Heuristic Methods for Finding Pathogenic Variants in Gene Coding Sequences

T hese are exciting times, with a plethora of new technologies that are expediting discovery of the genetic underpinnings of human disease. Comprehensive resequencing of the human genome is now feasible and affordable, allowing each person’s entire genetic makeup to be revealed. The major focus of attention in genetics studies has been the small portion (1%) of the human genome that comprises the protein-coding sequences in genes (the “exome”), and the majority of causal disease-associated variants identified to date have been located in these regions. A remarkable extent of genetic variation in the protein-coding regions has been found, with at least 20 000 single-nucleotide polymorphisms (SNPs) present even in normal healthy subjects. Half these SNPs are nonsynonymous changes that result in an amino acid substitution that could potentially affect protein function. The greatest challenge now facing investigators is data interpretation and the development of strategies to identify the minority of gene-coding variants that actually cause or confer susceptibility to disease. To address this problem, bioinformatics tools have been developed to predict the likelihood of pathogenicity. A bewildering array of options is available, and users need to be aware of the programs most suited to their needs as well as the strengths and weaknesses of the various methods employed. Here, we provide an introductory overview of some commonly used pathogenicity prediction programs as well as a set of illustrative cardiac examples. This article is tailored for readers who are not bioinformatics experts and is relevant to cardiovascular researchers undertaking human genetics studies as well as to clinicians performing genetic testing. For comprehensive reviews of available methods, detailed technical explanations of the bioinformatics and validation of individual programs, and comparative analyses in large variant data sets, we refer the reader to excellent articles published elsewhere. The important “take-home” message is that although bioinformatics prediction programs are extremely useful, the results cannot necessarily be taken at face value because all programs have inherent limitations, and additional supporting evidence is required to confirm that predicted deleterious variants have a role in disease processes.