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Identification of intragenic variants in pediatric patients with intellectual disability in Peru

BMC Medical Genomics volume 18, Article number: 76 (2025) Cite this article

Abstract

Background

Intellectual disability in Latin America can reach a frequency of 12% of the population, these may include nutritional deficiencies, exposure to toxic or infectious agents, and the lack of universal neonatal screening programs. In 90% of patients with intellectual disability, the etiology can be attributed to variants in the genome.

Objective

to determine intragenic variants in patients with intellectual disability between 5 and 18 years old at Instituto Nacional de Salud del Niño.

Methods

It is a descriptive cross-sectional study with convenience sampling. A total of 124 children diagnosed with intellectual disability were selected based on psychological test results and availability for whole exome sequencing. In addition, a chromosomal analysis of 6.55 M was performed on ten patients with a negative result in sequencing. Relative and absolute frequencies and measures of central tendency and dispersion were determined according to their nature. In addition, multiple linear regression and Poisson regression were used to determine the association between some clinical characteristics and the probability of occurrence in patients with positive results.

Results

The median age of the patients was 6.3 (IQR = 5.95), males accounted for 57.3%, and 91.9% of the cases had mild intellectual disability. Exome sequencing determined the etiology in 30.6% of patients with intellectual disability, of which 52.6% were autosomal dominant inheritance. The most frequent genes found were MECP2, STXBP1 and LAMA2. A broad genotype-phenotype correlation was identified, highlighting the genetic heterogeneity of intellectual disability in this population. The presence of dermatologic lesions, dystonia, peripheral neurological disorders, and fourth finger flexion limitation were observed more frequently in patients with intellectual disability with “positive results”.

Conclusions

This study shows that one-third of patients with intellectual disability exhibit intragenic variants, highlighting the importance of genetic analysis for accurate diagnosis. The identification of genes such as MECP2, STXBP1, and LAMA2 underscores the genetic heterogeneity of intellectual disability in the studied population. These findings emphasize the need for genetic testing in clinical management and the implementation of early detection programs in Peru.

Peer Review reports

Background

The prevalence of intellectual disability (ID) worldwide is 1–3% [1]. However, some studies mention that this figure could reach ≈ 12–18% in Latin America and low-income countries [2,3,4]. In a previous study in Peru, chromosomal microarray or CMA (750 K) was performed on 124 children with intellectual disability. Pathogenic and likely pathogenic copy number variants (CNV) were detected in 36.1%, and ≈ 6% showed regions of homozygosity (ROH) greater than 2.56%, suggesting a potential etiology of autosomal recessive inheritance [5].

To date, more than 3000 genes have associated with ID, highlighting its high genetic heterogeneity (https://sysndd.dbmr.unibe.ch/Genes). Massive sequencing tests detect single nucleotide variants (SNV) in 29.8–60% of patients with ID [6,7,8,9,10,11]. They have been observed to be more effective when the patient had a prior CMA test, analysis of a greater number of genes, evaluation of intronic regions, or even inclusion of parental studies (trio). While meta-analyses show that massive sequencing tests identify phenotype related SNVs in 31 to 68% of cases [12,13,14].

The frequency of the genes related to ID is heterogeneous, depending on factors such as population or sample size. For example, in China, MECP2 variants are observed in 1% of cases [15]. In Latin America, including Peru, there is a lack of studies characterizing genetic variants in patients with ID, which limits the understanding of this condition in the region.

The objective of the present study was to determine pathogenic or likely pathogenic intragenic variants associated with the phenotype of intellectual disability observed in patients at Instituto Nacional de Salud del Niño during 2021–2023. We conducted CMA (6.55 M) and massive sequencing to analyze genetic variations and their correlation with phenotypes. Our findings revealed a high genetic heterogeneity, with several genes correlating with the observed phenotypes, including dermatologic lesions, dystonia, and neurological disorders. This study aims to bridge the gap in genetic characterization of ID in the Peruvian population and provide valuable insights into its genetic underpinnings.

Materials and methods

This is a cross-sectional, descriptive study that included 124 children diagnosed with intellectual disability, seen at the Servicio de Genética & EIM of the Instituto Nacional de Salud del Niño Breña between 2021 and 2023.

Eligibility criteria

The eligibility criteria included children with informed parental consent, a diagnosis of ID by the Neuropsychology Department, based on clinical assessments and standardized cognitive evaluations documented in medical records, normal CGG triplet repeat number in the FMR1 gene, patients with negative CMA results or with ROH greater than 2.56%, or those with an ROH exceeding 10 Mb in one chromosome. Additionally, patients without previous CMA or FMR1 studies but with a phenotype indicating a specific inheritance pattern (clinical criterion) requiring exome sequencing as the initial examination were eligible.

Exclusion criteria

Patients were excluded if they had intellectual disability with a CNV consistent with the phenotype or with a trinucleotide expansion in the FMR1 gene.

Sample processing

For WES, peripheral blood collected in EDTA was used. Genomic DNA was isolated using the gSYNC DNA Extraction Kit (Geneaid, Taiwan), and concentrations were determined using Qubit dsDNA HS Assays Kit (Thermo Fisher Scientific, USA). The sample was ligated with an adapter and barcode using the Ion Xpress kit, and DNA purification was carried out with Agencourt Ampure XP beads (Beckman Coulter, Indianapolis, IN, USA). The Ion Chef™ was used for template preparation and chip loading, while the Ion GeneStudio S5 system was used for sequencing (both instruments from Thermo Fisher Scientific, Waltham, MA, USA). Runs comprised 200-base reads (500 flows) on Ion 540™ Chips (Thermo Fisher Scientific, Waltham, MA, USA). Sequenced reads were aligned to the hg19 human reference genome. Variant calling and annotation of single nucleotide variants were performed using the Varstation® platform in Fasta format.

Bioinformatics analysis

In the bioinformatics analysis, findings were correlated with the phenotype with input from three independent reviewers. CMA 6.55 M was also performed on ten patients to detect intragenic deletions or duplications.

Candidate variants related to the phenotype were searched in databases such as ClinVar, Franklin, and Varsome to verify their pathogenicity.

Characterization of SNVs

Sequencing results (independent variables) were SNVs, and genetic testing identified multiple nucleotide variants (MNV). These were classified as pathogenic (P), likely pathogenic (LP), uncertain significance (VUS), or “not related to the phenotype”. Additionally, they were grouped into positive results (P or PP) or negative results (VUS or no variants).

Variables

ID was categorized as mild, moderate, severe, or profound according to DSM-5 criteria. It was also classified into two groups: isolated and syndromic (associated with other clinical features).

Clinical characteristics were labeled according to Human Phenotype (HP) codes extracted from Phenomizer (https://compbio.charite.de/phenomizer/) or HPO (https://hpo.jax.org/app/). HPs are systematically defined resources with a logical organization of human phenotypes, used not only for diagnostic interpretation but also for gene discovery, disease-associated mechanisms, and cohort analysis [16].

Human phenotypes (HP) were categorized in 17 groups based on clinical criteria set by the authors: dermatological lesions, thoracoabdominal and spinal anomalies, craniofacial dysmorphisms, neuromuscular disorders, upper limb anomalies, genitourinary malformations, seizures or abnormal movements, ocular anomalies, endocrinological alterations, high or low stature, other neurodevelopmental disorders, hearing impairment, congenital heart diseases, tumors, and lower limb anomalies.

