Unraveling MECP2 structural variants in previously elusive Rett syndrome cases through IGV interpretation

Rett syndrome (RTT) is a rare severe neurodevelopmental disorder, that stands as the second most common cause of genetic neurodevelopmental disorder in females^1,2. It is characterized by normal development followed by motor and cognitive skills regression³. Individuals with RTT exhibit neurodevelopmental and motor delay, loss of hand usage, hand stereotypes, seizures, and autonomic symptoms including breathing abnormalities and social withdrawal^3,4.

RTT exemplifies the role of epigenetic mechanisms in disease pathology. Its etiology is primarily linked to mutations in MECP2, which encodes methyl-CpG binding protein 2 – a key epigenetic modulator in the brain that controls gene expression and modulates chromatin architecture through binding to methylated DNA⁵. The association between MECP2 and RTT is evident, as pathogenic variants in that gene have been identified in 90–95% of classic RTT cases and 50–70% of atypical RTT cases^6,7. However, a significant number of clinically diagnosed Rett patients lack a genetic diagnosis^8,9,10. Rett-like syndrome, exhibiting a phenotypic resemblance to classical RTT, also represents a considerable proportion of undiagnosed genetic cases^11,12,13,14. The mutations underlying these genetically unsolved cases remain elusive in routine screening of RTT or RTT-like patients. It is often presumed that a second gene or locus may be involved¹³, or that mutations are situated within regulatory non-coding regions of MECP2, which are not routinely screened¹⁵. Another possibility is the occurrence of SVs that may go undetected by routine PCR-based screening approaches and sequencing, particularly if a breakpoint extends beyond the genomic positions targeted by commonly used PCR primers^16,17.

We now successfully identify and clearly define three novel SVs within MECP2 in three distinct RTT patients who lacked a genetic diagnosis for many years. We emphasize the need to routinely search for SVs in RTT patients and other genetically unsolved diseases.

Clinical findings

We studied three cases of typical Rett syndrome whose underlying mutations remained elusive despite extensive clinical genetic testing.

Case 1: A 7.5-year-old girl born following a normal pregnancy, delivered via cesarean section due to placenta previa. The initial year of her life was marked by tranquility, alongside episodes of gastroesophageal reflux. However, at age one year, behavioral changes were noticed, with restlessness, frequent crying spells, and disrupted sleep patterns. These manifestations were coupled with a temporary decline in eye contact, which was regained at the age of three to the level of intense eye-pointing. She experienced delays in developmental milestones: walking at 20 months with a wide-based apraxic and ataxic gait, and limited verbal communication skills, using only a few words, which she primarily used during moments of stress. A formal diagnosis of autism spectrum disorder was made at the age of 18 months. By the age of 4.5 years, a decline in hand usage became evident, with minimal manipulation of objects observed, except for holding a bottle. Concurrently, she developed rapid, non-simultaneous hand tapping (left > right), predominantly on her abdomen and other surfaces, impairing her hand function. She developed daytime bruxism, and her sleep quality deteriorated, characterized by prolonged periods of sleep disruption, marked by crying or laughing episodes. Moreover, she displayed anxiety in diverse situations, resulting in episodes of freezing, especially when facing changes in her environment, such as navigating stairs or encountering different ground surfaces. She displayed a preference for human faces over objects and demonstrated heightened sensitivity to emotional fluctuations in her surroundings.

At the age of 6.5 years, she developed episodes of hyperventilation and short breath-holding. Although she used a few words, her intense tapping persisted, synchronizing with bilateral central spikes observed on video EEG recordings, which ceased when the tapping hand was restrained. Nocturnal spike activity persisted, intensifying during sleep to a degree that raised suspicion of electrical status epilepticus during sleep (ESES). This prompted a trial of Sulthiame combined with Clobazam, resulting in a decrease in the frequency of sleep spikes and potentially improving her sleep patterns. Physical examination at that point revealed a head circumference of 51 cm (50th percentile) with no dysmorphic features, intense eye contact, continuous one-hand tapping on her abdomen, apraxic atactic gait while wandering aimlessly in the room, mild hypotonia, and no evidence of cold extremities. MRI yielded normal results.

