ACt value for the well is not within a modified range of ACt values determined by the modified Gaussian mixture model).
  • a genomic DNA sample or a control DNA sample having zero copies of the target gene may fail quality acceptability criteria if a target Ct value for a well is less than a specified threshold (e.g., 32.0), a reference Ct value for a well is greater than or equal to a specified threshold (e.g., 32.0), or if the copy number result is "no call" (e.g., the ACt value for the well is not within a range specified for a copy number of zero).
  • a specified threshold e.g., 32.0
  • a reference Ct value for a well is greater than or equal to a specified threshold (e.g., 32.0)
  • the copy number result e.g., the ACt value for the well is not within a range specified for a copy number of zero.
  • An NTC sample may fail quality acceptability criteria if a target Ct value or a reference Ct value for a well is less than a specified threshold (e.g., 32.0) or if the copy number result is not "no call.” Additionally, a control DNA sample having a known target gene copy number may fail quality acceptability criteria if one or more replicates have calculated ACt values that do not fall within the appropriate modified range of ACt values corresponding to the appropriate copy number as determined by the modified Gaussian mixture model.
  • a specified threshold e.g. 32.0
  • FIG. 4 is a block diagram of an example computing system 410 capable of implementing at least a portion of one or more of the embodiments described and/or illustrated herein.
  • computing system 410 may perform and/or be a means for performing, either alone or in combination with other elements, one or more of the steps described herein (such as one or more of the steps illustrated in FIG. 3).
  • All or a portion of computing system 410 may also perform and/or be a means for performing any other steps, methods, or processes described and/or illustrated herein.
  • Computing system 410 broadly represents any single or multi-processor computing device or system capable of executing computer-readable instructions. Examples of computing system 410 include, without limitation, workstations, laptops, client-side terminals, servers, distributed computing systems, handheld devices, or any other computing system or device. In its most basic configuration, computing system 410 may include at least one processor 414 and a system memory 416.
  • Processor 414 generally represents any type or form of physical processing unit (e.g., a hardware-implemented central processing unit) capable of processing data or interpreting and executing instructions.
  • processor 414 may receive instructions from a software application or module. These instructions may cause processor 414 to perform the functions of one or more of the example embodiments described and/or illustrated herein.
  • System memory 416 generally represents any type or form of volatile or non-volatile storage device or medium capable of storing data and/or other computer-readable instructions. Examples of system memory 416 include, without limitation, Random Access Memory (RAM), Read Only Memory (ROM), flash memory, or any other suitable memory device. Although not required, in certain embodiments computing system 410 may include both a volatile memory unit (such as, for example, system memory 416) and a non-volatile storage device (such as, for example, primary storage device 432, as described in detail below). In one example, one or more of modules 232 from FIG. 2 may be loaded into system memory 416. [0069] In some examples, system memory 416 may store and/or load an operating system 440 for execution by processor 414.
  • RAM Random Access Memory
  • ROM Read Only Memory
  • flash memory or any other suitable memory device.
  • computing system 410 may include both a volatile memory unit (such as, for example, system memory 416) and a non-volatile storage device (such as, for example, primary storage device 432
  • operating system 440 may include and/or represent software that manages computer hardware and software resources and/or provides common services to computer programs and/or applications on computing system 410.
  • Examples of operating system 440 include, without limitation, LINUX, JUNOS, MICROSOFT WINDOWS, WINDOWS MOBILE, MAC OS, APPLE'S IOS, UNIX, GOOGLE CHROME OS, GOOGLE'S ANDROID, SOLARIS, variations of one or more of the same, and/or any other suitable operating system.
  • example computing system 410 may also include one or more components or elements in addition to processor 414 and system memory 416.
  • computing system 410 may include a memory controller 418, an Input/Output (I/O) controller 420, and a communication interface 422, each of which may be interconnected via a communication infrastructure 412.
  • Communication infrastructure 412 generally represents any type or form of infrastructure capable of facilitating communication between one or more components of a computing device. Examples of communication infrastructure 412 include, without limitation, a communication bus (such as an Industry Standard Architecture (ISA), Peripheral Component Interconnect (PCI), PCI Express (PCIe), or similar bus) and a network.
  • ISA Industry Standard Architecture
  • PCI Peripheral Component Interconnect
  • PCIe PCI Express
  • Memory controller 418 generally represents any type or form of device capable of handling memory or data or controlling communication between one or more components of computing system 410.
  • memory controller 418 may control communication between processor 414, system memory 416, and I/O controller 420 via communication infrastructure 412.
  • I/O controller 420 generally represents any type or form of module capable of coordinating and/or controlling the input and output functions of a computing device.
  • I/O controller 420 may control or facilitate transfer of data between one or more elements of computing system 410, such as processor 414, system memory 416, communication interface 422, display adapter 426, input interface 430, and storage interface 434.
  • computing system 410 may also include at least one display device 424 coupled to I/O controller 420 via a display adapter 426.
  • Display device 424 generally represents any type or form of device capable of visually displaying information forwarded by display adapter 426.
  • display adapter 426 generally represents any type or form of device configured to forward graphics, text, and other data from communication infrastructure 412 (or from a frame buffer, as known in the art) for display on display device 424.
  • example computing system 410 may also include at least one input device 428 coupled to I/O controller 420 via an input interface 430.
  • Input device 428 generally represents any type or form of input device capable of providing input, either computer or human generated, to example computing system 410. Examples of input device 428 include, without limitation, a keyboard, a pointing device, a speech recognition device, variations or combinations of one or more of the same, and/or any other input device.
  • example computing system 410 may include additional I/O devices.
  • example computing system 410 may include I/O device 436.
