This paper analyzes sustainable investments of Swedish mutual funds. Morningstar’s CarbonRisk Score (CRS) - funds exposure to a future of low-carbon economy - is analysed in termsof returns, management fees and flows. The CRS measure was introduced March 2018 with ahistorical series from March 2017, without the market being aware. Analysing CRS before theintroduction is therefore greenwashing-bias free. An empirical approach with regressions findthat there is a payoff between return and alignment with a low-carbon economy future, CRS.A 1% increased abnormal return causes a 0.13 standard deviations higher CRS. Regressionsalso find no relationship between management fee and CRS. A correlation between flow andCRS is found but no causality. The shown payoff between return and CRS implies that fundswhich are well-performing are less sustainable. Fund managers maximising their returnthereby lead to unsustainable investments. To handle this, a policy of tax relief or subsidyshould be implemented for investing sustainable. The tax relief or subsidy should beproportional to the increased return renounced when investing sustainable.

Indiana University-Purdue University Indianapolis (IUPUI) / Determining an individual’s genetic susceptibility in complex diseases like Alzheimer’s disease (AD) is challenging as multiple variants each contribute a small portion of the overall risk. Polygenic Risk Scores (PRS) are a mathematical construct or composite that aggregates the small effects of multiple variants into a single score. Potential applications of PRS include risk stratification, biomarker discovery and increased prognostic accuracy. A systematic review demonstrated that methodological refinement of PRS is an active research area, mostly focused on large case-control genome-wide association studies (GWAS). In AD, where there is considerable phenotypic and genetic heterogeneity, we hypothesized that PRS based on endophenotypes, and pathway-relevant genetic information would be particularly informative. In the first study, data from the NIA Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to develop endophenotype-based PRS based on amyloid (A), tau (T), neurodegeneration (N) and cerebrovascular (V) biomarkers, as well as an overall/combined endophenotype-PRS. Results indicated that combined phenotype-PRS predicted neurodegeneration biomarkers and overall AD risk. By contrast, amyloid and tau-PRSs were strongly linked to the corresponding biomarkers. Finally, extrinsic significance of the PRS approach was demonstrated by application of AD biological pathway-informed PRS to prediction of cognitive changes among older women with breast cancer (BC). Results from PRS analysis of the multicenter Thinking and Living with Cancer (TLC) study indicated that older BC patients with high AD genetic susceptibility within the immune-response and endocytosis pathways have worse cognition following chemotherapy±hormonal therapy rather than hormonal-only therapy. In conclusion, PRSs based on biomarker- or pathway- specific genetic information may provide mechanistic insights beyond disease susceptibility, supporting development of precision medicine with potential application to AD and other age-associated cognitive disorders.

Genome-wide Association Studies (GWAS) is the study used to associate common variants and phenotypes and has uncovered thousands of disease-associated variants. However, there is limited research on the contribution of a rare variant. The UK Biobank (UKB) contains detailed medical records and genetic information for nearly 500,000 individuals and offers a great opportunity for genetic association studies on rare variants. Here we focused on the role of rare protein-coding variants on UKB phenotypes. We selected three diseases for analysis: breast cancer, hypothyroidism and type II diabetes. We defined criteria for qualifying variants and pruned the control group to reduce interference signals from similar phenotypes. We identified the most known biomarkers for those diseases, such as BRCA1 and BRCA2 gene for breast cancer, TG and TSHR gene for hypothyroidism and GCK for type II diabetes. This result supports the model validity and clarifies the contribution of rare variants to diseases. Moreover, we also tried the geneset based collapsing method to aggregate information across genes to strengthen the signal from rare variants and build a diagnosis model that only relies on the genetic information. Our model could achieve great performance with an AUC of more than 20% improvement for type II diabetes and breast cancer and more than 90% accuracy for hypothyroidism.

Cardiopulmonary resuscitation (CPR) is one of the most commonly performed medical interventions. However, the true effectiveness of CPR remains unknown as it presents significant challenges for evaluation and research. Many resuscitation practices are driven by nonquantitative reasoning and may not be evidence based. Several studies have been published on survival after in-hospital CPR. However, the reported survival rates from one hospital to another vary significantly due to a number of reasons such as type of hospital, presence of specialized cardiac units, patient demographics, differences in inclusion criteria, outcome definitions and so on. Further research is indicated to evaluate the true effectiveness of CPR for in-hospital cardiac arrest.