The diagnostic evaluation, which included chromosomal microarray analysis (CMA) and trio exome sequencing, returned negative results. Subsequent investigation, which involved testing for Rett syndrome-associated genes through an Invitae panel and Multiplex ligation-dependent probe amplification (MLPA) to detect copy number variations (CNVs) within MECP2, also yielded negative findings. Despite these outcomes, the clinical features strongly suggested Rett syndrome (atypical preserved speech variant). Thus, driven by this compelling clinical suspicion, we decided to pursue Whole Genome Sequencing (WGS) to explore non-coding regions and identify potential hidden structural variants.

Case 2: A 5-year-old girl who was initially evaluated at age 2 years. She was born following a normal pregnancy and delivery. During her first year, she exhibited hypotonia, feeding difficulties, and poor weight gain, all of which were accompanied by developmental delays. At the age of 2, her head circumference measured 45 cm. Despite being a visually appealing child who could sit independently and stand with support, she did not achieve independent walking. Instead, she demonstrated progress through “bunny jumping”. Hand usage, initially reasonable, declined in the second year, and was limited primarily to holding a bottle. Intense hand stereotypes emerged, including hand washing, hand tapping on the mouth, and clapping. Additionally, she experienced pronounced daytime bruxism. While eye contact was lost toward the end of her first year, it was regained a few months later, albeit intermittently accompanied by bilateral squint. Other issues included poor chewing and swallowing, mild constipation, and disrupted sleep patterns, though her sleep improved using melatonin. By the age of 3, she began experiencing hyperventilation and breath-holding episodes, alongside the onset of epileptic seizures consistent with focal with partial unawareness seizures. Repeated EEGs revealed mildly slow background activity and increased frequency of central asynchronous spikes during sleep, typical for RTT patients. These seizures were effectively managed with Valproic acid. MRI imaging was normal. Despite negative results from chromosomal microarray analysis (CMA), trio whole-exome sequencing (WES), and an RTT-like genes panel by Invitae, her clinical presentation strongly suggested RTT.

Case 3: A 19-year-old female, the fifth child in a family of ten, was born after a normal pregnancy with early contractures. Her birth weight was 3750 grams, and her perinatal course was uneventful. She exhibited motor and generalized developmental delays: she sat at 1 year, crawled at 13 months, and stood and walked around furniture at 30 months. She lost the ability to walk at 4 years old but regained the ability to walk with a walker a few years later. At 13 months, she had a vocabulary of few words, which she lost by 21 months, but demonstrated a high level of communication using eye-gaze equipment. She could reach for toys and transfer objects between hands until around 24 months when she developed hand stereotypes. Awake bruxism, mild tremors, and eye-rolling events, non-epileptic and typical for RTT patients, also appeared. Her head growth decelerated from the 50th percentile at birth to below the 3rd percentile by 18 months. MRI was normal, and EEG showed a slow background without epileptiform activity. She had mild breathing abnormalities (hyperventilation alternating with apneas), a good appetite, and severe constipation. At her last clinic visit (age 18 years), she was wheelchair-bound, had consistent hand stereotypes requiring bracing, mild to moderate lower limb spasticity, and scoliosis. She is treated with Valproic acid for infrequent generalized tonic-clonic seizures that began at age 10 years. Her phenotype was consistent with typical RTT syndrome; however, MECP2 sequencing, CMA, MECP2 MLPA, and trio exome sequencing were negative.

