Identifying patients and assessing variant pathogenicity for an autosomal dominant disease-driving gene

• Published in: STAR protocols 2022-03, Vol.3 (1), p.101150, Article 101150
• Main Authors: Lee, Winston, de Prisco, Nicola, Gennarino, Vincenzo A.
• Format: Article
• Language: English
• Subjects: Clinical Protocol; Genes, Dominant; Genetic Diseases, Inborn; Genetics; Health Sciences; Humans; Mutation, Missense - genetics; Protocol; Sequence analysis; Sequencing
• ISSN: 2666-1667; 2666-1667
• DOI: 10.1016/j.xpro.2022.101150

Identifying a disease gene and determining its causality in patients can be challenging. Here, we present an approach to predicting the pathogenicity of deletions and missense variants for an autosomal dominant gene. We provide online resources for identifying patients and determining constraint metrics to isolate the causal gene among several candidates encompassed in a shared region of deletion. We also provide instructions for optimizing functional annotation programs that may be otherwise inaccessible to a nonexpert or novice in computational approaches. For complete details on the use and execution of this protocol, please refer to Gennarino et al. (2018). [Display omitted] •Recruit affected patients harboring variation in a candidate gene of interest•Identify a single causal gene within a large genomic deletion spanning multiple loci•Annotate genetic variants with multiple pathogenicity prediction scores•Assess pathogenicity range of singleton missense variants from the general population Identifying a disease gene and determining its causality in patients can be challenging. Here, we present an approach to predicting the pathogenicity of deletions and missense variants for an autosomal dominant gene. We provide online resources for identifying patients and determining constraint metrics to isolate the causal gene among several candidates encompassed in a shared region of deletion. We also provide instructions for optimizing functional annotation programs that may be otherwise inaccessible to a nonexpert or novice in computational approaches.