Additionally, phenotypes were grouped using hierarchical clustering with dissimilarity measured by Hamming distance and agglomeration method with complete linkage and Calinsky & Harabasz truncation, resulting in 34 final clusters.

Other variables considered included patient age, sex, parental ages, grandparents’ origin, and the number of genes and variants analyzed.

According to the sequencing results, the inheritance pattern was incorporated (dominant or recessive).

Statistical methods

The sample size was determined by including all patients with ID (censal approach) who underwent exome sequencing between 2021 and 2023 (n = 124). A formal power analysis was not performed, instead, statistical power was assessed based on the number of genes analyzed in positive and negative cases, achieving 100%. The sampling was non-probabilistic based on convenience.

In the univariate analysis of qualitative variables, data were presented using pie charts or tables to determine absolute and relative frequencies. Additionally, confidence intervals for relative frequencies were calculated for sex, type of variant found (SNVs/MNVs/absent), autosomal dominant (AD), autosomal recessive (AR), X-linked dominant (XLD), and X-linked recessive (XLR) inheritance patterns, grouped and isolated phenotypes, and origin of grandparents. For conditions involving autosomal or X-linked inheritance recessive, the ancestral origin of grandparents was established.

In the univariate analysis of quantitative variables, measures of central tendency (mean or median) and dispersion (standard deviation or interquartile range) were determined based on the normal distribution using Kolmogorov-Smirnov tests.

A robust Poisson regression model was employed for exploratory bi- and multivariate analysis. Crude and adjusted prevalence ratios were calculated between patients with any genetic variant detected (SNVs or MNVs) and the type of ID (isolated or syndromic). Likewise, the same test was used between patients with ID and pathogenic or likely pathogenic variants based on grouped phenotypes and isolated HPs. Additionally, multiple linear regression was used to assess the relationship between psychomotor development milestones and the presence or absence of pathogenic or likely pathogenic variants. Patients with positive results from the CMA 6,5 M test not included in this analysis.

Furthermore, the ANOVA test was used to identify differences in the number of HPs found among groups of patients with pathogenic or likely pathogenic variants, variants of uncertain significance, and non-pathogenic variants. The student’s t-test was used to determine differences in the number of genes and variants between groups with negative results and those with any identified variant.

Finally, box plots were used in patients with positive results to display differences in paternal and maternal ages based on dominant and recessive inheritance types.

The data collected in Microsoft Excel® was transferred to STATA® version 14 for the corresponding analysis, which had a 95% confidence interval and a significance level (p-value) less than 0.05.

The research was approved by the Ethics Committee of the Instituto Nacional de Salud del Niño under the reference number: 284/2021-CIEI-INSN and ID: PI-81/20.

Results

Personal and family history

The median age of the patients and parents was 6.3 and 33.8 years, respectively. Among the patients, 57.3% (n = 71) were male, and 91.9% (n = 114) had mild intellectual disability (Table 1).

Table 1 Patients with intellectual disability with genomic studies in Peruvian children (n = 124)
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In terms of prenatal history, there was no higher prevalence of threatened abortion (RP = 1.13, CI = 0.56–2.28; p = 0.739) or fever (RP = 1.14, CI = 0.107–12.27; p = 0.9087) in patients with “positive” results. Similarly, for psychomotor development milestones, there was no difference in age of acquisition between individuals with pathogenic or likely pathogenic variants and the rest of the cohort (Supplementary Material 1).

Phenotypic findings and classification

The number of observed Human Phenotypes (HPs) was 274, with the most common being developmental delay (84.7%), microcephaly (27.4%), seizures (25.0%), short stature (19.4%), autism spectrum disorder (ASD) (16.9%), generalized hypotonia (10.5%), speech and language delay (9.7%), strabismus (8.1%), hypertonia (6.5%), neurodevelopmental regression (6.5%), downturned corners of the mouth (6.5%), and limitation of flexion of the distal phalanx of the 4th finger (6.5%) (Supplementary Material 2). When grouped into 17 clinical categories, other neurodevelopmental disorders (89.5%), craniofacial dysmorphisms (70.2%), seizures, and abnormal movements (30.7%) were observed (Table 1).

Hierarchical clustering analysis (Supplementary Material 3) showed that some groups consisted of a single HP. Specifically, strabismus and pectus excavatum were independently grouped, with similar results when analyzed separately (crude prevalence ratio). No significant differences were observed in the remaining groups analyzed.

Variant distribution and classification

Exome sequencing identified pathogenic or likely pathogenic variants (positive results) in 30.6% (n = 38), and variants of uncertain significance in 38.7% (n = 48) (Table 1). Among the patients who underwent WES as the initial test, 58.9% (n = 73) had negative 750k CMA results (Supplementary Material 4). No differences in sequencing outcomes were observed based on prior CMA 750k testing or patient sex (p = 0.1821 and 0.1389, respectively).

Subsequently, ten patients with uncertain significance variants or no phenotype-related variants (negative result) from WES underwent CMA using 6.55 M markers (6.55 M XON array®).

Using CMA 6.55 M, deletions were found in 2 of 10 patients who initially had negative WES or 750k CMA results. One patient exhibited two deletions affecting the NBEA (exons 14–16) and SPOP (exon 1) genes, while another patient had a deletion in the YWAG gene (exon 1) (Table 1). Variants in NBEA gene have associated to neurodevelopmental disorder with or without early-onset generalized epilepsy (MIM #619157), while variants in SPOP gene are known to cause Nabais Sa-de Vries syndrome (MIM #618828/618829). Variants in the YWAG gene have associated to developmental and epileptic encephalopathy 56 (MIM #605356).

Phenotype-genotype correlation

The most frequently observed HP phenotypes in patients with pathogenic or likely pathogenic results from WES were limitation of flexion of the distal phalanx of the 4th finger, thick vermilion of the lower lip, and dystonia at 189.0%, 89.9%, and 188.0%; respectively (Table 2). In contrast, patients with ID presenting with downturned corners of the mouth, strabismus, or epicanthus had a lower frequency of pathogenic or likely pathogenic variants (Table 2).

Table 2 HP phenotypes and sequencing results in patients with intellectual disability in Peruvian children
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Dermatological alterations, thoracoabdominal or spinal column anomalies, and alterations in tone and muscle strength/dystrophies/neuropathies were 2.35; 2.21 and 1.8 times more likely to have pathogenic or likely pathogenic variants detected by WES (Table 3).

Table 3 Clustered HP phenotypes and sequencing results in patients with intellectual disabilities in Peruvian children
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The mean number of HPs per patient was 5.9, with similar values across the three groups: pathogenic/probably pathogenic (5.6), uncertain (6.4), and without variants (5.8) (p = 0.361).

Patients with syndromic ID were less likely to have a pathogenic or likely pathogenic variant (RPc = 0.455; 95% CI = 0.247–0.837) compared to those with isolated ID (p = 0.0433). No difference was observed when using CMA 6.55 M.

In patients with pathogenic, likely pathogenic, or uncertain significance variants determined by WES, the described genes were more frequently expressed at the cellular level in the nucleus (36.1%) (Supplementary Material 5).

It was observed that individuals with pathogenic, likely pathogenic, or uncertain variants, compared those without related variants had a higher number of genes detected by the bioinformatics program (18,181.5, SD = 285.7 vs. 18,045.7, SD = 266.9; p = 0.0136) and variants identified in the study (133,398.7, SD = 15,906.3 vs. 125,928.8, SD = 15,518.9; p = 0.0156).

