Laurens van de Wiel is Dutch scientist from Berghem, The Netherlands. Laurens spent his undergrad in Software Development (BSc, Avans Hogeschool ‘s-Hertogenbosch) and Computing Science (MSc, Radboud University Nijmegen). Laurens continued his career at a start-up, where he created large-scale, real-time analytical software. Laurens continued on his academic trajectory at the Radboudumc in Nijmegen, where he started his PhD in bioinformatics.
During his PhD, Laurens integrated genetic data with protein 3D structures and protein domains. He utilized the skills he obtained before setting out on his academic trajectory; building large-scale, robust, reliable software. Exemplified by the MetaDome Web server (https://stuart.radboudumc.nl/metadome/). During his PhD, he developed novel methodologies for the interpretation of genetic variants of unknown clinical significance and, by integrating structural and evolutionary biology with genomics, Laurens identified 36 novel disease-gene associations for developmental disorders. These discoveries enabled diagnosis for over 500 families worldwide.
Laurens’ areas of expertise are (bioinformatic) software development, data integration of genetic variation with other omics, and his research aims are:
1.) Lessons long-learned in computer science aid computational biology
2.) Multi-omic data integration allows the impact measurement of genetic variation
3.) Diagnosing undiagnosed disorders will uncover novel insights into biology.
4.) International and multidisciplinary collaborations are key in diagnosing rare disorders.
At Stanford University, under guidance of Dr. Matthew Wheeler, he is conducting his postdoctoral studies in line with his research aims.
Honors & Awards
Rubicon postdoctoral fellowship grant, The Netherlands Organisation for Scientific Research (NWO) (04/14/2022)
Best master thesis in computing science of 2014, AIA Software / Radboud University Nijmegen, the Netherlands (2014)
Doctor of Philosophy, Katholieke Universiteit Nijmegen (2021)
Master of Science, Katholieke Universiteit Nijmegen (2014)
Bachelor of Applied Science, Unlisted School (2010)
Ph.D, Radboud University Medical Center, Bioinformatics (2021)
MSc, Radboud University, Computing Science (2014)
B.A.Sc, Avans University of Applied Science, Computer Science (2010)
Matthew Wheeler, Postdoctoral Research Mentor
Matthew Wheeler, Postdoctoral Faculty Sponsor
De novo mutation hotspots in homologous protein domains identify function-altering mutations in neurodevelopmental disorders.
American journal of human genetics
Variant interpretation remains a major challenge in medical genetics. We developed Meta-Domain HotSpot (MDHS) to identify mutational hotspots across homologous protein domains. We applied MDHS to a dataset of 45,221 de novo mutations (DNMs) from 31,058 individuals with neurodevelopmental disorders (NDDs) and identified three significantly enriched missense DNM hotspots in the ion transport protein domain family (PF00520). The 37 unique missense DNMs that drive enrichment affect 25 genes, 19 of which were previously associated with NDDs. 3D protein structure modeling supports the hypothesis of function-altering effects of these mutations. Hotspot genes have a unique expression pattern in tissue, and we used this pattern alongside in silico predictors and population constraint information to identify candidate NDD-associated genes. We also propose a lenient version of our method, which identifies 32 hotspot positions across 16 different protein domains. These positions are enriched for likely pathogenic variation in clinical databases and DNMs in other genetic disorders.
View details for DOI 10.1016/j.ajhg.2022.12.001
View details for PubMedID 36563679
Mind the Gap: The Complete Human Genome Unlocks Benefits for Clinical Genomics.
View details for DOI 10.1093/clinchem/hvac133
View details for PubMedID 36112529
Evidence for 28 genetic disorders discovered by combining healthcare and research data
2020; 586 (7831): 757-+
De novo mutations in protein-coding genes are a well-established cause of developmental disorders1. However, genes known to be associated with developmental disorders account for only a minority of the observed excess of such de novo mutations1,2. Here, to identify previously undescribed genes associated with developmental disorders, we integrate healthcare and research exome-sequence data from 31,058 parent-offspring trios of individuals with developmental disorders, and develop a simulation-based statistical test to identify gene-specific enrichment of de novo mutations. We identified 285 genes that were significantly associated with developmental disorders, including 28 that had not previously been robustly associated with developmental disorders. Although we detected more genes associated with developmental disorders, much of the excess of de novo mutations in protein-coding genes remains unaccounted for. Modelling suggests that more than 1,000 genes associated with developmental disorders have not yet been described, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of genes associated with developmental disorders.