  • I/O device 436 may include and/or represent a user interface that facilitates human interaction with computing system 410.
  • Examples of I/O device 436 include, without limitation, a computer mouse, a keyboard, a monitor, a printer, a modem, a camera, a scanner, a microphone, a touchscreen device, variations or combinations of one or more of the same, and/or any other I/O device.
  • Communication interface 422 broadly represents any type or form of communication device or adapter capable of facilitating communication between example computing system 410 and one or more additional devices.
  • communication interface 422 may facilitate communication between computing system 410 and a private or public network including additional computing systems.
  • Examples of communication interface 422 include, without limitation, a wired network interface (such as a network interface card), a wireless network interface (such as a wireless network interface card), a modem, and any other suitable interface.
  • communication interface 422 may provide a direct connection to a remote server via a direct link to a network, such as the Internet.
  • Communication interface 422 may also indirectly provide such a connection through, for example, a local area network (such as an Ethernet network), a personal area network, a telephone or cable network, a cellular telephone connection, a satellite data connection, or any other suitable connection.
  • communication interface 422 may also represent a host adapter configured to facilitate communication between computing system 410 and one or more additional network or storage devices via an external bus or communications channel.
  • host adapters include, without limitation, Small Computer System Interface (SCSI) host adapters, Universal Serial Bus (USB) host adapters, Institute of Electrical and Electronics Engineers (IEEE) 1394 host adapters, Advanced Technology Attachment (ATA), Parallel ATA (PATA), Serial ATA (SAT A), and External SATA (eSATA) host adapters, Fibre Channel interface adapters, Ethernet adapters, or the like.
  • Communication interface 422 may also allow computing system 410 to engage in distributed or remote computing. For example, communication interface 422 may receive instructions from a remote device or send instructions to a remote device for execution.
  • system memory 416 may store and/or load a network communication program 438 for execution by processor 414.
  • network communication program 438 may include and/or represent software that enables computing system 410 to establish a network connection 442 with another computing system (not illustrated in FIG. 4) and/or communicate with the other computing system by way of communication interface 422.
  • network communication program 438 may direct the flow of outgoing traffic that is sent to the other computing system via network connection 442. Additionally or alternatively, network communication program 438 may direct the processing of incoming traffic that is received from the other computing system via network connection 442 in connection with processor 414.
  • network communication program 438 may alternatively be stored and/or loaded in communication interface 422.
  • network communication program 438 may include and/or represent at least a portion of software and/or firmware that is executed by a processor and/or Application Specific Integrated Circuit (ASIC) incorporated in communication interface 422.
  • ASIC Application Specific Integrated Circuit
  • example computing system 410 may also include a primary storage device 432 and a backup storage device 433 coupled to communication infrastructure 412 via a storage interface 434.
  • Storage devices 432 and 433 generally represent any type or form of storage device or medium capable of storing data and/or other computer- readable instructions.
  • storage devices 432 and 433 may be a magnetic disk drive (e.g., a so-called hard drive), a solid state drive, a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash drive, or the like.
  • Storage interface 434 generally represents any type or form of interface or device for transferring data between storage devices 432 and 433 and other components of computing system 410.
  • storage devices 432 and 433 may be configured to read from and/or write to a removable storage unit configured to store computer software, data, or other computer-readable information.
  • suitable removable storage units include, without limitation, a floppy disk, a magnetic tape, an optical disk, a flash memory device, or the like.
  • Storage devices 432 and 433 may also include other similar structures or devices for allowing computer software, data, or other computer-readable instructions to be loaded into computing system 410.
  • storage devices 432 and 433 may be configured to read and write software, data, or other computer-readable information.
  • Storage devices 432 and 433 may also be a part of computing system 410 or may be a separate device accessed through other interface systems.
  • computing system 410 may be connected to many other devices or subsystems. Conversely, all of the components and devices illustrated in FIG. 4 need not be present to practice the embodiments described and/or illustrated herein.
  • the devices and subsystems referenced above may also be interconnected in different ways from that shown in FIG. 4.
  • Computing system 410 may also employ any number of software, firmware, and/or hardware configurations.
  • one or more of the example embodiments disclosed herein may be encoded as a computer program (also referred to as computer software, software applications, computer-readable instructions, or computer control logic) on a computer- readable medium.
  • the term "computer-readable medium,” as used herein, generally refers to any form of device, carrier, or medium capable of storing or carrying computer-readable instructions.
  • Examples of computer-readable media include, without limitation, transmission- type media, such as carrier waves, and non-transitory-type media, such as magnetic-storage media (e.g., hard disk drives, tape drives, and floppy disks), optical-storage media (e.g., Compact Disks (CDs), Digital Video Disks (DVDs), and BLU-RAY disks), electronic-storage media (e.g., solid-state drives and flash media), and other distribution systems.
  • transmission- type media such as carrier waves
  • non-transitory-type media such as magnetic-storage media (e.g., hard disk drives, tape drives, and floppy disks), optical-storage media (e.g., Compact Disks (CDs), Digital Video Disks (DVDs), and BLU-RAY disks), electronic-storage media (e.g., solid-state drives and flash media), and other distribution systems.
  • transmission- type media such as carrier waves
  • non-transitory-type media such as magnetic-stor
  • the computer-readable medium containing the computer program may be loaded into computing system 410. All or a portion of the computer program stored on the computer-readable medium may then be stored in system memory 416 and/or various portions of storage devices 432 and 433.
