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Associative gene networks reveal novel candidates important for ADHD and dyslexia comorbidity
BMC Medical Genomics volume 16, Article number: 208 (2023)
Abstract
Background
Attention deficit hyperactivity disorder (ADHD) is commonly associated with developmental dyslexia (DD), which are both prevalent and complicated pediatric neurodevelopmental disorders that have a significant influence on children’s learning and development. Clinically, the comorbidity incidence of DD and ADHD is between 25 and 48%. Children with DD and ADHD may have more severe cognitive deficiencies, a poorer level of schooling, and a higher risk of social and emotional management disorders. Furthermore, patients with this comorbidity are frequently treated for a single condition in clinical settings, and the therapeutic outcome is poor. The development of effective treatment approaches against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and treatment. In this study, we developed bioinformatical methodology for the analysis of the comorbidity of these two diseases. As such, the search for candidate genes related to the comorbid conditions of ADHD and DD can help in elucidating the molecular mechanisms underlying the comorbid condition, and can also be useful for genotyping and identifying new drug targets.
Results
Using the ANDSystem tool, the reconstruction and analysis of gene networks associated with ADHD and dyslexia was carried out. The gene network of ADHD included 599 genes/proteins and 148,978 interactions, while that of dyslexia included 167 genes/proteins and 27,083 interactions. When the ANDSystem and GeneCards data were combined, a total of 213 genes/proteins for ADHD and dyslexia were found. An approach for ranking genes implicated in the comorbid condition of the two diseases was proposed. The approach is based on ten criteria for ranking genes by their importance, including relevance scores of association between disease and genes, standard methods of gene prioritization, as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analyzed genes. Among the top 20 genes with the highest priority DRD2, DRD4, CNTNAP2 and GRIN2B are mentioned in the literature as directly linked with the comorbidity of ADHD and dyslexia. According to the proposed approach, the genes OPRM1, CHRNA4 and SNCA had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the most relevant genes are involved in biological processes related to signal transduction, positive regulation of transcription from RNA polymerase II promoters, chemical synaptic transmission, response to drugs, ion transmembrane transport, nervous system development, cell adhesion, and neuron migration.
Conclusions
The application of methods of reconstruction and analysis of gene networks is a powerful tool for studying the molecular mechanisms of comorbid conditions. The method put forth to rank genes by their importance for the comorbid condition of ADHD and dyslexia was employed to predict genes that play key roles in the development of the comorbid condition. The results can be utilized to plan experiments for the identification of novel candidate genes and search for novel pharmacological targets.
Background
Attention deficit hyperactivity disorder (ADHD) and developmental dyslexia (DD) are both prevalent and complicated pediatric neurodevelopmental disorders that have a significant influence on children’s learning and development. More than 80% of children with ADHD and 60% of children with dyslexia fulfill at least one problem diagnostic criterion [1, 2]. ADHD is diagnosed individuals who exhibit inattention, hyperactivity, and impulsive behavior problems, with a prevalence of about 7.2% according to a recent meta-analysis [3]. The prognosis of ADHD is poor, with some children retaining symptoms into adulthood, and a study found that the risk of premature death is twice as high in those affected with ADHD, bringing a heavy burden to society and families [4]. Learning challenges are also a major motivator for ADHD patients to seek medical attention. ADHD is most commonly connected with DD among learning disorders [5]. Clinically, the comorbidity incidence of DD and ADHD is as high as 25–48% [6]. DD is a condition in which an individual with a normal IQ and educational opportunities, without hearing or nervous system problems, has much lower reading ability than the equivalent age level [7]. In nations that use alphabets, the identification rate of phonetic characters varies between 5 and 17.5% [8, 9]. In nations that use ideograms, such as Chinese characters, the detection rate of minors ranges from 3 to 16% [10, 11]. Dyslexia, as a chronic handicap, not only impedes children’s language acquisition, but also has an impact on arithmetic and other subjects. The reduction of academic performance may result in poorer self-esteem and increased loneliness in children, leading to psychological disorders such as anxiety, depression, and fatigue [12].
According to several studies, comorbid dyslexia may be a sign of ADHD in a subset of affected children who have more severe cognitive impairments and impaired executive and non-executive functioning [13]. Children with comorbidities exhibited greater secondary difficulties, such as low self-esteem, behavioral problems, and higher school dropout rates, than children with ADHD or dyslexia alone [14]. Furthermore, those with comorbid dyslexia and ADHD are more likely to develop additional debilitating diseases, and the prevalence of comorbid symptoms increases with age [15].
Both ADHD and dyslexia are related to impaired brain function [16]. They may share common abnormal changes in brain structure and functional characteristics, resulting in common cognitive impairment, which may be affected by common genetic factors [17]. The pathological etiology of ADHD and dyslexia is not fully understood, but genetic factors play a significant role in both diseases. It has been reported that children with a family history of ADHD or dyslexia were at a higher risk [18]. According to twin studies, the heritability of ADHD is around 0.74 [19], whereas the heritability of dyslexia is 0.4 ~ 0.6 [20].
Candidate genes for ADHD are mostly related to dopamine, 5-hydroxytryptophane, norepinephrine, and other neurotransmitter systems [21]. Several GWAS studies on ADHD have identified genes involved in processes important for brain development, such as cell adhesion and synapse formation [22]. DCDC2, DYX1C1, and KIAA0319 may be pathogenic genes that contribute to dyslexia [23]. According to linkage studies, multiple chromosomal regions have been linked to ADHD and dyslexia, including 6q12-q14, 15q, 16p, 5p, and 17p [24]. The most researched chromosomal region is 6p21-22, which contains significant genes including DCDC2 and KIAA0319 [25]. The DCDC2 gene is now known to be linked to dyslexia. According to a recent meta-analysis, the C allele of the rs807701 polymorphism of the DCDC2 gene can increase the risk of dyslexia [26]. Couto et al. investigated the link between the DCDC2 gene and ADHD. Their findings revealed a robust link between attention deficit, hyperactivity, and impulsive symptoms [27]. DCDC2 may influence ADHD symptoms via gene-gene interaction with KIAA3019, as well as gene-environment interactions with factors related to socioeconomic status [28]. Furthermore, functional impairment of the norepinephrine system plays a significant role in the pathogenic mechanism of ADHD and dyslexia [29], with the ADRA2A gene being related to ADHD in a number of studies, as well as ADHD with comorbid dyslexia [30]. Other frequent susceptibility genes for ADHD and dyslexia include DYX1C1 and DRD4.
Nowadays, there are large amounts of data on these diseases, allowing the construction of associative gene networks that describe the potential molecular mechanisms of interactions between the diseases. There are a number of resources in that allow the reconstruction of such associative gene networks, such as MetaCore [31], Ingenuity [32], and ANDSystem [33, 34]. A number of studies were performed using the ANDSystem tool, including an analysis of proteomic data on Helicobacter pylori infection [35], an analysis of the effect of tissue-specific gene knockouts and the search for potential drug targets [36], analysis of gene networks related to the life cycle of hepatitis C virus [37], as well as analyses of the comorbidity of bronchial asthma and tuberculosis [38], preeclampsia, diabetes, obesity and glaucoma [39], or of asthma and hypertension [40].
The purpose of this study was to prioritize potential genes based on gene network reconstruction and analysis explaining the connection between ADHD and dyslexia. The relationship between the two is detailed in depth in this research via the association network reconstructed using the ANDSystem tool, and the comorbidity-related genes were sourced from the GeneCards web server. Standard prioritizing approaches (Endeavour and ToppGene) were used, as well as novel methods that took into consideration the topology of the ADHD/dyslexia gene network and the correlation of gene polymorphisms with disease risk. The predicted genes can be used to plan genotyping experiments in order to find genes that predict a susceptibility for dyslexia and ADHD comorbidity.
Methodology
The reconstruction of associative gene networks related to ADHD and dyslexia was carried out using the ANDSystem tool [33, 34], which can automatically analyze scientific publications to extract data on the molecular genetic interactions and associations of proteins, genes, metabolites, drugs, and microRNAs with diseases, biological processes, drug side effects, or the phenotypes of various organisms. The main modules of ANDSystem are knowledge extraction module, ANDCell knowledge base and user interface ANDVision. The knowledge extraction module is based on text mining technology, using object name dictionary and semantic template. The compilation of the dictionary is based on the automatic extraction of the names and synonyms of biological objects from external databases and scientific publications. Semantic templates are structured records that list object types, dictionaries, regular expressions for text analysis, and interactive semantic descriptions. The ANDSystem knowledge base was built on the basis of a large-scale analysis of over 25 million abstracts of scientific papers listed in the PubMed database. Information on molecular genetic interactions from different factual databases, such as IntAct, MINT, and others, was integrated into ANDSystem. In total, more than seven million facts regarding molecular genetic interactions and associations are available in the ANDSystem knowledge base. In the current study, we used ANDSystem version 2021 [41], which is based on the analysis of all PubMed abstracts up to 2021, as well as information obtained from external databases that were available in 2021. No custom changes have been made in present study. As the ANDVisio allows to analyze the molecular-genetic networks it was applied to find the node connectivity and betweenness centrality coefficients of nodes in the hypoglycemia gene network. These parameters were calculated with function “Statistics” of the “Analysis” section of ANDVisio. The cross-talk specificity (CTS) values were calculated by ANDVisio function “Intelligent Filtration.” CTS was calculated according to the formula: CTS = Ki/Mi, where Ki is a number of links that the i gene has in the analyzed gene network; Mi is a number of links that the i gene has in the global human gene network of ANDSystem .
Enriched gene ontology (GO) biological processes were identified using the DAVID service [42], with default settings. To evaluate the centrality of vertices in the graphs of gene networks, Cytoscape was used to calculate the degree centrality (DC), total centrality (CC), and betweenness centrality (BC) [43].
A schematic illustration of the gene prioritization algorithm that includes 10 criteria is shown in Fig. 1.
A general scheme for calculating criteria of gene
Criterion 1
calculated using the GeneCards for gene prioritization, (https://www.genecards.org/) [44, 45]: Rank1i = Rank(Xi), where X is a sorted list of genes according to average of the relevance score rank. GeneCards is a comprehensive human gene database that provides brief information on all known and anticipated human genes, proteins, transcription, genetics, and functions. The link between genes and illnesses is also included in the information function of GeneCards, and the relevance score of the link between genes and diseases may be calculated. In this case, genes are sequenced based on their relevance score.
