Autism spectrum disorder assessment via alternative polyadenylation analysis

Abstract

The present invention relates to a method for assessing an autism spectrum disorder (ASD) in a sample from a subject, the method comprising (a) determining a parameter of alternative polyadenylation (APA parameter) in said sample; (b) comparing the APA parameter determined in step (a) to a reference, and (c) based on the comparison in step (b), assessing said ASD. The present invention also relates to polynucleotides, means for determining an APA parameter for use in diagnosis, databases, devices, and further methods related thereto.

Description

[0001] Deutsches Krebsforschungszentrum Stiftung des öffentlichen Rechts Universität Heidelberg 1 DK16729PC __________________________________________________________________________________ Autism Spectrum Disorder Assessment via alternative Polyadenylation Analysis __________________________________________________________________________________ The present invention relates to a method for assessing an autism spectrum disorder (ASD) in a sample from a subject, the method comprising (a) determining a parameter of alternative polyadenylation (APA parameter) in said sample; (b) comparing the APA parameter determined in step (a) to a reference, and (c) based on the comparison in step (b), assessing said ASD. The present invention also relates to polynucleotides, means for determining an APA parameter for use in diagnosis, databases, devices, and further methods related thereto. A minority of ASD cases are syndromic forms caused by highly penetrant single-gene mutations or chromosomal abnormalities; these forms are often accompanied by additional phenotypes, such as intellectual disability, epilepsy and craniofacial dysmorphology. By contrast, most ASD cases are idiopathic, with genetic causality residing in polygenic risk involving small effect-size variants in hundreds of genes. Thus, there is no available molecular test that could be used for diagnosis, and the main diagnostic criteria remains the abnormal social and repetitive behavior. Recently, alternative polyadenylation (APA), which is known to play an essential role in brain development, was used to identify APA-linked susceptibility genes in samples of brain tissue, e.g. for Parkinson's disease and amyotrophic lateral sclerosis (Cui et al. (2023), Nat Comm 14:583). APA was also used in assessment of cancer (Zhang et al.2021), J Exp Clin Cancer Res 40:51). Transcriptomic changes and APA were also identified in brain tissue from autism patients (Göpferich et al. (2020), doi.org / 10.1101 / 2020.08.12.247627, Szkop et al. (2017), Front Mol Neurosci 10, Article 279), also by single cell transcriptomics (Velmeshev et al. (2019), Science 364:6441; He et al. (2019), Sci Rep 9:11818), and involvement of CPEB4 was reported (Parras et al. (2018), Nature 560:7719). However, brain tissue is not easily accessible as sample material, in particular since ASD diagnosis is desirable at a very young age, in order to mitigate symptoms before they appear, if possible (Carruthers et al. (2023), J Child Psychol Psychiatry, doi:10.1111 / jcpp.13798). Thus, transcriptomic analyses in blood samples of ASD patients was suggested (Tylee et al. (2017), Am J Med Genet Neuropsychiatr Genet 174(3):181) as a diagnostic means, however, only with moderate success. There is, nonetheless, still a need in the art for improved means and methods for diagnosis of ASD, in particular autism, and reagents related thereto, avoiding the drawbacks as referred to above. In particular, improved methods for assigning patients to possible treatments are required. In accordance, the present invention relates to a method for assessing an autism spectrum disorder (ASD) in a sample from a subject, the method comprising (a) determining a parameter of alternative polyadenylation (APA parameter) in said sample; (b) comparing the APA parameter determined in step (a) to a reference, and (c) based on the comparison in step (b), assessing said ASD. In a preferred embodiment, the present invention relates to a method for assessing a light form of an autism spectrum disorder (ASD), preferably ASD level 1, in a sample from a subject, the method comprising (a) determining a parameter of alternative polyadenylation (APA parameter) in said sample; (b) comparing the APA parameter determined in step (a) to a reference, and (c) based on the comparison in step (b), assessing said light form of ASD, wherein said APA parameter preferably comprises at least one mRNA 3'UTR length of ATP5F1E, BUB3, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPN1, RPS15A, SERTAD3, SLC25A44, TSC22D3, UBE2A, ZBTB7B, EEF1A1, EIF4A3, HDAC6, HNRNPH2, IFFO1, INPP5D, POLR2F, and / or RPS19. In general, terms used herein are to be given their ordinary and customary meaning to a person of ordinary skill in the art and, unless indicated otherwise, are not to be limited to a special or customized meaning. As used in the following, the terms “have”, “comprise” or “include” or any arbitrary grammatical variations thereof are used in a non-exclusive way. Thus, these terms may both refer to a situation in which, besides the feature introduced by these terms, no further features are present in the entity described in this context and to a situation in which one or more further features are present. As an example, the expressions “A has B”, “A comprises B” and “A includes B” may both refer to a situation in which, besides B, no other element is present in A (i.e. a situation in which A solely and exclusively consists of B) and to a situation in which, besides B, one or more further elements are present in entity A, such as element C, elements C and D or even further elements. Also, as is understood by the skilled person, the expressions "comprising a" and "comprising an" preferably refer to "comprising one or more", i.e. are equivalent to "comprising at least one". In accordance, expressions relating to one item of a plurality, unless otherwise indicated, preferably relate to at least one such item, more preferably a plurality thereof; thus, e.g. identifying "a cell" relates to identifying at least one cell, preferably to identifying a multitude of cells. Also, the term "multitude", as referred to herein, relates to a number of more than one, i.e. at least two, preferably at least three, more preferably at least four, even more preferably at least five. A multitude may however, also be a number of at least ten, at least 25, at least 100, or more. Further, as used in the following, the terms "preferably", "more preferably", "most preferably", "particularly", "more particularly", "specifically", "more specifically" or similar terms are used in conjunction with optional features, without restricting further possibilities. Thus, features introduced by these terms are optional features and are not intended to restrict the scope of the claims in any way. The invention may, as the skilled person will recognize, be performed by using alternative features. Similarly, features introduced by "preferably" or similar expressions are intended to be optional features, without any restriction regarding further embodiments of the invention, without any restrictions regarding the scope of the invention and without any restriction regarding the possibility of combining the features introduced in such way with other optional or non-optional features of the invention. The methods specified herein below, preferably, are in vitro methods. The method steps may, in principle, be performed in any arbitrary sequence deemed suitable by the skilled person, but preferably are performed in the indicated sequence; also, one or more, preferably all, of said steps may be assisted or performed by automated equipment. Moreover, the methods may comprise steps in addition to those explicitly mentioned above. As used herein, if not otherwise indicated, the term "about" relates to the indicated value with the commonly accepted technical precision in the relevant field, preferably relates to the indicated value ± 20%, more preferably ± 10%, most preferably ± 5%. Further, the term "essentially" indicates that deviations having influence on the indicated result or use are absent, i.e. potential deviations do not cause the indicated result to deviate by more than ± 20%, more preferably ± 10%, most preferably ± 5%. Thus, “consisting essentially of” means including the components specified but excluding other components except for materials present as impurities, unavoidable materials present as a result of processes used to provide the components, and components added for a purpose other than achieving the technical effect of the invention. For example, a composition defined using the phrase “consisting essentially of” encompasses any known acceptable additive, excipient, diluent, carrier, and the like. Preferably, a composition consisting essentially of a set of components will comprise less than 5% by weight, more preferably less than 3% by weight, even more preferably less than 1% by weight, most preferably less than 0.1% by weight of non-specified component(s). The degree of identity (e.g. expressed as "%identity") between two biological sequences, preferably DNA, RNA, or amino acid sequences, can be determined by algorithms well known in the art. Preferably, the degree of identity is determined by comparing two optimally aligned sequences over a comparison window, where the fragment of the sequence in the comparison window may comprise additions or deletions (e.g., gaps or overhangs) as compared to the sequence it is compared to for optimal alignment. The percentage is calculated by determining, preferably over the whole length of the polynucleotide or polypeptide, the number of positions at which the identical residue occurs in both sequences to yield the number of matched positions, dividing the number of matched positions by the total number of positions in the window of comparison and multiplying the result by 100 to yield the percentage of sequence identity. Optimal alignment of sequences for comparison may be conducted by the local homology algorithm of Smith and Waterman (1981), by the homology alignment algorithm of Needleman and Wunsch (1970), by the search for similarity method of Pearson and Lipman (1988), by computerized implementations of these algorithms (e.g. BLAST, GAP, BESTFIT, PASTA, or TFASTA), or by visual inspection. Given that two sequences have been identified for comparison, GAP and BESTFIT are preferably employed to determine their optimal alignment and, thus, the degree of identity. Preferably, the default values of 5.00 for gap weight and 0.30 for gap weight length are used. More preferably, the Basic Local Alignment Search Tool (BLAST) implementation is used with default parameter values for alignment. In the context of biological sequences referred to herein, the term "essentially identical" indicates a %identity value of at least 80%, preferably at least 90%, more preferably at least 98%, most preferably at least 99%. As will be understood, the term essentially identical includes 100% identity. The aforesaid applies to the term "essentially complementary" mutatis mutandis. The term "fragment" of a biological macromolecule, preferably of a polynucleotide or polypeptide, is used herein in a wide sense relating to any sub-part, preferably subdomain, of the respective biological macromolecule comprising the indicated sequence, structure and / or function. Thus, the term includes sub-parts generated by actual fragmentation of a biological macromolecule, but also sub-parts derived from the respective biological macromolecule in an abstract manner, e.g. in silico. Thus, as used herein, an Fc or Fab fragment, but also e.g. a single-chain antibody, a bispecific antibody, and a nanobody may be referred to as fragments of an immunoglobulin. Also, unless specifically indicated otherwise herein, the compounds specified, in particular the polynucleotides and polypeptides, may be comprised in larger structures, e.g. may be covalently or non-covalently linked to further sequences, carrier molecules, retardants, and other excipients. The term “polypeptide”, as used herein, refers to a molecule consisting of several, typically at least 20 amino acids that are covalently linked to each other by peptide bonds. Molecules consisting of less than 20 amino acids covalently linked by peptide bonds are usually considered to be "peptides". Preferably, the polypeptide comprises of from 50 to 1000, more preferably of from 75 to 1000, still more preferably of from 100 to 500, most preferably of from 110 to 400 amino acids. Preferably, the polypeptide is comprised in a fusion polypeptide and / or a polypeptide complex. The term “polynucleotide” is known to the skilled person. As used herein, the term includes nucleic acid molecules comprising or consisting of a nucleic acid sequence or nucleic acid sequences as specified herein, optionally further comprising a chemical molecule allowing detection of the polynucleotide, i.e. an indicator agent, such as a dye. The polynucleotide of the present invention shall be provided, preferably, either as an isolated polynucleotide, i.e. isolated from its natural context, or in genetically modified form. The polynucleotide, preferably, is DNA, including cDNA, or is RNA. The term encompasses single as well as double stranded polynucleotides, as well as circularly closed and linear polynucleotides. Preferably, the polynucleotide is a chimeric molecule, i.e., preferably, comprises at least one nucleic acid sequence, preferably of at least 20 bp, more preferably at least 100 bp, heterologous to the residual nucleic acid sequence(s) or being an artificial nucleic acid sequence. Moreover, preferably, comprised are also chemically modified polynucleotides including naturally occurring modified polynucleotides such as glycosylated or methylated polynucleotides or artificial modified ones such as biotinylated polynucleotides, peptide nucleic acids, locked nucleic acids, and the like. The polynucleotide may, in principle, have any length deemed appropriate by the skilled person and / or as specified herein. In concurrence with common use of the terms, a polynucleotide comprising less than 25 nucleotides may also be referred to as an "oligonucleotide", thus, a probe polynucleotide with a length of less than 25 nucleotides may also be referred to as a "probe oligonucleotide", and a primer polynucleotide with a length of less than 25 nucleotides may be referred to as a "primer oligonucleotide" or as a "primer". Unless specifically indicated, the term polynucleotide includes polynucleotide variants. The term “polynucleotide variant”, as used herein, relates to a variant of a polynucleotide referred to herein comprising a nucleic acid sequence characterized in that the sequence can be derived from the aforementioned specific nucleic acid sequence by at least one nucleotide substitution, addition and / or deletion, wherein the polynucleotide variant shall have the activity as specified for the specific polynucleotide. Preferably, said polynucleotide variant is an ortholog, a paralog or another homolog of the specific polynucleotide. Also preferably, said polynucleotide variant is or is derived from a non-naturally occurring allele of the specific polynucleotide. Polynucleotide variants also encompass polynucleotides comprising a nucleic acid sequence which is capable of hybridizing to the aforementioned specific polynucleotides, preferably, under stringent hybridization conditions. These stringent conditions are known to the skilled worker and can be found in Current Protocols in Molecular Biology, John Wiley & Sons, N. Y. (1989), 6.3.1-6.3.6. The skilled worker knows how to determine the hybridization conditions required by referring to textbooks such as the textbook mentioned above, or the following textbooks: Sambrook et al., "Molecular Cloning”, Cold Spring Harbor Laboratory, 1989; Hames and Higgins (Ed.) 1985, ”Nucleic Acids Hybridization: A Practical Approach”, IRL Press at Oxford University Press, Oxford; Brown (Ed.) 1991, "Essential Molecular Biology: A Practical Approach”, IRL Press at Oxford University Press, Oxford. Further, variants include polynucleotides comprising nucleic acid sequences which are at least 70%, preferably at least 80%, more preferably at least 90%, even more preferably at least 95%, still more preferably at least 98%, most preferably at least 99%, identical to the specifically indicated nucleic acid sequences. The percent identity values are, preferably, calculated over the entire nucleic acid sequence region, preferably as specified herein above. The polynucleotides of the present invention either consist, essentially consist of, or comprise the aforementioned nucleic acid sequences. Thus, they may contain further nucleic acid sequences as well. Also, the polynucleotides and variants thereof may be comprised in a vector. The method for assessing of the present invention, preferably, is an in vitro method. Moreover, it may comprise steps in addition to those explicitly mentioned above. For example, further steps may relate, e.g., to providing a sample for step (a), or performing additional diagnostic steps before or after step (c). The term "autism spectrum disorder", which may be abbreviated as "ASD", is known to the skilled person to relate to a diverse group of conditions of human subjects characterized by at least some degree of difficulty with social interaction and communication. Other characteristics are atypical patterns of activities and behaviors, such as difficulty with transition from one activity to another, a focus on details and unusual reactions to sensations. Preferably, ASD is autism, Asperger's syndrome, or childhood disintegrative disorder. Symptoms of ASD are known in the art and may include in particular reduced eye contact, lack of response to their name or indifference to caregivers, withdrawal from social interaction, aggressive behavior, loss of language skills acquired previously, and development of unique pattern of behavior. Further symptoms which may be diagnosed include difficulty learning, problems in communicating, in applying knowledge in everyday life, and in adjusting to social situations. In a preferred embodiment, ASD is a light form of ASD, wherein the term "light form of ASD" preferably relates to a form of ASD referred to as ASD level 1. Thus, ASD may be ASD level 1. ASD level 1, as referred to herein, preferably relates to ASD of a severity level defined as "level 1" in the 2022 text revision of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5-TR). Thus, a subject with ASD level 1 preferably has, relative to a healthy subject, difficulties communicating with neurotypical subjects, including their peers, in particular saying the right thing at the right time or reading social cues and body language; has difficulties moving from one activity to another or trying new things, and / or has difficulties to organize and plan their lives. As the skilled person understands, such difficulties may only become apparent when the subject reaches an age where such capabilities are deemed normal for non-ASD subjects. Thus, the methods of the instant invention may predict ASD, in particular ASD level 1, may enable preventive therapy, and thereby may prevent symptoms of ASD level 1 or higher from occurring. Also in a preferred embodiment, ASD is not ASD level 2 or higher, i.e. the ASD subject preferably does not engage in repetitive behaviors that are far from accepted, neurotypical behaviors and / or does not show behavior that neurotypical subjects view as a mismatch with time and place. The term “assessing”, as used herein, refers to establishing information about the status of the indicated disease or condition, in particular its severity, symptoms, localization, prognosis and / or other relevant information. Said assessing, preferably, is an aid in diagnosing the indicated disease, i.e. ASD, preferably autism. As the skilled person will understand, establishing a diagnosis may be based on the