From 5,000 to 10,000 to one million and beyond. The scale of so-called genome-wide association studies (GWAS) has grown at a voracious pace over the last decade. And there’s no sign the initiatives are slowing down. As sequencing and data analysis costs continue to drop, public and private teams will likely push forward with even more ambitious projects.
But what if the data isn’t as worthwhile as it seems? That’s a question several researchers have raised in a compelling new paper published in Cell.
The three authors; Jonathan Pritchard, Evan Boyle, and Yang Li, all hail from the department of genetics at Stanford University.
In “An Expanded View of Complex Traits: From Polygenic to Omnigenic,” they argue that there is no smoking gun for complex traits. Instead, “association signals tend to be spread across most of the genome—including near many genes without an obvious connection to disease.”
This runs counter to the idea that disease-causing variants cluster around key biological pathways. According to the authors, essentially all genes expressed in relevant tissues affect traits.
This observation led to a new hypothesis the authors call the “omnigenic” model (omni- means “all”), instead of the current polygenic approach.
A Deep-dive Into Specialty Pharma
A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.
“To be very clear we do not argue that GWAS ‘failed,'” Pritchard stressed on Twitter.
But if the aim is to understand complex traits and the huge amount of data produced through GWAS, researchers should work on mapping regulatory networks in cells, they said.
If the aim is to link genes with diseases, the authors recommend scientists focus their efforts on rare mutations and how those genes impact human health and disease. Such variants are typically too rare to be identified in GWAS.
The idea that findings from large genomic studies need to be taken with a grain of salt is hardly new. An August 2013 post on the CDC’s Genomics and Health Impact Blog, underscores a number of caveats — some of which resonate with the Stanford scientists’ argument:
“GWAS have many limitations, such as their inability to fully explain the genetic/familial risk of common diseases; the inability to assess rare genetic variants; the small effect sizes of most associations; the difficulty in figuring out true causal associations; and the poor ability of findings to predict disease risk.”
But there’s no denying that GWAS have played a huge part in developing genomics to this point.
If anything, it’s humbling to be reminded of how much more we have to learn. As Pritchard shared on Twitter, “scientists study nature as it is, and not as we might wish it to be (i.e. simpler).”
Photo: farakos, Getty Images
