Genetic engineering is now a fast path to cures for inherited diseases, those in which the cause is mutation in a single gene. Remove or suppress the bad gene, and insert the fixed version. This sort of approach, suppressing or editing a few specific genes, is unlikely to be as broadly and directly effective for age-related conditions, however. This is because, as is the case for aging, the disease state is influenced by thousands of genes, but directly caused by none of them. There are arguments, such as the research noted here, that suggest we should expect discoveries such as myostatin knockout for muscle growth, or ASGR1 and ANGPTL4 for reduced cardiovascular disease risk, to be usual in the magnitude of their effects. When in search of genetic alterations to beneficially change a disease state, we should expect to come up more or less empty handed most of the time.
Here I am focused on the standard approach of editing or manipulating expression of one gene or a few genes, and not on more extensive engineering projects such as the SENS rejuvenation research program that aims to copy altered forms of mitochondrial DNA into the cell nucleus as a backup to remove the consequences of age-related damage to mitochondrial DNA. Or consider the Oisin Biotechnologies delivery of programmable DNA machinery to destroy a cell depending on its state. There will no doubt prove to be a role for other ambitious and essentially genetic reworkings of the cell, ways to improve redundancy of components or expand capabilities to ensure greater resilience, such as by providing a package of new enzymes capable of tackling problematic forms of metabolic waste.
This is, however, a very different class of approach to the single gene editing efforts that make up most of the field at present. This may be an era of medicine dominated by genetics, but we must, I think, recognize where it is limited as well as where there is the greatest potential. To my mind I see far too much enthusiasm for the sort of focus on genetic personalized medicine and single gene manipulations put forward by the likes of Human Longevity, to pick a representative example, while there just isn’t enough of a benefit in their approach to be excited by it.
In a provocative new perspective piece, researchers say that disease genes are spread uniformly across the genome, not clustered in specific molecular pathways, as has been thought. The gene activity of cells is so broadly networked that virtually any gene can influence disease, the researchers found. As a result, most of the heritability of diseases is due not to a handful of core genes, but to tiny contributions from vast numbers of peripheral genes that function outside disease pathways. Any given trait, it seems, is not controlled by a small set of genes. Instead, nearly every gene in the genome influences everything about us. The effects may be tiny, but they add up.
The researchers call their provocative new understanding of disease genes an “omnigenic model” to indicate that almost any gene can influence diseases and other complex traits. In any cell, there might be 50 to 100 core genes with direct effects on a given trait, as well as easily another 10,000 peripheral genes that are expressed in the same cell with indirect effects on that trait. Each of the peripheral genes has a small effect on the trait. But because those thousands of genes outnumber the core genes by orders of magnitude, most of the genetic variation related to diseases and other traits comes from the thousands of peripheral genes. So, ironically, the genes whose impact on disease is most indirect and small end up being responsible for most of the inheritance patterns of the disease.
Researchers have thought of genetically complex traits as conforming to a polygenic model, in which each gene has a direct effect on a trait, whether that trait is something like height or a disease. In earlier work on the genetics of height, researchers were surprised to find that essentially the entire genome influenced height. “It was really unintuitive to me. To be honest, I thought that it was probably wrong. I gradually started to realize that the data don’t really fit the polygenic model. We started to think, ‘If the whole genome is involved in a complex trait like height, then how does that work?'”
The polygenic model leads researchers to focus on the short list of core genes that function in molecular pathways known to impact diseases. So, therapeutic research typically means addressing those core genes. A common approach to gene discovery is to do larger and larger genome-wide association studies, but the team argues against this approach because the sample sizes are expensive and the thousands of peripheral genes uncovered are likely to have tiny, indirect effects. “After you get the first 100 hits, you’ve probably found most of the core genes you’re going to get through genomewide association studies.” Instead, the team recommends switching to deep sequencing the core genes to hunt down rare variants that might have bigger effects. “If this model is right, it’s telling us something profound about how cells work that we don’t really understand very well. And so maybe that puts us a little bit further away from using genome-wide association studies for therapeutics. But in terms of understanding how genetics encodes disease risk, it’s really important.”