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During evolution, living species have adapted to environmental constraints according to the mechanism of natural selection; when a mutation that aids the survival (and reproduction) of an individual appears in the genome, it then spreads throughout the rest of the species until, after several hundreds or even thousands of generations, it is carried by all individuals. But does this selection, which occurs on a specific gene in the genome of a species, also occur on the same gene in neighboring species? On which set of genes has natural selection acted specifically in each species? Researchers in the Dynamique et Organisation des Génomes team at the Institut de Biologie of the Ecole Normale Supérieure (CNRS/ENS/INSERM) have studied the genome of humans and three other primate species (chimpanzee, orangutan and macaque) using bioinformatics tools. Their work consisted in comparing the entire genomes of each species in order to identify the genes having undergone selection during the past 200,000 years. The result was that a few hundred genes have recently undergone selection in each of these species. These include around 100 genes detected in man that are shared by two or three other species, which is twice as many as might be anticipated as a random phenomenon. Thus a not inconsiderable proportion of the genes involved in human adaptation are also present in the chimpanzee, orangutan or macaque, and sometimes in several species at the same time. Natural selection acts not only by distancing different species from each other when new traits appear. But by acting on the same gene, it can also give rise to the same trait in species that have already diverged, but still have a relatively similar genome. This study thus provides a clearer understanding of the group of genes that are specifically implicated in human evolution (during the past 200,000 years), as it allows the identification of those genes which did not undergo selection in another primate line. An example that has been confirmed by this study is the well-known case of the lactase gene that can metabolize lactose during adulthood (a clear advantage with the development of agriculture and animal husbandry). The researchers have also identified a group of genes involved in some neurological functions and in the development of muscles and skeleton.

Statistical validation of results, as Shaffer described it, simply involves testing the null hypothesis: that the pattern you detect in your data occurs at random. If you can reject the null hypothesis—and science and medicine have settled on rejecting it when there's only a five percent or less chance that it occurred at random—then you accept that your actual finding is significant. The problem now is that we're rapidly expanding our ability to do tests. Various speakers pointed to data sources as diverse as gene expression chips and the Sloan Digital Sky Survey, which provide tens of thousands of individual data points to analyze. At the same time, the growth of computing power has meant that we can ask many questions of these large data sets at once, and each one of these tests increases the prospects than an error will occur in a study; as Shaffer put it, "every decision increases your error prospects." She pointed out that dividing data into subgroups, which can often identify susceptible subpopulations, is also a decision, and increases the chances of a spurious error. Smaller populations are also more prone to random associations. In the end, Young noted, by the time you reach 61 tests, there's a 95 percent chance that you'll get a significant result at random. And, let's face it—researchers want to see a significant result, so there's a strong, unintentional bias towards trying different tests until something pops out. Young went on to describe a study, published in JAMA, that was a multiple testing train wreck: exposures to 275 chemicals were considered, 32 health outcomes were tracked, and 10 demographic variables were used as controls. That was about 8,800 different tests, and as many as 9 million ways of looking at the data once the demographics were considered.