  • a computer program loaded into computing system 410 may cause processor 414 to perform and/or be a means for performing the functions of one or more of the example embodiments described and/or illustrated herein. Additionally or alternatively, one or more of the example embodiments described and/or illustrated herein may be implemented in firmware and/or hardware.
  • computing system 410 may be configured as an Application Specific Integrated Circuit (ASIC) adapted to implement one or more of the example embodiments disclosed herein.
  • ASIC Application Specific Integrated Circuit
  • Two qPCR assays were run using a real-time PCR system on genomic DNA samples from different individuals in wells of a 384-well assay plate.
  • One of the two qPCR assays utilized a TAQMAN RNase P Copy Number Reference Assay Mix (Applied Biosystems, Foster City, Calif.) that detects Ribonuclease P (RNase P) RNA component HI (HIRNA) gene (RPPHl) on chromosome 14, cytoband 14ql 1.2 as a first reference assay.
  • the other qPCR assay utilized a TAQMAN TERT Copy Number Reference Assay Mix (Applied Biosystems) that targets the telomerase reverse transcriptase (TERT) gene located on chromosome 5, cytoband 5pl5.33 as a second reference assay.
  • An ALB reference assay that targets the albumin (ALB) gene located on chromosome 4, cytoband 4ql3.3 was available as a third reference probe for follow-up qPCR assays to determine the SMN1 copy number for any of the genomic DNA samples in the event either of the initial two qPCR assays resulted in conflicting or inconclusive copy number calls.
  • Genomic DNA samples were extracted from EDTA-blood or Oragene- saliva.
  • AN SMN1 Copy Number Assay Mix for determining SMN1 copy number for the SMN1 gene was prepared with the reagents shown in Table 2 in a DNA suspension buffer.
  • AN RNase P Master Mix was prepared with a TAQMAN Master Mix (TAQMAN Genotyping Master Mix or TAQMAN GTXpress Master Mix from Applied Biosystems) according to the composition shown in Table 3: Table 3: RNase P Master Mix
  • a TERT Master Mix was prepared according to the composition shown in Table 4:
  • DNA samples having known SMN1 copy numbers were used.
  • Four positive (two heterozygote 1-copy, two homozygote 0-copy) and four negative (wildtype 2- and 3-copy) SMN1 controls were present on each assay plate run.
  • Two no-template (PCR reagent) controls were present on each plate run to confirm the absence of background contamination.
  • FIG. 5 shows the plate layout used for Example 1. As shown in FIG.
  • the assay plate included replicate wells of samples S01 through S86 with the RNase P (RP) master mix and TERT (TT) master mix as reference assays. Additionally, the assay plate included replicate wells of 0-copy controls (0CC), 1-copy controls (ICC), 2-copy controls (2CC), 3-copy controls (3CC), and no-template controls (NTC) with the RNase P (RP) master mix and TERT (TT) master mix as reference assays.
  • the assay plate was loaded into an ABI ViiA 7 RT-PCR instrument (Counsyl, San Francisco, Calif.) for qPCR processing.
  • the qPCR processing included a hold stage including one cycle of 10 minutes at 95°C and a PCR stage including 40 cycles of 15 second at 95°C and one minute at 60°C. Amplification measurements of fluorescence from each of the wells were taken during each cycle of the PCR stage. Delta reporter values (ARn) for each of the assay plate wells was calculated by normalizing fluorescent signals from the SMN1 probes, the RNase P reference probes, and the TERT reference probes to a fluorescent signal from a passive reference dye (e.g., ROX dye, etc.) and subtracting a baseline signal.
  • a passive reference dye e.g., ROX dye, etc.
  • FIG. 6 shows an amplification plot of ARn values for a sample well in the assay plate plotted on a log scale Y-axis at each of 40 cycles for the SMN1 assay (SMN1 amplification curve 602), RNase P assay (RNase P amplification curve 604), and TERT assay (TERT amplification curve 606).
  • SMN1 amplification curve 602 shows the SMN1 assay
  • RNase P assay RNase P amplification curve 604
  • TERT assay TERT assay
  • a Ct threshold line 610 passing through the linear phase of RNase P amplification curve 604 indicates the location of the Ct value for the RNase P assay
  • a Ct threshold line 612 passing through the linear phase of TERT amplification curve 606 indicates the location of the Ct value for the RNase P assay.
  • Ct values determined for each of the SMN1, RNase P, and TERT assays were used to calculate ACt values for each of the assay plate wells. For each of the wells in the odd- numbered columns of the 384-well optical PCR plate, which contained the SMN1 Assay Mix and the RNase P Copy Number Reference Assay Mix, ACt values were calculated as the difference between the Ct value for the SMN1 assay and the Ct value for the RNase P reference assay.
  • ACt values were calculated as the difference between the Ct value for the SMN1 assay and the Ct value for the TERT reference assay.
  • FIG. 7 is a plot showing ACt values for a plurality of the samples and controls tested in the 384-well optical PCR plate based on the modified Gaussian mixture model.
  • ACt range 702 corresponds to a copy number of 1
  • ACt range 704 corresponds to a copy number of 2
  • ACt range 706 corresponds to a copy number of 3.
  • Values outside ranges 702, 704, and 706 were designated as no-call / zero copy number represented at position 708.
  • FIG. 7 also shows a no-call range 710 between the 1- and 2-copy regions.
  • samples S74, S76, S78, S79, S80, S81, S82, S83, S84, S85, and S86 and each of the 2-copy control (2CC) samples correspond to genomic DNA samples having an SMNl copy number of 2.
  • Samples S07, S35, S44, S54, S57, and S67 and each of the 3-copy control (3CC) samples correspond to genomic DNA samples having an SMNl copy number of 3.
  • Sample S08 corresponds to a genomic DNA sample that resulted in an inconclusive result due to a disparity in the replicate results (two of the replicates for sample S08 had ACt values within ACt range 702 and two replicates had ACt values within ACt range 704).
  • Sample S08 was flagged for further testing and analysis using another reference assay, such as an ALB reference assay.
  • the no-copy control (NTC) samples correspond to no-template controls having no DNA present in the respective sample wells. The accuracy of all called copy numbers for the assayed samples was determined to be 100%.
  • Sequence Listing for the application.
  • the Sequence Listing is disclosed on a computer-readable ASCII text file title "sequence.txt", created on Jan. 19, 2017.
  • the sequence.txt file is 1 kb in size.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Genetics & Genomics (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Biophysics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Biotechnology (AREA)
  • Evolutionary Biology (AREA)
  • Theoretical Computer Science (AREA)
  • Molecular Biology (AREA)
  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Analytical Chemistry (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Databases & Information Systems (AREA)
  • Bioethics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • Epidemiology (AREA)
  • Evolutionary Computation (AREA)
  • Public Health (AREA)
  • Software Systems (AREA)
  • Apparatus Associated With Microorganisms And Enzymes (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