View details for DOI 10.1038/s41586-020-2832-5
View details for Web of Science ID 000577265800001
View details for PubMedID 33057194
View details for PubMedCentralID PMC7116826
De novo CLTC variants are associated with a variable phenotype from mild to severe intellectual disability, microcephaly, hypoplasia of the corpus callosum, and epilepsy
GENETICS IN MEDICINE
2020; 22 (4): 797-802
To delineate the genotype-phenotype correlation in individuals with likely pathogenic variants in the CLTC gene.We describe 13 individuals with de novo CLTC variants. Causality of variants was determined by using the tolerance landscape of CLTC and computer-assisted molecular modeling where applicable. Phenotypic abnormalities observed in the individuals identified with missense and in-frame variants were compared with those with nonsense or frameshift variants in CLTC.All de novo variants were judged to be causal. Combining our data with that of 14 previously reported affected individuals (n = 27), all had intellectual disability (ID), ranging from mild to moderate/severe, with or without additional neurologic, behavioral, craniofacial, ophthalmologic, and gastrointestinal features. Microcephaly, hypoplasia of the corpus callosum, and epilepsy were more frequently observed in individuals with missense and in-frame variants than in those with nonsense and frameshift variants. However, this difference was not significant.The wide phenotypic variability associated with likely pathogenic CLTC variants seems to be associated with allelic heterogeneity. The detailed clinical characterization of a larger cohort of individuals with pathogenic CLTC variants is warranted to support the hypothesis that missense and in-frame variants exert a dominant-negative effect, whereas the nonsense and frameshift variants would result in haploinsufficiency.
View details for DOI 10.1038/s41436-019-0703-y
View details for Web of Science ID 000523113900018
View details for PubMedID 31776469
De Novo Variants in SPOP Cause Two Clinically Distinct Neurodevelopmental Disorders
AMERICAN JOURNAL OF HUMAN GENETICS
2020; 106 (3): 405-411
Recurrent somatic variants in SPOP are cancer specific; endometrial and prostate cancers result from gain-of-function and dominant-negative effects toward BET proteins, respectively. By using clinical exome sequencing, we identified six de novo pathogenic missense variants in SPOP in seven individuals with developmental delay and/or intellectual disability, facial dysmorphisms, and congenital anomalies. Two individuals shared craniofacial dysmorphisms, including congenital microcephaly, that were strikingly different from those of the other five individuals, who had (relative) macrocephaly and hypertelorism. We measured the effect of SPOP variants on BET protein amounts in human Ishikawa endometrial cancer cells and patient-derived cell lines because we hypothesized that variants would lead to functional divergent effects on BET proteins. The de novo variants c.362G>A (p.Arg121Gln) and c. 430G>A (p.Asp144Asn), identified in the first two individuals, resulted in a gain of function, and conversely, the c.73A>G (p.Thr25Ala), c.248A>G (p.Tyr83Cys), c.395G>T (p.Gly132Val), and c.412C>T (p.Arg138Cys) variants resulted in a dominant-negative effect. Our findings suggest that these opposite functional effects caused by the variants in SPOP result in two distinct and clinically recognizable syndromic forms of intellectual disability with contrasting craniofacial dysmorphisms.
View details for DOI 10.1016/j.ajhg.2020.02.001
View details for Web of Science ID 000519101800016
View details for PubMedID 32109420
View details for PubMedCentralID PMC7058825
De Novo Variants Disturbing the Transactivation Capacity of POU3F3 Cause a Characteristic Neurodevelopmental Disorder
AMERICAN JOURNAL OF HUMAN GENETICS
2019; 105 (2): 403-412
POU3F3, also referred to as Brain-1, is a well-known transcription factor involved in the development of the central nervous system, but it has not previously been associated with a neurodevelopmental disorder. Here, we report the identification of 19 individuals with heterozygous POU3F3 disruptions, most of which are de novo variants. All individuals had developmental delays and/or intellectual disability and impairments in speech and language skills. Thirteen individuals had characteristic low-set, prominent, and/or cupped ears. Brain abnormalities were observed in seven of eleven MRI reports. POU3F3 is an intronless gene, insensitive to nonsense-mediated decay, and 13 individuals carried protein-truncating variants. All truncating variants that we tested in cellular models led to aberrant subcellular localization of the encoded protein. Luciferase assays demonstrated negative effects of these alleles on transcriptional activation of a reporter with a FOXP2-derived binding motif. In addition to the loss-of-function variants, five individuals had missense variants that clustered at specific positions within the functional domains, and one small in-frame deletion was identified. Two missense variants showed reduced transactivation capacity in our assays, whereas one variant displayed gain-of-function effects, suggesting a distinct pathophysiological mechanism. In bioluminescence resonance energy transfer (BRET) interaction assays, all the truncated POU3F3 versions that we tested had significantly impaired dimerization capacities, whereas all missense variants showed unaffected dimerization with wild-type POU3F3. Taken together, our identification and functional cell-based analyses of pathogenic variants in POU3F3, coupled with a clinical characterization, implicate disruptions of this gene in a characteristic neurodevelopmental disorder.