aforesaid assessment, however, more preferably, is based on the aforesaid assessment in combination with further diagnostic information, such as anamnesis data and / or general physical and mental examination findings. Thus, assessing ASD may relate to assessing whether a subject suffers from ASD, is at risk of suffering from ASD, exhibits a medical condition which deteriorates with respect to ASD, and / or establishing a prognosis with respect to ASD. Accordingly, assessing as used herein includes diagnosing ASD, predicting the risk for developing ASD, and / or predicting any deterioration of the health condition of the subject, in particular, with respect to signs and symptoms accompanying ASD. Assessment referred to herein preferably is the assessment of a risk of developing and / or suffering from ASD. Also preferably, assessment may be the prediction of the risk that the subject’s (health) condition will deteriorate. Moreover, it will be understood that if the risk of developing ASD or risk of the deterioration of the health condition is predicted, typically, the prediction is made within a predictive window. More typically, said predictive window is of from newborn to 5 years, more preferably of from 6 months to two years. Assessing as referred to herein may relate to a rule-in assessment, i.e. to identifying a subject as belonging to a group of subjects sharing a common feature, e.g. suffering from ASD, in a preferred embodiment ASD level 1 as specified herein above. Assessing, however, may also relate to a rule-out assessment, i.e. to identifying a subject as not belonging to a group of subjects sharing a common feature, e.g. as not suffering from ASD, in a preferred embodiment ASD level 1. Thus, the assessment may aid in establishing a diagnosis; the assessment may, however, also aid in excluding a diagnosis. In a preferred embodiment, assessing comprises classifying a patient into an "ASD level 1" group or a "healthy" group, wherein the term "healthy" relates to an apparently normal state with regards to ASD. In a further preferred embodiment, assessing comprises differentiating an ASD patient from a healthy sibling. In a preferred embodiment, the aforesaid assessment is provided at a young age of the subject, preferably before the age of 10 years, more preferably before the age of five years, still more preferably before the age of 3 years, even more preferably before the age of two years, most preferably before the age of one year. Since the assessment may also be performed e.g. on a blood sample, it may in a preferred embodiment also be performed shortly after birth, e.g. within two weeks, more preferably within four weeks, still more preferably within three months, even more preferably within six months, most preferably within twelve months after birth. In a further preferred embodiment, the assessment is performed before the subject develops speech, i.e. preferably before the age of one year. As will be understood by those skilled in the art, the assessment made in accordance with the present invention, although usually preferred to be, may not be correct for 100% of the investigated subjects. However, the term typically requires that a statistically significant portion of subjects can be correctly assessed. Whether a portion is statistically significant can be determined without further ado by the person skilled in the art using various well known statistic evaluation tools, e.g., determination of confidence intervals, p-value determination, Student´s t-test, Mann-Whitney test, etc. Details may be found in Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York 1983. Typically envisaged confidence intervals are at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%. The p-values are, typically, 0.2, 0.1, or 0.05. The term "sample" refers to a sample of separated cells or to a sample from a tissue or an organ, preferably of blood cells. Preferably, the sample is a sample of a bodily fluid comprising cells, such as blood, saliva, urine, mucus, lacrimal fluid, or any other sample comprising at least one type of nucleated cells deemed appropriate by the skilled person. The body fluid sample can be obtained from the subject by routine techniques which are well known to the person skilled in the art, e.g., venous or arterial puncture, lavage, or any other method deemed appropriate by the skilled person. Preferably, the sample is a blood sample or a blood-derived sample, i.e. preferably comprises blood cells, preferably nucleated blood cells, more preferably peripheral blood monocytic cells (PBMC). Also preferably, the sample is a sample comprising cells from the nasopharynx, more preferably is a saliva sample, a sample of a gargling fluid, a nasal or oral swab, or the like, more preferably is a saliva sample. Thus, the sample may in particular be a blood sample or a blood-derived sample or a saliva sample. The sample may, however also be a tissue sample, preferably of easily accessible tissue, such as of a mucous membrane or skin. As is known to the skilled person, tissue or organ samples may be obtained from any tissue or organ by, e.g., biopsy, surgery, or any other method deemed appropriate by the skilled person. Separated cells may be obtained from the body fluids, such as lymph, blood, cerebrospinal fluid (CSF) and other, or from the tissues or organs by separating techniques such as centrifugation or cell sorting. Preferably, the sample is a non-brain sample, i.e. is not a sample of intracranial tissue; thus, the sample preferably does not comprise neurons and / or glial cells. The term "subject", as referred to herein, relates to a mammal, preferably a human. Preferably, the subject is suspected to suffer from ASD, or is suspected to develop ASD in the future, preferably within two to five years, more preferably within two years. Suspicion of suffering or developing ASD may arise e.g. from family analysis showing an increased probability of ASD, or from one or more symptoms of ASD as specified herein above. Thus, the subject may have a suspicion diagnosis of ASD. Preferably, the subject is at an age of from 0 to 10 years, more preferably of from 0 to 5 years, most preferably of from 0 to two years. The term "alternative polyadenylation" is, in principle, understood by the skilled person to relate to a post-transcriptional regulatory mechanism that processes RNA polynucleotides in a cell, preferably depending on their 3′-untranslated region (3′-UTR) specific sequence signal. APA processing can generate several mRNA isoforms from a single gene, which may have different biological functions. In accordance, a "parameter of APA", as the term is referred to herein, relates to any and all parameters being a measure of APA. Thus, an APA parameter may be the average length of the 3' untranslated region (3'UTR) of transcripts of a marker gene, preferably selected from the marker genes of Table 1. The APA parameter may, however, also be a parameter derived from lengths of 3'UTRs of a multitude of genes as specified herein, preferably at least 3 of said genes, more preferably at least ten of said genes. Thus, the APA parameter may be an absolute (average) length of at least one 3'UTR, a relative length, e.g. compared to a reference length, as well as any value or parameter which correlates thereto or can be derived therefrom, preferably by standard mathematical operations. As is understood by the skilled person in view of the description herein, in particular Table 1, depending on the marker gene evaluated, lengthening or shortening of the 3'UTR, relative to a subject not suffering from ASD, may be indicative of a subject suffering from ASD, e.g. based on signed earth movers distance (wasserstein distance) (EMD) values. As indicated in Table 1, positive EMD values indicate marker genes in which a lengthening of 3'UTRs is indicative of ASD, and negative EMD values are indicative of marker genes for which shortening of 3'UTRs is indicative of ASD. As the skilled person understands, in case 3'UTR lengths of a multitude of marker genes is determined, the results obtained may be mathematically combined into one or more parameter(s) of APA, e.g. a single score. As the skilled person understands from the description herein, a parameter of APA may also be determined by determining the presence or absence of nucleic acid sequences which are present in a normal (or extended) variant of a 3'UTR, but not in a shortened (or normal) variant of said 3'UTR. Also in such case, determinations of 3'UTRs from more than one marker gene may be combined into a score. Preferably, the APA parameter comprises at least one mRNA 3'UTR length of RPN1, UBE2A and / or MKRN2. Also preferably, the APA parameter comprises mRNA 3'UTR lengths of RPN1, UBE2A, and MKRN2. More preferably, the APA parameter comprises mRNA 3'UTR lengths of RPN1, UBE2A, MKRN2, UHMK1, IER3, BUB3, MLX, CA2, ITM2A, and / or NT5C3A. Still more preferably, the APA parameter comprises mRNA 3'UTR lengths of at least two, preferably at least three, more preferably at least four, even more preferably at least five, still more preferably at least six, still more preferably at least seven, still more preferably at least eight, still more preferably at least nine, most preferably all ten genes selected from the list consisting of RPN1, UBE2A, MKRN2, UHMK1, IER3, BUB3, MLX, CA2, ITM2A, and NT5C3A. Even more preferably, the APA parameter comprises mRNA 3'UTR lengths of the genes RPN1, UBE2A, MKRN2, UHMK1, IER3, BUB3, MLX, CA2, ITM2A, and NT5C3A. In a preferred embodiment, in particular in case the assessing is assessing ASD level 1, the APA parameter comprises at least one mRNA 3'UTR length of ATP5F1E, BUB3, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPN1, RPS15A, SERTAD3, SLC25A44, TSC22D3, UBE2A, and / or ZBTB7B or of ATP5F1E, BUB3, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPN1, RPS15A, SERTAD3, SLC25A44, TSC22D3, UBE2A, ZBTB7B, EEF1A1, EIF4A3, HDAC6, HNRNPH2, IFFO1, INPP5D, POLR2F, and / or RPS19. In a further preferred embodiment, in particular in case the assessing is assessing ASD level 1, the APA parameter comprises mRNA 3'UTR lengths of at least five, preferably at least ten, more preferably at least 15, even more preferably at least 20, still more preferably at least 25, still more preferably at least all genes selected from the list consisting of ATP5F1E, BUB3, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPN1, RPS15A, SERTAD3, SLC25A44, TSC22D3, UBE2A, and ZBTB7B; or consisting of ATP5F1E, BUB3, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPN1, RPS15A, SERTAD3, SLC25A44, TSC22D3, UBE2A, ZBTB7B, EEF1A1, EIF4A3, HDAC6, HNRNPH2, IFFO1, INPP5D, POLR2F, and RPS19. In a further preferred embodiment, in particular in case the assessing is assessing ASD level 1, the APA parameter comprises mRNA 3'UTR lengths of ATP5F1E, BUB3, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPN1, RPS15A, SERTAD3, SLC25A44, TSC22D3, UBE2A, and ZBTB7B or of ATP5F1E, BUB3, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPN1, RPS15A, SERTAD3, SLC25A44, TSC22D3, UBE2A, ZBTB7B, EEF1A1, EIF4A3, HDAC6, HNRNPH2, IFFO1, INPP5D, POLR2F, and RPS19. The term "marker gene", as used herein, relates to a gene, preferably of a subject and preferably expressed by cells in a sample, from which at least one transcript is transcribed which shows differential polyadenylation in subjects suffering from ASD as compared to subjects not suffering from ASD. Thus, marker genes, preferably, are marker genes of ASD and, more preferably, determining an APA parameter of a gene product thereof allows the assessment to be made. Methods for identifying a marker gene as referred to herein are provided on an exemplary basis herein in the Examples. Preferred marker genes are provided in Table 1. In a preferred embodiment, marker genes are provided in Table 14; thus, in a further prefered embodiment, the marker gene(s) is / are selected independently from Table 1 and / or Table 14. The marker genes of Table 1 and Table 14 are, in principle known to the skilled person and can be accessed by the indicated accession numbers. Unless indicated otherwise, all database accession numbers in this description refer to the respective entry current at the filing date of this description. It is understood by the skilled person that the marker genes are referenced as biomarkers, not as specific polynucleotides or polypeptides. Accordingly, the gene products of the marker genes, in particular polynucleotides and polypeptides having the specific sequences deposited under the database accession numbers, are to be understood as exemplary sequences representing a marker gene. Encompassed as gene products according to the present invention are also variant polynucleotides which vary due to at least one nucleotide addition, substitution and / or deletion from the polynucleotide having the specific sequence deposited in the database as long as they are also suitable as biomarkers as discussed above. Preferably, the variant polynucleotides are at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98% or at least 99% identical to the specific polynucleotides. Preferably, the APA parameter is determined based on all RNA transcript gene products of a marker gene present in a sample. Preferably, a multitude of marker genes is used in the assessment. Thus, preferably, the marker gene is RPN1, UBE2A and / or MKRN2, more preferably is RPN1, UBE2A, MKRN2, UHMK1, IER3, BUB3, MLX, CA2, ITM2A, and / or NT5C3A. In a preferred embodiment, the marker gene is ATP5F1E, BUB3, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPN1, RPS15A, SERTAD3, SLC25A44, TSC22D3, UBE2A, and / or ZBTB7B. In a further preferred embodiment , the marker gene is ATP5F1E, BUB3, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPN1, RPS15A, SERTAD3, SLC25A44, TSC22D3, UBE2A, ZBTB7B, EEF1A1, EIF4A3, HDAC6, HNRNPH2, IFFO1, INPP5D, POLR2F, and / or RPS19. Table 1: Preferred marker genes, with database accession numbers and EMD values; EMD: earth movers distance (wasserstein distance), positive EMD values indicate larger UTR in ASD patients; SEQ ID NOs of exemplary 3'UTRs of the respective genes are indicated. Gene NCBI Genbank Acc No EMD SEQ ID NO GeneID RPN1 6184 NM_002950.4 -0,3697 1 UBE2A 7319 NM_003336.4 -0,2578 2 MKRN2 23609 NM_014160.5 0,0134 3 UHMK1 63877 NM_175866.5 0,7158 4 IER3 8870 NM_003897.4 -0,1085 5 BUB3 7529 NM_004725.4 -0,5448 6 MLX 4691 NM_198204.2 -0,3364 7 CA2 760 NM_000067.3 -0,5689 8 ITM2A 9452 NM_004867.5 -0,4221 9 NT5C3A 6117 NM_001002010.5 -0,2225 10 FGR 2268 NM_005248.3 -0,0212 11 M6PR 3382 NM_002355.4 -0,2149 12 AK2 204 NM_001625.4 -0,6316 13 IFRD1 3475 NM_001550.4 0,0056 14 RHBDF1 64285 NM_022450.5 0,3113 15 MMP25 11188 NM_022468.5 0,0063 16 CALCOCO1 23034 NM_020898.3 -0,0540 17 NUB1 51507 NM_001243351.2 -0,1461 18 IFNGR1 3459 NM_000416.3 0,0013 19 TRIT1 51742 NM_017646.6 0,2333 20 OFD1 8481 NM_003611.3 0,0749 21 VAMP3 9341 NM_004781.4 -0,1084 22 GDI2 2665 NM_001494.4 -0,0147 23 ATP11B 4904 NM_014616.3 0,0036 24 MRPS35 7158 NM_021821.4 -0,2359 25 CASP8 841 NM_001372051.1 -0,0729 26 ZBTB11 27107 NM_014415.4 -0,0736 27 ISOC1 5566 NM_016048.2 -0,3355 28 SMG6 5362 NM_017575.5 -0,0653 29 FGF22 7163 NM_020637.2 -0,4825 30 DGCR2 9618 NM_005137.3 -0,0685 31 CAMK2A 815 NM_015981.4 -2.8818 32 RPL31 6160 NM_000993.5 0,0006 33 TRIB2 28951 NM_021643.4 -0,3149 34 FAM3A 56925 NM_021806.4 0,2052 35 RAB7A 7879 NM_004637.6 -0,7569 36 LXN 27131 NM_020169.4 -0,0863 37 KEAP1 5037 NM_203500.2 -0,0881 38 PCNP 11213 NM_020357.3 -0,4134 39 EPB41L3 23136 NM_012307.5 -0,4042 40 ATRX 546 NM_000489.6 -0,1989 41 SLC25A24 23411 NM_013386.5 0,2250 42 RBM22 3483 NM_018047.3 -0,0597 43 FKBP1A 2280 NM_000801.5 -0,0204 44 SNX5 9701 NM_014426.4 -0,2775 45 PABPC4 8761 NM_001135653.2 -0,0064 46 TINF2 26277 NM_001099274.3 -0,0545 47 COMT 1312 NM_000754.4 -0,0591 48 FBXO7 6367 NM_012179.4 0,0524 49 PDGFB 5155 NM_002608.4 -0,6634 50 SGSM3 27352 NM_015705.6 0,0340 51 RBX1 9978 NM_014248.4 0,0043 52 RBM23 9738 NM_001077351.2 0,1260 53 NIN 1512 NM_020921.4 0,0471 54 SNW1 8767 NM_012245.3 -0,0102 55 PSMC1 5700 NM_002802.3 0,3254 56 SRP54 6729 NM_003136.4 -0,1561 57 TPD52L2 7165 NM_003288.4 -0,0647 58 RIOK3 8780 NM_003831.5 -0,4522 59 SLC35A2 857 NM_005660.3 0,0571 60 PIN4 835 NM_006223.4 -0,0711 61 BEX4 29886 NM_001080425.4 -0,0291 62 STK24 4899 NM_001032296.4 -0,9167 63 TSC22D1 6009 NM_183422.4 -0,1838 64 USB1 55664 NM_024598.4 0,0009 65 CYB5B 55848 NM_030579.3 -0,1190 66 ELOB 6923 NM_007108.4 -0,0084 67 R3HCC1 57606 NM_001136108.3 0,5893 68 UBXN8 7993 NM_005671.4 -0,0736 69 ASAH1 427 NM_177924.5 -0,3875 70 LIN7B 23321 NM_022165.3 -0,0895 71 C19orf53 3312 NM_014047.3 0,0024 72 ZFR2 1656 NM_015174.2 1.04315 73 KDELR1 10945 NM_006801.3 -0,0278 74 GRWD1 6404 NM_031485.4 -0,1524 75 PDE4C 1389 NM_001098818.4 0,6518 76 SMG9 7421 NM_019108.4 0,5413 77 SEC61B 6908 NM_006808.3 0,0037 78 ARHGAP21 10802 NM_020824.4 -0,1659 79 RPL28 6158 NM_000991.5 -0,0249 80 TSPAN14 54464 NM_030927.4 1.45294 81 RAB5C 5878 NM_004583.4 -0,1263 82 LPXN 5000 NM_004811.3 -0,0068 83 MS4A6A 4780 NM_022349.4 0,0009 84 IL10RA 3587 NM_001558.4 -0,0113 85 CDKN1B 1027 NM_004064.5 -0,4726 86 COPZ1 8934 NM_016057.3 -0,0768 87 RAB5B 4179 NM_002868.4 -0,2625 88 COPS7A 5825 NM_001164094.2 0,0013 89 CCNC 892 NM_005190.4 -0,0176 90 SNX3 8724 NM_003795.6 -0,0196 91 COX7A2 1347 NM_001366293.2 0,0003 92 BTN2A1 11120 NM_007049.5 -0,0394 93 SPARC 6678 NM_003118.4 -0,0462 94 GNPDA1 39 NM_005471.5 -0,0874 95 TRIM23 373 NM_001656.4 0,3286 96 PDGFRB 5159 NM_002609.4 -0,5664 97 PDCD10 1475 NM_007217.4 -0,0188 98 KYNU 8942 NM_003937.3 0,0069 99 RPL22 2739 NM_000983.4 -0,5883 100 QSOX1 5768 NM_002826.5 0,2102 101 MFN2 9927 NM_014874.4 -0,0474 102 ACADM 34 NM_000016.6 -0,3463 103 CD46 4179 NM_172351.3 -0,0671 104 RSRP1 8678 NM_020317.5 -0,0761 105 RPS6KA1 6195 NM_002953.4 -0,0314 106 SPCS2 9789 NM_014752.3 -0,3796 107 FBXL5 26234 NM_012161.4 0,0359 108 FOXO3 2309 NM_001455.4 0,1891 109 GLE1 2733 NM_001003722.2 0,0729 110 EIF2B2 4738 NM_014239.4 0,0767 111 IFIT2 3433 NM_001547.5 0,0330 112 DENND10 10452 NM_207009.4 -0,2297 113 PCMT1 5110 NM_001360452.2 -0,1144 114 PAIP2 27293 NM_016480.5 -0,5181 115 ZCCHC17 7376 NM_016505.4 -0,1106 116 FLT3 2322 NM_004119.3 -0,5422 117 HNRNPA2B1 3181 NM_002137.4 0,1747 118 VPS26A 9559 NM_004896.5 -0,2886 119 ATF1 466 NM_005171.5 -1.15692 120 VAPB 759 NM_004738.5 0,0534 121 ATP5F1E 406 NM_006886.4 -0,0001 122 DDX27 8932 NM_017895.8 -0,0153 123 F13A1 2162 NM_000129.4 0,0439 124 TBCC 6903 NM_003192.3 -0,1527 125 DEK 3559 NM_003472.4 0,0701 126 TM9SF2 1824 NM_004800.3 -0,2031 127 CENPB 1059 NM_001810.6 0,0204 128 MAX 3178 NM_002382.5 -0,7005 129 HECTD3 4193 NM_024602.6 0,0328 130 EPS15L1 54606 NM_001258374.3 -0,1242 131 SEM1 7979 NM_006304.2 -0,0021 132 CALU 813 NM_001219.5 0,0553 133 ANAPC13 3662 NM_015391.4 -0,0087 134 MBD4 8930 NM_001276270.2 -0,0074 135 PARP2 10038 NM_001042618.2 -0,0234 136 KLF16 56267 NM_031918.4 0,1557 137 RPL36 953 NM_033643.3 -0,0059 138 JUND 3727 NM_005354.6 0,5709 139 TRPM4 54795 NM_017636.4 -0,1036 140 ZC3H4 901 NM_015168.2 -0,0275 141 DNMT1 1786 NM_001130823.3 -0,2308 142 COX7B 1349 NM_001866.3 0,0010 143 LIN28A 79727 NM_024674.6 -2.01022 144 DNAJB1 597 NM_006145.3 -0,3151 145 KHDC4 22889 NM_014949.4 -0,3939 146 POMP 51371 NM_015932.6 -0,0365 147 SLC39A11 201266 NM_139177.4 -0,3284 148 GIMAP4 55303 NM_018326.3 -0,3454 149 MKRN1 23608 NM_013446.4 0,1376 150 NOTCH2 4853 NM_024408.4 0,0241 151 TMEM106C 55827 NM_001143842.2 -0,0778 152 RPS15A 27 NM_001019.5 -0,0008 153 DOCK2 1794 NM_004946.3 -0,1474 154 SLC43A3 9898 NM_199329.3 -0,0069 155 OSTF1 2987 NM_012383.5 -0,2453 156 NAT10 55226 NM_024662.3 -0,0942 157 CD164 8763 NM_006016.6 -0,1386 158 PIGC 5279 NM_153747.2 -0,0679 159 LAMC1 3915 NM_002293.4 0,0149 160 TMBIM1 55016 NM_022152.6 -0,0104 161 TBC1D4 9882 NM_014832.5 -0,0356 162 LCP1 3936 NM_002298.5 0,0036 163 SRSF1 6426 NM_006924.5 -0,0362 164 SLC31A2 1318 NM_001860.3 -0,2645 165 MRPL50 54534 NM_019051.3 0,0668 166 UQCC2 23245 NM_032340.4 -0,0637 167 CPEB2 114789 NM_001177382.2 0,0018 168 THAP12 5612 NM_004705.4 0,0072 169 IL18BP 4851 NM_001039660.2 0,0018 170 RPLP1 6176 NM_001003.3 -0,0028 171 ACTR2 10097 NM_005722.4 -1.16604 172 ACTR1A 10121 NM_005736.4 -0,2435 173 ASCC1 22866 NM_001198800.3 -0,2152 174 METTL5 29081 NM_014168.4 -0,0321 175 SLC40A1 10666 NM_014585.6 -0,2452 176 FAM13A 10144 NM_014883.4 0,5000 177 NEDD1 9531 NM_152905.4 0,3288 178 MBNL2 10150 NM_001382683.1 -0,1987 179 TPM1 1105 NM_001018005.2 -0,3315 180 GLYR1 5239 NM_032569.4 -0,3526 181 ADAMTS10 81794 NM_030957.4 -0,1348 182 EVI5L 90139 NM_001159944.3 -0,1048 183 ZNF614 80110 NM_025040.4 -0,3550 184 EVA1B 55194 NM_001304762.2 0,1749 185 MOB3C 9577 NM_201403.3 -0,2473 186 CD53 963 NM_000560.4 -0,0033 187 PRUNE1 50848 NM_021222.3 -0,0270 188 PSEN2 5664 NM_000447.3 0,0998 189 PYCR2 10020 NM_013328.4 -0,0069 190 SNRPG 4731 NM_003096.4 -0,0003 191 HSPD1 3329 NM_002156.5 -0,1738 192 RAB5A 917 NM_004162.5 -0,3088 193 RPL32 1234 NM_000994.4 -0,0008 194 LRIG1 10898 NM_015541.3 -0,0961 195 ABHD10 55347 NM_018394.4 -0,2438 196 TCTA 6988 NM_022171.3 -0,0676 197 RPL37 6154 NM_000997.5 -0,4038 198 MSN 4478 NM_002444.3 -0,0041 199 ATP6V1B2 526 NM_001693.4 -0,0664 200 GOLGA7 51125 NM_001002296.2 -0,4190 201 STOM 2040 NM_004099.6 -0,0670 202 NRBF2 29982 NM_030759.5 -0,2690 203 GLUD1 2746 NM_005271.5 -0,0583 204 ADD3 120 NM_016824.5 0,1587 205 BTBD10 8314 NM_032320.7 -0,0965 206 PTPRJ 5795 NM_002843.4 -0,0307 207 FADS1 3992 NM_013402.7 -0,4964 208 PDCD4 27250 NM_014456.5 0,3721 209 ANK3 288 NM_020987.5 -0,4812 210 ADAM17 6868 NM_003183.6 0,2512 211 ACSL1 2180 NM_001995.5 -0,0591 212 BAG3 1073 NM_004281.4 -0,0840 213 ZNF547 27069 NM_173631.4 0,1429 214 CDYL 9425 NM_004824.4 -0,6848 215 HNRNPU 3192 NM_031844.3 -0,5278 216 NRGN 4900 NM_006176.3 -0,0321 217 OBSCN 84033 NM_001386125.1 0,7444 218 PTPRN2 5799 NM_002847.5 -0,1626 219 HSPA13 6782 NM_006948.5 -0,6801 220 CNOT8 9337 NM_001301073.2 -0,5799 221 ATP5MJ 9556 NM_004894.3 -0,0002 222 UQCRB 7381 NM_006294.5 -0,0942 223 MFSD14A 5871 NM_033055.3 -0,1467 224 NECAP2 28511 NM_018090.5 -0,3412 225 TSC22D3 1831 NM_198057.3 -0,0217 226 PRXL2B 55602 NM_152371.5 -0,0148 227 PFKFB1 5207 NM_002625.4 0,6597 228 CCAR2 55974 NM_001393997.1 -0,0471 229 EPB41 2035 NM_001376013.1 0,0745 230 CYB5R1 686 NM_016243.3 -0,0035 231 BTG2 5028 NM_006763.3 -0,1691 232 GATD3A 8209 NM_004649.8 -0,2105 233 ITGB2 3689 NM_000211.5 0,0021 234 CXCR5 643 NM_001716.5 -0,0128 235 ZBTB7B 51043 NM_001256455.2 -0,2054 236 SLC25A44 1901 NM_014655.4 -0,0475 237 ALDH16A1 26156 NM_153329.4 -0,5467 238 RBBP4 5928 NM_005610.3 0,7580 239 ARPC5 10092 NM_005717.4 -0,3545 240 SNED1 25992 NM_001080437.3 0,0772 241 SLC66A3 130814 NM_152391.5 -0,0898 242 CTSS 1520 NM_004079.5 -0,0192 243 BOLA3 2804 NM_212552.3 -0,0157 244 IFI16 3428 NM_001376587.1 -0,0068 245 AIM2 9447 NM_004833.3 0,0342 246 TPRA1 64837 NM_001136053.4 0,0166 247 ARHGEF3 10025 NM_019555.3 -0,1061 248 ZNF691 5771 NM_001242739.2 -0,1225 249 BDH2 56898 NM_020139.4 -0,5845 250 UQCRQ 27089 NM_014402.5 -0,0101 251 DCBLD1 9632 NM_001366458.2 -0,2683 252 TAGAP 117289 NM_054114.5 -0,3103 253 TP53INP1 28976 NM_033285.4 -0,0183 254 PSIP1 6666 NM_033222.5 0,1530 255 HIF1AN 55662 NM_017902.3 0,0959 256 TAF10 6881 NM_006284.4 -0,3953 257 PDIA3 2923 NM_005313.5 -0,0061 258 DUSP18 51363 NM_152511.5 -0,0578 259 ATP5MG 10632 NM_006476.5 -0,0175 260 TEPSIN 146705 NM_001363764.2 -0,0698 261 OR51E2 51218 NM_030774.4 -0,0278 262 SERTAD3 29946 NM_203344.3 -0,0112 263 LENG8 57118 NM_052925.4 -1.11753 264 FAM234A 80258 NM_032039.4 0,6884 265 DDIT4 54541 NM_019058.4 -0,0045 266 CAVIN2 8436 NM_004657.6 -0,8100 267 NSMCE1 54584 NM_145080.4 0,0064 268 HINT1 3094 NM_005340.7 -0,0079 269 RNF34 50626 NM_025126.4 -0,0354 270 LSM3 27258 NM_014463.3 -0,0261 271 MTM1 4534 NM_000252.3 -0,0179 272 RGS19 10287 NM_005873.3 0,0083 273 MRM3 55178 NM_018146.4 -0,0103 274 CD8B 926 NM_004931.5 0,0490 275 CLEC7A 23660 NM_197947.3 -0,1687 276 ZNF131 7690 NM_001330707.2 0,1668 277 SUCLG2 8801 NM_003848.4 -0,1723 278 RPL38 6169 NM_000999.4 -0,0085 279 FAM222B 6840 NM_001077498.3 0,0703 280 PSMD1 5707 NM_002807.4 0,2878 281 RBM4B 23607 NM_031492.4 -0,0163 282 KCTD13 10479 NM_178863.5 -0,1429 283 PACS1 2475 NM_018026.4 -0,0004 284 GOLGA8A 23015 NM_181077.5 -0,3506 285 UNC119B 55251 NM_001080533.3 -0,1739 286 ZBTB8OS 170371 NM_178547.5 -0,1151 287 HIC1 3090 NM_006497.4 -0,4995 288 PPFIA3 8541 NM_003660.4 -0,5861 289 ATOX1 475 NM_004045.4 0,0397 290 BET1L 51272 NM_001098787.2 -0,0819 291 ERFE 4056 NM_001291832.2 0,3203 292 CALR 811 NM_004343.4 -0,0119 293 GADD45GIP1 3672 NM_052850.4 -0,0202 294 R3HDM2 22864 NM_001394031.1 -0,0267 295 MYLPF 29895 NM_013292.5 -0,4223 296 OAZ2 4947 NM_002537.3 -0,1811 297 CCDC66 2074 NM_001141947.3 0,0142 298 H2BC4 8347 NM_003526.3 -0,0263 299 FAM83H 286077 NM_198488.5 0,0257 300 PJA1 64219 NM_001032396.4 -0,1127 301 ERCC6L2 2938 NM_020207.7 0,2926 302 SKA2 4067 NM_182620.4 -1.37698 303 RPL35A 6165 NM_000996.4 0,0199 304 PTTG1IP 754 NM_004339.4 -0,1055 305 DAZAP2 1523 NM_014764.4 -0,0902 306 TENT5C 54855 NM_017709.4 2.03747 307 TM2D3 80213 NM_078474.3 -0,1112 308 CCR10 2826 NM_016602.3 -0,3041 309 H2BC21 8349 NM_003528.3 -0,2252 310 CDK10 572 NM_052988.5 -0,1842 311 FAAP100 204 NM_025161.6 0,0366 312 BRWD1 292 NM_033656.4 0,0833 313 IFIT1 3434 NM_001548.5 -0,0047 314 RPS23 6224 NM_001025.5 0,0097 315 TET3 55703 NM_001287491.2 -0,9239 316 ZNF490 23034 NM_020714.3 -0,7715 317 COMMD6 9821 NM_203495.4 -0,0200 318 PARVB 29780 NM_013327.5 -0,0461 319 TMEM120B 55334 NM_001080825.2 0,0016 320 HMGB1 3146 NM_002128.7 -0,2417 321 BLOC1S2 23274 NM_173809.5 -0,2350 322 HSH2D 55733 NM_001382417.1 0,0340 323 FLNA 2316 NM_001110556.2 -0,6667 324 C5AR1 728 NM_001736.4 0,0920 325 RPF2 51665 NM_032194.3 -0,5533 326 RPL37A 6168 NM_000998.5 -0,0151 327 TOR4A 54863 NM_017723.3 0,1898 328 RPL23A 6147 NM_000984.6 -0,0472 329 MSRB1 51734 NM_016332.4 -0,0629 330 TSEN15 55160 NM_052965.4 -0,1058 331 MPIG6B 634 NM_138272.3 -0,0154 332 BAG6 1653 NM_001387994.1 -0,8485 333 HLA-C 3107 NM_002117.6 -0,0141 334 CDSN 1041 NM_001264.5 -0,0334 335 PPP1R11 6992 NM_021959.3 -0,0854 336 UQCC3 3595 NM_001085372.3 -0,0380 337 E2F4 1874 NM_001950.4 -0,0041 338 PRR13 5026 NM_018457.4 -0,2853 339 CNEP1R1 81550 NM_001281789.2 -0,2255 340 C17orf107 334 NM_001145536.2 -1.65591 341 ATF6B 1388 NM_004381.5 -0,0062 342 IRF9 10379 NM_006084.5 -0,0543 343 CEBPD 1052 NM_005195.4 -0,0355 344 RBM14 10432 NM_006328.4 0,1242 345 ZNF564 27349 NM_144976.4 -0,2228 346 ATXN7L3B 25828 NM_001136262.2 -0,2616 347 SCRT1 9321 NM_031309.6 -0,0762 348 NBPF11 80344 NM_001385469.3 -0,2063 349 S1PR2 6789 NM_004230.4 1.21635 350 NBPF9 1522 NM_001388367.1 -0,1406 351 NBPF10 994 NM_001302371.3 -0,0324 352 Table 14: Preferred marker genes, in particular for assessing ASD level 1, with database accession numbers and EMD values; EMD: earth movers distance (wasserstein distance), positive EMD values indicate larger UTR in ASD patients; SEQ ID NOs of exemplary 3'UTRs of the respective genes are indicated. Gene NCBI Genbank Acc No EMD SEQ ID NO GeneID ATP5F1E NM_006886.4 -0.0011 BUB3 NM_004725.4 -0.2512 CDK10 NM_052988.5 -0.1068 CENPB NM_001810.6 0.0240 COX7B NM_001866.3 0.0039 ELOB NM_007108.4 -0.0073 FAM3A NM_021806.4 0.2514 IFIT1 NM_001548.5 -0.0445 JUND NM_005354.6 0.5657 NSMCE1 NM_145080.4 0.0083 OSTF1 NM_012383.5 -0.4100 see Table 1 PDCD10 NM_007217.4 -0.0111 PRR13 see Table 1 NM_018457.4 -0.3206 QSOX1 NM_002826.5 0.1853 RPL32 NM_000994.4 0.0010 RPL35A NM_000996.4 0.0171 RPL36 NM_033643.3 -0.0006 RPLP1 NM_001003.3 -0.0030 RPN1 NM_002950.4 -0.4109 RPS15A NM_001019.5 0.0021 SERTAD3 NM_203344.3 -0.0102 SLC25A44 NM_014655.4 -0.0402 TSC22D3 NM_198057.3 -0.0146 UBE2A NM_003336.4 -0.2370 ZBTB7B NM_001256455.2 -0.2537 IFFO1 25900 NM_001193457.2 -0,0755 2056 HDAC6 10013 NM_006044.4 0,0100 2057 POLR2F 5435 NM_021974.5 -0,0713 2058 RPS19 6223 NM_001022.4 -0,0262 2059 HNRNPH2 3188 NM_019597.5 -0,0030 2060 EIF4A3 9775 NM_014740.4 0,0095 2061 EEF1A1 1915 NM_001402.6 0,0078 2062 INPP5D 3635 NM_001017915.3 0,0052 2063 The term "3' untranslated region", abbreviated as "3'UTR", of a transcript is understood by the skilled person. Preferably, said term includes all sequences of a transcript, e.g. an mRNA, downstream of the open reading frame. Thus, the 3'UTR preferably starts with the first nucleotide of the transcript 3' of the (last) stop codon of an open reading frame and ends with the last 3' nucleotide of the transcript. More preferably, the 3'UTR starts with the first nucleotide of the transcript 3' of the (last) stop codon of an open reading frame and ends with the last nucleotide 5' of the poly-A tail of the transcript. Preferred 3'UTRs are those of SEQ ID NOs: 1 to 352, and, in a preferred embodiment, SEQ ID NOs: 2056 to 2063. The term “determining” as used herein refers to semiquantitative or quantitative determination of an APA parameter referred to herein. Determining the value of an APA parameter may be carried out by any technique which allows for establishing a measure of quantity of an APA parameter in a semiquantitative or quantitative manner. Suitable techniques are in principle known in the art, e.g. from the references cited herein above and in the Examples, and are discussed elsewhere herein in more detail. Typically, an APA parameter may be determined by any means deemed appropriate by the skilled person, e.g. for determining a length of a 3'UTR or a part thereof. Thus, 3'UTR or a part thereof may be sequenced, e.g. by bulk RNA sequencing or single cell sequencing techniques, but also by standard sequencing methods, optionally with preceding steps of transcribing RNAs, preferably mRNAs, into cDNA and, preferably amplification, e.g. by PCR. As indicated herein above, a parameter of APA may also be determined by detecting the presence or absence of specific 3'UTR subsequences. Preferred 3'UTR subsequences are disclosed herein as SEQ ID NOs:353 to 2048 and, in a preferred embodimennt, SEQ ID NOs:2064 to 2089. As the skilled person understands, the 3'UTR sequences shown in SEQ ID NOs:1 to 352 and 2056 to 2063 are exemplary sequences, since the corresponding gene products are subject to APA and, as a consequence, there is variation in 3'UTR sequences. In accordance, the subsequences of SEQ ID NOs:353 to 2048 and, optionally, 2064 to 2089 may or may not be comprised in the corresponding exemplary 3'UTR indicated, may be only comprised partially, may comprise additional sequences, and / or may comprise single nucleotide polymorphisms or other mutations. Preferably, determining the presence or an increased amount, compared to a healthy control, of 3'UTR subsequences designated by an indication including "ASD", in particular those of Table 12 and, in a preferred embodiment Table 15, is indicative of a subject suffering from ASD. Thus, preferably, determining the presence or an increased amount, compared to a healthy control, of a nucleic acid sequence selected from the list consisting of SEQ ID NOs:353 to 1280 and / or, in a preferred embodiment SEQ ID NOs:2064 to 2074 is indicative of a subject suffering from ASD. Also preferably, determining the absence or a decreased amount, compared to a healthy control, of 3'UTR subsequences designated by an indication including "ASD", in particular those of Table 12 and, in a preferred embodiment Table 15, in particular of a nucleic acid sequence selected from the list consisting of SEQ ID NOs:353 to 1280 and optionally 2064 to 2074 is indicative of a subject not suffering from ASD. Also preferably, determining the presence or an increased oro equal amount, compared to a healthy control, of 3'UTR subsequences designated by an indication including "CTRL", in particular those of Table 13 or, in a preferred embodiment Table 16, is indicative of a subject not suffering from ASD. Thus, preferably, determining the presence or an increased or equal amount, compared to a healthy control, of a nucleic acid sequence selected from the list consisting of SEQ ID NOs:1281 to 2048, or optionally SEQ ID NOs:2075 to 2089, is indicative of a subject not suffering from ASD. Also preferably, determining the absence or a decreased amount, compared to a healthy control, of 3'UTR subsequences designated by an indication including "CTRL", in particular those of Table 13 or, in a preferred embodiment Table 16, in particular of a nucleic acid sequence selected from the list consisting of SEQ ID NOs:1281 to 2048 or optionally 2075 to 2089, is indicative of a subject suffering from ASD. The aforesaid sequences may be determined by any method deemed appropriate by the skilled person, such as sequencing as specified herein above, hybridization methods, including PCr methods, and the like. As the skilled person understands, it may be sufficient to determine a subsequence of any of the aforesaid 3'UTR sequences or 3'UTR subsequences; preferably, in such case, any subsequence determined is selected to have a length providing for unambiguous allocation to the corresponding gene, 3'UTR, and, referably, 3'UTR subsequence, all as specified herein above. Thus, the subsequence determined preferably comprises at least 10, more preferably at least 12, even more preferably at least 14, contiguous nucleotides of any one of SEQ ID NO:1 to 2048 and / or optionally 2056 to 2089, in particular SEQ ID NOs:353 to 2048 and / or optionally 2064 to 2089. As an alternative to sequencing or in addition thereto, an APA parameter may also be determined e.g. by classical hybridization methods, such as DNA / DNA blot or DNA / RNA blot methods, amplification of polynucleotides using subsequence-specific primers, and the like. Also, probe polynucleotides, e.g. coupled to at least one indicator agent such as a dye, may be used to determine the amount(s) of the aforesaid subsequences, thereby determining a parameter of APA. Thus, preferably, a polynucleotide comprising a sequence of from 10, preferably from 12, more preferably from 14, still more preferably from 15, even more preferably from 16, to all contiguous nucleotides of any one of SEQ ID NOs:1 to 2048 and / or optionally from SEQ ID NOs: 2056 to 2089, or a reverse complement sequence thereof, is used as a probe polynucleotide and / or as a primer polynucleotide in said determination. The term “reference”, as used herein, relates to a value, e.g. a value of an APA parameter, such as a 3'UTR length, or any value derived therefrom, e.g. a score, which can be correlated to the medical condition ASD and, preferably, which allows for the assessment of the invention to be made, more preferably enables allocation of a subject into either a group of subjects suffering from a disease or condition or being at risk for developing it, or a group of subjects which do not suffer from said disease or condition or which are not at risk for developing it. Such a reference can be a threshold value, e.g. a threshold 3'UTR length, which separates these groups from each other. Accordingly, the reference may be a value which allows for allocation of a subject into a group of subjects suffering from a disease or condition or being at risk for developing it, or not. For example, the reference may be a value which allows for allocation of a subject into a group of subjects suffering from ASD, or not being at risk of developing ASD. The reference may, however, also be a reference range, e.g., preferably, a range of values for which ASD can be excluded. Furthermore, the reference may be a value calculated from the aforesaid APA parameter values, e.g. from two or more marker genes, preferably to provide a score. A suitable reference separating the two groups can be provided without further ado e.g. by the statistical tests referred to herein elsewhere based on values of marker genes from suitable reference groups as specified herein below. As the skilled person understands, it may not always be possible, although preferred, to provide a reference unambiguously allocating each and every possible value of an APA parameter to one of the aforesaid groups; thus, there may be a range of values for which a clear assessment cannot be provided. Preferably, however, as indicated above, a reference enables the assessment to be made for each and every value of an APA parameter which may be measured. As the skilled person understands, the specific value of a reference may depend on the assessment intended and on parameters thereof; thus, the reference value for assessing ASD may be different from the reference value for assessing e.g. autism. Relevant parameters having an influence on the reference may in particular be the desired sensitivity and specificity of assessment. As indicated herein above, a reference may in particular be derived from at least one reference group, the term "reference group" relating to a group of subjects with known status with regard to the assessment. Thus the reference group may e.g. be a group of subjects for which it is known whether they suffer from ASD. The population of subjects in a reference group preferably comprises a plurality of subjects, e.g. at least 5, 10, 50, 100, 1,000, or 10,000 subjects. Typically, the subject to be diagnosed and the subjects of the said reference group are of the same species. The reference applicable for an individual subject may vary depending on various physiological parameters such as age, gender, or subpopulation. As is understood by the skilled person, prevalence of ASD in the population is low; thus, a reference may be derived also from the average population. Assuming that contribution of actually afflicted subjects is low, such an average population reference group may be treated as a reference group known not to suffer from ASD; preferably, in such case, the size of the reference group is sufficiently large, e.g. at least 100, more preferably at least 1000, more preferably at least 10000 subjects. In view of the description herein, the skilled person understands that a reference group may, in principle, also be a mixed population of subjects with regard to ASD, provided that the status of each member of said mixed population with regards to ASD is or becomes known before deriving a reference from such group. The term “comparing” as used herein encompasses comparing the determined value of an APA parameter as referred to herein to a reference. It is to be understood that comparing as used herein refers to any kind of comparison made between the value determined with the reference. However, it is to be understood that, preferably, identical types of values are compared with each other, e.g., if an average length is determined, the reference shall also be an average length, if a relative length is determined, the reference shall also be a relative length, etc. The term comparing also encompasses comparing a calculated score with a suitable reference score. The comparison may be carried out manually or computer assisted. The value of the amount and the reference can be, e.g., compared to each other and the said comparison can be automatically carried out by a computer program executing an algorithm for the comparison. The computer program carrying out the said evaluation will provide the desired assessment in a suitable output format. As set forth above, it is also envisaged to calculate a score, in particular a single score, and to compare this score to a reference score. The calculated score preferably combines information on the determinations in a multitude of marker genes. Moreover, in the score, the marker genes may be weighted in accordance with their contribution to the establishment of the differentiation, wherein the weighting factor of the individual marker genes may be different. The score can be regarded as a classifier parameter for the assessing as set forth herein. In particular, it enables providing the assessment based on a single score. Thus, the skilled person preferably does not have to interpret the entire information obtained from the individual marker genes. Also, using a scoring system, values of different dimensions or units may be used, since the values may be mathematically transformed into the score. The reference score to be applied may be elected based on the desired sensitivity and / or the desired specificity. How to elect a suitable reference score is well known in the art. The method of assessing