Genetic Investigation of Inherited Variants in a Multiplex Autism Spectrum Disorder (ASD) Family Using Whole-Genome Sequencing (WGS)

Mehdi Agha Gholizadeh; Farkhondeh Behjati; Masoud Garshasbi

doi:10.18502/ijph.v54i5.18643

Genetic Investigation of Inherited Variants in a Multiplex Autism Spectrum Disorder (ASD) Family Using Whole-Genome Sequencing (WGS)

Mehdi Agha Gholizadeh

Farkhondeh Behjati

Masoud Garshasbi

Abstract

Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by early-onset challenges in social communication, repetitive behaviors, and clinical diversity. ASD is a highly heritable disorder, however, the exact mechanism by which inherited variants contribute to ASD in multiplex families, where more than one affected individual within a family is presented, remains unclear. We aimed to identify inherited genes in patients with ASD in a family with two affected siblings using Whole Genome Sequencing (WGS).
Methods: We performed WGS on two patients from a single family diagnosed with ASD. All of the patients were diagnosed with ASD using the gold-standard Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). We used various bioinformatics approaches to identify a list of prioritized candidate genes that may be associated with ASD or other neurodevelopmental disorders in this family.
Results: Our WGS analysis identified three potential candidate genes (EVI5:c.-82+866C>T, RAPGEF1:c.668C>T;p. Thr223Ile and PDZD4:c.-457G>A)) associated with ASD shared by the two patients. Additionally, utilizing various in-silico prediction tools and analysis of bioinformatics databases revealed that these rare variants are predicted to be deleterious and may contribute to ASDs. The identified variants are the first variants reported in ASD patients in the Iranian population that could be subjected to further validation studies.
Conclusion: These findings shed light on the genetic diversity of ASD within multiplex families and emphasize the complexity of genetic basis of ASD. Understanding the underlying genetic architecture of ASD is pivotal for advancing precise diagnostics and tailored therapeutic strategies.

