Diagnostic chromosome marker

Abstract

To provide a process for identifying a prognosis for autism spectrum disorder (ASD).SOLUTION: A process for identifying a prognosis for autism spectrum disorder (ASD) in an individual, the process comprising detecting the presence or absence of one or more chromosomal interactions in a sample derived from the individual, wherein the chromosomal interactions include, for example, a chromosomal interaction on chromosome 7 formed by a first region at chromosomal position 106136913-106136942 and a second region at chromosomal position 106155637-106155666, the presence of which is associated with severe ASD.SELECTED DRAWING: None

Description

Technical Field

[0001] The present invention relates to disease markers.

Background Art

[0002] Autism spectrum disorder (ASD) is thought to be associated with a combination of genetic and environmental factors. Risk factors include certain infectious diseases, toxins, autoimmune diseases, cocaine, and air pollution. Worldwide, autism is estimated to affect 24.8 million people (estimated in 2015). In developed countries, nearly 1.5% of children are diagnosed with ASD (estimated in 2017). This rate has increased significantly from the estimated 0.7% in 2000.

Summary of the Invention

[0003] The present invention is based on the finding that disseminating individual chromosomal conformations that are specific for autism spectrum disorder (ASD) and various forms of ASD can be identified by measuring global significant differences in 3D genome architecture detected in proxy whole-body profiling using chromosomal conformation signatures.

[0004] Therefore, the present invention is a process for detecting chromosomal states representing subgroups within a population, comprising the step of determining whether chromosomal interactions associated with those chromosomal states are present or absent within defined regions of the genome; and - The chromosomal interactions have been identified, optionally, by a method for determining which chromosomal interactions are associated with chromosomal states corresponding to subgroups of the population, the method comprising contacting a first set of nucleic acids from subgroups having different states of a chromosome with a second set of index nucleic acids, and enabling hybridization of complementary sequences, wherein the nucleic acids in the first and second sets of nucleic acids represent ligated products containing sequences from both chromosomal regions that have come together in a chromosomal interaction, and the pattern of hybridization between the first set and the second set of nucleic acids enables determination of which chromosomal interactions are specific to that subgroup; and - The subgroup is associated with the prognosis of autism spectrum disorder (ASD), and the chromosomal interaction is (i) present in any of the regions or genes listed in any of Tables 1, 2, 3 or 4, and / or (ii) corresponding to any of the chromosomal interactions represented by any of the probes shown in any of Tables 1, 2, 3 or 4, and / or (iii) present in a 4,000 base region that includes or is adjacent to (i) or (ii), provides a process.

[0005] Furthermore, the present invention provides a process for identifying the prognosis of ASD, comprising identifying whether a chromosomal interaction as represented in any of Tables 1, 2, 3 or 4 is present or absent, thereby determining the prognosis. In one aspect, the present invention provides a process for identifying the prognosis of ASD, comprising identifying whether a chromosomal interaction as represented in any of Tables 8, 9 or 10 is present or absent, thereby determining the prognosis. The present invention also provides a process for identifying the prognosis of ASD comprising identifying whether a chromosomal interaction as represented by any of the tables described herein is present or absent, thereby determining the prognosis.

Brief Description of the Drawings

[0006]

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Mode for Carrying Out the Invention

[0007] Embodiments of the present invention The present invention relates to the determination of the prognosis of ASD, including the severity and / or type of ASD, and whether it is aggressive or indolent. This determination is made herein, for example, by classifying any of the associated markers disclosed in any of the tables, or preferred combinations of markers, or markers in defined specific regions disclosed herein. Thus, the present invention relates to a method of classifying an individual to determine the state of ASD, for example, to diagnose ASD or its type, or to determine the prognosis of ASD or its type.

[0008] Essentially, in the process of the present invention, subpopulations of ASD can be identified by the classification of markers. Thus, the present invention relates to, for example, a panel of epigenetic markers associated with prognostic ASD. Therefore, the present invention enables the provision of individualized treatment to patients that accurately reflects their needs. Any treatment mentioned herein, such as a drug, can be administered to an individual based on the results of the classification. Thus, the process of the present invention can be carried out to select an individual for treatment.

[0009] Preferably, the markers classified in the process are those represented by the probe or primer sequences of the table.

[0010] Process of the invention The process of the present invention includes a classification system for detecting chromosome interactions related to prognosis. This classification can be carried out using the EpiSwitch™ system referred to herein, which is based on the cross-linked regions of chromosomes that are joined together by chromosome interactions, cleaving the chromosomal DNA and then ligating the nucleic acids present in the cross-linked entities to derive ligated nucleic acids having sequences from both regions that formed the chromosome interaction. Detection of this ligated nucleic acid enables determination of the presence or absence of specific chromosome interactions.

[0011] Chromosomal interactions can be identified using the above-described methods in which populations of first and second nucleic acids are used. These nucleic acids can also be generated using the technology of EpiSwitch™.

[0012] Epigenetic interactions related to the present invention As used herein, the terms “epigenetic” and “chromosomal” interactions typically mean interactions between distal regions of chromosomes, and such interactions are dynamic and change, form, or are disrupted depending on the state of the chromosomal regions.

[0013] In certain processes of the invention, chromosomal interactions are typically detected by generating ligated nucleic acids that contain sequences from both regions of the chromosome that are part of the interaction. In such processes, the regions can be cross-linked by any suitable means. In a preferred embodiment, the interaction is cross-linked using formaldehyde, although any aldehyde, or D-biotinoyl-e-aminocaproic acid-N-hydroxysuccinimide ester or digoxigenin-3-O-methylcarbonyl-e-aminocaproic acid-N-hydroxysuccinimide ester can also be used for cross-linking. Paraformaldehyde can cross-link DNA strands that are 4 angstroms apart. Preferably, the chromosomal interactions are on the same chromosome and are optionally 2 to 10 angstroms apart.

[0014] Chromosomal interactions can reflect the state of chromosomal regions, for example, when transcriptionally active or repressed in response to a change in physiological state. Chromosomal interactions specific to subgroups as defined herein have been found to be stable and thus provide a reliable means of measuring differences between two subgroups.

[0015] Furthermore, chromosomal interactions specific to characteristics (such as prognosis) usually occur early in biological processes, for example, compared to other epigenetic markers such as changes in methylation or histone protein binding. Therefore, the process of the present invention can detect the early stages of biological processes. This enables earlier intervention (such as treatment), which is more effective as a result. Chromosomal interactions can also be used to evaluate changes in prognosis because they reflect the current state of an individual. Furthermore, there are few changes in related chromosomal interactions among individuals within the same subgroup. Detecting chromosomal interactions is very beneficial as there can be up to 50 different interactions that can be considered for each gene, and thus the process of the present invention can collate 500,000 different interactions.

[0016] There is no one-to-one correspondence between chromosomal interactions and other types of epigenetic markers such as gene markers or methylation. Therefore, chromosomal interactions represent a distinct control modality.

[0017] Preferred marker sets As used herein, the term "marker" or "biomarker" refers to specific chromosomal interactions that can be detected (classified) in the present invention. Specific markers are disclosed herein and any of them can be used in the present invention. Additional sets of markers can be used, for example, in the combinations or numbers disclosed herein. The specific markers disclosed in the tables herein are not only preferred, but also markers present in the genes and regions mentioned in the tables herein. These can be classified by any suitable method, for example, the PCR or probe-based methods disclosed herein, including the qPCR method. Markers are defined herein by their position or by probe and / or primer sequences.

[0018] Location and cause of chromosomal interactions Chromosomal interactions can be overlapping and can include regions of chromosomes that have been shown to encode related genes or genes not yet described, but can equally be present in intergenic regions. It should further be noted that the inventors have discovered that epigenetic interactions in all regions are equally important in determining the state of chromosomal loci. These interactions do not necessarily exist in the coding regions of specific genes located at the loci, but can be present in intergenic regions.

[0019] The chromosomal interactions detected in the present invention can be affected by changes to the underlying DNA sequence due to environmental factors, DNA methylation, non-coding antisense RNA transcripts, non-mutagenic carcinogens, histone modifications, chromatin remodeling, and specific local DNA interactions. Changes leading to chromosomal interactions can be affected by changes to the underlying nucleic acid sequence, which themselves do not directly affect gene products or the mode of gene expression. Such changes can be, for example, SNPs within and / or outside genes, gene fusions and / or intergenic DNA, microRNAs, and deletions of non-coding RNAs. For example, since approximately 20% of SNPs are known to be present in non-coding regions, the described processes are also beneficial in non-coding situations. In one aspect, the regions of the chromosome that come together to form an interaction are less than 5 kb, 3 kb, 1 kb, 500 base pairs, or 200 base pairs apart on the same chromosome.

[0020] The detected chromosomal interactions preferably exist within any of the genes mentioned in any of Tables 1, 2, 3, or 4. However, it can also be upstream or downstream of the gene, for example, up to 50,000, up to 30,000, up to 20,000, up to 10,000, or up to 5000 base pairs upstream or downstream from the gene or coding sequence.

[0021] Subgroups, time points, and individual treatments In one aspect, the present invention determines prognosis. This can be done at one or more defined time points, for example, at least 1, 2, 5, 8 or 10 different time points. The period between at least 1, 2, 5 or 8 time points can be at least 5, 10, 20, 50, 80 or 100 days.

[0022] As used herein, "subgroup" preferably refers to a population subgroup, more preferably a subgroup within a population of a particular animal such as a particular eukaryote, or a mammal (e.g., human, non-human, non-human primate, or rodent such as a mouse or rat). Most preferably, "subgroup" refers to a subgroup within the human population.

[0023] The present invention includes detecting and treating a particular subgroup within a population. The inventors have discovered that chromosomal interactions differ between subsets (e.g., at least two subsets) within a given population. By identifying these differences, a physician can classify a patient as part of one subset of the population, as described in the process. Thus, the present invention provides a physician with a process for personalizing a drug for a patient based on epigenetic chromosomal interactions, such as a drug and / or its dosage and / or its frequency of administration.