comprises step (a) determining a parameter of alternative polyadenylation (APA parameter) in said sample. Parameters of APA and methods for their determination have been described herein above. Preferably, the APA parameter is determined in a blood-derived sample as described herein above. Preferably, the parameter is determined as specified herein above; to the value obtained, standard mathematical and statistical operations may be applied, such as standardization, normalization, log- transformation, e.g. log10 transformation, and the like. The method of assessing further comprises step (b) comparing the APA parameter determined in step (a) to a reference. References have been described herein above. As the skilled person understands, in case determination is based on a multitude of marker genes, these are preferably compared to references and / or are included in calculating the score as well. As is also understood by the skilled person, values of parameters determined for marker genes are typically compared to corresponding references, as specified herein above. Also, an APA parameter determined for a marker gene X will be compared to a reference derived from measurements relating to said marker gene X. Preferably, in case a score is calculated from the amounts determined in step (a), said score is compared to a reference score calculated from references based on the same marker gene(s) by the same mathematical operations. The specific comparison made preferably depends on the specific reference(s) used. In case the reference is a threshold, the comparison may comprise determining whether the value of the sample in question is beyond said threshold; in case the reference is a reference range, the comparison may comprise establishing whether the value of the sample in question is within the reference range, or not. The method of assessing further comprises step (c) based on the comparison in step (b), assessing said ASD. The term "assessing" has been specified herein above. Typically, the assessment in step (c) may in particular be based on the status of the reference group(s) and the reference derived therefrom. As the skilled person understands, a parameter of APA may be decreased or increased in an afflicted subject compared to a healthy reference. Thus, in case the reference is derived from a group of subjects known to be afflicted with the disease or condition, a parameter value essentially identical to the reference preferably leads to an assessment of the subject under investigation being afflicted with the disease or condition, and a value different, in an embodiment significantly different, from said reference preferably leads to an assessment of the subject under investigation not being afflicted with the disease or condition. Conversely, in case the reference is derived from a group of subjects known not to be afflicted with the disease or condition, a parameter value essentially identical to the reference preferably leads to an assessment of the subject under investigation not being afflicted with the disease or condition, and a value different, preferably significantly different, from said reference preferably leads to an assessment of the subject under investigation being afflicted with the disease or condition. Further, as indicated above, the reference may be a threshold value; such a threshold value may be derived from a reference group known to suffer from the disease of condition, from a reference group known not to suffer from the disease of condition, or from a reference group known to suffer from the disease of condition and a reference group known not to suffer from the disease of condition. Typically, in case a parameter value of a subject under investigation exceeds the aforesaid threshold reference toward the values of the reference group known to suffer from the disease or condition, it will be assumed that the subject suffers from the disease of condition; and in case a parameter value of a subject under investigation exceeds the aforesaid threshold toward the values of the reference group known to not suffer from the disease or condition, it will be assumed that the subject does not suffer from the disease or condition. Thus, depending on the specific APA parameter and its correlation with a disease or condition, values found in a sample which are higher than or equal to a threshold may be indicative for the presence of a medical condition while those being lower may be indicative for the absence of the medical condition; or, it may also be that values found in a sample to be investigated which are lower or identical than the threshold are indicative for the presence of a medical condition while those being higher are indicative for the absence of the medical condition. As the skilled person understands in view of the description herein, the above applies mutatis mutandis to a score, which may incorporate values of more than one marker gene, preferably all marker genes. As specified herein above, a reference score may in particular be calculated by the same mathematical operations applied to calculate a score, but using values from one or more reference group(s). The reference score also may be e.g. a threshold score or a reference score range. As the skilled person will understand as well, the assessment following from the comparison of a score to a reference score will depend on the specificities of score calculation; thus, whether a score above or below a threshold score is indicative of ASD will depend on the specific way of calculating the score. The result of the assessment in step (c) preferably is a statement concerning the status of the subject with regards to ASD, in a preferred embodiment ASD level 1. Said result may be implicit, e.g. by juxtaposing the value determined in the sample to one or more reference(s), which may be further evaluated, e.g. by a medical practitioner. The result may, however, also be explicit, e.g. by annunciating that the comparison suggests a specific status with regards to ASD. Thus, the method may further comprise a step of annunciating the result of the assessment of step (c). Alternatively or in addition, the result of the assessment in step (c) may also be used in further assessments, e.g. by inclusion into or combination with results of further assessments, e.g. further diagnostic measures, such as behavioral assessment, and / or determining subject age and / or gender. The result of the assessment is step (c) may form the basis for a treatment of the subject or a decision thereon. Thus, in case it assessed that the subject suffers from ASD, said subject may be treated for ASD. Appropriate treatments are known in the art and include in particular therapy mitigating progress. Thus, treatment may in particular comprise recommending behavioral therapy; treatment may also comprise pharmaceutical treatment with compounds known to the skilled person, e.g. oxytocin administration. Advantageously, it was found in the work underlying the present invention that 3'UTR sequences of marker genes listed herein can be used as biomarkers of autism, thus enabling diagnosis of ASD independently of e.g. behavioral assessments. This makes possible e.g. early diagnosis before symptoms develop and thus enables improved treatment with a possibility to improve the course of disease. The definitions made above apply mutatis mutandis to the following. Additional definitions and explanations made further below also apply for all embodiments described in this specification mutatis mutandis. The present invention further relates to a detection polynucleotide comprising of from 10 to all contiguous nucleotides of a nucleic acid sequence present in the 3'UTR of a marker gene from a subject suffering from ASD but not in a subject not suffering from ASD; or present in the 3'UTR of a marker gene from a subject not suffering from ASD but not present in a subject suffering from ASD. Thus, the detection polynucleotide is preferably derived from sequence differing between a subject suffering from ASD and a subject not suffering from ASD. Preferably, said detection polynucleotide comprises, preferably consists of, a nucleic acid sequence of any one of SEQ ID NOs:353 to 2048 and / or optionally 2064 to 2089, of a fragment comprising at least 10, more preferably at least 12, even more preferably at least 14, contiguous nucleotides thereof, as the only gene specific sequence of a marker gene specified in Table 1 or, in a preferred embodiment, Table 14. The term polynucleotide has been specified herein above; in concurrence, the term “detection polynucleotide”, as used herein, refers to a linear or circular nucleic acid molecule having the specified properties. The detection polynucleotides of the invention have the activity of specifically binding to a 3'UTR from a subject suffering from ASD, or specifically binding to a 3'UTR from a subject not suffering from ASD. Methods for testing whether a condidate detection polynucleotide has the aforesaid activity are known in the art. Unless specifically indicated otherwise, reference to specific detection polynucleotides herein preferably includes detection polynucleotide variants. A detection polynucleotide variant referred to herein has the activity as specified herein above. Thus, the detection polynucleotide or variant thereof preferably comprises a sequence which is essentially the reverse complement of the sequence of a 3'UTR of a transcript of a marker gene referred to herein, preferable comprises a sequence which is the reverse complement of such transcript. The present invention also relates to a means for determining an APA parameter for use in diagnosis; and to a use of means for determining an APA parameter for the manufacture of a diagnostic. Moreover, the present invention relates to a means for determining an APA parameter for use in diagnosing ASD; and to a use of a means for determining an APA parameter for the manufacture of a diagnostic for diagnosing ASD. The term "means for determining an APA parameter" is understood by the skilled person in view of the description herein and in principle includes each and every means deemed suitable by the skilled person to determine an APA parameter as specified elsewhere herein. Preferably, said means is specific for determining an APA parameter. Also preferably, the means is essential for determining an APA parameter, i.e. preferably is required to make a determination of an APA parameter possible. As referred to herein, the term preferably relates to means directly interacting with at least one 3'UTR of a marker gene as specified herein above. Thus, the means for determining an APA parameter preferably is a polynucleotide comprising a nucleic acid sequence specifically hybridizing to a transcript of at least one of the marker genes specified in Table 1, more preferably specifically hybridizes to a 3'UTR of at least one of said genes. Thus, the means for determining an APA parameter preferably is a probe polynucleotide or a primer polynucleotide, preferably comprising a sequence of at least 10 contiguous nucleotides of any one of SEQ ID NOs:1 to 2048, or, in a preferred embodiment, 2056 to 2089, or a reverse complement thereof, more preferably is a detection polynucleotide of the present invention. As is understood by the skilled person in view of the description herein, the polynucleotide being a means for determining an APA parameter does not necessarily have to be specific for a sequence specific for a subject suffering from ASD or for a subject not suffering from ASD. I.e. said means may be any means allowing such an APA parameter to be determined. Thus, said means may e.g. be a primer polynucleotide enabling amplification and / or sequencing, and thereby length determination, of a 3'UTR of a marker gene referred to herein. Said means may also be a probe polynucleotide, which may enable detection of a specific sequence, but may also enable length determination of a 3'UTR of a marker gene referred to herein, as is the case e.g. in traditional RNA / DNA blotting techniques ("Northern blotting"). The term “diagnosing”, as used herein, refers to assessing the probability according to which a subject is suffering from a disease or condition or is at risk of developing a disease or condition. Accordingly, the method may provide an aid for diagnosis since it might be necessary to further strengthen or confirm said diagnosis by, e.g., a medical practitioner. In particular, as will be understood by those skilled in the art, such an assessment, although preferred to be, may not be correct for 100% of the subjects to be diagnosed. The term, preferably, requires that a statistically significant portion of subjects can be correctly identified as suffering from the disease and / or that a statistically significant portion of subjects can be correctly identified as not suffering from the disease, wherein correct identification preferably is verified based on a population of subjects with a clinical ASD diagnosis established according to applicable guidelines. Whether a portion is statistically significant can be determined without further ado by the person skilled in the art using various well known statistic evaluation tools, e.g., determination of confidence intervals, p-value determination, Student´s t-test, Mann-Whitney test, Likelihood ratio test, etc. Details are found in Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York 1983. Preferred confidence intervals are at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or at least 95%. Preferred p-values are 0.1, 0.05, 0.01, 0.05, or 0.001. It will be understood, moreover, that the methods of the present invention essentially provide an aid for diagnosis and may be included into or supplemented by other diagnostic measures. Diagnosing according to the present invention includes confirmation and classification of the relevant disease or its symptoms. Confirmation relates to the strengthening or substantiating a diagnosis already established using other indicators or markers. Classification relates to allocating the diagnosis according to the strength or kind of symptoms into different classes, such as those indicated herein above. The present invention also relates to a database, preferably tangibly embedded on a data carrier, comprising an identifier of at least one of the marker genes specified in Table 1 and / or, in a preferred embodiment, Table 14, allocated to at least one APA parameter. The term “database”, as used herein, refers to a collection of data which may be physically and / or logically grouped together. Accordingly, the database preferably comprises an allocation of at least one identifier of at least one of the marker genes specified in Table 1 to at least one APA parameter. The APA parameter preferably is a reference as specified herein above. The APA parameter may, however, also be an APA parameter value, e.g. of a subject of known ASD status; in accordance, in particular in case the database comprises APA parameter values from a multitude of non-identical subjects with known ASD status, a reference may be calculated from such APA parameter values. Accordingly, the database preferably further comprises, allocated to the APA parameter, an identifier of a diagnosis allocated to said APA parameter value or reference. Thus, the database enables allocating a marker gene to an APA parameter value, and, preferably, allocating an APA parameter value determined in a sample to a (tentative) diagnosis. As the skilled person understands from the description herein above, the reference may be a score, so the APA parameter comprised in the database may also be said score. The database preferably comprises further data, such as upper and / or lower detection limits, references for further marker genes, in particular those described herein above, data relevant for plausibility checks, and the like. Preferably, the database comprises data on one or more determining methods to use, lot-specific data, e.g. for means for determination of an APA parameter, and the like. Preferably, the database may be implemented in a single data storage medium or in physically separated data storage media being operatively linked to each other. Preferably, the database comprises a data collection on a suitable storage medium, preferably tangibly embedded thereon. Moreover, the database preferably further comprises a database management system. The database management system preferably is a network-based, hierarchical or object-oriented database management system. Also, the database may be a federal or integrated database. Also preferably, the database will be implemented as a distributed (federal) system, e.g. as a Client-Server- System. Preferably, the database is structured as to allow a search algorithm to compare a test data set with the data sets, in particular references, comprised by the data collection. Specifically, by using such an algorithm, the database can be searched for similar or identical data sets being indicative for a subpopulation or effect as set forth above (e.g. a query search). Thus, preferably, if a data set fulfilling the comparison criteria as detailed elsewhere herein can be identified in the database, the test data set will be associated with the diagnosis. Consequently, the information obtained from the database can be used, e.g., as a reference for the methods described elsewhere herein. The present invention further relates to a data carrier comprising the database a referred to herein above. As used herein, the term "data carrier" is used in a wide sense and includes any device adapted for carrying data. The data carrier may be human readable and / or machine readable. Accordingly, the data carrier may be a printed information comprising the database. Preferably, the data carrier is a device carrying data in a machine-readable form e.g. as a CD, DVD, memory device or memory unit of a device specified herein, mass storage unit, and the like. The present invention also relates to a device comprising a means for determining an APA parameter according to the present invention, a database according to the present invention and / or a data carrier according to the present invention. As used herein, the term "device" includes any and all apparatuses comprising the components specified. Preferably, the device is adapted to perform a method as specified herein, in particular an assessment method, so the device may e.g. be a diagnostic device. Thus, the device preferably comprises (i) an analysis unit comprising a means for determining an APA parameter in a sample, and, operatively connected thereto (ii) an evaluation unit comprising tangibly embedded executable instructions for performing a method as specified herein. Typical means for determining an APA parameter are described herein above The analysis unit preferably further comprises means for determining binding of a means for determining an APA parameter to a transcript of a marker gene; said means for determining binding may in particular be a detector, e.g. detecting a signal provided by a probe polynucleotide. The means for determining may, however, also e.g. be a sequencing unit, a qPCR unit, or any other means deemed appropriate by the skilled person. An evaluation unit may be any means capable of providing the analysis as specified; preferably, the evaluation means is a data processing means, such as a microprocessor, a handheld device such as a mobile phone, or a computer. How to link the means in an operating manner will depend on the type of means included into the device. Preferably, the aforesaid means are comprised by a single device. Said device may accordingly include (i) an analyzing unit and a (ii) computer unit for processing the resulting data for the evaluation. Preferably, the instructions and interpretations are comprised in an executable program code comprised in the device, such that, as a result of determination, an assessment of ASD can be provided. Typical devices are those which can be applied without the particular knowledge of a specialized technician, e.g., electronic