Whole genome sequencing predicts novel human disease models in rhesus macaques

Abstract

Rhesus macaques are an important pre-clinical model of human disease. To advance our understanding of genomic variation that may influence disease, we surveyed genome-wide variation in 21 rhesus macaques. We employed best-practice variant calling, validated with Mendelian inheritance. Next, we used alignment data from our cohort to detect genomic regions likely to produce inaccurate genotypes, potentially due to either gene duplication or structural variation between individuals. We generated a final dataset of >16 million high confidence variants, including 13 million in Chinese-origin rhesus macaques, an increasingly important disease model. We detected an average of 131 mutations predicted to severely alter protein coding per animal, and identified 45 such variants that coincide with known pathogenic human variants. These data suggest that expanded screening of existing breeding colonies will identify novel models of human disease, and that increased genomic characterization can help inform research studies in macaques.

Keywords: Chinese-origin; Genome; Indian-origin; Macaca mulatta; Nonhuman primate; SIV; SNP; Variant discovery.

Figure 1

High quality mapping rates in Indian- and Chinese-origin animals. The fraction of reads aligning with high mapping quality (>Q20) is shown for each geographic origin. Dots indicate mapping rate of each individual.

Figure 2

Discrepancies between reference genome and alignment data. All alignments against the MacaM reference genome were analyzed, and genomic positions where more than two alleles were detected in a single individual were identified. The graph shows the number of times these tri-allelic positions were detected in the genome.

Figure 3

High confidence SNVs detected by population. A) Venn diagram showing overlap of high-confidence SNVs detected in Indian- and Chinese-origin animals. B) Histograms showing the distribution of allele frequency of variants detected in Indian-origin (top) and Chinese-origin (bottom) animals. C) Principal component analysis of IBS clustering using all high-confidence SNVs detected in our cohort. Indian-origin animals are in green, Chinese-origin animals are in black, and hybrid animals are in red.