Remember Jurassic Park? I can. I still get shivers thinking about the dinosaurs crashing through the trees and devouring anything that moves. I also recall the adorable animation the park visitors watched to better understand how the dinosaurs were brought back from extinction via the DNA in their blood cells trapped in those ancient mosquitoes encased in amber. This introduction to genetic engineering via Hollywood initiated my scientific endeavor for learning more about nucleic acids. I was hooked.
Since then, I have joined the countless masses who have become enthralled with the double-helix. We believed that the message that dictated our uniqueness resided in that simple, repetitive molecule. Thanks to technological advances, reading these messages has become both easy and affordable. As a result, the scientific literature is inundated with genome wide association studies (GWAS), which search for genetic changes that are related to many different diseases and conditions (Manolio, 2017).
Boyle et al. raised the question as to whether or not they should limit their focus to just a small subset of genes identified from GWAS to better understand disease or inheritable traits (Boyle, Li, & Pritchard, 2017). In their recent Cell publication, the group of scientists concluded that while a “core” set of genes might be a cause for something, the overall phenotype (physical or observable characteristics) resulted from many small contributions from many more genes. The authors stated that while the initial impulse may be to do even more GWAS, a different approach may be needed. They recommended doing an omnigenics analysis of GWAS data.
What is “omnigenics” you may ask? For complex traits or diseases, any gene variant in the genome could be contributing to the manifestation of the disease or trait. Hence, to truly understand the genetic cause of a disease or trait, all DNA mutations need to be considered. Even if the mutation seems to affect a system seemingly unrelated to the disease or trait of interest, it should be included in the analysis.
Recently, a study was published that showed that individuals were carrying disease-causing gene variations, yet the carriers showed no physical manifestation of the disease (Chen et al., 2016). The reason could be what the Boyle et al. had suggested: The manifestation is dictated by the genomic forest and not by a few gene trees.
This omnigenics approach sounds lovely, but it comes with its own issues. How do we truly separate legitimate changes from the background to understand the causes of disease? If we look at the data, is it possible that we are just looking at a pile of sawdust trying to pretend to be the desired forest? With so much information embedded in DNA, it can be a daunting challenge to establish causality no matter how good the software that sifts through the info.
Instead of considering our DNA, maybe it would be worthwhile to change focus and look at the proteomic (all proteins that constitute our bodies) forest. Being the products of our genes, proteins offer a timelier view of what is happening in our bodies. Understanding how changes in the protein, such as concentration, correlate with disease or inheritable traits may shed more light onto how different biological systems interact with one another in the body. It could also help explain exactly how simple genetic change(s) in an unrelated pathway could affect a physical trait. This capability would certainly explain what happened in the Jurassic Park dinosaurs, which had frog DNA inserted into their genomes, to allow some of them to become male.
Boyle, E. A., Li, Y. I., & Pritchard, J. K. (2017). An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell, 169(7), 1177-1186. doi:10.1016/j.cell.2017.05.038
Chen, R., Shi, L., Hakenberg, J., Naughton, B., Sklar, P., Zhang, J., . . . Friend, S. H. (2016). Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases. Nat Biotechnol, 34(5), 531-538. doi:10.1038/nbt.3514
Manolio, T. A. (2017). In Retrospect: A decade of shared genomic associations. Nature, 546(7658), 360-361. doi:10.1038/546360a