devices which merely require loading with a sample. The results may be given as output of raw data which need interpretation by a technician. Preferably, the output of the device is, however, processed, i.e. evaluated, raw data, the interpretation of which does not require a technician. Further typical devices comprise the analyzing units / devices (e.g., biosensors, arrays, solid supports coupled to ligands specifically recognizing binding, Plasmon surface resonance devices, NMR spectrometers, mass-spectrometers etc.) or evaluation units / devices referred to above in accordance with the method of the invention. Preferably, the device further comprises a memory unit, preferably comprising a database as specified herein above and or a data carrier as specified herein above. Also, the present invention relates to a kit comprising at least one detection polynucleotide of the present invention, preferably comprised in a housing. The term “kit”, as used herein, refers to a collection of the aforementioned compounds, means or reagents which may or may not be packaged together. The components of the kit may be comprised by separate vials (i.e. as a kit of separate parts) or provided in a single vial, e.g. as a diagnostic composition. The housing of the kit preferably allows translocation of the compounds of the kit, in particular common translocation; thus, the housing may in particular be a transportable container comprising all specified components. Moreover, it is to be understood that the kit of the present invention may be used for practicing the methods referred to herein above. It is, preferably, envisaged that all components are provided in a ready-to-use manner for practicing the methods referred to above. Further, the kit preferably contains instructions for carrying out said methods. The instructions can be provided by a user's manual on paper or in electronic form. For example, the manual may comprise instructions for interpreting the results obtained when carrying out the aforementioned methods using the kit. Preferably, the kit comprises further compounds, such as a reaction buffer, a hybridization solution, a lysis buffer, one or more diluents, or the like. Preferably, the kit is adapted for use in a method of the present invention, more preferably is adapted to comprise all reagents required to perform said method or methods. The present invention also relates to a method for treating and / or preventing an ASD in a subject, said method comprising (A) diagnosing or having diagnosed said ASD in said subject, and (B) treating and / or preventing said ASD in said subject. The method of treating and / or preventing of the present invention, preferably, is an in vivo method. Moreover, it may comprise steps in addition to those explicitly mentioned above. For example, further steps may relate, e.g., to further diagnostic steps preceding, in, or following step a). The terms "treating" and “treatment” refer to an amelioration of the diseases or disorders referred to herein or the symptoms accompanied therewith to a significant extent. Said treating as used herein may also include an entire restoration of health with respect to the diseases or disorders referred to herein. It is to be understood that treating, as the term is used herein, may not be effective in all subjects to be treated. However, the term shall require that, preferably, a statistically significant portion of subjects suffering from a disease or disorder referred to herein can be successfully treated. Whether a portion is statistically significant can be determined without further ado by the person skilled in the art using various well known statistic evaluation tools, e.g., as described herein above. Preferably, treating ASD is reducing severity of at least one symptom of ASD, preferably as specified herein above. As the skilled person understands, ASD may not be treatable in a manner leading to an improvement; thus, treating ASD may also be conservative treatment to avoid deterioration and / or to delay onset of symptoms. Also treatment may be preventing or delaying deterioration of symptoms. Thus, treatment preferably comprises in particular behavioral therapy and / or pharmaceutical treatment with compounds known to the skilled person, e.g. oxytocin administration. The term “preventing” refers to retaining health with respect to the diseases or disorders referred to herein for a certain period of time in a subject. It will be understood that the said period of time may be dependent on the type and intensity of treatment and individual factors of the subject discussed elsewhere in this specification. It is to be understood that prevention may not be effective in all subjects treated with the compound according to the present invention. However, the term requires that, preferably, a statistically significant portion of subjects of a cohort or population are effectively prevented from suffering from a disease or disorder referred to herein or its accompanying symptoms. Preferably, a cohort or population of subjects is envisaged in this context which normally, i.e. without preventive measures according to the present invention, would develop a disease or disorder as referred to herein. Whether a portion is statistically significant can be determined without further ado by the person skilled in the art using various well known statistic evaluation tools discussed elsewhere in this specification. Furthermore, the present invention relates to a method for identifying a subject susceptible for treating and / or preventing an ASD, said method comprising diagnosing said ASD in said subject, and, thereby, identifying a subject susceptible for treating and / or preventing said ASD. The method of treating and / or preventing of the present invention, preferably, is an in vitro method. Moreover, it may comprise steps in addition to those explicitly mentioned above. For example, further steps may relate, e.g., to further diagnostic steps for providing the diagnosis. Preferably, the diagnosing comprises the steps of the method for assessing of the present invention. Moreover, the present invention relates to a use of a polynucleotide according to the present invention an oligonucleotide according to the present invention, a database according to the present invention, a data carrier according to the present invention, a device according to the present invention, and / or a kit according to the present invention, for diagnosing, treating, and / or preventing an ASD. Furthermore, the present invention relates to a method of providing at least one marker gene for use in the method of assessing an ASD of the present invention, the method comprising (I) providing 3'UTR sequencing read starting sites of a multitude of gene transcripts in samples of a multitude subjects from a reference group, wherein a fraction of said multitude of subjects is suffering from ASD; (II) allocating the 3'UTR sequencing read starting sites of step (I) to reference 3'UTR coordinates and determining 3'UTR lengths (transcript 3'UTR lengths); (III) modeling the distribution of transcript 3'UTR lengths in a multivariate probability distribution, preferably a Dirichlet-Multinomial distribution; and (IV) identifying genes with 3'UTR length differences between ASD and non-ASD samples via a Likelihood Ratio test, thereby providing at least one marker gene for use in the method of assessing an ASD. The method of providing is an in vitro method and may comprise steps in addition to those specifically referred to. E.g. step (III) may be followed by step (V) ranking the genes identified in step (IV) via Jensen-Shannon distance, or Wasserstein distance between ASD and non- ASD samples; also, the method may further comprise step (VI) selecting at least one gene with a permutation-corrected p-value from step (IV) below 0.01 and preferably, an absolute distance from step (V) above the 50% percentile, preferably the 75% percentile, more preferably the 85% percentile. Preferably, the method comprises at least one further feature as specified herein in the Examples. As the skilled person will understand in view of the description herein, the method of providing may also be used to provide one or more reference(s) for an APA parameter. Thus, the method may also be a method for providing at least one APA parameter for use in the method of assessing an ASD of the present invention, or a method for providing at least one marker gene and an allocated APA parameter for use in the method of assessing an ASD of the present invention. The term "providing" is used herein in a broad sense including any and all means and methods of making the indicated information or item available. Thus, a 3'UTR sequencing read starting site may be provided as an information, preferably tangibly embedded on a data carrier. In accordance with the description herein above, the 3'UTR sequencing read starting site may be known as such to the skilled person, or may be newly identified, preferably as specified herein in the Examples. Thus, providing a 3'UTR sequencing read starting site may be identifying, preferably experimentally identifying, the 3'UTR sequencing read starting site. Preferably, a multitude of 3'UTR sequencing read starting sites is provided. Also preferably, at least one 3'UTR sequencing read starting site is provided per marker gene. The statistical methods referred to herein, in particular including Dirichlet-Multinomial distribution, Likelihood Ratio test, Jensen-Shannon distance, and Wasserstein distance calculations are textbook knowledge available to the skilled person. In view of the above, the following embodiments are particularly envisaged: Embodiment 1: A method for assessing an autism spectrum disorder (ASD) in a sample from a subject, the method comprising (a) determining a parameter of alternative polyadenylation (APA parameter) in said sample; (b) comparing the APA parameter determined in step (a) to a reference, and (c) based on the comparison in step (b), assessing said ASD. Embodiment 2: The method of embodiment 1, wherein said sample is a non-brain sample. Embodiment 3: The method of embodiment 1 or 2, wherein said sample is a sample of a bodily fluid. Embodiment 4: The method of any one of embodiments 1 to 3, wherein said sample is a sample comprising cells. Embodiment 5: The method of any one of embodiments 1 to 4, wherein said sample is a blood sample or a blood-derived sample. Embodiment 6: The method of any one of embodiments 1 to 6, wherein said APA parameter comprises an mRNA 3' untranslated region (3'UTR) length of at least one marker gene selected from the list consisting of RPN1, UBE2A, MKRN2, UHMK1, IER3, BUB3, MLX, CA2, ITM2A, NT5C3A, FGR, M6PR, AK2, IFRD1, RHBDF1, MMP25, CALCOCO1, NUB1, IFNGR1, TRIT1, OFD1, VAMP3, GDI2, ATP11B, MRPS35, CASP8, ZBTB11, ISOC1, SMG6, FGF22, DGCR2, CAMK2A, RPL31, TRIB2, FAM3A, RAB7A, LXN, KEAP1, PCNP, EPB41L3, ATRX, SLC25A24, RBM22, FKBP1A, SNX5, PABPC4, TINF2, COMT, FBXO7, PDGFB, SGSM3, RBX1, RBM23, NIN, SNW1, PSMC1, SRP54, TPD52L2, RIOK3, SLC35A2, PIN4, BEX4, STK24, TSC22D1, USB1, CYB5B, ELOB, R3HCC1, UBXN8, ASAH1, LIN7B, C19orf53, ZFR2, KDELR1, GRWD1, PDE4C, SMG9, SEC61B, ARHGAP21, RPL28, TSPAN14, RAB5C, LPXN, MS4A6A, IL10RA, CDKN1B, COPZ1, RAB5B, COPS7A, CCNC, SNX3, COX7A2, BTN2A1, SPARC, GNPDA1, TRIM23, PDGFRB, PDCD10, KYNU, RPL22, QSOX1, MFN2, ACADM, CD46, RSRP1, RPS6KA1, SPCS2, FBXL5, FOXO3, GLE1, EIF2B2, IFIT2, DENND10, PCMT1, PAIP2, ZCCHC17, FLT3, HNRNPA2B1, VPS26A, ATF1, VAPB, ATP5F1E, DDX27, F13A1, TBCC, DEK, TM9SF2, CENPB, MAX, HECTD3, EPS15L1, SEM1, CALU, ANAPC13, MBD4, PARP2, KLF16, RPL36, JUND, TRPM4, ZC3H4, DNMT1, COX7B, LIN28A, DNAJB1, KHDC4, POMP, SLC39A11, GIMAP4, MKRN1, NOTCH2, TMEM106C, RPS15A, DOCK2, SLC43A3, OSTF1, NAT10, CD164, PIGC, LAMC1, TMBIM1, TBC1D4, LCP1, SRSF1, SLC31A2, MRPL50, UQCC2, CPEB2, THAP12, IL18BP, RPLP1, ACTR2, ACTR1A, ASCC1, METTL5, SLC40A1, FAM13A, NEDD1, MBNL2, TPM1, GLYR1, ADAMTS10, EVI5L, ZNF614, EVA1B, MOB3C, CD53, PRUNE1, PSEN2, PYCR2, SNRPG, HSPD1, RAB5A, RPL32, LRIG1, ABHD10, TCTA, RPL37, MSN, ATP6V1B2, GOLGA7, STOM, NRBF2, GLUD1, ADD3, BTBD10, PTPRJ, FADS1, PDCD4, ANK3, ADAM17, ACSL1, BAG3, ZNF547, CDYL, HNRNPU, NRGN, OBSCN, PTPRN2, HSPA13, CNOT8, ATP5MJ, UQCRB, MFSD14A, NECAP2, TSC22D3, PRXL2B, PFKFB1, CCAR2, EPB41, CYB5R1, BTG2, GATD3A, ITGB2, CXCR5, ZBTB7B, SLC25A44, ALDH16A1, RBBP4, ARPC5, SNED1, SLC66A3, CTSS, BOLA3, IFI16, AIM2, TPRA1, ARHGEF3, ZNF691, BDH2, UQCRQ, DCBLD1, TAGAP, TP53INP1, PSIP1, HIF1AN, TAF10, PDIA3, DUSP18, ATP5MG, TEPSIN, OR51E2, SERTAD3, LENG8, FAM234A, DDIT4, CAVIN2, NSMCE1, HINT1, RNF34, LSM3, MTM1, RGS19, MRM3, CD8B, CLEC7A, ZNF131, SUCLG2, RPL38, FAM222B, PSMD1, RBM4B, KCTD13, PACS1, GOLGA8A, UNC119B, ZBTB8OS, HIC1, PPFIA3, ATOX1, BET1L, ERFE, CALR, GADD45GIP1, R3HDM2, MYLPF, OAZ2, CCDC66, H2BC4, FAM83H, PJA1, ERCC6L2, SKA2, RPL35A, PTTG1IP, DAZAP2, TENT5C, TM2D3, CCR10, H2BC21, CDK10, FAAP100, BRWD1, IFIT1, RPS23, TET3, ZNF490, COMMD6, PARVB, TMEM120B, HMGB1, BLOC1S2, HSH2D, FLNA, C5AR1, RPF2, RPL37A, TOR4A, RPL23A, MSRB1, TSEN15, MPIG6B, BAG6, HLA-C, CDSN, PPP1R11, UQCC3, E2F4, PRR13, CNEP1R1, C17orf107, ATF6B, IRF9, CEBPD, RBM14, ZNF564, ATXN7L3B, SCRT1, NBPF11, S1PR2, NBPF9, and NBPF10. Embodiment 7: The method of embodiment 7, wherein said APA parameter comprises mRNA 3'UTR lengths of at least two, preferably at least three, more preferably at least four, even more preferably at least five, marker genes selected from said list. Embodiment 8: The method of any one of embodiments 1 to 7, wherein said APA parameter comprises mRNA 3'UTR lengths of at least ten preferably at least 25, marker genes selected from said list. Embodiment 9: The method of any one of embodiments 1 to 8, wherein said APA parameter comprises at least one mRNA 3'UTR length of RPN1, UBE2A and / or MKRN2. Embodiment 10: The method of any one of embodiments 1 to 9, wherein said APA parameter comprises mRNA 3'UTR lengths of RPN1, UBE2A, and MKRN2. Embodiment 11: The method of any one of embodiments 1 to 10, wherein said APA parameter comprises mRNA 3'UTR lengths of RPN1, UBE2A, MKRN2, UHMK1, IER3, BUB3, MLX, CA2, ITM2A, and / or NT5C3A. Embodiment 12: The method of any one of embodiments 1 to 11, wherein said APA parameter comprises mRNA 3'UTR lengths of at least two, preferably at least three, more preferably at least four, even more preferably at least five, still more preferably at least six, still more preferably at least seven, still more preferably at least eight, still more preferably at least nine, most preferably all ten, marker genes selected from the list consisting of RPN1, UBE2A, MKRN2, UHMK1, IER3, BUB3, MLX, CA2, ITM2A, and NT5C3A. Embodiment 13: The method of any one of embodiments 1 to 12, wherein said APA parameter comprises mRNA 3'UTR lengths of the marker genes RPN1, UBE2A, MKRN2, UHMK1, IER3, BUB3, MLX, CA2, ITM2A, and NT5C3A. Embodiment 14: The method of any one of embodiments 1 to 7, wherein said APA parameter comprises mRNA 3'UTR lengths of all of said marker genes. Embodiment 15: The method of any one of embodiments 1 to 14, wherein said APA parameter is determined from 3' mRNA reads. Embodiment 16: The method of any one of embodiments 1 to 15, wherein a polynucleotide comprising the reverse complement sequence of from 10 to all contiguous nucleotides of any one of SEQ ID NOs:1 to 352 and, optionally 2056 to 2063, is used as a probe polynucleotide and / or as a primer polynucleotide. Embodiment 17: A detection polynucleotide comprising a sequence of from 10 to all contiguous nucleotides of any one of SEQ ID NOs:1 to 2048 and, optionally 2056 to 2089, or a reverse complement thereof, as the only gene specific sequence of the genes specified in any one of embodiments 7 to 13. Embodiment 18: A means for determining an APA parameter for use in diagnosis. Embodiment 19: Use of means for determining an APA parameter for the manufacture of a diagnostic. Embodiment 20: A means for determining an APA parameter for use in diagnosing ASD. Embodiment 21: Use of a means for determining an APA parameter for the manufacture of a diagnostic for diagnosing ASD. Embodiment 22: The subject matter of any one of embodiments 18 to 21, wherein said means for determining an APA parameter is a polynucleotide comprising a nucleic acid sequence specifically hybridizing to a marker gene specified in Table 1. Embodiment 23: The subject matter of any one of embodiments 18 to 22, wherein said polynucleotide specifically hybridizes to a 3'UTR of at least one marker gene specified in any one of embodiments 6 to 13. Embodiment 24: The subject matter of any one of embodiments 18 to 23, wherein said polynucleotide is a probe polynucleotide. Embodiment 25: The subject matter of any one of embodiments 18 to 24, wherein said polynucleotide is primer polynucleotide. Embodiment 26: A database, preferably tangibly embedded on a data carrier, comprising an identifier of at least one of the marker genes specified in embodiment any one of embodiments 6 to 13 allocated to at least one APA parameter. Embodiment 27: The database of embodiment 26, wherein said identifier is allocated to at least one APA parameter value derived from an apparently healthy subject and / or to at least one APA parameter value derived from at least one subject known to suffer from ASD. Embodiment 28: The database of embodiment 26 or 27, wherein said APA parameter is a reference. Embodiment 29: A data carrier comprising the database of any one of embodiments 24 to 27. Embodiment 30: A device comprising a means for determining an APA parameter, a database according to any one of embodiments 25 to 28 and / or a data carrier according to embodiment 29. Embodiment 31: A kit comprising at least one detection polynucleotide according to embodiment 17, preferably comprised in a housing. Embodiment 32: The kit of embodiment 31, further comprising a data carrier according to embodiment 28. Embodiment 33: A method for treating and / or preventing an ASD in a subject, said method comprising (A) diagnosing or having diagnosed said ASD in said subject, and (B) treating and / or preventing said ASD in said subject. Embodiment 34: A method for identifying a subject susceptible for treating and / or preventing an ASD, said method comprising diagnosing said ASD in said subject, and, thereby, identifying a subject susceptible for treating and / or preventing said ASD. Embodiment 35: The method of embodiment 33 or 34, wherein said diagnosing comprises the steps of the method of any one of embodiments 1 to 16. Embodiment 36: Use of a detection polynucleotide according to embodiment 17, a database according to any one of embodiments 26 to 28, a data carrier according to embodiment 29, a device according to embodiment 30, and / or a kit according to embodiment 31 or 32, for diagnosing, treating, and / or preventing an ASD. Embodiment 37: The use of embodiment 36, wherein said use is for diagnosing and wherein said diagnosing is in vitro diagnosing. Embodiment 38: A method of providing at least one marker gene for use in the method of assessing an ASD of the present invention, the method comprising (I) providing 3'UTR sequencing read starting sites of a multitude of gene transcripts in samples of a multitude subjects from a reference group, wherein a fraction of said multitude of subjects is suffering from ASD; (II) allocating the 3'UTR sequencing read starting sites of step (I) to reference 3'UTR coordinates and determining 3'UTR lengths (transcript 3'UTR lengths); (III) modeling the distribution of transcript 3'UTR lengths in a multivariate probability distribution, preferably a Dirichlet-Multinomial distribution; and (IV) identifying genes with 3'UTR length differences between ASD and non-ASD samples via a Likelihood Ratio test, thereby providing at least one marker gene for use in the method of assessing an ASD. Embodiment 39: The method of embodiment 38 further having a feature of one of embodiments 1 to 37. Embodiment 40: The subject matter of any one of embodiments 1 to 39, wherein said APA parameter comprises an mRNA 3' untranslated region (3'UTR) length of at least one marker