title: Quantification of Phenotype Information Aids the Identification of Novel Disease Genes.
abstract: Next-generation sequencing led to the identification of many potential novel disease genes. The presence of mutations in the same gene in multiple unrelated patients is, however, a priori insufficient to establish that these genes are truly involved in the respective disease. Here, we show how phenotype information can be incorporated within statistical approaches to provide additional evidence for the causality of mutations. We developed a broadly applicable statistical model that integrates gene-specific mutation rates, cohort size, mutation type, and phenotype frequency information to assess the chance of identifying de novo mutations affecting the same gene in multiple patients with shared phenotype features. We demonstrate our approach based on the frequency of phenotype features present in a unique cohort of 6,149 patients with intellectual disability. We show that our combined approach can decrease the number of patients required to identify novel disease genes, especially for patients with combinations of rare phenotypes. In conclusion, we show how integrating genotype-phenotype information can aid significantly in the interpretation of de novo mutations in potential novel disease genes.
authors.affiliation: Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.
authors.forename: Anneke T
authors.initials: AT
authors.lastname: Vulto-van Silfhout
authors.affiliation: Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.
authors.forename: Christian
authors.initials: C
authors.lastname: Gilissen
authors.affiliation: Department for Health Evidence, Radboud university medical center, Nijmegen, The Netherlands.|Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
authors.forename: Jelle J
authors.initials: JJ
authors.lastname: Goeman
authors.affiliation: Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.
authors.forename: Sandra
authors.initials: S
authors.lastname: Jansen
authors.affiliation: Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.
authors.forename: Claudia J M
authors.initials: CJM
authors.lastname: van Amen-Hellebrekers
authors.affiliation: Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.
authors.forename: Bregje W M
authors.initials: BWM
authors.lastname: van Bon
authors.affiliation: Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.
authors.forename: David A
authors.initials: DA
authors.lastname: Koolen
authors.affiliation: Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Erik A
authors.initials: EA
authors.lastname: Sistermans
authors.affiliation: Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.
authors.forename: Han G
authors.initials: HG
authors.lastname: Brunner
authors.affiliation: Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.
authors.forename: Arjan P M
authors.initials: APM
authors.lastname: de Brouwer
authors.affiliation: Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.
authors.forename: Bert B A
authors.initials: BBA
authors.lastname: de Vries
mesh_terms: D056726:Genetic Association Studies / Q000379:methods; D020022:Genetic Predisposition to Disease; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D015203:Reproducibility of Results
keywords: de novo mutations; intellectual disability; patient cohorts; phenotype features; statistical approach; systematic phenotyping