Cardiopulmonary resuscitation (CPR) is one of the most commonly performed medical interventions. However, the true effectiveness of CPR remains unknown as it presents significant challenges for evaluation and research. Many resuscitation practices are driven by nonquantitative reasoning and may not be evidence based. Several studies have been published on survival after in-hospital CPR. However, the reported survival rates from one hospital to another vary significantly due to a number of reasons such as type of hospital, presence of specialized cardiac units, patient demographics, differences in inclusion criteria, outcome definitions and so on. Further research is indicated to evaluate the true effectiveness of CPR for in-hospital cardiac arrest.

Budgets for transportation improvements are limited so it is important for governments to focus on improving locations most in need of safety funding. The objective of the Two-Output Model for Safety (TOMS) is to provide the Utah Department of Transportation (UDOT) a reliable method to prioritize safety improvements on state-owned roadways among the different regions. This research will improve the existing Crash Analysis Methodology for Segments (CAMS) and Intersection Safety Analysis Methodology (ISAM) being used to analyze crashes on Utah roadways. The scope of this project is improving on the existing CAMS and ISAM to work together within R, to incorporate segment and intersection severity in safety hot spot analysis, to develop overall severity distributions, and to develop limited recommendations and conclusions related to the research. TOMS uses UDOT data to create a statistical input. Each segment is homogenous with respect to five variables: average annual daily traffic, functional class, number of through lanes, speed limit, and urban code. Intersections are provided as a separate dataset. In the statistical analyses performed on the data, five years of crash data (2016-2020) are used to determine a weighted risk score for segments and intersections of similar characteristics. Those segments or intersections with excess weighted risk scores are designated as crash hot spots. Two-page technical reports with road characteristics and crash data are created for the top 10 hot spots for segments and intersections in Utah. The reports are sent to UDOT where region engineers may review and determine which locations might be addressed.

Background: Canada continues to face an opioid epidemic with 5,368 opioid apparent related deaths occurring between January and September of 2021. Methadone Maintenance Treatment (MMT), a form of Medication Assisted Treatment used to treat Opioid Use Disorder (OUD), has been reported to decrease opioid cravings and opioid use, however, individual differences exist in the effective dose of methadone. Further, individuals living with an OUD have higher rates of substance use including cannabis. A genetic component has been suggested to exist for both cannabis use and MMT outcomes, however inconsistent findings have been reported. Methods: Knowledge synthesis and primary genetic association studies were conducted. A protocol was prepared for the planning of a systematic review for Genome-Wide Association Studies (GWASs) of cannabis use. The full systematic review was then conducted, providing an assessment of the literature and a description of studies quality. A GWAS and Polygenic Risk Score (PRS) was then conducted for cannabis use and MMT outcomes, separately, in Europeans only. The top Single Nucleotide Polymorphisms (SNPs) were then analyzed separately by sex and sex interactions were conducted. Results: The systematic review included 6 studies, identifying 96 genetic variants associated with cannabis use. The GWASs for both cannabis use and MMT outcomes did not identify any significant results. A significant PRS was found for regular cannabis use and methadone dose. No sex-specific results were identified. Discussion: This thesis summarised the evidence on the genetics of cannabis use as well as employed GWASs and PRSs to investigate cannabis use and MMT outcomes within a European population. We were able to highlight gaps within the genetic literature of cannabis and MMT outcomes as well as identify areas of interest for future research. / Dissertation / Doctor of Philosophy (PhD) / Cannabis use rates in Canada are increasing, with Opioid Use Disorder (OUD) patients having high rates of cannabis use despite inconsistent findings on the impacts. To combat the opioid crisis, Methadone Maintenance Treatment (MMT) is utilized to reduce opioid cravings and use. However, individuals on MMT are likely to use other substances, including cannabis. This thesis explores the genetic literature on cannabis use and conducts a Genome-Wide Association Study (GWAS) and a Polygenetic Risk Score (PRS). The GWAS investigates genetic variants throughout the whole genome associated with a trait, while the PRS creates a genetic weight risk score. GWAS and PRS methods were used to investigate cannabis use and MMT outcomes within Europeans with OUD. While no significant GWAS results were found, a statistically significant PRS was found for regular cannabis use and methadone dose, suggesting each respective score can estimate an individual’s risk of that trait.