Molecular genetic analysis

Case 1: We conducted trio WGS to identify relevant variants within MECP2. Using MANTA software (https://github.com/Illumina/manta), we detected two points flagged as potentially bordering an SV within MECP2 (hg38: chrX:154,032,101, chrX:154,032,109). Visualization of this region using IGV revealed that approximately 50% of the reads surrounding a specific point (hg38: chrX:154,032,104) were aligned towards this center point. Upon utilizing the ‘view as pair’ option in IGV, we observed that the reads positioned to the left of this point had their pairs located on chromosome 6 (hg38: chr6:162,662,479), while those on the right also had their pairs on chromosome 6, albeit approximately 200Kbp distant (hg38: chr6:162,862,870). These two points on chromosome 6 were located within genes, PRKN and PACRG, respectively (Fig. 1A). The reads on chromosome X were directed towards their paired counterparts on chromosome 6, suggesting a plausible scenario where they are indeed situated adjacently on the mutated allele. Upon visualizing the direction and location of the reads, we inferred that the most plausible explanation was a translocation of 200,391 bp from chromosome 6 to chromosome X, disrupting the MECP2 gene between exons 3 and 4 (Fig. 1A, B). PCR amplification using primers surrounding both SV borders, yielded amplification only in the affected individual’s DNA and not in either parent’s or two other siblings’ DNA. Sanger sequencing of the amplicon accurately identified the precise entry site and borders of the SV (Fig. 1B).

A Schematic representation of the translocation breakpoints in the reference wild-type genome. Red square marks the translocated region from chromosome 6. Each color represents a distinct gene: green for PACRG, blue for PRKN, and pink for MECP2. The red arrow highlights the translocation from chromosome 6 to chromosome X. IGV visualization shows: Brown reads span the translocation entry site on chromosome X, Yellow reads flank the translocated borders on chromosome 6. B Schematic representation of the predicted mutated patient genome. The red square identifies the translocated region from chromosome 6, which encompasses two genes: green for PACRG and blue for PRKN. Paired-read visualization illustrates the directions of the aligned reads: brown reads (aligned to ChrX) are adjacent to and directed toward yellow reads (aligned to Chr6). Sanger sequencing confirms the SV breakpoints. Figure created using biorender.com.

Full size image

Case 2: WGS was performed for the affected individual to identify mutations within MECP2. Using MANTA software, a specific possible breakpoint was identified between exons 2 and 3 of MECP2 (hg38: ChrX:154,040,681). Upon visualization in IGV, it became apparent that approximately 50% of the reads in this region had dis-concordant paired reads located approximately 170Kbp away (hg38: ChrX:153,873,102) within the L1CAM gene, also situated on chromosome X. Notably, the reads at both points were directed towards the downstream region of chromosome X. Further investigation involved checking for additional points identified on chromosome X by MANTA. Another point was detected within TMEM187 (hg38: ChrX:153,980,877), approximately 60Kbp away from the suspected MECP2 breakpoint. IGV visualization of this TMEM187 point similarly revealed approximately 50% aberrant reads. Upon examining the pairs, it was observed that all suspected reads had pairs located on the L1CAM gene (hg38: ChrX:153,867,860), with the reads pointing in the same direction, towards the upstream of chromosome X (Fig. 2A). By visualizing all the abnormal split reads and interpreting the unusual distances and orientations between the paired-end reads, we inferred the presence of a complex structural variant (cSV). The logical deduction was that two distinct deletion sites, along with an inversion site between them, might account for our observations in MANTA and IGV (Fig. 2A). To validate this hypothesis, we examined on IGV the proposed deleted regions, and indeed noted the presence of only homozygous SNPs, accompanied by a decreased read depth, thereby reinforcing our hypothesis of deleted areas. For molecular validation, we conducted PCR amplification on both border sides of the complex structural cSV, operating under the assumption of both a deletion and an inversion. We successfully obtained an amplicon only from the affected family member, and not from the parents or the two healthy siblings. Subsequently, we confirmed and precisely defined the borders through Sanger sequencing (Fig. 2B).

A Schematic representation of the cSV breakpoints in wild-type genome. Each color represents a distinct gene: Yellow for L1CAM, Green for TMEM187 and Pink for MECP2. Each colored square represent distinct variant type: Blue square for inversion region, Red square for two deletion sites and Green square for the unaffected regions. IGV visualization shows the analyzed reads directions by Cyan, and Blue arrows. B Schematic representation of the predicted mutated patient genome. Visualization of the paired-read directions aligned to the mutated allele: cyan represents the left border of the cSV, and blue represents the right border. Sanger sequencing confirms the cSV breakpoints. Purple squares indicate unrelated insertions within the cSV breakpoints. Figure created using biorender.com.