Among patients with pathogenic or likely pathogenic variants, the most frequent inheritance types were autosomal dominant and recessive (52.6% and 23.7%, respectively). The most frequently affected genes in this group were MECP2 (12.8%), associated with X-linked dominant diseases; LAMA2 (7.7%, autosomal recessive), and STXBP1 (5.1%; autosomal dominant) (Supplementary Material 6).

The median age of patients in the “positive” results group (n = 38) was 7.3 years, and 52.6% (n = 20) were female (Table 4). The diseases associated with the genetic changes, according to the MIM (Mendelian Inheritance in Man) catalog, involved more than two entities in 34.2% (Table 4).

Table 4 Pathogenic and likely pathogenic variance in patients with intellectual disability in Peruvian children (n = 38)
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The types of variants found were missense mutations (n = 17), nonsense mutations (n = 12), frameshift mutations with a downstream stop codon (n = 7), and frameshift mutations (n = 2). Substitution variants were observed in 77.3%, while insertions, deletions, or duplications in 22.8% (Table 4).

Variants related to inborn errors of metabolism were found in 4.9% of patients. These included GLUT1 deficiency syndrome (n = 1), cerebral creatine deficiency syndrome 1 (n = 1), Menkes disease (n = 1), mut (0) type methylmalonic aciduria (n = 1), methylmalonic aciduria and hmocystinuria type cblD (n = 1), and homocystinuria due to cystathionine beta-synthase deficiency (n = 1) (Table 4).

In patients with pathogenic or likely pathogenic variants, the median paternal and maternal ages in dominant (autosomal or X-linked) inheritance cases were 33.9 years and 29.3 years, respectively. In patients with recessive (autosomal or X-linked) inheritance, the median paternal and maternal ages were 32.3 and 29.3 years, respectively (Supplementary Material 7).

Ancestry information according to medical records

Information on the grandparents’ origins was retrieved in a group of medical records. Among patients with pathogenic or likely pathogenic variants related to autosomal recessive diseases (n = 7), the grandparents’ origins were from the departments of Ancash (n = 7), Lima (n = 6), Piura (n = 4), Loreto (n = 3), Lambayeque (n = 2), Arequipa (n = 2), Apurimac (n = 2), Puno (n = 1), and Cajamarca (n = 1). Among all identified variants, two variants could be confidently associated with specific ancestral origins: LAMA2:c.3928 G > T (Lima) and CA8:c.823 C > T (Apurímac) (Fig. 1A).

Fig. 1
figure 1

(A) Ancestry of maternal and paternal grandparents in patients with autosomal recessive intellectual disability. (B) Ancestry of the maternal grandmother in patients with X-linked recessive intellectual disability. The probable origin of the variants is shown. Red: higher probability

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In patients with X-linked recessive inheritance (n = 4), the maternal grandmother’s origin was most likely from Cajamarca (n = 2), Ayacucho (n = 1), and Venezuela (n = 1) (Fig. 1B).

No new candidate genes for ID were identified in this study; however, new variants were identified in the LETM1, BICRA, CREBBP, EP300, TAF1, CYFIP2, SLC6A8, SLC2A1, FGFR1, GRIN2B, NF1, MED13L, MUT, CA8 and CBS, genes.

Discussion

Frequency of pathogenic and likely pathogenic variants and inheritance types

It was observed that 30.6% of patients with ID who underwent exome sequencing had pathogenic or likely pathogenic variants. This finding falls within the range reported in previous meta-analyses (28.6–68%) [12,13,14, 17]. However, it is important to note that there are publications where the observed frequency was higher (38–43%) [6, 9]. This variability in frequencies is likely due to the type of technology used (e.g., Ion Torrent® vs. Illumina®) [18], the number of genes analyzed (e.g., gene panels, whole exome sequencing, or genome sequencing), patient selection criteria, or the extension of the study to family members (e.g., trio sequencing). Other factors that could influence this heterogeneity in results include the number of genomic changes found in genes associated with a previously described condition or the increasing number of disease-associated genes identified each year [19, 20].

No significant differences were found in the prevalence of pathogenic or likely pathogenic variants between sexes. However, Kim et al. showed that in males, the presence of “positive” results was higher compared to females (OR 7.8; CI = 2.4–25.7; p = 0.0007) [10]. This difference is attributed to one of the most frequently identified genes having an X-linked dominant inheritance pattern (MECP2) and being observed only in females. Nonetheless, males have a higher prevalence in the total population studied, likely explained by the greater density of genes related to intellectual functioning located on the X chromosome compared to autosomal chromosomes [21, 22].

The most frequently observed inheritance type in patients with “positive” results was autosomal dominant (52.6%), which differs from other studies (7.7-76%). The same situation was observed in those with autosomal recessive (23.7% vs. 12–89%) and X-linked recessive inheritance (23.7% vs. 3.3–15.5%) [6,7,8, 10]. This difference is likely due to the genomic composition of each population. In populations where consanguineous unions are common, the risk of autosomal recessive diseases associated with ID increases. Alternatively, delaying parenthood in some societies may increase the likelihood of de novo-dominant diseases [23,24,25].

Patients with syndromic ID showed a lower frequency of pathogenic or likely pathogenic variants (54.5%) compared to those with isolated ID. This could be due to the presence of genetic modifiers in syndromic disorders, which may affect the phenotypic expression and penetrance of pathogenic variants. However, these findings should be interpreted with caution and corroborated with a larger sample size.

The prevalence of positive results did not improve in cases where patients underwent prior CMA 750k testing. This reinforces sequencing as the first-choice test in patients with ID [26].

Frequency of variants of uncertain significance

Variants of uncertain significance (VUS) were reported in 38.6% (7), which is higher than what has been previously reported (11-19.4%) [7, 10]. This is because WES was not performed on any of the parents (trios), thereby not allowing the verification of whether the genetic change was de novo or inherited. According to the American College of Medical Genetics and Genomics criteria, parental determination of VUS is recommended based on entity penetrance and the presence or absence of the variants in the parents. VUS can be reclassified as likely benign or pathogenic [27]. Another reason is the need for a population-based variant frequency database for Peru, making determining which variants might be polymorphic complex. Previous studies have demonstrated significant ethnic and racial differences in patients who underwent germline genetic studies. Asians and Hispanics are more likely to have VUS, and specific genes with pathogenic variants have been identified in different races. This happens because these populations typically have less access to genetic studies; therefore, fewer common variants are observed in widely used databases like Gnomad [28]. Accordingly, variant frequencies might be rare according to Gnomad® or other platforms, but they may be classified as common changes in Peru [29, 30].

In some cases, even when a variant is detected in the parents, its pathogenicity cannot be definitively determined, particularly in diseases with low penetrance or recessive inheritance. This uncertainty can place a significant emotional and psychological burden on patients and families, potentially leading to inappropriate management [30]. In such situations, protein functional analysis or RNA expression tests would provide a more precise determination of pathogenicity [27, 31].

Milestones of psychomotor development

In most studies, developmental delay is a commonly reported but is better viewed as part of the early manifestation spectrum in patients with ID.

When assessing if specific developmental milestone was more delayed in patients with ID and a “positive” result, no relationship was found. This contrasts with the study by Deciphering Developmental Disorders Study which observed delays in first words and sitting without support by 0.205 and 0.125 years, respectively [9]. The discrepancy may be due to the broad genotypic and phenotypic variability, as well as the population frequency of genes linked to ID.

