Application of knowledge discovery and data mining methods in livestock genomics for hypothesis generation and identification of biomarker candidates influencing meat quality traits in pigs

Abstract

Recent advancements in genomics and genome profiling technologies have lead to an increase in the amount of data available in livestock genomics. Yet, most of the studies done in livestock genomics have been following a reductionist approach and very few studies have either followed data mining or knowledge discovery concepts or made use of the wealth of information available in the public domain to gain new knowledge. The goals of this thesis were: (i) the adoption of existing analysis strategies or the development of novel approaches in livestock genomics for integrative data analysis following the principles of data mining and knowledge discovery and (ii) demonstrating the application of such approaches in livestockgenomics for hypothesis generation and biomarker discovery. A pig meat quality trait termed androstenone measurement in backfat was selected as the target phenotype for the experiments. <br /> Two experiments were performed as a part of this thesis. The first one followed a knowledge driven approach merging high-throughput expression data with metabolic interaction network. Based on the results from this experiment, several novel biomarker candidates and a hypothesis regarding different mechanisms regulating androstenone synthesis in porcine testis samples with divergent androstenone measurements in back fat were proposed. The model proposed that the elevated levels of androstenone synthesis in sample population could be due to the combined effect of cAMP/PKA signaling, elevated levels of fatty acid metabolism and anti lipid peroxidation activity of members of glutathione metabolic pathway. The second experiment followed a data driven approach and integrated gene expression data from multiple porcine populations to identify similarities in gene expression patterns related to hepatic androstenone metabolism. The results indicated that one of the low androstenone phenotype specific co-expression cluster was functionally enriched in pathways related to androgen and androstenone metabolism and that the members of this cluster exhibited weak co-expression in high androstenone phenotype. Based on the results from this experiment, this co-expression cluster was proposed as a signature cluster for hepatic androstenone metabolism in boars with low androstenone content in back fat. The results from these experiments indicate that integrative analysis approaches following data mining and knowledge discovery concepts can be used for the generation of new knowledge from existing data in livestock genomics. But, limited data availability in livestock genomics is a hindrance to the extensive use such analysis methods in livestock genomics field for gaining new knowledge. <br /> In conclusion, this study was aimed at demonstrating the capabilities of data mining and knowledge discovery methods and integrative analysis approaches to generate new knowledge in livestock genomics using existing datasets. The results from the experiments hint the possibilities of further exploring such methods for knowledge generation in this field. Although the application of such methods is limited in livestock genomics due to data availability issues at present, the increase in data availability due to evolving high throughput technologies and decrease in data generation costs would aid in the wide spread use of such methods in livestock genomics in the coming future.