4040 SNPs for genomic analysis in the rhesus macaque (Macaca mulatta).

Although the rhesus macaque (Macaca mulatta) is commonly used for biomedical research and becoming a preferred model for translational medicine, quantification of genome-wide variation has been slow to follow the publication of the genome in 2007. Here we report the properties of 4040 single nucleotide polymorphisms discovered and validated in Chinese and Indian rhesus macaques from captive breeding colonies in the United States. Frequency-matched measures of linkage disequilibrium were much greater in the Indian sample. Although the majority of polymorphisms were shared between the two populations, rare alleles were over twice as common in the Chinese sample. Indian rhesus had higher rates of heterozygosity, as well as previously undetected substructure, potentially due to admixture from Burma in wild populations and demographic events post-captivity.

High-throughput single-nucleotide polymorphism discovery and the search for candidate genes for long-term SIVmac nonprogression in Chinese rhesus macaques (Macaca mulatta).

BACKGROUND: Genetic differences between Indian and Chinese rhesus macaques contribute to the phenotypic variance of clinical trials, including infection with SIVmac. The completion of the rhesus genome has facilitated the discovery of several thousand markers. METHODS: We developed a genome-wide SNP map for rhesus macaques containing 3869 validated markers with an average distance of 0.88 Mb and used the program VarLD to identify genomic areas with significant differences in linkage disequilibrium (LD) between Indian-derived and Chinese rhesus macaques. RESULTS: Forty-one statistically significant differences in LD between Chinese and Indian-origin rhesus were detected on chromosomes 1, 4, 5 and 11. The region of greatest LD difference was located on the proximal end of chromosome one, which also contained the genes ELAVL4, MAST2 and HIVEP3. CONCLUSION: These genomic areas provide entry to more detailed studies of gene function. This method is also applicable to the study of differences in biomarkers between regional populations of other species.