denovo-db: a compendium of human<i>de novo</i>variants

Turner, Tychele N.; Qian, Yi; Krumm, Niklas; Huddleston, John; Hoekzema, Kendra; Stessman, Holly A.F.; Doebley, Anna-Lisa; Bernier, Raphael; Nickerson, Deborah A.; Eichler, Evan E.

doi:10.1093/nar/gkw865

Cited by 186 publications

(198 citation statements)

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“…13 Here, we developed a method to identify genes with spatially clustered DNMs and applied this to DNMs identified in a large cohort of individuals with ID/DD. 14 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.…”

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confidence: 99%

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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

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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confidence: 99%

“…We first validated our method on a dataset of DNMs identified in 2,448 unaffected siblings and healthy control studies 14,[17][18][19][20][21][22] (Table S3). In this cohort, we failed to identify genes for which clustering of de novo missense mutations reached statistical significance (Table S4).…”

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

101

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“…To assess the behavior of DOMINO on real sets of exome / genome data, we tested it on genotypes from denovo-db, a database of de novo variants identified by NGS, 26 Tables S7 and S8). By virtue of their heterozygous de novo inheritance (i.e., dominant in following generations), their presence in the same gene in more than one person, and of strict filtering procedures, all these DNA changes likely represent pathogenic mutations, and therefore all genes harboring them represent true AD genes detected by real NGS experiments.…”

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confidence: 99%

DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders

Quinodoz

Royer‐Bertrand²,

Cisarova

et al. 2017

The American Journal of Human Genetics

109

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In contrast to recessive conditions with biallelic inheritance, identification of dominant (monoallelic) mutations for Mendelian disorders is more difficult, because of the abundance of benign heterozygous variants that act as massive background noise (typically, in a 400:1 excess ratio). To reduce this overflow of false positives in next-generation sequencing (NGS) screens, we developed DOMINO, a tool assessing the likelihood for a gene to harbor dominant changes. Unlike commonly-used predictors of pathogenicity, DOMINO takes into consideration features that are the properties of genes, rather than of variants. It uses a machine-learning approach to extract discriminant information from a broad array of features (N ¼ 432), including: genomic data, intra-, and interspecies conservation, gene expression, protein-protein interactions, protein structure, etc. DOMINO's iterative architecture includes a training process on 985 genes with well-established inheritance patterns for Mendelian conditions, and repeated cross-validation that optimizes its discriminant power. When validated on 99 newly-discovered genes with pathogenic mutations, the algorithm displays an excellent final performance, with an area under the curve (AUC) of 0.92. Furthermore, unsupervised analysis by DOMINO of real sets of NGS data from individuals with intellectual disability or epilepsy correctly recognizes known genes and predicts 9 new candidates, with very high confidence. In summary, DOMINO is a robust and reliable tool that can infer dominance of candidate genes with high sensitivity and specificity, making it a useful complement to any NGS pipeline dealing with the analysis of the morbid human genome.

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“…The third database in this issue recognized by the NAR reviewers and editors with the ‘Breakthrough’ designation is denovo-db , a database of mutations that have been found in human subjects but which were missing in both of their parents (4). The database lists ∼32 000 sites in the genome with data obtained from >16 000 patients carrying some kind of a disease and >17,000 control individuals.…”

Section: New and Updated Databasesmentioning

confidence: 99%

“…The database lists ∼32 000 sites in the genome with data obtained from >16 000 patients carrying some kind of a disease and >17,000 control individuals. The majority of disease variants were from individuals with autism and congenital heart disease with smaller samples coming from schizophrenia, epilepsy, and other neurodevelopmental disorders (4). There is no doubt that this collection will find a variety of uses, from analyzing de novo mutations linked to a particular disease to studying the frequencies of mutations in certain parts of the genome.…”

Section: New and Updated Databasesmentioning

confidence: 99%

The 24th annualNucleic Acids Researchdatabase issue: a look back and upcoming changes

2017

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This year's Database Issue of Nucleic Acids Research contains 152 papers that include descriptions of 54 new databases and update papers on 98 databases, of which 16 have not been previously featured in NAR. As always, these databases cover a broad range of molecular biology subjects, including genome structure, gene expression and its regulation, proteins, protein domains, and protein–protein interactions. Following the recent trend, an increasing number of new and established databases deal with the issues of human health, from cancer-causing mutations to drugs and drug targets. In accordance with this trend, three recently compiled databases that have been selected by NAR reviewers and editors as ‘breakthrough’ contributions, denovo-db, the Monarch Initiative, and Open Targets, cover human de novo gene variants, disease-related phenotypes in model organisms, and a bioinformatics platform for therapeutic target identification and validation, respectively. We expect these databases to attract the attention of numerous researchers working in various areas of genetics and genomics. Looking back at the past 12 years, we present here the ‘golden set’ of databases that have consistently served as authoritative, comprehensive, and convenient data resources widely used by the entire community and offer some lessons on what makes a successful database. The Database Issue is freely available online at the https://academic.oup.com/nar web site. An updated version of the NAR Molecular Biology Database Collection is available at http://www.oxfordjournals.org/nar/database/a/.

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denovo-db: a compendium of humande novovariants

Cited by 186 publications

References 54 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

DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders

The 24th annualNucleic Acids Researchdatabase issue: a look back and upcoming changes

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