gene selected from the list consisting of ATP5F1E, BUB3, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPN1, RPS15A, SERTAD3, SLC25A44, TSC22D3, UBE2A, and / or ZBTB7B, preferably the list consisting of ATP5F1E, BUB3, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPN1, RPS15A, SERTAD3, SLC25A44, TSC22D3, UBE2A, ZBTB7B, EEF1A1, EIF4A3, HDAC6, HNRNPH2, IFFO1, INPP5D, POLR2F, and / or RPS19. Embodiment 41: The subject mater of any one of embodiments 1 to 40, wherein said APA parameter comprises an mRNA 3' untranslated region (3'UTR) length of at least five, preferably at least ten, more preferably at least 15, still more preferably at least 20, even more preferably at least 25, most preferably all, marker gene selected from the list consisting of ATP5F1E, BUB3, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPN1, RPS15A, SERTAD3, SLC25A44, TSC22D3, UBE2A, and / or ZBTB7B, preferably the list consisting of ATP5F1E, BUB3, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPN1, RPS15A, SERTAD3, SLC25A44, TSC22D3, UBE2A, ZBTB7B, EEF1A1, EIF4A3, HDAC6, HNRNPH2, IFFO1, INPP5D, POLR2F, and / or RPS19. Embodiment 42: The subject matter of any one of embodiments 1 to 41, wherein assessing ASD is assessing ASD level 1. All references cited in this specification are herewith incorporated by reference with respect to their entire disclosure content and the disclosure content specifically mentioned in this specification. Figure Legends Fig.1: Experimental setup to study APA from blood of ASD patients and healthy controls. A: Overview of samples used for analysis of APA differences in blood as a diagnostic tool for ASD. B: Schematic representation of SPRI bead- based size selection before and after cDNA amplification to remove contaminating RBC transcripts (HBB & HBA1 / 2) that are smaller in fragment size than most of the PBMC cDNA. Fig.2: Overview of the protocol for the preparation of 3’UTR library for sequencing the APA landscape from whole blood. Fig. 3: APA changes in ASD versus healthy controls. A: UTR length differences in ASD vs healthy controls. Triangles represent the 334 UTRs deemed significantly longer or shorter in ASD vs controls using a chi-squared test on a nested Dirichlet-MNR model. The six best candidates are labeled in the figure. B: Performance of an ASD classifier in diagnosing ASD built using the top 10 significant UTRs identified using the Dirichlet Multinomial. ROC curve for a Logistic Regression classifier. All UTRs used for the classifier were required to have a Permutation p-value < 0.00001, Transcripts > 1000 UMIs, Total number of PAS per gene / 3’UTR < 10, Sorted by Jenson-Shannon (JS) distance. Fig.4: APA changes in ASD Level 1 versus healthy controls. A: UTR length differences in ASD Level 1 vs healthy controls. Triangles represent the 33 UTRs deemed significantly longer or shorter in ASD vs controls using a chi-squared test on a nested Dirichlet-MNR model, and with the following restrictions: Permutation p-value < 0.05, Transcripts > 1000 UMIs, Total number of PAS per gene / 3’UTR < 10. The five best candidates are labeled in the figure. B: Performance of an ASD Level 1 classifier in diagnosing ASD built using the significant UTRs identified using the Dirichlet Multinomial. ROC curve for a Logistic Regression classifier. The following Examples shall merely illustrate the invention. They shall not be construed, whatsoever, to limit the scope of the invention. Example 1: Diagnosis of ASD using whole blood In order to investigate ASD changes from whole blood, a total of 71 samples were processed. This comprised of 39 ASD children, 26 healthy parents, 5 healthy children and 1 ASD adult. (Figure 1). 1.1 RNA extraction from whole blood (Fresh / Frozen) Whole blood was collected in EDTA tubes and depending on the need for storage of samples the blood was mixed with 3 volumes of RNALater to prevent RNA degradation. RNA extraction from whole blood was performed according to manufacturer’s instruction (RiboPure™ RNA Purification Kit, Thermo, AM1928). Either 500µl of fresh blood or 2 ml of blood in RNA Later was used in this study. Samples with RNALater, were additionally centrifuged for 1 min at 16,000 g RT to discard the supernatant. To the pellet / fresh blood 800 µl of Lysis Solution and 50 µl of sodium acetate solution were added and the blood cells were lysed by vigorously vortexing the samples until a homogeneous mixture was observed. To this mixture 500 µl of acid-phenol:chloroform mixture was added and the sample was vortexed. The samples were incubated at RT for 5 mins and then centrifuged for 1 min at 16,000 g RT to separate the aqueous and the organic phases. The upper aqueous phase containing the RNA was then transferred to a new 2 ml Eppendorf tube while the lower organic phase was discarded. To the aqueous phase 600 µl of 100% EtOH was added, and the samples were vortexed. This mixture was passed through a filter cartridge to facilitate the binding of RNA to the column and later centrifuged (16,000 g, 10 s, RT) to discard the flow through. The column was washed with 3 times with wash solutions 1, 2 / 3 (16,000 g, 10 s, RT) and transferred to a new collection tube. Finally, the RNA was eluted in 100 µl of preheated (75°C) elution solution after an incubation of 20 s by centrifuging the column at max speed for 30 s. Additionally, DNase treatment was performed to removing contaminating DNA. 1.2 cDNA preparation for Bulk 3’UTR Libraries The cDNA prep and the subsequent library prep was performed according to a previously described protocol (Hennig et al., (2018) G3 Genes|Genomes|Genetics 8(1):79).80 ng starting RNA from each sample was taken for each cDNA reaction. For samples where the RNA concentration was low, and the calculated RNA volume was more than 2 µl a 0.8x SPRI bead purification was performed to bind RNA to the beads and then perform an on-bead RT. The RNA was incubated with dNTPs, PEG (50%) and oligo dT primers for 10 mins at 70°C before proceeding to the reverse transcription step. The oligo dT Primers used in this application has a 3’ end identifier sequence (CCAA) that allows to specifically enrich for the 3’ ends of the transcripts in the library prep step. It also contains an 8bp UMI to identify each unique molecule of mRNA as well as a unique 6 bp sample barcode that allows multiplexing samples for sequencing. The Pre-RT mix reaction was as follows: Table 2: Pre-RT mix for cDNA preparation with on-bead RT After the 10 minutes of incubation the RT master mix is added to the pre-RT mix to synthesise the cDNA. The RT-mix was as follows: Table 3: RT mix for cDNA preparation of all samples Once the 10 µl RT reaction is set up the following program was run on the thermocycler: Table 4: Thermocycler cycling conditions for reverse transcription After the RT reaction the cDNA was amplified to have enough material for the tagmentation protocol. The following reaction composition was used: Table 5: cDNA amplification PCR reaction mix After setting the PCR reaction the following PCR program was set up. Table 6: cDNA Amplification PCR program Post PCR amplification, from fresh whole blood RNA samples, two rounds of a 0.8X SPRI bead purification was performed after the RT reaction to avoid primer carry over into the cDNA. Finally, the resulting cDNA was eluted in 11 µl of nuclease-free water.1 µl of cDNA was used to quantify its concentration using qubit high sensitivity DNA kit and another 1 µl was used to run on the bioanalyzer to look at cDNA profiles. 1.3 RBC transcript depletion The main issue for patient samples was that they were frozen for transportation. This caused inefficient RBC lysis as this step relied on the cell membrane integrity of RBCs that therefore resulted in RBC transcript contamination (HBB, HBA 1 / 2) of PBMC cDNA. In order to deplete these contaminating transcripts, we tested 2 strategies. First, we used the SPRI bead-based size exclusion of the RBC transcripts. As the size of the RBC transcripts were around 650-700 bp a 0.5x SPRI bead purification resulted in a substantial depletion of these transcripts. Two rounds of 0.5X SPRI bead purification was performed after RT in addition to the purification steps after cDNA amplification to ensure substantial reduction of RBC transcript contamination. Despite such a depletion, the resulting 3’UTR libraries sequenced still had a large number of reads used up by the RBC transcripts. In addition to this, the size-based transcript selection arbitrarily excluded all transcripts below a certain length This implied that there was a bias to longer transcripts while shorter transcripts and even shorter 3’UTRs from the same gene could be excluded from the 3’UTR libraries. We therefore adapted a second strategy which took advantage of locked nucleic acid (LNA) primers that bind to the RBC transcripts only and prevented its specific amplification (Everaert et al. (2022), BioRxiv DOI:10.1101 / 2022.03.11.483910). HBB_LNA (GCTCGGTGCCTTTAGTGATG, SEQ ID NO:2049) & HBA1 / 2_LNA (ACCAAGACCTACTTCCCGCA, SEQ ID NO:2050). Thus we could enrich the PBMC transcripts without adding any size based bias to the 3’UTR libraries and subsequent APA analysis. 1.4 Modified tagmentation approach for 3’UTR library preparation The previously described tagmentation protocol by Hennig et al was used to enrich for the 3’ ends of mRNA transcripts (Hennig et al., (2018) G3 Genes|Genomes|Genetics 8(1):79). In brief, the transposase (Tn5; 0.85 mg / ml) was loaded with the adapter Tn5ME-B / Rev (12.5 µM) (1:10; Tn5:adapter) by incubating the mix at 23°C for 60 mins. This loaded Tn5 was then added to the cDNA and the tagmentation was performed for 5 mins at 55°C (5 µl rxn vol). The following tagmentation reaction was used. Table 7: Tagmentation reactionmix for 3‘UTR library prep Immediately after the tagmentation, 1.25 µl of 0.2 % SDS was added to the reaction and incubated at RT for 5 mins to strip off the transposase. The normal gap filling step after tagmentation was avoided in order to enrich specifically for the 3’ ends of the mRNA transcripts (Fig.2). In order to amplify the tagmented product the following reaction was set up (Table 8) with using the PCR program described in Table 9. Table 8: Library PCR reaction mix post tagmentation of PBMC cDNA Table 9: Library PCR program to amplify final 3’UTR libraries Finally, two rounds of 0.7X bead purification was performed and the final 3’UTR libraries were eluted into 11µl of nuclease-free water. The library concentrations were determined using qubit DNA high sensitivity kit (1 µl) and then the average library peak size was determined by running 1 µl of library on the bioanalyzer. The equimolar volumes of samples were pooled for sequencing using a combination of unique dual indexed libraries (i7 + sample barcode). The final libraries were run on the NextSeq2000 sequencing platform using a P3200 cycles kit with paired end sequencing (130+8+0+100; R1+i7+i5+R2). The amount of the library loaded on to the sequencer was 1100 pM (with 14% PhiX spike-in) and using a custom polyA-seq primer (Hennig et al., (2018) G3 Genes|Genomes|Genetics 8(1):79) for sequencing read1. The read2 was long enough (100bp) to go through the poly(A) tail into the ends of the 3’UTR thus allowing to identify true 3’UTR ends. Table 10: Primers used for 3’UTR bulk 1.5 Bioinformatics analysis The alignments for all sequencing data were performed using STAR (Dobin et al., (2013) Bioinformatics 29(1):15). Read1 was used to precisely map the 3’ sequenced reads onto genomic coordinates and the 3’UTR regions were identified using Ensembl annotations.3’UTR peak calling was performed using kernel density estimation (KDE) (McCarthy & O’Callaghan, (2014) Bioinformatics 30(9)1302). For the ASD blood APA data a Dirichlet-MNR (Chen & Li, (2013), The Annals of Applied Statistics 7(1):418; Mosimann, (1962) Biometrika 49(1-2):65) was performed to correct for overdispersion. Two regressions were performed for each gene per replicate; one with the ASD status taken into consideration and another excluding information on ASD status. The significance in the deviation between the two Dirichlet-MNRs was calculated using a chi-squared test and to corroborate this result permutation tests were used. This procedure allows the determination of APA genes with significant changes in ASD versus controls combining all replicates. An EMD was performed to visualize the APA changes in whole blood between ASD patients and healthy controls. Positive changes implied 3’UTR lengthening in ASD while negative changes implied shortening in the same. From the list of significant APA genes determined using the Dirichlet-MNR the top 10 candidates were selected on upon meeting the following criteria: a permutation p-value < 0.00001, transcripts count > 1000 UMIs, total number of PAS per gene / 3’UTR < 10 & sorted by Jenson-Shannon (JS) distance. These selected genes were then used to build an unbiased Logistic Regression ASD classification pipeline, with 10-fold cross-validation (Fig.3). Example 2: Method usage for Diagnosis To show an application of the method described herein, we analyzed a blood sample from a male child clinically diagnosed with ASD. The child was diagnosed with the following co- morbidities: dysmorphic traits, language disabilities, learning disabilities, macrocephaly, and mobility issues. 2.1 Sample preparation To derive an ASD diagnosis for an individual via RNA-sequencing, the blood was collected as described in Example 1, section 1.1., and cDNA preparation and RBC transcript depletion was performed as described in Example 1, sections 1.2 and 1.3, respectively, Then, the sample was tagmented and sequenced as described in Example 1, section 1.4. 2.23’UTR size determination The sequenced sample’s information, in the form of a fastq file containing both reads’ nucleotide sequences, was aligned to the Human genome (GRCh38 assembly) using STAR. In total, 4.226.937 reads were sequenced, with 68% of those uniquely mapped to the genome. As described in Example 1, the read 1 was used to map the 3’ sequenced reads onto genomic coordinates and the 3’UTR regions were identified using Ensembl annotations. From these coordinates, each transcript’s UTR size was calculated. Next, these raw transcript UTR sizes were assigned to the nearest reference UTR polyadenylation peak, obtained in Example 1, generating a matrix with transcript counts per peak for the analyzed sample. 2.3 Diagnosis prediction Finally, the relative counts of each peak were calculated in relation to the total counts per UTR (Table 11) and this matrix was used as input for the ASD classification model obtained and described in Example 1. The sample was predicted by the logistic regression model to be from an individual with ASD, with a probability of 89.7%, confirming the previous behavioral diagnosis. Table 11: Proportion of peak counts per UTR for a selected group of 3 UTRs. Example 3: RNA-sequencing-free methods for diagnosis To enable methods of diagnosis that are not reliant on RNA-sequencing technology, and are thus more applicable to procedures already present in most clinics that work hand-in-hand with medical doctors, the sequences described in SEQ ID NOs:1 to 2048, in particular SEQ ID NOs:353 to 2048, are used to produce oligonucleotide probes, e.g. DNA oligonucleotide probes, specifically hybridizing to the aforesaid sequences. The probes derived from the sequences designated by an indication including "ASD" are more prevalent in samples from individuals with ASD, and those derived from the sequences designated by an indication including "CTRL" are more frequent in healthy individuals. This can be used to assess an ASD spectrum disorder by any hybridization method deemed appropriate by the skilled person by methods in principle known to the skilled person. In an exemplary embodiment, multiple probes are immobilized on a chip for RNA analysis such as microarrays, e.g. as proposed by Schena M, et al. (1995) Science 270(5235):467. As an example of this method, an RNA extract from the patient is fluorescently labeled and loaded to the microarray. The immobilized probes hybridize with the sample RNA molecules, thus resulting in an increase of fluorescence intensity over a background level, which can be measured using a fluorescent scanner. The relative flourescence of each probe can be obtained by dividing the probe’s fluorescence intensity by the sum of intensity of all probes for each UTR. This procedure generates a matrix comparable to the one described in Example 2, section 2.3. which can then be used as input for the ASD classification model obtained and described in Example 1, generating a binary and / or probability-based ASD diagnosis prediction. In a further exemplary embodiment, oligonucleotide probes hybridizing specifically to at least one sequence from Table 12 or Table 13, and / or optionally Table 15 and / or Table 16, are labeled in a detectable manner. Specific binding of a probe hybridizing to a sequence of Table 12, or optionally Table 15, is indicative of ASD, while specific binding of a probe of Table 13, or optionally Table 16, is indicative of non-ASD. Thus, e.g. a multitude of oligonucleotide probes specifically hybridizing to a multitude of nucleic acid sequences of Table 12, and optionally Table 15, can be labeled with a first color, e.g. green, and / or a multitude of oligonucleotide probes specifically hybridizing to a multitude of nucleic acid sequences of Table 13, and optionally Table 16, can be labeled with a second color, e.g. red. The aforesaid oligonucleotide probes are hybridized to RNA or cDNA prepared from a sample of a subject, and the resulting color intensity caused by bound oligonucleotide probes or their ratio is used as a parameter to asses ASD. In a further exemplary embodiment, length of UTRs and / or presence of sequences from Table 12 and / or Table 13, and / or optionally Table 15 and / or Table 16, is determined by qRT-PCR by methods known to the skilled person. Example 4: Method usage for Diagnosis of ASD level 1 Patients were classified as 'ASD Level 1' if a practicing professional diagnosed them with a severity level of 1, based on the criteria outlined in the 2022 text revision of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5-TR). 4.1 Sample preparation To obtain an ASD Level 1 diagnosis from RNA sequencing, blood was collected as outlined in Example 1, section 1.1. The preparation of cDNA and the depletion of RBC transcripts were performed following the protocols described in sections 1.2 and 1.3 of Example 1, respectively. Subsequently, the sample underwent tagmentation and sequencing as detailed in section 1.4 of Example 1. 4.23’UTR size determination As explained in Example 1, read 1 was used to align the 3' sequenced reads to genomic coordinates, identifying 3'UTR regions based on Ensembl annotations. From these coordinates, the UTR size for each transcript was calculated. These raw UTR sizes were then mapped to the nearest reference UTR polyadenylation peak, as obtained in Example 1, creating a matrix of transcript counts per peak for the analyzed sample. 4.3 Candidate selection Candidates were selected as in Example 1 section 1.5, modeling transcript counts per peak with Dirichlet-Multinomial nested regressions followed by a chi-squared test on the nested models and a permutation test. However, in this Example only the ASD Level 1 samples were used to train the model. Furthermore, the candidates were selected on upon meeting the following criteria: a permutation p-value < 0.05, transcripts count > 1000 UMIs, total number of PAS per gene / 3’UTR < 10. 4.4 Diagnosis prediction The relative counts for each peak were calculated as a proportion of the total counts per UTR, following the method described in Example 2. This matrix served as input for the ASD classification model shown in Example 1. The logistic regression model predicted that the sample came from an individual with ASD Level 1, with a 77.5% probability, corroborating the previous behavioral diagnosis. The overall efficiency of the classifier on the test data is illustrated in Fig.4.