[0024] The present invention relates to any particular condition included in the broad definition of ASD. In one aspect, the condition is autism or childhood autism. The condition may be Asperger's syndrome, PDD-NOS (pervasive developmental disorder), or childhood disintegrative disorder. The present invention relates to any PDD-NOS condition including addictive conditions such as addiction to drugs or digital media devices. The ASD EpiSwitch markers reveal epigenetic deregulation and control defects in addiction pathways, neurexin and neuroligin control pathways, estrogen signaling, TH17 differentiation and control of NK cells, Hippo, IL4 and IL13 control, HPV infection and mTOR signaling.

[0025] Generation of ligated nucleic acids Certain aspects of the invention utilize ligated nucleic acids, particularly ligated DNA. These contain sequences from both regions that come together in chromosomal interactions and thus provide information regarding the interactions. The EpiSwitch™ method described herein uses the generation of such ligated nucleic acids to detect chromosomal interactions.

[0026] Thus, the processes of the invention can include the step of generating ligated nucleic acids (e.g., DNA) by the following steps (including methods that include these steps): (i) Crosslinking epigenetic chromosomal interactions present at chromosomal loci, preferably in vitro; (ii) Optionally, isolating the crosslinked DNA from the chromosomal loci; (iii) Subjecting the crosslinked DNA to cleavage by restriction digestion using an enzyme that cleaves at least once, particularly an enzyme that cleaves at least once within the chromosomal loci; (iv) Ligating the crosslinked cleaved DNA ends (particularly to form DNA loops); and (v) Optionally, identifying the presence of the ligated DNA and / or the DNA loops using techniques such as PCR (polymerase chain reaction) to identify the presence of specific chromosomal interactions.

[0027] These steps can be performed to detect chromosomal interactions for any aspect referred to herein. These steps can also be performed to generate the first and / or second set of nucleic acids referred to herein.

[0028] PCR (Polymerase Chain Reaction) can be used to detect or discriminate ligated nucleic acids and can be used, for example, to identify the status of a locus because the size of the generated PCR product can suggest the specific chromosomal interactions present. In a preferred embodiment, at least 1, 2, or 3 primers or primer pairs shown in Table 5 are used in the PCR reaction. In other embodiments, at least 1, 10, 20, 30, 50, or 80 primers or primer pairs shown in Table 1, 2, 3, or 4 are used in the PCR reaction. Those skilled in the art will recognize a number of restriction enzymes that can be used to cleave DNA within the chromosomal locus of interest. It will be apparent that the particular enzyme used depends on the locus being studied and the sequence of the DNA located therein. A non-limiting example of a restriction enzyme that can be used to cleave DNA as described in the present invention is TaqI.

[0029] EpiSwitch™ Technology EpiSwitch™ Technology also relates to the use of microarray EpiSwitch™ marker data in the detection of epigenetic chromosomal conformation signatures specific to a phenotype. There are several advantages to embodiments such as EpiSwitch™ that utilize nucleic acids ligated by the methods described herein. They have, for example, a low level of probabilistic noise because nucleic acid sequences from a first set of nucleic acids of the invention hybridize or do not hybridize to a second set of nucleic acids. This provides binary results that enable a relatively simple method of measuring complex mechanisms at the epigenetic level. EpiSwitch™ Technology also has a fast processing time and low cost. In one embodiment, the processing time is from 3 to 6 hours.

[0030] Samples and Sample Processing The process of the present invention is typically performed on a sample. The sample can be obtained at a defined time point, for example, at any time point defined herein. The sample typically contains DNA from an individual. It typically contains cells. In one aspect, the sample can be obtained by minimally invasive means, for example, a blood sample. The DNA can be extracted and cut with standard restriction enzymes. This allows for the pre-determination of which chromosomal conformations are retained and detected by the EpiSwitch™ platform. By synchronizing chromosomal interactions between tissues and blood, including horizontal transmission, chromosomal interactions within tissues such as tissues related to diseases can be detected using blood samples.

[0031] Properties of the Nucleic Acids of the Invention The present invention relates to specific nucleic acids such as ligated nucleic acids described herein as being used or generated in the process of the present invention. These can be the same as the first and second nucleic acids referred to herein, or can have any of their properties. The nucleic acids of the present invention typically comprise two parts, each of which contains a sequence from one of two regions of a chromosome that come together in a chromosomal interaction. Typically, each part is at least 8, 10, 15, 20, 30 or 40 nucleotides in length, for example 10 - 40 nucleotides in length. Preferred nucleic acids contain a sequence from any of the genes referred to in any of the tables. Typically, preferred nucleic acids contain a specific probe sequence referred to in Table 1, 2, 3 or 4, or a fragment and / or homolog of such a sequence.

[0032] Preferably, the nucleic acid is DNA. It is understood that when a specific sequence is provided, the present invention can use the complementary sequence as required in a particular aspect. Preferably, the nucleic acid is DNA. It is understood that when a specific sequence is provided, the present invention can use the complementary sequence as required in a particular aspect.

[0033] The primers shown in Tables 1, 2, 3, or 4 can also be used in the present invention as described herein. In one aspect, a primer is used that comprises either the sequences shown in Tables 1, 2, 3, or 4, or a fragment and / or homolog of any of the sequences shown in Tables 1, 2, 3, or 4.

[0034] "First" and "second" nucleic acids In one aspect of the invention: - The second set of nucleic acids is derived from a larger group of individuals than the first set of nucleic acids; and / or - The first set of nucleic acids is derived from at least 8 individuals; and / or - The first set of nucleic acids is derived from at least 4 individuals from a first subgroup and, preferably, at least 4 individuals from a second subgroup that does not overlap with the first subgroup.

[0035] In a further aspect of the invention: - The second set of nucleic acids represents a non-selected group; and / or - The second set of nucleic acids is bound to the array at defined positions; and / or - The second set of nucleic acids represents chromosomal interactions in at least 100 different genes; and / or - The second set of nucleic acids comprises at least 1000 different nucleic acids that represent at least 1000 different chromosomal interactions; and / or - The first set of nucleic acids and the second set of nucleic acids comprise at least 100 spreads having a length of 10 to 100 nucleotide bases.

[0036] The second set of nucleic acids - "index" sequences The second set of nucleic acid sequences has the function of being a set of index sequences and is essentially a set of nucleic acid sequences suitable for identifying subgroup-specific sequences. They can represent "background" chromosomal interactions and can or cannot be selected in some way. They are generally a subset of all possible chromosomal interactions.

[0037] The second set of nucleic acids can be derived by any suitable process. They can be derived computationally or based on an individual's chromosomal interactions. They typically represent a larger population group than the first set of nucleic acids. In one particular embodiment, the second set of nucleic acids represents all possible epigenetic chromosomal interactions in a specific set of genes. In another particular embodiment, the second set of nucleic acids represents a majority of all possible epigenetic chromosomal interactions present in the population described herein. In one particular embodiment, the second set of nucleic acids represents at least 50% or at least 80% of the epigenetic chromosomal interactions in at least 20, 50, 100 or 500 genes, for example 20 - 100 or 50 - 500 genes.

[0038] The second set of nucleic acids typically represents at least 100 possible epigenetic chromosomal interactions that modify, regulate, or mediate in some way the phenotype in the population. The second set of nucleic acids can represent chromosomal interactions that affect a disease state (typically associated with diagnosis or prognosis) in a species. The second set of nucleic acids typically includes sequences that represent both epigenetic interactions associated with and not associated with a prognostic subgroup.

[0039] In one particular embodiment, the second set of nucleic acids is at least partially derived from sequences of natural origin in the population and is typically obtained by an in silico process. The nucleic acids may further comprise one or more mutations as compared to the corresponding portions of the nucleic acids present in nucleic acids of natural origin. Mutations include deletions, substitutions, and / or additions of one or more nucleotide base pairs. In one particular embodiment, the second set of nucleic acids may comprise sequences representing homologs and / or orthologs having at least 70% sequence identity to the corresponding portions of the nucleic acids present in a species of natural origin. In another particular embodiment, at least 80% or at least 90% sequence identity to the corresponding portions of the nucleic acids present in a species of natural origin is provided.

[0040] Properties of the second set of nucleic acids In one particular embodiment, the second set of nucleic acids comprises at least 100 different nucleic acid sequences, preferably at least 1000, 2000 or 5000 different nucleic acid sequences, and up to 100,000, 1,000,000 or 10,000,000 different nucleic acid sequences. Typical numbers are in the range of 100 to 1,000,000, such as 1,000 to 100,000 different nucleic acid sequences. All or at least 90% or at least 50% of these correspond to different chromosomal interactions.

[0041] In one particular embodiment, the second set of nucleic acids represents chromosomal interactions at at least 100, at least 500, at least 1000, or at least 5000 different loci or genes, such as at least 20 different loci or genes, preferably at least 40 different loci or genes, more preferably 100 to 10,000 different loci or genes. The length of the second set of nucleic acids is suitable for specifically hybridizing to the first set of nucleic acids according to Watson-Crick base pairs in order to enable the identification of chromosomal interactions specific to a subgroup. Typically, the second set of nucleic acids comprises two portions corresponding in sequence to two chromosomal regions that come together in a chromosomal interaction. The second set of nucleic acids typically comprises a nucleic acid sequence that is at least 10 bases (nucleotides) in length, preferably 20 bases in length, preferably also 30 bases in length. In another embodiment, the nucleic acid sequence can be at most 500 base pairs in length, preferably at most 100 base pairs in length, preferably also at most 50 base pairs in length. In a preferred embodiment, the second set of nucleic acids comprises a nucleic acid sequence between 17 base pairs and 25 base pairs. In one embodiment, at least 100%, 80%, or 50% of the second set of nucleic acid sequences have the lengths as described above. Preferably, the different nucleic acids do not have overlapping sequences, e.g., at least 100%, 90%, 80%, or 50% of the nucleic acids do not have the same sequence over at least 5 consecutive nucleotides.

[0042] Considering that the second set of nucleic acids acts as an "index", the same set of second nucleic acids can be used with various sets of first nucleic acids representing subgroups for various characteristics, i.e., the second set of nucleic acids can represent a "universal" set of nucleic acids that can be used to identify chromosomal interactions associated with various characteristics.