Criterion 2
calculated using the Endeavour system for gene prioritization, (https://endeavour.esat.kuleuven.be/Endeavour.aspx) [46, 47]: Rank2i = Rank(Xi), where X is a sorted list of genes according to Endeavour output, and i is the gene number. The algorithm was used with default settings, and the list of genes from the complete ADHD/dyslexia network was utilized as the input for the test and training sets.
Criterion 3
calculated with the gene prioritization system, ToppGene (https://toppgene.cchmc.org/prioritization.jsp) [48, 49]: Rank3i = Rank(Xi), where X is a sorted list of genes according to ToppGene output, and i is the gene number. The algorithm was used with default settings. The genes from the complete ADHD/dyslexia network were entered as the input, and the list of genes from the complete ADHD/dyslexia network, from which the analyzed genes were excluded, was provided as a training set.
Criterion 4
GO biological processes enriched in the complete and genetic regulatory networks of ADHD/dyslexia. This score was computed as Rank4i = Rank(Xi), where X is a sorted list of genes according to Ni=N1i + N2i, where N1i is the total number of enriched GO biological processes in complete networks of ADHD/dyslexia in which gene i was involved, N2 was calculated the same as N1 for genetic regulatory networks of ADHD/dyslexia (see Additional file 1: Table S2).
Criterion 5
calculated for gene i as Rank5i = Rank(Xi), where X is a sorted list of genes according to average measure of the value of DC, CC, and BC for each gene from the complete network of ADHD/dyslexia.
Criterion 6
calculated in the same way as Criterion 5 using the genetic regulatory network of ADHD/ dyslexia instead of the complete network of ADHD/ dyslexia.
Criterion 7
Rank7i = Rank(Xi), where X is a sorted list of genes according to specificity of the connection of genes with biological processes associated with ADHD and dyslexia. To arrive at this score, at the first step, a list of biological processes connected with ADHD and dyslexia according to ANDSystem was constructed. In ANDSystem, the biological processes are named “Pathways”. They were filtered manually or using a script to leave only the Gene Ontology biological processes. Then the list of Gene Ontology biological processes associated with ADHD was intersected with the list of Gene Ontology biological processes associated with dyslexia. This intersection constitutes the biological processes connected simultaneously with ADHD and dyslexia. Then, all the interactions between all human genes (proteins) and all Gene Ontology biological processes were downloaded from the Gene Ontology server (http://current.geneontology.org/products/pages/downloads.html). Furthermore, we established the interactions between studied genes and biological processes using the downloaded files. We then divided all the Gene Ontology biological processes into 2 groups. The first group (the test set) contained the biological processes connected simultaneously with both studied diseases from step 1. The second group (control set) encompassed all of the remaining GO-based biological processes. The specificity of the connection between genes/proteins and the test set of Gene Ontology biological processes was then evaluated by applying Student’s t-test using the function stats.ttest_ind with the parameter equal_var = false, using the scipy.stats package in Python [50, 51]. An FDR correction for multiple comparisons was conducted using the p.adjust function (Y, “FDR”) of the “stats” package in the programming language R.
Criterion 8
Rank8i = 1 if SNPs from list Y was present in gene i, otherwise Rank8i was equal to the maximal rank for list X (Rank8i = 231), since the presence of such polymorphisms is of great importance for genotyping. List Y included all SNPs for each gene from X that were found in the eQTL gene region with a frequency of the minor allele in at least 5% of the sequences in the 1000Genomes database. A threshold of 5% allows the detection of MAF polymorphisms with a high degree of probability using available genotyping arrays, and it is often used in genomic analysis [52, 53]. To calculate this score, the GTEx resource (http://www.gtexportal.org) [54] was consulted. It provides information on the variability of global expression of genes and SNPs affecting the level of gene expression. For the analyzed genes, all SNPs localized in the region of the eQTL were taken from the database. Such SNPs may be relevant to the development of diseases [55, 56]. Then, only the SNPs that alter the expression of the analyzed genes in the brain were selected. As the next step, the prevalence of the minor alleles among the sequences in the 1000Genomes database was estimated for SNPs in the eQTL region. The analysis was carried out using the NCBI database (https://www.ncbi.nlm.nih.gov/omim/) [57]. For further analysis, only SNPs that had a minor allele frequency of at least 5% among the sequences in the 1000Genomes database were selected (for most of the found SNPs, the minor allele frequency was 20% or higher).
Criterion 9
Rank9i = 1 if any gene i has an SNP associated with either ADHD or dyslexia that is present in list Y, otherwise Rank8i was equal to 231.
Criterion 10
Rank10i = 1 if in list Y for gene i an SNP is associated with some disease comorbid with ADHD or dyslexia was present, otherwise Rank9i was equal to 231. Manual analysis of PubMed publications was conducted to generate a list of diseases comorbid with ADHD and dyslexia. For ADHD, we manually examined 594 PubMed publications found by the query, “ADHD comorbid diseases”, and filtered via the parameter, “Free full text”. For dyslexia, 422 PubMed publications obtained with the query “dyslexia comorbid diseases”, and filtered by the parameter, “Free full text”, were analyzed. For each gene, the final score was computed as the average value of ranks formulated according to criteria 1–10.
Results and discussion
Associative gene networks related to ADHD and dyslexia
In order to identify potential molecular genetic mechanisms underlying the development of ADHD and dyslexia, we compiled a list of 599 genes/proteins associated with ADHD and 167 genes/proteins associated with dyslexia according to ANDSystem (Additional file 1: Table S1). The gene network of ADHD included 148,978 interactions between 321 genes and 278 proteins, including 623 activity regulations, 16 degradation regulations, 863 expression regulations, 4931 transport regulations, 4838 downregulations, 131,844 associative interactions, and 5863 protein-protein interactions. In the ANDSystem, associative interaction is a special category of interactions reflecting any type of relations between two objects listed above.
The gene network of dyslexia included 27,083 interactions between 92 genes and 75 proteins, including 31 activity regulations, 1 degradation regulations, 48 expression regulations, 266 transport regulations, 381 downregulations, 26,023 associative interactions, and 333 protein-protein interactions. In order to complement the gene set of the molecular genetic mechanisms underlying the development of ADHD and dyslexia, we compiled a list of 1842 genes associated with ADHD and 921 genes associated with dyslexia according to GeneCards (Additional file 1: Table S1). There are suggestions in the literature that putative candidate genes for the development of comorbid conditions between a pair of diseases are often simultaneously associated with both diseases [58, 59]. The network of interactions between genes and proteins, associated simultaneously with ADHD and dyslexia (complete ADHD/dyslexia network), which was constructed by intersecting the ADHD and dyslexia networks, included 231 genes and 353 proteins.
The enriched GO biological processes (p-value < 0.01 with FDR correction) for genes/proteins associated with ADHD were identified using the DAVID system. The most significant GO biological processes included positive regulation of transcription from RNA polymerase II promoters, positive regulation of transcription, signal transduction, positive regulation of cell proliferation, immune response, negative regulation of transcription from RNA polymerase II promoters, inflammatory response, negative regulation of transcription, positive regulation of GTPase activity, transcription, G-protein coupled receptor signaling pathways, and chemical synaptic transmission(Additional file 1: Table S2). For genes/proteins associated with dyslexia, the most significant GO biological processes included regulation of chemical synaptic transmission, nervous system development, response to drugs, negative regulation of apoptotic process, transport, cell proliferation, intracellular signal transduction, signal transduction, positive regulation of GTPase activity, positive regulation of transcription from RNA polymerase II promoters, negative regulation of transcription, and negative regulation of transcription from RNA polymerase II promoters (Additional file 1: Table S2).
The most highly enriched GO biological processes for genes/proteins associated simultaneously with ADHD and dyslexia (complete ADHD/dyslexia network) included chemical synaptic transmission, ion transmembrane transport, response to amphetamine, neuronal migration, locomotory behavior, nervous system development, transport, cell adhesion, and signal transduction (Additional file 1: Table S2). These processes may be the most significant for the comorbid relationship between ADHD and dyslexia.
The GO biological processes that were enriched for the ADHD network, and were not featured in the list of enriched processes for the complete ADHD/dyslexia network, included G-protein coupled receptor signaling pathway, circadian regulation of gene expression, neuropeptide signaling pathway, and chemokine mediated signaling pathway. The GO biological processes that were enriched only in the dyslexia network included store-operated calcium entry, G2/M transition of the mitotic cell cycle, homophilic cell adhesion via plasma membrane adhesion molecules, potassium ion transmembrane transport, and ER to Golgi vesicle-mediated transport. Such processes appear to be more pertinent to the mechanisms of development of either ADHD or dyslexia without mutual comorbidity.
The GO biological processes that were enriched only for the complete ADHD/dyslexia network and not for the individual ADHD or dyslexia networks included chemical synaptic transmission, signal transduction, response to amphetamine, and negative regulation of neuronal apoptotic processes.
It is known that genetic regulation is paramount for the genetic variability in diseases across patients [60, 61]. The genetic regulatory network of ADHD/dyslexia, including interactions between genes involved in expression and activity regulation, including up- and downregulation, as well as transport regulation, is shown in Fig. 2. As shown in Fig. 2 the general regulatory network can be divided into at least five subnetworks, including four small subnetworks containing from 2 to 3 participants (for example, DRD2 protein → SLC6A3 gene). These subnetworks appear to be unconnected with the core of the regulatory network, because in the ANDSystem classification they were connected only by associative interaction type. It was also important to evaluate the enrichment of GO biological processes for genes/proteins from the genetic regulatory network of ADHD/dyslexia (Additional file 1: Table S2). Notably, some new enriched GO biological processes were identified in this network, including G-protein coupled receptor signaling pathway, response to estradiol, circadian rhythm, positive regulation of MAPK cascade, neuropeptide signaling pathway, positive regulation of tyrosine phosphorylation of Stat3 protein, intracellular signal transduction, and G2/M transition of mitotic cell cycle. These processes were not significantly enriched within the whole ADHD/dyslexia network. The GO processes that were simultaneously significantly enriched in the complete and genetic regulatory network of ADHD/ dyslexia include the negative regulation of signal transduction, positive regulation of transcription from RNA polymerase II promoters, chemical synaptic transmission, response to drugs, ion transmembrane transport, nervous system development, cell adhesion, and neuronal migration.
