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Veltman, J. A.

Publications and source records attributed to Veltman, J. A..

3 recordsLinked to original sources
bioRxiv2018

A systematic review and standardized clinical validity assessment of male infertility genes

Study questionWhich genes are confidently linked to human male infertility?\n\nSummary answerOur systematic literature search and clinical validity assessment reveals that a total of 67 genes are currently confidently linked to 81 human male infertility phenotypes.\n\nWhat is known alreadyThe discovery of novel male infertility genes is rapidly accelerating with the availability of Next-Generation Sequencing methods, but the quality of evidence for gene-disease relationships varies greatly. In order to improve genetic research, diagnostics and counseling, there is a need for an evidence-based overview of the currently known genes.\n\nStudy design, size, durationWe performed a systematic literature search and evidence assessment for all publications in Pubmed until June 2018 covering genetic causes of male infertility and/or defective male genitourinary development.\n\nParticipants/materials, setting, methodsTwo independent reviewers conducted the literature search and included papers on the monogenic causes of human male infertility and excluded papers on genetic association or risk factors, karyotype anomalies and/or copy number variations affecting multiple genes. Next, the quality and the extent of all evidence supporting selected genes was weighed by a standardized scoring method and used to determine the clinical validity of each gene-disease relationship as expressed by the following six categories: no evidence, limited, moderate, strong, definitive or unable to classify.\n\nMain results and the role of chanceFrom a total of 23,031 records, we included 1,286 publications about monogenic causes of male infertility leading to a list of 471 gene-disease relationships. The clinical validity of these gene-disease relationships varied widely and ranged from definitive (n=36) to strong (n=12), moderate (n=33), limited (n=86) or no evidence (n=154). A total of 150 gene-disease relationships could not be classified.\n\nLimitations, reasons for cautionOur literature search was limited to Pubmed.\n\nWider implications of the findingsThe comprehensive overview will aid researchers and clinicians in the field to establish gene lists for diagnostic screening using validated gene-disease criteria and identify gaps in our knowledge of male infertility. For future studies, the authors discuss the relevant and important international guidelines regarding research related to gene discovery and provide specific recommendations to the field of male infertility.\n\nStudy funding/competing interest(s)This work was supported by a VICI grant from The Netherlands Organisation for Scientific Research (918-15-667 to JAV).

genetics↗
bioRxiv2018

Identifying long indels in exome sequencing data of patients with intellectual disability

Exome sequencing is a powerful tool for detecting both single and multiple nucleotide variation genome wide. However long indels, in the size range 20 - 200bp, remain difficult to accurately detect. By assessing a set of common exonic long indels, we estimate the sensitivity of long indel detection in exome sequencing data to be 92%. To clarify the role of pathogenic long indels in patients with intellectual disability (ID), we analysed exome sequencing data from 820 patients using two variant callers, Pindel and Platypus. We identified three indels explaining the patients clinical phenotype by disrupting the UBE3A, PGAP3 and MECP2 genes. Comparison of different tools demonstrated the importance of both correct genotyping and annotation variants. In conclusion, specialized long indel detection can improve diagnostic yield in ID patients.

genomics↗
bioRxiv2017

Germline De Novo Mutation Clusters Arise During Oocyte Aging In Genomic Regions With Increased Double-Strand Break Incidence

Clustering of mutations has been found both in somatic mutations from cancer genomes and in germline de novo mutations (DNMs). We identified 1,755 clustered DNMs (cDNMs) within whole-genome sequencing data from 1,291 parent-offspring trios and investigated the underlying mutational mechanisms. We found that the number of clusters on the maternalallele was positively correlated with maternal age and that these consist of more individual mutations with larger intra-mutational distances compared to paternal clusters. More than 50% of maternal clusters were located on chromosomes 8, 9 and 16, in regions with an overall increased maternal mutation rate. Maternal clusters in these regions showed a distinct mutation signature characterized by C>G mutations. Finally, we found that maternal clusters associate with processes involving double-stranded-breaks (DSBs) such as meiotic gene conversions and de novo deletions events. These findings suggest accumulation of DSB-induced mutations throughout oocyte aging as an underlying mechanism leading to maternal mutation clusters.

genomics↗