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This thesis presents novel investigations of three common ageing phenotypes in human population studies, using microarray technology to assess ‘transcriptome-wide’ expression in whole blood to identify mechanisms and biomarkers. Muscle strength is related to frailty and is predictive of disability in older persons. I assessed the association between transcript abundance in the InCHIANTI peripheral blood samples (N=695) and muscle strength. One gene (CEBPB) passed the multiple testing criteria, and is involved in macrophage-mediated repair of damaged muscle. I extended this work with a meta-analysis of over 7,781 individuals in four collaborating cohorts; expression of over 222 genes were significantly associated with strength, less than half of which have previously been linked to muscle in the literature. CEBPB did not replicate in these younger cohorts. I then performed the first human analysis of gene expression and cognitive function (and separately with decline in cognitive ability over nine years) in the InCHIANTI cohort (N=681), and one gene was identified; CCR2, a chemokine receptor. Evidence in mice has implicated this gene in the accumulation of β-amyloid and cognitive impairment. Finally, in a collaborative project with the Framingham Heart Study I studied age-related inflammation – another hallmark of ageing - using a novel approach to ‘transcriptome-wide’ analysis; each transcript was assessed for the proportion of the association between age and interleukin-6 (IL6) that it statistically mediated. Very few of the genes associated with IL6 alone also mediated the relationship with age. Findings include; SLC4A10, the strongest mediator, not previously linked to inflammation, and interleukin-1 beta and perforin, a cytokine and cytotoxic protein, respectively. These novel analyses highlight key molecular pathways associated with age-related phenotypes in whole blood and provide links between mouse models and humans. They provide biological insight and directions for future research.

This work focuses on the evaluation of a bagging EB method in terms of its ability to select a subset of QTL-related markers for accurate EBV prediction. Experiments were performed on several simulated and real datasets consisting of SNP genotypes and phenotypes. The simulated datasets modeled different dominance levels and different levels of background noises. Our results show that the bagging EB method is able to detect most of the simulated QTL, even with large background noises. The average recall of QTL detection was $0.71$. When using the markers detected by the bagging EB method to predict EBVs, the prediction accuracy improved dramatically on the simulation datasets compared to using the entire set of markers. However, the prediction accuracy did not improve much when doing the same experiments on the two real datasets. The best accuracy of EBV prediction we achieved for the dairy dataset is 0.57 and the best accuracy for the beef dataset is 0.73.

In this thesis I describe a functional genomics resource in which each yeast gene, with its native promoter and 3’UTR, is cloned on a uniquely barcoded low-copy vector. We refer to this resource as the Molecular Barcoded Yeast ORF (MoBY-ORF) library 1.0. Each gene carried by MoBY-ORF 1.0 should mimic its native expression and thus is best suited for complementation cloning. The vector backbone of MoBY-ORF 1.0 is compatible with the mating-assisted genetically integrated cloning (MAGIC) system for recombination cloning in bacterial cells, which allows the transfer of the ORF fragment and its barcoded cassette to other vector backbones. Taking advantage of the MAGIC system, we created a multi-copy version of the library, which we refer to as MoBY-ORF 2.0. I used MoBY-ORF 1.0 to map drug resistant mutants by complementation cloning with a barcode microarray readout. I investigated several drugs with known targets in my proof-of-principle experiments and showed the feasibility of this method. I identified a single mutation that causes resistance to two different natural products, theopalauamide and stichloroside. By doing so, I was able to link these two chemicals to their cellular target, ergosterol. In fact, theopalauamide represents a new class of sterol binding chemical. I also describe the use of MoBY-ORF 2.0 to clone dosage suppressors of conditional temperature-sensitive mutants. By doing so, and combing our own data with published literature, we showed that dosage suppression interactions often overlap with protein-protein interactions and negative genetic interactions but not positive interactions; however the majority of dosage suppression interactions are unique and thus they represent an unique edge on a global functional interaction map. We also describe the first genome-wide dosage suppressor interaction map of budding yeast.

In this thesis I describe a functional genomics resource in which each yeast gene, with its native promoter and 3’UTR, is cloned on a uniquely barcoded low-copy vector. We refer to this resource as the Molecular Barcoded Yeast ORF (MoBY-ORF) library 1.0. Each gene carried by MoBY-ORF 1.0 should mimic its native expression and thus is best suited for complementation cloning. The vector backbone of MoBY-ORF 1.0 is compatible with the mating-assisted genetically integrated cloning (MAGIC) system for recombination cloning in bacterial cells, which allows the transfer of the ORF fragment and its barcoded cassette to other vector backbones. Taking advantage of the MAGIC system, we created a multi-copy version of the library, which we refer to as MoBY-ORF 2.0. I used MoBY-ORF 1.0 to map drug resistant mutants by complementation cloning with a barcode microarray readout. I investigated several drugs with known targets in my proof-of-principle experiments and showed the feasibility of this method. I identified a single mutation that causes resistance to two different natural products, theopalauamide and stichloroside. By doing so, I was able to link these two chemicals to their cellular target, ergosterol. In fact, theopalauamide represents a new class of sterol binding chemical. I also describe the use of MoBY-ORF 2.0 to clone dosage suppressors of conditional temperature-sensitive mutants. By doing so, and combing our own data with published literature, we showed that dosage suppression interactions often overlap with protein-protein interactions and negative genetic interactions but not positive interactions; however the majority of dosage suppression interactions are unique and thus they represent an unique edge on a global functional interaction map. We also describe the first genome-wide dosage suppressor interaction map of budding yeast.