Human phenotype ontology - HPO

The number of described HPs was 274, similar to a previous report of 333 [7]. Higher frequencies of microcephaly (27.4% vs. 9.16%) and short stature (19.4% vs. 4.85%) were observed compared to earlier studies. However, lower frequencies of macrocephaly (1.6% vs. 9.7%), seizures (25.0% vs. 45.3%), speech delay (9.7% vs. 68.7%), ASD (16.9% vs. 25.6%), and hypotonia (10.5% vs. 20.2%) were reported [7]. Another study highlighted only microcephaly and macrocephaly as key phenotypes, seen in 30.5% and 12.9% of cases [10].

There was no difference observed in this research between those HPs that previous publications found to have a higher prevalence in patients with pathogenic or likely pathogenic variants, including the occurrence of seizures, hypotonia, microcephaly, and short stature [7].

Regarding HPs, we found that fourth finger flexion limitation, thick lower lip vermilion, and dystonia were more frequent in patients with “positive” results. No previous studies have analyzed these associated phenotypes.

When grouping HPs, dermatological features, thoracoabdominal and spinal abnormalities, as well as muscle tone-strength alterations, dystrophies or neuropathies, were more prevalent in patients with ID and pathogenic or likely pathogenic variants. The higher prevalence of skin lesions may be explained by development genes being expressed simultaneously or asynchronously in multiple organs (e.g., CNS and skin), both of ectodermal origin [32, 33], or by the differential expression of alternative transcripts in various systems [33].

While numerous genes have been associated in the phenotypes of limitation of fourth finger flexion, dermatologic lesions, dystonia, and peripheral neurological disorders, we did not identify specific syndromes associated with these HPs. This may be due to the complex nature of these phenotypes and its possible genetic and environmental etiologies. However, the presence of these HPs in individuals with ID suggests an underlying genetic etiology. Therefore, exome sequencing is a valuable diagnostic tool for these patients, since the co-occurrence of these HPs and ID increases the possibilities of identifying pathogenic or likely pathogenic variants.

The number of reported HPs and the differences in associations of these phenotypes with “positive” results may be influenced by the examiner’s experience, interpretation of the clinical characteristic, familiarity with the HP nomenclature, and subjectivity in the understanding clinical features, which should ideally be measurable and comparable according to corresponding percentiles [34,35,36]. Less examiner bias increases the likelihood of identifying a variant related to the clinical presentation. Additionally, using a more “in-depth” or specific HP terms enhances the probability of finding a genomic change [37]. Another factor could be that geneticists tend to perform clinical assessment and genomic tests more frequently on patients with a higher number of dysmorphias or more severe clinical features, which could be related to the dissimilarity of perception between specialists. Clearly stating that the patient’s illness manifests this dissimilarity because the patient does not display the clinical characteristics of the human typical idea, designating the patient as an illness, or as a result of the patient and doctor’s power imbalance [38].

The accuracy of identifying relevant variants could be enhanced by utilizing programs that search for probable genes based on HPs. Additionally, incorporating software that analyzes photographs to propose clinical diagnostic possibilities could further refine the diagnostic process. These tools can reduce examiner bias, improve precision in phenotype interpretation, and increase the likelihood of discovering clinically relevant genomic changes. By integrating such technology, we can achieve more objective assessments and streamline the diagnostic workflow [39, 40].

Frequency of pathogenic or likely pathogenic variants

Disorders associated with MECP2

The most frequently reported gene associated with ID in our study was MECP2, with a frequency of 12.8% among the positive cases (n = 4), significantly higher than the 1% reported in other studies [15]. Variants in MECP2 have been found in 95–97% of typical Rett syndrome patients and 85% of atypical Rett syndrome patients, estimating a population frequency of 7.1 per 100,000 women [41]. The MECP2 gene is primarily expressed in neurons and functions as a methylome reader of the DNA, which regulates the expression of other genes [42].

Rett syndrome (MIM #312750) linked to variants in the MECP2 gene [43]. The syndrome is characterized by normal psychomotor development until 6 to 8 months of age, although early clinical features such as hypotonia and deceleration of head growth may be observed frequently [44]. Additionally, it is characterized by developmental delay, motor regression or arrest, loss or severe deficiency of language, ASD and ID [44, 45]. Other commonly associated features include gastrointestinal disorders (e.g. constipation), respiratory issues and cardiac arrhythmias [44]. Other clinical characteristics are epilepsy, ataxia, apraxia, and Parkinson-like symptoms. The survival rate reaches 70% by age 45 years [46].

Variants in LAMA2

The second most frequently affected gene found was LAMA2, which encodes the Alpha 2 subunit of the heterotrimeric laminin 2 protein. Alterations in this gene causes a broad clinical spectrum, ranging from a type of congenital muscular dystrophy to a late-onset progressive disease [47, 48]. LAMA2 encodes a molecule found in the extracellular matrix that stabilizes myotubules and is associated with apoptosis [48].

LAMA2 variants were found to be more prevalent in this study compared to previous reports (7.7% vs. 1.3%) [47]. A study found that 18.2% with LAMA2 variants had ID [49]. In some cases linked to cortical malformations (e.g. polymicrogyria, lissencephaly), although ID has been observed without these anomalies [49].

Developmental and epileptic encephalopathy type 4

Developmental and epileptic encephalopathies are a genetically heterogeneous group with either dominant or recessive inheritance patterns, encompassing over 110 distinct types identified to date [50]. The clinical feature is the presence of frequent and abundant epileptiform activity, which may even occur in the absence of seizures and is commonly associated with severe behavioral and cognitive impairments [51].

The third most common cause associated with ID involves variants in the STXBP1 gene, which causes Developmental and epileptic encephalopathy Type 4. All patients present ID and 85% have some form of epilepsy [52]. The function of this gene is to maintain a protein complex (SNARE) that controls neurotransmitter release [53]. It has been reported that the prevalence of STXBP1 variants in patients with ID is comparable to what was found in this study (5,1% vs. 5,9%) [54].

Inborn errors of metabolism (IEM)

More than 116 inborn errors of metabolism can lead to intellectual disability, highlighting the importance of early diagnosis to enable treatments that may reduce symptoms or prevent neurodevelopmental disorders [55].

The prevalence of individuals with ID and variants associated with IEMs is consistent with reports from other authors (0-8.4%), although the mean frequency is 1% [56]. This variability is due to the use of specific technologies for this group of disorders, such as tandem mass spectrometry (MS/MS), high-performance liquid chromatography (HPLC), as well as differences in sample populations across studies [55, 57].

Parental age in patients with pathogenic and likely pathogenic variants

The number of de novo variants per year due to maternal and paternal age is 0.0172 and 0.03606 per year, respectively [9]. Consequently, the risk of having a child with ID is 20% higher for parents in their fifth decade compared to those in their twenties [58]. Although Wang et al. noted a linear association between paternal age over 35 years shows and an increasing risk of having a child with ID [59]. In this sense, it was observed that paternal age in dominant conditions tends to be higher than in recessive disorders, although not significantly. These de novo variants occurring during spermatogenesis are attributed to increased DNA replication errors, reduced DNA repair efficiency, telomere shortening, or exposure to mutagens-factors that rise with the increase in cell division proportionally associated with age [60, 61].

