Fawcett et al. BMC Genomics 2011, 12:311 http://www.biomedcentral.com/1471-2164/12/311 assemblies [31]. Therefore we wished to determine whether our validated SNPs had homology to assembled contigs placed in chrUr, which might represent unrecog- nized duplications. To test this, we split chrUr into 150 bp length “reads” and scanned this simulated “read” data using e-genotype probes created from all of our validated rhesus SNPs (3,271,622 probes, representing the reference position at all locations as well as the one or two non-reference validated alleles). None of the probes matched sequence from the unmapped reference sequence provided in chrUr. Given these various results, we conclude that there is no clear evidence for a signifi- cant number of false positive SNP calls due to dupli- cated genomic regions in our SNP data. SNP annotation and Polyphen Analysis All the validated SNPs were further examined to charac- terize their position relative to known coding regions and possible functionality (Figure 3). Due to the incom- plete annotation of the rhesus reference genome, the annotations of rhesus SNPs are based in part on map- ping to homologous human transcripts. As expected, Figure 3 SNP annotation. Genomic context of annotated validated SNPs. All annotations were obtained from Ensemble build 57 (released March 2010). The number of SNPs that fit into each category represented by a bar is listed over the respective bar. The bar for “Additional transcripts” represents only those SNPs where the specific annotation placement information was different between different transcripts for the same SNP. Page 5 of 13 most validated SNPs map to intergenic regions (84.2% of validated SNPs). Intronic SNPs were the second most prevalent at 15.3%. Very few (601 or 0.02%) of the SNPs fell into known or predicted splice sites. Among coding SNPs, about equal numbers are synonymous changes (4,605, 0.15% of total) and non-synonymous (4,472, 0.15% of total). A number of SNPs fell into regions of alternative splicing such that the genomic placement of the SNP varied by transcript. All such SNPs (n = 418) are associated with a coding sequence of at least one transcript, and with an alternative transcript where the placement differed: synonymous coding (21.5%), non- synonymous coding (19.1%), 5’UTR (28.9%), 3’UTR (30.4%). Potentially functional polymorphisms in rhesus maca- ques are of particular interest, especially those in genes that may correlate with human disease. The rhesus gen- ome annotation is incomplete and based primarily upon gene prediction algorithms that rely upon human-to- rhesus homology [32-34]. Therefore, we relied upon a homology-based method (see Methods) to generate hypotheses about the functionality of non-synonymous SNPs (nsSNPs) (Table 2). A total of 4,439 SNPs from the full list of nsSNPs (4,472) were successfully con- verted from rheMac2 coordinates to hg18 coordinates. After removing all SNPs where the hg18 reference allele matched the new variant macaque allele, 4,177 nsSNPs were submitted to Polyphen-2 (http://genetics.bwh.har- vard.edu/pph2/[35]) [36]. Of those, 411 were scored as probably damaging and 325 scored as possibly dama- ging. SNPs that affect binding sites or are located in transmembrane regions can often be expected to alter protein function. Twelve of the probably damaging mutations are annotated as falling in transmembrane regions, two affect a metal binding site, and one affects a modified residue. Of the possibly damaging mutations, six are in transmembrane regions, one is in a propep- tide, two affect modified residues, and one affects a car- bohydrate binding site. We performed GeneGo (http:// www.genego.com/[37]) pathway analysis to determine which diseases were associated with the genes contain- ing predicted deleterious rhesus SNPs from the Poly- Phen-2 analysis. These genes were involved in one or more critical organism process such as apoptotic path- ways, DNA repair, development or inflammation. Table 2 displays a non-exhaustive list of diseases associated with the relevant pathways, many of which are due to a few well-known genes: Brca2, Ppgca1b, Map3k1, and Casp7. Finally, we compared the list of validated Indian-origin rhesus macaque SNPs to the locations of known SNPs in the human genome (dbSNP, build 132). The evolu- tionary lineage leading to rhesus macaques diverged from the human lineage about 22-26 million years ago,