From genetics to biochemistry

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A few months ago, I pointed out a paper identifying variants near the FTO gene as being involved in obesity. I noted how strikingly little was known about this gene, concluding:

So essentially, nothing is known about this gene. Thanks to this study, this is unlikely to be the case for long.

Little did I know it would only take a few months to get the ball rolling! From this week’s Science:

Variants in the FTO (fat mass and obesity associated) gene are associated with increased body mass index in humans. Here, we show by bioinformatics analysis that FTO shares sequence motifs with Fe(II)- and 2-oxoglutarate–dependent oxygenases. We find that recombinant murine Fto catalyzes the Fe(II)- and 2OG-dependent demethylation of 3-methylthymine in single-stranded DNA, with concomitant production of succinate, formaldehyde, and carbon dioxide. Consistent with a potential role in nucleic acid demethylation, Fto localizes to the nucleus in transfected cells. Studies of wild-type mice indicate that Fto messenger RNA (mRNA) is most abundant in the brain, particularly in hypothalamic nuclei governing energy balance…

This is an absolutely beautiful example of the hypothesis-generating power of genome-wide association studies. Studying the genetic variation underlying a trait is simply a great way to get at the mechanism by which the trait works. This point is lost on many people–even if the “environment”, however you want to define it, plays the most important role in a trait (like it may in obesity, for example), there are an infinite number of hypotheses about which environmental variables might be relevant, and once you find a correlation, it’s both difficult to establish causality and you get very little information about the mechanism by which the trait works (yes, eating a lot leads to increased weight in most people, but how?). In genetics, there is a finite number of hypotheses (there are many millions of genetic variants in humans, and all of them will eventually be testable), the road to establishing causality is much clearer (ie. this genetic variant leads to increased probability of obesity–it would be difficult to argue the inverse), and you immediately have your foot in the door to study the molecules involved in the trait. Again, this is a wonderful example of all of these points.

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4 Comments

  1. This why I like genetics so much. With little prior knowledge of the underlying biology of a trait, you can use results of genomewide scans to help generate hypothesis and gain insight in the biology. 
    Thanks for posting this.

  2. fat mass and obesity associated 
     
    i initially read it: fat ass and obesity associated ;-)

  3. How much does this type of analysis cost? 
     
    For example, let’s say I want to determine the set of genomes that affect a certain trait such as red hair. I imagine I would gather a sample of people, obtain their genomes and do a statistical analysis to find a certain set of genes that I can atrribute to red hair. 
     
    From there you would obviously want to determine the mechanism by which these genes effect this attribute. However, I imagine this second step is very difficult. 
     
    But how much is the first step? Using 100 people with a cost of $1000 per person to do the genome analysis + research costs gives me the impression it would be less than 1 million dollars. Is that the right ball park?

  4. But how much is the first step? Using 100 people with a cost of $1000 per person to do the genome analysis + research costs gives me the impression it would be less than 1 million dollars. Is that the right ball park? 
     
    it depends on the trait. if effect sizes are small (ie. the alleles involved in a trait have tiny effects, as is true for most traits), you need > 1000 individuals. with a couple million dollars, you could do a solid study.  
     
    for “simpler” traits (like red hair, for example), it would be less.

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