View details for DOI 10.1016/j.ajhg.2019.06.007
View details for Web of Science ID 000478022200014
View details for PubMedID 31303265
View details for PubMedCentralID PMC6698880
MetaDome: Pathogenicity analysis of genetic variants through aggregation of homologous human protein domains
2019; 40 (8): 1030-1038
The growing availability of human genetic variation has given rise to novel methods of measuring genetic tolerance that better interpret variants of unknown significance. We recently developed a concept based on protein domain homology in the human genome to improve variant interpretation. For this purpose, we mapped population variation from the Exome Aggregation Consortium (ExAC) and pathogenic mutations from the Human Gene Mutation Database (HGMD) onto Pfam protein domains. The aggregation of these variation data across homologous domains into meta-domains allowed us to generate amino acid resolution of genetic intolerance profiles for human protein domains. Here, we developed MetaDome, a fast and easy-to-use web server that visualizes meta-domain information and gene-wide profiles of genetic tolerance. We updated the underlying data of MetaDome to contain information from 56,319 human transcripts, 71,419 protein domains, 12,164,292 genetic variants from gnomAD, and 34,076 pathogenic mutations from ClinVar. MetaDome allows researchers to easily investigate their variants of interest for the presence or absence of variation at corresponding positions within homologous domains. We illustrate the added value of MetaDome by an example that highlights how it may help in the interpretation of variants of unknown significance. The MetaDome web server is freely accessible at https://stuart.radboudumc.nl/metadome.
View details for DOI 10.1002/humu.23798
View details for Web of Science ID 000480595600004
View details for PubMedID 31116477
View details for PubMedCentralID PMC6772141
De Novo and Inherited Pathogenic Variants in KDM3B Cause Intellectual Disability, Short Stature, and Facial Dysmorphism
AMERICAN JOURNAL OF HUMAN GENETICS
2019; 104 (4): 758-766
By using exome sequencing and a gene matching approach, we identified de novo and inherited pathogenic variants in KDM3B in 14 unrelated individuals and three affected parents with varying degrees of intellectual disability (ID) or developmental delay (DD) and short stature. The individuals share additional phenotypic features that include feeding difficulties in infancy, joint hypermobility, and characteristic facial features such as a wide mouth, a pointed chin, long ears, and a low columella. Notably, two individuals developed cancer, acute myeloid leukemia and Hodgkin lymphoma, in childhood. KDM3B encodes for a histone demethylase and is involved in H3K9 demethylation, a crucial part of chromatin modification required for transcriptional regulation. We identified missense and truncating variants, suggesting that KDM3B haploinsufficiency is the underlying mechanism for this syndrome. By using a hybrid facial-recognition model, we show that individuals with a pathogenic variant in KDM3B have a facial gestalt, and that they show significant facial similarity compared to control individuals with ID. In conclusion, pathogenic variants in KDM3B cause a syndrome characterized by ID, short stature, and facial dysmorphism.
View details for DOI 10.1016/j.ajhg.2019.02.023
View details for Web of Science ID 000463474700016
View details for PubMedID 30929739
View details for PubMedCentralID PMC6451728
Heterozygous missense variants of LMX1A lead to nonsyndromic hearing impairment and vestibular dysfunction
2018; 137 (5): 389-400
Unraveling the causes and pathomechanisms of progressive disorders is essential for the development of therapeutic strategies. Here, we identified heterozygous pathogenic missense variants of LMX1A in two families of Dutch origin with progressive nonsyndromic hearing impairment (HI), using whole exome sequencing. One variant, c.721G > C (p.Val241Leu), occurred de novo and is predicted to affect the homeodomain of LMX1A, which is essential for DNA binding. The second variant, c.290G > C (p.Cys97Ser), predicted to affect a zinc-binding residue of the second LIM domain that is involved in protein-protein interactions. Bi-allelic deleterious variants of Lmx1a are associated with a complex phenotype in mice, including deafness and vestibular defects, due to arrest of inner ear development. Although Lmx1a mouse mutants demonstrate neurological, skeletal, pigmentation and reproductive system abnormalities, no syndromic features were present in the participating subjects of either family. LMX1A has previously been suggested as a candidate gene for intellectual disability, but our data do not support this, as affected subjects displayed normal cognition. Large variability was observed in the age of onset (a)symmetry, severity and progression rate of HI. About half of the affected individuals displayed vestibular dysfunction and experienced symptoms thereof. The late-onset progressive phenotype and the absence of cochleovestibular malformations on computed tomography scans indicate that heterozygous defects of LMX1A do not result in severe developmental abnormalities in humans. We propose that a single LMX1A wild-type copy is sufficient for normal development but insufficient for maintenance of cochleovestibular function. Alternatively, minor cochleovestibular developmental abnormalities could eventually lead to the progressive phenotype seen in the families.
