Burden of rare deleterious variants in WNT signaling genes among 511 myelomeningocele patients

Hebert, Luke; Hillman, Paul; Baker, Craig; Brown, M. R. W.; Ashley–Koch, Allison; Hixson, James E.; Morrison, Alanna C.; Au, Kit Sing

doi:10.1371/journal.pone.0239083

Cited by 9 publications

(10 citation statements)

References 68 publications

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“…Hence, it is evident that these four genes, namely, NCOA3 , KMT2C , KMT2D , and TNRC6B are interrelated to each other in their function. Also, it has been reported that the TNRC6B gene shows an association with myelomeningocele in the Mexican American population ( Hebert et al, 2020 ). Therefore, these genes might have roles in SB.…”

Section: Discussionmentioning

confidence: 98%

Identification of the Key Regulators of Spina Bifida Through Graph-Theoretical Approach

et al. 2021

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Spina Bifida (SB) is a congenital spinal cord malformation. Efforts to discern the key regulators (KRs) of the SB protein-protein interaction (PPI) network are requisite for developing its successful interventions. The architecture of the SB network, constructed from 117 manually curated genes was found to self-organize into a scale-free fractal state having a weak hierarchical organization. We identified three modules/motifs consisting of ten KRs, namely, TNIP1, TNF, TRAF1, TNRC6B, KMT2C, KMT2D, NCOA3, TRDMT1, DICER1, and HDAC1. These KRs serve as the backbone of the network, they propagate signals through the different hierarchical levels of the network to conserve the network’s stability while maintaining low popularity in the network. We also observed that the SB network exhibits a rich-club organization, the formation of which is attributed to our key regulators also except for TNIP1 and TRDMT1. The KRs that were found to ally with each other and emerge in the same motif, open up a new dimension of research of studying these KRs together. Owing to the multiple etiology and mechanisms of SB, a combination of several biomarkers is expected to have higher diagnostic accuracy for SB as compared to using a single biomarker. So, if all the KRs present in a single module/motif are targetted together, they can serve as biomarkers for the diagnosis of SB. Our study puts forward some novel SB-related genes that need further experimental validation to be considered as reliable future biomarkers and therapeutic targets.

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Section: Discussionmentioning

confidence: 98%

Identification of the Key Regulators of Spina Bifida Through Graph-Theoretical Approach

et al. 2021

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“…The correlation between variants pathogenic prediction on protein function or structure and abnormal clinical outcomes are validated by experimental facts at the current stage. For certain phenotypes, an evident enrichment of deleterious variants in a set of disease-related genes, such as the increased mutational burden in essential genes in autism spectrum disorder ( Ji et al, 2016 ), WNT signaling genes in myelomeningocele ( Hebert et al, 2020 ), a set of 5 genes in epilepsy ( Leu et al, 2015 ). The gap between observed higher burden genes and clinical phenotype is then bridged by functional or mechanical experimental studies.…”

Section: Discussionmentioning

confidence: 99%

Computational approaches for predicting variant impact: An overview from resources, principles to applications

et al. 2022

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One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.

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“…They also included all the variants inherited from unaffected healthy parents, potentially explaining their high percentage of variant detection in known candidate genes. Hebert et al (2020) evaluated the burden of rare deleterious variants in WNT signaling genes among 511 MMC patients and showed that 10 WNT signaling genes were disrupted with a higher mutational burden among Mexican American MMC subjects compared to reference subjects. Lemay et al (2015) showed that, by applying WES in 43 trio NTDs families, loss-of-function (LOF)…”

Section: Discussionmentioning

confidence: 99%

“…In order to include a larger cohort of SB cases, we screened our collaborator's SB WES cohort of 511 MMC individuals (Hebert et al, 2020) for the variants in the total of 18 potential candidate genes. However, no likely deleterious variants were identified in these 18 genes.…”

Section: Analysis Of 136 Potential Candidate Genes Identifies Likely ...mentioning

confidence: 99%

Whole exome sequencing identifies potential candidate genes for spina bifida derived from mouse models

Wang

Seltzsam

Zheng

et al. 2022

American J of Med Genetics Pt A

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Spina bifida (SB) is the second most common nonlethal congenital malformation. The existence of monogenic SB mouse models and human monogenic syndromes with SB features indicate that human SB may be caused by monogenic genes. We hypothesized that whole exome sequencing (WES) allows identification of potential candidate genes by (i) generating a list of 136 candidate genes for SB, and (ii) by unbiased exome‐wide analysis. We generated a list of 136 potential candidate genes from three categories and evaluated WES data of 50 unrelated SB cases for likely deleterious variants in 136 potential candidate genes, and for potential SB candidate genes exome‐wide. We identified 6 likely deleterious variants in 6 of the 136 potential SB candidate genes in 6 of the 50 SB cases, whereof 4 genes were derived from mouse models, 1 gene was derived from human nonsyndromic SB, and 1 gene was derived from candidate genes known to cause human syndromic SB. In addition, by unbiased exome‐wide analysis, we identified 12 genes as potential candidates for SB. Identification of these 18 potential candidate genes in larger SB cohorts will help decide which ones can be considered as novel monogenic causes of human SB.

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Burden of rare deleterious variants in WNT signaling genes among 511 myelomeningocele patients

Cited by 9 publications

References 68 publications

Identification of the Key Regulators of Spina Bifida Through Graph-Theoretical Approach

Identification of the Key Regulators of Spina Bifida Through Graph-Theoretical Approach

Computational approaches for predicting variant impact: An overview from resources, principles to applications

Whole exome sequencing identifies potential candidate genes for spina bifida derived from mouse models

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