About this research group

This research group focuses around three central themes that are strongly connected:

1. The development and application of bioinformatics methods and new technologies to better identify genetic variation

Next-generation sequencing techniques such as exome or whole genome sequencing have become the standard techniques for  genetic testing and research. In addition, new technologies that allows us to better identify variation in the human genome are steadily developed. Our department is at the forefront of testing and implementing these technologies. With all of these technologies, new bioinformatics methods are required and we have done performed various studies showing the clinical utility of novel techniques, or developing novel methods that improve on variant identification of existing technologies:

  • Accurate detection of clinically relevant uniparental disomy from exome sequencing data. Yauy K, de Leeuw N, Yntema, HG, Pfundt R#, Gilissen C#. Genet Med. 2019 Nov 26. PMID:31767986.
  • Comparison of Exome and Genome Sequencing Technologies for the Complete Capture of Protein Coding Regions.Lelieveld SH, Spielmann M, Mundlos S, Veltman JA, Gilissen C. Hum Mutat. 2015 May 14. PMID: 25973577.
  • Genome sequencing identifies major causes of severe intellectual disability.Gilissen C*, Hehir-Kwa JY*, … Brunner HG#, Vissers LELM#, Veltman JA#. Nature. 2014 Jul 17;511(7509):344-7. PMID: 24896178.
  • A Post-Hoc Comparison of the Utility of Sanger Sequencing and Exome Sequencing for the Diagnosis of Heterogeneous Diseases. Neveling K*, Feenstra I*, Gilissen C*, … Scheffer H, Nelen MR. Hum Mutat. 2013 Dec;34(12):1721-6. PMID: 24123792.

In ongoing studies we’re investigating the potential of long-read sequencing technologies for medical genetics, and the recycling of exome sequencing data for identifying novel types of genetic variation.

2. The development and application of bioinformatics data-integration methods to understand the effect of genetic variation in the context of human disease, in order to identify the genetic cause of disease

With the availability of large datasets of genetics information, we can try and understand more about the biology of disease by performing large scale data-integration, either integrating different large genetic datasets, or by combining genetics with other types of biological data. We have worked on using statistical approaches to identify candidate genes in which mutations give rise to neurodevelopmental disorders:

  • Integrating healthcare and research genetic data empowers the discovery of 49 novel developmental disorders.Joanna Kaplanis*, Kaitlin E. Samocha*, Laurens Wiel*, Zhancheng Zhang*,  … Matthew E. Hurles#, Christian Gilissen#, Kyle Retterer#. bioRxiv 797787; doi: https://doi.org/10.1101/797787
  • Spatial Clustering of de Novo Missense Mutations Identifies Candidate Neurodevelopmental Disorder-Associated Genes. Lelieveld SH*, Wiel L*, … Vissers LELM#, Gilissen C#. Am J Hum Genet. 2017 Aug 30. PMID: 28867141.
  • Meta-analysis of 2,104 trios provides support for 10 new genes for intellectual disability Lelieveld SH*, Reijnders MRF*, … , Vissers LELM#, Brunner HG#, Gilissen C#. Nat Neurosci. 2016 Aug 1. PMID: 27479843.

3. Bioinformatic investigations into the biological mechanisms that underlie new mutations in humans

De novo mutations are a frequent cause of human disease, such as neurodevelopmental disorders but are also the drivers of human evolution. However, we still know very little about why and how exactly these types of mutations arise. Through bioinformatics analysis of large whole genome sequencing datasets we try to identify patterns of these de novo mutations that give us insight into the likely underlying mechanisms that cause these mutations to occur:

  • De Novo Mutations Reflect Development and Aging of the Human Germline.Goldmann JM, Veltman JA, Gilissen C. Trends Genet. 2019 Oct 11. PMID: 31610893.
  • Germline de novo mutation clusters arise during oocyte aging in genomic regions with high double strand-break incidence.Goldmann JM*, Seplyarskiy VB*, Wong WSW*, Vilboux T, Neerincx PB, Bodian DL, Solomon BD, Veltman JA, Deeken JF, Gilissen C#, Niederhuber JE#. Nat Genet. 2018 Mar 5. PMID: 29507425.
  • Parent-of-origin specific signatures of de novo mutations. Goldmann JM*, Wong WSW*, Pinelli M, Farrah T, Bodian D, Stittrich AB, Glusman G, Vissers LELM, Hoischen A, Roach JC, Vockley JG, Veltman JA, Solomon BD, Gilissen C#, Niederhuber JE#. Nat Genet. 2016 Jul 20. PMID: 27322544. 

Patient care

This research group is strongly connected to the division of Genome Diagnostics that offers state-of-the-art genetic testing for patients with genetic disorders. This allows us to test and validate new bioinformatic methods on large cohorts of patient data, but also facilitates the quick adoption of new techniques into patient care.

Research Research groups Genome bioinformatics

About this research group

Developing and applying bioinformatics methods to gain insight into the causes and mechanisms of genetic disorders such as intellectual disability. read more

Research group leader

prof. dr. Christian Gilissen

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