Methods and Models for the Accurate Estimation of the Effects of Single Nucleotide Polymorphisms (SNP) in Beef Cattle

Abstract

Genetic markers provide the Australian beef industry with the opportunity to increase rates of genetic gains. However, accurate estimates of the gene frequencies and the marker size of effects are first required. Stochastic simulation was used to examine the methods and models required to estimate SNP effects. Results showed for a single SNP explaining 2% of the phenotypic variation, 1,500 animals were required to estimate the SNP effects when the favourable allele frequency (p) was 0.5. However, increasing the additive SNP effects decreased the number of animals required. SNP effect estimates were inflated when the power to detect genotype effects was low. In addition, when the allele frequency was rare (p=0.1), biased dominance effects were estimated. For SNPs that were in linkage disequilibrium with the causative SNP, the SNP effects were accurately estimated when linkage disequilibrium was greater than D'=0.9. This thesis found that when there was linkage disequilibrium between the two direct SNPs (explaining 8% of the phenotypic variation, collectively), and one SNP was ignored in the model, estimates of the SNP effects were biased upwards. Ignoring epistatic effects (1% of the phenotypic variance) also increased the estimates of the SNP effects. To estimate accurate SNP and epistatic effects 3,000 animals were required. If SNPs were excluded in the model, the SNP and epistatic variance was partitioned as polygenic and residual variances, respectively. The inclusion of SNPs was shown to increase the accuracy of the estimated breeding value (EBV); the more phenotypic variation explained by the SNPs the higher the increase in EBV accuracy (or estimated genetic merit when non-additive (i.e. epistasis) effects were included). This thesis shows that the population size, allele frequency, statistical power to detect genotype effects, statistical models and the data structure all affect the ability to accurately estimate the size of SNP effects.