Cette invention concerne un procédé de détermination quantitative du nombre de copies d'un gène d'intérêt dans un échantillon d'ADN génomique pouvant comprendre (i) la détermination d'une valeur de seuil de cycle pour un gène cible à nombre de copies inconnu et d'une valeur de seuil de cycle pour un gène de référence dans chaque puits d'une pluralité de puits d'une plaque de dosage, (ii) le calcul d'une valeur de seuil de cycle delta pour chaque puits de ladite pluralité de puits, (iii) l'ajustement d'un modèle de mélange de Gaussiennes aux valeurs de seuils de cycle delta, (iv) la génération d'un modèle de mélange de Gaussiennes modifié par exécution d'une routine d'espérance-maximisation itérative sur le modèle de mélange de Gaussiennes, et (v) la détermination du nombre de copies du gène cible dans chaque échantillon de la pluralité d'échantillons d'ADN génomiques en fonction du modèle de mélange de Gaussiennes modifié. Divers autres procédés et systèmes sont en outre décrits.
PCT/US2018/014163 2017-01-31 2018-01-18 Systèmes et procédés de détermination quantitative du nombre de copies d'un gène WO2018144228A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201762452974P 2017-01-31 2017-01-31
US62/452,974 2017-01-31

Publications (1)

Publication Number Publication Date
WO2018144228A1 true WO2018144228A1 (fr) 2018-08-09

