Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

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Informationsbeteende och inlärningsmiljöer : en kvalitativ studie av medicine studerandes informationsbeteende i två olika utbildningsprogram

Avhandlingens övergripande syfte är att granska relationerna mellan olika undervisningsmetoder och studenters informationsbeteende, vilket i denna undersökning inbegriper även deras informationskompetens. Vikten av att undersöka dessa förhållanden kan motiveras med att både kunskap om de faktorer som påverkar utvecklandet av informationskompetens och forskning som tar fram olika mönster i studenternas informationsbeteende behövs för att sådana inlärningsmiljöer, informationssystem och -tjänster som stöder studenternas inlärning skall kunna utvecklas. I avhandlingen söks svar på följande frågor: 1. Vilka faktorer i inlärningsmiljöerna, dvs. problembaserad inlärningsmiljö (pbl) och traditionell inlärningsmiljö, påverkar informationsbeteendet och hur påverkar dessa faktorer? 2. Hurdan information behövs i inlärningsprocessen? Hur anskaffas informationen? Vilka informationskanaler och -källor används och hur används de? 3. Hur används information i samband med inlärningen? I undersökningen används en kvalitativ forskningsansats och det huvudsakliga undersökningsmaterialet består av intervjuer med 16 medicine studerande som studerar enligt en problembaserad inlärningsmetod och 15 studerande som studerar i ett traditionellt ämnesbaserat utbildningsprogram. Den empiriska delen av undersökningen utfördes i slutet av 1990-talet. Resultaten indikerar att en problembaserad inlärningsmiljö utvecklar förståelsen av kunskap, aktiverar informationsanskaffningen och informationsanvändningen, samt främjar utvecklingen av studenternas informationskompetens såsom den definierades i denna undersökning. Högre nivå av informationskompetens och aktiv informationsanvändning förekom emellertid i båda utbildningsprogrammen även bland studenter som hade påbörjat de fördjupade studiernas slutarbete, vilket framhäver motivationens och de verkliga informationsbehovens roll i informationsbeteendet och i utvecklandet av informationskompetensen.

Influence of surface functionalization on the behavior of silica nanoparticles in biological systems

Personalized nanomedicine has been shown to provide advantages over traditional clinical imaging, diagnosis, and conventional medical treatment. Using nanoparticles can enhance and clarify the clinical targeting and imaging, and lead them exactly to the place in the body that is the goal of treatment. At the same time, one can reduce the side effects that usually occur in the parts of the body that are not targets for treatment. Nanoparticles are of a size that can penetrate into cells. Their surface functionalization offers a way to increase their sensitivity when detecting target molecules. In addition, it increases the potential for flexibility in particle design, their therapeutic function, and variation possibilities in diagnostics. Mesoporous nanoparticles of amorphous silica have attractive physical and chemical characteristics such as particle morphology, controllable pore size, and high surface area and pore volume. Additionally, the surface functionalization of silica nanoparticles is relatively straightforward, which enables optimization of the interaction between the particles and the biological system. The main goal of this study was to prepare traceable and targetable silica nanoparticles for medical applications with a special focus on particle dispersion stability, biocompatibility, and targeting capabilities. Nanoparticle properties are highly particle-size dependent and a good dispersion stability is a prerequisite for active therapeutic and diagnostic agents. In the study it was shown that traceable streptavidin-conjugated silica nanoparticles which exhibit a good dispersibility could be obtained by the suitable choice of a proper surface functionalization route. Theranostic nanoparticles should exhibit sufficient hydrolytic stability to effectively carry the medicine to the target cells after which they should disintegrate and dissolve. Furthermore, the surface groups should stay at the particle surface until the particle has been internalized by the cell in order to optimize cell specificity. Model particles with fluorescently-labeled regions were tested in vitro using light microscopy and image processing technology, which allowed a detailed study of the disintegration and dissolution process. The study showed that nanoparticles degrade more slowly outside, as compared to inside the cell. The main advantage of theranostic agents is their successful targeting in vitro and in vivo. Non-porous nanoparticles using monoclonal antibodies as guiding ligands were tested in vitro in order to follow their targeting ability and internalization. In addition to the targeting that was found successful, a specific internalization route for the particles could be detected. In the last part of the study, the objective was to clarify the feasibility of traceable mesoporous silica nanoparticles, loaded with a hydrophobic cancer drug, being applied for targeted drug delivery in vitro and in vivo. Particles were provided with a small molecular targeting ligand. In the study a significantly higher therapeutic effect could be achieved with nanoparticles compared to free drug. The nanoparticles were biocompatible and stayed in the tumor for a longer time than a free medicine did, before being eliminated by renal excretion. Overall, the results showed that mesoporous silica nanoparticles are biocompatible, biodegradable drug carriers and that cell specificity can be achieved both in vitro and in vivo.