[0043] The first set of nucleic acids The first set of nucleic acids is typically from a subgroup related to prognosis. The first nucleic acid can have any of the characteristics and properties of the second set of nucleic acids referred to herein. The first set of nucleic acids is usually derived from samples from individuals who have undergone the treatments and processes described herein, particularly the cross-linking and cleavage steps of EpiSwitch™. Typically, the first set of nucleic acids represents all or at least 80% or 50% of the chromosomal interactions present in a sample taken from an individual.

[0044] Typically, the first set of nucleic acids represents a smaller population of chromosomal interactions across the loci or genes represented by the second set of nucleic acids as compared to the chromosomal interactions represented by the second set of nucleic acids, i.e., the second set of nucleic acids represents a background or index set of interactions at a defined set of loci or genes.

[0045] Library of nucleic acids Any of the types of nucleic acid populations referred to herein can exist in the form of a library containing at least 200, at least 500, at least 1000, at least 5000, or at least 10,000 different nucleic acids of that type, such as "first" or "second" nucleic acids. Such libraries can be in a form bound to an array. The library can contain some or all of the probe or primer pairs shown in Tables 1, 2, 3, or 4. The library can be in the form of a composition or in the form of a kit where the nucleic acids are provided in separate containers.

[0046] Hybridization The present invention requires means for enabling the hybridization of completely or partially complementary nucleic acid sequences from the first set of nucleic acids and the second set of nucleic acids. In one aspect, all of the first set of nucleic acids are contacted with all of the second set of nucleic acids in a single assay, i.e., in a single hybridization step. However, any suitable assay can be used.

[0047] Labeled nucleic acids and hybridization patterns The nucleic acids referred to herein can preferably be labeled using an independent label such as a fluorophore (fluorescent molecule) or a radioactive label that aids in the detection of successful hybridization. Certain labels can be detected under UV light. The hybridization pattern represents, for example on an array as described herein, the difference in epigenetic chromosomal interactions between two subgroups, and thus provides a process for comparing epigenetic chromosomal interactions and a determination of which epigenetic chromosomal interactions are specific to the subgroups in the population of the invention.

[0048] The term "hybridization pattern" broadly covers the presence or absence of hybridization between a first set of nucleic acids and a second set of nucleic acids, i.e., which specific nucleic acids from the first set hybridize with which specific nucleic acids from the second set, and thus is not limited to any particular assay or technique, or the requirement to have a surface or array capable of detecting the "pattern".

[0049] Selection of subgroups having specific characteristics The present invention provides a process comprising the step of detecting the presence or absence of chromosomal interactions, typically 5 to 20 or 5 to 500 such interactions, preferably 20 to 300 or 50 to 100 interactions, in order to determine the presence or absence of characteristics associated with the prognosis of an individual. Preferably, the chromosomal interactions are in any of the genes referred to herein. In one aspect, the classified chromosomal interactions are those represented by the nucleic acids of Tables 1, 2, 3 or 4. The column in the table titled "Detected loops" indicates which subgroups are detected by each probe. The detection can be the detection of the presence or absence of chromosomal interactions within that subgroup.

[0050] Individuals to be tested The individuals to be tested are typically of any species mentioned herein. Additionally, the individuals tested in the process of the present invention can be selected in any way. The individuals can be susceptible to any disease mentioned herein and / or may require any treatment mentioned. The individuals may have received any treatment mentioned herein. In particular, the individuals have or are suspected of having ASD.

[0051] The individuals may be suspected of having certain conditions included in the broad definition of ASD. It can be autism, childhood autism, Asperger's syndrome, PDD-NOS (pervasive developmental disorder), childhood disintegrative disorder, addictions (such as addictions to drugs or digital media devices).

[0052] Classifying combinations of markers The present invention is a process in which specific combinations of chromosomal interactions are classified, comprising: (i) including all chromosomal interactions represented by the probes of Table 1, 2, 3 or 4; and / or (ii) including at least 25, 50, 100, 150 or 200 chromosomal interactions represented by the probes of Table 1, 2, 3, or 4; and / or (iii) these together being present in at least 10, 20, 30 or 40 regions or genes listed in Table 1, 2, 3, or 4; and / or (iv) at least 10, 20, 30, or 40 of the chromosomal interactions to be classified being present in a 4,000-base region that includes or is adjacent to the chromosomal interactions represented by the probes of Table 1, 2, 3 or 4, including the process.

[0053] Typically, in the process of the present invention, at least 20, 30, 40 or 50 chromosomal interactions are classified.

[0054] Preferred gene regions, loci, genes and chromosomal interactions For all aspects of the present invention, preferred gene regions, loci, genes and chromosomal interactions are described in tables, such as Tables 1, 2, 3 or 4. Typically, in the process of the present invention, chromosomal interactions are detected from at least 10, 20, 30, 40 or 50 of the genes listed in Table 1. Typically, in the process of the present invention, chromosomal interactions are detected from at least 10, 20, 30, 40 or 50 of the genes listed in Table 2. Typically, in the process of the present invention, chromosomal interactions are detected from at least 10, 20, 30, 40 or 50 of the genes listed in Table 3. Typically, in the process of the present invention, chromosomal interactions are detected from at least 10, 20, 30, 40 or 50 of the genes listed in Table 4.

[0055] Preferably, the presence or absence of at least 10, 20, 50, 150 or 200 of the relevant specific chromosomal interactions represented by the probe sequences of Table 1 is detected. Preferably, the presence or absence of at least 10, 20, 50, 150 or 200 of the relevant specific chromosomal interactions represented by the probe sequences of Table 2 is detected. Preferably, the presence or absence of at least 10, 20, 50, 150 or 200 of the relevant specific chromosomal interactions represented by the probe sequences of Table 3 is detected. Preferably, the presence or absence of at least 10, 20, 50, 150 or 200 of the relevant specific chromosomal interactions represented by the probe sequences of Table 4 is detected. Chromosomal interactions can be upstream or downstream of any of the genes described herein, for example, within 50 kb upstream or 20 kb downstream of the coding sequence.

[0056] Preferred combinations and number of markers In one aspect, the present invention relates to classifying the markers shown in Table 1. In this aspect, the markers to be classified may or may not be present in other tables. Thus, the present invention includes a process for determining the prognosis of ASD by classifying one or more of the chromosomal interactions shown in Table 1. Typically, the presence or absence of at least 1, 5, 8, 10, 15, 20 chromosomal interactions from Table 1 is detected.

[0057] In one aspect, the present invention relates to classifying the markers shown in Table 2. In this aspect, the markers to be classified may or may not be present in other tables. Thus, the present invention includes a process for determining the prognosis of ASD by classifying one or more of the chromosomal interactions shown in Table 2. Typically, the presence or absence of at least 1, 5, 8, 10, 15, 20 chromosomal interactions from Table 2 is detected.

[0058] In one aspect, the present invention relates to classifying the markers shown in Table 3. In this aspect, the markers to be classified may or may not be present in other tables. Thus, the present invention includes a process for determining the prognosis of ASD by classifying one or more of the chromosomal interactions shown in Table 3. Typically, the presence or absence of at least 1, 5, 8, 10, 15, 20 chromosomal interactions from Table 3 is detected.

[0059] In one aspect, the present invention relates to classifying the markers shown in Table 4. In this aspect, the markers to be classified may or may not be present in other tables. Thus, the present invention includes a process for determining the prognosis of ASD by classifying one or more of the chromosomal interactions shown in Table 4. Typically, the presence or absence of at least 1, 5, 8, 10, 15, 20 chromosomal interactions from Table 4 is detected.

[0060] In one aspect, the present invention relates to classifying the markers shown in Table 8. In this aspect, the markers to be classified may or may not be present in other tables. Thus, the present invention includes a process for determining the prognosis of ASD by classifying one or more of the chromosomal interactions shown in Table 8. Typically, the presence or absence of at least 1, 5, 8, 10, 15, 20 chromosomal interactions from Table 8 is detected. In one aspect, the presence or absence of at least 30, 50, 80, 100 or 150 interactions from Table 8 is detected.

[0061] Table 8 includes groups of markers defined as follows. Group A: Markers 1 to 12 Group B: Markers 13 to 76 and 139 to 167 Group C: Markers 77 to 138 Group D: Markers 168 to 183

[0062] Typically, the presence or absence of at least 1, 5, 8, 10 or all chromosomal interactions from Group A of Table 8 is detected. In one aspect, the presence or absence of at least 1, 5, 8, 10, 15, 20 or all chromosomal interactions from Group B of Table 8 is detected. Typically, the presence or absence of at least 1, 5, 8, 10, 15, 20 or all chromosomal interactions from Group C of Table 8 is detected. In one aspect, the presence or absence of at least 1, 5, 8, 10, 15, 20 or all chromosomal interactions from Group D of Table 8 is detected.

[0063] In one aspect, the present invention relates to classifying the markers shown in Table 9. In this aspect, the markers to be classified may or may not be present in other tables. Thus, the present invention includes a process for determining the prognosis of ASD by classifying one or more of the chromosomal interactions shown in Table 9. Typically, the presence or absence of at least 1, 5, 8, 10, 15, 20 chromosomal interactions from Table 9 is detected. In one aspect, the presence or absence of at least 30, 50, 80, 100 or 150 interactions from Table 9 is detected.

[0064] Table 9 includes groups of markers defined as follows. Group A: Markers from 2 to 7 and from 9 to 15 Group B: Markers 1, 8, 16 to 87 and 149 to 171 Group C: Markers from 88 to 148 Group D: Markers from 172 to 182

[0065] Typically, the presence or absence of at least 1, 5, 8, 10 or all of the chromosomal interactions from Group A of Table 9 is detected. In one aspect, the presence or absence of at least 1, 5, 8, 10, 15, 20 or all of the chromosomal interactions from Group B of Table 9 is detected. Typically, the presence or absence of at least 1, 5, 8, 10, 15, 20 or all of the chromosomal interactions from Group C of Table 9 is detected. In one aspect, the presence or absence of at least 1, 5, 8, 10, 15, 20 or all of the chromosomal interactions from Group D of Table 9 is detected.

[0066] In one aspect, the presence or absence of at least 1, 5, 8, 10, 15, 20 or all of the chromosomal interactions from Table 10 is detected. In a preferred aspect, one or both of the first two markers in Table 10 are classified.

