Derek Lowe's commentary on drug discovery and the pharma industry. An editorially independent blog from the publishers of Science Translational Medicine . All content is Derek’s own, and he does not in any way speak for his employer.

I wrote a couple of years ago about the long-running study of mutations in a serotonin transporter gene. Over the years, polymorphism in the gene have been correlated with all sorts of human behavior and psychiatry, in keeping with the importance of serotonin signaling in human cognition. Depression, anxiety, that whole end of human behavior seemed to be affected by just what sort of genetic variation one had. Hundreds and hundreds of studies have appeared in the literature, many of them with truly impressive p-values.

Well, as that old post shows, people have been throwing cold water on this idea for a while now as well, and now there’s a paper that should (you’d think) expunge the whole idea of 5-HTTLPR variations having anything coherent to tell us about human disease. It doesn’t stop there: the authors go on to demolish every other “depression gene” connection in the existing literature. They went after the lot:

Utilizing data from large population-based and case-control samples (Ns ranging from 62,138 to 443,264 across subsamples), the authors conducted a series of preregistered analyses examining candidate gene polymorphism main effects, polymorphism-by-environment interactions, and gene-level effects across a number of operational definitions of depression (e.g., lifetime diagnosis, current severity, episode recurrence) and environmental moderators (e.g., sexual or physical abuse during childhood, socioeconomic adversity).

Nothing. No clear evidence for any given gene, in any polymorphic form, with any effect on depression, as either measured by itself or in combination with any other environmental effect. At this point it seems safe to say that there are no single standout genes that can be associated with depression. That’s not to say that there’s no genetic influence at all, but what this means is that (like so many other things) it’s a complex mix of dozens, hundreds, thousands of genetic factors tangled with environmental ones. It may well be that many of these end up binning into similar phenotypes or heading down common pathways, but we don’t know that for sure, either. What we do know is that talk of a “depression gene” is nonsense.

Looking back, the single biggest problem with all these earlier proposals (and there have been plenty) is that their sample sizes were wildly, hilariously small. Once again, it’s all down to effect size. The paper calculates that the largest-effect-size gene candidates in this field would still need samples in the tens of thousands to detect. And what has the median sample size been over the years? 345 patients. Right. This literature is all noise, all false positives, all junk. As you actually move to larger and larger studies, everything disappears, which is what noise does. Real stuff, on the other hand, should become stronger and harder to ignore as you increase the N, with tighter error bars and better signal/noise.

Here’s an excellent writeup on Slate Star Codex (whose author is a psychiatrist himself). He’s trying to be judicious throughout, but the frustration shows. I particularly like this part:

First, what bothers me isn’t just that people said 5-HTTLPR mattered and it didn’t. It’s that we built whole imaginary edifices, whole castles in the air on top of this idea of 5-HTTLPR mattering. We “figured out” how 5-HTTLPR exerted its effects, what parts of the brain it was active in, what sorts of things it interacted with, how its effects were enhanced or suppressed by the effects of other imaginary depression genes. This isn’t just an explorer coming back from the Orient and claiming there are unicorns there. It’s the explorer describing the life cycle of unicorns, what unicorns eat, all the different subspecies of unicorn, which cuts of unicorn meat are tastiest, and a blow-by-blow account of a wrestling match between unicorns and Bigfoot.

He goes on to note that there are a number of diagnostic tests that are supposed to help practitioners prescribe antidepressants based on gene sequence. But work like this latest paper strongly suggests that this is not well-founded. Some of these tests are for metabolic enzyme isoforms that could affect blood levels of specific compounds – and that’s not a stupid idea, although it’s often harder to realize in practice than just doing a sequence on someone. But there are companies using the exact same genes whose connection to depression is being invalidated. Slate Star Codex again:

Remember, GeneSight and their competitors refuse to release the proprietary algorithms they use to make predictions. They refuse to let any independent researchers study whether their technique works. They dismiss all the independent scientists saying that their claims are impossible by arguing that they’re light-years ahead of mainstream science and can do things that nobody else can. If you believed them before, you should be more cautious now. They are not light-years ahead of mainstream science. They took some genes that mainstream science had made a fuss over and claimed they could use them to predict depression. Now we think they were wrong about those. What are the chances they’re right about the others?

So far, there appears to be little or no reliable evidence that such testing is useful. That’s not to say that it can’t ever be, just that the people who are trying to sell it to you right now don’t have a very strong case. Psychiatric indications, out of the entire landscape of medical therapy, are really the most difficult and treacherous region to try to navigate with any sorts of molecule-level explanations. We don’t know enough to get that granular. We really don’t. If you start digging into the details of depression, anxiety, OCD, bipolar disorder and all the rest, the molecular and cellular-level explanations start coming apart in your hands like wet tissue paper. The field is littered with failed hypotheses, with just-so stories and compelling-but-wrong correlations, and with sources of unreliable data ranging from big, expensive, and completely honest efforts all the way down to plenty of outright charlatanry. Caveat emptor, and how.