Christian Gilissen associate professor
Christian Gilissen studied computer sciences at the Radboud University Nijmegen after which he started his PhD in 2006 at the department of Human genetics of the Radboud University Medical Hospital in Nijmegen under the supervision of Prof. Dr. Ir. Joris Veltman and Prof. Dr. Han Brunner. He obtained his PhD in 2012 on “Disease gene identification through Next Generation Sequencing”, for which he received the Dutch bioinformatics young researcher award. After his PhD he continued his work in Nijmegen as a post-doctoral researcher and worked for 3 months at the Computational Biology Group at the Charité-Universitätsmedizin Berlin, under supervision of Prof. Peter Robinson. In 2013 he received a personal Veni grant from the Dutch Organization for Scientific Research to develop novel methodologies based on normal variation in order to predict gene function and the impact of mutations. At the European Society of Human genetics meeting in 2014 he received the international Isabelle Oberlé award for his work on genome sequencing of patients with intellectual disability that was published in Nature. His work was also rewarded by the young investigator award of the Dutch Society of Human Genetics in that same year. From 2017 onwards he became the head of bioinformatics within the division of genome diagnostics and an associate professorship in the division of within the department of Human Genetics in Nijmegen. His most recent work involved the large-scale analyses of patients with intellectual disabilities and the characterization of genome-wide de novo mutations. In June 2017 he obtained a prestigious Vidi research grant on the integration of exome sequencing and metabolomics. His work has been published in high-impact journals such as Nature genetics, Nature Neuroscience, American Journal of Human Genetics and others.
Personal prizes & awards national & international
- Dutch society of human genetics (NVHG) Young Investigator award 2014
- European Society of Human Genetics, Isabelle Oberlé Award for an outstanding presentation in the field of intellectual disability 2014
- European Journal of Human Genetics, Nature Citation Award, 1st prize most cited article 2014
- Nominee European Society of Human Genetics Young Investigator award
- Dutch Bioinformatics (NBIC) Young Investigator award 2012
- European NGS meeting, Best presentation award
- Member of the Scientific program committee of the American Society of Human Genetics (ASHG)
- Head of bioinformatics Genome Diagnostics Nijmegen and Maastricht UMC
- Chairman FWO panel med2 for personal PhD and post-doc grants
- Member of the Scientific program committee of the European Society of Human Genetics (ESHG)
- Member of the NWO Veni committee
Field of studyHuman Genetics, Bioinformatics
- Wiel L, Baakman C, Gilissen D, Veltman JA, Vriend G and Gilissen C
MetaDome: Pathogenicity analysis of genetic variants through aggregation of homologous human protein domains. Hum Mutat, 2019 May 22.
- Goldmann JM*, Seplyarskiy VB*, Wong WSW, Vilboux T, Neerincx PB, Bodian DL, Solomon BD, Veltman JA, Deeken JF, Gilissen C¥, Niederhuber JE¥.
Germline de novo mutation clusters arise during oocyte aging in genomic regions with high double-strand-break incidence. Nat Genet. 2018 Apr;50(4):487-492.
- Lelieveld SH, et al. Spatial Clustering of de Novo Missense Mutations Identifies Candidate Neurodevelopmental Disorder-Associated Genes. Am J Hum Genet. 2017 Sep 7;101(3):478-484. doi: 10.1016/j.ajhg.2017.08.004. Epub 2017 Aug 31. PMID: 28867141
- Wiel L, et al. Aggregation of population-based genetic variation over protein domain homologues and its potential use in genetic diagnostics.
Hum Mutat. 2017 Nov;38(11):1454-1463. doi: 10.1002/humu.23313. Epub 2017 Aug 31. PMID: 28815929
- Vulto-van Silfhout AT, et al. Quantification of Phenotype Information Aids the Identification of Novel Disease Genes. Hum Mutat. 2017 May;38(5):594-599. PMID: 28074630.
- Lelieveld SH, et al. Meta-analysis of 2,104 trios provides support for 10 new genes for intellectual disability. Nat Neurosci. 2016 Aug 1. PMID: 27479843.
- Goldmann JM, et al. Parent-of-origin specific signatures of de novo mutations. Nat Genet. 2016 Jun 20. PMID: 27322544.
- Vissers LE, et al. Genetic studies in intellectual disability and related disorders. Nat Rev Genet. 2015 Oct 27. PMID: 26503795.
- Lelieveld SH, et al. Novel bioinformatic developments for exome sequencing. Hum Genet. 2016 Apr 13. PMID: 27075447
- Acuna-Hidalgo R, et al. Post-zygotic Point Mutations Are an Underrecognized Source of De Novo Genomic Variation. Am J Hum Genet. 2015 Jun 3. PMID: 26054435.
- Lelieveld SH, et al. Comparison of Exome and Genome Sequencing Technologies for the Complete Capture of Protein Coding Regions. Hum Mutat. 2015 May 14. PMID: 25973577.
- Gilissen C, et al. Nature. 2014 Jul 17;511(7509):344-7. PMID: 24896178.
- Neveling K, et al. A Post-Hoc Comparison of the Utility of Sanger Sequencing and Exome Sequencing for the Diagnosis of Heterogeneous Diseases. Hum Mutat. 2013 Dec;34(12):1721-6. PMID: 24123792.
- van Bon BW, et al. Cantú Syndrome Is Caused by Mutations in ABCC9. Am J Hum Genet. 2012 May 16. PMID: 22608503.
- Gilissen C, et al. Exome Sequencing Identifies Truncating Mutations in Human SERPINF1 in Autosomal-Recessive Osteogenesis Imperfecta. Am J Hum Genet. 2011, PMID: 21353196.
