Genetics of disease resistance: application to bovine Tuberculosis

Abstract

Bovine Tuberculosis (bTB) is a disease of significant economic importance, being one of the most persistent animal health problems in the UK and the Republic of Ireland and increasingly constituting a public health concern especially for the developing world. Limitations of the currently available diagnostic and control methods, along with our incomplete understanding of bTB transmission, prevent successful eradication. This Thesis addresses the development of a complementary control strategy which will be based on animal genetics and will allow us to identify animals genetically predisposed to be more resistant to disease. Specifically, the aim of my PhD project is to investigate the genetic architecture of resistance to bTB and demonstrate the feasibility of whole genome prediction for the control of bTB in cattle. Genomic selection for disease resistance in livestock populations will assist with the reduction of the in herd-level incidence and the severity of potential outbreaks. The first objective was to explore the estimation of breeding values for bTB resistance in UK dairy cattle, and test these genomic predictions for situations when disease phenotypes are not available on selection candidates. Through using dense SNP chip data the results of Chapter 2 demonstrate that genomic selection for bTB resistance is feasible (h2 = 0.23(SE = 0.06)) and bTB resistance can be predicted using genetic markers with an estimate of prediction accuracy of r(g, ĝ) = 0.33 in this data. It was shown that genotypes help to predict disease state (AUC ≈ 0.58) and animals lacking bTB phenotypes can be selected based on their genotypes. In Chapter 3, a novel approach is presented to identify loci displaying heterozygote (dis)advantage associated with resistance to M. bovis, hypothesising underlying non-additive genetic variation, and these results are compared with those obtained from standard genome scans. A marker was identified suggesting an association between locus heterozygosity and increased susceptibility to bTB i.e. a heterozygote disadvantage, with the heterozygotes being significantly more in the cases than in the controls (x2 = 11.50, p<0.001). Secondly, this thesis focused on conducting a meta-analysis on two dairy cattle populations with bTB phenotypes and SNP chip genotypes, identifying genomic regions underlying bTB resistance and testing genomic predictions by means of cross-validation. In Chapter 4, exploration of the genetic architecture of the trait revealed that bTB resistance is a moderately polygenic, complex trait with clusters of causal variants spread across a few major chromosomes collectively controlling the trait. A region was identified on chromosome 6, putatively associated with bTB resistance and this chromosome as a whole was shown to contribute a major proportion (hc 2= 0.051) of the observed variation in this dataset. Genomic prediction for bTB was shown to be feasible even when only distantly related populations are combined (r(g,ĝ)=0.33 (SE = 0.05)), with the chromosomal heritability results suggesting that the accuracy arises from the SNPs capturing linkage disequilibrium between markers and QTL, as well as additive relationships between animals (~80% of estimated genomic h2 is due to relatedness). To extend the analysis, in Chapter 5, high density genotypes were inferred by means of genotype imputation, anticipating that these analyses will allow the identification of genomic regions associated with bTB resistance more closely, and that would increase the prediction accuracy. Genotype imputation was successful, however, using all imputed genotypes added little information. The limiting factor was found to be the number of animals and the trait definitions rather than the density of genotypes. Thirdly, a quantitative genetic analysis of actual Single Intradermal Comparative Cervical Test (SICCT) values collected during bTB herd testing was conducted aiming to investigate if selection for bTB resistance is likely to have an impact on the SICCT diagnostic test. This analysis demonstrated that the SICCT has a negligibly low heritability (h2=0.0104 (SE = 0.0032)) and any effect on the responsiveness to the test is likely to be small. In conclusion, breeding for disease resistance in livestock is feasible and we can predict the risk of bTB in cattle using genomic information. Further, putative QTLs associated with bTB resistance were identified, and exploration of the genetic architecture of bTB resistance revealed a moderately polygenic trait. These results suggest that given that larger datasets with more phenotyped and genotyped animals will be available, we can breed for bTB resistance and implement the genomic selection technology in breeding programmes aiming to improve the disease status and overall health of the livestock population. Using the genomics this can be continued as the epidemic declines.