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Review

Aberrant Short Tandem Repeats: Pathogenicity, Mechanisms, Detection, and Roles in Neuropsychiatric Disorders

by
Yuzhong Liu
1,2 and
Kun Xia
1,2,*
1
Institute of Cytology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang 421001, China
2
MOE Key Lab of Rare Pediatric Diseases, School of Basic Medicine, Hengyang Medical College, University of South China, Hengyang 421001, China
*
Author to whom correspondence should be addressed.
Genes 2025, 16(4), 406; https://doi.org/10.3390/genes16040406
Submission received: 3 March 2025 / Revised: 17 March 2025 / Accepted: 19 March 2025 / Published: 30 March 2025
(This article belongs to the Section Molecular Genetics and Genomics)
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Abstract

:
Short tandem repeat (STR) sequences are highly variable DNA segments that significantly contribute to human neurodegenerative disorders, highlighting their crucial role in neuropsychiatric conditions. This article examines the pathogenicity of abnormal STRs and classifies tandem repeat expansion disorders(TREDs), emphasizing their genetic characteristics, mechanisms of action, detection methods, and associated animal models. STR expansions exhibit complex genetic patterns that affect the age of onset and symptom severity. These expansions disrupt gene function through mechanisms such as gene silencing, toxic gain-of-function mutations leading to RNA and protein toxicity, and the generation of toxic peptides via repeat-associated non-AUG (RAN) translation. Advances in sequencing technologies—from traditional PCR and Southern blotting to next-generation and long-read sequencing—have enhanced the accuracy of STR variation detection. Research utilizing these technologies has linked STR expansions to a range of neuropsychiatric disorders, including autism spectrum disorders and schizophrenia, highlighting their contribution to disease risk and phenotypic expression through effects on genes involved in neurodevelopment, synaptic function, and neuronal signaling. Therefore, further investigation is essential to elucidate the intricate interplay between STRs and neuropsychiatric diseases, paving the way for improved diagnostic and therapeutic strategies.

1. Introduction of Short Tandem Repeats and Tandem Repeat Expansion Disorders

Short tandem repeats (STRs) are tandemly repeated DNA sequences consisting of repeat motifs of 1–6 base pairs (bp) [1,2]. STRs represent one of the most variable types of DNA sequences within the genome, constituting approximately 3–5% of the human genome—exceeding the proportion occupied by protein-coding genes [1,2,3]. These sequences are ubiquitously present across nearly all human chromosomes, yet their distribution is notably uneven [4,5,6]. Chromosome 19 boasts the highest density of STRs [4], whereas chromosomes 1 and X exhibit relatively lower STR densities [4,5,6]. The majority of STRs are located in non-coding regions, such as promoters, enhancers, and introns [4], where they regulate gene expression by influencing DNA methylation, chromatin structure, and RNA processing [7,8]. In contrast, the minority of STRs situated in coding regions are subject to stronger selective pressure due to their potential impact on protein function and structure [9,10,11]. Non-coding STRs are primarily associated with the regulation of genetic variation [12,13], particularly regarding modifiers in conditions such as Huntington’s disease (HD). Beyond the CAG expansion within the HTT gene, several other STRs—such as those found within the introns of the MLH1 and MSH3 genes—modulate disease onset and severity by influencing DNA repair efficiency [14,15]. Fuchs endothelial corneal dystrophy (FECD), the non-coding CTG repeat expansion in the TCF4 gene, causes RNA aggregation, triggering cell death in corneal endothelial cells through the activation of the unfolded protein response [16]. On the other hand, coding STRs may exert direct toxic effects on proteins [12,13]. For example, spinal bulbar muscular atrophy (SBMA) is closely linked to the expansion of CAG trinucleotide repeats within the androgen receptor (AR) gene [14]. When the number of CAG repeats exceeds the normal threshold (typically over 38 repeats), the resulting polyglutamine (polyQ) protein can form misfolded aggregates, which in turn leads to neurotoxicity [17,18].
Tandem Repeat Expansion Disorders (TREDs) are a class of hereditary diseases caused by the abnormal expansion of specific DNA sequence repeats beyond their physiological range [19,20,21]. These repeat expansions typically occur in either coding or non-coding regions of the genome, resulting in altered protein function or abnormal gene expression [21]. The etiology of these diseases may involve genetic factors, environmental influences, or a complex interplay between both [22,23]. In recent years, with the advancements in sequencing technologies such as targeted sequencing and whole-genome long-read sequencing (LRS), substantial progress has been made in identifying new repeat expansion loci and elucidating motif structures, interruption patterns, and allelic diversity [24,25,26]. Increasing evidence suggests that TREDs predominantly affect the nervous system [27,28,29,30]. To date, STR expansions have been implicated in 65 neurological diseases and 14 neuromuscular diseases [31]. Based on the systems they affect, repeat expansion disorders can be broadly categorized into Neurological Disorders, Psychiatric/Mental Disorders (Table 1) and Non-Neuropsychiatric Disorders [32,33]. Neurological Disorders associated with tandem repeat expansions (TREs) are characterized by degenerative changes in the central or peripheral nervous system. These disorders involve various types of repeats and pathogenic mechanisms. Examples include Fragile X Syndrome (FXS) [34,35,36], HD [10,37,38], and Neuronal Intranuclear Inclusion Disease (NIID) [39]. In these conditions, abnormal STR expansions directly result in gene function loss or toxic protein accumulation, leading to neurological dysfunction [40,41,42,43,44]. Psychiatric or Mental Disorders within the spectrum of repeat expansion disorders are characterized by psychiatric symptoms caused directly or indirectly by abnormal TREs in specific genes. The pathological mechanisms underlying these disorders are complex, such as RNA toxicity or protein aggregation [18] and epigenetic regulation or synaptic plasticity alterations [18,45,46,47,48]. Examples include Autism Spectrum Disorder (ASD) [28,29], Fragile X-associated Neuropsychiatric Disorders (FXAND), Bipolar Disorder (BD), and Schizophrenia (SCZ) [30,49]. In these cases, STR variations increase susceptibility by modulating the expression and function of multiple related genes [50,51,52,53]. Non-Neuropsychiatric Disorders are repeat expansion disorders that do not directly involve the central nervous system or mental/behavioral functions [54]. Examples include Blepharophimosis, Ptosis, and Epicanthus Inversus Syndrome (BPES) [55], Congenital Central Hypoventilation Syndrome (CCHS) [56], and Cleidocranial Dysplasia (CLCD) [57]. The pathological manifestations of these disorders primarily impact muscles, the metabolic system, epithelial tissues, the cardiovascular system, or the endocrine organs, demonstrating the broad systemic pathogenic effects of repeat expansions across multiple organ systems [55,56,57]. In-depth research into STRs not only enhances our understanding of the pathogenic mechanisms underlying these disorders but also provides new avenues for their diagnosis and treatment.
This review provides an overview of the genetic patterns and characteristics of diseases associated with STRs and highlights the significant role of STR expansions in various genetic and neurodevelopmental disorders, such as HD, muscular dystrophy, and ASD. Additionally, the review explores the mechanisms by which STR expansions lead to disease, including RNA and protein toxicity, RAN translation, and gene silencing. This review particularly focuses on the role of STRs in neuropsychiatric disorders and discusses the advancements in sequencing technologies and their application in research within this field.

2. Methods for Detecting Variations in STR Sequence Composition

With technological advancements, the detection of variations in the sequence composition of STRs has undergone significant transformation(Figure 1). Initially, PCR-based methods and Southern blot analysis were the primary techniques utilized by clinical laboratories to diagnose repeat expansion disorders [58,59,60,61]. Southern blotting was considered the gold standard for genotyping highly expanded full mutations (>250 triplet repeats), although it was incapable of characterizing interruptions within the repeat sequences [19,62,63]. Conventional PCR assays, which involve amplifying the repeat region with two primers followed by restriction digestion, can distinguish interrupted alleles from uninterrupted ones, but these require prior knowledge of the interruption motif and its sequence [64,65].
However, these early methods faced limitations in detecting interruptions within repeats [31]. The advent of Sanger sequencing and improved PCR techniques significantly enhanced the ability to resolve repeat length and sequence composition [31]. Sanger sequencing allows for the estimation of repeat lengths and can determine the sequence composition of alleles with up to approximately 250–300 triplet repeats [66]. Improved PCR methods, such as repeat-primed PCR (RP-PCR), enable the mapping of sequence composition variations by generating heterogeneous mixtures of amplified fragments [62,67,68,69,70]. RP-PCR can detect most repeat expansions and reveal the positions of interruptions within repeats, but its genotyping capability is limited to repeats shorter than approximately 200 triplets [67,71].
Optical genome mapping (OGM) has emerged as another approach for identifying structural variations, including copy number variations and structural changes larger than 500 base pairs [72]. OGM provides indirect evidence of repeat expansions by identifying increased distances between tags flanking STR regions [73]. However, it cannot resolve precise breakpoints or the base-by-base composition of the repeats [74].
As technology progresses, more advanced sequencing techniques are addressing the limitations of molecular detection methods. Both short-read sequencing (NGS) and LRS have been applied to achieve higher-resolution STR analysis [31]. NGS offers a high-throughput platform for characterizing STR sequence compositions. Bioinformatics tools, such as ExpansionHunter Denovo (EHdn) [75] and STRling [76], can identify both reference and non-reference expansions. Nevertheless, resolving highly repetitive sequences and addressing technology-specific biases require specialized algorithms and validation studies [77].
LRS technologies, such as those developed by Pacific Biosciences or Oxford Nanopore Technologies, are capable of generating read lengths of approximately 15–20 kilobases or more [19,78,79,80,81,82,83,84,85,86]. This allows for the resolution of non-canonical motifs and simultaneous methylation analysis [24]. These technologies can fully span most repetitive expansions, providing detailed characterization of pathogenic repeat expansions [19,78,79,80,81,82,83,84,85,86]. A significant challenge in LRS data analysis lies in distinguishing true interruptions from sequencing errors, necessitating rigorous validation studies [80,87]. As LRS becomes more affordable and sequencing accuracy continues to improve, it may evolve into the standard method for detecting repeat expansions [88].
In addition to the continuous advancements in sequencing technologies, the genetic methods used to analyze the relationship between STRs and affected disorders have also expanded significantly. For instance, linkage analysis methods can be employed in family studies that utilize STR markers to co-segregate with disease phenotypes [89], thereby localizing pathogenic genes [90,91]. Typically, a lod score exceeding 3 indicates significant linkage [90,91,92]; however, this method tends to be less sensitive for polygenic disorders [93], such as those characterized by multiple genetic factors. To enhance accuracy, high heterozygosity STR markers and parametric models, such as the Elston–Stewart algorithm, are often utilized [94].
Moreover, Genome-Wide Association Studies (GWAS) can be employed to compare the frequency differences of STRs between case and control groups, facilitating the identification of risk loci. For example, ASD research has uncovered 54 significantly associated STRs through GWAS [95]. Additionally, expression STRs (eSTRs) analysis integrates RNA-seq data to identify STRs that regulate gene expression. These eSTRs can account for 10–15% of gene expression variance and are associated with complex diseases such as Crohn’s disease [96]. Polygenic Risk Scores (PRS) are then utilized to evaluate the cumulative effects of multiple STR expansions; for instance, patients with SCZ exhibit a higher burden of STR expansions [97].
With the development of sequencing technology, it is now possible to directly determine the relationship between repeat sequences and diseases by sequencing [83]. For example, LRS utilizing nanopore or SMRT technology allows for the direct measurement of long repeat sequences, enabling the precise analysis of pathogenic expansions, such as the CAG repeat in HD, along with their methylation status [24,98]. Targeted sequencing further enhances clinical applicability by designing probes for known pathogenic STRs (e.g., FMR1, HTT), thus reducing costs [99,100].
In addition, bioinformatics tools can be employed to identify the relationship between STRs and diseases. Tools such as LobSTR [7], HipSTR [101], and GangSTR [102] can infer STR lengths from short-read data, though challenges remain regarding alignment errors and GC content bias. Expanded detection tools like ExpansionHunter [99] and STRique [100] are designed to identify pathogenic expansions. Furthermore, three-dimensional genome analysis techniques (such as Hi-C) have revealed co-localization between STRs and chromatin domain boundaries [103]. These advancements in technology have rendered the investigation of the relationship between STRs and diseases more comprehensive and profound.