[0067] Classifying the chromosomal interactions depicted in the figure In one aspect, the method of the present invention comprises classifying one or more chromosomal interactions associated with any gene described in any of the figures (as defined in the table). Typically, at least 1, 5, 8, 10, 15 or 20 such interactions are classified.

[0068] Classifying different types of ASD As can be seen from the table, different markers are specific to different types of ASD (defined by either their presence or absence). The process of the present invention typically has the following characteristics: (i) Present in healthy controls (HC) but not in mild and severe ASD (ii) Specific to either mild or severe ASD and not present in HC (iii) Common to or present in both severe and mild ASD but not present in HC (iv) Present or absent in either severe or mild ASD and comprises classifying markers having any of these.

[0069] In one aspect, at least 1, 5, 8, 10, 15 or 20 chromosomal interactions are classified and have characteristic (i). In a further aspect, at least 1, 5, 8, 10, 15 or 20 chromosomal interactions are classified and have characteristic (ii). In one aspect, at least 1, 5, 8, 10, 15 or 20 chromosomal interactions are classified and have characteristic (iii). In a further aspect, at least 1, 5, 8, 10, 15 or 20 chromosomal interactions are classified and have characteristic (vi).

[0070] Type of chromosomal interaction In one aspect, a locus (including the gene and / or location where chromosomal interactions are detected) may include a CTCF binding site. This is any sequence that can bind to the transcriptional repressor CTCF. The sequence may consist of, or include, the sequence CCCTC which may be present in 1, 2, or 3 copies at the locus. The CTCF binding site sequence may include the sequence CCGCGNGGNGGCAG (IUPAC notation). The CTCF binding site may be within at least 100, 500, 1000, or 4000 bases of the chromosomal interaction, or within any of the chromosomal regions shown in Tables 1, 2, 3, or 4. The CTCF binding site may be within at least 100, 500, 1000, or 4000 bases of the chromosomal interaction, or within any of the chromosomal regions shown in Tables 1, 2, 3, or 4.

[0071] In one aspect, the detected chromosomal interactions are present in any of the gene regions shown in Tables 1, 2, 3, or 4. If ligated nucleic acids are detected in the process, sequences shown in any of the probe sequences of Tables 1, 2, 3, or 4 may be detected.

[0072] Thus, typically, sequences from both regions of the probe (i.e., from both sites of the chromosomal interaction) may be detected. In a preferred aspect, a probe containing or consisting of a sequence the same as or complementary to any of the probes shown in any table is used in the process. In some aspects, a probe containing a sequence homologous to any of the probe sequences shown in the table is used.

[0073] In one aspect, one or more chromosomal interactions to be classified are (i) include a single nucleotide polymorphism (SNP); and / or (ii) express a microRNA (miRNA); and / or (iii) express a non-coding RNA (ncRNA); and / or (iv) express a nucleic acid sequence encoding at least 10 consecutive amino acid residues; and / or (v) represent a regulatory element; and / or (vii) containing CTCF binding sites in the locus / region.

[0074] Explanation of the table Table 1 shows markers that are present in healthy controls but not in severe and mild autism. The designation "mHC" means that it is absent in both mild and severe cases (meaning from the mild comparison with HC). The designation "sHC" means that it is absent in both mild and severe cases (meaning from the severe comparison with HC).

[0075] Table 2 shows specific markers that are present in mild and severe autism. The designation "sAD" means that it is absent in controls and mild cases. The designation "mAD" means that it is absent in controls and severe cases.

[0076] Table 3 shows common markers that are present in severe and mild autism. The designation "sAD" means that it is present in mild cases (meaning from the severe comparison with HC). The designation "mAD" means that it is present in severe cases (meaning from the mild comparison with HC).

[0077] Table 4 shows specific markers that are absent in either severe or mild autism. The designation "sHC" means that it is present in healthy controls but only for the comparison between severe and HC patients. It says nothing about the mild state. The designation "mHC" means that it is present in healthy controls but only for the comparison between mild and HC patients. It says nothing about the severe state.

[0078] Table 8 shows markers related to severe autism. This table shows four marker groups, groups A, B, C, and D, as defined above and shown in the table. Markers can be selected from the entire table or from the groups.

[0079] Table 9 shows markers associated with mild autism. This table shows four marker groups, groups A, B, C, and D, as defined above and shown in the table. Markers can be selected from the entire table or from the groups.

[0080] Table 10 shows high-performance markers and is an optimized panel. In particular, this panel includes chromosomal interactions related to NAMPT and MAP2 (marker numbers 1 and 2 in the table).

[0081] In the LS column of all tables, there is either a "1" or a "-1". This reflects how the comparison is made, where healthy controls are always the numerator, and thus significant markers present in the HC are 1, and disease samples (mild or severe) are always the denominator, and thus significant markers present in the disease samples are always -1.

[0082] The tables show probe (EpiSwitch™ marker) data and gene data representing chromosomal interactions related to prognosis. The probe sequences are sequences that can be used to detect ligated products generated from both sites of the gene region joined in the chromosomal interaction, i.e., the probe contains a sequence complementary to the sequence in the ligated product. The first two sets of start - end positions indicate the probe positions, and the second two sets of start - end positions indicate the related 4 kb regions. The following information is provided in the table of probe data: - HyperG_Stats: p - value for the probability of finding that number of significant EpiSwitch™ markers within a locus based on parameters of hypergeometric enrichment - Total number of probes: Total number of EpiSwitch™ conformations tested at the locus - Number of significant probes: Number of EpiSwitch™ conformations found to be statistically significant at the locus - FDR HyperG: Hypergeometric p-value corrected by multi-test (Fimmunoresposivenesse Discovery Rate) - Percent Significance: Percentage of significant EpiSwitch™ markers relative to the number of markers tested at the locus - logFC: Base-2 logarithm of the epigenetic ratio (FC) - AveExpr: Average log2 expression for probes across all arrays and channels - T: Moderated t-statistic - p-value: Raw p-value - Adjusted p-value: Adjusted p-value or q-value - B-B statistic (Rod or B) is the log odds that the gene is differentially expressed - FC - Non-log fold change - FC_1 - Non-log fold change centered around zero - LS - Binary value, which is related to the FC_1 value. FC_1 values less than -1.1 are set to -1, and if the FC_1 value exceeds 1.1, it is set to 1. Values between these are 0

[0083] The table shows genes where related chromosomal interactions are known to occur. The p-values in the locus table are the same as HyperG Stats (p-value for the probability of finding that number of significant EpiSwitch™ within the locus based on hypergeometric enrichment parameters). The LS column indicates the presence or absence of an interaction related to that specific subgroup (prognostic state).

[0084] Probes are designed to be 30bp away from the Taq1 site. In the case of PCR, PCR primers are typically designed to detect the ligated product, but the position from the Taq1 site varies

[0085] Probe position: Start1 - 30 bases upstream of the Taql site on fragment 1 TaqI restriction site on Fragment 1 - End1 TaqI restriction site on Fragment 2 - Start2 30 bases downstream of the TaqI site on Fragment 2 - End2

[0086] 4 kb sequence position: 4000 bases upstream of the TaqI site on Fragment 1 - Start1 TaqI restriction site on Fragment 1 - End1 TaqI restriction site on Fragment 2 - Start2 4000 bases downstream of the TaqI site on Fragment 2 - End2

[0087] GLMNET values related to the procedure of fitting the entire lasso or elastic net regularization (lambda is set to 0.5 (elastic net)).

[0088] Certain markers are shown twice when related to shared markers, once for presence / absence in mild autism and once for presence / absence in severe autism.

[0089] Preferred embodiments for sample preparation and detection of chromosomal interactions Methods for preparing samples and methods for detecting chromosomal conformations are described herein. Optimized (different from the prior art) versions of these methods can be used as described, for example, in this section.

[0090] Typically, the sample contains at least 2×10 5 cells. The sample may contain up to 5×10 5 cells. In one embodiment, the sample contains 2×10 5 to 5.5×10 5 cells.

[0091] Crosslinking of epigenetic chromosomal interactions present at chromosomal loci is described herein. This can be performed before cell lysis occurs. Cell lysis can be performed for 3 - 7 minutes, such as 4 - 6 minutes or about 5 minutes. In some embodiments, cell lysis is performed for at least 5 minutes and less than 10 minutes.

[0092] Digestion of DNA by restriction enzymes is described herein. Typically, DNA restriction is performed at about 55°C to about 70°C, such as about 65°C, for a period of about 10 - 30 minutes, such as about 20 minutes.

[0093] Preferably, frequently cutting restriction enzymes are used, resulting in ligated DNA fragments having an average fragment size up to 4000 base pairs. Optionally, by restriction enzymes, the resulting ligated DNA fragments have an average fragment size of about 200 - 300 base pairs, such as about 256 base pairs. In one embodiment, typical fragment sizes are from 200 base pairs to 4000 base pairs, such as 400 - 2,000 or 500 - 1,000 base pairs.

[0094] In one embodiment of the EpiSwitch method, a DNA precipitation step is not performed between the DNA restriction digestion step and the DNA ligation step.

[0095] DNA ligation is described herein. Typically, DNA ligation is performed for 5 - 30 minutes, such as about 10 minutes.

[0096] Proteins in the sample can be enzymatically digested, for example, using proteinase, optionally proteinase K. Proteins can be enzymatically digested for about 30 minutes to 1 hour, such as about 45 minutes. In one embodiment after digestion of the protein, such as proteinase K digestion, there is no crosslink reversal or phenol DNA extraction step.

[0097] In one aspect, PCR detection can preferably detect a single copy of the ligated nucleic acid using a binary readout value for the presence or absence of the ligated nucleic acid.

[0098] Figure 10 shows a preferred method for detecting chromosomal interactions.

