This thesis investigated the association between several genetic factors and autoantibodies and the development of undifferentiated arthritis (UA) and rheumatoid arthritits (RA). Second, this... Show moreThis thesis investigated the association between several genetic factors and autoantibodies and the development of undifferentiated arthritis (UA) and rheumatoid arthritits (RA). Second, this thesis described a prediction model that estimates the chance to progress from UA to RA. The most important genetic risk factor for RA are the HLA-Class II alleles that encode for a common amino acid sequence, called the ‘Shared Epitope’. Investigating the progression to RA from UA revealed that the HLA-Shared Epitope alleles are not primarily a risk factor for RA but for the presence of anti-CCP antibodies, that are known to be specific for RA. Smoking in the presence of HLA-Shared Epitope alleles particularly increased the risk on anti-CCP-positive RA.. The HLA-DR3 alleles were associated with anti-CCP-negative RA. The presence of HLA-alleles encoding for D70ERAA correlated with a lower risk on RA and a less severe disease course. The presence of the PTPTN22 T-allele conferred an increased risk for both UA and RA. The knowledge on risk factors for RA-development was translated in a model that estimates the chance to progress to RA in patients that present with UA by using 9 clinical variables. The discriminative ability was high and this model allows individualized treatment decisions in UA. Show less
In this thesis novel statistical methods are developed for the analysis of high dimensional microarray data. In short: Chapter 1 gives an overview of the most important research methods developed... Show moreIn this thesis novel statistical methods are developed for the analysis of high dimensional microarray data. In short: Chapter 1 gives an overview of the most important research methods developed so far. Chapter 2 describes a method for testing association of the expression of gene sets (pathways) with a patient level response variable, which can be continuous or two-valued. Chapter 3 extends the methodology of chapter 2 to survival as a response variable. Chapter 4 presents a goodness-of-fit test for the multinomial regression model, which can be used to extend the methodology of chapter 2 to multi-valued outcomes. Chapter 5 presents a general theoretical framework in for the tests of chapters 2-4 and derives optimality properties for these tests. Chapter 6 presents a method for predicting a response variable from high dimensional data, based on latent variables. Chapter 7 presents a visualization tool for improved presentation of scatterplots with many thousands of dots. Show less
