•

the Blog

## Is there a gene for alcoholism? (1)

By Guillaume Filion, filed under
causality,
genetics,
series: is there a gene for alcoholism?,
independence,
information.

• 26 August 2012 •

This is usually the next thing I hear when I say that I am a geneticist. Behind this question and its variants lies a profound and natural interrogration, which could be phrased as *"how much of me is the product of my genes?"* I made a habit of not answering that question but instead, highlight its inaneness by lecturing people about genetics. So, for once, and exclusively on my blog, here is the tl;dr answer: *no, there is not*. Now comes the lecture about genetics.

I will start with mental retardation — unrelated with my opinion of those claims, really — and more precisely with the fragile X syndrome. James Watson, the co-discoverer of the structure of DNA and the pioneer of the Human Genome Project declared:

I think it was the first triumph of the Human Genome Project. With fragile X we've got just one protein missing, so it's a simple problem. So, you know, if I were going to work on something with the thought that I were going to solve it, oh boy, I'd work on fragile X.

In other words, there seems to be a gene for mental retardation. The incidence...

## The fallacy of (in)dependence

By Guillaume Filion, filed under
information,
causality,
probability,
independence.

• 04 May 2012 •

In the post Why p-values are crap I argued that independence is a key assumption of statistical testing and that it almost never holds in practical cases, explaining how p-values can be insanely low even in the absence of effect. However, I did not explain how to test independence. As a matter of fact I did not even *define* independence because the concept is much more complex than it seems.

Apart from the singular case of Bayes theorem, which I referred to in my previous post, the many conflicts of probability theory have been settled by axiomatization. Instead of saying what probabilities *are*, the current definition says what properties they have. Likewise, independence is defined axiomatically by saying that events $(A)$ and $(B)$ are independent if $(P(A \cap B) = P(A)P(B))$, or in English, if the probability of observing both is the product of their individual probabilities. Not very intuitive, but if we recall that $(P(A|B) = P(A \cap B)/P(B))$, we see that an alternative formulation of the independence of $(A)$ and $(B)$ is $(P(A | B) = P(A))$. In other words, if $(A)$ and $(B)$ are independent, observing...