[0099] The processes and uses of the present invention The process of the present invention can be described in various ways. It can be described as a method for producing a ligated nucleic acid, which method includes (i) a step of crosslinking chromosomal regions joined together by chromosomal interactions in vitro, (ii) a step of subjecting the crosslinked DNA to cleavage or restriction digestion cleavage, and (iii) a step of ligating the crosslinked cleaved DNA ends to form a ligated nucleic acid, and the detection of the ligated nucleic acid can be used to determine the chromosomal state at a locus, preferably, - the locus can be any of the loci, regions, or genes referred to in Tables 1, 2, 3, or 4, and / or - the chromosomal interaction can be any of the chromosomal interactions corresponding to any of the probes referred to herein or disclosed in Tables 1, 2, 3, or 4, and / or - the ligated product can have or contain a sequence that is identical or homologous to any of the probe sequences disclosed in Tables 1, 2, 3, or 4 or (ii) a sequence complementary to (ii).

[0100] The process of the present invention can be described as a process for detecting chromosomal states representing different subgroups within a population, which process includes determining whether a chromosomal interaction is present or absent within a defined epigenetically active region of the genome, preferably, - the subgroups are defined by the presence or absence of a state or by the type of state, and / or - The chromosomal state can be any locus, region, or gene referred to in Tables 1, 2, 3, or 4, and / or - The chromosomal interaction can be any of those referred to in Tables 1, 2, 3, or 4 or corresponding to any of the probes disclosed in Tables 1, 2, 3, or 4.

[0101] The present invention includes the detection of chromosomal interactions at any locus, gene, or region referred to in Tables 1, 2, 3, or 4. The present invention includes the use of the nucleic acids and probes referred to herein for detecting chromosomal interactions, e.g., the use of at least 1, 5, 10, 20, or 50 such nucleic acids or probes for detecting chromosomal interactions. The nucleic acid or probe preferably detects chromosomal interactions at at least 1, 5, 10, 20, or 50 different loci or genes. The present invention includes the detection of chromosomal interactions using any of the primers or primer pairs listed in Tables 1, 2, 3, or 4, or variants of these primers described herein (sequences including the primer sequence or sequences including fragments and / or homologs of the primer sequence).

[0102] When analyzing whether a chromosomal interaction occurs within a defined gene, region, or position, both parts of the chromosomes that come together in the interaction are within the defined gene, region, or position, or in some embodiments, only one part of the chromosome is within the defined gene, region, or position.

[0103] The markers shown in the tables are "disseminating" markers whose presence or absence is associated with a specific ASD state (shown in the relevant table) defined herein. Thus, the results of the process of the present invention are analyzed with reference to the way in which the marker is associated with the ASD state.

[0104] Use of the method of the present invention for identifying new treatments Knowledge of chromosomal interactions can be used to identify new treatments for ASD. The present invention provides, for example, methods and uses of chromosomal interactions as defined herein for identifying or designing new therapeutic agents related to the treatment of ASD.

[0105] Homolog Homologs of polynucleotide / nucleic acid (e.g., DNA) sequences are referred to herein. Such homologs typically have at least 70% homology, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, or at least 99% homology over a region of, for example, at least 10, 15, 20, 30, 100 or more consecutive nucleotides, or a portion of a nucleic acid derived from a region of a chromosome involved in chromosomal interactions. Homology can be calculated based on nucleotide identity (sometimes referred to as "hard homology").

[0106] Thus, in certain embodiments, homologs of polynucleotide / nucleic acid (e.g., DNA) sequences are referred to herein by reference to percentage sequence identity. Typically, such homologs have at least 70% sequence identity, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, or at least 99% sequence identity over a region of, for example, at least 10, 15, 20, 30, 100 or more consecutive nucleotides, or a portion of a nucleic acid derived from a region of a chromosome involved in chromosomal interactions.

[0107] For example, the UWGCG package provides the BESTFIT program that can be used to calculate homology and / or % sequence identity (e.g., as used in its default settings) (Devereux et al (1984) Nucleic Acids Research 12, p387-395). The PILEUP and BLAST algorithms can be used to calculate homology and / or % sequence identity and / or to align sequences (such as identifying equivalent or corresponding sequences (typically in their default settings)) as described, for example, in Altschul S. F. (1993) J Mol Evol 36: 290-300; Altschul, S, F et al (1990) J Mol Biol 215: 403-10.

[0108] Software for performing BLAST analysis is publicly available through the National Center for Biotechnology Information. This algorithm first identifies high-scoring sequence pairs (HSPs) by identifying short words of length W in the query sequence that match or satisfy some positive-valued threshold score T when aligned with words of the same length within the database sequences. T is referred to as the neighborhood word score threshold (Altschul et al., supra). These initial neighborhood word hits serve as seeds to initiate a search to find the HSPs that contain them. The word hits are extended in both directions along each sequence as long as the cumulative alignment score can be increased. Extension of the word hits in each direction stops when: the cumulative alignment score drops by an amount X from its maximum achieved value; the cumulative alignment score becomes less than or equal to zero due to the cumulative addition of one or more negative-score residue alignments; or the end of either sequence is reached. The BLAST algorithm parameters W, T, and X determine the sensitivity and speed of the alignment. The BLAST programs, by default, use a word length (W) of 11, a BLOSUM62 scoring matrix (see Henikoff and Henikoff (1992) Proc. Natl. Acad. Sci. USA 89: 10915-10919) alignment (B) of 50, an expectation value (E) of 10, M = 5, N = 4, and comparison of both strands.

[0109] The BLAST algorithm performs a statistical analysis of similarity between two sequences (see, e.g., Karlin and Altschul (1993) Proc. Natl. Acad. Sci. USA 90: 5873-5787). One measure of similarity provided by the BLAST algorithm is the minimum total probability (P(N)), which provides an indication of the probability that a match between two polynucleotide sequences occurs by chance. For example, if the minimum total probability in a comparison of a first sequence and a second sequence is less than about 1, preferably less than about 0.1, more preferably less than about 0.01, and most preferably less than about 0.001, the sequences are considered to be similar to another sequence.

[0110] Homologous sequences typically differ by only 1, 2, 3, 4 or more bases, such as less than 10, 15, or 20 bases (which can be nucleotide substitutions, deletions, or insertions). These changes can be measured over any of the regions described above in relation to the calculation of homology and / or % sequence identity.

[0111] The homology of a "primer pair" can be calculated, for example, for the purpose of later comparison to another primer pair (which is also considered as a single sequence), by considering the two sequences as a single sequence (as if the two sequences were joined together).

[0112] Array A second set of nucleic acids can be bound to the array. In one aspect, there are at least 15,000, 45,000, 100,000 or 250,000 different second nucleic acids that are bound to the array, which preferably represent at least 300, 900, 2000 or 5000 loci. In one aspect, one, or more, or all different populations of the second nucleic acids are bound to separate regions of more than one of the arrays, and are in effect repeated on the array, allowing for error detection. The array can be based on the Agilent SurePrint G3 Custom CGH microarray platform. Detection of the binding of the first nucleic acid to the array can be performed by a dual-color system.

[0113] Therapeutic agent (e.g., selected based on the classification of an individual or selected based on a test according to the present invention)

[0114] Therapeutic agents are mentioned herein. The present invention provides such agents for use in preventing or treating the medical conditions of a particular individual, e.g., an individual identified by the process of the present invention. This can include administering to an individual in need of a therapeutically effective amount of the agent. The present invention provides the use of an agent in the manufacture of a medicament for preventing or treating a disease in a particular individual.

[0115] The formulation of the agent depends on the nature of the agent. The agent is provided in the form of a pharmaceutical composition comprising the agent and a pharmaceutically acceptable carrier or diluent. Suitable carriers and diluents include isotonic saline, e.g., phosphate buffered saline. Typical oral dosage forms include tablets, capsules, liquid solutions, and liquid suspensions. The agent can be formulated for parenteral, intravenous, intramuscular, subcutaneous, transdermal, or oral administration.

[0116] The dosage of the agent can be determined according to various parameters as follows, particularly according to the substance used; the age, weight, disease of the individual being treated; the route of administration; and the required regimen. A physician can determine the route of administration and dosage required for any particular agent. However, a suitable dosage can be, for example, from 0.1 to 100 mg / kg of body weight, such as from 1 to 40 mg / kg of body weight, taken once to three times a day.

[0117] The therapeutic agent can be any such agent disclosed herein, or can target any "target" disclosed herein, including any protein or gene disclosed in any table herein (including Tables 1, 2, 3, or 4).

[0118] ASD treatment Any anti-ASD therapy, such as any drug targeting ASD symptoms, for example, for the purpose of regulating behavior, etc., can be used in the present invention. The therapy may be a psychotropic drug, an antiepileptic drug, an antidepressant, or an antipsychotic drug. The antipsychotic drug can be risperidone or aripiprazole.

[0119] The forms of substances referred to in this specification Any of the substances such as nucleic acids or therapeutic agents referred to in this specification can be in a purified or isolated form. They can be in a form different from those found in nature, for example, they can exist in combination with other substances not occurring in nature. Nucleic acids (including portions of the sequences defined herein) can have sequences different from those found in nature, for example, having at least 1, 2, 3, 4 or more nucleotide changes in the sequence as described in the section on homology. Nucleic acids can have heterologous sequences at the 5' or 3' ends. Nucleic acids can be chemically different from those found in nature, for example, they can be modified in some way, but it is preferred that Watson-Crick base pairing is still possible. Where appropriate, nucleic acids are provided in double-stranded or single-stranded form. The present invention provides all of the specific nucleic acid sequences referred to herein in single-stranded or double-stranded form, and thus includes the complementary strand to any of the disclosed sequences.

[0120] The present invention provides a kit for performing any process of the present invention, including the detection of chromosomal interactions related to prognosis. Such a kit can include a specific binding agent capable of detecting related chromosomal interactions, such as an agent capable of detecting the ligated nucleic acids produced by the process of the present invention. Preferred agents present in the kit include, for example, as described herein, a probe capable of hybridizing to the ligated nucleic acids or primer pairs and capable of amplifying the ligated nucleic acids in a PCR reaction.

[0121] The present invention provides a device capable of detecting related chromosomal interactions. The device preferably includes any specific binding agent, probe or primer pair capable of detecting chromosomal interactions, such as any such agent, probe or primer pair described herein.

