Status quo of annotation of human disease variants

Venselaar, Hanka; Camilli, Franscesca; Gholizadeh, Shima; Snelleman, Marlou; Brunner, Han G.; Vriend, Gert

doi:10.1186/1471-2105-14-352

Cited by 3 publications

(2 citation statements)

References 60 publications

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“…44 The impact of a surface change, however, depends entirely on the spatial context and is therefore less likely to result in misfolding and subsequent protein degradation. 45 Consequently, de novo disease-causing missense mutations preventing proper folding cause protein degradation, and thus indirectly lead to HI, similar to protein truncating mutations in such genes. To test the hypothesis that our clustered de novo missense mutations do not generally result in HI due to protein misfolding, we modeled mutations onto the 3D protein structure using YASARA & WHAT IF Twinset.…”

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confidence: 99%

Spatial Clustering of de Novo Missense Mutations Identifies Candidate Neurodevelopmental Disorder-Associated Genes

Lelieveld

Wiel

Venselaar

et al. 2017

The American Journal of Human Genetics

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101

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SummaryHaploinsufficiency (HI) is the best characterized mechanism through which dominant mutations exert their effect and cause disease. Non-haploinsufficiency (NHI) mechanisms, such as gain-of-function and dominant-negative mechanisms, are often characterized by the spatial clustering of mutations, thereby affecting only particular regions or base pairs of a gene. Variants leading to haploinsufficency might occasionally cluster as well, for example in critical domains, but such clustering is on the whole less pronounced with mutations often spread throughout the gene. Here we exploit this property and develop a method to specifically identify genes with significant spatial clustering patterns of de novo mutations in large cohorts. We apply our method to a dataset of 4,061 de novo missense mutations from published exome studies of trios with intellectual disability and developmental disorders (ID/DD) and successfully identify 15 genes with clustering mutations, including 12 genes for which mutations are known to cause neurodevelopmental disorders. For 11 out of these 12, NHI mutation mechanisms have been reported. Additionally, we identify three candidate ID/DD-associated genes of which two have an established role in neuronal processes. We further observe a higher intolerance to normal genetic variation of the identified genes compared to known genes for which mutations lead to HI. Finally, 3D modeling of these mutations on their protein structures shows that 81% of the observed mutations are unlikely to affect the overall structural integrity and that they therefore most likely act through a mechanism other than HI.De novo mutations affecting protein-coding genes are a major cause of intellectual disability (ID) and other developmental disorders (DDs). We downloaded all DNMs occurring in individuals with ID/DD from de novo-db version 1.3 14 identified through WES and whole genome sequencing which were then reannotated with our in-house variant annotation pipeline. The de novo mutations included in the analysis were previously validated by a second independent method or showed a high validation rate for a subset of de novo mutations. In addition, we added 1,183 de novo variants identified in the exomes of an in-house ID cohort that was previously published. 8 To further reduce the risk of including sequencing artifacts and/or genotyping errors, we excluded all de novo variants that were present more than once in the ExAC dataset (Table S1). 15 These efforts resulted in 6,495 protein coding DNMs, including 4,061 missense mutations, in 5,302 individuals with ID/DD (Table S2). We set out to determine for any gene whether the observed de novo missense mutations cluster more than expected compared to random permutations. Hereto, we selected for each the longest representative transcript (i.e., part of the GENCODE basic set) 16 and calculated the geometric mean distance d g over all missense DNMs on

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mentioning

confidence: 99%

Spatial Clustering of de Novo Missense Mutations Identifies Candidate Neurodevelopmental Disorder-Associated Genes

Lelieveld

Wiel

Venselaar

et al. 2017

The American Journal of Human Genetics

Self Cite

101

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“…However, these tools have moderate sensitivity and low specificity -one study found that 31-32 % of pathogenic variants were predicted to be benign, while 84-87 % of benign variants were predicted to be pathogenic (Flanagan et al 2010;Dorfman et al 2010). More accurate assessments may be gained by considering each variant in the context of the three-dimensional structure of the protein (Venselaar et al 2013;Preeprem and Gibson 2014).…”

Section: Interpreting the Relevance Of Genetic Variation To Phenotypimentioning

confidence: 99%

Insights into the Genetic Foundations of Human Communication

2015

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The human capacity to acquire sophisticated language is unmatched in the animal kingdom. Despite the discontinuity in communicative abilities between humans and other primates, language is built on ancient genetic foundations, which are being illuminated by comparative genomics. The genetic architecture of the language faculty is also being uncovered by research into neurodevelopmental disorders that disrupt the normally effortless process of language acquisition. In this article, we discuss the strategies that researchers are using to reveal genetic factors contributing to communicative abilities, and review progress in identifying the relevant genes and genetic variants. The first gene directly implicated in a speech and language disorder was FOXP2. Using this gene as a case study, we illustrate how evidence from genetics, molecular cell biology, animal models and human neuroimaging has converged to build a picture of the role of FOXP2 in neurodevelopment, providing a framework for future endeavors to bridge the gaps between genes, brains and behavior.

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Predicted Molecular Effects of Sequence Variants Link to System Level of Disease

et al. 2016

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Developments in experimental and computational biology are advancing our understanding of how protein sequence variation impacts molecular protein function. However, the leap from the micro level of molecular function to the macro level of the whole organism, e.g. disease, remains barred. Here, we present new results emphasizing earlier work that suggested some links from molecular function to disease. We focused on non-synonymous single nucleotide variants, also referred to as single amino acid variants (SAVs). Building upon OMIA (Online Mendelian Inheritance in Animals), we introduced a curated set of 117 disease-causing SAVs in animals. Methods optimized to capture effects upon molecular function often correctly predict human (OMIM) and animal (OMIA) Mendelian disease-causing variants. We also predicted effects of human disease-causing variants in the mouse model, i.e. we put OMIM SAVs into mouse orthologs. Overall, fewer variants were predicted with effect in the model organism than in the original organism. Our results, along with other recent studies, demonstrate that predictions of molecular effects capture some important aspects of disease. Thus, in silico methods focusing on the micro level of molecular function can help to understand the macro system level of disease.

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Status quo of annotation of human disease variants

Cited by 3 publications

References 60 publications

Spatial Clustering of de Novo Missense Mutations Identifies Candidate Neurodevelopmental Disorder-Associated Genes

Spatial Clustering of de Novo Missense Mutations Identifies Candidate Neurodevelopmental Disorder-Associated Genes

Insights into the Genetic Foundations of Human Communication

Predicted Molecular Effects of Sequence Variants Link to System Level of Disease

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