3. The Genetic Characteristics of Neurological Disorders Caused by Repeat Expansion Disorders

Large expansions of STRs can become pathogenic and are the underlying cause of various primary neurological disorders [19]. The earliest identified repeat expansion disorder can be traced back to FXS, which was the first repeat expansion disorder to have its molecular basis uncovered. As early as 1991, researchers employed Southern blotting along with other cytogenetic and molecular techniques to ascertain that the abnormal expansion of the CGG trinucleotide repeat sequence within the 5′ UTR of the FMR1 gene was the causative factor of FXS. These expanded STRs lead to the silencing of FMR1 gene transcription through promoter methylation, ultimately resulting in the silencing of the gene and subsequent neuronal dysfunction [104,105,106,107]. Since then, STR expansion disorders and the pathogenic mechanisms of STRs have been gradually identified and reported [108]. In 1993, research teams employed gene cloning techniques to isolate the HTT gene and utilized methods such as Southern blotting and PCR to uncover the pathogenic cause of HD [10,37,38]. They discovered that the abnormal elongation of the CAG repeat sequence in exon 1 of the HTT gene results in the formation of an excessively long polyQ structure at the N-terminus of the huntingtin protein [109,110]. This mutant protein adopts misfolded conformations within neurons and has a propensity to aggregate, forming inclusions, thereby impairing neuronal function and ultimately contributing to the onset and progression of the disease [111,112]. Similarly, in 1922, researchers employed STR linkage analysis in conjunction with trinucleotide repeat-primed PCR (TP-PCR) technology to identify the presence of CTG repeat expansions in the 3′ UTR of the DMPK gene in patients with type 1 myotonic dystrophy (DM1). The expanded CTG repeat sequences are transcribed into CUG repeat RNA, which forms hairpin structures that aberrantly bind RNA-binding proteins (RBPs), such as muscleblind-like splicing regulator 1 (MBNL1). This interaction disrupts splicing regulation, leading to multisystem dysfunction [113,114]. In addition to the aforementioned disorders, numerous other repeat expansion disorders fall under the category of single-gene inheritance patterns [32,33].
With the rapid advancement of sequencing technology and the widespread application of large-scale sequencing in the study of STRs [24,25,26], numerous studies have shown that many STR loci are associated with multifactorial complex diseases [28,29,30,49,115,116,117]. Currently, modern STR variant detection technology integrates traditional detection methods (such as PCR detection and Southern blot analysis) [58,59], various sequencing techniques (including NGS and LRS), and a variety of STR genotyping tools [3,31]. This integration has achieved the precise diagnosis and treatment of multifactorial complex diseases, providing strong support for the precise diagnosis and treatment of such diseases. Similarly, a 2025 study on Alzheimer’s disease (AD) utilized whole-genome sequencing (WGS) on AD patients and control groups, integrating bioinformatics tools such as ExpansionHunter for the comprehensive detection of STR expansion loci, lengths, and frequency distributions [97]. The study found that AD patients exhibited a 1.19-fold increase in STR expansions compared to healthy elderly controls. Individuals with over 30 STR expansions had a 3.69-fold increased risk of developing AD and presented with more severe neuropathological features of the disease [97]. Further analysis revealed the significant enrichment of AD-associated STR expansions in active promoter regions of postmortem hippocampal tissue, particularly within SINE-VNTR-Alu (SVA) retrotransposons [97]. This suggests a tight correlation between STR expansions in active promoter regions and AD risk [97]. The ExpansionHunter demonstrated high accuracy in estimating STR numbers and reliably identified STRs with normal lengths under 150 base pairs as well as abnormal expansions exceeding 200 base pairs, proving its efficacy in handling long repeat sequences and complex structural variations [95,118,119].

4. The Genetic Characteristics of Mental Disorders Associated with Tandem Repeats

STRs have long been regarded as “genetic stutters” with little functional relevance [120,121]. However, mounting evidence now implicates these dynamic mutations as key players in the pathogenesis of Mental Disorders [20,122]. Unlike single nucleotide variants, STRs exert their influence through repeat expansions or contractions that disrupt gene expression, protein function, and neural circuitry [30,123,124]. However, the role of these tandem repeats in polygenic Mental Disorders remains largely elusive [28,125]. Even so, over the past few decades, researchers have gained new insights into the psychiatric disorders caused by TREs, thanks to the continuous advancements in technological methodologies.
In a 2020 study, researchers harnessed WGSdata from the MSSNG project [126], the Simons Simplex Collection (SSC) [127], and the 1000 Genomes Project [128] to meticulously explore the role of TREs in ASD [29]. Utilizing the EHdn algorithm [75] alongside a suite of validation techniques such as PCR, Sanger sequencing, RP-PCR, and Southern blot, they conducted an in-depth analysis [29]. The findings revealed that rare TREs are significantly more prevalent in individuals with ASD compared to their unaffected siblings [29]. This prevalence was notably higher in regions proximal to exons and splice junctions, particularly within genes implicated in neurodevelopment and the cardiovascular or muscular systems [29]. Collectively, these rare TREs account for a 2.6% contribution to ASD risk. Among the genes exhibiting such expansions are DMPK and FXN, as well as FGF14 and CACNB1 [29].
Another 2021 study conducted an in-depth analysis of WGS data from the SSC [127], utilizing advanced algorithms such as GangSTR [102] and MonSTR [28]. The study uncovered that the genomes of children with ASD harbored a significantly higher number of TR mutations, which were also larger in scale, compared to their unaffected siblings [28]. These mutations were predominantly concentrated in regulatory regions of genes tied to fetal brain development, suggesting a potential role in modulating gene expression and thus influencing neurodevelopmental processes [28]. Furthermore, from an evolutionary perspective, these mutations appear more likely to be deleterious, potentially affecting the pathogenesis of ASD through alterations in protein function or regulatory elements [28,129]. Notably, new TR mutations arising from the maternal germline were found to be larger in size than those originating from the paternal germline and were more prone to instigate gradual changes in copy number [28].
Recent research focusing on the expansion of STRs in cortex-specific genes has meticulously analyzed WGS data from 634 ASD families [53]. Utilizing the STRling algorithm [76] alongside various validation techniques such as satellite marker analysis, RP-PCR, and nanopore LRS, the study provided profound insights [53]. It was discovered that rare STR expansions predominantly occur in early cortical development genes involved in neurodevelopment within the ASD patient population [53]. This expansion disrupts the regulatory activity of enhancers and promoters, thereby contributing to the pathogenesis of ASD [53]. Furthermore, individuals carrying ASD-associated STR expansions exhibit more pronounced ASD phenotypic characteristics and experience reduced adaptive abilities compared to non-carriers [53].
Another study on SCZ conducted WGS on 1154 Swedish patients diagnosed with SCZ and 934 matched controls [130]. The findings revealed a significantly higher burden of rare TREs in SCZ patients, particularly within genic regions, such as postsynaptic genes, brain-expressed genes, and genes that are differentially expressed between SCZ patients and controls. Furthermore, these TRE-associated genes are often more constrained, primarily affecting synaptic and neuronal signaling functions.
In a 2018 study, researchers analyzed 181 human cell lines (encompassing diverse ethnic groups) and postmortem brain tissue samples, with a particular focus on neural tissue [131]. This investigation combined LRS technologies, such as PacBio, with GWAS to elucidate the tandem repeat variations in the third intronic region of the CACNA1C gene, aiming to validate the polymorphism of STRs and their associations with BD and SCZ. The results revealed that the tandem repeat located within the non-coding region of the third intron of the CACNA1C gene consists of a 30 bp unit repeated hundreds of times, with lengths reaching several kilobases [131]. This repeat exhibits enhancer activity, capable of activating reporter gene expression in human neural progenitor cells. Different allelic variants of the repeat array displayed varying enhancing activities, with risk-associated alleles demonstrating significantly reduced enhancer functionality [131]. The aberrant expression linked to these alleles may influence neuronal excitability and synaptic plasticity through the calcium signaling pathway, thereby increasing the susceptibility to psychiatric disorders [131].
Furthermore, STR variations are associated with a multitude of other psychiatric disorders. For instance, in Attention Deficit Hyperactivity Disorder (ADHD), the variable number tandem repeats (VNTRs) in the DAT1 gene, particularly the 10-repeat allele, have been linked to an increased risk of ADHD, potentially influencing the efficiency of dopamine reuptake [132].
For another instance, in the context of depression, certain tandem repeat variations within the BDNF gene may contribute to the pathogenesis by influencing the expression of neurotrophic factors. However, the precise mechanisms underlying this association require further validation [133].
Overall, these studies suggest that short TREs may play a pivotal role in Mental Disorders (Table 2), necessitating further functional studies to elucidate the specific mechanisms of STR involvement [134].

5. The Genetic Characteristics of Repeat Expansion Disorders

As research delves deeper into the significance of STRs, coupled with advancements in sequencing technologies, scientists have identified several common genetic characteristics inherent in STR expansion disorders [25,77,118]. A notable aspect of these disorders is that they rarely present as a simple binary contrast between healthy and affected individuals. Instead, they manifest a spectrum of quantifiable traits, such as the age of onset and the severity of symptoms, which are modulated by the number of tandem repeats [49]. Due to the intergenerational instability of these tandem repeat sequences, they are regarded as dynamic mutations [135]. Variations in repeat number within certain ranges can lead to differences in age of onset, severity, penetrance, and clinical phenotype [136,137,138]. Thus, dynamic mutations can be categorized into full mutations, pre-mutations, gray zones, and normal alleles [9,139].
Full mutation alleles are pathogenic and exhibit somatic instability, often increasing in size when passed to subsequent generations [20,140]. In FXS, individuals with over 200 CGG repeats in the FMR1 gene’s promoter region experience hypermethylation and transcriptional silencing, leading to a deficiency in fragile X mental retardation protein (FMRP), crucial for CNS synaptic plasticity [35,36]. Similarly, over 1000 CTG repeats in the DMPK gene cause congenital DM1 [141]. These characteristics influence the age of onset, progression, and severity of certain repeat expansion disorders [142,143].
Pre-mutation alleles, positioned between normal and fully mutated states, may result in mild or variable phenotypes [144,145,146]. For instance, pre-mutations like 50–150 CTG repeats in the DMPK gene are associated with cataracts and mild myotonia, whereas higher repeat counts, such as 100–1000 CTG repeats, lead to classic severe conditions like muscle weakness and cardiac arrhythmias [147,148]. Furthermore, pre-mutations are characterized by genetic instability, with a pronounced tendency to expand into full mutations during maternal transmission. And pre-mutations could enhance promoter activity and transcription, leading to excessive RNA production and the formation of toxic aggregates, which directly impair cellular function [49,149].
The gray zone encompasses repeat sequences that lie between normal and pathogenic ranges, typically not causing disease but potentially expanding in subsequent generations [150,151]. Normal repeat sequences are vital for biological functions, regulating gene expression by influencing promoter activity [152,153].
Another genetic characteristic of STR expansion disorders is that their clinical manifestations and pathogenic mechanisms are influenced by the composition of the STR motifs. For instance, expanded alleles composed of uninterrupted repeat motifs often exhibit a high degree of structural instability [54,135]. For example, the abnormal expansion of the hexanucleotide repeat GGGGCC in the C9orf72 gene can form secondary structures such as G-quadruplexes [154,155]. These structures can disrupt DNA replication and repair processes, leading to replication fork stalling or slippage, which in turn exacerbates repeat expansion or contraction [155,156,157]. This mechanism is implicated in the pathogenesis of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) [158]. Notably, the genetic stability of certain STR loci can be significantly enhanced through the interruption of their repeat sequences by heterologous motifs. A classic example is found in the FMR1 gene associated with FXS. The insertion of AGG trinucleotides at regular intervals within the CGG repeat sequence effectively suppresses the conversion of pre-mutation alleles (55–200 repeats) to full mutation states (>200 repeats) during maternal transmission, substantially reducing the risk of FXS in offspring [145,159,160,161]. Moreover, there is a notable correlation between specific motif types and disease phenotypes. For instance, CGG-rich repeat sequences, due to their propensity to form unique secondary structures, often disrupt the expression regulation of genes involved in neurodevelopment, thereby showing significant pathogenic associations with various neurodevelopmental disorders, such as FXS and FXTAS [162].
In the investigation of genetic diseases, the disparities in STR variations among different ethnicities or specific populations, as well as their associations with neuropsychiatric disorders, may be closely linked to the founder effect [20]. Notably, larger repeat expansions are often associated with single or predominant haplotypes, resulting in the clustering of diseases within particular geographic regions or leading to significant variations in prevalence rates [20]. For instance, DM1 and Friedreich’s ataxia (FRDA) are exceedingly rare in Africa and Japan [163,164], suggesting that non-expanded alleles remain relatively stable, while risk haplotypes exist in a state of linkage disequilibrium with large normal alleles at homologous repeat loci [158]. This phenomenon implies a potentially multistep evolutionary process, where an initial mutation gives rise to a large normal allele, which subsequently expands over time, ultimately resulting in pathogenic alleles [20]. In a study conducted in 2023, researchers analyzed the STR length variability in 850 children suspected of having genetic disorders and 996 parents, encompassing over a thousand loci from six distinct ancestral groups (Africans, Europeans, East Asians, admixed Americans, non-admixed Americans, and Pacific Islanders) [165]. The results revealed that the STR lengths in admixed Americans were most similar to those of Europeans, while the African population exhibited the greatest variability. The study also identified significant expansions of STRs in known pathogenic genes such as TCF4, AR, and DMPK across multiple individuals, with lengths exceeding established pathogenic thresholds [165]. Additionally, it confirmed individuals with significant expansions at highly conserved loci and discovered two cases of exonic STR expansions associated with X-linked intellectual disability [165]. Overall, this research lays a foundation for understanding the differences in STR variations across different ethnicities or specific populations, providing crucial insights into the underlying mechanisms of certain genetic disorders.