Departmental origin of recessive diseases

We have likely identified the departmental origin of certain variants related to autosomal recessive or X-linked diseases. This might be due to genetic drift, which is explained by founder effects or bottleneck effects. The founder effect occurs when a small proportion of individuals from a large population become isolated and form a “new population” [62]. The bottleneck effect is observed when a significant population is drastically reduced by environmental factors, leading to the non-stochastic selection of a specific variant, thereby increasing the population frequency of a genetic disease [62]. Another possibility is that the frequency of recessive variants is inherent to each human being, and variability is observed across different populations or methodologies used. For example, in Brazil, 1.32 pathogenic or likely pathogenic variants per person related to autosomal recessive diseases have been reported [63].

However, research shows that the number of autosomal recessive alleles per person is 2 to 2.8, And between 50 and 100 variants are associated with genetic diseases [64,65,66].

Deletions and duplications detected by CMA 6,55 M

The 6.55 M CMA (XON array ®) analyzes the genome regions currently linked to any genetic disease, identifying deletions or duplications of the exome, while the 750 K could detect if the variant contains a larger gene.

In our cohort, a patient had two pathogenic deletions involving exons 14 to 16 of the NBEA gene and exon one of the SPOP gene. Heterozygous variants in NBEA are associated with neurodevelopmental disorder with or without early-onset generalized epilepsy (MIM #619157). NBEA encodes neurobeachin, an anchoring kinase protein linked to vesicle trafficking and synaptic function and structure [67]. Clinical characteristics are psychomotor regression, ASD, microcephaly, epilepsy of onset between 1 and 4 years, with seizures of different types with epileptiform activity verified by EEG [67]. These features are consistent with the evaluated patient’s phenotype.

In a cohort of 24 patients with variants in this gene, six presented deletions located in different exons (40–43, 2–34, 1–64, 40–43, 38–40, 37–39) [67]. This suggests that this region has an increased susceptibility to deletions or duplications, likely due to a high rate of non-allelic homologous recombination [68].

The same patient presented an additional variant in the SPOP gene, which encodes the speckle-type BTB/POZ protein, this molecule is involved in protein degradation via the ubiquitin-proteasome pathway and plays a role in the development of multiple organs [69]. Variants in this gene are associated with a variable clinical spectrum and have been categorized based on phenotypic features into the Nabais Sa-de Vries Syndrome Type 1 (SNSDV 1, MIM #618828) and 2 (SNSDV 1 MIM #618829) [69]. SNSDV 1 is characterized by microcephaly, depressed nasal bridge, and micrognathia, whereas SNSDV 2 include macrocephaly, broad forehead, bulbous nose, and protruded nasal bridge are present [70]. Only eight patients with variants in this gene have been reported, so the genotype-phenotype correlation remains unknown [69, 70].

In the second affected individual, a pathogenic deletion in exon 1 of the YWAG gene was found. Variants in this gene are associated with developmental and epileptic encephalopathy type 56 (MIM #617665), myoclonic epilepsy in infancy, or febrile seizures [71, 72]. The protein encoded by the YWHAG gene is highly conserved, and it is expressed in the mammalian brain, participating in neuronal migration [72].

Among the limitations of the study is the number of patients analyzed compared to previous reports. However, this is an initial report of Peruvian patients with ID, with results comparable to those observed in other populations. Another significant limitation is the lack of a baseline study in a cohort of healthy individuals, as has been done in other geographical areas [73] or the absence of parental testing for variants of uncertain significance; however, the bioinformatic tools could nevertheless aid in determining pathogenic or likely pathogenic variants with greater confidence. Additionally, the verification of pathogenic or likely pathogenic variants (P/LP) through confirmatory techniques such as Sanger sequencing or segregation analysis in families could not be conducted due to limitations in access and budget. This could potentially impact the robustness of some genetic findings and highlights the need for increased resources to support comprehensive genetic analyses.

While the number of described phenotypes (HPs) is very similar to those reported in another research, we consider that they may not be fully represented or could overdiagnosed. Using programs such as photogrammetry would be providing valuable support in addressing these issues. Although some HPs were found to be significantly more frequent, the study’s design may have certain limitations.

A more detailed psychomotor development assessment, like the Bayley Scales, could improve ID risk prediction. Nonetheless, the median age for etiological diagnosis is within an acceptable range.

The 6.55 M CMA was used for ten individuals with negative WES results, highlighting the importance of identifying etiology to reduce the diagnostic odyssey and relieve social and emotional burden on families. This also emphasizes the clinical utility of expanding genetic testing capacities, with third-generation sequencing as a promising future option.

Lastly, for patients without prior CMA 750 K testing and negative WES results without SNVs, bioinformatic CNV calling was not conducted, limiting detection assessment. Reanalysis for these patients is planned.

Conclusions

Exome sequencing is an essential diagnostic tool in patients with ID, not only to determine the risk of individual or familial recurrence but also to establish prognosis and the use of targeted treatment, as observed in some patients with inborn errors of metabolism (e.g., Methylmalonic aciduria with homocystinuria). However, a more significant proportion of patients with ID were found to have variants of uncertain significance. In the same sense, complementary technologies, such as CMA 6.55 M, expand diagnostic coverage, reducing the gaps inherent in current technology. Most frequent HPs, such as limitation of flexion of the fourth finger, dermatological lesions, dystonia, or thick lower lip vermilion, could be good predictors of diagnostic utility in patients with ID, where the number of positive results for WES would be higher.

Performing WES and HPs analysis on the general population will not only assist the diagnostic algorithm in patients with ID but also in any other monogenic disorder or in establishing the presence of carriers of recessive variants (autosomal or X-linked) or variants with low penetrance (e.g. Hereditary breast and ovarian cancer associated with BRCA1).

Expanding the diagnostic genetic and genomic testing offering at INSN, such as 6.55 M CMA, Sanger Sequencing, MLPA (multiple ligation probe amplification) or Q-PCR (quantitative PCR), enzymatic activity, amino acid quantification (e.g. HPLC), among others. Similarly, other genetic laboratories in Peru should implement these genomic diagnostic technologies following the technological and scientific advances of the present century.

Determine, through public policies, which population groups are more likely to have individuals with ID, such as by identifying endogamous regions or promoting a decrease in parental age within certain socioeconomic levels.

Data availability

Data, including variants from whole exome sequencing and phenotype, supporting the findings of this study, are available in the ClinVar repository under https://www.ncbi.nlm.nih.gov/clinvar/submitters/509781.

References

  1. Maulik PK, Mascarenhas MN, Mathers CD, Dua T, Saxena S. Prevalence of intellectual disability: a meta-analysis of population-based studies. Res Dev Disabil. 2011;32:419–36. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ridd.2010.12.018

    Article  PubMed  Google Scholar 

  2. Maulik PK, Darmstadt GL. Childhood disability in Low- and Middle-Income countries: overview of screening, prevention, services, legislation, and epidemiology. Pediatrics. 2007;120:S1–55. https://doiorg.publicaciones.saludcastillayleon.es/10.1542/peds.2007-0043B

    Article  PubMed  Google Scholar 

  3. Mercadante MT, Evans-Lacko S, Paula CS. Perspectives of intellectual disability in Latin American countries: epidemiology, policy, and services for children and adults. Curr Opin Psychiatry. 2009;22:469–74. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/YCO.0b013e32832eb8c6

    Article  PubMed  Google Scholar 

  4. Ropers HH. Genetics of early onset cognitive impairment. Annu Rev Genomics Hum Genet. 2010;11:161–87. https://doiorg.publicaciones.saludcastillayleon.es/10.1146/annurev-genom-082509-141640