Full size image

Case 3: We conducted WGS of the affected family member to identify variants within MECP2. In this instance, we independently scanned MECP2 using IGV software, without utilizing MANTA. Two suspicious points (hg38: ChrX:154,027,486 and ChrX:154,030,665) were identified, each with 50% colored dis-concordant reads in the respective regions. Reads from both sides were found to be paired with each other in the right direction towards each other, and the distance between the two points was approximately 3200 bp. Additionally, we observed a drop in depth in WGS reads of around 50% in the region between these two sites, suggesting a potential heterozygous deletion. The deleted region includes a significant coding portion of exon 4 and a large segment of the 3’ end of the MECP2 gene (Fig. 3A). Afterward, we conducted PCR amplification using primers targeting both sides of the suspected deleted region, which generated an amplicon only in the affected individual, who carries the deletion SV, and not in any other family member. Subsequent Sanger sequencing accurately pinpointed the exact deletion site (Fig. 3B).

A Red square illustrates the c-terminal deletion site within MECP2. IGV visualization shows the deletion borders. Brown reads represents the paired-reads encompassing the deleted area. B Sanger sequencing validated the deleted region borders. Figure created using biorender.com.

Full size image

Causative mutations in MECP2 have been identified in 90–95% of classic RTT cases and 50–70% of atypical RTT cases^6,7. However, many clinically evident RTT or RTT-like cases remain with no molecular diagnosis. Identifying the genetic basis of RTT cases is crucial, as it provides families with a definitive diagnosis of their child’s condition, alleviates uncertainty and anxiety regarding future pregnancies, and allows for appropriate medical management tailored to the underlying genetic cause – including opening opportunities to participate in clinical trials targeting MECP2-specific pathways. Many other clinical conditions can imitate cases of Rett syndrome^18,19, and misdiagnosis may lead to ineffective or potentially harmful treatments. Thus, uncovering the genetic underpinnings of these cases is essential for optimizing patient care and advancing prevention and treatment strategies.

At the Israeli Rett clinic at Sheba Medical Center, 225 patients with a clinical diagnosis consistent with Rett syndrome are being followed^20,21. Among these, 10 patients have remained without a molecular diagnosis for years. We set out to solve the genetic riddle of 3 of these cases: two with typical RTT and one with the atypical preserved speech variant (PSV). These patients were meticulously clinically diagnosed by a highly experienced pediatric neurologist specializing in Rett syndrome, but had no genetic diagnosis despite extensive testing over many years. We solved all three cases using WGS, revealing that each was caused by a distinct SV within MECP2.

We demonstrated that all three RTT cases were caused by disrupting both of the main isoforms of MECP2: MECP2_e1 (NM_004992.4) and MECP2_e2 (NM_001110792.3)²². In Case 1, we identified a ~ 200 Kbp translocation from chromosome 6 to the X chromosome; t(6;X)(q26;q28). The translocation breakpoint within MECP2 (hg38: chrX:154,032,104) is located between exons 3 and 4 (Fig. 1). This ~200 kbp insertion encompasses the complete exons 1 and 2 of PACRG and the entire exon 1 of PRKN (Fig. 1). We hypothesize that this translocation may disrupt normal splicing, potentially triggering nonsense-mediated decay (NMD) or leading to the production of an aberrant MECP2 protein. We consider it highly unlikely that this insertion within the core region of MECP2 would result in the synthesis of a normal WT protein. In Case 2, we found a cSV involving a large deletion that removes the entire downstream portion of both MECP2 isoforms, including exons 3 and 4, as well as the entire 3’ UTR (Fig. 2). This likely results in the transcript undergoing NMD, preventing protein production. In Case 3, we identified an approximately 3,200 bp deletion in the downstream region of MECP2 (NC_000023.11:g.154027486_154030665del). This deletion removes the terminal portion of exon 4, resulting in a stop-loss mutation and it also includes a 2,889 bp of the 3’ UTR (Fig. 3). The deletion affects amino acids 397 to 498 and causes a frameshift, generating a novel amino acid sequence of 149 residues before encountering a premature stop codon.