6. The Pathogenic Mechanisms of STRs

Repeat expansion disorders have diverse pathogenic mechanisms, which vary depending on the location of the expanded STR within the gene locus. STRs can occur in exons, introns, untranslated regions, or intergenic areas [30]. These region-specific STRs can interfere with one another through diverse pathogenic mechanisms, ultimately collaborating to culminate in the manifestation of disease [21]. Current research generally suggests that repeat expansion disorders cause disease through the following four molecular mechanisms [21,162].
Loss of gene function is the inhibition of gene transcription caused by nucleotide repeat amplification, which affects normal gene expression and function. For example, FXS is caused by a CGG repeat amplification on the X chromosome, which results in methylation and silencing of the FMR1 gene [35,166]. This results in the absence of the FMRP protein, which affects neurodevelopment and synaptic plasticity, ultimately leading to disease onset [167].
RNA-mediated gain of function is associated with the formation of aberrant structures, such as hairpin structures or circular RNA, in RNA molecules produced by nucleotide repeat amplification [45,168,169]. These structures interact with RBPs, leading to the disruption of RNA metabolism and transcriptional regulation. In ankylosing muscular dystrophy disease, the amplification of CTG repeats in the DMPK gene results in abnormal RNA molecules containing CUG repeat sequences [170,171]. These molecules bind to the MBNL1 protein and form nuclear inclusions that affect splicing regulation and muscle function leading to disease [45,46,47,172].
The gain of function of classic translation proteins containing expanded repeat sequences occurs when nucleotide repeat amplification takes place within the coding region of a gene, resulting in the production of proteins with abnormal amino acid repeat sequences, which could lead to increased toxicity or altered protein function [18,173]. For example, HD is caused by the amplification of CAG repeats in the HTT gene, which leads to the production of proteins with polyQ sequences in the gene. This causes protein aggregation and the disruption of neuronal survival and signaling, which ultimately leads to disease [9,38].
Repeat-associated RAN translation refers to a non-canonical translation process using non-AUG start codons to translate RNA molecules containing expanded nucleotide repeat sequences. This process results in polypeptide chains with abnormal amino acid repeats that form inclusions within cells. For instance, in ALS and FTD, the expansion of G4C2 repeat sequences leads to the production of polypeptides like polyG and polyP through RAN translation [174]. These polypeptides aggregate into cytotoxic inclusions that disrupt protein homeostasis, organelle function, and cell signaling, potentially damaging cell membranes and leading to cell death, particularly in neurons [169,175,176]. In diseases such as ALS and FTD, the accumulation of these peptides in neurons significantly contributes to neuronal dysfunction and disease progression [169,176,177,178,179].
Research has demonstrated that the synergistic effects of diverse pathogenic mechanisms may contribute to the onset of these disorders, with multifactorial pathological mechanisms being a prevalent phenomenon rather than an exception in neuropsychiatric diseases [21]. For instance, in C9 ALS/FTD, the abnormal expansion of the GGGGCC hexanucleotide repeat within the intronic region of the C9orf72 gene represents a major genetic etiology [180]. This expanded repeat sequence induces intron retention, resulting in diminished expression of the C9orf72 gene, while concurrently extending the half-life of the repeat sequence RNA, thereby exacerbating the sequestration of RBPs [181,182,183]. Furthermore, intron retention may also enhance the production of dipeptide repeat sequences, activating multiple downstream pathogenic pathways [180]. In HD, neurotoxicity is instigated through a dual mechanism wherein the expanded CAG repeat sequence alters RNA processing, affecting the recognition of the donor splice site for exon 1 [184]. This alteration leads to the production of a truncated HTT protein characterized by a polyQ) stretch, a form of the protein that exhibits heightened toxicity [185]. Additionally, RAN translation may also occur within the CAG repeat sequence. In FXTAS/FXS, the CGG repeat sequence not only has the capacity to sequester RBPs [186] but also enhances the RAN translation of upstream open reading frames (uORFs), resulting in a reduced initiation of FMRP translation [167,187].

7. The Common Animal Models Used in the Study of STR Variations in Neuropsychiatric Disorders

In the realm of psychiatric research, the utilization of animal models is of paramount importance. Each type of animal model possesses unique characteristics that allow for the effective simulation of human psychiatric disorders through the application of CRISPR/Cas9 technology to knock out or insert specific STR loci.
Rodents, such as mice and rats, are widely employed in studies to model diseases like SCZ, ASD, and depression [95]. For instance, the YAC128 mouse model for HD expresses the full-length human HTT gene with an expanded CAG repeat sequence [188]; conditional frataxin knock-out mouse models involve the deletion of the FXN gene [189]; and knock-in mouse models for fragile X-associated tremor/ataxia syndrome (FXTAS) feature a CGG repeat insertion [190]. The advantages of utilizing rodent models include the relative ease of constructing STR mutation models. Additionally, these animals are cost-effective, breed rapidly, and are suitable for the large-scale screening of drug effects or genetic interventions [191]. However, notable limitations exist. Rodent brains exhibit insufficient neuroanatomical complexity; their prefrontal cortex and other advanced brain regions are underdeveloped, making it challenging to fully replicate human psychiatric diseases [192]. Furthermore, the phenotypic expression in these models is limited, as certain symptoms—such as language deficits and complex cognitive impairments—cannot be adequately represented through animal behavior [193,194].
In contrast, non-human primates, such as macaques and marmosets, offer unique advantages for investigating the impact of STR variations on social behavior and cognitive function. For example, macaque models can be employed to explore the association between STR polymorphisms in the AVPR1a gene and social behaviors [195]. The benefits of using non-human primates include their highly conserved neuroanatomical structures and functions, with the prefrontal cortex and limbic system exhibiting remarkable similarity to those of humans, making them particularly suitable for exploring complex cognitive disorders [196,197]. Additionally, they can model intricate behaviors, such as social hierarchies and cooperative interactions [196,197]. However, significant drawbacks are also evident. The costs associated with using non-human primates can be prohibitively high, and their developmental timelines are long, with sexual maturity occurring at four to five years of age [198]. Furthermore, the expenses related to gene editing in these animals are substantial [198].
Zebrafish are primarily employed to study the effects of STR variations on early neurodevelopment, exemplified by ASD-like behaviors associated with mutations in the shank3b gene [199]. The advantages of using zebrafish include their suitability for high-throughput screening; and their transparent embryos facilitate in vivo imaging and rapid genetic screening [200]. Additionally, they are cost-effective and have a short lifecycle, with embryonic development completing within five days, making them ideal for large-scale drug testing [200]. However, considerable limitations are present. The behavioral phenotypes exhibited by zebrafish are relatively simplistic and fail to capture complex cognitive symptoms, such as deficits in working memory [201]. Moreover, the neural circuits in zebrafish differ from those in mammals, and their lack of cortical structures restricts the depth of mechanistic studies [201].

8. Conclusions

In conclusion, STRs, once considered inconsequential “junk DNA,” are now recognized as critical players in the complex landscape of neuropsychiatric disorders. This review has highlighted the multifaceted nature of STR pathogenicity, ranging from direct gene disruption through repeat expansions leading to loss of function or toxic gain of function (via RNA, protein, or RAN translation mechanisms) to more subtle modulations of gene expression impacting neurodevelopment and neuronal signaling. Advanced sequencing technologies, particularly LRS, are revolutionizing our ability to detect and characterize STR variations, revealing a spectrum of repeat instability and its correlation with disease onset, severity, and phenotypic expression. Compelling evidence links STR expansions to a growing list of neuropsychiatric conditions, including ASD and SCZ, solidifying their contribution to disease risk. While considerable progress has been made, further research is crucial to fully unravel the intricate interplay between STRs and these complex disorders. This includes investigating the functional consequences of specific STR expansions, exploring potential therapeutic targets based on the identified pathogenic mechanisms, and developing more precise diagnostic tools for improved patient stratification and personalized interventions. The ongoing exploration of STRs promises to unlock new avenues for understanding and ultimately treating these debilitating conditions.

Author Contributions

Y.L. was primarily responsible for the literature search and data analysis, and drafted the initial version of the review. K.X. made significant edits and revisions to the initial draft and provided crucial academic guidance. Y.L. was responsible for integrating the literature and constructing the arguments, ensuring the coherence and logic of the content. All authors have read and agreed to the published version of the manuscript.

Funding

This review was supported by National Natural Science Foundation of China (nos. 82361138573 to K.X.).

Institutional Review Board Statement

The writing and publication of this review did not involve any ethical review, as it did not include experimental research or data collection.

Data Availability Statement

The data for this review are sourced from publicly available literature, primarily through comprehensive literature searches conducted in databases such as PubMed.

Conflicts of Interest

The authors declare that there are no potential conflicts of interest, and all authors have no financial or personal relationships related to this research.