- Gilissen C, et al. Unlocking Mendelian disease using exome sequencing. Genome Biol. 2011 Sep 14;12(9):228. PMID: 21920049. IF = 6.63
- Vissers LE, et al. A de novo paradigm for mental retardation. Nat Genet. 2010 Dec;42(12):1109-12. PMID: 21076407.
- Gilissen C*, et al. Exome Sequencing Identifies WDR35 Variants Involved in Sensenbrenner Syndrome. Am J Hum Genet. 2010 Sep 10;87(3):418-23. PMID: 20817137.
- Hoischen A*, et al. De novo mutations of SETBP1 cause Schinzel-Giedion syndrome. Nat Genet. 2010 Jun;42(6):483-5. PMID: 20436468.
- Nikopoulos K, et al. Next-generation sequencing of a 40 Mb linkage interval reveals TSPAN12 mutations in patients with familial exudative vitreoretinopathy. Am J Hum Genet. 2010 Feb 12;86(2):240-7. PMID: 20159111.
- Hoischen A, et al. Massively parallel sequencing of ataxia genes after array-based enrichment. Hum Mutat. 2010 Apr;31(4):494-9. PMID: 20151403.
Integrating Metabolomics and Genomics for understanding human Disease" (Vidi project)The computational integration of large biomedical datasets allows the identification of biologically and clinically meaningful patterns that are otherwise not detectable. Work done in my group over the last 5 years has contributed new approaches and methods to identify disease-causing genes from large-scale genomics data and now allow clinicians worldwide to routinely identify disease-causing DNA variants among the overwhelming amount of normal variation present in any patient. However, genetics approaches provide a diagnosis in only 50-60% of patients and contribute limited knowledge to the underlying disease mechanisms. High-throughput untargeted metabolomics profiling (UMP) is now feasible and provides a complimentary and completely new perspective on the functional effects of genetic variation.
I will firstly develop bioinformatics analysis pipelines and statistical methods for the large-scale interpretation of the highly complex patterns that are generated by UMP. Secondly, I will integrate UMP results with those from genomics data into a “functional genomics” approach that is unique and applicable to the large-scale patient datasets currently being generated in my host institute and elsewhere. Thirdly, I will apply these methods using intellectual disabilities (ID) as a model disease where I aim to:
(1) Identify novel genetic causes of disease and improve patient diagnostics
(2) Gain insights into the biology underlying ID and identify novel biomarkers
(3) Pioneer novel approaches to the interpretation of non-coding genome variation
If successful, this project will create a functional genomics paradigm that is applicable to genomics disease testing across a wide range of genetic diseases.
A holistic approach to the analysis of genetic variation in patientsNext Generation Sequencing (NGS) technologies have enabled large-scale DNA sequencing projects of patient cohorts. The very large number of rare variants present in every individual’s genome has made the interpretation of genomic variation the main obstacle in identifying the genetic cause of disease. Current bioinformatic tools that predict the effect of variants are not sensitive enough to identify a single cause of disease amongst all of these rare variants. As a side-effect of large sequencing studies, information about many benign variants has become available. We and others have shown that this `normal’ variation occurring within the healthy population can be used to characterize genes based on the number and types of benign variants that a gene can tolerate. Genes with low amounts of normal variation apparently are less tolerant and more likely involved in disease. Here we propose to generalize this approach by modeling benign genomic variation that occurs within the normal population onto protein 3D structures. From this we will identify protein substructures (domains) that are tolerant to genetic variation and therefore less important for normal protein function. We hypothesize that the pathogenicity of a novel genomic variant in such a substructure can be estimated from the presence/absence of benign variants in that substructure. This is a completely novel way of predicting the functional impact of variants that will provide information complimentary to existing methods, and will lead to high-quality predictions for many hitherto hard to analyse mutations. We will immediately apply our predictions in the routine diagnostics exome workflow to improve patient diagnosis, but also in ongoing research projects for the identification of novel disease genes. This project is based on the unusual collaboration of two distinct fields of research, protein modelling and human genetics and we believe that this unique combination will results in a better mutual understanding, an exchange of ideas and novel research, and ultimately better (personalized) healthcare.
- Dutch science foundation (NWO) Vidi grant “Integrating Metabolomics and Genomics for understanding human Disease". €800,000 (10/2017 - 10/2022)
- Junior Principle Investigator, Radboud UMC. €150,000 (05/2015 – 05/2018)
- RIMLS PhD grant “A holistic approach to the analysis of genetic variation in patients". €250,000 (09/2015 – 09/2018)
- Province of Gelderland, Robust investment impulse", “Compute infrastructure for Clinical genome sequencing”. €1,845,000 (03/2014)
- Dutch science foundation (NWO) Veni grant “Population variation for genome interpretation". €250,000 (01/2014 – 03/2017)
- ZONMW Technology hotel grant: “Uncovering hidden genetic variation in the human genome.” € 30,000 (01-01-2018 – 01-01-2019)
- BMS31, Omics data analysis for systems biology - Biomedical Sciences (MSc)
- Research Projects - Medicine/Biomedical Sciences (BSc)
- MOL055, Molecular basis of diseases - Medical biology (BSc)
- MM3CF Molecular mechanisms of disease - Biomedical Sciences (MSc)
- MMST Omics data analysis and interpretation
In the news
BNR interview about finding 10 new genes (publication 1 on the list key publications):
About publication 2 on the list (NL):
About publication 5 on the list (EN):
Science webinar (EN):
Interview for computer science education(NL):
28 New genes identified as cause of severe developmental disorders14 October 2020
Christian Gilissen & Laurens van de Wiel with GeneDx and the Wellcome Sanger Institute, have published a major study in the scientific journal Nature, showing the need to bring data together.read more