Genetic and epigenetic mechanisms associated with child abuse: A bioinformatics study

Introduction

Child abuse is a pervasive social and public health issue with profound and long-lasting effects on the psychological, physiological and neurological well-being of an individual (1). Exposure to maltreatment during childhood has been linked to an increased risk of developing mental health disorders, including depression, anxiety and post-traumatic stress disorder (2), as well as physiological conditions, such as cardiovascular diseases and immune dysregulation (3). While environmental and psychosocial factors play a crucial role in the consequences of child abuse, emerging evidence suggests that genetic and epigenetic mechanisms may significantly influence the susceptibility of an individual to adversity and their ability to cope with trauma (4).

Genetic factors, including polymorphisms in stress-related genes, have been found to be associated with variations in the response of an individual to trauma, potentially predisposing some individuals to more severe psychological outcomes (5). Additionally, epigenetic modifications, such as DNA methylation and histone modifications, can alter gene expression without changing the DNA sequence, influencing how individuals respond to early-life stressors (6). Studies have indicated that childhood adversity can lead to persistent epigenetic changes that affect stress response systems, such as the hypothalamic-pituitary-adrenal (HPA) axis, and contribute to an increased risk of developing psychiatric disorders later in life (7,8).

Despite growing interest in this field, there is still limited research applying bioinformatics approaches to systematically identify genetic and epigenetic mechanisms associated with child abuse (9). The present study aimed to bridge this gap by conducting a bioinformatics analysis of genetic variations and epigenetic modifications linked to child abuse. By integrating data from genome-wide association studies (GWAS) and epigenetic databases, the present study aimed to identify potential biomarkers that may enhance the current understanding of the biological underpinnings of child abuse-related psychopathology (10). The findings presented herein may contribute to the development of early risk assessment tools and targeted interventions for affected individuals (11).

Data and methods

Dataset collection

The key terms ‘Child abuse’, ‘Child sexual abuse’, ‘Child maltreatment’, ‘Child neglect’, ‘Child physical abuse’ and ‘Child sexual abuse’ were entered into the MEDLINE and PubMed databases without date restrictions. English-language publications related to these terms were searched, transformed from the PubMed online database (https://pubmed.ncbi.nlm.nih.gov/) and merged (downloaded and merged) into Medline format. PubMed is a search engine for life sciences and biomedical articles as it contains abstracts and references, but often also full articles with open access. It is part of the NCBI (National Center for Biotechnology Information) (12).

Data filtering and pre-analysis

In this step, key terms related to abuse, maltreatment and neglect were extracted. The process was carried out in the bioinformatics environment of MATLAB Bioinformatics Toolbox using regular expression and the corresponding PUBMED ID, during which the data was evaluated in order to extract and store the results. Using bioinformatics techniques, publications from different datasets were filtered to remove publications that appear more than once. This was performed in order to create the complete set of unique publications.

Extraction of single nucleotide polymorphisms (SNPs) and data annotation

SNPs associated with child abuse were extracted and annotated from various genomic databases. The identified SNPs were located in genes previously implicated in stress response and neurodevelopment. Additionally, non-coding regions, including long intergenic non-coding (LINC) RNAs and provisional gene identifiers (LOC), were analyzed.

LINC RNA genes represent a category of long non-coding RNAs that do not overlap with protein-coding genes and are involved in gene regulation. LOC genes serve as provisional gene identifiers assigned in genomic databases when their precise function or official gene symbol has yet to be determined.

For annotation, databases such as dbSNP (https://www.ncbi.nlm.nih.gov/snp/), GWAS Catalog (https://www.ebi.ac.uk/gwas/) and Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.genome.jp/kegg/) were utilized to classify the SNPs based on their genomic location, functional impact, and association with biological pathways.

Semantic analysis and gene targets classification

In this step, the annotated gene targets were scored through semantic analysis in order to identify and isolate the most direct and likely targets associated with child abuse. Thus, a scoring function was created in which we can differentiate the ‘related SNPs’ as well as the ‘strongly related SNPs’, which were ranked and rated in terms of the frequency of occurrence of polymorphisms in the articles studied and the frequency of occurrence of polymorphisms based on each genetic target.

The score genetic targets were as follows:

where i is the number of corresponding SNPs per genetic target and n is the total number of the SNPs per genetic target. Scoring function was calculated as follows: Related_Genetic Targets: Score <3, strongly related genetic target: score ≥3.

All SNPs were identified and classified into these two main categories, strongly related genetic targets and related genetic targets based on their correlation rate with the particular terms studied.

Genetic targets, disease, biological pathway analysis and dark DNA matters in adversity genes

From group A ‘Strongly related Genetic Target’ of genetic targets with high correlation, their related SNPs were analyzed in order to collect all disease ontologies. A bioinformatics algorithm was then used to identify the disease ontologies that occur with higher frequency. This precise result of the most frequently appearing key terms was captured and visualized as a word cloud in Fig. 1.