Genetic regulatory network of ADHD/dyslexia. Proteins are presented by circles and genes are represented by DNA helices. The diagram was rendered using the ANDVisio plugin ANDSystem, and gene/protein notations are given according to the ANDVisio output
One of the most central regulatory nodes for both the complete and genetic regulatory network of ADHD/dyslexia is TNF-α (Fig. 2). We observed that this gene is involved in a large number of enriched GO biological processes. For example, it participates in negative regulation of transcription from RNA polymerase II promoters, MAPK cascade, positive regulation of apoptotic processes, the inflammatory response, positive regulation of transcription, immune response, and negative regulation of transcription.
TNF neurobiological impacts include the promotion of the development, differentiation, and repair of keratinocytes and neurons [62], neuroendocrine control [63], as well as changes in the production and metabolism of numerous neurotransmitters [64]. TNF may be involved in the development of neuropsychological deficits and peripheral neuropathies, offering clues for the genetic basis of DD and ADHD comorbidity.
TNF can directly alter pain, sleep, and other behavioral activities [65], implying that TNF can influence ADHD behavior in both direct and indirect ways. TNF-induced inflammatory and immunological responses are significant in the etiology of ADHD [66]. The pro-inflammatory cytokine TNF-α is thought to play a key role in brain inflammation [67]. Increased pro-inflammatory cytokine production triggers free radical formation and leads to alterations in glutamatergic neurotransmission, resulting in an increase of neuronal excitotoxicity [68]. Additionally, pro-inflammatory cytokines play a key role in tryptophan metabolism and dopaminergic pathways that are involved in the pathogenesis of ADHD [69]. According to research, there is aberrant TNF-α expression in animal models of attention deficit, and there is a close association between the neurotrophic factor gene and brain development [70]. TNF-α can play a role in neurobiology by promoting the restoration of nerve cells and neuronal differentiation, but a lower amount will impact neurotransmitter metabolism and synthesis. High levels of prenatal testosterone can act independently across development on both the thymus and the brain [71]. Thus, hemispheric laterality, immune function [72], and the incidence of dyslexia (via a direct effect on early brain development) would all be affected [73]. Galaburda et al. performed autopsies on the brains of patients with DD and found typical neuronal abnormalities, including ectopic, smaller somatic cells in the cerebellar gyrus and sensory thalamus [74]. The link between dyslexia and immune dysfunction laid the groundwork for further research. The present study does not cover all aspects linking ADHD and DD, but rather suggests an association between ADHD and DD comorbidity via inflammatory pathways. It is interesting that the TNF gene, which has high centrality, was found to be connected in the regulatory network with BDNF, which also has a high centrality value (Fig. 2). It is known that biological networks are characterized by a low degree of assortative connectivity, i.e. vertexes with a large number of connections are rarely connected with each other. Thus, the uncovered interactions between TNF and BDNF may indicate a special role of this connection for the comorbid state of ADHD and DD. Furthermore, BDNF can suppress the expression of TNF and decrease its activity [75]. It was previously demonstrated that the activity of BDNF is reduced in both ADHD and DD [76, 77]. As shown in Fig. 2, BDNF is able to enhance the level of transport regulation of the COMT gene [78]. Additionally, the various biological processes featured in the pathogenesis of ADHD and DD, as well as their comorbid development, including chemical synaptic transmission, nervous system development and signal transduction, are connected through regulatory interactions.
Prioritization of candidate genes
Gene prioritization is an important task in studies aimed at candidate gene identification. The available tools for gene prioritization include GeneCards, Endeavour [41, 42], ToppGene [43, 44], and DIR [79]. In GeneCards (http://www.genecards.org/), the query and correlation of illness-related genes with the rating score (relevance score) is sorted by relevance score from high to low. Endeavour, ToppGene, and DIR allow one to rank a test set of genes based on a training set of genes according to certain criteria characterizing the proximity of genes from the test set to the genes from the training set. The methods of these resources employ properties of the vertices of gene network graphs, genetic information (co-localization in the genome), functional properties of genes (involvement in the same GO categories), and other aspects. To search for candidate genes that might play an important role in the molecular genetic mechanisms of ADHD and DD comorbidity, we utilized a combination of GeneCards (criterion 1), Endeavour (criterion 2) and ToppGene (criterion 3). Additionally, to take into account the structure of the gene network, describing the interactions between ADHD and dyslexia, as well as polymorphisms in the genes associated with the studied diseases, criteria 4–10 were used. In particular, information about polymorphisms was used in criteria 8–10. All genes with known polymorphisms had a minimal rank (equal to 1), while the rank of remaining genes had the maximal value (equal to 213). This allowed us to provide criteria 8–10 with more weight compared to other criteria. We believe that the presence of polymorphisms in the studied genes is important for the development of comorbidity. The values of the listed scores for the top ten genes from the complete ADHD/dyslexia network are shown in Table 1. According to criterion 1, the top ten most important genes/proteins, sorted by the “relevance score” indicator, were DRD4, MAP1B, SLC6A3, DRD2, DRD3, CNTNAP2, DCDC2, GRIN2B, KIAA0319, and FOXP2 (Additional file 3: Table S3). According to criterion 2, the top ten most important genes/proteins, sorted by the “P-value” indicator, were GRIN1, GRIA1, GABRA1, GABRG2, GABRB1, GABRB3, GRIN2B, PSEN1, CHRNA4, and SLC1A2 (Additional file 4:Table S4). For criterion 3 the list of the top ten genes/proteins, ranked according to the “Average Score” indicator, included CDK5, GRIN2B, GRIN1, ESR1, RYR1, CHRNA7, CHRNB2, DRD4, CHRNA4, and GRIA1 (Additional file 5:Table S5). Criterion 4 suggested that for both the complete and genetic regulatory network of ADHD/dyslexia, TNF was involved in the greatest number of over-represented GO biological categories, including 122 processes (Additional file 6: Table S6). Ranking by criterion 4 demonstrated that for the 20 genes/proteins (TNF, IL6, DRD2, APOE, SNCA, CDK5, IL10, APP, TLR2, DRD1, CHRNB2, DRD3, ESR1, RELN, IFNG, ADRA2A, GRIN1, DMD, OPRM1, CASP3), the total number of GO biological processes in which these genes/proteins participated with respect to the complete and genetic regulatory network of ADHD/dyslexia was more than 70 (Fig. 2).
According to criteria 5 and 6, the genes/proteins with the highest centrality index for both the complete and genetic regulatory network of ADHD/dyslexia were SLC6A3, IL6, GFAP, GFAP, and DLG4. The highest centrality index for only the complete network was for genes LPL, IL6, CDH2, ESR2 and SLC1A2, and for the genetic regulatory network of ADHD/dyslexia, genes BDNF, APP, GRIN2B, COMT, DLG4 (Additional file 7: Table S7).
According to criterion 7, 80 genes/proteins are specifically associated with the test set of biological processes with a FDR corrected p-value < 0.05 (Additional file 8: Table S8). Among the genes most significantly associated with the test set were GRIN1, DRD2, CHRNB2, CDK5, CHRNA4, DRD3, DRD4, SLC6A3, GRIN2B, and DRD1.
Criterion 8 showed that of the 231 analyzed genes, 93 had SNPs found in the eQTL database. Moreover, we revealed that there were 6170 SNPs (Additional file 9: Table S9). The highest number of SNPs (> 7 per 1000 nucleotides) was observed for the genes OPRM1, MAPT, DRD2, DCDC2, KIAA0319, and STS (Fig. 3). Among these, two polymorphisms in the OPRM1 and STS genes were associated with ADHD, while ten polymorphisms in DCDC2 and KIAA0319 were linked to dyslexia (Fig. 3). These genes had the highest priority according to criterion 9. The eight polymorphisms in genes OPRM1, CHRNA4, SNCA, PICK1, and MAPT were linked to any disease comorbid with ADHD or dyslexia (e.g., nicotine dependence, alcohol dependence, Alzheimer’s disease, general substance dependence, addiction to opioids or heroin, schizophrenia, obesity, Parkinson’s disease, or major depressive disorder). According to criterion 10, the genes OPRM1, CHRNA4, SNCA, PICK1, and MAPT had the highest priority(Additional file 10: Table S10).
Network of interactions between genes/proteins that had SNPs determined by criteria 8–10
After reviewing the average rank (Additional file 11: Table S11), twenty genes (OPRM1, CHRNA4, SNCA, GRIN1, MAPT, DRD2, DLG4, GABRA6, GRM3, ESR1, DRD4, CACNA1A, GABBR1, CNTNAP2, SCN1A, GABRA1, DRD5, IL6, GRIN2B, PICK1) had the highest priority (Table 1). According to the OMIM [80] and MalaCards [81] databases, all genes presented in Table 1, except for SNCA, CACNA1A, SCN1A, and GABRA1, appeared to be associated with ADHD and/or dyslexia. However, the associations of these genes with ADHD and/or dyslexia were subject to controversy in the literature[[82, 83]].
The top gene OPRMI (1st) is found on chromosome 6q24-25, a genomic region linked to ADHD and dyslexia. OPRMI encodes human opioid receptor, which is a member of the G-protein-coupled receptor family and is involved in substance-related reward pathways. The opioid system may be implicated in the reward and reinforcement control abnormalities seen in ADHD, notably the defective signaling of delayed rewards caused by changes in motivational processes [84]. The same opioid receptor gene (OPRM1) polymorphism has been linked to dispositional and neural sensitivity to social rejection [85], as well as to parent-child relationships [86], implying that the opioid (reward) system is also involved in the regulation of social relations and behavior. The current findings point to the opioid system playing a particular pathophysiological role in the increased sensitivity to both behavior disruption (ADHD) and problematic drug use. However, there is no direct discussion in the literature on OPRM1 about the comorbidity of ADHD and dyslexia.
