Ch1 Intro

1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. Ch6 con. • Verify the association between pQTL and N-A genes.