mirDNMR: a gene-centered database of backgroundde novomutation rates in human

Abstract

De novo germline mutations (DNMs) are the rarest genetic variants proven to cause a considerable number of sporadic genetic diseases, such as autism spectrum disorders, epileptic encephalopathy, schizophrenia, congenital heart disease, type 1 diabetes, and hearing loss. However, it is difficult to accurately assess the cause of DNMs and identify disease-causing genes from the considerable number of DNMs in probands. A common method to this problem is to identify genes that harbor significantly more DNMs than expected by chance, with accurate background DNM rate (DNMR) required. Therefore, in this study, we developed a novel database named mirDNMR for the collection of gene-centered background DNMRs obtained from different methods and population variation data. The database has the following functions: (i) browse and search the background DNMRs of each gene predicted by four different methods, including GC content (DNMR-GC), sequence context (DNMR-SC), multiple factors (DNMR-MF) and local DNA methylation level (DNMR-DM); (ii) search variant frequencies in publicly available databases, including ExAC, ESP6500, UK10K, 1000G and dbSNP and (iii) investigate the DNM burden to prioritize candidate genes based on the four background DNMRs using three statistical methods (TADA, Binomial and Poisson test). As a case study, we successfully employed our database in candidate gene prioritization for a sporadic complex disease: intellectual disability. In conclusion, mirDNMR (https://www.wzgenomics.cn/mirdnmr/) can be widely used to identify the genetic basis of sporadic genetic diseases.

Distinct genetic patterns of shared and unique genes across four neurodevelopmental disorders

“…Details are shown in Table S1. Four background de novo mutation rates (DNMRs), including DNMR by GC content (DNMR‐GC; [Sanders et al, 2012]), DNMR by sequence context (DNMR‐SC; [Samocha et al, 2014]), DNMR by multiple factors (DNMR‐MF; (Francioli et al, 2015) and DNMR by local DNA methylation level (DNMR‐DM; built by our in‐house pipelines) were collected from the mirDNMR database (Jiang et al, 2017). And then, mirTrios with integrated ANNOVAR (Li et al, 2015) and multiple allele frequency databases including ExAC (http://exac.broadinstitute.org/), 1,000 Genomes (http://www.1000genomes.org/) and ESP6500 (http://evs.gs.washington.edu/EVS/) to annotate all variants and focused on only rare protein‐coding variants with minor allele frequency (MAF) < 0.1%.…”

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

“…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. The mirDNMR database is also a collection of de novo mutations with specific focus on the background mutation rates calculated by several statistical approaches (5). …”

Prioritized High-Confidence Risk Genes for Intellectual Disability Reveal Molecular Convergence During Brain Development

“…All DNM datasets in this study were available from 11 published cohorts for ID and control, and detailed information is shown in Supplementary Table S1 . In addition, the four background DNM rates (DNMRs), including DNMR-GC ( Sanders et al, 2012 ), DNMR-SC ( Samocha et al, 2014 ), DNMR-MF ( Francioli et al, 2015 ), and DNMR-DM ( Jiang et al, 2017 ), were retrieved from the mirDNMR database ( Jiang et al, 2017 ).…”