Promoting Healthy Lifestyles with Personalized, APOE Genotype Based Health Information: The Effects on Psychological-, Health Behavioral and Clinical Factors

There is an increasing demand for individualized, genotype-based health advice. The general population-based dietary recommendations do not always motivate people to change their life-style, and partly following this, cardiovascular diseases (CVD) are a major cause of death in worldwide. Using genotype-based nutrition and health information (e.g. nutrigenetics) in health education is a relatively new approach, although genetic variation is known to cause individual differences in response to dietary factors. Response to changes in dietary fat quality varies, for example, among different APOE genotypes. Research in this field is challenging, because several non-modifiable (genetic, age, sex) and modifiable (e.g. lifestyle, dietary, physical activity) factors together and with interaction affect the risk of life-style related diseases (e.g. CVD). The other challenge is the psychological factors (e.g. anxiety, threat, stress, motivation, attitude), which also have an effect on health behavior. The genotype-based information is always a very sensitive topic, because it can also cause some negative consequences and feelings (e.g. depression, increased anxiety). The aim of this series of studies was firstly to study how individual, genotype-based health information affects an individual’s health form three aspects, and secondly whether this could be one method in the future to prevent lifestyle-related diseases, such as CVD. The first study concentrated on the psychological effects; the focus of the second study was on health behavior effects, and the third study concentrated on clinical effects. In the fourth study of this series, the focus was on all these three aspects and their associations with each other. The genetic risk and health information was the APOE gene and its effects on CVD. To study the effect of APOE genotype-based health information in prevention of CVD, a total of 151 volunteers attended the baseline assessments (T0), of which 122 healthy adults (aged 20 – 67 y) passed the inclusion criteria and started the one-year intervention. The participants (n = 122) were randomized into a control group (n = 61) and an intervention group (n = 61). There were 21 participants in the intervention Ɛ4+ group (including APOE genotypes 3/4 and 4/4) and 40 participants in the intervention Ɛ4- group (including APOE genotypes 2/3 and 3/3). The control group included 61 participants (including APOE genotypes 3/4, 4/4, 2/3, 3/3 and 2/2). The baseline (T0) and follow-up assessments (T1, T2, T3) included detailed measurements of psychological (threat and anxiety experience, stage of change), and behavioral (dietary fat quality, consumption of vegetables, - high fat/sugar foods and –alcohol, physical activity and health and taste attitudes) and clinical factors (total-, LDL- HDL cholesterol, triglycerides, blood pressure, blood glucose (0h and 2h), body mass index, waist circumference and body fat percentage). During the intervention six different communication sessions (lectures on healthy lifestyle and nutrigenomics, health messages by mail, and personal discussion with the doctor) were arranged. The intervention groups (Ɛ4+ and Ɛ4-) received their APOE genotype information and health message at the beginning of the intervention. The control group received their APOE genotype information after the intervention. For the analyses in this dissertation, the results for 106/107 participants were analyzed. In the intervention, there were 16 participants in the high-risk (Ɛ4+) group and 35 in the low-risk (Ɛ4-) group. The control group had 55 participants in studies III-IV and 56 participants in studies I-II. The intervention had both short-term (≤ 6 months) and long-term (12 months) effects on health behavior and clinical factors. The short-term effects were found in dietary fat quality and waist circumference. Dietary fat quality improved more in the Ɛ4+ group than the Ɛ4- and the control groups as the personal, genotype-based health information and waist circumference lowered more in the Ɛ4+ group compared with the control group. Both these changes differed significantly between the Ɛ4+ and control groups (p<0.05). A long-term effect was found in triglyceride values (p<0.05), which lowered more in Ɛ4+ compared with the control group during the intervention. Short-term effects were also found in the threat experience, which increased mostly in the Ɛ4+ group after the genetic feedback (p<0.05), but it decreased after 12 months, although remaining at a higher level compared to the baseline (T0). In addition, Study IV found that changes in the psychological factors (anxiety and threat experience, motivation), health and taste attitudes, and health behaviors (dietary, alcohol consumption, and physical activity) did not directly explain the changes in triglyceride values and waist circumference. However, change caused by a threat experience may have affected the change in triglycerides through total- and HDL cholesterol. In conclusion, this dissertation study has given some indications that individual, genotypebased health information could be one potential option in the future to prevent lifestyle-related diseases in public health care. The results of this study imply that personal genetic information, based on APOE, may have positive effects on dietary fat quality and some cardiovascular risk markers (e.g., improvement in triglyceride values and waist circumference). This study also suggests that psychological factors (e.g. anxiety and threat experience) may not be an obstacle for healthy people to use genotype-based health information to promote healthy lifestyles. However, even in the case of very personal health information, in order to achieve a permanent health behavior change, it is important to include attitudes and other psychological factors (e.g. motivation), as well as intensive repetition and a longer intervention duration. This research will serve as a basis for future studies and its information can be used to develop targeted interventions, including health information based on genotyping that would aim at preventing lifestyle diseases. People’s interest in personalized health advices has increased, while also the costs of genetic screening have decreased. Therefore, generally speaking, it can be assumed that genetic screening as a part of the prevention of lifestyle-related diseases may become more common in the future. In consequence, more research is required about how to make genetic screening a practical tool in public health care, and how to efficiently achieve long-term changes.