SVs are genetic structural alterations, typically defined as genomic variants larger than 50 bps. These variations can include deletions, duplications, insertions, inversions, and translocations, which may impact gene function and regulation. SVs can significantly impact health by disrupting gene function, leading to a wide range of diseases, including developmental disorders, intellectual disabilities, and various congenital conditions^23,24,25. Small-intermediate SVs typically range from 50 bp to 50kbp in size, whereas large SVs encompass alterations exceeding 50kbp^26,27.

Small-intermediate SVs pose a unique challenge in detection compared to single nucleotide variants (SNVs), indels, and large SVs, as they fall within a range where conventional sequencing methods may fail to accurately identify and characterize due to limitations in read length and resolution methods^23,28,29. Short read sequencing (SRS), in particular, encounters significant difficulties in detecting small-intermediate SVs^29,30. This is because it often fails to span the exact SV breakpoints and map them accurately, hindering the detection of such variants, especially those located in non-coding regions or characterized by combinations of structural changes, known as cSVs³⁰. Additionally, relying solely on SV detection software is complex, as it typically identifies thousands of SVs per genome, many of which are false positives or inaccurately predicted variants^31,32.

Since the discovery of Rett syndrome by Andreas Rett in 1966, numerous cases caused by MECP2 mutations have been solved using conventional methods. SNVs, indels, and copy number variants (CNVs) were mostly detected via routine genetic panels, whole exome sequencing (WES), and directed targeted sequencing of MECP2^{33,34,35,36,37,38,39}. Large SVs were primarily detected using chromosomal microarray (CMA) or fluorescence in situ hybridization (FISH)^{40,41,42,43,44,45,46}. Multiplex ligation-dependent probe amplification (MLPA) identifies large deletions^35,47, that might be missed by routine PCR-based screening strategies. For example, one group succeeded in detecting deletions ranging from 1235 bp to 85 kb within MECP2 using MLPA⁴⁸; but, to characterize the rearrangements and locate the exact nucleotide positions of the breakpoints, they had to use real-time quantitative PCR (qPCR) and long-range PCR. Therefore, it is understandable that general screening of the Human Gene Mutation Database (HGMD)⁴⁹ has shown these small to intermediate SVs to be much less recognized over the years compared to other types of mutations.

We present an effective approach that allowed us to uncover novel disease-causing SVs in MECP2 using conventional short-read WGS. In two of the three cases (Cases 1,2), MANTA software facilitated the identification of potential SV breakpoints within MECP2. MANTA software can detect discordant read pairs, split reads, and abnormal read depth, which are indicative of various SVs such as deletions, duplications, inversions, and translocations. By integrating these different types of evidence, MANTA can suggest genomic loci suspected to be SVs breakpoint borders. However, due to MANTA’s tendency to produce false positives and its limitations in predicting exact SV subtypes, especially cSVs, we found it necessary to directly analyze and visualize the BAM file alignment in the regions that MANTA recognized. We utilized the IGV to visualize read mapping, which enabled us to interpret and hypothesize what were the distinct SV types in each case, and accurately determine their respective boundaries. The utility of IGV was highlighted when Case 3 was resolved solely through visualizing MECP2 on IGV, bypassing the need for MANTA. Eventually, the borders of identified SVs that we hypothesized to exist were verified by PCR and Sanger sequencing. That approach enabled us to detect pathogenic novel, elusive small to intermediate SVs within MECP2, that evaded detection using any other diagnostic tool (such as CMA, Trio exome, RTT-like genes panel, MLPA, etc.).

It is worth noting that cSVs pose great challenges in SRS mapping interpretation, beyond those in simple SVs. The presence of multiple SVs within the same genomic region can obscure and impede their identification. For instance, in case 2, the deletion sites were not apparent in the BAM visualization due to the presence of the inversion between them. We could infer the presence of deletions only by speculating on what would be logical and how the paired-reads might align, assuming there might be an inversion. Our findings highlight the importance of direct searching for SVs when conventional methods prove insufficient in detecting any mutations in MECP2 in RTT cases^48,49.

