There are no common disorders (just extremes of quantitative traits)

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On the basis of recent Genome-wide association research, a review by Plomin et al. (2009) predicts that, in line with R.A. Fisher’s reconciliation of Mendelian inheritance and quantitative genetics, investigations “on polygenic liabilities will eventually lead to a focus on quantitative dimensions rather than qualitative disorders”. Basically, they are proposing a shift in thinking: moving from medical diagnoses and towards a broader level of analysis using quantitative traits.

By doing this, we should begin to get a better understanding of pleiotropic relationships and quantitative traits. As the authors highlight using an example of fat mass and obesity (FTO):

Although medical diagnoses (such as obesity) provide a convenient pragmatic framework for the initial discovery of genetic variants, in scientific terms there are no real ‘genes for disorders’. On the contrary, the genetic variants that are implicated in complex traits are associated with quantitative traits at every level of analysis. Thinking and researching quantitatively will provide a much richer picture of the complex biological pathways that lead from genes to disorders and will help us to generate biologically meaningful models of disease aetiology.

They also discuss the possibility of using weighted sets of variants to predict the polygenic risk score, which refers “to the set of multiple DNA variants that are associated with a disorder”. From here, a particularly salient point is raised about the inherent limitations of traditional case-control studies: that the control subjects are normally chosen on the basis of them not having the disorder in question, even though their phenotypic score may be near to the actual cases. By this, they mean that if we characterise disorders in terms of quantitative traits, then on a normal distribution some members of the control group fall very close to the low-end tail. To enhance the statistical power of these studies, the authors propose two alternative risk distributions: either contrast both ends of the distribution (the actual cases versus what they dub as super controls) or assign each participant their own phenotypic score and then study the entire distribution (what I dub as ambitious).

Still, there are limitations to this approach, namely: “for most disorders, we do not know what the relevant quantitative traits are”.

Here’s the abstract:

After drifting apart for 100 years, the two worlds of genetics – quantitative genetics and molecular genetics – are finally coming together in genome-wide association (GWA) research, which shows that the heritability of complex traits and common disorders is due to multiple genes of small effect size. We highlight a polygenic framework, supported by recent GWA research, in which qualitative disorders can be interpreted simply as being the extremes of quantitative dimensions. Research that focuses on quantitative traits – including the low and high ends of normal distributions – could have far-reaching implications for the diagnosis, treatment and prevention of the problematic extremes of these traits.

Citation: Plomin, Haworth & Davis. Common disorders are quantitative traits. Nature Reviews Genetics, 2009; 10, 872–878. DOI: 10.1038/nrg2670.

Hat-tip F1000.


  1. haven’t read the review…but it seems in terms of medical applications it is much sketchier to transmit statistical-type data to patients so that they can make decisions. IOW, i wonder if a focus on qualitatively salient illness and the like is going to be maintained by human stupidity. i guess theoretically genetic counselors are supposed to be the translators.

  2. >Still, there are limitations to this approach, namely: “for most disorders, we do not know what the relevant quantitative traits are”.

    this is more of a fatal flaw rather than a limitation, no?

  3. @Razib: Human stupidity may very well prevail. But I think if (and it’s a big if) the methods are presented correctly, then people should be able to grasp the data made available — especially if they have new ways of visualising and managing the data.

    @p-ter: Well, perhaps it’s a fatal flaw. I think the authors are hoping the data fairies will bestow them with gifts of larger sample sizes and more sensitive measurements.

  4. If you’ve got a way to measure the quantitative trait, there are plenty of statistical techniques you can use. e.g. if you have IQ tests, you can see what’s corrrelated with the numeric IQ score, rather than doing a case-control study with participants divided into “smart” and “less smart”.
    (Here, I won’t go into the usual concerns over whether IQ tests are a suitable measure of “intelligence”).

    But if you don’t have any idea how to measure the trait quantitatively, having more data is not going to help much.

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