[0122] Detection method In one aspect, quantitative detection of ligated sequences related to chromosomal interactions is performed using a probe that is detectable upon activation during a PCR reaction, wherein the ligated sequences include sequences from two chromosomal regions that come together in an epigenetic chromosomal interaction, and the method includes contacting the ligated sequences with the probe during the PCR reaction and detecting the degree of activation of the probe, and the probe binds to the ligated site. In this method, typically, a dual-labeled fluorescent hydrolysis probe can be used to detect specific interactions in a method compliant with MIQE.

[0123] Probes are generally labeled with detectable labels in inactive and active states and are thus detected only when activated. The degree of activation is related to the degree of template (ligated product) present in the PCR reaction. Detection can be performed during all or part of the PCR, for example, during at least 50% or 80% of the PCR cycles.

[0124] The probe can include a fluorophore covalently attached to one end of the oligonucleotide and a quencher attached to the other end of the nucleotide, such that the fluorescence of the fluorophore is quenched by the quencher. In one embodiment, the fluorophore is attached to the 5' end of the oligonucleotide and the quencher is covalently attached to the 3' end of the oligonucleotide. Fluorophores that can be used in the methods of the present invention include FAM, TET, JOE, Yakima Yellow, HEX, Cyanine 3, ATTO 550, TAMRA, ROX, Texas Red, Cyanine 3.5, LC 610, LC 640, ATTO 647N, Cyanine 5, Cyanine 5.5, and ATTO 680. Quenchers that can be used with suitable fluorophores include TAM, BHQ1, DAB, Eclip, BHQ2, and BBQ650, and optionally, the fluorophore is selected from HEX, Texas Red, and FAM. Preferred combinations of fluorophore and quencher include FAM and BHQ1, and Texas Red and BHQ2.

[0125] Use of the probe in a qPCR assay The hydrolysis probes of the present invention are typically optimized for temperature gradient with a negative control of matching concentration. Preferably, the one-step PCR reaction is optimized. More preferably, a standard curve is calculated. The advantage of using specific probes that bind across the junction of the ligated sequences is that it is possible to achieve specificity for the ligated sequences without using a nested PCR approach. The methods described herein enable accurate and precise quantification of low-copy number targets. Before optimization of the temperature gradient, the ligated sequences of the target can be purified, for example, by gel purification. The ligated sequences of the target can be sequenced. Preferably, the PCR reaction is carried out using about 10 ng, or 5 - 15 ng, or 10 - 20 ng, or 10 - 50 ng, or 10 - 200 ng of template DNA. The forward and reverse primers are designed such that one primer binds to one of the sequences of the chromosomal region represented by the ligated DNA sequence and the other primer binds to another chromosomal region represented by the ligated DNA sequence, for example, by being complementary to the sequence.

[0126] Selection of ligated DNA targets The present invention includes the step of selecting primers and probes for use in the PCR methods defined herein, including selecting primers based on their ability to bind to and amplify ligated sequences and selecting characteristics based on the probe sequences of the target sequences to which they bind, particularly the curvature of the target sequences.

[0127] Probes are typically designed / selected to bind to ligated sequences that are juxtaposed restriction fragments spanning a restriction site. In one aspect of the invention, the predicted curvature of sequences that may be ligated in relation to a particular chromosomal interaction is calculated using, for example, a particular algorithm referenced herein. Curvature can be expressed as degrees per helical turn, for example, 10.5° per helical turn. Ligated sequences are selected for targeting if the ligated sequences have a curvature trend peak score of at least 5° per helical turn, typically at least 10°, 15° or 20° per helical turn, for example 5° to 20° per helical turn. Preferably, the curvature trend peak score per helical turn is calculated for at least 20, 50, 100, 200 or 400 bases, such as 20 to 400 bases upstream and / or downstream of the ligated site. Thus, in one aspect, target sequences in the ligated product have any of these levels of curvature. Target sequences can also be selected based on the free energy of the lowest thermodynamic structure.

[0128] Particular aspects In one aspect, only intra-chromosomal interactions are classified / detected and extra-chromosomal interactions (between different chromosomes) are not classified / detected.

[0129] In particular aspects, certain chromosomal interactions, such as any specific interaction referred to herein (such as as defined by any probe or primer pair referred to herein) are not classified. In some aspects, chromosomal interactions are not classified in any of the genes referred to herein.

[0130] In one aspect, markers not listed in any of the tables are not classified, for example only the markers listed in Table 10 are classified.

[0131] Screening methods The present invention provides a method for determining which chromosomal interactions are associated with chromosomal states corresponding to prognostic subgroups of a population, the method comprising contacting a first set of nucleic acids from subgroups having different states of a chromosome with a second set of index nucleic acids, and enabling complementary sequences to hybridize, wherein the nucleic acids in the first and second sets of nucleic acids represent ligated products containing sequences from both chromosomal regions that have come together in a chromosomal interaction, and the hybridization pattern between the first set of nucleic acids and the second set of nucleic acids enables determination of which chromosomal interactions are specific to the prognostic subgroup. The subgroup can be any of the specific subgroups defined herein, for example, with reference to a particular disease or treatment.

[0132] The present invention further provides a process for using the prognostic / detection method of the present invention to identify or design a therapeutic agent for ASD, - preferably, the process is used to detect whether a candidate agent can cause a change in the chromosomal state associated with ASD; - the chromosomal interaction is represented by any of the probes of Tables 1, 2, 3 or 4; and / or - the chromosomal interaction is present in any region or gene listed in Tables 1, 2, 3 or 4; and optionally, - the chromosomal interaction is identified by a method for determining which chromosomal interactions are associated with the chromosomal state defined in claim 1, and / or - changes in the chromosomal interaction are monitored using (i) a probe having at least 70% identity to any of the probe sequences described in Tables 1, 2, 3 or 4, and / or (ii) a primer pair having at least 70% identity to any of the primer pairs of Tables 1, 2, 3 or 4 The process is provided.

[0133] Classification of "disseminating" markers The data provided herein indicates that the marker is a "disseminating" marker that can distinguish cases from non-cases with respect to the associated disease condition. Thus, in practicing the present invention, one of ordinary skill in the art will be able to determine by detecting the interactions of the subgroup to which the individual belongs. In one aspect, a threshold of detection of at least 70% of the test markers, in a form associated with the associated disease condition (either by absence or presence), can be used to determine whether an individual belongs to the associated subgroup. In other aspects, thresholds of at least 80% or at least 90% are used.

[0134] In one aspect, a classifier can be used to utilize a trained algorithm that includes information related to, for example, one or more disseminating markers, as described in any one of the tables herein, as part of the detection process.

[0135] Publication The content of all publications referred to herein is incorporated herein by reference and can be used to further define features related to the present invention.

[0136] The approach employed to identify markers and panels of markers The invention described herein relates to chromosomal conformation profiles and 3D architectures as control modalities that are closely related to phenotypes. Biomarker discovery was based on pattern recognition and annotation by screening in a representative cohort of clinical samples representing phenotypic differences. For the identification of statistically disseminating consistent conditional disseminating chromosomal conformations, important parts of the genome were annotated and screened beyond the coding and non-coding parts of known genes and over large fluctuations in non-coding 5' and 3'. This stitches together, for example, non-coding sites within (introns) or outside of open reading frames.

[0137] In the selection of the best markers, we are driven by statistical data on marker read and P-values. The chromosomal conformations within the selected and validated signatures are themselves disseminating stratifying entities, regardless of the expression profiles of the genes used as reference. Further work may be done on the relevant control modalities, such as SNPs at anchor sites, changes in gene transcription profiles, changes in the levels of H3K27ac, etc.

[0138] We also address the differences in clinical phenotypes and their stratification problems based on the underlying biology and epigenetic controls of the phenotypes, for example, from the framework of regulatory networks. Thus, to assist in stratification, it is preferably done through the signatures of several biomarkers by following machine learning algorithms for marker reduction, including being able to capture changes in the network and, for example, evaluating the optimal number of markers for stratifying the test cohort with minimal noise. This can be ended with 3 to 20 markers.

[0139] The selection of markers for the panel can be done by the statistical performance of cross-validation (and not, for example, by the functional relatedness of adjacent genes used as reference names).

[0140] The panel of markers (with the names of adjacent genes) is the product of a clustered selection from screening across important parts of the genome, in an unbiased way of analyzing the statistical disseminating power against 14,000 - 60,000 annotated EpiSwitch sites across important parts of the genome. It should not be considered as an adjusted capture of chromosomal conformations on genes with known functional values for the stratification problem. The total number of sites of chromosomal interaction is 12 million, and the number of potential combinations is 1.2 million to the power of 1.2 million. Nevertheless, the approach followed by the inventors enables the identification of relevant chromosomal interactions.

[0141] The specific markers provided by this application have passed the selection and are statistically (significantly) associated with the condition, as evidenced by the data in the relevant table. Each marker can be considered to represent a biological epigenetic event as part of the deregulation of the network that appears in the relevant condition. In practice, these markers mean that they are prevalent in the entire group of patients when compared to the control. On average, for example, individual markers can typically be present in 80% of the patients tested and 10% of the controls tested.

[0142] The simple addition of all markers does not represent the network interrelationships between some deregulations. Here, standard multivariate biomarker analysis, GLMNET (R package), is introduced. The GLMNET package helps identify the interdependence between several markers, which reflects the joint role in achieving the deregulation that leads to the disease phenotype. Modeling and subsequent testing of the markers with the highest GLMNET scores not only identify the minimum number of markers that accurately identify the patient cohort, but also provide the minimum number that provides the fewest false positive results in the patient control group due to the low prevalence in the control group, which is background statistical noise. Typically, a group (combination) of selected markers (such as 3 - 10) provides the best balance between both sensitivity and specificity and emerges from the individual properties of all selected statistically significant markers with respect to the conditions in the context of multivariate analysis.

[0143] The tables herein show the junctions between long - range interaction sites, chromosome numbers, and the reference names of array probes (60 - mer) for array analysis that overlap the start and end of two juxtaposed chromosomal fragments.

[0144] Specific embodiments The EpiSwitch trademark platform technology detects epigenetic regulatory signatures of regulatory changes between normal and abnormal states at loci. The EpiSwitch trademark platform identifies and monitors the fundamental epigenetic level of gene regulation associated with the control of the higher-order structure of human chromosomes, also known as chromosomal conformation signatures. Chromosomal signatures are distinct major steps in the cascade of gene deregulation. These are higher-order biomarkers with a unique set of advantages over biomarker platforms that utilize late epigenetic and gene expression biomarkers such as DNA methylation and RNA profiling.