    Article  CAS  PubMed  Google Scholar 

  5. Abarca-Barriga H, Sotomayor F, de MV, Trubnykova M, Velasquez FC, Pastor MAC, Jugo BEG, et al. Variantes En El número de Copias En Trastornos Del neurodesarrollo, síndrome malformativo y Talla Baja En Perú. Acta Médica Peru. 2020;37. https://doiorg.publicaciones.saludcastillayleon.es/10.35663/amp.2020.372.915

  6. Anazi S, Maddirevula S, Faqeih E, Alsedairy H, Alzahrani F, Shamseldin HE, et al. Clinical genomics expands the morbid genome of intellectual disability and offers a high diagnostic yield. Mol Psychiatry. 2017;22:615–24. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/mp.2016.113

    Article  CAS  PubMed  Google Scholar 

  7. Bowling KM, Thompson ML, Amaral MD, Finnila CR, Hiatt SM, Engel KL, et al. Genomic diagnosis for children with intellectual disability and/or developmental delay. Genome Med. 2017;9:43. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13073-017-0433-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chérot E, Keren B, Dubourg C, Carré W, Fradin M, Lavillaureix A, et al. Using medical exome sequencing to identify the causes of neurodevelopmental disorders: experience of 2 clinical units and 216 patients. Clin Genet. 2018;93:567–76. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/cge.13102

    Article  CAS  PubMed  Google Scholar 

  9. Deciphering Developmental Disorders Study. Prevalence and architecture of de Novo mutations in developmental disorders. Nature. 2017;542:433–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/nature21062

    Article  CAS  Google Scholar 

  10. Kim SH, Kim B, Lee JS, Kim HD, Choi JR, Lee S-T, et al. Proband-Only clinical exome sequencing for neurodevelopmental disabilities. Pediatr Neurol. 2019;99:47–54. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.pediatrneurol.2019.02.017

    Article  PubMed  Google Scholar 

  11. Wright CF, McRae JF, Clayton S, Gallone G, Aitken S, FitzGerald TW, et al. Making new genetic diagnoses with old data: iterative reanalysis and reporting from genome-wide data in 1,133 families with developmental disorders. Genet Med Off J Am Coll Med Genet. 2018;20:1216–23. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/gim.2017.246

    Article  Google Scholar 

  12. Abarca-Barriga HH, Vásquez-Sotomayor F. Utilidad diagnóstica de La secuenciación de Segunda y tercera generación En Pacientes Con Discapacidad intelectual: revisión Rápida. Psiquiatr Biológica. 2023;30. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.psiq.2023.100392

  13. Malinowski J, Miller DT, Demmer L, Gannon J, Pereira EM, Schroeder MC, et al. Systematic evidence-based review: outcomes from exome and genome sequencing for pediatric patients with congenital anomalies or intellectual disability. Genet Med. 2020;22:986–1004. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41436-020-0771-z

    Article  PubMed  PubMed Central  Google Scholar 

  14. Srivastava S, Love-Nichols JA, Dies KA, Ledbetter DH, Martin CL, Chung WK, et al. Meta-analysis and multidisciplinary consensus statement: exome sequencing is a first-tier clinical diagnostic test for individuals with neurodevelopmental disorders. Genet Med Off J Am Coll Med Genet. 2019;21:2413–21. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41436-019-0554-6

    Article  Google Scholar 

  15. Gu Y, Xiang B, Zhu L, Ma X, Chen X, Cai T. Three intellectual disability-associated de Novo mutations in MECP2 identified by trio-WES analysis. BMC Med Genet. 2020;21:99. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12881-020-01042-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Köhler S, Gargano M, Matentzoglu N, Carmody LC, Lewis-Smith D, Vasilevsky NA, et al. The human phenotype ontology in 2021. Nucleic Acids Res. 2021;49:D1207–17. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/nar/gkaa1043

    Article  CAS  PubMed  Google Scholar 

  17. Stefanski A, Calle-López Y, Leu C, Pérez-Palma E, Pestana-Knight E, Lal D. Clinical sequencing yield in epilepsy, autism spectrum disorder, and intellectual disability: A systematic review and meta-analysis. Epilepsia. 2021;62:143–51. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/epi.16755

    Article  PubMed  Google Scholar 

  18. Slatko BE, Gardner AF, Ausubel FM. Overview of next generation sequencing technologies. Curr Protoc Mol Biol. 2018;122:e59. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/cpmb.59

    Article  PubMed  PubMed Central  Google Scholar 

  19. Amberger JS, Bocchini CA, Scott AF, Hamosh A. OMIM.org: leveraging knowledge across phenotype-gene relationships. Nucleic Acids Res. 2019;47:D1038–43. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/nar/gky1151

    Article  CAS  PubMed  Google Scholar 

  20. Posey JE, O’Donnell-Luria AH, Chong JX, Harel T, Jhangiani SN, Coban Akdemir ZH, et al. Insights into genetics, human biology and disease gleaned from family based genomic studies. Genet Med Off J Am Coll Med Genet. 2019;21:798–812. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41436-018-0408-7

    Article  Google Scholar 

  21. Skuse DH. X-linked genes and mental functioning. Hum Mol Genet. 2005. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/hmg/ddi112. 14 Spec 1:R27-32.

    Article  PubMed  Google Scholar 

  22. Zechner U, Wilda M, Kehrer-Sawatzki H, Vogel W, Fundele R, Hameister H. A high density of X-linked genes for general cognitive ability: a run-away process shaping human evolution? Trends Genet. 2001;17:697–701. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0168-9525(01)02446-5

    Article  CAS  PubMed  Google Scholar 

  23. Mills M, Rindfuss RR, McDonald P, te Velde E, ESHRE Reproduction and Society Task Force. Why do people postpone parenthood? Reasons and social policy incentives. Hum Reprod Update. 2011;17:848–60. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/humupd/dmr026

    Article  PubMed  PubMed Central  Google Scholar 

  24. Taşkıran EZ, Karaosmanoğlu B, Koşukcu C, Ürel-Demir G, Akgün-Doğan Ö, Şimşek-Kiper PÖ, et al. Diagnostic yield of whole-exome sequencing in non-syndromic intellectual disability. J Intellect Disabil Res JIDR. 2021;65:577–88. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/jir.12835

    Article  PubMed  Google Scholar 

  25. Wood KA, Goriely A. The impact of paternal age on new mutations and disease in the next generation. Fertil Steril. 2022;118:1001–12. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.fertnstert.2022.10.017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Manickam K, McClain MR, Demmer LA, Biswas S, Kearney HM, Malinowski J, et al. Exome and genome sequencing for pediatric patients with congenital anomalies or intellectual disability: an evidence-based clinical guideline of the American college of medical genetics and genomics (ACMG). Genet Med Off J Am Coll Med Genet. 2021;23:2029–37. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41436-021-01242-6

    Article  Google Scholar 

  27. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American college of medical genetics and genomics and the association for molecular pathology. Genet Med Off J Am Coll Med Genet. 2015;17:405–24. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/gim.2015.30

    Article  Google Scholar 

  28. Caswell-Jin JL, Gupta T, Hall E, Petrovchich IM, Mills MA, Kingham KE, et al. Racial/ethnic differences in multiple-gene sequencing results for hereditary cancer risk. Genet Med Off J Am Coll Med Genet. 2018;20:234–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/gim.2017.96

    Article  Google Scholar 

  29. Gudmundsson S, Singer-Berk M, Watts NA, Phu W, Goodrich JK, Solomonson M, et al. Variant interpretation using population databases: lessons from GnomAD. Hum Mutat. 2021. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/humu.24309

    Article  PubMed  Google Scholar 

  30. Kobayashi Y, Yang S, Nykamp K, Garcia J, Lincoln SE, Topper SE. Pathogenic variant burden in the ExAC database: an empirical approach to evaluating population data for clinical variant interpretation. Genome Med. 2017;9:13. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13073-017-0403-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Postel MD, Culver JO, Ricker C, Craig DW. Transcriptome analysis provides critical answers to the variants of uncertain significance conundrum. Hum Mutat. 2022;43:1590–608. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/humu.24394

    Article  CAS  PubMed  Google Scholar 

  32. Bruce M, Carlson. Embriología humana y biología Del desarrollo. 978th-84th-9022nd-464th–9th ed. Elsevier Sanders; 2014.

