Genetic control of canine leishmaniasis: genome-wide association study and genomic selection analysis

Abstract

Background: The current disease model for leishmaniasis suggests that only a proportion of infected individuals develop clinical disease, while others are asymptomatically infected due to immune control of infection. The factors that determine whether individuals progress to clinical disease following Leishmania infection are unclear, although previous studies suggest a role for host genetics. Our hypothesis was that canine leishmaniasis is a complex disease with multiple loci responsible for the progression of the disease from Leishmania infection.

Methodology/principal findings: Genome-wide association and genomic selection approaches were applied to a population-based case-control dataset of 219 dogs from a single breed (Boxer) genotyped for ~170,000 SNPs. Firstly, we aimed to identify individual disease loci; secondly, we quantified the genetic component of the observed phenotypic variance; and thirdly, we tested whether genome-wide SNP data could accurately predict the disease.

Conclusions/significance: We estimated that a substantial proportion of the genome is affecting the trait and that its heritability could be as high as 60%. Using the genome-wide association approach, the strongest associations were on chromosomes 1, 4 and 20, although none of these were statistically significant at a genome-wide level and after correcting for genetic stratification and lifestyle. Amongst these associations, chromosome 4: 61.2-76.9 Mb maps to a locus that has previously been associated with host susceptibility to human and murine leishmaniasis, and genomic selection estimated markers in this region to have the greatest effect on the phenotype. We therefore propose these regions as candidates for replication studies. An important finding of this study was the significant predictive value from using the genomic information. We found that the phenotype could be predicted with an accuracy of ~0.29 in new samples and that the affection status was correctly predicted in 60% of dogs, significantly higher than expected by chance, and with satisfactory sensitivity-specificity values (AUC = 0.63).

Figure 1. Receiver Operating Characteristic (ROC) curves.

Sensitivity and specificity values were obtained for increasing classification thresholds to produce the ROC curves. In the legend, the values for the area under the ROC curve (AUC) are indicated in parenthesis for each model. AUC can range between 0.5 (randomness, dashed line) and 1.0 (ideally).

Figure 2. Fraction of correct predictions.

For increasing classification thresholds percentages of correct classifications were compared to those expected by chance. Calculations for the random expectation and the random 95% limit were drawn from a hypergeometric distribution and are detailed in Text S1.