[0145] EpiSwitch trademark array assay The custom EpiSwitch trademark array screening platform has four densities of unique chromosomal conformations: 15K, 45K, 100K, and 250K, and each chimeric fragment is repeated four times on the array, resulting in effective densities of 60K, 180K, 400K, and 1 million, respectively.

[0146] Custom-designed EpiSwitch trademark array The 15K EpiSwitch trademark array can screen the entire genome for approximately 300 loci collated with EpiSwitch trademark biomarker discovery technology. The EpiSwitch trademark array is constructed on the Agilent SurePrint G3 Custom CGH microarray platform, which offers four densities of 60K, 180K, 400K, and 1 million probes. Since each EpiSwitch trademark probe is presented as a quadruplicate, the density per array is reduced to 15K, 45K, 100K, and 250K, thus enabling a statistical evaluation of reproducibility. The average number of potential EpiSwitch trademark markers collated per locus is 50, and the number of loci that can be investigated is 300, 900, 2000, and 5000.

[0147] EpiSwitch (trademark) Custom Array Pipeline The EpiSwitch (trademark) Array is a dual-color system that, after generation of the EpiSwitch (trademark) library, comprises one set of samples labeled with Cy5 and another set of samples (controls) to be compared / analyzed labeled with Cy3. The array is scanned using an Agilent SureScan Scanner, and the resulting features are extracted using Agilent Feature Extraction software. The data are then processed using the EpiSwitch (trademark) Array processing script in R. The array is processed using the standard dual-color package in Bioconductor in R:Limma*. Normalization of the array is performed using the normalizedWithinArrays function in Limma*, which is done against on-chip Agilent positive controls and EpiSwitch (trademark) positive controls. To analyze using Limma*, the data are filtered based on Agilent Flag calls, Agilent control probes are removed, and technical replicate probes are averaged. Probes are modeled based on the differences between the two scenarios being compared and corrected using the false discovery rate. Probes with a coefficient of variation (CV) <= 30% and a p-value <= 0.1 FDR that pass a p-value of <= -1.1 or => 1.1 are used for further screening. To reduce the probe set, further multiple factor analysis is performed using the FactorMineR package in R.

[0148] *Note: LIMMA is a linear model and empirical Bayes process for assessing differential expression in microarray experiments. Limma is an R package for analyzing gene expression data arising from microarrays or RNA-Seq.

[0149] The probe pool is first selected based on adjusted p-value, FC and CV < 30% (any cut-off point) parameters and finally picked. Further analysis and the final list are created based on only the first two parameters (adjusted p-value; FC).

[0150] Statistical pipeline The EpiSwitch™ screening array is processed using the EpiSwitch™ analysis package in R to select high-value EpiSwitch™ markers for conversion to the EpiSwitch™ PCR platform.

[0151] Step 1 Probes are selected based on their adjusted p-values (false discovery rate, FDR), which are the product of a modified linear regression model. Select probes with p-value <= 0.1. Then further reduce by the epigenetic ratio (ER), and the probe ER must be <= -1.1 or => 1.1 for selection for further analysis. The final filter is the coefficient of variation (CV), and the probe must be <= 0.3.

[0152] Step 2 Select the top 40 markers from the statistical list based on their ER for selection as markers for PCR conversion. The top 20 markers with the highest negative ER load and the top 20 markers with the highest positive ER load form the list.

[0153] Step 3 The markers resulting from Step 1, the statistically significant probes, form the basis for enrichment analysis using hypergeometric enrichment (HE). This analysis enables marker reduction from the list of significant probes and, together with the markers from Step 2, forms a list of probes converted to the EpiSwitch™ PCR platform.

[0154] Statistical probes are processed by HE to determine which genetic locations have a statistically significant enrichment of probes, which indicates which genetic locations are hubs of epigenetic differences.

[0155] The most significant enrichment loci based on the corrected p-value are selected for the creation of the probe list. Genetic locations with a p-value less than 0.3 or 0.2 are selected. Using the markers from step 2, the statistical probes mapped to these genetic locations form high-value markers for EpiSwitch™ PCR conversion.

[0156] Array Design and Processing Array Design 1. Process the loci as follows using SII software (currently v3.2). a. Retrieve the genomic sequences at these specific loci (gene sequences 50 kb upstream and 20 kb downstream). b. Define the probability that the sequences within this region are involved in CC. c. Cut the sequences using specific REs. d. Determine which restriction fragments tend to interact in a specific orientation. e. Rank the likelihood that different CCs interact together. 2. Determine the array size and thus the number of available probe positions (x). 3. Extract x / 4 interactions. 4. For each interaction, define the sequences of 30 bp from part 1 to the restriction site and 30 bp from the restriction site of part 2. Confirm that those regions are not repetitive; if they are, exclude them and write the next interaction onto the list. Combine both 30 bp to define the probe. 5. Create a list of x / 4 probes + defined control probes, replicate 4 times, and create the list to be made on the array. 6. Upload the list of probes to the Agilent SureDesign website for custom CGH arrays. 7. Use the set of probes to design an Agilent custom CGH array.

[0157] Array processing 1. Process the samples using the EpiSwitch™ standard operating procedure (SOP) for template creation. 2. Clean up by ethanol precipitation by the array processing lab. 3. Process the samples according to the Agilent SureTag Complete DNA Labeling Kit - Agilent Oligonucleotide Array-based CGH for Genomic DNA Analysis Enzymatic labelling for Blood, Cells or Tissues. 4. Scan using an Agilent C Scanner with Agilent Feature Extraction software.

[0158] The signatures of EpiSwitch™ biomarkers demonstrate high robustness, sensitivity, and specificity in stratifying complex disease phenotypes. This technology utilizes the latest breakthroughs in epigenetics science, monitoring and evaluating chromosome conformation signatures as a highly useful class of epigenetic biomarkers. Current research methodologies deployed in academic settings require 3 - 7 days of biochemical processing of cell material to detect CCS. These procedures are limited in sensitivity and reproducibility and furthermore do not benefit from the targeted insights provided by the EpiSwitch™ analysis package at the design stage.

[0159] In silico biomarker identification for EpiSwitch™ arrays The CCS sites across the genome are directly evaluated on clinical samples from the test cohort by the EpiSwitch™ array for the identification of all relevant layer-specific lead biomarkers. The EpiSwitch™ array platform is used for marker identification due to its high throughput capacity and its ability to rapidly screen a large number of loci. The array used was an Agilent custom CGH array, which allows for the validation of markers identified by in-silico software.

[0160] EpiSwitch(™) PCR Subsequently, potential markers identified by the EpiSwitch™ array are verified by either EpiSwitch™ PCR or a DNA sequencer (i.e., Roche 454, Nanopore MinION, etc.). Top PCR markers that are statistically significant and show the best reproducibility are selected for further reduction to the final EpiSwitch™ signature set and verified in a cohort of independent samples. EpiSwitch™ PCR can be performed by trained technicians following an established standardized operating procedure protocol. The manufacture of all protocols and reagents is carried out under ISO 13485 and 9001 accreditation to ensure the quality of the research and the ability to transfer the protocol. The EpiSwitch™ PCR and EpiSwitch™ array biomarker platforms are compatible with the analysis of both whole blood and cell lines. The tests are sensitive enough to detect abnormalities at very low copy numbers using a small amount of blood.

[0161] Example 1 Identifying specific disseminating individual chromosome interactions not only for autism spectrum disorder but also for more specific differences between mild and severe autism and healthy controls The study of the causes of ASD has been complicated in current diagnosis and care, which is affected by the limitations, boundaries, and differences among psychiatric, psychological, and pediatric approaches, by the inability to identify objectively meaningful biomarkers for biologically distinct subgroups within the autism population. Current diagnostic tools are based on: 1) the Autism Diagnostic Interview-Revised (ADI-R); 2) the Autism Diagnostic Observation Schedule (ADOS); and particularly the Childhood Autism Rating Scale (CARS) for assessing severity by observing children. Diagnostic interviews for social and communication disorders are also used. Generally, autism spectrum disorder is distinguished from other developmental disorders by social developmental disorders.

[0162] Current drug treatments target ASD symptoms to regulate behavioral therapy in the social environment. Psychotropic drugs, anticonvulsants, antidepressants, antipsychotics. For example, the antipsychotics risperidone and aripiprazole are FDA-approved. The side effects of these treatments: weight gain, drooling, aggression often outweigh the benefits. Without objective biomarkers associated with the biological subtypes of disease dysregulation, it remains difficult to select beneficial drug treatments for individual patients from the perspective of personalized medicine.

[0163] Unbiased whole-genome screening of blood profiles by chromosome conformation was used for 12 individual patients annotated with clinically mild ASD and 12 patients with severe ASD, relative to the average profile of pooled healthy controls.

[0164] The detection of chromosomal conformations in these comparisons is not caused by bias or interest in a particular gene or genes. Identifying statistically significant chromosomal conformations for one of the subgroups identifies the regulatory domains of assembled topological autonomous chromosome domains that affect the control of genes captured within range by the nature of the 3D architecture. This enables an assessment of biological relevance by comparing unbiased systemic biomarkers obtained from EpiSwitch screening for genes discovered, for their biological relevance to ASD. Physically, the target sites / anchor points of long-range interactions of chromosomal conformations are always within the non-coding parts of the genome (including introns). They do not change the genomic sequence (i.e., are non-genetic by their regulatory nature) and thus have no direct relation to any effect on the protein amino acid composition from any gene.

[0165] Figure 1 shows the top 200 statistically significant markers present in either mild or severe ASD (identified against healthy controls (HC)). These two groups of markers overlap (145 markers were statistically significant when compared to HC and were present in both severe and mild ASD). 51 severe ASD markers were specific to the severe type. 55 mild ASD markers were specific to the mild type.

[0166] Figure 2 shows significant markers established as chromatin long-range domains, with the closest coding region - gene - identified in multiple scenarios of overlap in the upstream, downstream, and within the domain. In multiple combinations of overlap scenarios, the closest coding region (gene) was identified. All these combinations of overlap with the protein coding region are considered to have biological examples of how chromosomal conformation domains affect gene regulation.