The second-ranked gene, CHRNA7, is found on human chromosome 15q13.3, a region of the genome linked to ADHD and dyslexia [87]. CHRNA7 encodes a nicotinic acetylcholine receptor. Neuro-nicotinic basic cholinergic receptors are ligand-gated ion channels found in the human cerebral cortex, hippocampus, thalamus, and striatum. They play roles in attention, memory, and cognition, with links to neurodegenerative disorders, epilepsy, nicotine dependency, ADHD, and dyslexia [88]. Dopamine and acetylcholine are both neurotransmitters that play crucial roles in the human body, and dopamine release can be enhanced by stimulating the nicotinic acetylcholine receptor [89]. Clinical studies using nicotine and nicotine agonists have shown increased performance in attention-related activities, including in individuals with ADHD [90]. In addition to the standard auditory and phonological problems, dyslexia is characterized by delayed attention transfer [91]. The nicotinic system of the brain is primarily responsible for attention shift, and some studies have shown a particular link between attention and CHRNA4 [92], which also establishes the pathogenic basis of the nicotinic receptor gene in dyslexia.
A genetic and phenotypic overlap between ADHD and dyslexia was also discussed. A well-studied risk gene for ADHD is SNCA (3rd), which encodes α-synuclein, a protein located primarily in the presynaptic vesicles, which has been suggested to play a role in the modulation of dopamine transporter function [93]. In ADHD, the concentration of dopamine and other neurotransmitters in the brain decreases, which weakens their inhibitory activities and leads to hyperactivity. Dopamine transporter is the target of psychostimulants for the treatment of ADHD and plays a key role in regulating the dopamine concentration in the synaptic cleft [94]. Dopaminergic neurons are mostly found in the ventral midbrain and transmit to the striatum and prefrontal cortex [95]. Executive function, learning, mood control, reward sensitivity, and other functions are all affected by this protein [96]. According to previous research, disruption of the dopamine signaling system is a significant event the neuropsychiatric etiology of ADHD symptoms and dyslexia [97].
Among these top genes, DRD2, DRD4, CNTNAP2 and GRIN2B (6th, 11th, 14th, 19th place in Table 1) are directly discussed in the literature in the context of the comorbidity of ADHD and dyslexia [98,99,100,101]. The DRD2 gene, located at 11Q23.1, encodes the D2 dopamine receptor, which is involved in dopaminergic synaptic transmission. The dopamine receptor is prevalent in dopamine-containing brain regions, most notably the neostriatum, olfactory nodule, substantia nigra, ventral tegmental area, and nucleus accumbens [102]. The DRD4 gene, which encodes the D4 dopamine receptor, is found in the p15.5 region of chromosome 11. The DRD4 receptor is found at the synaptic end of neurons and has a high affinity for dopamine and norepinephrine in the synaptic gap [103]. It is a catecholamine receptor that participates in the transmission of the DA neurotransmitter between neurons. The D2 and D4 are presynaptic dopamine receptors that regulate dopamine synthesis and release, as well as controlling motor behavior, drug misuse, hormone production, and antipsychotic targets in schizophrenia [104]. As an excitatory neurotransmitter in the brain, dopamine primarily governs a number of central nervous system activities. When dopamine levels in the brain are low, children’s brains lose their capacity to manage irrelevant inputs, causing them to selectively filter irrelevant stimuli and become unable to maintain focus, resulting in ADHD. Accordingly, the expression of DRD2 and DRD4 was observed to be up-regulated in ADHD [105]. DRD2 and DRD4 are established drug targets for the treatment of ADHD [106, 107] and a number of SNPs in these genes are associated with ADHD [108, 109]. Dopaminergic function is considered to be critical for the modulation of neural activity in the striato-thalamo-cortical circuit, which is involved in complex goal-directed or context-dependent changes in human speech and bird song output [110]. Moreover, the dopaminergic system also plays an important role in maintaining linguistic functions such as speech fluency and reading, and a number of genetic polymorphisms in this system have been identified as important risk factors for dyslexia. For instance, a dyslexia susceptibility locus (DYX7) has been found to be linked to the dopamine D4 receptor (DRD4) region on chromosome 11p15.5 in participants of European ancestry [111]. At the same time, an association between DRD2 and stuttering has been found in the Chinese population through high-density genotyping [112]. DRD2 and DRD4 mutations are linked to selective cognitive impairment of working memory and behavioral flexibility. Working memory is a type of temporary information processing and storage that aids in the coordination of many actions and tasks. People with dyslexia have been shown to have deficiencies not just in particular language abilities, but also in working memory. Thus, mutations in the DRD2 and DRD4 genes might potentially impair working memory in children with dyslexia. The DRD4 and DRD2 genes have been linked to ADHD and dyslexia comorbidity. However, given the vast disparities in the linguistic and genetic background of different populations, the association of the DRD4 and DRD2 genes and ADHD with dyslexia comorbidity warrants more investigation.
Other interesting genes are CNTNAP2 and GRIN2B, which ranked at the 14th and 19th places in Table 1, respectively. The CNTNAP2 gene, located at 7q35, encodes a neuronal adhesion molecule called presynaptic membrane extension protein, which is produced in the developing human cerebral cortex [113]. The presynaptic membrane extensor protein interacts with neuroligin in the postsynaptic membrane and is essential for the construction, differentiation, and information transmission function of synapses. Common variants in the CNTNAP2 gene have been linked to lower white and gray matter volume in the cerebellum, fusiform gyrus, occipital lobe, and frontal lobe, as well as structural connections across different brain areas, according to neuroimaging studies in healthy adults [114]. The CNTNAP2 gene has been linked to changes in regions related to language function and neural networks in the brain. Transgenic animal models imply that CNTNAP2 is required for appropriate brain development because it encodes glial adhesin in myelinated axons [115]. Neural progenitor cells from CNTNAP2 heterozygous patients exhibited a substantial decrease in neural migration, whereas hiPSC-derived neurons from carriers exhibited changes in the expression of genes associated with synaptic transmission and neuronal activity [101]. Some phenotypic traits are shared across species, such as hyperactivity, which is observed in both mice and zebrafish and is one of the most noticeable signs of ADHD [116]. These studies demonstrated that CNTNAP2 is a candidate gene contributing to ADHD and dyslexia comorbidity.
The NMDA receptors are a glutamate receptor family that is involved in the excitatory neurotransmission of Ca2+ signals in the central nervous system (CNS) [116]. They perform critical physiological functions in neuronal differentiation and migration, synaptic plasticity, and long-term hippocampal potential control. GRIN2B encodes the GluN2B subunit of the NMDA receptor, and is located on chromosome 12p13.1 [117]. High levels of GRIN2B protein expression are detected in the hippocampus and the medial prefrontal cortex, an important brain region for spatial learning and memory tasks [118]. In view of its importance in psychological cognition, GRIN2B mutations have been associated with a number of neurodevelopmental diseases, including ADHD and dyslexia.
Biological processes underlying ADHD and dyslexia
Currently, there is increased interest among researchers in the effects of various pathological processes. The most relevant genes identified in this study were found to be involved in biological processes related to signal transduction, positive regulation of transcription from RNA polymerase II promoter, chemical synaptic transmission, response to drugs, ion transmembrane transport, nervous system development, cell adhesion, and neuronal migration. To further assess the biological processes potentially underlying ADHD/dyslexia, we evaluated the gene cluster by calculating the degree centrality (DC), total centrality (CC), and betweenness centrality (BC) using Cytoscape plugin Network Analyzer and R language. For a given network, each gene is characterized as a node and the interactions between the genes are known as edges. DC is the sum of edges linked to it, therefore, a high degree signifies the hub genes owning chief biological functions. BC shows the importance of a node by the number of small paths passing through each node. The BC of the node is computed as:
Where, n is node, s and t are nodes in the network other than node n, σst denotes the number of shortest paths from s to t, and σst (n) is the number of shortest paths from s to t of node n. Whereas, CC is the number of connected pairs or edges between the nodes. It is defined as follows:
Where, kn is the number of neighbors of node n and en is the number of connected pairs between all neighbors.
The value of BC and CC always lies between 0 and 1. Therefore, the decisive network was visualized centred on BC, CC and a node degree, and the hub genes were generated based on higher BC values, CC values, and the node degree with the cut off ≥ 0.005, ≥ 0.2 and > 40 respectively.
In particular, GO:0007165 ~ signal transduction (Fig. 4A) plays the most important role among all biological processes for the complete network of ADHD/dyslexia. Signal transduction is mostly represented by the proteins GABRB3, GRIA1, GABRB1, CD83, CHRNA2, CHRNA4, CHRNA7, ELK3, CACNA1I, SMPD1, TSPO, NLRP3, FLNB, GTF2I, SH2B1, CHRNB2, BCL11B, GABRA6, GABRA5, ESR1, ADRA2A, ESR2, KITLG, FGF14, DLG4, CRH, and CHRFAM7A. The onset of dyslexia and ADHD is based on signal transduction, and is related to mechanisms involving neurotransmitters such as dopamine, adrenaline, and acetylcholine [119]. GO:0045944 ~ positive regulation of transcription from RNA polymerase II promoters (Fig. 4B) was represented by the proteins APP, CEBPD, TNF, ELK3, NEUROD6, NLRP3, DRD2, DRD3, IL10, AUTS2, GSX1, BCL11B, LMO4, PAX6, TBR1, ESR1, IL2, GRIN1, CDK8, PER1, IL1A, IL6, MEIS1, IFNG, TNIP1, TET3, CDH13, RAF1, and TLR2. The biological process of RNA polymerase II promoter transcription controls gene expression directly. A considerable number of studies in recent years have found links of RNA polymerase II promoter transcription with ADHD and dyslexia. The function of miRNAs in processes important to central nervous system development, such as cell proliferation and differentiation, synaptogenesis, synaptic plasticity, and apoptosis, might lead to the creation of novel biomarkers for the diagnosis and prognosis of ADHD and dyslexia [120]. GO:0007268 ~ chemical synaptic transmission (Fig. 4C) was represented by the proteins GRIA1, GABRB2, CHRNA4, GABRA5, CACNA1B, SLC1A2, HTR1A, GRIK1, OPRM1, SLC6A1, GRIN2B, GRIN1, GRM3, PTPRD, CDK5, DLG4, CRH, DLGAP1, GRIA4, DRD5, and PAFAH1B1. Chemical synaptic transmission regulates synaptosome-related protein transmission. Synaptic proteins, which are primarily synthesized in neurons, play a vital role in neurotransmitter release, attention management, behavioral inhibition, learning, planning and strategy formation, thinking and reasoning, as well as a variety of other higher cognitive tasks in the brain [121]. GO:0042493 ~ response to drugs (Fig. 4D) was represented by the proteins IL10, BCHE, ABCB1, MAOB, SLC1A2, LPL, ABAT, COMT, SLC6A3, ICAM1, SOD1, IL6, IFNG, CASP3, SMPD1, CRH, TSPO, DRD1, DRD2, DRD3, and SNCA. There is currently no pharmaceutical intervention for dyslexia. Several studies have been undertaken to treat dyslexia and ADHD due to their similar genetic origins. The medicine tomoxetine is used to treat the comorbidity of these two diseases. According to a study by Shaywitz et al. [122], tomoxetine improved both ADHD and reading symptoms in ADHD and dyslexia. Notably, the improvement of reading symptoms could not be explained by the improvement of ADHD symptoms. According to Shaywitz et al., tomoxetine can increase reading and decoding ability, as well as reading vocabulary capacity, in ADHD and dyslexic patients, and the improvement of reading performance of comorbid dyslexia was not directly caused by the treatment of ADHD symptoms [122]. This phenomenon might be explained by the biological mechanism of responses to drugs.