Figure 1 Visual representation of the identified key terms.

This word cloud visualizes the most frequently occurring terms related to child abuse in the dataset. Larger words indicate higher frequency within the dataset, highlighting the central themes related to child maltreatment and neglect.

In the same manner, using genetic targets, genes, pseudogenes, primates, characterized regions of dark DNA matter, such as long non coding RNAs and microRNAs (miRNAs/miRs), categories of biological pathways were discovered using the platform Reactome, in order to extract beneficial information regarding the major biological pathways of the key genetic targets.

The concept of ‘adversity genes’, as described by Levine et al (13) in 2017, is relevant to this analysis. Adversity genes refer to genetic elements that exhibit differential expression in response to environmental stressors, including childhood maltreatment. These genes, which include FKBP prolyl isomerase 5 (FKBP5), corticotropin releasing hormone receptor 1 (CRHR1), catechol-O-methyltransferase (COMT) and several non-coding RNA regions, play a pivotal role in the stress response of the body and the development of stress-related disorders (13). Their involvement in neuroendocrine signaling, epigenetic modulation, and immune system regulation further supports the hypothesis that adverse childhood experiences leave a lasting imprint at the genetic and epigenetic levels.

Epigenetic target analysis

All suspected SNPs potentially linked to epigenetics were studied through publications in order to understand the contribution of epigenetics to this subject. To obtain this, a search for specific polymorphisms corresponding to the genetic targets was performed and re-evaluated for their epigenetic contribution to this subject.

The suspected epigenetic polymorphisms were defined as polymorphisms corresponding to non-coding regions such as long non-coding RNAs (lncRNAs), LINCs RNAs, miRs and LOCs. The following terms were used:

LINC RNAs. A type of lncRNA that does not overlap protein-coding genes and plays a role in gene regulation, chromatin remodeling, and cellular processes.

LOC (locus). A placeholder name for genes or genomic regions that have been identified but are not yet fully characterized or named. These regions may contain functional elements, including non-coding RNAs or pseudogenes.

GWAS. A research approach that involves scanning complete sets of DNA (genomes) from multiple individuals to identify genetic variants associated with specific traits or diseases.

Epigenetics. The study of changes in gene expression that do not involve alterations in the DNA sequence, but are influenced by environmental and developmental factors, such as DNA methylation and histone modifications.

DNA methylation. A biochemical process that adds a methyl group to DNA, often silencing gene expression and playing a key role in regulating development and disease.

Histone modification. Chemical changes to histone proteins that affect the structure of chromatin and influence gene expression.

SNP. A variation in a single nucleotide at a specific position in the genome that may be associated with genetic predispositions to diseases or traits.

HPA axis. A major neuroendocrine system that regulates stress responses, metabolism, immune function, and mood through the release of hormones such as cortisol.

Results

Dataset collection

A systematic data mining and semantic analysis approach was employed to identify genes, variants and SNPs associated with child abuse, maltreatment and neglect. A total of 201,121 publications containing key terms, such as ‘Child abuse’, ‘Child sexual abuse’, ‘Child maltreatment’, ‘Child neglect’ and ‘Child physical abuse’ were retrieved from the MEDLINE and PubMed databases. The extracted data were filtered and processed to generate a dataset of genetic factors relevant to child abuse. The key words used in this analysis are closely related to the research subject and represent the most frequently occurring terms in the relevant literature (Table I).

Table I Key words used and directly related to the types of child abuse.

Table I

Key words used and directly related to the types of child abuse.

Key words	Definition
Child abuse	Physical, emotional, or sexual harm or potential harm inflicted on a child by an adult or another child.
• Child physical abuse
• Child emotional abuse
• Child sexual abuse
Child neglect	Failure to provide basic needs such as food, shelter, medical care, education, and supervision to a child. This can include neglecting a child's health, safety, or emotional well-being.
Child maltreatment	A broad term that encompasses both child abuse and neglect, as well as any other form of harmful behavior towards a child. Maltreatment is any form of harm or mistreatment that a child experiences at the hands of a caregiver or authority figure.

A visual representation of the identified key terms is presented in Fig. 1, which illustrates the frequency of key terms related to child abuse within the dataset. This visualization highlights the most commonly occurring terminology in the literature, providing insight into the breadth and focus of existing research. Additionally, a word cloud was generated to visually represent the most prominent terms extracted from the dataset (Fig. 2).

Figure 2 Word cloud presentation of identified key terms based on dataset input.

