Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting

For appropriate and effective patient treatment, relevant clinical information should be available to the clinician on demand. Accurate interpretation of gene test results, including phenotype association of gene variants, is an important component in customizing patient therapy. Recent endeavors such as the NCBI Genetic Testing Registry, MutaDATABASE, 1000 Genomes and the Human Variome Project draw attention to this growing interest in gene variant annotation and clinical interpretation in human disease [1-4]. Ongoing efforts to catalog human genome variation for many years have led to authoritative repositories of gene variants, with clear association to disease phenotype finally beginning to emerge [5-8].Rapidly evolving technologies such as SNP chip genome-wide association studies and next-generation sequencing have lowered the cost and increased the speed of genomic analysis, yielding much larger data sets [9]. Currently, gene variants are being discovered at an unprecedented pace. One recent report found an average of 3 million variants per personal genome [10]. Unfortunately, an ever-widening gap exists between this fast growing collection of genetic variation and practical clinical interpretation due to a lack of understanding of the phenotypic consequences (if any) of any given variant. Although the number of genetic testing laboratories has remained around 600 over the past several years, recent data show that clinical testing is currently available for well over 2,200 different genes or genetic conditions [11]. As medical records increasingly incorporate genetic test information, improved decision support approaches are needed to provide clinicians with the preferred course of treatment [12,13]. Furthermore, for decision support rules to be of value, the clinical relevance of laboratory information must be well understood [14,15].Updated recommendations have been proposed from the American College of Medical Geneticists (ACMG) on reporting and classificat