(A) signal transduction, (B) positive regulation of transcription from RNA polymerase II promoters, (C) chemical synaptic transmission, (D) response to drugs, (E) ion transmembrane transport, (F) nervous system development. The figure was plotted using Cytoscape. (G) cell adhesion, (H) neuronal migration. The figure was rendered using Cytoscape
GO:0034220 ~ ion transmembrane transport (Fig. 4E) was represented by the proteins GABRB3, GRIA1, RYR1, GABRB2, GABRA1, GABRB1, GABRA6, GABRA5, CHRNA7, GRIK1, ATP2C2, GABRG3, GRIN2B, GABRG2, DLG4, CHRFAM7A, RAF1, and GRIA4. Ion transmembrane transport, especially Ca2+ transport, is significantly related to the comorbidity of ADHD and dyslexia. The ATP2C2 gene, for example, encodes an adenosine triphosphatase that transfers Ca2+ and Mn2+ to the Golgi lumen [123]. ATPases have recently been implicated in neurodevelopmental disorders [124]. Ca2+ regulates a number of neural functions, including neuronal migration, axon guidance, synaptic plasticity, memory, neuronal excitability, neurosecretion, and senescence [125, 126]. Excessive intracellular Mn2+ buildup is hazardous and can cause apoptosis as well as manganese poisoning, a neurological condition marked by tremors and convulsions [127]. Dysregulation of these cations has been linked to ADHD and dyslexia [124]. GO:0007399 ~ nervous system development (Fig. 4F) was represented by the proteins EPM2A, LGI1, APP, BDNF, DYRK1A, GRIK1, DCLK1, ROBO1, FGF14, MOBP, NEUROD6, DLG4, MAP1B, SMPD1, SCN8A, and TPP1. It has been shown that both ADHD and dyslexia are linked to abnormalities in critical genes involved in central nervous system development [128, 129]. GO:0007155 ~ cell adhesion (Fig. 4G) was represented by the proteins APP, CNTNAP2, MOG, CNTN5, CTNND1, IL2, ROBO1, ICAM1, KITLG, RELN, NPHS1, CDH2, CDH11, CDH13, CNTNAP5, and BCAR1. These genes encode cell adhesion molecules that are thought to have a function in brain development and were previously linked to ADHD and dyslexia [130]. GO:0001764 ~ neuronal migration (Fig. 4H) was represented by the genes CDKL5, DCDC2, PAX6, PSEN1, DCLK1, ASPM, TUBB2B, RELN, CDK5, MAPT, DISC1, KIAA0319, and PAFAH1B1. These genes encode proteins that can either directly or indirectly affect cytoskeletal microtubules and actin filaments, both of which are required for neuronal migration and axonal growth [131]. The pathogenic process underlying ADHD and dyslexia include aberrant cell migration [132].
Conclusions
Computer reconstruction and analysis for gene networks opens up possibilities to the proposal of hypotheses concerning the molecular mechanisms underlying diseases. It has been also proved to be efficient in exploring the complicated correlations of comorbid diseases. The reconstructed ADHD/dyslexia gene network that depicts the latent molecular-genetic interactions of these two diseases contains 231 genes/proteins. With standard prioritization methods, and original criteria using the ADHD/dyslexia gene network structure, 20 candidate genes for genotyping and drug target search are raised. Genes OPRM1, CHRNA4, and SNCA had the highest priority. In addition, the highest ranking genes are engaged in biological processes associated with signal transduction, positive regulation on transcription from RNA polymerase II promoters, chemical synaptic transmission, response to drugs, ion transmembrane transport, nervous system development, cell adhesion, nd neuronal migration, which can all reasonably contribute to dyslexia and ADHD and their comorbidity. However, the roles of these genes remain unclear, worthy of attention in future experiments.
Data availability
All data generated or analysed during this study are included in supplementary information files.
References
Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF. Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review. Biology Psychiatry. 2005a;57:1336–46.
Willcutt EG, Pennington BF, Olson RK, Chhabildas N, Hulslander J. Neuropsychological analyses of comorbidity between reading disability and attention deficit hyperactivity disorder: in search of the common deficit. Dev Neuropsychol. 2005b;27:35–78.
Thomas R, Sanders S, Doust J, et al. Prevalence of attentiondeficit/hyperactivity disorder: a systematic review and metaanalysis. Pediatrics. 2015;135(4):e994–e1001.
Dalsgaard S, Østergaard SD, Leckman JF, et al. Mortality in children, adolescents, and adults with attention deficit hyperactivity disorder: a nationwide cohort study. The Lancet. 2015;385(9983):2190–6.
Kronenberger WG, Dunn DW. Learning disorders. Neurol Clin. 2003;1:941–52.
Semrud-Clikeman M, Bledsoe J. Updates on attention-deficit/hyperactivity disorder and learning disorders. Curr Psychiatry Rep. 2011;13(5):364–73.
Peterson RL, Pennington BF. Developmental Dyslexia[J]. Ann Rev Clin Psychol. 2015. https://doi.org/10.1146/annurev-clinpsy-032814-112842.
DEMONET J F, TAYLOR M J, CHAIX Y. Developmental dyslexia[J]. Lancet. 2004;363(9419):1451–60.
Nowling,Margaret J. Early identification and interventions for dyslexia:a contemporary view[J].Journal of Research in Special Educational,2013,DOI:https://doi.org/10.1111/j.1471-3802.2012.01262.x
Zheng xin. A study on the cognitive characteristics of chinese developmental dyslexia in Jingpo ethnic children [D]. Yunnan Normal University; 2019.
Memati E. Maihoprati Kanji. Study on the detection rate and reading level of Uyghur dyslexia children in grade 4 primary School[J].Journal of lanzhou institute of education,2020,36(02):85–87.
Germanò E. Gagliano Antonella,Curatolo Paolo. Comorbidity of ADHD and dyslexia.[J]. Developmental neuropsychology,2010,35(5)
Lonergan A, Doyle C, Cassidy C, et al. A meta-analysis of executive functioning in dyslexia with consideration of the impact of comorbidADHD. J Cogn Psychol. 2019;31(7):725–49.
Naomi Tistarelli,Corrado Fagnani,Miriam Troianiello,Maria Antonietta Stazi,Walter Adriani. The nature and nurture of ADHD and its comorbidities: A narrative review on twin studies[J]. Neuroscience and Biobehavioral Reviews,2020,109(C)
WANG JJ, SUN L, SHU H, et al. The Multidimensional Research Progress in attention-deficit hyperactivity disorder Comorbid with Dyslexia: from gene, brain to Behavior[J]. Prog Biochem Biophys. 2020;47(11):1135–44.
McGrath Lauren M, Stoodley Catherine J. Are there shared neural correlates between dyslexia and ADHD? A meta-analysis of voxel-based morphometry studies.[J]. Journal of neurodevelopmental disorders,2019,11(1)
Truong Li Miao T,DeMille, Malins Mellissa JG, Lovett Maureen W. Bosson-Heenan Joan,Gruen Jeffrey R,Frijters Jan C. Effect of READ1 on latent profiles of reading disorder and comorbid attention and language impairment subtypes.[J]. Child neuropsychology: a journal on normal and abnormal development in childhood and adolescence,2020,26(2)
Sánchez-Morán. Mirian,Hernández Juan Andrés,Duñabeitia Jon Andoni,Estévez Adelina,Bárcena Laura,González-Lahera Aintzane,Bajo María Teresa,Fuentes Luis J,Aransay Ana M,Carreiras Manuel. Genetic association study of dyslexia and ADHD candidate genes in a Spanish cohort: Implications of comorbid samples.[J]. PloS one,2018,13(10)
Faraone SV, Larsson H. Genetics of attention deficit hyperactivity disorder.Mol Psychiatry, 2019, 24(4):562–575
Ziegler A, König IR, Deimel W, et al. Developmental dyslexia-recurrence risk estimates from a german bi-center study using the single proband sib pair design. Hum Hered. 2005;59(3):136–43.
Gao Q, Liu L, Qian Q, et al. Advances in molecular genetic studies of attention deficit hyperactivity disorder in China. Shanghai Arch Psychiatry. 2014;26(4):194–206.
Yang L, Neale BM, Liu L et al. Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: genome-wide association study of both common and rare variants. American Journal of Medical Genetics Part B, Neuropsychiatric Genetics: the Official Publication of the International Society of Psychiatric Genetics, 2013, 162B(5): 419–430
Carrion-Castillo A, Franke B, Fisher SE. Molecular genetics of dyslexia: an overview. Dyslexia (Chichester England). 2013;19(4):214–40.
Price KM, Wigg KG, Feng Y et al. Genome-wide association study of word reading: overlap with risk genes for neurodevelopmental disorders. 2020, 19(6):e12648
Couto JM, Gomez L, Wigg K, et al. Association of attentiondeficit/hyperactivity disorder with a candidate region for reading disabilities on chromosome 6p. Biol Psychiatry. 2009;66(4):368–75.
