New & Noteworthy
February 28, 2013
Remember when sequencing the human genome was going to help us better understand and treat complex diseases like Type 2 diabetes or Parkinson’s? Well, ten years later, we’re still waiting.
Sure we’ve made some progress. Using genome wide association studies (GWAS), scientists have uncovered markers here and there that explain a bit about how a genetic disease is inherited. But despite a seemingly never-ending stream of these assays, scientists simply can’t explain all of the genetics behind most of these diseases.
So now scientists need to try to explain this missing heritability. If they can find out why they aren’t getting the answers they need from GWAS, then maybe they can restructure these assays to give better results.
As usual, when things get dicey genetically, scientists turn to the yeast Saccharomyces cerevisiae to help sort things out. And in a new study out in Nature, Bloom and coworkers have done just that.
In this study, they mated a laboratory and a wine strain of yeast to get 1,008 test subjects from their progeny. They extensively genotyped each of these 1008 and came up with a colony size assay that allowed them to determine how well each strain grew under various conditions. They settled on 46 different traits to study genetically.
What they found was that none of these traits was determined by a single gene. In fact, they found that each of the 46 different traits had between 5 and 29 different loci associated with it, with a median of 12 loci. This tells us that at least in yeast, many genetic loci each contribute a bit to the final phenotype. And if this is true in people, it could be a major factor behind the missing heritability in GWAS.
If a trait is dependent on many genetic loci that each have a small effect, then researchers need large populations in order to tease them out. In fact, when Bloom and coworkers restricted their population to 100 strains, they could only detect a subset of the genetic loci. For example, the number of loci went from 16 to 2 when they looked at growth in E6 berbamine.
So it may be that scientists are missing loci in GWAS because there are simply too few participants in their assays. If true, then the obvious answer is to increase the size of the populations being studied. Thank goodness DNA technologies get cheaper every year!
Of course as the authors themselves remind us, we do need to keep in mind that humans are a bit more complex than yeast. There may be other reasons that we aren’t turning up the genetic loci involved in various traits. It may be that we can’t as accurately measure the phenotypes in humans or that human traits are more complicated than the yeast ones studied. Another possibility is that in humans, there are more rare alleles that can contribute to a given trait. These would be very hard to find in any population studies like GWAS.
Still, this study at the very least tells us that larger populations will undoubtedly uncover more loci involved in human disease. Thank you again yeast.
August 9, 2012
The idea behind a genome wide association study (GWAS) makes perfect sense. Compare the DNA of one group of people with a disease to another group that doesn’t have the disease, identify the DNA region specific to the disease group, and then find the specific gene and mutations that lead to the disease.
In theory, this sort of study should have become routine once we had the human genome sequenced. In practice, it has turned out to be less useful than everyone hoped.
Now, this doesn’t appear to be any fault with the technique itself. Instead, it has more to do with the fact that many human diseases are simply too complex for GWAS to handle.
Most common human diseases appear to result from multiple genetic pathways and/or multiple genes. Throw in environmental effects and GWAS quickly becomes overwhelmed. At least for now, too many patients and controls would be needed for this powerful technique to have a real chance at deciphering most common human diseases.
But that doesn’t mean the technique isn’t useful. It is very good at finding single genes involved in strongly expressed traits. And this might be ideal for certain model organisms.
In a study just out in the latest issue of GENETICS, Connelly and Akey set out to investigate how well GWAS would work in the yeast, Saccharomyces cerevisiae. In many respects, this yeast appears to be made for GWAS.
It has a small, easily sequenced genome, there is on average a polymorphism every 168 base pairs or so, and its linkage disequilibrium is low. There are genome sequences from 36 wild and laboratory strains publicly available, all as diverse as can be.
But this yeast isn’t perfect. The chromosomal structure between strains tends to be much more varied than between two humans. This is predicted to introduce a high error rate. And this is just what Connelly and Akey saw when they ran some simulations.
They found that the error rate was too high in the simulations to draw any meaningful conclusions. But they also found that by using a more sophisticated analytical technique called EMMA, they were able to partly correct for some of these errors.
Simulations are one thing, but how about real life? Connelly and Akey next tested the method by applying it to a practical problem: identifying the genetic reasons for differences in mitochondrial DNA (mtDNA) copy number in yeast. What they found mimicked the simulation data.
Using more traditional analytical approaches on the data obtained from GWAS, they found 73 potential causative SNPs. But when they switched to analyzing the data with EMMA, they found a single SNP that was significant. It took a bit of hand waving, but the gene associated with this SNP could possibly be implicated in mtDNA copy number. And then again, it might not.
This “significant” SNP was found amidst lots of errors and in a background of high p values. In other words, this finding may not be a real one after all. This experiment does not give confidence that GWAS can be used when all known strains of yeast are compared.
But if the strains to be included are selected more carefully, it may still prove to be a useful tool. When Connelly and Akey focused on strains that were structurally similar, they found that the error rate was much lower. Low enough that in the near term, scientists may be using GWAS to figure out how things work in model organisms.
Hopefully the findings from GWAS applied to model organisms will illuminate disease mechanisms in humans. Then maybe GWAS can realize its full potential, although not in the way it was originally envisioned.