Until recently, behavioural geneticists had to use twin samples to estimate the heritability of traits. The standard method estimates heritability - the contribution of genes - by exploiting the fact that identical twins are more genetically alike than nonidentical twins, who are more alike than unrelated people. A drawback of this method is that twin samples are hard to find. Recently, a method has become available that circumvents this problem; it's called genome-wide complex trait analysis (GCTA). The basic idea is to use differences in the actual genetic makeup of people to calculate heritability. It should not be confused with the genome-wide association technique, which "hunts" for specific "genes for" some outcome (the "gene for depression" or what have you).
A recent paper by Maciej Trzaskowski, Philip S. Dale and Robert Plomin (abstract; via) compares heritability estimates on the basis of standard and GCTA techniques. Results for the dependent variables show that GCTA estimates are considerably smaller than standard estimates for height, weight and intelligence. The real shocker are the results for "behaviour problems" (such as depression or hyperactivity), though. While the standard analyses suggest considerable heritability, most GCTA estimates are zero or close to zero. Here's the result for self-report measures, with standard results on the left and GCTA results on the right:
Results for parent and teacher reports are broadly similar.
What's it all mean? Well, the authors include a long discussion section in their paper, but, frankly, much of it is above my head due to my very limited knowledge of genetics and associated research methods. The most important take-home message, though, is that at least one of the common methods for estimating the contribution of genes to human outcomes yields misleading results. This is very important, and it is to be hoped that the paper gets lots of exposure. I've done my part.