References

  1. Horton, C.A.; Alexandari, A.M.; Hayes, M.G.B.; Marklund, E.; Schaepe, J.M.; Aditham, A.K.; Shah, N.; Suzuki, P.H.; Shrikumar, A.; Afek, A.; et al. Short tandem repeats bind transcription factors to tune eukaryotic gene expression. Science 2023, 381, eadd1250. [Google Scholar] [CrossRef]
  2. Ziaei Jam, H.; Li, Y.; DeVito, R.; Mousavi, N.; Ma, N.; Lujumba, I.; Adam, Y.; Maksimov, M.; Huang, B.; Dolzhenko, E.; et al. A deep population reference panel of tandem repeat variation. Nat. Commun. 2023, 14, 6711. [Google Scholar] [CrossRef]
  3. Tanudisastro, H.A.; Deveson, I.W.; Dashnow, H.; MacArthur, D.G. Sequencing and characterizing short tandem repeats in the human genome. Nat. Rev. Genet. 2024, 25, 460–475. [Google Scholar] [CrossRef] [PubMed]
  4. Subramanian, S.; Mishra, R.K.; Singh, L. Genome-wide analysis of microsatellite repeats in humans: Their abundance and density in specific genomic regions. Genome Biol. 2003, 4, R13. [Google Scholar] [CrossRef] [PubMed]
  5. Tóth, G.; Gáspári, Z.; Jurka, J. Microsatellites in different eukaryotic genomes: Survey and analysis. Genome. Res. 2000, 10, 967–981. [Google Scholar] [CrossRef]
  6. Lander, E.S.; Linton, L.M.; Birren, B.; Nusbaum, C.; Zody, M.C.; Baldwin, J.; Devon, K.; Dewar, K.; Doyle, M.; FitzHugh, W.; et al. Initial sequencing and analysis of the human genome. Nature 2001, 409, 860–921. [Google Scholar] [PubMed]
  7. Gymrek, M.; Golan, D.; Rosset, S.; Erlich, Y. lobSTR: A short tandem repeat profiler for personal genomes. Genome. Res. 2012, 22, 1154–1162. [Google Scholar] [CrossRef]
  8. Thornton, C.A. Myotonic dystrophy. Neurol. Clin. 2014, 32, 705–719. [Google Scholar] [CrossRef]
  9. Orr, H.T.; Zoghbi, H.Y. Trinucleotide repeat disorders. Annu. Rev. Neurosci. 2007, 30, 575–621. [Google Scholar] [CrossRef]
  10. Ross, C.A.; Tabrizi, S.J. Huntington’s disease: From molecular pathogenesis to clinical treatment. Lancet Neurol. 2011, 10, 83–98. [Google Scholar] [CrossRef]
  11. Fondon, J.W., 3rd; Garner, H.R. Molecular origins of rapid and continuous morphological evolution. Proc. Natl. Acad. Sci. USA 2004, 101, 18058–18063. [Google Scholar] [CrossRef] [PubMed]
  12. Gemayel, R.; Cho, J.; Boeynaems, S.; Verstrepen, K.J. Beyond junk-variable tandem repeats as facilitators of rapid evolution of regulatory and coding sequences. Genes 2012, 3, 461–480. [Google Scholar] [CrossRef] [PubMed]
  13. Kelkar, Y.D.; Strubczewski, N.; Hile, S.E.; Chiaromonte, F.; Eckert, K.A.; Makova, K.D. What is a microsatellite: A computational and experimental definition based upon repeat mutational behavior at A/T and GT/AC repeats. Genome Biol. Evol. 2010, 2, 620–635. [Google Scholar] [PubMed]
  14. Hannan, A.J. Repeating themes of plastic genes and therapeutic schemes targeting the ‘tandem repeatome’. Brain. Commun. 2024, 6, fcae047. [Google Scholar] [CrossRef]
  15. Sfera, A.; Sfera, D.O.; Sasannia, S.; Hazan, S. The MSH3 Gene From A Neuropsychiatrist’s Perspective. Adv. Clin. Med. Res. 2022, 3, 1–6. [Google Scholar] [CrossRef]
  16. Zarouchlioti, C.; Efthymiou, S.; Facchini, S.; Dominik, N.; Bhattacharyya, N.; Liu, S.; Costa, M.A.; Szabo, A.; Sadan, A.N.; Jun, A.S.; et al. Tissue-specific TCF4 triplet repeat instability revealed by optical genome mapping. EBioMedicine 2024, 108, 105328. [Google Scholar]
  17. La Rosa, P.; Petrillo, S.; Bertini, E.S.; Piemonte, F. Oxidative Stress in DNA Repeat Expansion Disorders: A Focus on NRF2 Signaling Involvement. Biomolecules 2020, 10, 702. [Google Scholar] [CrossRef]
  18. Zoghbi, H.Y.; Orr, H.T. Glutamine repeats and neurodegeneration. Annu. Rev. Neurosci. 2000, 23, 217–247. [Google Scholar]
  19. Chintalaphani, S.R.; Pineda, S.S.; Deveson, I.W.; Kumar, K.R. An update on the neurological short tandem repeat expansion disorders and the emergence of long-read sequencing diagnostics. Acta Neuropathol. Commun. 2021, 9, 98. [Google Scholar]
  20. Depienne, C.; Mandel, J.L. 30 years of repeat expansion disorders: What have we learned and what are the remaining challenges? Am. J. Hum. Genet. 2021, 108, 764–785. [Google Scholar]
  21. Malik, I.; Kelley, C.P.; Wang, E.T.; Todd, P.K. Molecular mechanisms underlying nucleotide repeat expansion disorders. Nat. Rev. Mol. Cell. Biol. 2021, 22, 589–607. [Google Scholar] [CrossRef] [PubMed]
  22. Inserra, A.; De Gregorio, D.; Gobbi, G. Psychedelics in Psychiatry: Neuroplastic, Immunomodulatory, and Neurotransmitter Mechanisms. Pharmacol. Rev. 2021, 73, 202–277. [Google Scholar] [PubMed]
  23. Magwai, T.; Shangase, K.B.; Oginga, F.O.; Chiliza, B.; Mpofana, T.; Xulu, K.R. DNA Methylation and Schizophrenia: Current Literature and Future Perspective. Cells 2021, 10, 2890. [Google Scholar]
  24. Stevanovski, I.; Chintalaphani, S.R.; Gamaarachchi, H.; Ferguson, J.M.; Pineda, S.S.; Scriba, C.K.; Tchan, M.; Fung, V.; Ng, K.; Cortese, A.; et al. Comprehensive genetic diagnosis of tandem repeat expansion disorders with programmable targeted nanopore sequencing. Sci. Adv. 2022, 8, eabm5386. [Google Scholar] [CrossRef]
  25. Xi, J.; Wang, X.; Yue, D.; Dou, T.; Wu, Q.; Lu, J.; Liu, Y.; Yu, W.; Qiao, K.; Lin, J.; et al. 5’ UTR CGG repeat expansion in GIPC1 is associated with oculopharyngodistal myopathy. Brain 2021, 144, 601–614. [Google Scholar]
  26. Firdaus, Z.; Li, X. Unraveling the Genetic Landscape of Neurological Disorders: Insights into Pathogenesis, Techniques for Variant Identification, and Therapeutic Approaches. Int. J. Mol. Sci. 2024, 25, 2320. [Google Scholar] [CrossRef]
  27. Höijer, I.; Tsai, Y.C.; Clark, T.A.; Kotturi, P.; Dahl, N.; Stattin, E.L.; Bondeson, M.L.; Feuk, L.; Gyllensten, U.; Ameur, A. Detailed analysis of HTT repeat elements in human blood using targeted amplification-free long-read sequencing. Hum. Mutat. 2018, 39, 1262–1272. [Google Scholar] [PubMed]
  28. Mitra, I.; Huang, B.; Mousavi, N.; Ma, N.; Lamkin, M.; Yanicky, R.; Shleizer-Burko, S.; Lohmueller, K.E.; Gymrek, M. Patterns of de novo tandem repeat mutations and their role in autism. Nature 2021, 589, 246–250. [Google Scholar]
  29. Trost, B.; Engchuan, W.; Nguyen, C.M.; Thiruvahindrapuram, B.; Dolzhenko, E.; Backstrom, I.; Mirceta, M.; Mojarad, B.A.; Yin, Y.; Dov, A.; et al. Genome-wide detection of tandem DNA repeats that are expanded in autism. Nature 2020, 586, 80–86. [Google Scholar]
  30. Fotsing, S.F.; Margoliash, J.; Wang, C.; Saini, S.; Yanicky, R.; Shleizer-Burko, S.; Goren, A.; Gymrek, M. The impact of short tandem repeat variation on gene expression. Nat. Genet. 2019, 51, 1652–1659. [Google Scholar] [CrossRef]
  31. Rajan-Babu, I.S.; Dolzhenko, E.; Eberle, M.A.; Friedman, J.M. Sequence composition changes in short tandem repeats: Heterogeneity, detection, mechanisms and clinical implications. Nat. Rev. Genet. 2024, 25, 476–499. [Google Scholar]
  32. Uguen, K.; Michaud, J.L.; Génin, E. Short Tandem Repeats in the era of next-generation sequencing: From historical loci to population databases. Eur. J. Hum. Genet. 2024, 32, 1037–1044. [Google Scholar] [PubMed]
  33. Xiao, X.; Zhang, C.Y.; Zhang, Z.; Hu, Z.; Li, M.; Li, T. Revisiting tandem repeats in psychiatric disorders from perspectives of genetics, physiology, and brain evolution. Mol. Psychiatry 2022, 27, 466–475. [Google Scholar] [CrossRef] [PubMed]
  34. Zhou, Z.D.; Jankovic, J.; Ashizawa, T.; Tan, E.K. Neurodegenerative diseases associated with non-coding CGG tandem repeat expansions. Nat. Rev. Neurol. 2022, 18, 145–157. [Google Scholar] [CrossRef]
  35. Verkerk, A.J.; Pieretti, M.; Sutcliffe, J.S.; Fu, Y.H.; Kuhl, D.P.; Pizzuti, A.; Reiner, O.; Richards, S.; Victoria, M.F.; Zhang, F.P.; et al. Identification of a gene (FMR-1) containing a CGG repeat coincident with a breakpoint cluster region exhibiting length variation in fragile X syndrome. Cell 1991, 65, 905–914. [Google Scholar]
  36. Mor-Shaked, H.; Eiges, R. Reevaluation of FMR1 Hypermethylation Timing in Fragile X Syndrome. Front. Mol. Neurosci. 2018, 11, 31. [Google Scholar]
  37. Walker, F.O. Huntington’s disease. Lancet 2007, 369, 218–228. [Google Scholar] [PubMed]
  38. The Huntington’s Disease Collaborative Research Group. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell 1993, 72, 971–983. [Google Scholar]
  39. Paulson, H. Repeat expansions in leukoencephalopathy. Ann. Neurol. 2019, 86, 809–811. [Google Scholar]
  40. Hsu, P.D.; Zhang, F. Dissecting neural function using targeted genome engineering technologies. ACS Chem. Neurosci. 2012, 3, 603–610. [Google Scholar] [CrossRef]
  41. Orack, J.C.; Deleidi, M.; Pitt, D.; Mahajan, K.; Nicholas, J.A.; Boster, A.L.; Racke, M.K.; Comabella, M.; Watanabe, F.; Imitola, J. Concise review: Modeling multiple sclerosis with stem cell biological platforms: Toward functional validation of cellular and molecular phenotypes in inflammation-induced neurodegeneration. Stem. Cells. Transl. Med. 2015, 4, 252–260. [Google Scholar] [CrossRef]
  42. Johnstone, S.E.; Baylin, S.B. Stress and the epigenetic landscape: A link to the pathobiology of human diseases? Nat. Rev. Genet. 2010, 11, 806–812. [Google Scholar] [CrossRef] [PubMed]
  43. Teleanu, R.I.; Niculescu, A.G.; Roza, E.; Vladâcenco, O.; Grumezescu, A.M.; Teleanu, D.M. Neurotransmitters-Key Factors in Neurological and Neurodegenerative Disorders of the Central Nervous System. Int. J. Mol. Sci. 2022, 23, 5954. [Google Scholar] [CrossRef] [PubMed]
  44. Boivin, M.; Deng, J.; Pfister, V.; Grandgirard, E.; Oulad-Abdelghani, M.; Morlet, B.; Ruffenach, F.; Negroni, L.; Koebel, P.; Jacob, H.; et al. Translation of GGC repeat expansions into a toxic polyglycine protein in NIID defines a novel class of human genetic disorders: The polyG diseases. Neuron 2021, 109, 1825–1835.e1825. [Google Scholar] [CrossRef]
  45. Napierała, M.; Krzyzosiak, W.J. CUG repeats present in myotonin kinase RNA form metastable “slippery” hairpins. J. Biol. Chem. 1997, 272, 31079–31085. [Google Scholar] [CrossRef]
  46. Mankodi, A.; Takahashi, M.P.; Jiang, H.; Beck, C.L.; Bowers, W.J.; Moxley, R.T.; Cannon, S.C.; Thornton, C.A. Expanded CUG repeats trigger aberrant splicing of ClC-1 chloride channel pre-mRNA and hyperexcitability of skeletal muscle in myotonic dystrophy. Mol. Cell. 2002, 10, 35–44. [Google Scholar] [CrossRef]
  47. Kanadia, R.N.; Johnstone, K.A.; Mankodi, A.; Lungu, C.; Thornton, C.A.; Esson, D.; Timmers, A.M.; Hauswirth, W.W.; Swanson, M.S. A muscleblind knockout model for myotonic dystrophy. Science 2003, 302, 1978–1980. [Google Scholar] [CrossRef] [PubMed]
  48. Prashad, S.; Gopal, P.P. RNA-binding proteins in neurological development and disease. RNA Biol. 2021, 18, 972–987. [Google Scholar] [CrossRef]
  49. Hannan, A.J. Tandem repeats mediating genetic plasticity in health and disease. Nat. Rev. Genet. 2018, 19, 286–298. [Google Scholar] [CrossRef]
  50. Subramanian, M.; Timmerman, C.K.; Schwartz, J.L.; Pham, D.L.; Meffert, M.K. Characterizing autism spectrum disorders by key biochemical pathways. Front. Neurosci. 2015, 9, 313. [Google Scholar] [CrossRef]
  51. Sullivan, P.F.; Daly, M.J.; O’Donovan, M. Genetic architectures of psychiatric disorders: The emerging picture and its implications. Nat. Rev. Genet. 2012, 13, 537–551. [Google Scholar] [PubMed]
