There is increasing interest in studying the molecular mechanisms of recent adaptations caused by positive selection in the genomics era. Such endeavors to detect recent positive selection, however, have been severely handicapped by false positives due to the confounding impact of demography and the population structure. To reduce false positives, it is critical to conduct a functional analysis to identify the true candidate genes/mutations from those that are filtered through neutrality tests. However, the extremely high cost of such functional analysis may restrict studies within a small number of model species. In particular, when the false positive rate of neutrality tests is high, the efficiency of the functional analysis will also be very low. Therefore, although the recent improvements have been made in the (joint) inference of demography and selection, our ultimate goal, which is to understand the mechanism of adaptation generally in a wide variety of natural populations, may not be achieved using the currently available approaches. More attention should thus be spent on the development of more reliable tests that could not only free themselves from the confounding impact of demography and the population structure but also have reasonable power to detect selection.

Natural selection is the differential reproductive success of individuals due to variation in traits. Positive selection increases the frequency of beneficial alleles and negative selection decreases the frequency of harmful alleles. Natural selection is one of the most important mechanisms for us to understand evolution. A lot of methods have been developed to detect positive selection. However, relatively less attention has been paid to false positives of candidates, mainly due to the confounding effects of demography. By reviewing the advantages and disadvantages of different methods, we suggest that new methods robust with demography should be developed in the future.