[0002] Table 12: ASD subsequences of marker FAM3A 451 - 452 genes RAB7A_UTR5 453 - 458 marker gene ASD LXN_UTR2 459 subsequence(s), KEAP1 460 SEQ ID NOs PCNP_UTR4 461 - 469 RPN1 353 - 355 EPB41L3 470 - 473 UBE2A_UTR4 356 ATRX_UTR24 474 MKRN2_UTR6 357 - 359 SLC25A24 475 - 476 UHMK1 360 - 365 RBM22 477 - 479 IER3 366 FKBP1A 480 - 482 BUB3 367 - 371 SNX5_UTR3 483 MLX 374 PABPC4 484 CA2_UTR6 375 - 380 TINF2 485 ITM2A 381 COMT_UTR3 486 - 488 NT5C3A 382 FBXO7_UTR5 489 FGR 383 PDGFB 490 - 492 M6PR 384 - 387 SGSM3 493 AK2 388 - 392 RBX1 494 IFRD1 393 - 395 RBM23 495 - 500 RHBDF1_UTR14 396 NIN_UTR22 501 MMP25_UTR7 397 SNW1_UTR5 502 CALCOCO1 398 - 400 PSMC1 503 - 504 NUB1_UTR7 401 - 403 SRP54_UTR12 505 - 506 IFNGR1 404 - 405 TPD52L2 507 - 508 TRIT1 406 - 407 RIOK3_UTR4 509 - 517 OFD1_UTR12 408 SLC35A2_UTR2 518 VAMP3 409 - 413 PIN4_UTR2 519 GDI2 414 - 416 BEX4 520 - 521 ATP11B_UTR12 417 - 418 STK24_UTR2 522 MRPS35_UTR7 419 - 421 TSC22D1 523 - 528 CASP8_UTR7 422 - 424 USB1_UTR6 529 ZBTB11 425 - 426 CYB5B 530 - 533 ISOC1 427 - 428 ELOB 534 SMG6 429 R3HCC1_UTR5 535 FGF22 430 - 431 UBXN8_UTR8 536 DGCR2 432 - 435 ASAH1 537 - 543 CAMK2A_UTR5 436 - 441 LIN7B_UTR3 544 RPL31 442 - 447 C19orf53_UTR3 545 TRIB2_UTR2 448 - 450 ZFR2_UTR7 546 - 547 KDELR1 548 - 549 DENND10 677 GRWD1 550 - 553 PCMT1_UTR10 678 - 679 PDE4C_UTR7 554 PAIP2 680 - 683 SMG9 555 - 556 ZCCHC17 684 SEC61B 557 FLT3 685 ARHGAP21 558 HNRNPA2B1_UTR5 686 - 693 RPL28_UTR4 559 - 560 VPS26A_UTR6 694 - 699 TSPAN14 561 - 572 ATF1_UTR5 700 - 703 RAB5C 573 - 575 VAPB 704 LPXN 576 ATP5F1E 705 - 707 MS4A6A 577 DDX27_UTR4 708 IL10RA_UTR7 578 F13A1 709 - 710 CDKN1B 579 - 587 TBCC 711 - 712 COPZ1_UTR4 588 DEK_UTR3 713 - 714 RAB5B_UTR3 589 - 592 TM9SF2 715 - 718 COPS7A_UTR9 593 - 595 CENPB 719 CCNC 596 - 597 MAX_UTR5 720 - 725 SNX3 598 - 601 HECTD3 726 COX7A2 602 EPS15L1_UTR10 727 BTN2A1_UTR6 603 SEM1_UTR14 728 SPARC 604 - 609 CALU 729 - 734 GNPDA1_UTR2 610 ANAPC13 735 - 736 TRIM23 611 - 614 MBD4 737 - 738 PDGFRB_UTR9 615 PARP2_UTR4 739 PDCD10 616 - 618 KLF16 740 - 741 KYNU_UTR10 619 RPL36 742 RPL22 620 - 622 JUND 743 - 744 QSOX1_UTR6 623 - 628 TRPM4_UTR16 745 MFN2_UTR18 629 - 631 ZC3H4 746 ACADM_UTR15 632 - 636 DNMT1_UTR13 747 CD46_UTR2 637 - 641 COX7B_UTR3 748 RSRP1 642 - 643 LIN28A 749 - 752 RPS6KA1_UTR22 644 - 648 DNAJB1 753 - 754 SPCS2 649 - 651 KHDC4 755 - 758 FBXL5 652 - 653 POMP 759 - 760 FOXO3_UTR2 654 - 664 SLC39A11 761 - 763 GLE1_UTR19 665 - 669 GIMAP4 764 - 765 EIF2B2_UTR5 670 - 672 MKRN1_UTR5 766 IFIT2 673 - 676 NOTCH2_UTR12 767 TMEM106C_UTR6 768 SNRPG 854 RPS15A 769 - 770 HSPD1 855 - 856 DOCK2 771 RAB5A_UTR5 857 - 862 SLC43A3 772 RPL32 863 OSTF1 773 - 774 LRIG1 864 - 865 NAT10_UTR2 775 ABHD10_UTR4 866 - 867 CD164 776 - 777 TCTA 868 - 869 PIGC 778 RPL37 870 - 873 LAMC1 779 MSN 874 - 877 TMBIM1 780 - 781 ATP6V1B2_UTR8 878 - 879 TBC1D4 782 - 783 GOLGA7_UTR2 880 - 882 LCP1 784 - 790 STOM 883 - 889 SRSF1 791 - 799 NRBF2 890 - 892 SLC31A2_UTR2 800 - 803 GLUD1 893 - 895 MRPL50 804 ADD3 896 - 898 UQCC2_UTR2 805 BTBD10 899 CPEB2_UTR2 806 - 807 PTPRJ_UTR2 900 THAP12 808 - 809 FADS1 901 - 905 IL18BP 810 PDCD4 906 - 910 RPLP1 811 - 812 ANK3 911 - 912 ACTR2_UTR4 813 - 819 ADAM17 913 - 916 ACTR1A_UTR3 820 - 821 ACSL1 917 - 922 ASCC1 822 - 824 BAG3 923 - 924 METTL5_UTR2 825 ZNF547_UTR4 925 SLC40A1 826 - 828 CDYL_UTR4 926 FAM13A_UTR6 829 HNRNPU_UTR11 927 NEDD1_UTR3 830 - 833 NRGN_UTR3 928 - 932 MBNL2_UTR5 834 - 835 OBSCN 933 - 934 TPM1_UTR12 836 PTPRN2 935 GLYR1 837 - 838 HSPA13 936 - 937 ADAMTS10 839 CNOT8_UTR5 938 - 939 EVI5L_UTR2 840 - 841 ATP5MJ 940 ZNF614_UTR2 842 UQCRB 941 - 944 EVA1B 843 MFSD14A 945 MOB3C 844 - 846 NECAP2_UTR5 946 - 950 CD53 847 - 849 TSC22D3 951 - 957 PRUNE1 850 PRXL2B 958 PSEN2 851 - 852 PFKFB1 959 PYCR2 853 CCAR2_UTR5 960 - 961 EPB41_UTR17 962 - 964 HINT1_UTR2 1068 - 1070 CYB5R1 965 RNF34 1071 - 1072 BTG2 966 - 975 LSM3 1073 - 1074 GATD3A_UTR4 976 - 977 MTM1 1075 ITGB2 978 - 979 RGS19 1076 - 1077 CXCR5 980 MRM3_UTR4 1078 ZBTB7B 981 CD8B_UTR3 1079 - 1082 SLC25A44 982 - 984 CLEC7A 1083 - 1087 ALDH16A1_UTR8 985 ZNF131_UTR3 1088 - 1089 RBBP4_UTR10 986 - 990 SUCLG2_UTR2 1090 - 1091 ARPC5 991 - 1000 RPL38_UTR2 1092 SNED1_UTR4 1001 - 1002 FAM222B 1093 - 1094 SLC66A3_UTR4 1003 - 1004 PSMD1_UTR17 1095 CTSS_UTR5 1005 RBM4B 1096 BOLA3 1006 KCTD13_UTR6 1097 - 1099 IFI16 1007 PACS1_UTR4 1100 AIM2 1008 GOLGA8A 1101 - 1102 TPRA1 1009 UNC119B 1103 - 1104 ARHGEF3 1010 - 1011 ZBTB8OS_UTR2 1105 - 1106 ZNF691 1012 HIC1 1107 BDH2 1013 - 1014 PPFIA3_UTR10 1108 UQCRQ 1015 ATOX1 1109 DCBLD1_UTR5 1016 BET1L_UTR3 1110 - 1111 TAGAP 1017 - 1027 ERFE 1112 TP53INP1 1028 - 1029 CALR_UTR2 1113 - 1114 PSIP1_UTR2 1030 - 1031 GADD45GIP1 1115 HIF1AN_UTR5 1032 - 1036 R3HDM2 1116 TAF10 1037 MYLPF 1117 PDIA3_UTR14 1038 - 1043 OAZ2 1118 - 1119 DUSP18_UTR3 1044 - 1046 CCDC66_UTR20 1120 ATP5MG_UTR2 1047 H2BC4 1121 - 1122 TEPSIN 1048 FAM83H 1123 - 1124 OR51E2 1049 PJA1 1125 SERTAD3 1050 ERCC6L2_UTR22 1126 - 1127 LENG8 1051 - 1055 SKA2 1128 - 1131 FAM234A_UTR4 1056 RPL35A_UTR4 1132 DDIT4 1057 PTTG1IP 1133 - 1140 CAVIN2 1058 - 1066 DAZAP2_UTR2 1141 - 1146 NSMCE1 1067 TENT5C 1147 - 1178 TM2D3_UTR3 1179 - 1180 TSEN15_UTR5 1238 - 1239 CCR10 1181 MPIG6B_UTR3 1240 - 1241 H2BC21 1182 - 1185 BAG6_UTR7 1242 CDK10_UTR12 1186 - 1188 HLA-C 1243 - 1244 FAAP100 1189 CDSN 1245 BRWD1_UTR2 1190 PPP1R11 1246 - 1248 IFIT1 1191 - 1192 UQCC3 1249 - 1250 RPS23 1193 - 1194 E2F4_UTR6 1251 TET3 1195 - 1199 PRR13_UTR3 1252 - 1255 ZNF490 1200 - 1203 CNEP1R1_UTR3 1256 - 1258 COMMD6 1204 C17orf107 1259 - 1263 PARVB 1205 - 1208 ATF6B 1264 TMEM120B_UTR2 1209 IRF9_UTR7 1265 - 1266 HMGB1 1210 - 1217 CEBPD 1267 BLOC1S2 1218 - 1220 RBM14_UTR3 1268 HSH2D_UTR3 1221 ZNF564 1269 - 1271 FLNA_UTR5 1222 ATXN7L3B 1272 - 1275 C5AR1 1223 - 1226 SCRT1 1276 RPF2_UTR8 1227 - 1229 NBPF11 1277 RPL37A_UTR2 1230 - 1233 S1PR2 1278 TOR4A 1234 NBPF9 1279 RPL23A_UTR2 1235 NBPF10 1280 MSRB1 1236 - 1237 NT5C3A 1302 - 1303 Table 13: CTRL (control) subsequences of FGR 1304 - 1305 marker genes M6PR 1306 - 1308 AK2 1309 - 1310 marker gene CTRL IFRD1 1311 subsequence(s), RHBDF1 1312 SEQ ID NOs MMP25 1313 RPN1 1281 CALCOCO1 1314 UBE2A 1282 - 1283 NUB1 1315 MKRN2 1284 - 1287 IFNGR1 1316 - 1317 UHMK1 1288 - 1290 TRIT1 1318 - 1319 IER3 1291 OFD1 1320 BUB3 1292 - 1293 VAMP3 1321 - 1322 MLX 1296 GDI2 1323 - 1324 CA2 1297 - 1298 ATP11B 1325 - 1326 ITM2A 1299 - 1301 MRPS35 1327 CASP8 1328 USB1 1404 ZBTB11 1329 CYB5B 1405 - 1408 ISOC1 1330 ELOB 1409 SMG6 1331 R3HCC1 1410 FGF22 1332 UBXN8 1411 DGCR2 1333 - 1335 ASAH1 1412 - 1414 CAMK2A 1336 - 1340 LIN7B 1415 - 1416 RPL31 1341 - 1343 C19orf53 1417 - 1418 TRIB2 1344 - 1345 ZFR2 1419 FAM3A 1346 - 1348 KDELR1 1420 RAB7A 1349 - 1352 GRWD1 1421 - 1422 LXN 1353 PDE4C 1423 KEAP1 1354 SMG9 1424 - 1427 PCNP 1355 - 1356 SEC61B 1428 EPB41L3 1357 ARHGAP21 1429 - 1430 ATRX 1358 RPL28 1431 - 1434 SLC25A24 1359 TSPAN14 1435 - 1442 RBM22 1360 - 1361 RAB5C 1443 FKBP1A 1362 - 1363 LPXN 1444 SNX5 1364 MS4A6A 1445 PABPC4 1365 IL10RA 1446 TINF2 1366 - 1367 CDKN1B 1447 - 1451 COMT 1368 - 1369 COPZ1 1452 FBXO7 1370 RAB5B 1453 - 1457 PDGFB 1371 - 1372 COPS7A 1458 SGSM3 1373 CCNC 1459 RBX1 1374 - 1375 SNX3 1460 - 1462 RBM23 1376 - 1380 COX7A2 1463 NIN 1381 BTN2A1 1464 SNW1 1382 SPARC 1465 - 1469 PSMC1 1383 - 1385 GNPDA1 1470 SRP54 1386 TRIM23 1471 TPD52L2 1387 - 1388 PDGFRB 1472 - 1473 RIOK3 1389 - 1393 PDCD10 1474 - 1476 SLC35A2 1394 KYNU 1477 PIN4 1395 - 1397 RPL22 1478 - 1480 BEX4 1398 QSOX1 1481 - 1482 STK24 1399 MFN2 1483 TSC22D1 1400 - 1403 ACADM 1484 - 1485 CD46 1486 - 1488 COX7B 1582 RSRP1 1489 - 1490 LIN28A 1583 - 1587 RPS6KA1 1491 - 1493 DNAJB1 1588 - 1589 SPCS2 1494 - 1496 KHDC4 1590 - 1591 FBXL5 1497 POMP 1592 - 1597 FOXO3 1498 - 1506 SLC39A11 1598 GLE1 1507 - 1510 GIMAP4 1599 - 1601 EIF2B2 1511 - 1512 MKRN1 1602 IFIT2 1513 - 1516 NOTCH2 1603 DENND10 1517 - 1518 TMEM106C 1604 PCMT1 1519 RPS15A 1605 - 1608 PAIP2 1520 - 1521 DOCK2 1609 ZCCHC17 1522 - 1523 SLC43A3 1610 FLT3 1524 - 1525 OSTF1 1611 - 1612 HNRNPA2B1 1526 - 1530 NAT10 1613 VPS26A 1531 - 1532 CD164 1614 - 1620 ATF1 1533 - 1536 PIGC 1621 VAPB 1537 LAMC1 1622 ATP5F1E 1538 - 1541 TMBIM1 1623 DDX27 1542 TBC1D4 1624 F13A1 1543 - 1547 LCP1 1625 - 1629 TBCC 1548 SRSF1 1630 DEK 1549 SLC31A2 1631 TM9SF2 1550 - 1551 MRPL50 1632 - 1633 CENPB 1552 UQCC2 1634 MAX 1557 CPEB2 1635 - 1636 HECTD3 1558 THAP12 1637 EPS15L1 1559 - 1560 IL18BP 1638 SEM1 1561 RPLP1 1639 - 1641 CALU 1562 - 1564 ACTR2 1642 - 1655 ANAPC13 1565 ACTR1A 1656 MBD4 1566 ASCC1 1657 PARP2 1567 METTL5 1658 KLF16 1568 - 1571 SLC40A1 1659 - 1661 RPL36 1572 FAM13A 1662 JUND 1573 - 1578 NEDD1 1663 TRPM4 1579 MBNL2 1664 - 1667 ZC3H4 1580 TPM1 1668 - 1670 DNMT1 1581 GLYR1 1671 - 1672 ADAMTS10 1673 CNOT8 1765 - 1768 EVI5L 1674 ATP5MJ 1769 ZNF614 1675 UQCRB 1770 - 1774 EVA1B 1676 - 1677 MFSD14A 1775 - 1776 MOB3C 1678 - 1681 NECAP2 1777 - 1778 CD53 1682 TSC22D3 1779 PRUNE1 1683 PRXL2B 1780 PSEN2 1684 PFKFB1 1781 - 1782 PYCR2 1685 CCAR2 1783 SNRPG 1686 EPB41 1784 - 1785 HSPD1 1687 CYB5R1 1786 RAB5A 1688 - 1692 BTG2 1787 - 1792 RPL32 1693 - 1695 GATD3A 1793 LRIG1 1696 ITGB2 1794 ABHD10 1697 - 1698 CXCR5 1795 TCTA 1699 - 1700 ZBTB7B 1796 RPL37 1701 - 1708 SLC25A44 1797 - 1798 MSN 1709 - 1712 ALDH16A1 1799 ATP6V1B2 1713 RBBP4 1800 - 1804 GOLGA7 1714 - 1718 ARPC5 1805 - 1814 STOM 1719 - 1720 SNED1 1815 NRBF2 1721 - 1723 SLC66A3 1816 GLUD1 1724 - 1725 CTSS 1817 ADD3 1726 - 1727 BOLA3 1818 BTBD10 1728 IFI16 1819 - 1820 PTPRJ 1729 - 1731 AIM2 1821 FADS1 1732 - 1737 TPRA1 1822 PDCD4 1738 - 1743 ARHGEF3 1823 ANK3 1744 ZNF691 1824 ADAM17 1745 - 1748 BDH2 1825 ACSL1 1749 - 1751 UQCRQ 1826 - 1827 BAG3 1752 DCBLD1 1828 ZNF547 1753 TAGAP 1829 - 1832 CDYL 1754 - 1756 TP53INP1 1833 HNRNPU 1757 PSIP1 1834 NRGN 1758 - 1759 HIF1AN 1835 - 1837 OBSCN 1760 TAF10 1838 - 1842 PTPRN2 1761 PDIA3 1843 - 1845 HSPA13 1762 - 1764 DUSP18 1846 - 1848 ATP5MG 1849 H2BC4 1912 TEPSIN 1850 FAM83H 1913 OR51E2 1851 - 1852 PJA1 1914 SERTAD3 1853 - 1854 ERCC6L2 1915 LENG8 1855 - 1857 SKA2 1916 - 1919 FAM234A 1858 - 1860 RPL35A 1920 - 1922 DDIT4 1861 - 1862 PTTG1IP 1923 - 1929 CAVIN2 1863 - 1868 DAZAP2 1930 - 1931 NSMCE1 1869 TENT5C 1932 - 1947 HINT1 1870 - 1871 TM2D3 1948 - 1949 RNF34 1872 CCR10 1950 - 1951 LSM3 1873 - 1874 H2BC21 1952 - 1953 MTM1 1875 CDK10 1954 RGS19 1876 FAAP100 1955 - 1956 MRM3 1877 BRWD1 1957 CD8B 1878 IFIT1 1958 - 1959 CLEC7A 1879 RPS23 1960 - 1962 ZNF131 1880 - 1881 TET3 1963 - 1964 SUCLG2 1882 ZNF490 1965 - 1967 RPL38 1883 COMMD6 1968 - 1971 FAM222B 1884 - 1888 PARVB 1972 PSMD1 1889 TMEM120B 1973 - 1975 RBM4B 1890 HMGB1 1976 - 1986 KCTD13 1891 BLOC1S2 1987 - 1989 PACS1 1892 HSH2D 1990 GOLGA8A 1893 - 1895 FLNA 1991 UNC119B 1896 C5AR1 1992 - 1996 ZBTB8OS 1897 RPF2 1997 - 1998 HIC1 1898 - 1899 RPL37A 1999 - 2001 PPFIA3 1900 TOR4A 2002 ATOX1 1901 RPL23A 2003 BET1L 1902 MSRB1 2004 - 2005 ERFE 1903 TSEN15 2006 CALR 1904 - 1905 MPIG6B 2007 - 2009 GADD45GIP1 1906 - 1907 BAG6 2010 R3HDM2 1908 HLA-C 2011 - 2012 MYLPF 1909 CDSN 2013 - 2015 OAZ2 1910 PPP1R11 2016 - 2018 CCDC66 1911 UQCC3 2019 E2F4 2020 PRR13 2021 CNEP1R1 2022 - 2023 C17orf107 2024 - 2029 ATF6B 2030 - 2031 IRF9 2032 CEBPD 2033 RBM14 2034 ZNF564 2035 - 2036 ATXN7L3B 2037 - 2039 SCRT1 2040 - 2041 NBPF11 2042 - 2043 S1PR2 2044 - 2046 NBPF9 2047 NBPF10 2048 Table 16: further preferred CTRL (control) Table 15: further preferred ASD subsequences of marker genes subsequences of marker genes marker gene CTRL marker gene ASD subsequence(s), subsequence(s), SEQ ID NOs SEQ ID NOs IFFO1 2064-2065 IFFO1 2075 HDAC6 2066-2067 HDAC6 2076 POLR2F 2068 POLR2F 2077-2078 RPS19 2069 RPS19 2079-2081 HNRNPH2 2070 HNRNPH2 2082 EIF4A3 2071 EIF4A3 2083 EEF1A1 2072-2073 EEF1A1 2084-2088 INPP5D 2074 INPP5D 2089