Family

ID=63041027

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2018/014163 WO2018144228A1 (fr) 2017-01-31 2018-01-18 Systèmes et procédés de détermination quantitative du nombre de copies d'un gène

Country Status (1)

Country Link
WO (1) WO2018144228A1 (fr)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112048548A (zh) * 2019-06-06 2020-12-08 北京阅微基因技术有限公司 以smnp作为对照检测smn基因拷贝数的方法
WO2021045947A1 (fr) * 2019-09-05 2021-03-11 Illumina, Inc. Procédés et systèmes de diagnostic à partir de données de séquençage du génome entier
CN113378116A (zh) * 2021-05-26 2021-09-10 中国人民解放军陆军航空兵学院 一种航空发动机载荷谱载荷等级临界值确定方法
CN116246694A (zh) * 2023-03-24 2023-06-09 苏州国科芯感医疗科技有限公司 一种实时数字pcr定量确定方法及装置
WO2023179053A1 (fr) * 2022-03-22 2023-09-28 上海润达榕嘉生物科技有限公司 Procédé de détection du nombre de copies d'un gène cible

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100070185A1 (en) * 2008-09-12 2010-03-18 Roche Molecular Systems, Inc. Real-time pcr elbow calling by equation-less algorithm
US8557525B1 (en) * 2011-02-25 2013-10-15 Celera Corporation Composite metastasis score with weighted coefficients for predicting breast cancer metastasis, and uses thereof
US20160041153A1 (en) * 2008-11-12 2016-02-11 Kirk Brown Biomarker compositions and markers
US20160342733A1 (en) * 2015-05-18 2016-11-24 Regeneron Pharmaceuticals, Inc. Methods And Systems For Copy Number Variant Detection
US20170372002A1 (en) * 2016-06-23 2017-12-28 Canon U.S. Life Sciences, Inc. System and method for melting curve normalization

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100070185A1 (en) * 2008-09-12 2010-03-18 Roche Molecular Systems, Inc. Real-time pcr elbow calling by equation-less algorithm
US20160041153A1 (en) * 2008-11-12 2016-02-11 Kirk Brown Biomarker compositions and markers
US8557525B1 (en) * 2011-02-25 2013-10-15 Celera Corporation Composite metastasis score with weighted coefficients for predicting breast cancer metastasis, and uses thereof
US20160342733A1 (en) * 2015-05-18 2016-11-24 Regeneron Pharmaceuticals, Inc. Methods And Systems For Copy Number Variant Detection
US20170372002A1 (en) * 2016-06-23 2017-12-28 Canon U.S. Life Sciences, Inc. System and method for melting curve normalization