  33. Tress ML, Abascal F, Valencia A. Alternative splicing May not be the key to proteome complexity. Trends Biochem Sci. 2017;42:98–110. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.tibs.2016.08.008

    Article  CAS  PubMed  Google Scholar 

  34. Hall J, Allanson J, Gripp K, Slavotinek A. Handbook of physical measurements. Oxford University Press; 2006.

  35. Hennekam RC, Biesecker LG, Allanson JE, Hall JG, Opitz JM, Temple IK, et al. Elements of morphology: general terms for congenital anomalies. Am J Med Genet A. 2013;161A:2726–33. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/ajmg.a.36249

    Article  PubMed  Google Scholar 

  36. Tchuisseu-Kwangoua LA, Kamtchum-Tatuene J, Tekendo-Ngongang C, Pengelly RJ, Self J. Bridging the Language gap - A call for the wider use of human phenotype ontology by non-geneticist clinicians when requesting genomic tests. Eur J Med Genet. 2023;66:104679. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ejmg.2022.104679

    Article  PubMed  Google Scholar 

  37. Rojano E, Seoane-Zonjic P, Jabato FM, Perkins JR, Ranea JAG. Comprehensive analysis of patients with undiagnosed genetic diseases using the patient exploration tools suite (PETS). In: Rojas I, Valenzuela O, Rojas F, Herrera LJ, Ortuño F, editors. Bioinforma. Biomed. Eng. Cham: Springer International Publishing; 2020. pp. 775–86. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/978-3-030-45385-5_69

    Chapter  Google Scholar 

  38. Haque OS, Waytz A. Dehumanization in medicine: causes, solutions, and functions. Perspect Psychol Sci J Assoc Psychol Sci. 2012;7:176–86. https://doiorg.publicaciones.saludcastillayleon.es/10.1177/1745691611429706

    Article  Google Scholar 

  39. Mishima H, Suzuki H, Doi M, Miyazaki M, Watanabe S, Matsumoto T, et al. Evaluation of Face2Gene using facial images of patients with congenital dysmorphic syndromes recruited in Japan. J Hum Genet. 2019;64:789–94. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s10038-019-0619-z

    Article  PubMed  Google Scholar 

  40. Slavotinek A, Prasad H, Yip T, Rego S, Hoban H, Kvale M. Predicting genes from phenotypes using human phenotype ontology (HPO) terms. Hum Genet. 2022;141:1749–60. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s00439-022-02449-6

    Article  CAS  PubMed  Google Scholar 

  41. Petriti U, Dudman DC, Scosyrev E, Lopez-Leon S. Global prevalence of Rett syndrome: systematic review and meta-analysis. Syst Rev. 2023;12:5. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13643-023-02169-6

    Article  PubMed  PubMed Central  Google Scholar 

  42. Tillotson R, Bird A. The molecular basis of MeCP2 function in the brain. J Mol Biol. 2020;432:1602–23. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jmb.2019.10.004

    Article  CAS  PubMed  Google Scholar 

  43. Krishnaraj R, Ho G, Christodoulou J. RettBASE: Rett syndrome database update. Hum Mutat. 2017;38:922–31. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/humu.23263

    Article  PubMed  Google Scholar 

  44. Pejhan S, Rastegar M. Role of DNA Methyl-CpG-Binding protein MeCP2 in Rett syndrome pathobiology and mechanism of disease. Biomolecules. 2021;11:75. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/biom11010075

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Vidal S, Xiol C, Pascual-Alonso A, O’Callaghan M, Pineda M, Armstrong J. Genetic landscape of Rett syndrome spectrum: improvements and challenges. Int J Mol Sci. 2019;20:3925. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijms20163925

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Tarquinio DC, Hou W, Neul JL, Kaufmann WE, Glaze DG, Motil KJ, et al. The changing face of survival in Rett syndrome and MECP2-Related disorders. Pediatr Neurol. 2015;53:402–11. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.pediatrneurol.2015.06.003

    Article  PubMed  PubMed Central  Google Scholar 

  47. Lindstrand A, Eisfeldt J, Pettersson M, Carvalho CMB, Kvarnung M, Grigelioniene G, et al. From cytogenetics to cytogenomics: whole-genome sequencing as a first-line test comprehensively captures the diverse spectrum of disease-causing genetic variation underlying intellectual disability. Genome Med. 2019;11. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13073-019-0675-1

  48. Sarkozy A, Foley AR, Zambon AA, Bönnemann CG, Muntoni F. LAMA2-Related dystrophies: clinical phenotypes, disease biomarkers, and clinical trial readiness. Front Mol Neurosci. 2020;13:123. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fnmol.2020.00123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Camelo CG, Artilheiro MC, Martins Moreno CA, Ferraciolli SF, Serafim Silva AM, Fernandes TR, et al. Brain MRI abnormalities, epilepsy and intellectual disability in LAMA2 related Dystrophy– a genotype/phenotype correlation. J Neuromuscul Dis. 2023;Preprint:1–10. https://doiorg.publicaciones.saludcastillayleon.es/10.3233/JND-221638

    Article  Google Scholar 

  50. Phenotypic Series - PS. 308350 - OMIM n.d. https://omim.org/phenotypicSeries/PS308350 (accessed June 16, 2023).

  51. Scheffer IE, Liao J. Deciphering the concepts behind epileptic encephalopathy and developmental and epileptic encephalopathy. Eur J Paediatr Neurol EJPN Off J Eur Paediatr Neurol Soc. 2020;24:11–4. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ejpn.2019.12.023

    Article  Google Scholar 

  52. Stamberger H, Nikanorova M, Willemsen MH, Accorsi P, Angriman M, Baier H, et al. STXBP1 encephalopathy: A neurodevelopmental disorder including epilepsy. Neurology. 2016;86:954–62. https://doiorg.publicaciones.saludcastillayleon.es/10.1212/WNL.0000000000002457

    Article  CAS  PubMed  Google Scholar 

  53. Rizo J, Südhof TC. The membrane fusion enigma: snares, Sect. 1/Munc18 proteins, and their accomplices–guilty as charged? Annu Rev Cell Dev Biol. 2012;28:279–308. https://doiorg.publicaciones.saludcastillayleon.es/10.1146/annurev-cellbio-101011-155818

    Article  CAS  PubMed  Google Scholar 

  54. Rauch A, Wieczorek D, Graf E, Wieland T, Endele S, Schwarzmayr T, et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet Lond Engl. 2012;380:1674–82. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0140-6736(12)61480-9

    Article  CAS  Google Scholar 

  55. van Hoytema EMM, Wortmann SB, Koelewijn MJ, Tseng LA, Houben R, Stöckler-Ipsiroglu S, et al. Treatable inherited metabolic disorders causing intellectual disability: 2021 review and digital app. Orphanet J Rare Dis. 2021;16:170. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13023-021-01727-2