[0003] Literature American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders (5th ed., text rev.). https: / / doi.org / 10.1176 / appi.books.9780890425787 Carruthers et al. (2023), J Child Psychol Psychiatry, doi:10.1111 / jcpp.13798 Chen & Li, (2013), The Annals of Applied Statistics 7(1):418 Cui et al. (2023), Nat Comm 14:583 Dobin et al., (2013) Bioinformatics 29(1):15 Everaert et al. (2022), BioRxiv DOI:10.1101 / 2022.03.11.483910 Göpferich et al. (2020), doi.org / 10.1101 / 2020.08.12.247627 He et al. (2019), Sci Rep 9:11818 Hennig et al., (2018) G3 Genes|Genomes|Genetics 8(1):79 McCarthy & O’Callaghan, (2014) Bioinformatics 30(9)1302 Mosimann, (1962) Biometrika 49(1-2):65 Parras et al. (2018), Nature 560:7719 Schena M, et al. (1995) Science 270(5235):467 Szkop et al. (2017), Front Mol Neurosci 10, Article 279 Tylee et al. (2017), Am J Med Genet Neuropsychiatr Genet 174(3):181 Velmeshev et al. (2019), Science 364:6441 Zhang et al.2021), J Exp Clin Cancer Res 40:51

Claims

Deutsches Krebsforschungszentrum Stiftung des öffentlichen Rechts Universität Heidelberg 57 DK16729PC Claims 1. A method for assessing an autism spectrum disorder (ASD) in a sample from a subject, the method comprising (a) determining a parameter of alternative polyadenylation (APA parameter) in said sample; (b) comparing the APA parameter determined in step (a) to a reference, and (c) based on the comparison in step (b), assessing said ASD, wherein said sample is a non-brain sample, and wherein said APA parameter comprises mRNA 3' untranslated region (3'UTR) lengths of at least ten genes selected from the list consisting of RPN1, UBE2A, MKRN2, UHMK1, IER3, BUB3, MLX, CA2, ITM2A, NT5C3A, FGR, M6PR, AK2, IFRD1, RHBDF1, MMP25, CALCOCO1, NUB1, IFNGR1, TRIT1, OFD1, VAMP3, GDI2, ATP11B, MRPS35, CASP8, ZBTB11, ISOC1, SMG6, FGF22, DGCR2, CAMK2A, RPL31, TRIB2, FAM3A, RAB7A, LXN, KEAP1, PCNP, EPB41L3, ATRX, SLC25A24, RBM22, FKBP1A, SNX5, PABPC4, TINF2, COMT, FBXO7, PDGFB, SGSM3, RBX1, RBM23, NIN, SNW1, PSMC1, SRP54, TPD52L2, RIOK3, SLC35A2, PIN4, BEX4, STK24, TSC22D1, USB1, CYB5B, ELOB, R3HCC1, UBXN8, ASAH1, LIN7B, C19orf53, ZFR2, KDELR1, GRWD1, PDE4C, SMG9, SEC61B, ARHGAP21, RPL28, TSPAN14, RAB5C, LPXN, MS4A6A, IL10RA, CDKN1B, COPZ1, RAB5B, COPS7A, CCNC, SNX3, COX7A2, BTN2A1, SPARC, GNPDA1, TRIM23, PDGFRB, PDCD10, KYNU, RPL22, QSOX1, MFN2, ACADM, CD46, RSRP1, RPS6KA1, SPCS2, FBXL5, FOXO3, GLE1, EIF2B2, IFIT2, DENND10, PCMT1, PAIP2, ZCCHC17, FLT3, HNRNPA2B1, VPS26A, ATF1, VAPB, ATP5F1E, DDX27, F13A1, TBCC, DEK, TM9SF2, CENPB, MAX, HECTD3, EPS15L1, SEM1, CALU, ANAPC13, MBD4, PARP2, KLF16, RPL36, JUND, TRPM4, ZC3H4, DNMT1, COX7B, LIN28A, DNAJB1, KHDC4, POMP, SLC39A11, GIMAP4, MKRN1, NOTCH2, TMEM106C, RPS15A, DOCK2, SLC43A3, OSTF1, NAT10, CD164, PIGC, LAMC1, TMBIM1, TBC1D4, LCP1, SRSF1, SLC31A2, MRPL50, UQCC2, CPEB2, THAP12, IL18BP, RPLP1, ACTR2, ACTR1A, ASCC1, METTL5, SLC40A1, FAM13A, NEDD1, MBNL2, TPM1, GLYR1, ADAMTS10, EVI5L, ZNF614, EVA1B, MOB3C, CD53, PRUNE1, PSEN2, PYCR2, SNRPG, HSPD1, RAB5A, RPL32, LRIG1, ABHD10, TCTA, RPL37, MSN, ATP6V1B2, GOLGA7, STOM, NRBF2, GLUD1, ADD3, BTBD10, PTPRJ, FADS1, PDCD4, ANK3, ADAM17, ACSL1, BAG3, ZNF547, CDYL, HNRNPU, NRGN, OBSCN, PTPRN2, HSPA13, CNOT8, ATP5MJ, UQCRB, MFSD14A, NECAP2, TSC22D3, PRXL2B, PFKFB1, CCAR2, EPB41, CYB5R1, BTG2, GATD3A, ITGB2, CXCR5, ZBTB7B, SLC25A44, ALDH16A1, RBBP4, ARPC5, SNED1, SLC66A3, CTSS, BOLA3, IFI16, AIM2, TPRA1, ARHGEF3, ZNF691, BDH2, UQCRQ, DCBLD1, TAGAP, TP53INP1, PSIP1, HIF1AN, TAF10, PDIA3, DUSP18, ATP5MG, TEPSIN, OR51E2, SERTAD3, LENG8, FAM234A, DDIT4, CAVIN2, NSMCE1, HINT1, RNF34, LSM3, MTM1, RGS19, MRM3, CD8B, CLEC7A, ZNF131, SUCLG2, RPL38, FAM222B, PSMD1, RBM4B,Deutsches Krebsforschungszentrum Stiftung des öffentlichen Rechts Universität Heidelberg 58 DK16729PC KCTD13, PACS1, GOLGA8A, UNC119B, ZBTB8OS, HIC1, PPFIA3, ATOX1, BET1L, ERFE, CALR, GADD45GIP1, R3HDM2, MYLPF, OAZ2, CCDC66, H2BC4, FAM83H, PJA1, ERCC6L2, SKA2, RPL35A, PTTG1IP, DAZAP2, TENT5C, TM2D3, CCR10, H2BC21, CDK10, FAAP100, BRWD1, IFIT1, RPS23, TET3, ZNF490, COMMD6, PARVB, TMEM120B, HMGB1, BLOC1S2, HSH2D, FLNA, C5AR1, RPF2, RPL37A, TOR4A, RPL23A, MSRB1, TSEN15, MPIG6B, BAG6, HLA-C, CDSN, PPP1R11, UQCC3, E2F4, PRR13, CNEP1R1, C17orf107, ATF6B, IRF9, CEBPD, RBM14, ZNF564, ATXN7L3B, SCRT1, NBPF11, S1PR2, NBPF9, and NBPF10.

2. The method of claim 1, wherein said assessing ASD is diagnosing ASD.

3. The method of claim 1 or 2, wherein said sample is a blood sample or a blood-derived sample.

4. The method of any one of claims 1 to 3, wherein said APA parameter comprises mRNA 3'UTR lengths of RPN1, UBE2A, MKRN2, UHMK1, IER3, BUB3, MLX, CA2, ITM2A, and / or NT5C3A.

5. The method of ay one of claims 1 to 4, wherein said APA parameter comprises mRNA 3'UTR lengths of RPN1, UBE2A, MKRN2, UHMK1, IER3, BUB3, MLX, CA2, ITM2A, and NT5C3A.

6. The method of any one of claims 1 to 5, wherein said APA parameter comprises mRNA 3' untranslated region (3'UTR) lengths of the genes RPN1, UBE2A, BUB3, ATP5F1E, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPS15A, SERTAD3, SLC25A44, TSC22D3, and ZBTB7B.

7. The method of any one of claims 1 to 6, wherein said APA parameter further comprises mRNA 3' untranslated region (3'UTR) lengths of the genes EEF1A1, EIF4A3, HDAC6, HNRNPH2, IFFO1, INPP5D, POLR2F, and RPS19.

8. The method of any one of claims 1 to 7, wherein said APA parameter comprises mRNA 3' untranslated region (3'UTR) lengths of the genes RPN1, UBE2A, BUB3, ATP5F1E, CDK10, CENPB, COX7B, ELOB, FAM3A, IFIT1, JUND, NSMCE1, OSTF1, PDCD10, PRR13, QSOX1, RPL32, RPL35A, RPL36, RPLP1, RPS15A, SERTAD3, SLC25A44, TSC22D3, ZBTB7B, EEF1A1, EIF4A3, HDAC6, HNRNPH2, IFFO1, INPP5D, POLR2F, and RPS19.Deutsches Krebsforschungszentrum Stiftung des öffentlichen Rechts Universität Heidelberg 59 DK16729PC 9. The method of any one of claims 1 to 8, wherein said APA parameter comprises mRNA 3'UTR lengths of at least 25, more preferably at least 50, even more preferably at least 100, most preferably all, of said marker genes.

10. The method of any one of claims 1 to 9, wherein said ASD is ASD level 1.

11. The method of any one of claims 1 to 10, wherein said assessing ASD is differentiating between ASD level 1 and a healty status with regard to ASD.

12. A kit for assessing ASD in a non-brain sample, comprising at least ten detection polynucleotides, wherein said detection polynucleotides comprise non-identical nucleic acid sequences of from 10 to all contiguous nucleotides of any one of SEQ ID NOs:353 to 2048 and 2064 to 2089, or a reverse complement thereof, as the only nucleic acid sequence specific for any gene specified in claim 1.

13. A kit according to claim 12 for use in diagnosing ASD, preferably for diagnosing ASD level 1, in a non-brain sample.

14. A database tangibly embedded on a data carrier, comprising an identifier of at least one of the marker genes specified in claim 1 allocated to at least one APA parameter as specified in claim 1.

15. The database of claim 14, wherein said APA parameter is a reference.

16. A method for identifying a subject susceptible for treating and / or preventing an ASD, said method comprising diagnosing said ASD in said subject according to the method of claims 1 to 11, and, thereby, identifying a subject susceptible for treating and / or preventing said ASD.

Images