Mascheretti S, Trezzi V, Giorda R, et al. Complex effects of dyslexia risk factors account for ADHD traits: evidence from two independent samples. J Child Psychol Psychiatry. 2017;58(1):75–82.
Stevenson J, Langley K, Pay H, et al. Attention deficit hyperactivity disorder with reading disabilities: preliminary genetic findings on the involvement of theADRA2A gene. J Child Psychol Psychiatry Allied Discip. 2005;46(10):1081–8.
Demontis D, Walters RK, Martin J, et al. Discovery of the first genome-wide significant risk loci for attention deficit/. hyperactivity disorder Nature Genetics. 2019;51(1):63–75.
Gialluisi A, Andlauer TFM, Mirza-Schreiber N, et al. Genomewide association scan identifies new variants associated with a cognitive predictor of dyslexia. Translational Psychiatry. 2019;9(1):77.
Davies G, Lam M, Harris SE et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function.Nature Communications, 2018, 9(1): 2098
Ekins S, Bugrim A, Brovold L, Kirillov E, Nikolsky Y, Rakhmatulin E, Sorokina S, Ryabov A, Serebryiskaya T, Melnikov A, Metz J, Nikolskaya T. Algorithms fornetwork analysis in systems-ADME/Tox using the MetaCore and MetaDrug platforms. Xenobiotica. 2006;36(10–11):877–901.
Mayburd AL, Martlinez A, Sackett D, Liu H, Shih J, Tauler J, Avis I, Mulshine JL. Ingenuity network-assisted transcription profiling: identification of a new pharmacologic mechanism for MK886. Clin Cancer Res. 2006;12(6):1820–7.
Demenkov PS, Ivanisenko TV, Kolchanov NA, Ivanisenko VA. ANDVisio: a new tool for graphic visualization and analysis of literature mined associative gene networks in the ANDSystem. silico biology. 2012;11(3, 4):149–61.
Ivanisenko VA, Saik OV, Ivanisenko NV, Tiys ES, Ivanisenko TV, Demenkov PS, Kolchanov NA. ANDSystem: an associative network discovery system for automated literature mining in the field of biology. BMC Syst Biol. 2015;9(2):2.
Momynaliev KT, Kashin SV, Chelysheva VV, Selezneva OV, Demina IA, Serebryakova MV, Alexeev D, Ivanisenko VA, Aman E, Govorun VM. Functional divergence of helicobacter pylori related to early gastric cancer. J Proteome Res. 2009;9(1):254–67.
Larina IM, Pastushkova LK, Tiys ES, Kireev KS, Kononikhin AS, Starodubtseva NL, Popov IA, Custaud MA, Dobrokhotov IV, Nikolaev EN. Kolchanov NA,Ivanisenko VA. Permanent proteins in the urine of healthy humans during the Mars-500 experiment. J Bioinforma Comput Biol. 2015;13(01):1540001.
Petrovskiy ED, Saik OV, Tiys ES, Lavrik IN, Kolchanov NA, Ivanisenko VA. Prediction of tissue-specific effects of gene knockout on apoptosis in different anatomical structures of human brain. BMC Genomics. 2015;16(13):3.
Popik OV, Petrovskiy ED, Mishchenko EL, Lavrik IN, Ivanisenko VA. Mosaic gene network modelling identified new regulatory mechanisms in HCV infection. Virus Res. 2016;218:71–8.
Bragina EY, Tiys ES, Freidin MB, Koneva LA, Demenkov PS, Ivanisenko VA, Kolchanov NA, Puzyrev VP. Insights into pathophysiology of dystropy through the analysis of gene networks: an example of bronchial asthma and tuberculosis. Immunogenetics. 2014;66(7–8):457–65.
Saik OV, Demenkov PS, Ivanisenko TV, Bragina EY, Freidin MB, Goncharova IA, Dosenko VE, Zolotareva OI, Hofestaedt R, Lavrik IN, Rogaev EI, Ivanisenko VA. Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks. BMC Med Genomics. 2018 Feb 13;11(Suppl 1):15.
Ivanisenko VA, Demenkov PS, Ivanisenko TV, Mishchenko EL, Saik OV. A new version of the ANDSystem tool for automatic extraction of knowledge from scientific publications with expanded functionality for reconstruction of associative gene networks by considering tissue-specific gene expression. BMC Bioinformatics. 2019 Feb 5;20(Suppl 1):34.
Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44.
Hagberg A, Swart P, Chult D. Exploring network structure, dynamics, and function using NetworkX. Los Alamos: Los Alamos National Laboratory (LANL); 2008.
Hauser ER, Crossman DC, Granger CB, Haines JL, Jones CJ, Mooser V, McAdam B, Winkelmann BR, Wiseman AH, Muhlestein JB, Bartel AG, Dennis CA, Dowdy E, Estabrooks S, Eggleston K, Francis S, Roche K, Clevenger PW, Huang L, Pedersen B, Shah S, Schmidt S, Haynes C, West S, Asper D, Booze M, Sharma S, Sundseth S, Middleton L, Roses AD, Hauser MA, Vance JM, Pericak-Vance MA, Kraus WE. A genomewide scan for early-onset coronary artery disease in 438 families: the GENECARD Study. Am J Hum Genet. 2004 Sep;75(3):436–47.
Jomini V, Oppliger-Pasquali S, Wietlisbach V, Rodondi N, Jotterand V, Paccaud F, Darioli R, Nicod P, Mooser V. Contribution of major cardiovascular risk factors to familial premature coronary artery disease: the GENECARD project. J Am Coll Cardiol. 2002 Aug 21;40(4):676 – 84.
Tranchevent LC, Ardeshirdavani A, ElShal S, Alcaide D, Aerts J, Auboeuf D, Moreau Y. Candidate gene prioritization with Endeavour. Nucleic Acids Res. 2016;44(W1):W117–21.
Aerts S, Lambrechts D, Maity S, Van Loo P, Coessens B, De Smet F, Tranchevent LC, De Moor B, Marynen P, Hassan B, Carmeliet P. Gene prioritization through genomic data fusion. Nat Biotechnol. 2006;24(5):537.
Chen J, Xu H, Aronow BJ, Jegga AG. Improved human disease candidate gene prioritization using mouse phenotype. BMC Bioinformatics. 2007;8(1):392.
Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009;37(suppl2):W305–11.
Oliphant TE. Python for scientific computing. Comput Sci Eng. 2007;9(3):10–20.
Millman KJ, Aivazis M. Python for scientists and engineers. Comput Sci Eng. 2011;13(2):9–12.
Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C, Ingle CE, Dunning M, Flicek P, Koller D, Montgomery S, Tavaré S, Deloukas P, Dermitzakis ET. Population genomics of human gene expression. Nat Genet. 2007;39(10):1217.
Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TF, McCarroll SA, Visscher PM. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747.
Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S, Hasz R, Walters G, Garcia F, Young N, Foster B. The genotype-tissue expression (GTEx). project Nat Genet. 2013;45(6):580–5.
Franke L, Jansen RC. eQTL analysis in humans. Cardiovasc Genomics Methods Protoc. 2009;573:311–28.
Raj T, Rothamel K, Mostafavi S, Ye C, Lee MN, Replogle JM, Feng T, Lee M, Asinovski N, Frohlich I, Imboywa S. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science. 2014;344(6183):519–23.
Schoch CL, Ciufo S, Domrachev M, Hotton CL, Kannan S, Khovanskaya R, Leipe D, Mcveigh R, O’Neill K, Robbertse B, Sharma S, Soussov V, Sullivan JP, Sun L, Turner S, Karsch-Mizrachi I. NCBI Taxonomy: a comprehensive update on curation, resources and tools. Database (Oxford). 2020 Jan 1;2020:baaa062.
Puzyrev VP. Genetic bases of human comorbidity. Russ J Genet. 2015;51(4):408–17.
Sundarrajan S, Arumugam M. Comorbidities of psoriasis-exploring the links by network approach. PLoS ONE. 2016;11(3):e0149175.
Lamontagne M, Timens W, Hao K, Bossé Y, Laviolette M, Steiling K, Campbell JD, Couture C, Conti M, Sherwood K, Hogg JC. Genetic regulation of gene expression in the lung identifies CST3 and CD22 as potential causal genes for airflow obstruction. Thorax. 2014. https://doi.org/10.1136/thoraxjnl-2014-205630.
Turpeinen H, Seppälä I, Lyytikäinen LP, Raitoharju E, Hutri-Kähönen N, Levula M, Oksala N, Waldenberger M, Klopp N, Illig T, Mononen N. A genome-wide expression quantitative trait loci analysis of proprotein convertase subtilisin/kexin enzymes identifies a novel regulatory gene variant for FURIN expression and blood pressure. Hum Genet. 2015;134(6):627–36.
Varfolomeev E, Vucic D. Intracellular regulation of TNF activity in health and disease. Cytokine. 2018 Jan;101:26–32.
Fischer R, Kontermann RE, Pfizenmaier K. Selective targeting of TNF receptors as a Novel Therapeutic Approach. Front Cell Dev Biol. 2020 May;26:8:401.
Probert L. TNF and its receptors in the CNS: The essential, the desirable and the deleterious effects.Neuroscience. 2015 Aug27;302:2–22.
Wallach D. The cybernetics of TNF: old views and newer ones. Semin Cell Dev Biol. 2016 Feb;50:105–14.
Corominas-Roso M, Armario A, Palomar G, Corrales M, Carrasco J, Richarte V, Ferrer R, Casas M, Ramos-Quiroga JA. IL-6 and TNF-α in unmedicated adults with ADHD: relationship to cortisol awakening response. Psychoneuroendocrinology. 2017 May;79:67–73.
Elsadek AE, Al-Shokary AH, Abdelghani WE, Kamal NM, Ibrahim AO, El-Shorbagy HH, Suliman HA, Barseem NF, Abdel Maksoud YH, Azab SM, Nour El Din DM. Serum levels of Interleukin-6 and tumor necrosis factor alpha in children with attention-deficit hyperactivity disorder. J Pediatr Neurosci. 2020 Oct-Dec;15(4):402–8.
