5-HTTLPR: A Pointed Review

The first 2/3 of this article describe the unstable foundation of genetic testing for depression and the last 1/3 the frightening consequences. It really starts at "Third, antidepressant pharmacogenomic testing." and ends with this brutal conclusion:

"But I think we should take a second to remember that yes, this is really bad. That this is a rare case where methodological improvements allowed a conclusive test of a popular hypothesis, and it failed badly. How many other cases like this are there, where there’s no geneticist with a 600,000 person sample size to check if it’s true or not? How many of our scientific edifices are built on air? How many useless products are out there under the guise of good science? We still don’t know."

This is a misleading paraphrasing of what the study said. There are plenty of effects which can be easily demonstrated with smaller sample sizes; it's just not complex effects like depression. What the authors said:

>genome-wide association studies (GWASs), which agnostically examine associations at millions of common single-nucleotide polymorphisms (SNPs) across the genome in large samples, have consistently found that individual SNPs exert small effects on genetically complex traits such as depression (18–20). For example, in the most recent GWAS of depression, which utilized a sample of 135,458 case subjects and 344,901 control subjects, the strongest individual signal detected (rs12552; odds ra- tio=1.044, p=6.07310219) would require a sample of ap- proximately 34,100 individuals to be detected with 80% power at an alpha level of 0.05, assuming a balanced case- control design (18).

They explain that given what we now know about polygenicity, the highest-effect-size genes require samples of about 34,000 people to detect, and so any study with fewer than 34,000 people that says anything about specific genes is almost definitely a false positive; they go on to show that the median sample size for previous studies in this area was 345.»

So you have to correct for this often by dividing the chosen p-value threshold (usually 0.05) by the number of comparisons (this is the simple Bonferonni correction). If you looked at 10,000 genes your new significance threshold would be 0.05/10000=0.000005. Therefore any gene difference between groups must have a p-value of less than 0.000005 to be considered significant if you're testing that many genes. However, if you chose to only look at 5-HTTLPR, then you don't need this correction and you only need p < 0.05.

>In retrospect, it should have been obvious that hypertext transport over line printers just wasn’t a good idea.