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Recent years have seen considerable progress in applying single nucleotide polymorphisms (SNPs) to population genetics studies. However, relatively few have attempted to use them to study the genetic differentiation of wild bird populations and none have examined possible differences of exonic and intronic SNPs in these studies. Here, using 144 SNPs, we examined population genetic differentiation in the saker falcon (Falco cherrug) across Eurasia. The position of each SNP was verified using the recently sequenced saker genome with 108 SNPs positioned within the introns of 10 fragments and 36 SNPs in the exons of six genes, comprising MHC, MC1R and four others. In contrast to intronic SNPs, both Bayesian clustering and principal component analyses using exonic SNPs consistently revealed two genetic clusters, within which the least admixed individuals were found in Europe/central Asia and Qinghai (China), respectively. Pairwise D analysis for exonic SNPs showed that the two populations were significantly differentiated and between the two clusters the frequencies of five SNP markers were inferred to be influenced by selection. Central Eurasian populations clustered in as intermediate between the two main groups, consistent with their geographic position. But the westernmost populations of central Europe showed evidence of demographic isolation. Our work highlights the importance of functional exonic SNPs for studying population genetic pattern in a widespread avian species.

One of the main topics in population genetics is identification of adaptive selection among populations. For this purpose, population history should be correctly inferred to evaluate the effect of random drift and exclude it in selection identification. With the rapid progress in genomics in the past decade, vast genome-scale variations are available for population genetic analysis, which however requires more sophisticated models to infer species' demographic history and robust methods to detect local adaptation. Here we aim to review what have been achieved in the fields of demographic modeling and selection detection. We summarize their rationales, implementations, and some classical applications. We also propose that some widely-used methods can be improved in both theoretical and practical aspects in near future.

The Tibetan Plateau is one of the top 10 biodiversity hotspots in the world and acts as a modern harbour for many rare species because of its relatively pristine state. In this article, we report a landscape genetic study on the Yunnan snub-nosed monkey (Rhinopithecus bieti), a primate endemic to the Tibetan Plateau. DNA was extracted from blood, tissue and fecal samples of 135 wild individuals representing 11 out of 15 extant monkey groups. Ten microsatellite loci were used to characterize patterns of genetic diversity. The most striking feature of the population structure is the presence of five subpopulations with distinct genetic backgrounds and unique spatial regions. The population structure of R. bieti appears to be shaped by anthropogenic landscape features as gene flow between subpopulations is strongly impeded by arable land, highways and human habitation. A partial Mantel test showed that 36.23% (r = 0.51, P = 0.01) of the genetic distance was explained by habitat gaps after controlling for the effect of geographical distance. Only 4.92% of the genetic distance was explained by geographical distance in the partial Mantel test, and no significant correlation was found. Estimation of population structure history indicates that environmental change during the last glacial maximum and human impacts since the Holocene, or a combination of both, have shaped the observed population structure of R. bieti. Increasing human activity on the Plateau, especially that resulting in habitat fragmentation, is becoming an important factor in shaping the genetic structure and evolutionary potential of species inhabiting this key ecosystem.