Data filtering and pre-analysis

A total of 529 abuse-related keywords were identified within the dataset, including ‘Child sexual abuse’, ‘Child emotional abuse’ and ‘Child neglect’. Semantic analysis was applied to refine the dataset, ensuring the inclusion of only the most relevant terms. The frequency of these key terms was analyzed, and the most frequently occurring words were identified (Table II). The table provides an overview of the most frequently occurring key terms within the dataset, demonstrating the breadth of terminology used in the literature related to child abuse.

Table II List of the most frequently shown key terms describing child abuse within the dataset (frequency >150).

Table II

List of the most frequently shown key terms describing child abuse within the dataset (frequency >150).

Name	Frequency
Child abuse	563
Legal approach	360
Population	305
Child maltreatment	295
Child sexual abuse	289
Sexual abuse	249
Adolescents	242
Behavior	234
Age factors	218
Youth	215
Professional patient relationship	205
Developed countries	200
Americas	190
Children	183
North America	171
Genetics and reproduction	171
Trauma	161
Depression	160
Economic factors	152

A visual representation of the identified key terms was created in the form of a word cloud (Fig. 2). The figure illustrates the most common key terms extracted from the dataset using semantic analysis. Words that appear more frequently in the relevant literature are depicted in larger fonts, illustrating the conceptual emphasis of the dataset.

Extraction of SNPs and data annotation

A total of 209 SNPs and 104 genetic targets, including genes, pseudogenes and transcription factors, were identified and extracted from online databases. The SNPs associated with child abuse were annotated using MATLAB algorithms, incorporating genomic information from dbSNP, GWAS Catalog and KEGG. The genes most frequently associated with child abuse were classified according to their functional role in stress response and neurodevelopment. The identified genetic targets and SNPs related to child abuse are listed in Table III. This table compiles the key genetic targets identified in association with child abuse. It includes genes, transcription factors, pseudogenes and non-coding RNAs, highlighting their role in neurodevelopmental and stress-related biological processes.

Table III List of genetic targets and SNPs extracted and associated with child abuse.

Table III

List of genetic targets and SNPs extracted and associated with child abuse.

