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Ch1 Intro • 1.1 Intro to QTL: general intro of QTL – QTL/ plural form QTL’s • 1.2 what’s the thesis about – References problem – The word “expression level” v.s. “matching” • 1.3 relevant literature – References problem again – QTL mapping / graphing Ch2 Data • Bioconductor -> affymetrix • 2.1 Data sources – Database Origin: affymetrix “R” & “Original” – Datasets: 2.1.2 QTL , 2.1.3 special gene groups • 2.2 Graphical overview – “5000 interesting genes” needs an explanation • 2.3 modelling bp v.s. cm Ch3 comparison • 3.1 “breakdown” – Gene’s way v.s. QTL’s way • 3.2 analysis of overall table – In gene’s way, odds ratio is to justify indep. • 3.3 GLM (Modeling) – 3.3.1 loglinear model:Model selection X2/G2 – 3.3.2 logit model: model selection Ch3 Cont’d • 3.4 GLMM – Chromosome as a random effect (in the thesis) – 3.4.1 Poisson regression from gene’s way – 3.4.2 GLMM from QTL’s way (chromosome not sig.) • 3.5 discussion – Summary of this chapter and stage conclusion Ch4 More comparison • MBH genes • 4.1 further stratified table • 4.2 GLMM from gene’s way – Model selection: almost every model is good – Choose the simplest one • 4.3 GLMM from QTL’s way – Choose the parsimonious model Ch5 simu. • Randomly chosen genes from every chr. • Count # rand. genes covered by pQTL • Compare with # N-A genes covered by pQTL • Compare with # N-A genes covered by bQTL Ch6 con. • Verify the association between pQTL and N-A genes.