“Common Disease Is More Complex Than Implied by the Core Gene Omnigenic Model”, 2018-06-14 (; backlinks; similar):
The evidence that most adult-onset common diseases have a polygenic genetic architecture fully consistent with robust biological systems supported by multiple back-up mechanisms is now overwhelming. In this context, we consider the recent “omnigenic” or “core genes” model. A key assumption of the model is that there is a relatively small number of core genes relevant to any disease. While intuitively appealing, this model may underestimate the biological complexity of common disease, and therefore, the goal to discover core genes should not guide experimental design. We consider other implications of polygenicity, concluding that a focus on patient stratification is needed to achieve the goals of precision medicine.
…In conclusion, et al 2017 are congratulated for their synthesis of current data and for articulation of a biological framework that has prompted extensive constructive discussion. We agree that understanding the cell-specific role of disease-associated variants is a crucial step for advancing knowledge of common disease. However, whereas those authors extrapolate results of analyses of GWAS summary statistics to make fundamental assumptions that rare variants of large effect in a small number of genes play the most critical roles in clinical conditions that attract a common disease diagnosis, we believe it would be a major disservice to the field to allow these assumptions to guide the next steps of research. To assume that a limited number of core genes are key to our understanding of common disease may underestimate the true biological complexity, which is better represented by systems genetics and network approaches ( et al 2017, et al 2015). While Boyle et al advocate for WES studies, they did not discuss the sample sizes needed for such discovery. We believe that in the short term, large samples recorded for key measures of phenotypic heterogeneity and genome-wide SNP data are the best next steps for research using human DNA samples in moving forward our understanding of complex genetic diseases. Large numbers of samples, biobanked for cellular reprogramming, will position us well for the next generation of sequencing and other new technologies. High-throughput phenotyping to characterize cellular properties associated with disease-associated genomes may be the key to penetrate the polygenic complexity of common disease and provide the data needed for patient stratification, as well as to progress toward the goal of new drug treatments. These are research paths that need to advance in parallel to advance the promise of precision medicine.