  52. Nakamori, M.; Thornton, C. Epigenetic changes and non-coding expanded repeats. Neurobiol. Dis. 2010, 39, 21–27. [Google Scholar] [PubMed]
  53. Kim, J.H.; Koh, I.G.; Lee, H.; Lee, G.H.; Song, D.Y.; Kim, S.W.; Kim, Y.; Han, J.H.; Bong, G.; Lee, J.; et al. Short tandem repeat expansions in cortical layer-specific genes implicate in phenotypic severity and adaptability of autism spectrum disorder. Psychiatry. Clin. Neurosci. 2024, 78, 405–415. [Google Scholar] [PubMed]
  54. Mirkin, S.M. Expandable DNA repeats and human disease. Nature 2007, 447, 932–940. [Google Scholar]
  55. Lin, Z.B.; Chen, Z.J.; Yang, H.; Ding, X.R.; Li, J.; Pan, A.P.; Sun, H.S.; Yu, A.Y.; Chen, S.H. Expanded phenotypic spectrum of FOXL2 Variant c.672_701dup revealed by whole-exome sequencing in a rare blepharophimosis, ptosis, and epicanthus inversus syndrome family. BMC Ophthalmol. 2023, 23, 446. [Google Scholar] [CrossRef]
  56. Bachetti, T.; Ceccherini, I. Causative and common PHOX2B variants define a broad phenotypic spectrum. Clin. Genet. 2020, 97, 103–113. [Google Scholar]
  57. Mundlos, S. Cleidocranial dysplasia: Clinical and molecular genetics. J. Med. Genet. 1999, 36, 177–182. [Google Scholar]
  58. Spector, E.; Behlmann, A.; Kronquist, K.; Rose, N.C.; Lyon, E.; Reddi, H.V. Laboratory testing for fragile X, 2021 revision: A technical standard of the American College of Medical Genetics and Genomics (ACMG). Genet. Med. 2021, 23, 799–812. [Google Scholar]
  59. Sequeiros, J.; Seneca, S.; Martindale, J. Consensus and controversies in best practices for molecular genetic testing of spinocerebellar ataxias. Eur. J. Hum. Genet. 2010, 18, 1188–1195. [Google Scholar]
  60. Krey, I.; Platzer, K.; Esterhuizen, A.; Berkovic, S.F.; Helbig, I.; Hildebrand, M.S.; Lerche, H.; Lowenstein, D.; Møller, R.S.; Poduri, A.; et al. Current practice in diagnostic genetic testing of the epilepsies. Epileptic. Disord. 2022, 24, 765–786. [Google Scholar] [CrossRef]
  61. Sequeiros, J.; Martindale, J.; Seneca, S.; Giunti, P.; Kämäräinen, O.; Volpini, V.; Weirich, H.; Christodoulou, K.; Bazak, N.; Sinke, R.; et al. EMQN Best Practice Guidelines for molecular genetic testing of SCAs. Eur. J. Hum. Genet. 2010, 18, 1173–1176. [Google Scholar] [CrossRef] [PubMed]
  62. Leferink, M.; Wong, D.P.W.; Cai, S.; Yeo, M.; Ho, J.; Lian, M.; Kamsteeg, E.J.; Chong, S.S.; Haer-Wigman, L.; Guan, M. Robust and accurate detection and sizing of repeats within the DMPK gene using a novel TP-PCR test. Sci. Rep. 2019, 9, 8280. [Google Scholar]
  63. Akimoto, C.; Volk, A.E.; van Blitterswijk, M.; Van den Broeck, M.; Leblond, C.S.; Lumbroso, S.; Camu, W.; Neitzel, B.; Onodera, O.; van Rheenen, W.; et al. A blinded international study on the reliability of genetic testing for GGGGCC-repeat expansions in C9orf72 reveals marked differences in results among 14 laboratories. J. Med. Genet. 2014, 51, 419–424. [Google Scholar] [CrossRef]
  64. Tomé, S.; Gourdon, G. Fast Assays to Detect Interruptions in CTG.CAG Repeat Expansions. Methods. Mol. Biol. 2020, 2056, 11–23. [Google Scholar] [PubMed]
  65. Jang, J.H.; Yoon, S.J.; Kim, S.K.; Cho, J.W.; Kim, J.W. Detection Methods and Status of CAT Interruption of ATXN1 in Korean Patients With Spinocerebellar Ataxia Type 1. Ann. Lab. Med. 2022, 42, 274–277. [Google Scholar] [CrossRef]
  66. Loomis, E.W.; Eid, J.S.; Peluso, P.; Yin, J.; Hickey, L.; Rank, D.; McCalmon, S.; Hagerman, R.J.; Tassone, F.; Hagerman, P.J. Sequencing the unsequenceable: Expanded CGG-repeat alleles of the fragile X gene. Genome. Res. 2013, 23, 121–128. [Google Scholar] [CrossRef]
  67. Rajan-Babu, I.S.; Law, H.Y.; Yoon, C.S.; Lee, C.G.; Chong, S.S. Simplified strategy for rapid first-line screening of fragile X syndrome: Closed-tube triplet-primed PCR and amplicon melt peak analysis. Expert. Rev. Mol. Med. 2015, 17, e7. [Google Scholar]
  68. Nethisinghe, S.; Kesavan, M.; Ging, H.; Labrum, R.; Polke, J.M.; Islam, S.; Garcia-Moreno, H.; Callaghan, M.F.; Cavalcanti, F.; Pook, M.A.; et al. Interruptions of the FXN GAA Repeat Tract Delay the Age at Onset of Friedreich’s Ataxia in a Location Dependent Manner. Int. J. Mol. Sci. 2021, 22, 7507. [Google Scholar] [CrossRef]
  69. Botta, A.; Rossi, G.; Marcaurelio, M.; Fontana, L.; D’Apice, M.R.; Brancati, F.; Massa, R.; G Monckton, D.; Sangiuolo, F.; Novelli, G. Identification and characterization of 5′ CCG interruptions in complex DMPK expanded alleles. Eur. J. Hum. Genet. 2017, 25, 257–261. [Google Scholar]
  70. Lian, M.; Law, H.Y.; Lee, C.G.; Chong, S.S. Defining the performance parameters of a rapid screening tool for myotonic dystrophy type 1 based on triplet-primed PCR and melt curve analysis. Expert. Rev. Mol. Diagn. 2016, 16, 1221–1232. [Google Scholar]
  71. Rajan-Babu, I.-S.; Chong, S.S. Molecular Correlates and Recent Advancements in the Diagnosis and Screening of FMR1-Related Disorders. Genes 2016, 7, 87. [Google Scholar] [CrossRef] [PubMed]
  72. Mantere, T.; Neveling, K.; Pebrel-Richard, C.; Benoist, M.; van der Zande, G.; Kater-Baats, E.; Baatout, I.; van Beek, R.; Yammine, T.; Oorsprong, M.; et al. Optical genome mapping enables constitutional chromosomal aberration detection. Am. J. Hum. Genet. 2021, 108, 1409–1422. [Google Scholar] [CrossRef]
  73. Yuan, Y.; Chung, C.Y.; Chan, T.F. Advances in optical mapping for genomic research. Comput. Struct. Biotechnol. J. 2020, 18, 2051–2062. [Google Scholar] [CrossRef]
  74. Corbett, M.A.; Depienne, C.; Veneziano, L.; Klein, K.M.; Brancati, F.; Guerrini, R.; Zara, F.; Tsuji, S.; Gecz, J. Genetics of familial adult myoclonus epilepsy: From linkage studies to noncoding repeat expansions. Epilepsia 2023, 64 (Suppl. S1), S14–S21. [Google Scholar] [CrossRef]
  75. Dolzhenko, E.; Bennett, M.F.; Richmond, P.A.; Trost, B.; Chen, S.; van Vugt, J.; Nguyen, C.; Narzisi, G.; Gainullin, V.G.; Gross, A.M.; et al. ExpansionHunter Denovo: A computational method for locating known and novel repeat expansions in short-read sequencing data. Genome Biol. 2020, 21, 102. [Google Scholar] [CrossRef]
  76. Dashnow, H.; Pedersen, B.S.; Hiatt, L.; Brown, J.; Beecroft, S.J.; Ravenscroft, G.; LaCroix, A.J.; Lamont, P.; Roxburgh, R.H.; Rodrigues, M.J.; et al. STRling: A k-mer counting approach that detects short tandem repeat expansions at known and novel loci. Genome Biol. 2022, 23, 257. [Google Scholar] [CrossRef] [PubMed]
  77. Dolzhenko, E.; Weisburd, B.; Ibañez, K.; Rajan-Babu, I.S.; Anyansi, C.; Bennett, M.F.; Billingsley, K.; Carroll, A.; Clamons, S.; Danzi, M.C.; et al. REViewer: Haplotype-resolved visualization of read alignments in and around tandem repeats. Genome Med. 2022, 14, 84. [Google Scholar] [CrossRef]
  78. Su, Y.; Fan, L.; Shi, C.; Wang, T.; Zheng, H.; Luo, H.; Zhang, S.; Hu, Z.; Fan, Y.; Dong, Y.; et al. Deciphering Neurodegenerative Diseases Using Long-Read Sequencing. Neurology 2021, 97, 423–433. [Google Scholar] [CrossRef] [PubMed]
  79. van Dijk, E.L.; Naquin, D.; Gorrichon, K.; Jaszczyszyn, Y.; Ouazahrou, R.; Thermes, C.; Hernandez, C. Genomics in the long-read sequencing era. Trends Genet. 2023, 39, 649–671. [Google Scholar] [CrossRef]
  80. Chiara, M.; Zambelli, F.; Picardi, E.; Horner, D.S.; Pesole, G. Critical assessment of bioinformatics methods for the characterization of pathological repeat expansions with single-molecule sequencing data. Brief. Bioinform. 2020, 21, 1971–1986. [Google Scholar] [CrossRef]
  81. Chiu, R.; Rajan-Babu, I.S.; Friedman, J.M.; Birol, I. Straglr: Discovering and genotyping tandem repeat expansions using whole genome long-read sequences. Genome Biol. 2021, 22, 224. [Google Scholar]
  82. Mitsuhashi, S.; Frith, M.C.; Mizuguchi, T.; Miyatake, S.; Toyota, T.; Adachi, H.; Oma, Y.; Kino, Y.; Mitsuhashi, H.; Matsumoto, N. Tandem-genotypes: Robust detection of tandem repeat expansions from long DNA reads. Genome Biol. 2019, 20, 58. [Google Scholar]
  83. Liu, Q.; Zhang, P.; Wang, D.; Gu, W.; Wang, K. Interrogating the "unsequenceable" genomic trinucleotide repeat disorders by long-read sequencing. Genome Med. 2017, 9, 65. [Google Scholar] [PubMed]
  84. English, A.C.; Dolzhenko, E.; Ziaei Jam, H.; McKenzie, S.K.; Olson, N.D.; De Coster, W.; Park, J.; Gu, B.; Wagner, J.; Eberle, M.A.; et al. Analysis and benchmarking of small and large genomic variants across tandem repeats. Nat. Biotechnol. 2024, 43, 431–442. [Google Scholar]
  85. Ren, J.; Gu, B.; Chaisson, M.J.P. Vamos: Variable-number tandem repeats annotation using efficient motif sets. Genome Biol. 2023, 24, 175. [Google Scholar]
  86. Jam, H.Z.; Zook, J.M.; Javadzadeh, S.; Park, J.; Sehgal, A.; Gymrek, M. Genome-wide profiling of genetic variation at tandem repeat from long reads. bioRxiv 2024. [Google Scholar] [CrossRef]
  87. Uppili, B.; Sharma, P.; Ahmad, I.; Sahni, S.; Asokachandran, V.; Nagaraja, A.B.; Srivastava, A.K.; Faruq, M. Sequencing through hyperexpanded Friedreich’s ataxia-GAA repeats by nanopore technology: Implications in genotype-phenotype correlation. Brain Commun. 2023, 5, fcad020. [Google Scholar] [CrossRef]
  88. Marx, V. Method of the year: Long-read sequencing. Nat. Methods. 2023, 20, 6–11. [Google Scholar]
  89. Weber, J.L.; May, P.E. Abundant class of human DNA polymorphisms which can be typed using the polymerase chain reaction. Am. J. Hum. Genet. 1989, 44, 388–396. [Google Scholar]
  90. Ott, J. Analysis of Human Genetic Linkage; Johns Hopkins University Press: Baltimore, MD, USA, 1999. [Google Scholar]
  91. Strachan, T.; Read, A. Human Molecular Genetics; CRC Press: Boca Raton, FL, USA, 2018. [Google Scholar]
  92. Morton, N.E. Sequential tests for the detection of linkage. Am. J. Hum. Genet. 1955, 7, 277–318. [Google Scholar]
  93. Risch, N.J. Searching for genetic determinants in the new millennium. Nature 2000, 405, 847–856. [Google Scholar] [CrossRef]
  94. Elston, R.C.; Stewart, J. A general model for the genetic analysis of pedigree data. Hum. Hered. 1971, 21, 523–542. [Google Scholar] [CrossRef] [PubMed]
  95. Sproviero, W.; Ghose, U.; Winchester, L.M.; Fernandes, M.; Newby, D.; Sproviero, D.; Amin, N.; Smets, B.; He, K.Y.; Khramtsova, E.A.; et al. The impact of Short Tandem Repeats on grey matter brain imaging derived phenotypes in UK Biobank. medRxiv 2023. [Google Scholar] [CrossRef]
  96. Mattick, J.; Amaral, P. A new understanding of molecular biology. In RNA, the Epicenter of Genetic Information; CRC Press: Boca Raton, FL, USA, 2022. [Google Scholar]
  97. Guo, M.H.; Lee, W.P.; Vardarajan, B.; Schellenberg, G.D.; Phillips-Cremins, J.E. Polygenic burden of short tandem repeat expansions promotes risk for Alzheimer’s disease. Nat. Commun. 2025, 16, 1126. [Google Scholar] [CrossRef]
  98. Giesselmann, P.; Brändl, B.; Raimondeau, E.; Bowen, R.; Rohrandt, C.; Tandon, R.; Kretzmer, H.; Assum, G.; Galonska, C.; Siebert, R.; et al. Analysis of short tandem repeat expansions and their methylation state with nanopore sequencing. Nat. Biotechnol. 2019, 37, 1478–1481. [Google Scholar] [CrossRef]
  99. Ciobanu, C.G.; Nucă, I.; Popescu, R.; Antoci, L.M.; Caba, L.; Ivanov, A.V.; Cojocaru, K.A.; Rusu, C.; Mihai, C.T.; Pânzaru, M.C. Narrative Review: Update on the Molecular Diagnosis of Fragile X Syndrome. Int. J. Mol. Sci. 2023, 24, 9206. [Google Scholar] [CrossRef] [PubMed]