Polygenic risk scores are scores used in precision medicine in order to assess an individual's risk of having a certain quantitative trait based on his or her genetics. Previous works have shown that machine learning, namely Gradient Boosted Regression Trees, can be successfully applied to calibrate the weights of the risk score to improve its predictive power in a target population. Neural networks are a very powerful class of machine learning algorithms that have demonstrated success in various elds of genetics, and in this work, we examined the predictive power of a polygenic risk score that uses neural networks to perform the weight calibration. Using a single neural network, we were able to obtain prediction R2 of 0.234 and 0.074 for height and BMI, respectively. We further experimented with changing the dimension of the input features, using ensembled models, and varying the number of splits used to train the models in order to obtain a nal prediction R2 of 0.242 for height and 0.0804 for BMI, achieving a relative improvement of 1.26% in prediction R2 for height. Furthermore, we performed extensive analysis of the behaviour of the neural network-calibrated weights. In our analysis, we highlighted several potential drawbacks of using neural networks, as well as machine learning algorithms in general when performing the weight calibration, and o er several suggestions for improving the consistency and performance of machine learning-calibrated weights for future research. / Thesis / Master of Science (MSc)

Background: South Asians are at an increased risk of developing dysglycemia during and after pregnancy. In pregnant women, dysglycemia often develops in the form of gestational diabetes mellitus (GDM), which may predispose their newborns to adverse health outcomes through abnormal cord blood insulin levels. However, reasons for the elevated risk of dysglycemia in South Asians have not been extensively studied. Genetic factors may contribute to the heritability of GDM and abnormal cord blood insulin levels in South Asians. Objectives: The objectives of this thesis were to test the association of: 1) A type 2 diabetes polygenic risk score with GDM in South Asian pregnant women from the South Asian Birth Cohort (START); 2) maternal and newborn insulin-based polygenic risk scores with cord blood insulin and glucose/insulin ratio in South Asian newborns from START Methods: Three polygenic risk scores were created to test their association with participant data (N=1012) from START. GDM was defined using cut-offs established by the Born in Bradford cohort of South Asian women. The type 2 diabetes polygenic risk score was created in 832 START mothers and included 35,274 independent variants. The maternal and newborn insulin-based polygenic risk scores were created in 604 START newborns and included 1128017 independent variants. Univariate and multiple logistic and linear regression models were used to test the associations between the polygenic risk scores and dysglycemia outcomes. Results: The type 2 diabetes polygenic risk score was associated with GDM in both univariate (OR: 2.00, 95% CI: 1.46-2.75, P<0.001), and multivariable models (OR: 1.81, 95% CI: 1.30-2.53, P<0.001). The maternal insulin-based polygenic risk score was not associated with cord blood insulin or cord glucose/insulin ratio. However, the newborn insulin-based polygenic risk score was associated with cord blood insulin in a multivariable model adjusted for maternal insulin-based polygenic risk score (β = 0.036, 95% CI: 0.002 – 0.069; P=0.038 among other factors. Conclusion: A type 2 diabetes polygenic risk score and a newborn insulin-based polygenic risk score may be associated with maternal and newborn dysglycemia. / Thesis / Master of Science (MSc) / Background: South Asians are approximately two times more at risk for developing gestational diabetes mellitus (GDM) compared to white Caucasians. Genetic factors may contribute to this elevated risk. Polygenic risk scores (PRSs), which combine the effects of multiple disease loci and variants associated with the disease into one variable could be useful in further understanding how GDM develops in South Asians. Methods: Data from the South Asian Birth Cohort (START) was used to test the association of three PRSs with the outcomes of interest. Results: The type 2 diabetes PRS was independently associated with GDM. The insulin-based maternal PRS was not associated with cord blood insulin but the insulin-based newborn PRS was independently associated with cord blood insulin. However, neither the insulin-based maternal nor newborn PRS was associated with cord blood glucose/insulin ratio. Conclusion: The PRSs suggests a possible genetic component, which contributes to abnormal glycemic status development in South Asian mothers and their newborns.

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