Long-read sequencing (LRS) is effective in identifying disease-associated SVs and cSVs⁵⁰. However, its high cost makes it impractical for routine genetic testing. As we demonstrate, such cases can be effectively resolved through advanced cost-effective bioinformatics analysis tools designed to detect SVs in SRS data. Our findings are consistent with a previous report that identified a 2.6 kb intronic insertion variant within MECP2 using MANTA software⁵¹. Additionally, our results demonstrate that even manually scanning IGV with a targeted focus on a specific gene can lead to the identification of SVs. We have shown that simply visualizing and carefully examining the MECP2 gene in the patient’s BAM file using IGV software can reveal inappropriately mapped reads that may indicate the presence of a disease-causing SV. Despite the obvious need for such software, there are currently no user-friendly and reliable bioinformatics tools available for routine use in identifying SVs in MECP2, nor is there a standard practice for directly examining the MECP2 gene in BAM files from Rett patients. This gap highlights the need for developing cost-effective and accessible techniques to improve the diagnostic process for SV detection.

The clinical phenotypes associated with MECP2 mutations exhibit significant variability, and prior studies have explored whether this variability is influenced by the type and location of the mutation^52,53,54. Bebbington et al. developed a phenotypic profile of C-terminal deletions in Rett syndrome, finding that such cases often present with milder disease phenotypes⁵⁵. These individuals are more likely to have normal head circumference and weight, a later onset of stereotypies, and earlier acquisition of walking skills. Additionally, deletions occurring downstream within the MECP2 gene were associated with lower average severity scores compared to those occurring upstream⁵⁵. However, the phenotypes observed in our cases diverge from this typical profile; in Case 3, we identified a deletion at the C-terminus of MECP2 (Fig. 3), resulting in a frameshift that alters the protein sequence starting at amino acid 397 and introduces a stop codon at position 545. Despite this C-terminal mutation, the patient’s phenotype aligns with classic Rett syndrome, except for the absence of seizures. She presented with microcephaly and has not achieved independent walking, which contrasts with the typically milder phenotype associated with C-terminal mutations. Similarly, in Case 2, the mutation involved a cSV, with a larger deletion encompassing exons 3 and 4 and the entire 3’ UTR (Fig. 2), and the patient also exhibited features consistent with classic Rett syndrome. Interestingly, in Case 1, the patient was diagnosed as PSV, a milder phenotype previously described in the literature⁵⁶. Given the large translocation identified in this case, a classic Rett syndrome phenotype might have been expected. While there are occasional genotype-phenotype correlations linked to the position of the mutation, other factors—such as the X-inactivation ratio, modifier genes, and additional, less well-known epigenetic influences—also contribute to phenotypic differences and severity^57,58,59.

Case 1 also raises the question of whether the two disrupted genes on chromosome 6 contribute to the patient’s phenotype. As described, the balanced translocation breakpoints in this case are located within two genes on chromosome 6: PRKN and PACRG. PRKN is primarily associated with Parkinson’s disease in the biallelic state; in this case, however, it is disrupted in a heterozygous state, which is not typically linked to the disease. Similarly, according to OMIM⁶⁰, PACRG has not been associated with any disease in the heterozygous state. The translocation site within MECP2, however, lies in a critical region that likely impacts the MECP2 transcript relevant to Rett syndrome (NM_004992.4, MECP2_e1), suggesting it is a primary contributor to the patient’s phenotype.

To date, dozens of variants in several genes have been suggested to be causative of Rett and Rett-like syndrome in cases where no MECP2 mutation was found^13,14,61,62. It is plausible that some of those proposed variants are not disease-causing, as causative small or intermediate SVs within MECP2 have possibly eluded detection, or because there may be a double diagnosis involving both MECP2 SVs changes and a pathogenic mutation in another gene, leading to a more complex phenotype.

We propose the following approach to address elusive SVs and cSVs cases (Fig. 4): in clinically diagnosed RTT cases without a detectable MECP2 mutation, it is crucial to investigate the presence of SVs. LRS or WGS should be considered as diagnostic tools. If feasible, LRS is preferred. When using WGS, SV breakpoints should be sought within the MECP2 gene. The patient’s BAM file can either be manually scanned or analyzed with SV detection tools such as MANTA, Delly, and others. Visualizing read mapping by IGV may uncover subtle clues within the SRS data that aid in accurately identifying SV boundaries. Finally, these identified SV breakpoints can be validated through PCR and Sanger sequencing.