  100. Jeck, W.R.; Parker, J.; Carson, C.C.; Shields, J.M.; Sambade, M.J.; Peters, E.C.; Burd, C.E.; Thomas, N.E.; Chiang, D.Y.; Liu, W.; et al. Targeted next generation sequencing identifies clinically actionable mutations in patients with melanoma. Pigment. Cell. Melanoma. Res. 2014, 27, 653–663. [Google Scholar] [CrossRef]
  101. Willems, T.; Zielinski, D.; Yuan, J.; Gordon, A.; Gymrek, M.; Erlich, Y. Genome-wide profiling of heritable and de novo STR variations. Nat. Methods 2017, 14, 590–592. [Google Scholar] [CrossRef]
  102. Mousavi, N.; Shleizer-Burko, S.; Yanicky, R.; Gymrek, M. Profiling the genome-wide landscape of tandem repeat expansions. Nucleic. Acids. Res. 2019, 47, e90. [Google Scholar] [CrossRef]
  103. Jakubosky, D.; D’Antonio, M.; Bonder, M.J.; Smail, C.; Donovan, M.K.R.; Young Greenwald, W.W.; Matsui, H.; D’Antonio-Chronowska, A.; Stegle, O.; Smith, E.N.; et al. Properties of structural variants and short tandem repeats associated with gene expression and complex traits. Nat. Commun. 2020, 11, 2927. [Google Scholar] [CrossRef]
  104. Dashnow, H.; Lek, M.; Phipson, B.; Halman, A.; Sadedin, S.; Lonsdale, A.; Davis, M.; Lamont, P.; Clayton, J.S.; Laing, N.G.; et al. STRetch: Detecting and discovering pathogenic short tandem repeat expansions. Genome Biol. 2018, 19, 121. [Google Scholar]
  105. Ciaccio, C.; Fontana, L.; Milani, D.; Tabano, S.; Miozzo, M.; Esposito, S. Fragile X syndrome: A review of clinical and molecular diagnoses. Ital. J. Pediatr. 2017, 43, 39. [Google Scholar] [CrossRef]
  106. Hagerman, R.J.; Berry-Kravis, E.; Hazlett, H.C.; Bailey, D.B., Jr.; Moine, H.; Kooy, R.F.; Tassone, F.; Gantois, I.; Sonenberg, N.; Mandel, J.L.; et al. Fragile X syndrome. Nat. Rev. Dis. Primers 2017, 3, 17065. [Google Scholar] [PubMed]
  107. Richter, J.D.; Zhao, X. The molecular biology of FMRP: New insights into fragile X syndrome. Nat. Rev. Neurosci. 2021, 22, 209–222. [Google Scholar] [PubMed]
  108. Leitão, E.; Schröder, C.; Depienne, C. Identification and characterization of repeat expansions in neurological disorders: Methodologies, tools, and strategies. Rev. Neurol. 2024, 180, 383–392. [Google Scholar]
  109. Zheng, Z.; Li, A.; Holmes, B.B.; Marasa, J.C.; Diamond, M.I. An N-terminal nuclear export signal regulates trafficking and aggregation of Huntingtin (Htt) protein exon 1. J. Biol. Chem. 2013, 288, 6063–6071. [Google Scholar]
  110. Harding, R.J.; Loppnau, P.; Ackloo, S.; Lemak, A.; Hutchinson, A.; Hunt, B.; Holehouse, A.S.; Ho, J.C.; Fan, L.; Toledo-Sherman, L.; et al. Design and characterization of mutant and wildtype huntingtin proteins produced from a toolkit of scalable eukaryotic expression systems. J. Biol. Chem. 2019, 294, 6986–7001. [Google Scholar]
  111. Ruz, C.; Alcantud, J.L.; Vives Montero, F.; Duran, R.; Bandres-Ciga, S. Proteotoxicity and Neurodegenerative Diseases. Int. J. Mol. Sci. 2020, 21, 5646. [Google Scholar] [CrossRef]
  112. Yang, H.; Yang, S.; Jing, L.; Huang, L.; Chen, L.; Zhao, X.; Yang, W.; Pan, Y.; Yin, P.; Qin, Z.S.; et al. Truncation of mutant huntingtin in knock-in mice demonstrates exon1 huntingtin is a key pathogenic form. Nat. Commun. 2020, 11, 2582. [Google Scholar]
  113. Emery, A.E. The muscular dystrophies. Lancet 2002, 359, 687–695. [Google Scholar]
  114. Mercuri, E.; Bönnemann, C.G.; Muntoni, F. Muscular dystrophies. Lancet 2019, 394, 2025–2038. [Google Scholar] [CrossRef]
  115. Gymrek, M.; Willems, T.; Guilmatre, A.; Zeng, H.; Markus, B.; Georgiev, S.; Daly, M.J.; Price, A.L.; Pritchard, J.K.; Sharp, A.J.; et al. Abundant contribution of short tandem repeats to gene expression variation in humans. Nat. Genet. 2016, 48, 22–29. [Google Scholar] [CrossRef]
  116. Oketch, J.W.; Wain, L.V.; Hollox, E.J. A comparison of software for analysis of rare and common short tandem repeat (STR) variation using human genome sequences from clinical and population-based samples. PLoS ONE 2024, 19, e0300545. [Google Scholar] [CrossRef] [PubMed]
  117. Hannan, A.J. Tandem repeat polymorphisms: Modulators of disease susceptibility and candidates for ‘missing heritability’. Trends Genet. 2010, 26, 59–65. [Google Scholar] [CrossRef]
  118. Dolzhenko, E.; Deshpande, V.; Schlesinger, F.; Krusche, P.; Petrovski, R.; Chen, S.; Emig-Agius, D.; Gross, A.; Narzisi, G.; Bowman, B.; et al. ExpansionHunter: A sequence-graph-based tool to analyze variation in short tandem repeat regions. Bioinformatics 2019, 35, 4754–4756. [Google Scholar] [CrossRef]
  119. Dolzhenko, E.; van Vugt, J.; Shaw, R.J.; Bekritsky, M.A.; van Blitterswijk, M.; Narzisi, G.; Ajay, S.S.; Rajan, V.; Lajoie, B.R.; Johnson, N.H.; et al. Detection of long repeat expansions from PCR-free whole-genome sequence data. Genome Res. 2017, 27, 1895–1903. [Google Scholar] [CrossRef] [PubMed]
  120. Edwards, A.O.; Civitello, A.; Hammond, H.; Caskey, C.T. DNA typing and genetic mapping with trimeric and tetrameric tandem repeats. Am. J. Hum. Genet. 1991, 49, 746–756. [Google Scholar] [PubMed]
  121. Press, M.O.; Carlson, K.D.; Queitsch, C. The overdue promise of short tandem repeat variation for heritability. bioRxiv 2014. [Google Scholar] [CrossRef]
  122. McMurray, C.T. Mechanisms of trinucleotide repeat instability during human development. Nat. Rev. Genet. 2010, 11, 786–799. [Google Scholar] [CrossRef]
  123. Henden, L.; Fearnley, L.G.; Grima, N.; McCann, E.P.; Dobson-Stone, C.; Fitzpatrick, L.; Friend, K.; Hobson, L.; Chan Moi Fat, S.; Rowe, D.B.; et al. Short tandem repeat expansions in sporadic amyotrophic lateral sclerosis and frontotemporal dementia. Sci. Adv. 2023, 9, eade2044. [Google Scholar]
  124. Fang, L.; Liu, Q.; Monteys, A.M.; Gonzalez-Alegre, P.; Davidson, B.L.; Wang, K. DeepRepeat: Direct quantification of short tandem repeats on signal data from nanopore sequencing. Genome Biol. 2022, 23, 108. [Google Scholar]
  125. Hannan, A.J. Repeat DNA expands our understanding of autism spectrum disorder. Nature 2021, 589, 200–202. [Google Scholar]
  126. RK, C.Y.; Merico, D.; Bookman, M.; Howe, J.L.; Thiruvahindrapuram, B.; Patel, R.V.; Whitney, J.; Deflaux, N.; Bingham, J.; Wang, Z.; et al. Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nat. Neurosci. 2017, 20, 602–611. [Google Scholar]
  127. Fischbach, G.D.; Lord, C. The Simons Simplex Collection: A resource for identification of autism genetic risk factors. Neuron 2010, 68, 192–195. [Google Scholar] [CrossRef] [PubMed]
  128. Auton, A.; Brooks, L.D.; Durbin, R.M.; Garrison, E.P.; Kang, H.M.; Korbel, J.O.; Marchini, J.L.; McCarthy, S.; McVean, G.A.; Abecasis, G.R. A global reference for human genetic variation. Nature 2015, 526, 68–74. [Google Scholar]
  129. Annear, D.J.; Vandeweyer, G.; Sanchis-Juan, A.; Raymond, F.L.; Kooy, R.F. Non-Mendelian inheritance patterns and extreme deviation rates of CGG repeats in autism. Genome Res. 2022, 32, 1967–1980. [Google Scholar] [PubMed]
  130. Halvorsen, M.; Huh, R.; Oskolkov, N.; Wen, J.; Netotea, S.; Giusti-Rodriguez, P.; Karlsson, R.; Bryois, J.; Nystedt, B.; Ameur, A.; et al. Increased burden of ultra-rare structural variants localizing to boundaries of topologically associated domains in schizophrenia. Nat. Commun. 2020, 11, 1842. [Google Scholar]
  131. Song, J.H.T.; Lowe, C.B.; Kingsley, D.M. Characterization of a Human-Specific Tandem Repeat Associated with Bipolar Disorder and Schizophrenia. Am. J. Hum. Genet. 2018, 103, 421–430. [Google Scholar] [PubMed]
  132. Šerý, O.; Paclt, I.; Drtílková, I.; Theiner, P.; Kopečková, M.; Zvolský, P.; Balcar, V.J. A 40-bp VNTR polymorphism in the 3′-untranslated region of DAT1/SLC6A3 is associated with ADHD but not with alcoholism. Behav. Brain Funct. 2015, 11, 21. [Google Scholar]
  133. Cichon, S.; Craddock, N.; Daly, M.; Faraone, S.V.; Gejman, P.V.; Kelsoe, J.; Lehner, T.; Levinson, D.F.; Moran, A.; Sklar, P.; et al. Genomewide association studies: History, rationale, and prospects for psychiatric disorders. Am. J. Psychiatry 2009, 166, 540–556. [Google Scholar]
  134. Wen, J.; Trost, B.; Engchuan, W.; Halvorsen, M.; Pallotto, L.M.; Mitina, A.; Ancalade, N.; Farrell, M.; Backstrom, I.; Guo, K.; et al. Rare tandem repeat expansions associate with genes involved in synaptic and neuronal signaling functions in schizophrenia. Mol. Psychiatry 2023, 28, 475–482. [Google Scholar] [CrossRef] [PubMed]
  135. Pearson, C.E.; Nichol Edamura, K.; Cleary, J.D. Repeat instability: Mechanisms of dynamic mutations. Nat. Rev. Genet. 2005, 6, 729–742. [Google Scholar] [CrossRef] [PubMed]
  136. Burgunder, J.M. Mechanisms underlying phenotypic variation in neurogenetic disorders. Nat. Rev. Neurol. 2023, 19, 363–370. [Google Scholar] [CrossRef]
  137. Paulson, H.; Shakkottai, V. Spinocerebellar Ataxia Type 3. In GeneReviews®; Adam, M.P., Feldman, J., Mirzaa, G.M., Pagon, R.A., Wallace, S.E., Amemiya, A., Eds.; University of Washington: Seattle, WA, USA, 1993. [Google Scholar]
  138. Cleary, J.D.; Subramony, S.H.; Ranum, L.P.W. Spinocerebellar Ataxia Type 8. In GeneReviews®; Adam, M.P., Feldman, J., Mirzaa, G.M., Pagon, R.A., Wallace, S.E., Amemiya, A., Eds.; University of Washington: Seattle, WA, USA, 1993. [Google Scholar]
  139. Bram, E.; Javanmardi, K.; Nicholson, K.; Culp, K.; Thibert, J.R.; Kemppainen, J.; Le, V.; Schlageter, A.; Hadd, A.; Latham, G.J. Comprehensive genotyping of the C9orf72 hexanucleotide repeat region in 2095 ALS samples from the NINDS collection using a two-mode, long-read PCR assay. Amyotroph. Lateral. Scler. Frontotemporal. Degener. 2019, 20, 107–114. [Google Scholar] [CrossRef] [PubMed]
  140. Gall-Duncan, T.; Sato, N.; Yuen, R.K.C.; Pearson, C.E. Advancing genomic technologies and clinical awareness accelerates discovery of disease-associated tandem repeat sequences. Genome Res. 2022, 32, 1–27. [Google Scholar] [CrossRef]
  141. Lanni, S.; Pearson, C.E. Molecular genetics of congenital myotonic dystrophy. Neurobiol. Dis. 2019, 132, 104533. [Google Scholar]
  142. Morales, F.; Vásquez, M.; Corrales, E.; Vindas-Smith, R.; Santamaría-Ulloa, C.; Zhang, B.; Sirito, M.; Estecio, M.R.; Krahe, R.; Monckton, D.G. Longitudinal increases in somatic mosaicism of the expanded CTG repeat in myotonic dystrophy type 1 are associated with variation in age-at-onset. Hum. Mol. Genet. 2020, 29, 2496–2507. [Google Scholar] [CrossRef]
  143. Ishiura, H.; Doi, K.; Mitsui, J.; Yoshimura, J.; Matsukawa, M.K.; Fujiyama, A.; Toyoshima, Y.; Kakita, A.; Takahashi, H.; Suzuki, Y.; et al. Expansions of intronic TTTCA and TTTTA repeats in benign adult familial myoclonic epilepsy. Nat. Genet. 2018, 50, 581–590. [Google Scholar] [CrossRef]
  144. Flavell, J.; Franklin, C.; Nestor, P.J. A Systematic Review of Fragile X-Associated Neuropsychiatric Disorders. J. Neuropsychiatry Clin. Neurosci. 2023, 35, 110–120. [Google Scholar] [CrossRef]
  145. Nolin, S.L.; Sah, S.; Glicksman, A.; Sherman, S.L.; Allen, E.; Berry-Kravis, E.; Tassone, F.; Yrigollen, C.; Cronister, A.; Jodah, M.; et al. Fragile X AGG analysis provides new risk predictions for 45-69 repeat alleles. Am. J. Med. Genet. A 2013, 161, 771–778. [Google Scholar] [CrossRef]
  146. Alonso, I.; Jardim, L.B.; Artigalas, O.; Saraiva-Pereira, M.L.; Matsuura, T.; Ashizawa, T.; Sequeiros, J.; Silveira, I. Reduced penetrance of intermediate size alleles in spinocerebellar ataxia type 10. Neurology 2006, 66, 1602–1604. [Google Scholar] [CrossRef] [PubMed]
