New DNA sequencing technologies are rapidly transforming the diagnosis of rare genetic diseases, but they also carry a risk: by allowing us to see all of the hundreds of “interesting-looking” variants in a patient’s genome, they make it potentially easy for researchers to spin a causal narrative around genetic changes that have nothing to do with disease status. Such false positive reports can have serious consequences: incorrect diagnoses, unnecessary or ineffective treatment, and reproductive decisions (such as embryo termination) based on spurious test results. In order to minimize such outcomes the field needs to decide on clear statistical guidelines for deciding whether or not a variant is truly causally linked with disease.
In a paper in Nature this week we report the consensus statement from a workshop sponsored by the National Human Genome Research Institute, on establishing guidelines for assessing the evidence for variant causality. We argue for a careful two-stage approach to assessing evidence, taking into account the overall support for a causal role of the affected gene in the disease phenotype, followed by an assessment of the probability that the variant(s) carried by the patient do indeed play a causal role in that patient’s disease state. We argue for the primacy of statistical genetic evidence for new disease genes, which can be supplemented (but not replaced by) additional informatic and experimental support; and we emphasize the need for all forms of evidence to be placed within a statistical framework that considers the probability of any of the reported lines of evidence arising by chance.
The paper itself is open access, so you can read the whole thing – we won’t rehash a complete summary here. However, we did want to discuss the back story and expand on a few issues raised in the paper.
Continue reading ‘Guidelines for finding genetic variants underlying human disease’