Name	Total unique SNPS/overall	SNPs	Type of SNPs (functional consequence)
FKBP5	15//87	rs3800373	genic_downstream_transcript_variant,intron_variant,3_prime_UTR_variant
		rs1360780	intron_variant
		rs4713916	intron_variant,genic_upstream_transcript_variant
		rs3777747	intron_variant
		rs2766533	genic_upstream_transcript_variant,intron_variant
		rs9296158	intron_variant
		rs737054	intron_variant
		rs9470080	intron_variant
		rs7771727	intron_variant,genic_downstream_transcript_variant
		rs4713902	intron_variant
		rs9394309	intron_variant
		rs9470079	intron_variant
		rs3798347	intron_variant
		rs10947563	intron_variant
		rs7748266	intron_variant
		rs947008	intron_variant
		rs1360870
LINC02210-CRHR1	11//47	rs7209436	intron_variant
		rs4792887	intron_variant
		rs110402	intron_variant
		rs17689882	intron_variant
		rs242924	intron_variant
		rs2664008	intron_variant
		rs12944712	intron_variant
		rs9900679	intron_variant
		rs1876831	intron_variant
		rs9900679	intron_variant
		rs16940665	synonymous_variant,coding_sequence_variant
CRHR1	10	rs7209436	intron_variant
		rs4792887	intron_variant
		rs110402	intron_variant
		rs17689882	intron_variant
		rs242924	intron_variant
		rs2664008	intron_variant
		rs12944712	intron_variant
		rs9900679	intron_variant
		rs1876831	intron_variant
		rs16940665	synonymous_variant,coding_sequence_variant
LOC112267956	7	rs9296158	intron_variant
		rs1360780	intron_variant
		rs3777747	intron_variant
		rs737054	intron_variant
		rs4713902	intron_variant
		rs3798347	intron_variant
		rs7748266	intron_variant
COMT	6	rs165599	genic_downstream_transcript_variant,3_prime_UTR_variant,intron_variant
		rs5993882	genic_upstream_transcript_variant,upstream_transcript_variant,intron_variant
		rs737866	2KB_upstream_variant,genic_upstream_transcript_variant,upstream_transcript_variant,intron_variant
		rs4680	2KB_upstream_variant,coding_sequence_variant,upstream_transcript_variant,missense_variant
		rs6267	2KB_upstream_variant,coding_sequence_variant,upstream_transcript_variant,missense_variant
		rs4633	2KB_upstream_variant,coding_sequence_variant,upstream_transcript_variant,synonymous_variant
		rs4818	2KB_upstream_variant,coding_sequence_variant,upstream_transcript_variant,synonymous_variant
OXTR	6	rs2268498	2KB_upstream_variant,upstream_transcript_variant
		rs1042778	3_prime_UTR_variant,intron_variant
		rs53576	intron_variant
		rs2254298	intron_variant
		rs237895	intron_variant
		rs237885	intron_variant
		rs237987
IL1B	6	rs16944	2KB_upstream_variant,upstream_transcript_variant
		rs1143623	2KB_upstream_variant,upstream_transcript_variant
		rs1143627	2KB_upstream_variant,upstream_transcript_variant
		rs1143643	intron_variant
		rs1143633	intron_variant
		rs1143634	synonymous_variant,coding_sequence_variant
HTR2A	5	rs7997012	intron_variant
		rs6561333	intron_variant
		rs1885884	non_coding_transcript_variant,intron_variant
		rs9316235	intron_variant
		rs6313	synonymous_variant,coding_sequence_variant,intron_variant
CRHR2	5	rs2190242	intron_variant
		rs2284217	intron_variant
		rs2014663	intron_variant
		rs4722999	intron_variant,3_prime_UTR_variant
		rs12701020	non_coding_transcript_variant,intron_variant
FOXP2	5	rs7783012	intron_variant
		rs10262462	intron_variant
		rs1456031	genic_downstream_transcript_variant,intron_variant
		rs2396753	intron_variant
		rs2253478	intron_variant,genic_upstream_transcript_variant
IL19	5	rs1800896	genic_upstream_transcript_variant,intron_variant,upstream_transcript_variant,2KB_upstream_variant
		rs1800871	genic_upstream_transcript_variant,intron_variant,upstream_transcript_variant,2KB_upstream_variant
		rs1800872	genic_upstream_transcript_variant,intron_variant,upstream_transcript_variant,2KB_upstream_variant
		rs1800890	genic_upstream_transcript_variant,intron_variant
		rs6676671	intron_variant,genic_upstream_transcript_variant
GABRA2	5	rs279826	intron_variant
		rs11503014	5_prime_UTR_variant,intron_variant
		rs279858	missense_variant,synonymous_variant,coding_sequence_variant
		rs211034	intron_variant
		rs211035	missense_variant,intron_variant,coding_sequence_variant
NOS1AP	4	rs4145621	genic_upstream_transcript_variant,intron_variant
		rs6680461	intron_variant,genic_upstream_transcript_variant
		rs3751284	genic_upstream_transcript_variant,missense_variant,coding_sequence_variant,synonymous_variant
		rs348624	synonymous_variant,coding_sequence_variant
NR3C2	4	rs17581262	intron_variant
		rs5522	missense_variant,non_coding_transcript_variant,coding_sequence_variant
		rs5534	non_coding_transcript_variant,3_prime_UTR_variant,genic_downstream_transcript_variant
		rs2070951	non_coding_transcript_variant,5_prime_UTR_variant
IL1RN	4	rs9005	3_prime_UTR_variant
		rs4251961	intron_variant,upstream_transcript_variant,genic_upstream_transcript_variant
		rs315952	missense_variant,synonymous_variant,coding_sequence_variant
		rs419598	synonymous_variant,coding_sequence_variant
GRN	3	rs3859268	intron_variant
		rs2879096	intron_variant
		rs3785817	intron_variant
SLC6A4	3	rs25531	2KB_upstream_variant,intron_variant,genic_upstream_transcript_variant,upstream_transcript_variant
		rs3813034	3_prime_UTR_variant
		rs1042173	3_prime_UTR_variant
LOC105371801	3	rs17689882	intron_variant
		rs16940665	synonymous_variant,coding_sequence_variant
		rs1876831	intron_variant
LOC101929309	3	rs3800373	genic_downstream_transcript_variant,intron_variant,3_prime_UTR_variant
		rs6910300	intron_variant,genic_downstream_transcript_variant
		rs7771727	intron_variant,genic_downstream_transcript_variant
IL10	3	rs1800896	genic_upstream_transcript_variant,intron_variant,upstream_transcript_variant,2KB_upstream_variant
		rs1800871	genic_upstream_transcript_variant,intron_variant,upstream_transcript_variant,2KB_upstream_variant
		rs1800872	genic_upstream_transcript_variant,intron_variant,upstream_transcript_variant,2KB_upstream_variant
IL6R	3	rs4845617	5_prime_UTR_variant,genic_upstream_transcript_variant,intron_variant
		rs4537545	intron_variant,genic_downstream_transcript_variant
		rs2228145	missense_variant,coding_sequence_variant,intron_variant,genic_downstream_transcript_variant
IFNG	3	rs1861494	intron_variant
		rs2069718	intron_variant
		rs2430561	intron_variant
CRHBP	3	rs7728378	intron_variant
		rs6453267	intron_variant
		rs10474485	genic_downstream_transcript_variant,intron_variant
SLC6A2	3	rs1814270	intron_variant
		rs2242446	upstream_transcript_variant,intron_variant,5_prime_UTR_variant,2KB_upstream_variantgenic_upstream_transcript_variant
		rs5569	missense_variant,synonymous_variant,coding_sequence_variant
NR3C1	3	rs12655166	intron_variant,genic_upstream_transcript_variant
		rs10482672	intron_variant
		rs6198	non_coding_transcript_variant,3_prime_UTR_variant,genic_downstream_transcript_variant
CRP	3	rs3093059	upstream_transcript_variant,2KB_upstream_variant
		rs1417938	intron_variant
		rs1130864	intron_variant,3_prime_UTR_variant
		rs2794520
		rs3093077

