Yes / Importance: Myopia is a leading cause of untreatable visual impairment and is increasing in prevalence worldwide. Interventions for slowing childhood myopia progression have shown success in randomized clinical trials; hence, there is a need to identify which children would benefit most from treatment intervention. Objectives: To examine whether genetic information alone can identify children at risk of myopia development and whether including a child’s genetic predisposition to educational attainment is associated with improved genetic prediction of the risk of myopia. Design, Setting, and Participants: Meta-analysis of 3 genome-wide association studies (GWAS) including a total of 711 984 individuals. These were a published GWAS for educational attainment and 2 GWAS for refractive error in the UK Biobank, which is a multisite cohort study that recruited participants between January 2006 and October 2010. A polygenic risk score was applied in a population-based validation sample examined between September 1998 and September 2000 (Avon Longitudinal Study of Parents and Children [ALSPAC] mothers). Data analysis was performed from February 2018 to May 2019. Main Outcomes and Measures: The primary outcome was the area under the receiver operating characteristic curve (AUROC) in analyses for predicting myopia, using noncycloplegic autorefraction measurements for myopia severity levels of less than or equal to −0.75 diopter (D) (any), less than or equal to -3.00 D (moderate), or less than or equal to −5.00 D (high). The predictor variable was a polygenic risk score (PRS) derived from genome-wide association study data for refractive error (n = 95 619), age of onset of spectacle wear (n = 287 448), and educational attainment (n = 328 917). Results: A total of 383 067 adults aged 40 to 69 years from the UK Biobank were included in the new GWAS analyses. The PRS was evaluated in 1516 adults aged 24 to 51 years from the ALSPAC mothers cohort. The PRS had an AUROC of 0.67 (95% CI, 0.65-0.70) for myopia, 0.75 (95% CI, 0.70-0.79) for moderate myopia, and 0.73 (95% CI, 0.66-0.80) for high myopia. Inclusion in the PRS of information associated with genetic predisposition to educational attainment marginally improved the AUROC for myopia (AUROC, 0.674 vs 0.668; P = .02), but not those for moderate and high myopia. Individuals with a PRS in the top 10% were at 6.1-fold higher risk (95% CI, 3.4–10.9) of high myopia. Conclusions and Relevance: A personalized medicine approach may be feasible for detecting very young children at risk of myopia. However, accuracy must improve further to merit uptake in clinical practice; currently, cycloplegic autorefraction remains a better indicator of myopia risk (AUROC, 0.87). / PhD studentship grant from the College of Optometrists (Drs Guggenheim and Williams; supporting Mr Mojarrad) entitled Genetic prediction of individuals at-risk for myopia development) and National Institute for Health Research (NIHR) Senior Research Fellowship award SRF-2015-08-005 (Dr Williams). The UK Medical Research Council and Wellcome grant 102215/2/13/2 and the University of Bristol provide core support for the Avon Longitudinal Study of Parents and Children (ALSPAC). A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). This research was conducted using the UK Biobank Resource (application 17351). The UK Biobank was established by the Wellcome Trust, the UK Medical Research Council, the Department for Health (London, England), the Scottish government (Edinburgh, Scotland), and the Northwest Regional Development Agency (Warrington, England). It also received funding from the Welsh Assembly Government (Cardiff, Wales), the British Heart Foundation, and Diabetes UK.