1. Association AP (2013). American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, Arlington. https://repository.poltekkes-kaltim.ac.id/657/1/Diagnostic%20and%20statistical%20manual%20of%20mental%20disorders%20_%20DSM-5%20(%20PDFDrive.com%20).pdf
2. American Psychiatric Association, DSM-5 Task Force (2013). Diagnostic and statistical manual of mental disorders: DSM-5™ (5th ed.). American Psychiatric Publishing, Inc.
3. Tick B, Bolton P, Happé F, Rutter M, Rijsdijk F (2016). Heritability of autism spectrum disorders: a meta‐analysis of twin studies. J Child Psychol Psychiatry, 57 (5):585-595.
4. Yuen RKC, Merico D, Bookman M, et al (2017). Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nat Neurosci, 20 (4):602-611.
5. Ramaswami G, Geschwind DH (2018). Genetics of autism spectrum disorder. Handb Clin Neurol, 147:321-329.
6. Fernandez BA, Scherer SW (2017). Syndromic autism spectrum disorders: moving from a clinically defined to a molecularly defined approach. Dialogues Clin Neurosci, 19 (4):353-371.
7. Jiang YH, Yuen RK, Jin X, et al (2013). Detection of clinically relevant genetic variants in autism spectrum disorder by whole-genome sequencing. Am J Hum Genet, 93 (2):249-263.
8. Sun Y, Liu F, Fan C, et al (2021). Characterizing sensitivity and coverage of clinical WGS as a diagnostic test for genetic disorders. BMC Med Genomics, 14:102.
9. Li H, Durbin R (2009). Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics, 25 (14):1754-1760.
10. Li H, Handsaker B, Wysoker A, et al (2009). The sequence alignment/map format and SAMtools. Bioinformatics, 25 (16):2078-2079.
11. McKenna A, Hanna M, Banks E, et al (2010). The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res, 20 (9):1297-1303.
12. McLaren W, Pritchard B, Rios D, et al (2010). Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics, 26 (16):2069-2070.
13. Wang K, Li M, Hakonarson H (2010). ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res, 38 (16):e164.
14. Kircher M, Witten DM, Jain P, et al (2014). A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet, 46 (3):310-315.
15. Rentzsch P, Witten D, Cooper GM, et al (2019). CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res, 47 (D1):D886-D894.
16. Liu X, Wu C, Li C, Boerwinkle E (2016). dbNSFP v3. 0: A one‐stop database of functional predictions and annotations for human nonsynonymous and splice‐site SNVs. Hum Mutat, 37 (3):235-241.
17. Chen KM, Wong AK, Troyanskaya OG, Zhou J (2022). A sequence-based global map of regulatory activity for deciphering human genetics. Nat Genet, 54 (7):940-949.
18. Ernst J, Kheradpour P, Mikkelsen TS, et al (2011). Mapping and analysis of chromatin state dynamics in nine human cell types. Nature, 473 (7345):43-49.
19. Karolchik D, Baertsch R, Diekhans M, et al (2003). The UCSC genome browser database. Nucleic Acids Res, 31 (1):51-54.
20. Bairoch A, Boeckmann B (1992). The SWISS-PROT protein sequence data bank. Nucleic Acids Res, 20 Suppl(Suppl):2019-22.
21. Abyzov A, Urban AE, Snyder M, Gerstein M (2011). CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res, 21 (6):974-984.
22. Jia L, Liu N, Huang F, et al (2020). intansv: an R package for integrative analysis of structural variations. PeerJ, 8:e8867.
23. Battle DE (2013) Diagnostic and Statistical Manual of Mental Disorders (DSM). Codas, 25(2):191-2.
24. Chaste P, Leboyer M (2012). Autism risk factors: genes, environment, and gene-environment interactions. Dialogues Clin Neurosci, 14(3):281-92.
25. Hallmayer J, Cleveland S, Torres A, et al (2011). Genetic heritability and shared environmental factors among twin pairs with autism. Arch Gen Psychiatry, 68 (11):1095-1102.
26. Trost B, Walker S, Haider SA, et al (2019). Impact of DNA source on genetic variant detection from human whole-genome sequencing data. J Med Genet, 56(12):809-817.
27. Mazdeh M, Rahimi M, Eftekharian MM, et al (2017). Ecotropic Viral Integration Site 5 (EVI5) expression analysis in multiple sclerosis patients. Hum Antibodies, 26 (3):113-119.
28. Razavian T, Shakib ME, Gharagozli K, et al (2020). Association of rs12487066, rs12044852, rs10735781, rs3135388, rs6897932, rs1321172, rs10492972, and rs9657904 polymorphisms with multiple sclerosis in Iranian population. Oman Med J, 35 (4):e150.
29. Piton A, Gauthier J, Hamdan F, et al (2011). Systematic resequencing of X-chromosome synaptic genes in autism spectrum disorder and schizophrenia. Mol Psychiatry, 16 (8):867-880.
30. Giovenino C, Trajkova S, Pavinato L, et al (2023). Skewed X-chromosome inactivation in unsolved neurodevelopmental disease cases can guide re-evaluation For X-linked genes. Eur J Hum Genet, 31(11):1228-1236.
31. Wu J, Yu P, Jin X, et al (2018). Genomic landscapes of Chinese sporadic autism spectrum disorders revealed by whole-genome sequencing. J Genet Genomics, 45 (10):527-538.
32. Lek M, Karczewski KJ, Minikel EV, et al (2016). Analysis of protein-coding genetic variation in 60,706 humans. Nature, 536 (7616):285-291.
33. Samocha KE, Robinson EB, Sanders SJ, et al (2014). A framework for the interpretation of de novo mutation in human disease. Nat Genet, 46 (9):944-950.
34. Voss AK, Britto JM, Dixon MP, et al (2008). C3G regulates cortical neuron migration, preplate splitting and radial glial cell attachment. Development, 135(12):2139-49.
35. Oskoui M, Gazzellone MJ, Thiruvahindrapuram B, et al (2015). Clinically relevant copy number variations detected in cerebral palsy. Nat Commun, 6:7949.
36. Li N, Zhou P, Yang M, et al (2021). Zebrafish modeling mimics developmental phenotype of patients with RAPGEF1 mutation. Clin Genet, 100 (2):144-155.
37. Voss AK, Krebs DL, Thomas T (2006). C3G regulates the size of the cerebral cortex neural precursor population. EMBO J, 25(15):3652-3663.

Files	XML PDF (2MB)
Issue	Vol 54 No 5 (2025)
Section	Original Article(s)
DOI	https://doi.org/10.18502/ijph.v54i5.18643
Keywords
Autism spectrum disorder (ASD) Whole-genome sequencing (WGS) Multiplex families Next generation sequence (NGS)

Rights and permissions
	This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite

Agha Gholizadeh M, Behjati F, Garshasbi M. Genetic Investigation of Inherited Variants in a Multiplex Autism Spectrum Disorder (ASD) Family Using Whole-Genome Sequencing (WGS). Iran J Public Health. 2025;54(5):1087-1097.

Vancouver

Download Citation