Finding rare variants involved in disease
It has been noticed in some diseases that common variants which lead to modest increases in risk are located in or near genes that also, when mutated, cause severe monogenic forms of a the same disease (eg. obesity). This naturally leads to the hypothesis that newly identified genes containing modest risk alleles will also contain rarer alleles of strong effect.
A new study tests this hypothesis in type I diabetes: the authors take 10 genes known to be involved in diabetes etiology (note that many of these genes were discovered by genome-wide association studies of common variants) and re-sequence them in a large set of cases and controls.
What do they find? As hypothesized, a number of rare protein-altering changes in one of the genes (IFIH1, a gene involved in response to viral infection) end up being strongly associated with type I diabetes. The effect sizes aren’t massive (the risk alleles have odds ratios around 2), but they certainly have stronger effects than the common variants identified (though because of their low frequencies, they explain only a minimal fraction of all the variance in diabetes risk).
This is only a proof-of-principle– expect many similar studies, including full exome re-sequencing, in the years to come.
Labels: Genetics
This is cool.
Step 1: Look for weak associations between a trait and common variants to get a list of potentially important DNA regions.
Step 2: Closely examine those DNA regions in people with extremes in that trait looking for rare variants with strong effect.
Step 3: Use the rare variants of strong effect to identify the important genes, proteins, and molecular pathways underlying a trait.
Step 4: Tie it all together to get a good DNA-protein-system model of the trait.
This would be a fast track method for predicting phenotype from genotype.
Sequence a person’s genome and identify variant DNA. Some of the variants will be common and have a known effect. Some of the variants will be in regions that are known to have little affect on the trait. Some of the novel variants will lie in DNA regions known to be important for that trait. Combined with a good model connecting the genotype through molecular mechanism to phenotype, one then predicts how the novel variant will affect the trait. E.g., a novel variant causes a change in a critical part of a protein. Knowing the protein’s function in the biological system the doctor then uses a model to predict the affect of that DNA variant on the trait.
Hot off the press
In related news it appears that most cases of Type I Diabetes are triggered by a common childhood enteroviral infection. Interestingly enough some cases of Type 2 Diabetes may be triggered by the same infection.
Sciencedaily (March 6, 2009) Type 1 Diabetes triggered by common childhood infection
I’ve had a hard time googling a definition for a person’s “risk” of diabetes or, say, multiple sclerosis – a notion that is not very intuitive, since you either have MS or you don’t.
Is this risk found by adding up all your blood relatives with MS and dividing by the total number of relatives (weighting each relative, of course, by the coefficient of relatedness)? This just seems a little odd – with MS prevalence being about 1%, most people would have no affected relatives and thus a risk of “zero”… which is all the more true for a much rarer disease like myasthenia gravis.