View details for DOI 10.1007/s00439-018-1880-5
View details for Web of Science ID 000433512300004
View details for PubMedID 29754270
View details for PubMedCentralID PMC5973959
Aggregation of population-based genetic variation over protein domain homologues and its potential use in genetic diagnostics
2017; 38 (11): 1454-1463
Whole exomes of patients with a genetic disorder are nowadays routinely sequenced but interpretation of the identified genetic variants remains a major challenge. The increased availability of population-based human genetic variation has given rise to measures of genetic tolerance that have been used, for example, to predict disease-causing genes in neurodevelopmental disorders. Here, we investigated whether combining variant information from homologous protein domains can improve variant interpretation. For this purpose, we developed a framework that maps population variation and known pathogenic mutations onto 2,750 "meta-domains." These meta-domains consist of 30,853 homologous Pfam protein domain instances that cover 36% of all human protein coding sequences. We find that genetic tolerance is consistent across protein domain homologues, and that patterns of genetic tolerance faithfully mimic patterns of evolutionary conservation. Furthermore, for a significant fraction (68%) of the meta-domains high-frequency population variation re-occurs at the same positions across domain homologues more often than expected. In addition, we observe that the presence of pathogenic missense variants at an aligned homologous domain position is often paired with the absence of population variation and vice versa. The use of these meta-domains can improve the interpretation of genetic variation.
View details for DOI 10.1002/humu.23313
View details for Web of Science ID 000412835700002
View details for PubMedID 28815929
View details for PubMedCentralID PMC5656839
Genome-scale detection of positive selection in nine primates predicts human-virus evolutionary conflicts
NUCLEIC ACIDS RESEARCH
2017; 45 (18): 10634-10648
Hotspots of rapid genome evolution hold clues about human adaptation. We present a comparative analysis of nine whole-genome sequenced primates to identify high-confidence targets of positive selection. We find strong statistical evidence for positive selection in 331 protein-coding genes (3%), pinpointing 934 adaptively evolving codons (0.014%). Our new procedure is stringent and reveals substantial artefacts (20% of initial predictions) that have inflated previous estimates. The final 331 positively selected genes (PSG) are strongly enriched for innate and adaptive immunity, secreted and cell membrane proteins (e.g. pattern recognition, complement, cytokines, immune receptors, MHC, Siglecs). We also find evidence for positive selection in reproduction and chromosome segregation (e.g. centromere-associated CENPO, CENPT), apolipoproteins, smell/taste receptors and mitochondrial proteins. Focusing on the virus-host interaction, we retrieve most evolutionary conflicts known to influence antiviral activity (e.g. TRIM5, MAVS, SAMHD1, tetherin) and predict 70 novel cases through integration with virus-human interaction data. Protein structure analysis further identifies positive selection in the interaction interfaces between viruses and their cellular receptors (CD4-HIV; CD46-measles, adenoviruses; CD55-picornaviruses). Finally, primate PSG consistently show high sequence variation in human exomes, suggesting ongoing evolution. Our curated dataset of positive selection is a rich source for studying the genetics underlying human (antiviral) phenotypes. Procedures and data are available at https://github.com/robinvanderlee/positive-selection.
View details for DOI 10.1093/nar/gkx704
View details for Web of Science ID 000413107400027
View details for PubMedID 28977405
View details for PubMedCentralID PMC5737536
Spatial Clustering of de Novo Missense Mutations Identifies Candidate Neurodevelopmental Disorder-Associated Genes
AMERICAN JOURNAL OF HUMAN GENETICS
2017; 101 (3): 478-484
Haploinsufficiency (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.
View details for DOI 10.1016/j.ajhg.2017.08.004
View details for Web of Science ID 000409530900014
View details for PubMedID 28867141
View details for PubMedCentralID PMC5591029
KeCo: Kernel-Based Online Co-agreement Algorithm
SPRINGER-VERLAG BERLIN. 2015: 308-315
View details for DOI 10.1007/978-3-319-24282-8_26
View details for Web of Science ID 000367678000026