title: Designing algorithms for determining significance of DNA missense changes.
abstract: Humans differ from each other in their genomes by <1 %. This determines the difference in susceptibility to disease, phenotypes, and traits. Predominantly, when looking for causal disease mutations, protein-coding sequences are screened first since those have the highest probability of affecting the function of a protein. Recent technological advances have seen a rise in the number of experiments being conducted to study a variety of diseases from monogenic to complex traits. Several computational approaches have been developed to extract putative functional missense variants. In this chapter we review some of these approaches and describe a standard step-by-step procedure that can be used to classify variants for the purpose of clinical care. We also provide two examples demonstrating this approach, one for a patient with a dilated cardiomyopathy diagnosis, and the other for a patient with an unknown etiology undergoing whole-genome sequencing (WGS).
authors.affiliation: Novartis Institutes for Biomedical Research, 500 Technology Square, 840-14, Cambridge, MA, 02139, USA, sivakumar.gowrisankar@novartis.com.
authors.forename: Sivakumar
authors.initials: S
authors.lastname: Gowrisankar
authors.forename: Matthew S
authors.initials: MS
authors.lastname: Lebo
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D020125:Mutation, Missense / Q000235:genetics*