Both phenotypic and genetic evidence for asymmetric hybridization between rhesus (Macaca mulatta) and cynomolgus (Macaca fascicularis) macaques has been observed in the region of Indochina where both species are sympatric. The large-scale sharing of MHC class II alleles between the two species in this region supports the hypothesis that genes, and especially genes involved in immune response, are being transferred across the species boundary. This differential introgression has important implications for the incorporation of cynomolgus macaques of unknown geographic origin in biomedical research protocols. Our study found that for 2808 SNP markers, the MAF and observed heterozygosity calculated from a sample of Vietnamese cynomolgus macaques was significantly different from those calculated from samples of both Chinese rhesus and Indonesian cynomolgus macaques. SNP alleles from Chinese rhesus macaques were overrepresented in a sample of Vietnamese cynomolgus macaques relative to their Indonesian conspecifics and located in genes functionally related to the primary immune system. These results suggest that Indochinese cynomolgus macaques represent a genetically and immunologically distinct entity from Indonesian cynomolgus macaques.

This study was designed to address issues regarding sample size and marker location that have arisen from the discovery of SNPs in the genomes of poorly characterized primate species and the application of these markers to the study of primate population genetics. We predict the effect of discovery sample size on the probability of discovering both rare and common SNPs and then compare this prediction with the proportion of common and rare SNPs discovered when different numbers of individuals are sequenced. Second, we examine the effect of genomic region on estimates of common population genetic data, comparing markers from both coding and non-coding regions of the rhesus macaque genome and the population genetic data calculated from these markers, to measure the degree and direction of bias introduced by SNPs located in coding versus non-coding regions of the genome. We found that both discovery sample size and genomic region surveyed affect SNP marker attributes and population genetic estimates, even when these are calculated from an expanded data set containing more individuals than the original discovery data set. Although none of the SNP detection methods or genomic regions tested in this study was completely uninformative, these results show that each has a different kind of genetic variation that is suitable for different purposes, and each introduces specific types of bias. Given that each SNP marker has an individual evolutionary history, we calculated that the most complete and unbiased representation of the genetic diversity present in the individual can be obtained by incorporating at least 10 individuals into the discovery sample set, to ensure the discovery of both common and rare polymorphisms.