Beukelman T, Xie F, Chen L, Baddley JW, Delzell E, Grijalva CG, Lewis JD, Ouellet-Hellstrom R, Patkar NM, Saag KG, Winthrop KL, Curtis JR, SABER Collaboration. Rates of hospitalized bacterial infection associated with juvenile idiopathic arthritis and its treatment. Arthritis Rheum. 2012 Aug;64(8):2773–80.
Beukelman T, Haynes K, Curtis JR, Xie F, Chen L, Bemrich-Stolz CJ, Delzell E, Saag KG, Solomon DH, Lewis JD. Safety Assessment of Biological therapeutics collaboration. Rates of malignancy associated with juvenile idiopathic arthritis and its treatment. Arthritis Rheum. 2012 Apr;64(4):1263–71.
Idriss HT, Naismith JH. TNF alpha and the TNF receptor superfamily: structure-function relationship(s). Microsc Res Tech. 2000 Aug 1;50(3):184 – 95.
Li J, Zhang H, Huang W, Qian H, Li Y. TNF-α inhibitors with anti-oxidative stress activity from natural products. Curr Top Med Chem. 2012;12(13):1408–21.
Gilger JW, Pennington BF, Harbeck RJ, DeFries JC, Kotzin B, Green P, Smith S. A twin and family study of the association between immune system dysfunction and dyslexia using blood serum immunoassay and survey data. Brain Cogn. 1998 Apr;36(3):310–33.
Vincent A, Deacon R, Dalton P, Salmond C, Blamire AM, Pendlebury S, Johansen-Berg H, Rajogopalan B, Styles P, Stein J. Maternal antibody-mediated dyslexia? Evidence for a pathogenic serum factor in a mother of two dyslexic children shown by transfer to mice using behavioural studies and magnetic resonance spectroscopy. J Neuroimmunol. 2002 Sep;130(1–2):243–7.
Giraldo-Chica M, Schneider KA. Hemispheric asymmetries in the orientation and location of the lateral geniculate nucleus in dyslexia. Dyslexia. 2018 May;24(2):197–203.
Zhang Y, Fang X, Fan W, Tang W, Cai J, Song L, Zhang C. Interaction between BDNF and TNF-α genes in schizophrenia. Psychoneuroendocrinology. 2018 Mar;89:1–6.
Liu DY, Shen XM, Yuan FF, Guo OY, Zhong Y, Chen JG, Zhu LQ, Wu J. The physiology of BDNF and its relationship with ADHD. Mol Neurobiol. 2015 Dec;52(3):1467–76.
Zakopoulou V, Vlaikou AM, Darsinou M, Papadopoulou Z, Theodoridou D, Papageorgiou K, Alexiou GA, Bougias H, Siafaka V, Zoccolotti P, Chroussos GP, Syrrou M, Michaelidis TM. Linking Early Life Hypothalamic-Pituitary-Adrenal Axis Functioning, Brain Asymmetries, and Personality Traits in Dyslexia: An Informative Case Study. Front Hum Neurosci. 2019 Oct 1;13:327.
Brunoni AR, Carracedo A, Amigo OM, Pellicer AL, Talib L, Carvalho AF, Lotufo PA, Benseñor IM, Gattaz W, Cappi C. Association of BDNF, HTR2A, TPH1, SLC6A4, and COMT polymorphisms with tDCS and escitalopram efficacy: ancillary analysis of a double-blind, placebo-controlled trial. Braz J Psychiatry. 2020 Apr;42(2):128–35.
Chen Y, Wang W, Zhou Y, Shields R, Chanda SK, Elston RC, Li J. In silico gene prioritization by integrating multiple data sources. PLoS ONE. 2011;6(6):e21137.
McKusick VA. Mendelian inheritance in man: a catalog of human genes and genetic disorders.JHU Press. 1998;1
Rappaport N, Twik M, Plaschkes I, Nudel R, Iny Stein T, Levitt J, Lancet D. MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Res. 2017;45(D1):D877–87.
Pawan Sharma,Rajesh Sagar,Raman Deep Pattanayak,Manju Mehta. Familial study of attentional and behavioural problems in children with Dyslexia and their first-degree relatives in Indian setting[J]. Asian Journal of Psychiatry,2018,33:
Mascheretti S. Trezzi Vittoria,Giorda Roberto,Boivin Michel,Plourde Vickie,Vitaro Frank,Brendgen Mara,Dionne Ginette,Marino Cecilia. Complex effects of dyslexia risk factors account for ADHD traits: evidence from two independent samples.[J].Journal of child psychology and psychiatry, and allied disciplines,2017,58(1)
Carpentier PJ, Arias Vasquez A, Hoogman M, Onnink M, Kan CC, Kooij JJ, Makkinje R, Iskandar S, Kiemeney LA, de Jong CA, Franke B, Buitelaar JK. Shared and unique genetic contributions to attention deficit/hyperactivity disorder and substance use disorders: a pilot study of six candidate genes. Eur Neuropsychopharmacol. 2013 Jun;23(6):448–57.
Sánchez-Mora C, Richarte V, Garcia-Martínez I, Pagerols M, Corrales M, Bosch R, Vidal R, Viladevall L, Casas M, Cormand B, Ramos-Quiroga JA, Ribasés M. Dopamine receptor DRD4 gene and stressful life events in persistent attention deficit hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet. 2015 Sep;168(6):480–91.
Bonvicini C, Faraone SV, Scassellati C. Common and specific genes and peripheral biomarkers in children and adults with attention-deficit/hyperactivity disorder. World J Biol Psychiatry. 2018 Mar;19(2):80–100.
Wigg KG, Feng Y, Crosbie J, Tannock R, Kennedy JL, Ickowicz A, Malone M, Schachar R, Barr CL. Association of ADHD and the protogenin gene in the chromosome 15q21.3 reading disabilities linkage region. Genes Brain Behav. 2008 Nov;7(8):877–86.
Cho K, Frijters JC, Zhang H, Miller LL, Gruen JR. Prenatal exposure to nicotine and impaired reading performance. J Pediatr. 2013 Apr;162(4):713–718e2.
Pyakurel P, Shin M, Venton BJ. Nicotinic acetylcholine receptor (nAChR) mediated dopamine release in larval Drosophila melanogaster.Neurochem Int. 2018Mar;114:33–41.
Vink JM, Treur JL, Pasman JA, Schellekens A. Investigating genetic correlation and causality between nicotine dependence and ADHD in a broader psychiatric context. Am J Med Genet B Neuropsychiatr Genet. 2021 Oct;186(7):423–9.
Gabay Y, Gabay S, Schiff R, Henik A. Visual and auditory interference control of attention in Developmental Dyslexia. J Int Neuropsychol Soc. 2020 Apr;26(4):407–17.
Quan J, Ong ML, Bureau JF, Sim LW, Sanmugam S, Abdul Malik AB, Wong E, Wong J, Chong YS, Saw SM, Kwek K, Qiu A, Holbrook JD, Rifkin-Graboi A, GUSTO Study Group. The influence of CHRNA4, COMT, and maternal sensitivity on orienting and executive attention in 6-month-old infants. Brain Cogn. 2017 Aug;116:17–28.
Puschmann A, Bhidayasiri R, Weiner WJ. Synucleinopathies from bench to bedside. Parkinsonism Relat Disord. 2012 Jan;18(Suppl 1):24–7.
Del Campo N, Chamberlain SR, Sahakian BJ, Robbins TW. The roles of dopamine and noradrenaline in the pathophysiology and treatment of attention-deficit/hyperactivity disorder.Biol Psychiatry. 2011 Jun15;69(12):e145-57.
Marino C, Giorda R, Vanzin L, Molteni M, Lorusso ML, Nobile M, Baschirotto C, Alda M, Battaglia M. No evidence for association and linkage disequilibrium between dyslexia and markers of four dopamine-related genes. Eur Child Adolesc Psychiatry. 2003 Aug;12(4):198–202.
Stergiakouli E, Thapar A. Fitting the pieces together: current research on the genetic basis of attention-deficit/hyperactivity disorder (ADHD). Neuropsychiatr Dis Treat. 2010 Sep;7:6:551–60.
Bailey T, Beyond DSM. The role of auditory processing in attention and its disorders. Appl Neuropsychol Child. 2012;1(2):112–20.
Chen H, Wang G, Xia J, Zhou Y, Gao Y, Xu J, Huen MS, Siok WT, Jiang Y, Tan LH, Sun Y. Stuttering candidate genes DRD2 but not SLC6A3 is associated with developmental dyslexia in Chinese population. Behav Brain Funct. 2014 Sep 1;10(1):29.
Asherson PJ, Curran S. Approaches to gene mapping in complex disorders and their application in child psychiatry and psychology. Br J Psychiatry. 2001 Aug;179:122–8.
Ludwig KU, Roeske D, Herms S, Schumacher J, Warnke A, Plume E, Neuhoff N, Bruder J, Remschmidt H, Schulte-Körne G, Müller-Myhsok B, Nöthen MM, Hoffmann P. Variation in GRIN2B contributes to weak performance in verbal short-term memory in children with dyslexia. Am J Med Genet B Neuropsychiatr Genet. 2010 Mar;5(2):503–11.
Dalla Vecchia E, Mortimer N, Palladino VS, Kittel-Schneider S, Lesch KP, Reif A, Schenck A, Norton WHJ. Cross-species models of attention-deficit/hyperactivity disorder and autism spectrum disorder: lessons from CNTNAP2, ADGRL3, and PARK2. Psychiatr Genet. 2019 Feb;29(1):1–17.
Berke JD. What does dopamine mean? Nat Neurosci. 2018 Jun;21(6):787–93.
Glazer J, King A, Yoon C, Liberzon I, Kitayama S. DRD4 polymorphisms modulate reward positivity and P3a in a gambling task: exploring a genetic basis for cultural learning. Psychophysiology. 2020 Oct;57(10):e13623.
Naumova D, Grizenko N, Sengupta SM, Joober R. DRD4 exon 3 genotype and ADHD: Randomised pharmacodynamic investigation of treatment response to methylphenidate. World J Biol Psychiatry. 2019 Jul;20(6):486–95.