[i] SNPs, single nucleotide polymorphisms; FKBP5, FKBP prolyl isomerase 5; CRHR1, corticotropin releasing hormone receptor 1; COMT, catechol-O-methyltransferase; OXTR, oxytocin receptor; IL1B, interleukin 1 beta; HTR2A, 5-hydroxytryptamine receptor 2A; CRHR2, corticotropin releasing hormone receptor 2; FOXP2, forkhead box P2; IL19, interleukin 19; GABRA2, gamma-aminobutyric acid type a receptor subunit alpha 2; NOS1AP, nitric oxide synthase 1 adaptor protein; NR3C2, nuclear receptor subfamily 3 group C member 2; IL1RN, interleukin 1 receptor antagonist; GRN, granulin precursor; SLC6A4, solute carrier family 6 member 4; IL10, interleukin 10; IL6R, interleukin 6 receptor; IFNG, interferon gamma; CRHBP, corticotropin releasing hormone binding protein; SLC6A2, solute carrier family 6 member 2; NR3C1, nuclear receptor subfamily 3 group C member 1; CRP, C-reactive protein.

The distribution of genetic targets and evidence found in the present study is visually presented in Fig. 3. This figure presents a graphical representation of the most frequently occurring genetic targets and non-coding regions associated with child abuse. The size of each genetic term corresponds to its relative frequency within the dataset, highlighting key genes, such as FKBP5, CRHR1 and COMT.

Figure 3 Word cloud representation of child abuse-related genes and other non-coding regions.

Semantic analysis and gene targets classification

A semantic analysis of the extracted SNPs revealed a genomic map of child abuse-related genetic targets. The genes, FKBP5, CRHR1, LINC02210-CRHR1 and COMT, were identified as the most frequently occurring targets. These genes are associated with stress response regulation, emotional resilience and neuroendocrine signaling pathways.

FKBP5. The FKBP5 gene is a part of the immunophilin proteins, which play a role in immune regulation and functions as a co-chaperone in glucocorticoid receptor activity in response to stressors, making it one of the most frequently encountered genes in studies of people who have undergone stress, and in particular, in children who have been abused (14). In addition to epigenetic modifications and other environmental factors, it appears that FKBP5 may modulate GR susceptibility by delaying or reducing its transcriptional activity (15).

CHRH1. The CRHR1 gene has been extensively studied due to its implication for sensitized reactivity in stressful conditions (16). Through these findings, a significant interaction of CHRH1 with childhood abuse and trauma and history of suicide attempts emerges. In the similar direction, in another study on 235 HPA axis SNPs, a trend of the rs2664008 polymorphism of the CRHR1 gene, early childhood abuse and suicide attempts in bipolar patients was indicated (17).

COMT. Variations in some genes, including the COMT gene, are known to be associated with susceptibility to stress and some mental disorders. The association between stressful events and genes is known to activate the mechanism of depression development (5).

Variations in these genes are associated with stress sensitivity and depressive cognitive biases. The interaction between genes and stressful events in childhood is considered to be a mechanism that plays a role in the development of depression and therefore helps to proactively identify symptoms of depression or other diseases through genetic susceptibility (5).

Genetic targets, disease, biological pathway analysis and dark DNA matters in adversity genes

The analysis of genetic targets revealed that the most common disorders associated with child abuse include neoplasms, depressive disorders, schizophrenia, neurodegenerative diseases and autoimmune conditions. These disorders share key biological pathways involved in the stress response, neurodevelopment and immune function. The most frequently occurring disease ontologies linked to child abuse are summarized in Table IV. The table categorizes the primary disease ontologies identified in the dataset, emphasizing conditions frequently associated with child abuse, such as neuropsychiatric disorders, metabolic syndromes, and immune-related pathologies.

Table IV List of the most frequently shown ontologies describing child abuse within the dataset.

Table IV

List of the most frequently shown ontologies describing child abuse within the dataset.

