Molecular, bioinformatic and statistical approaches to identify genes underlying complex traits in livestock

One of the primary goals for molecular geneticists working with livestock species is to identify and characterize genes underlying complex traits, the so-called quantitative trait loci (QTL). The primary strategy for identifying QTL involves several steps, one being fine mapping of a previously defined chromosomal region and another being identification of candidate genetic polymorphisms that may cause differences in phenotype. The studies presented in this dissertation address fine mapping methodology, use of the candidate gene approach for directly identifying candidate genetic polymorphisms and use of bioinformatic tools for identifying genetic polymorphisms in silico. Results from simulation studies suggest that two linkage disequilibrium-based fine mapping methods, one using haplotype information, the other using single marker information, provide QTL position estimates with comparable accuracy. Additional research is necessary to determine optimal fine mapping methods under experimental research conditions. The candidate gene studies presented, concerning the porcine connexin 37 (CX37) and bone morphogenetic factor 15 (BMP15) genes, highlight use of comparative sequence and biological information for identifying candidate genetic variants. Two synonymous mutations were discovered in the CX37 gene, which was subsequently mapped to SSC6 q24--31. However, these mutations were not significantly associated with fertility traits as hypothesized. Unfortunately, mutations could not be identified in BMP15, which was physically mapped to SSCX p11--13. Bioinformatic tools are shown here to be lucrative for identifying putative single nucleotide polymorphisms (SNPs) from redundant expressed sequence tag (EST) information in the pig. Using computer-derived SNPs, a correlation of 0.77 (p \u3c 0.00001) was found between the frequency of human and porcine SNPs in the coding regions (cSNPs) of 25 genes, while a correlation of 0.48 (p \u3c 0.0005) was found between the frequency of human and mouse cSNPs in 50 genes. This strong human-pig relationship should be verified in a larger sample so that SNP identification in pigs could be expedited by screening porcine genes homologous to human genes known to be SNP-dense in their coding regions. By capitalizing on statistical, bioinformatic and molecular tools in an integrated approach, the rate at which QTL are identified in livestock could be increased