  147. Yum, K.; Wang, E.T.; Kalsotra, A. Myotonic dystrophy: Disease repeat range, penetrance, age of onset, and relationship between repeat size and phenotypes. Curr. Opin. Genet. Dev. 2017, 44, 30–37. [Google Scholar] [CrossRef]
  148. Musova, Z.; Mazanec, R.; Krepelova, A.; Ehler, E.; Vales, J.; Jaklova, R.; Prochazka, T.; Koukal, P.; Marikova, T.; Kraus, J.; et al. Highly unstable sequence interruptions of the CTG repeat in the myotonic dystrophy gene. Am. J. Med. Genet. A. 2009, 149, 1365–1374. [Google Scholar] [CrossRef]
  149. Hunter, J.E.; Berry-Kravis, E.; Hipp, H.; Todd, P.K. FMR1 Disorders. In GeneReviews®; Adam, M.P., Feldman, J., Mirzaa, G.M., Pagon, R.A., Wallace, S.E., Amemiya, A., Eds.; University of Washington: Seattle, WA, USA, 1993. [Google Scholar]
  150. Kandola, T.; Venkatesan, S.; Zhang, J.; Lerbakken, B.T.; Von Schulze, A.; Blanck, J.F.; Wu, J.; Unruh, J.R.; Berry, P.; Lange, J.J.; et al. Pathologic polyglutamine aggregation begins with a self-poisoning polymer crystal. eLife 2023, 12, RP86939. [Google Scholar] [CrossRef]
  151. Tassone, F.; Iong, K.P.; Tong, T.H.; Lo, J.; Gane, L.W.; Berry-Kravis, E.; Nguyen, D.; Mu, L.Y.; Laffin, J.; Bailey, D.B.; et al. FMR1 CGG allele size and prevalence ascertained through newborn screening in the United States. Genome Med. 2012, 4, 100. [Google Scholar] [CrossRef]
  152. Peprah, E. Fragile X syndrome: The FMR1 CGG repeat distribution among world populations. Ann. Hum. Genet. 2012, 76, 178–191. [Google Scholar] [CrossRef] [PubMed]
  153. Bassell, G.J.; Warren, S.T. Fragile X syndrome: Loss of local mRNA regulation alters synaptic development and function. Neuron 2008, 60, 201–214. [Google Scholar] [CrossRef] [PubMed]
  154. Reddy, K.; Zamiri, B.; Stanley, S.Y.R.; Macgregor, R.B., Jr.; Pearson, C.E. The disease-associated r(GGGGCC)n repeat from the C9orf72 gene forms tract length-dependent uni- and multimolecular RNA G-quadruplex structures. J. Biol. Chem. 2013, 288, 9860–9866. [Google Scholar] [CrossRef]
  155. Haeusler, A.R.; Donnelly, C.J.; Periz, G.; Simko, E.A.; Shaw, P.G.; Kim, M.S.; Maragakis, N.J.; Troncoso, J.C.; Pandey, A.; Sattler, R.; et al. C9orf72 nucleotide repeat structures initiate molecular cascades of disease. Nature 2014, 507, 195–200. [Google Scholar] [CrossRef]
  156. Rizzo, A.; Salvati, E.; Porru, M.; D’Angelo, C.; Stevens, M.F.; D’Incalci, M.; Leonetti, C.; Gilson, E.; Zupi, G.; Biroccio, A. Stabilization of quadruplex DNA perturbs telomere replication leading to the activation of an ATR-dependent ATM signaling pathway. Nucleic Acids Res. 2009, 37, 5353–5364. [Google Scholar] [CrossRef]
  157. Sarkies, P.; Reams, C.; Simpson, L.J.; Sale, J.E. Epigenetic instability due to defective replication of structured DNA. Mol. Cell. 2010, 40, 703–713. [Google Scholar]
  158. Fratta, P.; Mizielinska, S.; Nicoll, A.J.; Zloh, M.; Fisher, E.M.; Parkinson, G.; Isaacs, A.M. C9orf72 hexanucleotide repeat associated with amyotrophic lateral sclerosis and frontotemporal dementia forms RNA G-quadruplexes. Sci. Rep. 2012, 2, 1016. [Google Scholar] [CrossRef] [PubMed]
  159. Nolin, S.L.; Glicksman, A.; Tortora, N.; Allen, E.; Macpherson, J.; Mila, M.; Vianna-Morgante, A.M.; Sherman, S.L.; Dobkin, C.; Latham, G.J.; et al. Expansions and contractions of the FMR1 CGG repeat in 5,508 transmissions of normal, intermediate, and premutation alleles. Am. J. Med. Genet. A 2019, 179, 1148–1156. [Google Scholar]
  160. Nolin, S.L.; Brown, W.T.; Glicksman, A.; Houck, G.E., Jr.; Gargano, A.D.; Sullivan, A.; Biancalana, V.; Bröndum-Nielsen, K.; Hjalgrim, H.; Holinski-Feder, E.; et al. Expansion of the fragile X CGG repeat in females with premutation or intermediate alleles. Am. J. Hum. Genet. 2003, 72, 454–464. [Google Scholar]
  161. Nolin, S.L.; Glicksman, A.; Ersalesi, N.; Dobkin, C.; Brown, W.T.; Cao, R.; Blatt, E.; Sah, S.; Latham, G.J.; Hadd, A.G. Fragile X full mutation expansions are inhibited by one or more AGG interruptions in premutation carriers. Genet. Med. 2015, 17, 358–364. [Google Scholar] [PubMed]
  162. Annear, D.J.; Kooy, R.F. Unravelling the link between neurodevelopmental disorders and short tandem CGG-repeat expansions. Emerg. Top Life Sci. 2023, 7, 265–275. [Google Scholar]
  163. Acton, R.T.; Rivers, C.A.; Watson, B.; Oh, S.J. DMPK-associated myotonic dystrophy and CTG repeats in Alabama African Americans. Clin. Genet. 2007, 72, 448–453. [Google Scholar] [PubMed]
  164. Cossée, M.; Schmitt, M.; Campuzano, V.; Reutenauer, L.; Moutou, C.; Mandel, J.L.; Koenig, M. Evolution of the Friedreich’s ataxia trinucleotide repeat expansion: Founder effect and premutations. Proc. Natl. Acad. Sci. USA 1997, 94, 7452–7457. [Google Scholar]
  165. Vijayaraghavan, P.; Batalov, S.; Ding, Y.; Sanford, E.; Kingsmore, S.F.; Dimmock, D.; Hobbs, C.; Bainbridge, M. The Genomic landscape of short tandem repeats across multiple ancestries. PLoS ONE 2023, 18, e0279430. [Google Scholar]
  166. Pieretti, M.; Zhang, F.P.; Fu, Y.H.; Warren, S.T.; Oostra, B.A.; Caskey, C.T.; Nelson, D.L. Absence of expression of the FMR-1 gene in fragile X syndrome. Cell 1991, 66, 817–822. [Google Scholar] [CrossRef]
  167. Sutcliffe, J.S.; Nelson, D.L.; Zhang, F.; Pieretti, M.; Caskey, C.T.; Saxe, D.; Warren, S.T. DNA methylation represses FMR-1 transcription in fragile X syndrome. Hum. Mol. Genet. 1992, 1, 397–400. [Google Scholar] [CrossRef] [PubMed]
  168. Ranum, L.P.; Cooper, T.A. RNA-mediated neuromuscular disorders. Annu. Rev. Neurosci. 2006, 29, 259–277. [Google Scholar] [CrossRef] [PubMed]
  169. Ash, P.E.; Bieniek, K.F.; Gendron, T.F.; Caulfield, T.; Lin, W.L.; Dejesus-Hernandez, M.; van Blitterswijk, M.M.; Jansen-West, K.; Paul, J.W., 3rd; Rademakers, R.; et al. Unconventional translation of C9ORF72 GGGGCC expansion generates insoluble polypeptides specific to c9FTD/ALS. Neuron 2013, 77, 639–646. [Google Scholar] [CrossRef]
  170. Miller, J.W.; Urbinati, C.R.; Teng-Umnuay, P.; Stenberg, M.G.; Byrne, B.J.; Thornton, C.A.; Swanson, M.S. Recruitment of human muscleblind proteins to (CUG)(n) expansions associated with myotonic dystrophy. Embo. J. 2000, 19, 4439–4448. [Google Scholar] [CrossRef]
  171. Warf, M.B.; Berglund, J.A. MBNL binds similar RNA structures in the CUG repeats of myotonic dystrophy and its pre-mRNA substrate cardiac troponin T. RNA 2007, 13, 2238–2251. [Google Scholar] [CrossRef] [PubMed]
  172. García-López, A.; Llamusí, B.; Orzáez, M.; Pérez-Payá, E.; Artero, R.D. In vivo discovery of a peptide that prevents CUG-RNA hairpin formation and reverses RNA toxicity in myotonic dystrophy models. Proc. Natl. Acad. Sci. USA 2011, 108, 11866–11871. [Google Scholar] [CrossRef]
  173. Ross, C.A.; Poirier, M.A. Protein aggregation and neurodegenerative disease. Nat. Med. 2004, 10, S10–S17. [Google Scholar]
  174. Zu, T.; Liu, Y.; Bañez-Coronel, M.; Reid, T.; Pletnikova, O.; Lewis, J.; Miller, T.M.; Harms, M.B.; Falchook, A.E.; Subramony, S.H.; et al. RAN proteins and RNA foci from antisense transcripts in C9ORF72 ALS and frontotemporal dementia. Proc. Natl. Acad. Sci. USA 2013, 110, E4968–E4977. [Google Scholar] [CrossRef]
  175. Gendron, T.F.; Bieniek, K.F.; Zhang, Y.J.; Jansen-West, K.; Ash, P.E.; Caulfield, T.; Daughrity, L.; Dunmore, J.H.; Castanedes-Casey, M.; Chew, J.; et al. Antisense transcripts of the expanded C9ORF72 hexanucleotide repeat form nuclear RNA foci and undergo repeat-associated non-ATG translation in c9FTD/ALS. Acta Neuropathol. 2013, 126, 829–844. [Google Scholar]
  176. Mori, K.; Weng, S.M.; Arzberger, T.; May, S.; Rentzsch, K.; Kremmer, E.; Schmid, B.; Kretzschmar, H.A.; Cruts, M.; Van Broeckhoven, C.; et al. The C9orf72 GGGGCC repeat is translated into aggregating dipeptide-repeat proteins in FTLD/ALS. Science 2013, 339, 1335–1338. [Google Scholar] [CrossRef]
  177. DeJesus-Hernandez, M.; Mackenzie, I.R.; Boeve, B.F.; Boxer, A.L.; Baker, M.; Rutherford, N.J.; Nicholson, A.M.; Finch, N.A.; Flynn, H.; Adamson, J.; et al. Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron 2011, 72, 245–256. [Google Scholar] [CrossRef]
  178. Renton, A.E.; Majounie, E.; Waite, A.; Simón-Sánchez, J.; Rollinson, S.; Gibbs, J.R.; Schymick, J.C.; Laaksovirta, H.; van Swieten, J.C.; Myllykangas, L.; et al. A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD. Neuron 2011, 72, 257–268. [Google Scholar] [CrossRef] [PubMed]
  179. Gitler, A.D.; Tsuiji, H. There has been an awakening: Emerging mechanisms of C9orf72 mutations in FTD/ALS. Brain Res. 2016, 1647, 19–29. [Google Scholar] [CrossRef]
  180. Liu, X.; Zhao, X.; He, J.; Wang, S.; Shen, X.; Liu, Q.; Wang, S. Advances in the Structure of GGGGCC Repeat RNA Sequence and Its Interaction with Small Molecules and Protein Partners. Molecules 2023, 28, 5801. [Google Scholar] [CrossRef] [PubMed]
  181. Balendra, R.; Isaacs, A.M. C9orf72-mediated ALS and FTD: Multiple pathways to disease. Nat. Rev. Neurol. 2018, 14, 544–558. [Google Scholar] [CrossRef]
  182. Shi, Y.; Lin, S.; Staats, K.A.; Li, Y.; Chang, W.H.; Hung, S.T.; Hendricks, E.; Linares, G.R.; Wang, Y.; Son, E.Y.; et al. Haploinsufficiency leads to neurodegeneration in C9ORF72 ALS/FTD human induced motor neurons. Nat. Med. 2018, 24, 313–325. [Google Scholar] [CrossRef] [PubMed]
  183. Shao, Q.; Liang, C.; Chang, Q.; Zhang, W.; Yang, M.; Chen, J.F. C9orf72 deficiency promotes motor deficits of a C9ALS/FTD mouse model in a dose-dependent manner. Acta Neuropathol. Commun. 2019, 7, 32. [Google Scholar] [CrossRef]
  184. Neueder, A.; Landles, C.; Ghosh, R.; Howland, D.; Myers, R.H.; Faull, R.L.M.; Tabrizi, S.J.; Bates, G.P. The pathogenic exon 1 HTT protein is produced by incomplete splicing in Huntington’s disease patients. Sci. Rep. 2017, 7, 1307. [Google Scholar] [CrossRef]
  185. Lieberman, A.P.; Shakkottai, V.G.; Albin, R.L. Polyglutamine Repeats in Neurodegenerative Diseases. Annu. Rev. Pathol. 2019, 14, 1–27. [Google Scholar] [CrossRef]
  186. Zhang, N.; Ashizawa, T. RNA toxicity and foci formation in microsatellite expansion diseases. Curr. Opin. Genet. Dev. 2017, 44, 17–29. [Google Scholar] [CrossRef]
  187. Coffee, B.; Zhang, F.; Ceman, S.; Warren, S.T.; Reines, D. Histone modifications depict an aberrantly heterochromatinized FMR1 gene in fragile x syndrome. Am. J. Hum. Genet. 2002, 71, 923–932. [Google Scholar]
  188. Michaelson, J.J. Genetic Approaches to Understanding Psychiatric Disease. Neurotherapeutics 2017, 14, 564–581. [Google Scholar]
  189. Puccio, H.; Simon, D.; Cossée, M.; Criqui-Filipe, P.; Tiziano, F.; Melki, J.; Hindelang, C.; Matyas, R.; Rustin, P.; Koenig, M. Mouse models for Friedreich ataxia exhibit cardiomyopathy, sensory nerve defect and Fe-S enzyme deficiency followed by intramitochondrial iron deposits. Nat. Genet. 2001, 27, 181–186. [Google Scholar]
  190. Yang, N.; MacArthur, D.G.; Gulbin, J.P.; Hahn, A.G.; Beggs, A.H.; Easteal, S.; North, K. ACTN3 genotype is associated with human elite athletic performance. Am. J. Hum. Genet. 2003, 73, 627–631. [Google Scholar] [CrossRef]
  191. Silver, L.M. Mouse Genetics Concepts and Applications; Oxford University Press: Oxford, UK, 1995. [Google Scholar]
