It is known that to achieve efficient scalability of an Evolutionary Algorithm (EA), dependencies (also known as linkage) must be properly taken into account during variation. In a Gray-Box... Show moreIt is known that to achieve efficient scalability of an Evolutionary Algorithm (EA), dependencies (also known as linkage) must be properly taken into account during variation. In a Gray-Box Optimization (GBO) setting, exploiting prior knowledge regarding these dependencies can greatly benefit optimization. We specifically consider the setting where partial evaluations are possible, meaning that the partial modification of a solution can be efficiently evaluated. Such problems are potentially very difficult, for example, non-separable, multimodal, and multiobjective. The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) can effectively exploit partial evaluations, leading to a substantial improvement in performance and scalability. GOMEA was recently shown to be extendable to real-valued optimization through a combination with the real-valued estimation of distribution algorithm AMaLGaM. In this article, we definitively introduce the Real-Valued GOMEA (RV-GOMEA), and introduce a new variant, constructed by combining GOMEA with what is arguably the best-known real-valued EA, the Covariance Matrix Adaptation Evolution Strategies (CMA-ES). Both variants of GOMEA are compared to L-BFGS and the Limited Memory CMA-ES (LM-CMA-ES). We show that both variants of RV-GOMEA achieve excellent performance and scalability in a GBO setting, which can be orders of magnitude better than that of EAs unable to efficiently exploit the GBO setting. Show less
Osteoarthritis (OA) mainly affects the articular cartilage covering the bones. In this thesis we investigated the relation between levels of inflammatory mediators, genes involved in their... Show moreOsteoarthritis (OA) mainly affects the articular cartilage covering the bones. In this thesis we investigated the relation between levels of inflammatory mediators, genes involved in their regulation and the disease status of OA. We investigated the role of genetic variation at the interleukin(IL)-1 gene cluster in the innate bio-availability of IL-1beta. A haplotype that associated to low innate bio-availability also associated to higher hand OA scores. Although this is counterintuitive with respect to the generally accepted hypothesis that a pro-inflammatory status is detrimental to the cartilage it underlines a complex relationship between inflammation and OA. For the C-reactive protein we identified a haplotype associated to high CRP levels as well as to severe hand OA, which is more in line with expected directions of associations. Analysis of baseline cytokine and chemokine levels indicated that chemokine levels associated to hand OA scores, again with low levels associated to high OA scores. In a follow up functional genomic analysis of a previously identified OA susceptibility gene (DIO2) in our studies we show that the risk allele of this gene is transcribed at higher levels as compared to the non-risk allele. Furthermore, we showed increased DIO2 protein presence in OA affected cartilage. Show less
Linkage analysis makes use of genetic markers to measure genetic similarity between relatives. By comparing this index of genetic similarity with phenotypic similarity, we can identify chromosomal... Show moreLinkage analysis makes use of genetic markers to measure genetic similarity between relatives. By comparing this index of genetic similarity with phenotypic similarity, we can identify chromosomal regions harbouring genes involved in the architecture of a phenotype of interest. Although linkage has been very successful in discovering genes responsible for simple Mendelian diseases, results have often been disappointing in gene mapping for complex traits. This thesis presents some attempts to improve the current design and analysis of linkage studies for complex traits. The statistical methodology adopted is driven by the fact that genes involved in complex traits have small effects, it therefore seems legitimate to use score tests because of their local optimality properties. In addition, score tests often give rise to tractable expressions, in the context of linkage these can be meaningfully interpreted in terms of regressions and quickly computed which is a crucial feature in genetics. Show less