Yes / Background: Myopia has been shown to have a complex mode of inheritance, being influenced by both genetic and environmental factors. Here, an introduction into myopia genetics is given, with the shortcomings of current genetic prediction for myopia discussed, including the proportionally limited research on genetic prediction in people of non-European ancestry. A previously developed genetic risk score derived from European participants was evaluated in participants of non-European ancestry. Methods: Participants from UK Biobank who self-reported their ethnicity as “Asian”, “Chinese”, or “Black” and who had refractive error and genetic data available were included in the analysis. Ancestral homogeneity was confirmed using principal component analysis, resulting in samples of 3500 Asian, 444 Chinese, and 3132 Black participants. A published refractive error GWAS meta-analysis of 711,984 participants of European ancestry was used to create a weighted genetic risk score model which was then applied to participants from each ethnic group. Accuracy of genetic prediction of refractive error was estimated as the proportion of variance explained (PVE). Receiver operating characteristic (ROC) curves were developed to estimate myopia prediction performance at three thresholds: any myopia (equal to or more than 0.75D), moderate myopia (between -3.00D and -4.99D) and high myopia (equal to or more than -5.00D). Odds ratios for myopia were calculated for the participants in the top 10th or 5th percentile of genetic risk score distribution, comparing them to the remainder of the population. Results: The PVE value for refractive error was 6.4%, 6.2%, and 1.5% for those with Asian, Chinese and Black ethnicity, respectively (compared to 11.2% in Europeans). Odds ratios for any myopia and moderate myopia development for those within the top 10th and 5th percentile of genetic risk were significant in all ethnic groups P<0.05). However, the genetic risk score was not able to reliably identify those at risk of high myopia, other than for participants of Chinese ethnicity (P<0.05). Conclusion: Prediction of refractive error in Asian, Chinese and Black participants was ~57%, 55% and 13% as accurate in comparison to prediction in European participants. Further research in diverse ethnic populations is needed to improve prediction accuracy. / This research has been conducted using the UK Biobank Resource (applications #17351). UK Biobank was established by the Wellcome Trust; the UK Medical Research Council; the Department for Health (London, UK); Scottish Government (Edinburgh, UK); and the Northwest Regional Development Agency (Warrington, UK). It also received funding from the Welsh Assembly Government (Cardiff, UK); the British Heart Foundation; and Diabetes UK. Collection of eye and vision data was supported by The Department for Health through an award made by the NIHR to the Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, and UCL Institute of Ophthalmology, London, United Kingdom (grant no. BRC2_009). Additional support was provided by The Special Trustees of Moorfields Eye Hospital, London, United Kingdom (grant no. ST 12 09). Many parts of this project were performed during the time that author Neema Ghorbani Mojarrad was supported by the College of Optometrists with a Postgraduate Scholarship.

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.

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.

Family-based and genome-wide association studies (GWAS) of alcohol dependence (AD) have reported numerous associated variants. The clinical validity of these variants for predicting AD compared to family history has not yet been reported. These studies aim to explore the aggregate impact of multiple genetic variants with small effect sizes on risk prediction in order to provide a clinical interpretation of genetic contributions to AD. Data simulations showed that given AD’s prevalence and heritability, a risk prediction model incorporating all genetic contributions would have an area under the receiver operating characteristic curve (AUC) approaching 0.80, which is often a target AUC for screening. Adding additional environmental factors could increase the AUC to 0.95. Using the Collaborative Study on the Genetics of Alcoholism (COGA) and the Study of Addiction: Genes and Environment (SAGE) GWAS samples, we used several different sources to capture genetic information associated with AD in discovery samples, and then tested genetic sum scores created based on this information for predictive accuracy in validation samples. Scores were assessed separately for single nucleotide polymorphisms (SNPs) associated in candidate gene studies and in GWAS analyses. Candidate gene sum scores did not exhibit significant predictive accuracy, but SNPs meeting less stringent p-value thresholds in GWAS analyses did, ranging from mean estimates of 0.549 for SNPs meeting p<0.01 to 0.565 for SNPs meeting p<0.50. Variants associated with subtypes of AD showed that there is similarly modest and significant predictive ability for an externalizing subtype. Scores created based on all individual SNP effects in aggregate across the entire genome accounted for 0.46%-0.57% of the variance in AD symptom count, and have AUCs of 0.527 to 0.549. Additional covariates and environmental factors that are correlated with AD increased the AUC to 0.865. Family history was a better classifier of case-control status than genetic sum scores, with an AUC of 0.686 in COGA and 0.614 in SAGE. This project suggests that SNPs from candidate gene studies and genome-wide association studies currently have limited clinical validity, but there is potential for enhanced predictive ability with better detection of genetic factors contributing to AD.

No description available.

京都大学 / 新制・論文博士 / 博士(医学) / 乙第13398号 / 論医博第2222号 / 新制||医||1051(附属図書館) / (主査)教授 妹尾 浩, 教授 中山 健夫, 教授 西浦 博 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM