Investigation of the Genetic and Environmental Causes of Autism: a Multi-Center Study

Introduction to the Project The project represents an innovative, multi-center study aimed at investigating the genetic, epigenetic, and environmental underpinnings of Autism Spectrum Disorder (ASD). ASD is a complex neurodevelopmental condition that typically presents in early childhood and affects individuals in diverse ways, particularly in social communication and behavior. The disorder is marked by significant variability in symptom severity and presentation across individuals. Despite years of research, the molecular mechanisms driving the onset and progression of ASD remain elusive, making diagnosis and treatment challenging.

This research aims to fill this gap by using a multi-omics approach to better understand the biological, genetic, and environmental factors contributing to ASD. Through the integration of genomic, epigenomic, and transcriptomic data, the project seeks to uncover molecular signatures and interactions that could explain the variability in ASD presentation and severity. Additionally, the study aims to identify potential biomarkers that could lead to earlier diagnosis, better prognosis, and more personalized treatment options.

The study will be conducted across multiple research centers, with a structured and well-coordinated approach to data collection, sample processing, and analysis. This multi-disciplinary project brings together a range of expertise, including genetics, bioinformatics, clinical psychiatry, and data science, ensuring that all aspects of the research are approached with scientific rigor and precision. By analyzing samples from individuals with ASD, the project will provide new insights into the genetic and epigenetic factors involved in ASD, while also investigating the potential influence of environmental factors.

Multi-Departmental Structure and Project Integrity The project is organized into multiple specialized departments, each with a specific role in maintaining the integrity of the study and ensuring that all research activities are conducted to the highest standards. This structure allows for seamless collaboration between departments and ensures that all data is collected, processed, and analyzed with the utmost accuracy.

Overall Coordination and Oversight At the highest level, the project is managed by an overall coordinator who oversees all aspects of the research. This coordinator ensures that each department adheres to the project's goals and timeline, fostering communication between teams and facilitating the integration of clinical, genetic, and epigenetic data. Coordination ensures that any challenges arising in the course of the study are promptly addressed and that the project remains on track to meet its objectives.

The role of the overall coordinator is critical for ensuring the integration of the multi-omics data from the various research centers. The coordinator also plays a key part in the interpretation of data, working closely with the data analysis teams to ensure that the findings are cohesive and meaningful. Regular updates are shared across departments to maintain alignment with the project's overarching research goals.

Clinical Operations: Adult and Pediatric Units The clinical operations of the project are divided into two main sectors: adult and pediatric. Each sector is managed by a dedicated team that oversees clinical centers responsible for data collection. These clinical centers are located across various regions, enabling the project to gather a broad and diverse range of clinical data from individuals diagnosed with ASD.

Each clinical center follows standardized protocols for data collection, ensuring that all patient data is collected in a uniform and consistent manner. The data collection process involves the use of internationally recognized diagnostic tools, including the Autism Diagnostic Observation Schedule (ADOS) and the Social Communication Questionnaire (SCQ). These tools are essential for diagnosing ASD and for evaluating the severity of symptoms.

The clinical operations also involve the collection of sociodemographic data, developmental histories, and information on comorbid conditions. This data is gathered through questionnaires and clinical interviews conducted by trained healthcare professionals. The collection of this comprehensive dataset is critical for understanding the clinical heterogeneity of ASD and for correlating clinical symptoms with genetic and environmental data.

Genetic and Omics Divisions The genetic and omics divisions form the core of the project, providing the molecular data that will be used to uncover the genetic and epigenetic mechanisms driving ASD. The division oversees the collection, processing, and analysis of biological samples, which include venous blood and nasal swabs obtained from study participants.

Sample Collection and Processing The biological samples collected from participants undergo a rigorous and standardized process of nucleic acid extraction. This process, managed by the nucleic acid extraction and purification teams, involves isolating high-quality DNA and RNA from the blood and nasal swab samples. These samples are essential for downstream sequencing and analyses. The quality of the extracted DNA and RNA is critical, as any impurities or degradation could affect the accuracy of the genetic and epigenetic analyses.

Once extracted, the DNA and RNA samples are subjected to multiple quality control checks. These checks are overseen by the quality control and storage teams, who ensure that the nucleic acids meet the required standards for sequencing. Samples that pass the quality control checks are stored under optimal conditions, preserving their integrity until they are ready to be sequenced.

Genomic Analysis The genomic analysis of DNA samples begins with whole genome sequencing (WGS), a comprehensive technique that allows researchers to examine the entire genetic code of each participant. WGS enables the identification of a wide range of genetic variants, including single nucleotide polymorphisms (SNPs), copy number variations (CNVs), insertions and deletions (InDels), and structural variants. These variants are evaluated to determine their potential contribution to ASD.

One of the primary analyses conducted within the genomic division is the genome-wide association study (GWAS). GWAS is used to identify genetic variants that are significantly associated with ASD by comparing the frequency of variants between individuals with ASD and those without the disorder. This analysis is particularly useful for identifying common genetic variants that may contribute to ASD risk.

In addition to GWAS, the project also conducts quantitative trait locus (QTL) analysis. QTL analysis examines how genetic variants influence quantitative traits related to ASD, such as symptom severity, cognitive functioning, or developmental milestones. This type of analysis helps to elucidate the genetic basis for phenotypic variability in ASD and identifies specific variants that may contribute to differences in symptom presentation.

Epigenetic Analysis The epigenetic component of the project focuses on how environmental factors influence gene expression and contribute to the risk of ASD. DNA methylation is one of the key epigenetic mechanisms studied in this project. Using whole genome bisulfite sequencing (WGBS), researchers analyze DNA methylation patterns across the entire genome. WGBS is considered the gold standard for high-resolution DNA methylation analysis, allowing the identification of methylation changes that are associated with ASD.

Differentially methylated regions (DMRs) are genomic regions where the methylation levels differ between individuals with ASD and unaffected controls. By identifying these DMRs, researchers can pinpoint specific genomic regions where environmental factors may be influencing gene expression in individuals with ASD.

Additionally, allele-specific methylation (ASM) is explored in this study. ASM analysis investigates how methylation patterns differ between the two copies of a gene inherited from each parent. This analysis helps researchers understand how genetic and epigenetic factors interact to influence gene expression and contribute to ASD risk.

As part of the epigenetic analysis, m-QTL (methylation quantitative trait loci) analysis will be conducted to investigate the relationship between genetic variants and DNA methylation patterns across the genome. m-QTLs are genomic loci where variations in DNA sequence are associated with differences in DNA methylation levels. By identifying these loci, the project aims to uncover how genetic variation influences epigenetic regulation and, subsequently, how these interactions may contribute to the risk of developing ASD. This analysis will provide valuable insights into the interplay between genetic and epigenetic factors, helping to elucidate the molecular mechanisms that underpin ASD. Additionally, m-QTLs may serve as potential biomarkers for understanding the environmental modulation of genetic susceptibility in individuals with ASD.

The epigenetic data is also integrated with genetic data to conduct epigenome-wide association studies (EWAS). EWAS examines how DNA methylation patterns correlate with ASD, with the goal of identifying methylation marks that could serve as biomarkers for the disorder. These analyses are essential for understanding how environmental exposures, such as prenatal stress, toxins, or maternal health, may affect ASD risk through epigenetic mechanisms.

Transcriptomic Analysis The transcriptomic analysis focuses on understanding how gene expression differs between individuals with ASD and those without the disorder. RNA sequencing (RNA-seq) is used to measure the levels of gene expression in both blood and nasal swab samples. RNA-seq provides a comprehensive view of the transcriptome, which represents the complete set of RNA transcripts present in a cell or tissue at a given time.

The RNA-seq data is used to perform differential gene expression (DGE) analysis, which identifies genes that are either upregulated or downregulated in individuals with ASD. This analysis helps to uncover the molecular pathways that are disrupted in ASD and highlights potential biomarkers that could be used for early diagnosis or treatment.

In addition to identifying differentially expressed genes, the transcriptomic data is used to perform pathway analysis. Pathway analysis examines how changes in gene expression affect biological processes and cellular functions. This analysis can identify key pathways that may be disrupted in ASD, providing insights into the biological mechanisms underlying the disorder.

By comparing the gene expression profiles from both blood and nasal swab samples, researchers can identify tissue-specific expression patterns. This comparison provides a more nuanced understanding of the molecular differences between tissues and may reveal tissue-specific biomarkers for ASD.

eQTL (Expression Quantitative Trait Loci) Analysis

In addition to m-QTL analysis, integrating eQTL (expression quantitative trait loci) analysis will allow the project to identify genetic variants that affect gene expression levels. eQTLs are loci where genetic variations are correlated with changes in gene expression. By analyzing eQTLs, the study can investigate how specific genetic variants influence the transcription of genes related to ASD. This approach helps uncover regulatory pathways and gene networks that contribute to the variability in ASD symptoms. The integration of eQTL analysis with transcriptomic data (such as RNA-Seq) provides a nuanced understanding of how genetic risk factors alter gene expression, ultimately impacting the phenotypic manifestation of ASD.

Epitranscriptomic Analysis

Beyond the study of DNA and RNA through genomics and transcriptomics, epitranscriptomics explores post-transcriptional modifications of RNA, such as N6-methyladen

• Collection and Completion of Genetic Raw Data (WGS and WGBS) [Срок оценки: Expected to complete within 30 months of study initiation]
• Integration of Genetic Data [Срок оценки: Expected to complete within 60 months of study initiation.]