Disease ontology	Count
Neoplasms	4,052
Depressive disorder	3,864
Schizophrenia	3,316
Pain	2,122
Breast neoplasms	1,707
Colorectal neoplasms	1,235
Stress disorders_post_traumatic	1,098
Hyperhomocysteinemia	1,061

A visual representation of the key disease associations with child abuse is provided in Fig. 4, illustrating the most prominent biological ontologies derived from the analysis. A word cloud displaying the most frequently identified biological ontologies related to child abuse is presented in Fig. 4. Terms related to neurodevelopmental processes, stress response and immune system regulation appear prominently, emphasizing their significance in the dataset.

Figure 4 Word cloud presentation of the ontologies related to child abuse.

Further overrepresentation analysis of genetic targets demonstrated marked enrichment in biological pathways related to HPA axis regulation, dopaminergic signaling and synaptic plasticity. These pathways are crucial in the adaptation of the body to stress and have been implicated in mental health disorders commonly observed in individuals with a history of child abuse. The identified pathways are summarized in Table V. This table details the biological pathways enriched in the dataset, focusing on molecular mechanisms linked to stress response, neurodevelopment and immune regulation. The pathways were identified using overrepresentation analysis.

Table V Biological ontology mechanisms that occurred in the search and are associated with child abuse.

Table V

Biological ontology mechanisms that occurred in the search and are associated with child abuse.

Mental disorders	Cancer	Metabolic disorders	Neurodegenerative diseases	Autoimmune diseases	Other
Depressive Disorder	Neoplasms	Hypertension	Alzheimer disease	Arthritis Rheumatoid	Pain
Schizophrenia	Breast Neoplasms	Obesity	Parkinson disease	Lupus Erythematosus	Hyperhomocysteinemia
				Systemic
Stress Disorders Post Traumatic	Colorectal Neoplasms	Diabetes Mellitus			Wounds And Injuries
Anxiety Disorders	Lung Neoplasms	Hypotension			Asthma
Mental Disorders	Stomach Neoplasms	DiabetesMellitus_Type2			Periodontitis
Autistic Disorder	Carcinoma Non Small Cell Lung				Infections
Depressive Disorder Major	Prostatic Neoplasms				Attention Deficit Disorder
					With Hyperactivity
Bipolar Disorder	Esophageal Neoplasms				Alcoholism
Pain Insensitivity Congenital					Sepsis
Psychoses Substance Induced					Radiation Pneumonitis
Anxiety					Tuberculosis
Dyskinesia Drug Induced					Hepatitis B
Substance Related Disorders					Headache Disorders Secondary
Adjustment Disorders					Pulmonary Disease Chronic
					Obstructive
					Irritable Bowel Syndrome
					Fibrosis
					Cognition Disorders
					Cerebral Infarction
					Weight Loss

A graphical representation of these enriched pathways is provided in Fig. 5, highlighting the statistical significance of biological processes associated with child abuse-related genetic targets. This heatmap visually represents the statistical significance (P-values) of the biological pathways linked to the most frequently occurring genes in child abuse, grouped by functional categories such as immune signaling, neurodevelopment, and stress-related mechanisms. Different colors correspond to varying levels of statistical significance, with darker shades representing lower P-values and greater relevance. The exact P-values are presented numerically within each cell of the heatmap, and statistical significance is shown using the -log10(P-value) scale, allowing for both visual and quantitative interpretation.

Figure 5 Heatmap visualization of enriched biological pathways related to child abuse. This heatmap visually represents the statistical significance (P-values) of the biological pathways linked to the most frequently occurring genes in child abuse, grouped by functional categories such as immune signaling, neurodevelopment, and stress-related mechanisms. Different colors correspond to varying levels of statistical significance, with darker shades representing lower P-values and greater relevance. The exact P-values are presented numerically within each cell of the heatmap, and statistical significance is shown using the -log10(P-value) scale, allowing for both visual and quantitative interpretation.

Table VI provides a detailed breakdown of the most frequently identified biological pathways and their associated genetic targets. It highlights key signaling mechanisms implicated in the biological response to childhood adversity. The most frequently occurring biological pathways associated with adversity genes are detailed in Table VI.

Table VI Biological pathways of the common genetic targets and the corresponding genes most frequently involved in child abuse.

Table VI

Biological pathways of the common genetic targets and the corresponding genes most frequently involved in child abuse.

Pathway	Genetic target	Corresponding genes
Immune system	• Innate immune system	- C-type lectin receptors
		- Toll like receptor cascades
	• Cytokine signaling in immune system	- Signaling by interleukins
Signal transduction	• Signaling by GPCR	- GPCR ligand binding
		- GPCR downstream signaling
	• MAPK family signaling cascades
	• Signaling by receptor tyrosine kinases
Gene expression (transcription)	• RNA polymerase II transcription	- Generic transcription pathway
Cellular responses to stimuli	• Cellular responses to stress
Programmed cell death	• Regulated necrosis
Disease	• Infectious disease	- Parasitic infection pathways
		- Bacterial infection pathways
	• Diseases of immune system	- Diseases associated with the TLR signaling cascade
	• Disorders of developmental biology
	• Disorders of transmembrane transporters
Developmental biology
Neuronal system

Epigenetic target analysis

An analysis of epigenetic targets identified key non-coding RNAs (LINC, LOC and miR) that may mediate the effects of child abuse. miR-195, implicated in breast cancer, was found to be epigenetically regulated, while LOC105369506 and LOC100287329 were associated with stress-related epigenetic modifications (18). These findings suggest that epigenetic alterations in non-coding genomic regions may contribute to the long-term consequences of childhood adversity. The epigenetically significant genetic targets related to child abuse are compiled in Table VII.

Table VII The ‘usual suspects’ genetic targets epigenetically associated with child abuse.

Table VII

The ‘usual suspects’ genetic targets epigenetically associated with child abuse.

Genetic target	SNPs	Type of SNPs		Epigenetic association with child abuse	Epigenetic link
LINC02210-CRHR1	rs7209436	intron_variant	locus	x
	rs4792887	intron_variant
	rs110402	intron_variant
	rs17689882	intron_variant
	rs242924	intron_variant
	rs2664008	intron_variant
	rs12944712	intron_variant
	rs9900679	intron_variant
	rs1876831	intron_variant
	rs9900679	intron_variant
	rs16940665	synonymous_variant,coding_sequence_variant
LOC112267956	rs9296158	intron_variant	locus	x
	rs1360780	intron_variant
	rs3777747	intron_variant
	rs737054	intron_variant
	rs4713902	intron_variant
	rs3798347	intron_variant
	rs7748266	intron_variant
LOC105371801	rs17689882	intron_variant	locus	x
	rs16940665	synonymous_variant,coding_sequence_variant
	rs1876831	intron_variant
LOC101929309	rs3800373	genic_downstream_transcript_variant,intron_variant,3_prime_UTR_variant	ncRNA	x
	rs6910300	intron_variant,genic_down
		stream_transcript_variant
	rs7771727	intron_variant,genic_down
		stream_transcript_variant
miR-4761	rs4680	2KB_upstream_variant,coding_sequence_variant,upstream_transcript_variant,missense_variant	short non-coding RNA	√	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7016268/
LOC107986321	rs6857715	genic_upstream_transcript_variant,intron_variant,upstream_transcript_variant,non_coding_transcript_variant		x
LOC105371720	rs25531	2KB_upstream_variant,intron_variant,genic_upstream_transcript_variant,upstream_transcript_variant	ncRNA	x
LOC107986777	rs3037354	intron_variant,2KB_upstream_variant,upstream_transcript_variant,genic_upstream_transcript_variant	ncRNA	x
LOC105377387	rs34043524	intron_variant	RNA, long non-coding	x
LOC105370115	rs1886797	intron_variant	RNA, long non-coding	x
LOC105377864	rs6296	intron_variant,genic_upstream_transcript_variant,5_prime_UTR_variant,coding_sequence_variant,synonymous		x
LOC100287329	rs1041981	coding_sequence_variant,upstream_transcript_variant,2KB_upstream_variant,missense_variant	RNA, long non-coding	√	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7232649/
LOC105369506	rs11215217	intron_variant	RNA, long non-coding	√	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6689282/

miR-195 is a type of miRNA that has been implicated in breast cancer. miRNAs are small RNA molecules that regulate gene expression by binding to messenger RNA (mRNA) molecules and preventing their translation into proteins. Research has demonstrated that the expression of miR-195 is downregulated, or less active, in breast cancer cells compared to normal breast tissue. This suggests that it may play a role in the development or progression of breast cancer. The expression of miR-4761 is regulated by histone modifications in breast cancer cells, suggesting that epigenetic changes may contribute to its dysregulation in breast cancer (18).

LOC105369506 is a gene that has been studied in relation to various behaviors and conditions, and research has suggested that certain epigenetic modifications can play a role in the development of antisocial behavior and the effects of child abuse. For example, studies have found that individuals who have experienced abuse or neglect during childhood may have differences in epigenetic marks on genes related to stress response and emotional regulation, which may increase their risk for antisocial behavior later in life (19-21).

LOC100287329 is a gene that is also known as miR-548AA2. It is a miRNA gene located on chromosome 14 in humans. Abuse, whether physical, emotional, or sexual, can have a significant impact on the health of an individual. There is evidence to suggest that exposure to stress and trauma can lead to epigenetic changes that may contribute to the development of MS and other diseases (22).

Discussion

The findings of the present study highlight the genetic and epigenetic factors associated with child abuse, providing new insight into the biological underpinnings of stress-related disorders. Through systematic data mining and bioinformatics analysis, the present study identified 209 SNPs and 104 genetic targets, including FKBP5, CRHR1 and COMT, that are strongly associated with childhood maltreatment. The results also revealed that LINC RNAs, provisional gene identifiers (LOC) and miRNAs contribute to the molecular effects of early-life adversity.