    Article  Google Scholar 

  56. Abarca Barriga HHA. Avances genómicos de La última Década y Su influencia En El Enfoque Diagnóstico de La Discapacidad intelectual. Rev Neuropsiquiatr. 2021;84:33–50. https://doiorg.publicaciones.saludcastillayleon.es/10.20453/rnp.v84i1.3936

    Article  Google Scholar 

  57. Han JY, Jang W, Park J, Kim M, Kim Y, Lee IG. Diagnostic approach with genetic tests for global developmental delay and/or intellectual disability: single tertiary center experience. Ann Hum Genet. 2019;83:115–23. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/ahg.12294

    Article  CAS  PubMed  Google Scholar 

  58. Taylor JL, Debost J-CPG, Morton SU, Wigdor EM, Heyne HO, Lal D, et al. Paternal-age-related de Novo mutations and risk for five disorders. Nat Commun. 2019;10:3043. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41467-019-11039-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Wang S-H, Wu C-S, Hsu L-Y, Lin M-C, Chen P-C, Thompson WK, et al. Paternal age and 13 psychiatric disorders in the offspring: a population-based cohort study of 7 million children in Taiwan. Mol Psychiatry. 2022;27:5244–54. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41380-022-01753-x

    Article  PubMed  PubMed Central  Google Scholar 

  60. Cioppi F, Casamonti E, Krausz C. Age-Dependent de Novo mutations during spermatogenesis and their consequences. Adv Exp Med Biol. 2019;1166:29–46. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/978-3-030-21664-1_2

    Article  CAS  PubMed  Google Scholar 

  61. Sharma R, Agarwal A, Rohra VK, Assidi M, Abu-Elmagd M, Turki RF. Effects of increased paternal age on sperm quality, reproductive outcome and associated epigenetic risks to offspring. Reprod Biol Endocrinol RBE. 2015;13:35–54. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12958-015-0028-x

    Article  Google Scholar 

  62. Jain A, Sharma D, Bajaj A, Gupta V, Scaria V. Founder variants and population genomes-Toward precision medicine. Adv Genet. 2021;107:121–52. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/bs.adgen.2020.11.004

    Article  CAS  PubMed  Google Scholar 

  63. Quaio CRDC, Chung CH, Perazzio SF, Dutra AP, Moreira CM, Filho GMN, et al. Frequency of carriers for rare recessive Mendelian diseases in a Brazilian cohort of 320 patients. Am J Med Genet C Semin Med Genet. 2021;187:364–72. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/ajmg.c.31932

    Article  CAS  PubMed  Google Scholar 

  64. 1000 Genomes Project Consortium, Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, et al. A map of human genome variation from population-scale sequencing. Nature. 2010;467:1061–73. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/nature09534

    Article  CAS  Google Scholar 

  65. Bell CJ, Dinwiddie DL, Miller NA, Hateley SL, Ganusova EE, Mudge J, et al. Carrier testing for severe childhood recessive diseases by Next-Generation sequencing. Sci Transl Med. 2011;3:65ra4. https://doiorg.publicaciones.saludcastillayleon.es/10.1126/scitranslmed.3001756

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Ceyhan-Birsoy O, Murry JB, Machini K, Lebo MS, Yu TW, Fayer S, et al. Interpretation of genomic sequencing results in healthy and ill newborns: results from the babyseq project. Am J Hum Genet. 2019;104:76–93. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ajhg.2018.11.016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Mulhern MS, Stumpel C, Stong N, Brunner HG, Bier L, Lippa N, et al. NBEA: developmental disease gene with early generalized epilepsy phenotypes. Ann Neurol. 2018;84:788–95. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/ana.25350

    Article  PubMed  PubMed Central  Google Scholar 

  68. Feuk L, Carson AR, Scherer SW. Structural variation in the human genome. Nat Rev Genet. 2006;7:85–97. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/nrg1767

    Article  CAS  PubMed  Google Scholar 

  69. Hu W, Fang H, Peng Y, Li L, Liu S, Liao H, et al. Nabais Sa-de Vries syndrome in a Chinese infant associated with a novel SPOP mutation: A clinical study and genetic report. Mol Genet Genomic Med. 2022;10:e2075. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/mgg3.2075

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Nabais Sá MJ, El Tekle G, de Brouwer APM, Sawyer SL, del Gaudio D, Parker MJ, et al. De Novo variants in SPOP cause two clinically distinct neurodevelopmental disorders. Am J Hum Genet. 2020;106:405–11. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ajhg.2020.02.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Guella I, McKenzie MB, Evans DM, Buerki SE, Toyota EB, Allen MIV, et al. De Novo mutations in YWHAG cause Early-Onset epilepsy. Am J Hum Genet. 2017;101:300–10. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ajhg.2017.07.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Ye X-G, Liu Z-G, Wang J, Dai J-M, Qiao P-X, Gao P-M, et al. YWHAG mutations cause childhood myoclonic epilepsy and febrile seizures: molecular Sub-regional effect and mechanism. Front Genet. 2021;12:632466. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fgene.2021.632466

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Auton A, Abecasis GR, Altshuler DM, Durbin RM, Abecasis GR, Bentley DR, et al. A global reference for human genetic variation. Nature. 2015;526:68–74. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/nature15393

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thank Marta Martina and Yamile Hurtado for their suggestions on our manuscript.

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Authors and Affiliations

  1. Instituto de Investigaciones de Ciencias Biomédicas, Facultad de Medicina Humana, Universidad Ricardo Palma, Av. Benavides 5440. Santiago de Surco, Lima, 1801, Perú

    Hugo Hernán Abarca-Barriga & Flor Vásquez Sotomayor

  2. Servicio de Genética & Errores Innatos del Metabolismo, Instituto Nacional de Salud del Niño– Breña, Lima, Perú

    Hugo Hernán Abarca-Barriga, Flor Vásquez Sotomayor, Renzo Punil-Luciano & María Cristina Laso-Salazar

  3. Facultad de Medicina Humana, Universidad Nacional Mayor de San Marcos. Lima, Lima, Perú

    Hugo Hernán Abarca-Barriga & Heli Jaime Barrón-Pastor

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  1. Hugo Hernán Abarca-Barriga
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  2. Flor Vásquez Sotomayor
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  3. Renzo Punil-Luciano
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  4. María Cristina Laso-Salazar
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  5. Heli Jaime Barrón-Pastor
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Contributions

HHAB, FVS and RPL interpreted the WES’s data. HHAB wrote the manuscript. FVS, MCLS, HBP and RPL edited and reviewed the document. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hugo Hernán Abarca-Barriga.

Ethics declarations

Ethics approval and consent to participate

All procedures performed in this study were conducted in accordance with the ethical standards of the Institutional Research Committee of the Instituto Nacional de Salud del Niño and the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from all parents or legal guardians. Additionally, for participants aged 16 years or older who had the capacity to provide it, their informed consent was also obtained.

The research was approved by the Ethics Committee of the Instituto Nacional de Salud del Niño under the reference number: 284/2021-CIEI-INSN and ID: PI-81/20.

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Not Applicable.

Competing interests

The authors declare no competing interests.

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Abarca-Barriga, H.H., Vásquez Sotomayor, F., Punil-Luciano, R. et al. Identification of intragenic variants in pediatric patients with intellectual disability in Peru. BMC Med Genomics 18, 76 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12920-025-02141-4

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BMC Medical Genomics

ISSN: 1755-8794

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