Loci targeted by directional selection are expected to show elevated geographical population structure relative to neutral loci, and a flurry of recent papers have used this rationale to search for genome regions involved in adaptation. Studies of functional mutations that are known to be under selection are particularly useful for assessing the utility of this approach. Antimalarial drug treatment regimes vary considerably between countries in Southeast Asia selecting for local adaptation at parasite loci underlying resistance. We compared the population structure revealed by 10 nonsynonymous mutations (nonsynonymous single-nucleotide polymorphisms [nsSNPs]) in four loci that are known to be involved in antimalarial drug resistance, with patterns revealed by 10 synonymous mutations (synonymous single-nucleotide polymorphisms [sSNPs]) in housekeeping genes or genes of unknown function in 755 Plasmodium falciparum infections collected from 13 populations in six Southeast Asian countries. Allele frequencies at known nsSNPs underlying resistance varied markedly between locations (F(ST) = 0.18-0.66), with the highest frequencies on the Thailand-Burma border and the lowest frequencies in neighboring Lao PDR. In contrast, we found weak but significant geographic structure (F(ST) = 0-0.14) for 8 of 10 sSNPs. Importantly, all 10 nsSNPs showed significantly higher F(ST) (P < 8 x 10(-5)) than simulated neutral expectations based on observed F(ST) values in the putatively neutral sSNPs. This result was unaffected by the methods used to estimate allele frequencies or the number of populations used in the simulations. Given that dense single-nucleotide polymorphism (SNP) maps and rapid SNP assay methods are now available for P. falciparum, comparing genetic differentiation across the genome may provide a valuable aid to identifying parasite loci underlying local adaptation to drug treatment regimes or other selective forces. However, the high proportion of polymorphic sites that appear to be under balancing selection (or linked to selected sites) in the P. falciparum genome violates the central assumption that selected sites are rare, which complicates identification of outlier loci, and suggests that caution is needed when using this approach.