A key finding of the present study was the prominent role of FKBP5 in child abuse-related pathways. This gene, which is involved in glucocorticoid receptor regulation (14,23,24), was one of the most frequently identified genetic targets in the dataset. The presence of CRHR1, a key regulator of the HPA axis, further supports the hypothesis that childhood stress alters neuroendocrine responses. The identification of COMT, which influences dopamine metabolism, aligns with increasing evidence that childhood trauma affects cognitive and emotional processing. Notably, the present study extends prior knowledge (24-26) by demonstrating that these genetic markers are not only statistically overrepresented in the child abuse dataset, but also frequently co-occur with stress-related SNPs.

Beyond individual genes, the study mapped the biological pathways most significantly associated with child abuse. Overrepresentation analysis revealed that child abuse-related genes are enriched in pathways linked to neuroinflammation, oxidative stress, and immune system dysregulation. These findings suggest that the physiological impact of childhood adversity extends beyond neurological effects to include systemic alterations that may predispose individuals to chronic diseases, including autoimmune disorders and metabolic conditions.

A particularly novel contribution of the present study is the identification of epigenetic markers associated with childhood trauma. The analysis uncovered miR-4761, LOC105369506 and LOC100287329 as key regulatory elements that may mediate the effects of abuse at the molecular level. The detection of these epigenetic factors underscores the role of non-coding genomic elements in shaping individual susceptibility to trauma-related disorders. Unlike previous studies, which have focused primarily on protein-coding genes, this study expands the scope of genetic investigation by incorporating non-coding RNA elements, providing a more comprehensive understanding of the genomic response to adversity.

The concept of adversity genes, first introduced by Levine et al (13), is reinforced by these findings. Their study demonstrated that genes exhibiting differential expression in response to childhood maltreatment are frequently involved in stress adaptation, neurodevelopment and immune regulation (13). While previous research has suggested that adversity genes contribute to vulnerability in trauma-exposed individuals, the present study provides a direct bioinformatics-based validation of their significance. The results confirm that these genes are consistently overrepresented in child abuse datasets, further solidifying their relevance in understanding the biological consequences of early-life stress.

These findings have critical clinical implications. The identification of genetic and epigenetic biomarkers associated with child abuse may pave the way for risk prediction models that could help identify individuals at increased risk of developing psychiatric or stress-related disorders. Additionally, these insights may contribute to the development of precision medicine approaches, where genetic screening informs targeted interventions for trauma survivors. From a forensic perspective, the biological evidence linking specific genetic variants to child abuse may also have applications in legal contexts, offering new tools for assessing the long-term consequences of maltreatment.

While the present study provides notable contributions to the field, certain limitations need to be acknowledged. Genetic predisposition alone does not determine individual outcomes, as environmental and social factors play a critical role in shaping resilience. Moreover, epigenetic modifications are dynamic, necessitating further longitudinal studies to assess their stability over time. Future research is thus required to focus on validating these findings through experimental approaches, including gene expression studies and methylation analyses in trauma-exposed populations. Additionally, integrating machine learning algorithms with bioinformatics pipelines may enhance predictive models for assessing genetic risk factors in child abuse cases.

In conclusion, the present study presents original evidence of the genetic and epigenetic alterations associated with child abuse. By identifying specific SNPs, gene targets, and regulatory elements linked to early-life adversity, these findings contribute to a growing understanding of how childhood trauma becomes biologically embedded. The integration of genetic, epigenetic, and pathway analysis provides a comprehensive framework for future research, with implications for both clinical practice and social policy. As the field progresses, these insights may help shape personalized intervention strategies aimed at mitigating the long-term impact of childhood maltreatment.

Acknowledgements

Not applicable.

Funding

Funding: No funding was received.

Availability of data and materials

The data generated in the present study may be requested from the corresponding author.

Authors' contributions

ED and DV conceived and designed the study. ED performed the data collection, data analysis and interpretation. LP contributed to the development of the bioinformatics methodology and figure processing. EE and GPC performed critical revisions and were involved in the analysis of the on the genetic data and its biological relevance, providing expert guidance. DV supervised the project and provided overall coordination and critical manuscript review. ED and LP drafted the manuscript. All authors contributed to the interpretation of results and manuscript preparation. All authors have read and approved the final manuscript. ED and DV confirm the authenticity of all the raw data.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

GPC is the Editor in Chief of the journal, and DV and EE are Editors of the journal. However, they had no personal involvement in the reviewing process, or any influence in terms of adjudicating on the final decision, for this article. The other authors declare that they have no competing interests.

References