  192. Ellenbroek, B.; Youn, J. Rodent models in neuroscience research: Is it a rat race? Dis. Model. Mech. 2016, 9, 1079–1087. [Google Scholar] [CrossRef]
  193. Nestler, E.J.; Hyman, S.E. Animal models of neuropsychiatric disorders. Nat. Neurosci. 2010, 13, 1161–1169. [Google Scholar] [PubMed]
  194. Kalueff, A.V.; Wheaton, M.; Murphy, D.L. What’s wrong with my mouse model? Advances and strategies in animal modeling of anxiety and depression. Behav. Brain. Res. 2007, 179, 1–18. [Google Scholar] [PubMed]
  195. Mulholland, M.M.; Navabpour, S.V.; Mareno, M.C.; Schapiro, S.J.; Young, L.J.; Hopkins, W.D. AVPR1A variation is linked to gray matter covariation in the social brain network of chimpanzees. Genes. Brain. Behav. 2020, 19, e12631. [Google Scholar] [PubMed]
  196. Bennett, B.T.; Abee, C.R.; Henrickson, R. Nonhuman Primates in Biomedical Research: Diseases; Academic Press: New York, NY, USA, 1998. [Google Scholar]
  197. Nelson, E.E.; Winslow, J.T. Non-human primates: Model animals for developmental psychopathology. Neuropsychopharmacology 2009, 34, 90–105. [Google Scholar]
  198. Capitanio, J.P.; Emborg, M.E. Contributions of non-human primates to neuroscience research. Lancet 2008, 371, 1126–1135. [Google Scholar]
  199. Liu, C.X.; Li, C.Y.; Hu, C.C.; Wang, Y.; Lin, J.; Jiang, Y.H.; Li, Q.; Xu, X. CRISPR/Cas9-induced shank3b mutant zebrafish display autism-like behaviors. Mol. Autism. 2018, 9, 23. [Google Scholar] [CrossRef] [PubMed]
  200. Kalueff, A.V.; Stewart, A.M.; Gerlai, R. Zebrafish as an emerging model for studying complex brain disorders. Trends. Pharmacol. Sci. 2014, 35, 63–75. [Google Scholar] [CrossRef]
  201. Lieschke, G.J.; Currie, P.D. Animal models of human disease: Zebrafish swim into view. Nat. Rev. Genet. 2007, 8, 353–367. [Google Scholar] [CrossRef] [PubMed]
Genes 16 00406 g001
Figure 1. STR Identification Technology Development Timeline. LoBSTR: a tool specifically designed for early short-read genotyping; RepeatSeq: an advanced tool aimed at optimizing short-read data processing; PacmonSTR: a tool that combines short-read and long-read data to enhance analytical capabilities; HipSTR: an innovative tool for efficiently detecting tandem repeat sequences; Expansion Hunter: detects disease-related expansions of long STRs; PopSTR: conducts population-scale STR genotyping; TREDPARSE: an improved alignment algorithm; RepeatHMM: modeling using Hidden Markov Models; exSTRA: a powerful tool that demonstrates exceptional performance in handling low-coverage data; STRetch: analyzes the expansion and variation of STRs in the genome; RepLong: an optimized processing workflow; GangSTR: a tool that incorporates population genetics models to enrich the depth of analysis; NanoSatellite: designed specifically for Oxford Nanopore Technologies (ONT) data; STRique: a tool that integrates short-read and long-read data; Tandem-genotypes: specifically designed for population-scale STR genotyping; EHdn: a robust tool for de novo identification of repeat sequences; Tricolor: a technology utilizing multicolor fluorescent labeling; STRaglr: an enhanced algorithm for optimized detection; STRling: a tool that performs joint analysis of short-read and long-read data to obtain more comprehensive results; DeepRepeat: the application of deep learning techniques; EnsembleTR: a tool that significantly enhances performance through ensemble learning methods; TRGT: an advanced genomic analysis algorithm.
Figure 1. STR Identification Technology Development Timeline. LoBSTR: a tool specifically designed for early short-read genotyping; RepeatSeq: an advanced tool aimed at optimizing short-read data processing; PacmonSTR: a tool that combines short-read and long-read data to enhance analytical capabilities; HipSTR: an innovative tool for efficiently detecting tandem repeat sequences; Expansion Hunter: detects disease-related expansions of long STRs; PopSTR: conducts population-scale STR genotyping; TREDPARSE: an improved alignment algorithm; RepeatHMM: modeling using Hidden Markov Models; exSTRA: a powerful tool that demonstrates exceptional performance in handling low-coverage data; STRetch: analyzes the expansion and variation of STRs in the genome; RepLong: an optimized processing workflow; GangSTR: a tool that incorporates population genetics models to enrich the depth of analysis; NanoSatellite: designed specifically for Oxford Nanopore Technologies (ONT) data; STRique: a tool that integrates short-read and long-read data; Tandem-genotypes: specifically designed for population-scale STR genotyping; EHdn: a robust tool for de novo identification of repeat sequences; Tricolor: a technology utilizing multicolor fluorescent labeling; STRaglr: an enhanced algorithm for optimized detection; STRling: a tool that performs joint analysis of short-read and long-read data to obtain more comprehensive results; DeepRepeat: the application of deep learning techniques; EnsembleTR: a tool that significantly enhances performance through ensemble learning methods; TRGT: an advanced genomic analysis algorithm.
Genes 16 00406 g001
Table 1. Comprehensive table of neuropsychiatric disorder-associated STR variants and inheritance patterns.
Table 1. Comprehensive table of neuropsychiatric disorder-associated STR variants and inheritance patterns.
GeneDiseasesOMIM IDRepeat MotifMode of InheritanceLocation on GeneMechanismAge of OnsetRepeat Arrangement in GRCh38
HTTHD143100CAGADExon 1GoF (polyQ)
GoF (RNA)
RAN translation		Pediatric/Adult (Mean: 35–44)(CAG)19 CAA CAG CCG CCA (CCG)7
FMR1FXTAS/
FXPOI		300623CGGXLD5′ UTRGoF (RNA)
RAN translation		Adult (FXTAS: 60–65. FXPOI < 40)19(CGG)10 AGG (CGG)9
FMR1FXAND-CGGXLD5′ UTRGoF (RNA)
RAN translation		Pediatric/Adult(CGG)10 AGG (CGG)9
FMR1FXS300624CGGXLD5′ UTRLoFPediatric(CGG)10 AGG (CGG)9
C9ORF72FTD and/
or ALS1		105550GGGGCCADIntron 1LoF
GoF (RNA)
RAN translation		Adult (Typical: 50–64)(GGGGCC)3
ATXN1SCA1164400CAGADExon 8GoF (polyQ)
LoF		Adult (Typical: 30–40)(CAG)12 CAT CAG CAT (CAG)14
ATXN2SCA2/
ALS13		183090CAGADExon 1GoF (polyQ)
GoF (RNA)
RAN translation		Adult (Typical: 40)(CAG)13 CAA (CAG)9
ATXN3SCA3/
MJD		109150CAGADExon 10GoF (polyQ)
GoF (RNA)
RAN translation		Pediatric/Adult (Typical: 20–50)(CAG)2 CAA AAG CAG CAA (CAG)8
ATXN7SCA7164500CAGADExon 3GoF (polyQ)
GoF (RNA)		Pediatric/Adult (Typical: >40)(CAG)10
ATXN8OS/
ATXN8		SCA8608768CTG/CAGAD3′ UTR exon 5GoF (polyQ)
GoF (RNA)
RAN translation		Pediatric/Adult (Typical: 30–50)(CTG/CAG)15
ATXN10SCA10603516ATTCTADIntron 9GoF (RNA)Pediatric/Adult (Range: 12–48)(ATTCT)14
TCF4FECD3613267CTGADIntron 3GoF (RNA)
RAN translation		Adult(CTG)24
PRNPCJD123400CCTCATGGTGGTGGCTGGGGGCAGADExon 2LoF?Adult(CCTCATGGTGGTGGCTGGGGGCAG)2 CCCCATGGTGGTGGCTGGGGGCAG CCTCATGGTGGTGGCTGGGGTCAA
ARX_1DEE1/
XLID29		308350/300419NGCXLRExon 2LoFPediatric(NGC)16
ARX_2DEE1/
PRTS/
XLID29		308350/309510/300419NGCXLRExon 2LoFPediatric(NGC)12
TBPSCA17607136CAGADExon 3LoF
GoF (polyQ)		Pediatric/Adult (Mean: 34.6)(CAG)3 CAA CAA CAA (CAG)8 CAA CAG CAA (CAG)19
ZIC2HPE5609637GCNADExon 3LoFPediatric(GCN)15
ZIC3VACTERLX314390GCCXLRExon 1UnknownPediatric(GCC)8 GCT GCC
NOTCH2NLCNIID/ETM6/PD603472/618866/618600GGCAD5′ UTRGoF (RNA)
GoF (polyG)		Adult (Mean: 60.5)(GGC)9 GGA GGA(GGC)2
DMPKDM1160900CTGAD3′ UTRGoF (RNA)
RAN translation		Pediatric/Adult (Range: birth–70)(CTG)20
HD, Huntington’s disease; FXTAS, fragile X-associated tremor/ataxia syndrome; FXPOI, fragile X-associated primary ovarian insufficiency; FXAND, fragile X-associated neuropsychiatric disorders; FXS, fragile X syndrome; FTD, frontotemporal dementia; ALS, amyotrophic lateral sclerosis; SCA, spinocerebellar ataxia; MJD, Machado–Joseph disease; FECD, Fuchs endothelial corneal dystrophy; CJD, Creutzfeldt–Jakob disease; DBQD, Desbuquois dysplasia; DEE, developmental and epileptic encephalopathy; XLID, X-linked intellectual developmental disorder; HPE, holoprosencephaly; VACTERLX, X-linked VACTERL association with or without hydrocephalus; NIID, neuronal intranuclear inclusion disease; ETM, hereditary essential tremor; PD, Parkinson’s disease; DM, myotonic dystrophy; GoF, gain of function; RAN, repeat-associated non-AUG; LoF, loss of function.
Table 2. Main findings and characteristics of STRs in mental disorders associated with tandem repeats.
Table 2. Main findings and characteristics of STRs in mental disorders associated with tandem repeats.
DiseaseAssociated Genes/LocationsFunctional ImpactGenetic CharacteristicsKey FindingsYear of Study
ASDDMPK, FXN, FGF14, CACNB1, genes related to cortical developmentAbnormalities in neurodevelopment, cardiovascular/muscular system gene functions; disruption of regulatory regions (enhancer/promoter)Maternal mutations tend to be more substantial; higher frequency of new TR mutations (compared to unaffected siblings)Rare tandem repeats (TREs) contribute to 2.6% of ASD risk
Mutations are enriched near exons/splice sites and regulatory regions of fetal brain development
Carriers of STR expansions exhibit more severe phenotypes and lower adaptive capacity		2020, 2021, 2023
SchizophreniaPostsynaptic genes, brain-expressed genes (e.g., intronic region of CACNA1C)Abnormal synaptic signaling, neuronal excitability, and calcium signaling pathwaysHigher burden of rare TREs in patients; reduced enhancer activity of risk allelesTREs are enriched in gene regions (e.g., postsynaptic genes)
Length of intronic STR in CACNA1C influences enhancer activity, correlating with disease risk		2021, 2023
Bipolar Disorder Non-coding region of the third intron of CACNA1C gene (30 bp repeat unit)Variations in enhancer activity lead to abnormalities in neuronal excitability and synaptic plasticitySignificantly reduced enhancer activity of risk allelesLong STR alleles are associated with increased risk of BD/SCZ
Repeat lengths can reach thousands of base pairs, impacting calcium signaling pathways		2018
Attention Deficit Hyperactivity Disorder (ADHD)VNTR of the DAT1 gene (10-repeat allele)Altered dopamine reuptake efficiencyVariations in dopamine transporter-encoding geneThe 10-repeat allele is associated with an increased risk of ADHD2015
DepressionTandem repeats in the BDNF geneAbnormal expression of neurotrophic factorsMechanism not fully understoodSpecific STR variations may contribute to pathogenesis by influencing BDNF expression2009
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Liu, Y.; Xia, K. Aberrant Short Tandem Repeats: Pathogenicity, Mechanisms, Detection, and Roles in Neuropsychiatric Disorders. Genes 2025, 16, 406. https://doi.org/10.3390/genes16040406

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Liu Y, Xia K. Aberrant Short Tandem Repeats: Pathogenicity, Mechanisms, Detection, and Roles in Neuropsychiatric Disorders. Genes. 2025; 16(4):406. https://doi.org/10.3390/genes16040406

Chicago/Turabian Style

Liu, Yuzhong, and Kun Xia. 2025. "Aberrant Short Tandem Repeats: Pathogenicity, Mechanisms, Detection, and Roles in Neuropsychiatric Disorders" Genes 16, no. 4: 406. https://doi.org/10.3390/genes16040406

APA Style

Liu, Y., & Xia, K. (2025). Aberrant Short Tandem Repeats: Pathogenicity, Mechanisms, Detection, and Roles in Neuropsychiatric Disorders. Genes, 16(4), 406. https://doi.org/10.3390/genes16040406

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