LRS; Long-read sequencing. SVs; Structural variants. IGV; Integrative Genomics Viewer.

Full size image

This methodology enabled us to successfully resolve all three investigated RTT cases. We believe that this approach could be valuable for resolving SV cases in other diseases, as these types of variants are not exclusive to Rett syndrome. We suggest that this approach should be considered for any genetic case exhibiting a distinctive phenotype with a limited number of related genes, or where there is a confined linkage area. In such instances, it would be feasible to avoid thousands of false positive variants and to investigate small to intermediate SVs within these specific loci or genes.

As we investigated 3 cases with a clear RTT phenotype, unraveling causative SVs in all 3 cases, it is plausible that SVs possibly constitute a common cause of RTT in yet unresolved cases. We have succeeded in elucidating the pathogenic SVs through tedious work using existing software and IGV visualization and interpretation. However, this process can be greatly facilitated through the generation of user-friendly clinical analysis software that would enable clinicians of various disciplines, not necessarily geneticists, to easily filter VCF files in search of SVs. We propose that it may be feasible to develop such software by integrating different programs capable of identifying suspected SV regions and combining them with the ability to filter by various loci, genes, or related phenotypes using databases such as OMIM or HPO. Such software could be routinely used and facilitate the resolution of many elusive SV and cSV cases, not only in Rett syndrome but also in other monogenic diseases.

Clinical phenotyping

The study was approved by the Soroka Medical Center Institutional Review Board (IRB; approval #5071 G) and the Israel Ministry of Health National Helsinki Committee (approval #920100319). Written informed consent was obtained from all individuals studied or their legal guardians. The phenotyping was conducted by senior neurologist and geneticist.

Molecular genetic analysis

DNA was extracted from peripheral blood leukocytes using the E.Z.N.A Blood DNA kit (Omega Bio-tek, Nor-cross, GA, USA) as was previously described⁶³. Whole-genome sequencing was performed by Macrogen® using Illumina’s NovaSeq X with TruSeq PCR-free kit generating 150 bp pair-end reads. Raw data reads were aligned to GRCh38 reference genome using BWA-MEM. Variant calling was done using GATK 4.3.0.0, and Manta 1.6.0⁶⁴. Data were analyzed using VARista (https://VARista.link)⁶⁵ and Genoox analysis software, Franklin (https://franklin.genoox.com/clinical-db/home). The Integrative Genomics Viewer (IGV) was employed to visualize and interpret the mapped reads and to investigate the variants within MECP2. Segregation analysis of the structural variants within the studied kindreds was done through PCR amplification and gel electrophoresis using primers flanking the junction points of the SVs regions: Case 1: Left side of the SV border- Forward 5′-CCTTTGCTCACCCTTCTCATGG-3′, Reverse 5′-CCAACCCAAGCCTTTGGAACAG-3′. (733 bp amplicon; annealing temperature 60 °C; extension time 44 s). Right side: Forward 5’- GAGATGCCTGAAAAGGCAACGG-3’. Reverse 5’ CCGCTCTGCTGGGAAGTATGAT-3’ (575 bp amplicon; annealing temperature 60 °C; extension time 35 s). Case 2: Left side of the cSV border: Forward 5’ TGAGGGTGGTAGAGGTCTGG-3’, Reverse 5’-AGGAAGAAGCCACGTGGAAG-3’. (880 bp amplicon; annealing temperature 60 °C; extension time 53 s). Right side: Forward 5’-GGAGCTTACTATGTCCCCTGC-3’, Reverse 5’-GTACACTGAGGTCTGTTGGCAT-3’. (635 bp amplicon; annealing temperature 60 °C; extension time 38 s). Case 3: Forward 5’-CAGGAGACCGTACTCCCCAT-3’, Reverse 5’-AAGGCGACTGACTCGTGATG-3’ (498 bp amplicon; annealing temperature 60 °C; extension time 30 s). Sanger sequencing of all the amplicons confirmed the precise junction points and the borders of the SVs.