Due to the hostility of this environment, it is unclear whether humans have ever subsisted in tropical forests without depending on external resources, such as agriculture or possible exchanges with neighboring populations. Evidence of societies living in such harsh conditions is scarce for contemporary modern humans, as well as for early Homo. Nonetheless, it is possible that humans have developed recent biological adaptations to tropical forests. A few examples of such adaptations have indeed been documented, the most well-known being the pygmy phenotype, defined by Perry and Dominy as small human body size. These authors argue that short-statured individuals may have advantages to cope with food limitation, thermoregulation, and mobility hardship in a dense forest and, with few exceptions, are thus found in hunter-gatherer populations living in tropical rainforests of Africa, Asia, Oceania, and the Americas. However, it has also been suggested that this phenotype could be a by-product of selection for early onset of reproduction, which could enable populations to overcome problems related to their life history and increased mortality. To investigate whether tropical forest dwellers have developed specific biological adaptations to this harsh environment, we searched for genome-wide signals of positive selection in populations from the Americas and Africa, specifically aiming at identifying 22-Oxacalcitriol convergent evolution signals, that is a significant signal of positive selection occurring at the same genomic region or biological pathway in populations belonging to two distinct evolutionary lineages. To that effect, we investigated populations living in tropical forests and others, genetically related, living outside these environments using publicly available genome-wide SNP data and a robust and sensitive FST-based method for inference of positive selection that explicitly includes a convergent selection model. A modified version of BayeScan was used to identify candidate targets for natural selection. The original methodology in this software is based on the multinomial-Dirichlet likelihood- based approach implemented via a Markov chain Monte Carlo algorithm. The approach assumes an 1,9-Dideoxyforskolin island model ��in which the subpopulations�� allele frequencies are correlated through a common migrant gene pool from which they differ by varying degrees��to calculate a population-specific FST coefficient. Logistically transformed FST coefficients are then decomposed into a population-specific component, shared by all loci, and a locus-specific component, shared by all the populations.