[0167] Markers specific to mild ASD (chromosomal conformations that are present only in mild ASD or absent only in ASD) were analyzed for genes that are potentially affected by being within their regulatory scope. Next, these genes via their protein products were used in standard Cytoscape network construction, and the proteins were queried against a known systematic database of protein control networks and pathways. The top of the list was the neurexin and neuroligin control pathways. The Hippo pathway was also identified.

[0168] A similar analysis was done for markers specific to known transcription factor binding sites (TFBS). This was done for markers specific to severe ASD, specific to mild ASD, and common to both severe and mild ASD. The number of TFs significantly enriched for each group was plotted in the VENN diagram of Figure 3. For example, for markers specific to mild ASD, 18 TFs were uniquely enriched, 7 + 5 TFs were shared with the enrichment observed in the region of severe ASD markers, 5 + 5 TFs were common with the TFs enriched for the common shared mild and severe ASD markers, and 5 TFs were shared as shared for all three groups of ASD markers as shared for mild, severe, shared. The identified groups of enriched TFs were used with standard STRINGS network and pathway enrichment tools to evaluate which control pathways and networks are affected by the three groups of markers by the TFs.

[0169] The String network with TFs for specific mild AD markers was enriched for estrogen signaling and control of Th17 differentiation. Figure 4 shows pathway enrichment with TFs for specific mild ASD markers. Note neurexin and neuroligin, hippo, intoxication pathways.

[0170] Table 5 shows the same analysis, based not only on chromosomal conformation markers that are specifically present in mild ASD, but also on chromosomal conformation markers that are not specifically present in mild ASD. The specific presence of markers has important practical benefits for the detection test, while the specific absence of markers provides valuable biological insights from a control perspective. The same analysis was done for markers shared by both mild and severe ASD, and IL4, IL13, and Hippo were identified.

[0171] Table 6 shows the same analysis as Table 5, but for shared (common) ASD markers for both mild and severe. Note the HPV infection pathway.

[0172] The Cytoscape network for severe ASD identified the IL-4 mediated signaling pathway.

[0173] Table 7 is a network analysis considering not only specifically present ones, but also chromosomal conformations that are not specifically present in all severe ASD.

[0174] Figure 7 is a repetition of Figure 3 as a cautionary note before the String network and enrichment analysis of severe ASD markers.

[0175] String analysis by TF and pathway enrichment for severe ASD markers identified the viral infection pathway, brain development, and estrogen-dependent GEX.

[0176] Figure 6 shows the selection of the identified mild ASD marker pathways for further analysis. Figure 7 shows the same but for severe ASD.

[0177] Figure 8 shows the principal component analysis based on the mild pathway. Large ellipse in the left hand - mild ASD, small ellipse in the left hand - severe ASD, ellipse in the right hand - healthy control. Note the complete separation from both types of ASD in HC with severe ASD driven in the tight group compared to mild ASD - clear differences from the perspective of mild ASD markers in the mild and severe ASD profiles.

[0178] Figure 9 shows the same analysis but using the severe ASD pathway markers. Note the strong separation from both ASD (ellipses in the left hand) in HC (ellipse in the right hand).

[0179] The locus GRIN2A was analyzed, and a significant EpiSwitch™ binary marker specific to severe or mild ASD detected in blood, with reference to the position of the peaks of H3K27ac detected in the post - biopsy brain (the peaks themselves are broad and carry background noise). H3K27ac is thought to correlate with elements of the 3D chromosomal architecture in certain cases. The non - invasive binary marker was retrospectively observed to correlate with the peak positions marked in two cases. Not all H3K27ac peaks correlate with significant discriminatory differences between mild and severe ASD patients.

[0180] Tables 8 - 10 provide a list and panel of additional markers with specific performance criteria. For Table 10, SHAP values, i.e., SHapley Additive exPlanations, are given, and the SHAP values indicate how each predictor contributes to the target variable.

[0181] Figure 11 shows the network and pathways related to markers specific to mild ASD.

[0182] Figure 12 shows the enhancement of the network developed with all mild ASD markers.

[0183] Figure 13 shows the pathway enhancement related to common ASD markers.

[0184] Figure 14 shows the enhancement of the network developed with all common ASD markers.

[0185] Figure 15 shows the networks and pathways related to markers specific to severe ASD.

[0186] Figure 16 shows the enhancement of the network developed from markers specific to severe ASD.

[0187] Figure 17 shows the enhancement of the network developed for all severe ASD markers.

[0188] Figure 18 shows the TF network constructed using severe ASD markers.

[0189] Figure 19 shows the performance characteristics of the markers in Table 10. The training data is on the left and the test is on the right.

[0190] Conclusion EpiSwitch custom Agilent CGH array whole-genome screening provided significantly statistically significant markers between the following groups. 1. Present in healthy controls (HC) but not in mild and severe ASD 2. Specific to either mild or severe ASD and not present in HC 3. Common to severe and mild ASD but not present in HC 4. Specific absence in either severe or mild ASD

[0191] From the perspective of subjective and conflicting means that can potentially cause confusion in identifying and sub - classifying patients with ASD, unbiased whole - genome screening at the level of chromosomal conformation in blood identified statistically significant non - invasive biomarkers using a dissemination power to consistently distinguish between healthy controls, and mild and severe ASD as a measure of systemic non - genetic deregulation associated with ASD. Analysis of genomic regions receiving the identified chromosomal conformation controls is consistent with the biological pathway mechanisms involved in ASD, which involve neurological deregulation (such as neurexin), intoxication, estrogen, HPV infection, and immune system resetting (NK cells). The immune response shows a special presence in the severe ASD subgroup.

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Claims

**Claim 1** A process for detecting chromosomal states representing subgroups in a population, comprising: identifying whether chromosomal interactions associated with the chromosomal state are present or absent within a defined region of the genome; and - said chromosomal interactions are optionally identified by a method for determining which chromosomal interactions are associated with a chromosomal state corresponding to a subgroup of the population, said method comprising contacting a first set of nucleic acids from subgroups having different states of a chromosome with a second set of index nucleic acids, and enabling hybridization of complementary sequences, wherein the nucleic acids in the first and second sets of nucleic acids represent ligated products containing sequences from both chromosomal regions joined in a chromosomal interaction, and the hybridization pattern between the first set and the second set of nucleic acids enables determination of which chromosomal interactions are specific to the subgroup; and - the subgroup is associated with the prognosis of autism spectrum disorder (ASD), and the chromosomal interaction is (i) present in any of the regions or genes listed in any of Tables 1, 2, 3 or 4, and / or (ii) corresponding to any of the chromosomal interactions represented by any of the probes shown in any of Tables 1, 2, 3 or 4, and / or (iii) present in a 4,000 base region containing or adjacent to (i) or (ii) A process. **Claim 2** - Table 1, or - Table 2, or - Table 3, or - Table 4 in which at least 10 chromosomal interactions are classified The process according to claim 1. **Claim 3** The process according to claim 1 or 2, in which at least 1, 5, 8 or 10 chromosomal interactions represented in Table 8, 9 or 10 are classified. **Claim 4** The process according to any one of claims 1 to 3, in which at least 1, 5, 8, 10, 15 or 20 chromosomal interactions represented in Table 10 are classified. **Claim 5** The chromosomal interaction is - classified in a sample from an individual, and / or - classified by detecting the presence or absence of a DNA loop at the site of the chromosomal interaction, and / or - classified by detecting the presence or absence of distal regions of chromosomes joined in a chromosomal conformation, and / or - Classified by detecting the presence of ligated nucleic acids that are generated between said classifications and contain two regions corresponding to regions of chromosomes whose sequences come together in chromosomal interactions, where the detection of the ligated nucleic acids is preferably by a probe having at least 70% identity to any of the specific probe sequences described in any of Tables 1, 2, 3 or 4, and / or (ii) by a primer pair having at least 70% identity to any of the primer pairs of any of Tables 1, 2, 3 or 4, The process according to any one of claims 1 to 4.

6. The detection of chromosomal interactions is (i) cross-linking chromosomal regions that come together in chromosomal interactions, (ii) optionally subjecting the cross-linked regions to cleavage by restriction digestion with an enzyme, and (iii) ligating the cross-linked and cleaved DNA ends to form a first set of nucleic acids (in particular including ligated DNA) The process according to any one of claims 1 to 5, which is carried out by a process comprising the steps of

7. The process according to any one of claims 1 to 6, which is carried out to determine the severity or type of ASD.

8. The process according to any one of claims 1 to 7, wherein at least 1, 5, 8, 10, 15 or 20 chromosomal interactions are classified as being specific for mild ASD.

9. The process according to any one of claims 1 to 8, wherein at least 1, 5, 8, 10, 15 or 20 chromosomal interactions are classified as being specific for severe ASD.

10. The classification or detection is by quantitative PCR (qPCR) using primers capable of amplifying the ligated product and a probe capable of binding to the ligation site during the PCR reaction, the probe containing sequences complementary to sequences each derived from chromosomal regions that come together in chromosomal interactions, preferably the probe is an oligonucleotide that specifically binds to the ligated product, and / or a fluorophore covalently attached to the 5' end of the oligonucleotide, and / or a quencher covalently attached to the 3' end of the oligonucleotide and, optionally, the fluorophore is selected from HEX, Texas Red and FAM, and / or The probe comprises a nucleic acid sequence having a nucleotide base length of 10 to 40, preferably 20 to 30 The process according to any one of claims 1 to 9. **Claim 11** - The result of the process is provided in a report, and / or - The result of the process is used to select a patient's treatment schedule, preferably to select a treatment specific to an individual The process according to any one of claims 1 to 10. **Claim 12** The process according to any one of claims 1 to 11, wherein the individual is preselected based on physical characteristics, risk factors or symptoms, and preferably based on having symptoms or risk factors of autism, childhood autism, Asperger's syndrome, PDD-NOS (pervasive developmental disorder), childhood disintegrative disorder or addiction **Claim 13** A therapeutic agent for use in a method of treating ASD in an individual identified as in need of a therapeutic agent by the process according to any one of claims 1 to 12.

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