Stolf AR, Cupertino RB, Müller D, Sanvicente-Vieira B, Roman T, Vitola ES, Grevet EH, von Diemen L, Kessler FHP, Grassi-Oliveira R, Bau CHD, Rovaris DL, Pechansky F, Schuch JB. Effects of DRD2 splicing-regulatory polymorphism and DRD4 48 bp VNTR on crack cocaine addiction. J Neural Transm (Vienna). 2019 Feb;126(2):193–9.
Hasler R, Salzmann A, Bolzan T, Zimmermann J, Baud P, Giannakopoulos P, Perroud N. DAT1 and DRD4 genes involved in key dimensions of adult ADHD. Neurol Sci. 2015 Jun;36(6):861–9.
Ballon N, Leroy S, Roy C, Bourdel MC, Olie JP, Charles-Nicolas A, Krebs MO, Poirier MF. Polymorphisms TaqI a of the DRD2, BalI of the DRD3, exon III repeat of the DRD4, and 3’ UTR VNTR of the DAT: association with childhood ADHD in male african-caribbean cocaine dependents? Am J Med Genet B Neuropsychiatr Genet. 2007 Dec;5(8):1034–41.
Patte KA, Davis CA, Levitan RD, Kaplan AS, Carter-Major J, Kennedy JL. A behavioral genetic model of the Mechanisms underlying the link between obesity and symptoms of ADHD. J Atten Disord. 2020 Aug;24(10):1425–36.
Słopién A, Dmitrzak-Weglarz M, Rybakowski F, Rajewski A, Hauser J. Udział czynników genetycznych w etiologii ADHD. Badania populacyjne, geny układu amin katecholowych [Genetic background of ADHD: population studies, genes of the catecholamine system]. Psychiatr Pol. 2006 Jan-Feb;40(1):19–31.
Sewall KB, Caro SP, Sockman KW. Song competition affects monoamine levels in sensory and motor forebrain regions of male Lincoln’s sparrows (Melospiza lincolnii). PLoS ONE. 2013;8(3):e59857.
Hsiung GY, Kaplan BJ, Petryshen TL, Lu S, Field LL. A dyslexia susceptibility locus (DYX7) linked to dopamine D4 receptor (DRD4) region on chromosome 11p15.5.Am J Med Genet B Neuropsychiatr Genet. 2004 Feb15;125B(1):112–9.
Lan J, Song M, Pan C, Zhuang G, Wang Y, Ma W, Chu Q, Lai Q, Xu F, Li Y, Liu L, Wang W. Association between dopaminergic genes (SLC6A3 and DRD2) and stuttering among Han Chinese. J Hum Genet. 2009 Aug;54(8):457–60.
Peter B, Raskind WH, Matsushita M, Lisowski M, Vu T, Berninger VW, Wijsman EM, Brkanac Z. Replication of CNTNAP2 association with nonword repetition and support for FOXP2 association with timed reading and motor activities in a dyslexia family sample. J Neurodev Disord. 2011 Mar;3(1):39–49.
Truong DT, Rendall AR, Castelluccio BC, Eigsti IM, Fitch RH. Auditory processing and morphological anomalies in medial geniculate nucleus of Cntnap2 mutant mice. Behav Neurosci. 2015 Dec;129(6):731–43.
Gu H, Hou F, Liu L, Luo X, Nkomola PD, Xie X, Li X, Song R. Genetic variants in the CNTNAP2 gene are associated with gender differences among dyslexic children in China. EBioMedicine. 2018 Aug;34:165–170.
Poot M. Connecting the CNTNAP2 networks with Neurodevelopmental Disorders. Mol Syndromol. 2015 Feb;6(1):7–22.
Hu C, Chen W, Myers SJ, Yuan H, Traynelis SF. Human GRIN2B variants in neurodevelopmental disorders. J Pharmacol Sci. 2016 Oct;132(2):115–121. doi: https://doi.org/10.1016/j.jphs.2016.10.002. Epub 2016 Oct 19. Erratum in: J Pharmacol Sci. 2017 Apr;133(4):280.
Dorval KM, Wigg KG, Crosbie J, Tannock R, Kennedy JL, Ickowicz A, Pathare T, Malone M, Schachar R, Barr CL. Association of the glutamate receptor subunit gene GRIN2B with attention-deficit/hyperactivity disorder. Genes Brain Behav. 2007 Jul;6(5):444–52.
Willcutt Erik G, Pennington Bruce F, Olson Richard K,DeFries, John C. Understanding comorbidity: a twin study of reading disability and attention-deficit/hyperactivity disorder.[J]. American journal of medical genetics. Part B, Neuropsychiatric genetics: the official publication of the International Society of Psychiatric Genetics,2007,144B(6)
Willcutt Erik G, Pennington Bruce FDuncan, Laramie D, Keenan Janice M. Wadsworth Sally,Defries John C,Olson Richard K. Understanding the complex etiologies of developmental disorders: behavioral and molecular genetic approaches.[J]. Journal of developmental and behavioral pediatrics: JDBP,2010,31(7)
Nagai J, Rajbhandari AK, Gangwani MR, Hachisuka A, Coppola G, Masmanidis SC, Fanselow MS, Khakh BS. Hyperactivity with Disrupted Attention by Activation of an Astrocyte Synaptogenic Cue.Cell. 2019 May16;177(5):1280–1292.e20.
Shaywitz S, Shaywitz B, Wietecha L, Wigal S, McBurnett K, Williams D, Kronenberger WG, Hooper SR. Effect of Atomoxetine Treatment on Reading and Phonological Skills in children with Dyslexia or Attention-Deficit/Hyperactivity disorder and Comorbid Dyslexia in a Randomized, Placebo-Controlled Trial. J Child Adolesc Psychopharmacol. 2017 Feb;27(1):19–28.
Müller B, Schaadt G, Boltze J, Emmrich F, LEGASCREEN Consortium, Skeide MA, Neef NE, Kraft I, Brauer J, Friederici AD, Kirsten H, Wilcke A. ATP2C2 and DYX1C1 are putative modulators of dyslexia-related MMR. Brain Behav. 2017 Oct;18(11):e00851.
Martinelli A, Rice ML, Talcott JB, Diaz R, Smith S, Raza MH, Snowling MJ, Hulme C, Stein J, Hayiou-Thomas ME, Hawi Z, Kent L, Pitt SJ, Newbury DF, Paracchini S. A rare missense variant in the ATP2C2 gene is associated with language impairment and related measures.Hum Mol Genet. 2021 Jun9;30(12):1160–1171.
Tang Y, Illes P. Regulation of adult neural progenitor cell functions by purinergic signaling.Glia. 2017Feb;65(2):213–230.
He W, Hu Z. The role of the Golgi-resident SPCA Ca2+/Mn2+ pump in ionic homeostasis and neural function. Neurochem Res. 2012 Mar;37(3):455–68.
Mezzaroba L, Alfieri DF, Colado Simão AN, Vissoci Reiche EM. The role of zinc, copper, manganese and iron in neurodegenerative diseases. Neurotoxicology. 2019 Sep;74:230–41.
Stoodley CJ. The Cerebellum and Neurodevelopmental Disorders. Cerebellum. 2016 Feb;15(1):34–7.
Grandjean P, Landrigan PJ. Neurobehavioural effects of developmental toxicity. Lancet Neurol. 2014 Mar;13(3):330–8.
Mascheretti S, Trezzi V, Giorda R, Boivin M, Plourde V, Vitaro F, Brendgen M, Dionne G, Marino C. Complex effects of dyslexia risk factors account for ADHD traits: evidence from two independent samples. J Child Psychol Psychiatry. 2017 Jan;58(1):75–82.
SHAO S, KONG R, ZOU L et al. The roles of genes in the neuronal migration and neurite outgrowth network in developmental dyslexia: single-and multiple-risk genetic variants.Mol Neurobiol,2016,53 (6):3967–3975.
Gharami K, Das M, Das S. Essential role of docosahexaenoic acid towards development of a smarter brain. Neurochem Int. 2015 Oct;89:51–62.
Acknowledgements
Authors are grateful to guidance the Professor Olga V. Saik of Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia. The main tool used in this study were provided by Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia.
Funding
The gene network of ADHD and dyslexia was analysed with the support of the National Natural Science Foundation of China “The neural mechanism of bilingual processing in Uyghur-Chinese bilingual dyslexia children”, project number: 81760597.
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Zuo Pengxiang, Ji Chun and He Hongyao development of criteria for prioritization and gene selection. He Hongyao, Zhang Jing and Zhong lin performed the reconstruction of gene networks. He Hongyao, Zuo Pengxiang and Gao xiaoyan produced results on analysis of ADHD gene network. Ji Chun, Gao xiaoyan and Liu fangfang performed SNPs analysis by GTEx resource. He Hongyao, Zuo Pengxiang and Li Zengchun produced results on analysis of dyslexia gene network. He Hongyao, Ji Chun, Zuo Pengxiang, Li Zengchun, Gao xiaoyan and Liu fangfang drafted the manuscript. Ji Chun, Zuo Pengxiang, He Hongyao, Li Zengchun, Gao xiaoyan, Liu fangfang and Ji Chun revised the manuscript. Zuo Pengxiang and Li Zengchun supervised the whole study. All authors have read the manuscript and approved the final version. He hongyao and Jichun are co-first authors who contributed equally to this manuscript.
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Additional file 1: Table S1.
Lists of genes associated with asthma and Hypertension in ANDsystem and GeneCard
Additional file 2: Table S2.
Results of gene ontology enrichment analysis
Additional file 3: Table S3.
Criterion 1 results
Additional file 4: Table S4.
Criterion 2 results
Additional file 5: Table S5.
Criterion 3 results
Additional file 6: Table S6.
Criteria 4 results
Additional file 7: Table S7.
Criteria 5–6 results
Additional file 8: Table S8.
Criterion 7 results
Additional file 9: Table S9.
Criterion 8 results
Additional file 10: Table S10.
Criteria 9–10 results
Additional file 11: Table S11.
Total score results
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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
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Hongyao, H., Chun, J., Xiaoyan, G. et al. Associative gene networks reveal novel candidates important for ADHD and dyslexia comorbidity. BMC Med Genomics 16, 208 (2023). https://doi.org/10.1186/s12920-023-01502-1
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DOI: https://doi.org/10.1186/s12920-023-01502-1