Cardiomyopathies (CMPs) and channelopathies (CLPs) are debilitating inherited diseases of the heart muscle and conduction system, which collectively affect well over one million patients in the United States, and can lead to heart failure and sudden cardiac death. Although some CMP/CLPs arise sporadically, many show a strong pattern of familial inheritance. Genetic testing applies knowledge of CMP/CLP genes towards the care of patients and their family members. In any given patient, if one knows the actual mutation responsible for their condition, one can very easily determine if other family members carry it, thereby facilitating careful surveillance and potential preventive therapies. Remarkably, despite knowing in many cases exactly which mutation causes the diseases in a given family, we can often do very little when it comes to predicting what is likely to befall a specific individual. This phenomenon, where not all individuals who inherit a mutation actually develop a disease is known as incomplete penetrance. It affects not only CMPs and CLPs, but nearly all inherited disease. Incomplete penetrance has been ascribed to the complex interplay between genes and environment, so that many modifying influences can influence the severity of the disease. Although this represents a conceptually satisfying explanation, it does little to help assess risk in patients. This failure of accurate prognostication, even in such highly heritable diseases, has real practical consequences, such as when it comes to deciding on such therapies as implantation of a defibrillator or recommendation, which themselves carry significant risk. In this proposal, I describe a stepwise approach to beginning to resolve incomplete penetrance in CMPs/CLPs. I first identify which genetic variants in the human population are likely to modify severity of disease in the CMPs and CLPs. This step requires harnessing remarkable recent advances in massively parallel genomic technology, where tens of thousands of variants can be interrogated at once for their effect on regulating gene activity. With this catalogue of variants in hand, one can build assays to assess the status of patients at each of these positions in the genome, as well as determine the identify of the underlying causal mutation(s). The final step is too look at actual CMP/CLP patients, and determine whether knowledge of modifying genetic variation can help predict who is likely to develop severe disease, and who will have a milder course.
|Deo, Rahul C (2016) Alternative Splicing, Internal Promoter, Nonsense-Mediated Decay, or All Three: Explaining the Distribution of Truncation Variants in Titin. Circ Cardiovasc Genet 9:419-425|
|Deo, Rahul C (2015) Machine Learning in Medicine. Circulation 132:1920-30|
|Roberts, Jason D; Longoria, James; Poon, Annie et al. (2015) Targeted deep sequencing reveals no definitive evidence for somatic mosaicism in atrial fibrillation. Circ Cardiovasc Genet 8:50-7|
|Vedantham, Vasanth; Galang, Giselle; Evangelista, Melissa et al. (2015) RNA sequencing of mouse sinoatrial node reveals an upstream regulatory role for Islet-1 in cardiac pacemaker cells. Circ Res 116:797-803|
|Shah, Sanjiv J; Katz, Daniel H; Selvaraj, Senthil et al. (2015) Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation 131:269-79|
|Zou, Jun; Tran, Diana; Baalbaki, Mai et al. (2015) An internal promoter underlies the difference in disease severity between N- and C-terminal truncation mutations of Titin in zebrafish. Elife 4:e09406|
|Deo, Rahul C; Musso, Gabriel; Tasan, Murat et al. (2014) Prioritizing causal disease genes using unbiased genomic features. Genome Biol 15:534|