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
ALIYU ET AL.: "Copy Number Variation in Transcriptionally Active Regions of Sexual and Apomictic Boechera Demonstrates Independently Derived Apomictic Lineages", THE PLANT CELL, vol. 25, 31 October 2013 (2013-10-31), pages 3808 - 3823, XP055531637 *
KUMASAKA ET AL.: "PlatinumCNV: a Bayesian Gaussian mixture model for genotyping copy number polymorphisms using SNP array signal intensity data", GENETIC EPIDEMIOLOGY, vol. 35, no. 8, 31 December 2011 (2011-12-31), pages 831 - 844, XP055531630 *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112048548A (zh) * 2019-06-06 2020-12-08 北京阅微基因技术有限公司 以smnp作为对照检测smn基因拷贝数的方法
WO2020244482A1 (fr) * 2019-06-06 2020-12-10 北京阅微基因技术有限公司 Procédé de détection d'un nombre de copies de gène smn à l'aide de smnp en tant que référence
WO2021045947A1 (fr) * 2019-09-05 2021-03-11 Illumina, Inc. Procédés et systèmes de diagnostic à partir de données de séquençage du génome entier
CN113228192A (zh) * 2019-09-05 2021-08-06 因美纳有限公司 用于从全基因组测序数据进行诊断的方法和系统
CN113378116A (zh) * 2021-05-26 2021-09-10 中国人民解放军陆军航空兵学院 一种航空发动机载荷谱载荷等级临界值确定方法
CN113378116B (zh) * 2021-05-26 2023-12-08 中国人民解放军陆军航空兵学院 一种航空发动机载荷谱载荷等级临界值确定方法
WO2023179053A1 (fr) * 2022-03-22 2023-09-28 上海润达榕嘉生物科技有限公司 Procédé de détection du nombre de copies d'un gène cible
CN116246694A (zh) * 2023-03-24 2023-06-09 苏州国科芯感医疗科技有限公司 一种实时数字pcr定量确定方法及装置
CN116246694B (zh) * 2023-03-24 2023-10-27 苏州国科芯感医疗科技有限公司 一种实时数字pcr定量确定方法及装置

Similar Documents

Publication Publication Date Title
AU2022205239B2 (en) Chromosome representation determinations
Liu et al. Interrogating the “unsequenceable” genomic trinucleotide repeat disorders by long-read sequencing
US20210158898A1 (en) Methods and processes for non-invasive assessment of genetic variations
WO2018144228A1 (fr) Systèmes et procédés de détermination quantitative du nombre de copies d'un gène
US9361426B2 (en) Copy number analysis of genetic locus
KR102540202B1 (ko) 유전적 변이의 비침습 평가를 위한 방법 및 프로세스
Luquette et al. Single-cell genome sequencing of human neurons identifies somatic point mutation and indel enrichment in regulatory elements
CN110383385B (zh) 从肿瘤样品中检测突变负荷的方法
US20190066842A1 (en) A novel algorithm for smn1 and smn2 copy number analysis using coverage depth data from next generation sequencing
Justino et al. Comprehensive massive parallel DNA sequencing strategy for the genetic diagnosis of the neuro-cardio-facio-cutaneous syndromes
US20180300450A1 (en) Systems and methods for performing and optimizing performance of dna-based noninvasive prenatal screens
Jiang et al. Identification of thalassemia gene cluster deletion by long‐read whole‐genome sequencing (LR‐WGS)
Jo et al. Distant regulatory effects of genetic variation in multiple human tissues
Kang et al. An advanced model to precisely estimate the cell-free fetal DNA concentration in maternal plasma
Yu et al. Quantitative real-time polymerase chain reaction for the verification of genomic imbalances detected by microarray-based comparative genomic hybridization
US11001880B2 (en) Development of SNP islands and application of SNP islands in genomic analysis
Barcia et al. Improving post-natal detection of mitochondrial DNA mutations
Geeleher et al. Cancer eQTLs can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18747345

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 18747345

Country of ref document: EP

Kind code of ref document: A1