Video: Making Sense of Genetic Testing Results
Next-generation sequencing gene panels for diagnostic or predisposition testing can be expected to uncover genetic variants in every patient tested. However, not all of these variants are disease causing. It is the role of the clinical testing lab to interpret and classify genetic variants as disease causing (pathogenic) or benign. For a health care provider who may order these tests, it is important to understand the rapidly evolving science and art of how these classifications are made.
Even a general understanding of the methods used in variant interpretation will give health care providers an appreciation for the limitations of currently available genomic medicine.
There is currently no set standard for classifying variants, but groups like the ClinGen consortium (www.clinicalgenome.org) are working to define standard procedures for classifying and curating sequence variants. The most widely used framework for classifying variants in disease genes is based on an earlier set of guidelines proposed by the American College of Medical Genetics and Genomics (ACMG). Because classification often occurs in the context of incomplete information, it is rarely definitive, but rather, couched in terms of certainty. The five-tiered ACMG classification (pathogenic, likely pathogenic, uncertain significance (VUS), likely benign, and benign) applies to moderately and highly penetrant disease genes. Low-penetrance disease gene variants, such as those that come from Genome-Wide Association Studies (GWAS), are almost always common variants and are categorized separately as risk factors. The new ACMG guidelines, updated in 2015, provide a much more detailed framework for variant classification. Not all laboratories follow this guideline exactly, nor do they necessarily use the same vocabulary when reporting results. However, most laboratories consider roughly the same evidence in making a determination about variant pathogenicity.
When evaluating a variant, two fundamental considerations are made: first, can the gene containing the variant be plausibly linked to a disease, and second, does the variant affect the function of the gene? All of the following discussion aims at answering these deceptively simple questions.
EVIDENCE USED IN ASSESSMENT OF PATHOGENICITY
Effect of variant on protein structure or function
Any type of variant can be pathogenic, although the likelihood decreases substantially when considering nonprotein-coding variants. Putative loss of function (LOF) variants (stop-gained, stop-lost, frameshift insertion/deletion, splice site disruptor AG/GT) are the most deleterious and best candidates for pathogenic variants. Missense variants can also be pathogenic, but it's difficult to know if any given missense variant will impact the protein in such a way as to cause disease. Computational algorithms that estimate deleteriousness based on features like conservation and location include programs like PhyloP, Polyphen, SIFT, MutationTaster, and others. These algorithms can best be described as informative ...