Genome sequencing identifies multiple deleterious variants in autism patients with more severe phenotypes View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12-03

AUTHORS

Hui Guo, Michael H. Duyzend, Bradley P. Coe, Carl Baker, Kendra Hoekzema, Jennifer Gerdts, Tychele N. Turner, Michael C. Zody, Jennifer S. Beighley, Shwetha C. Murali, Bradley J. Nelson, , Michael J. Bamshad, Deborah A. Nickerson, Raphael A. Bernier, Evan E. Eichler

ABSTRACT

PurposeTo maximize the discovery of potentially pathogenic variants to better understand the diagnostic utility of genome sequencing (GS) and to assess how the presence of multiple risk events might affect the phenotypic severity in autism spectrum disorders (ASD).MethodsGS was applied to 180 simplex and multiplex ASD families (578 individuals, 213 patients) with exome sequencing and array comparative genomic hybridization further applied to a subset for validation and cross-platform comparisons.ResultsWe found that 40.8% of patients carried variants with evidence of disease risk, including a de novo frameshift variant in NR4A2 and two de novo missense variants in SYNCRIP, while 21.1% carried clinically relevant pathogenic or likely pathogenic variants. Patients with more than one risk variant (9.9%) were more severely affected with respect to cognitive ability compared with patients with a single or no-risk variant. We observed no instance among the 27 multiplex families where a pathogenic or likely pathogenic variant was transmitted to all affected members in the family.ConclusionThe study demonstrates the diagnostic utility of GS, especially for multiple risk variants that contribute to the phenotypic severity, shows the genetic heterogeneity in multiplex families, and provides evidence for new genes for follow up. More... »

PAGES

1611-1620

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41436-018-0380-2

    DOI

    http://dx.doi.org/10.1038/s41436-018-0380-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1110301002

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30504930


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