Genome-wide approaches including polygenic risk scores (PRSs) are now widely used in medical research; however, few studies have been conducted in low- and middle-income countries (LMICs), especially in South America. This study was designed to test the transferability of psychiatric PRSs to individuals with different ancestral and cultural backgrounds and to provide genome-wide association study (GWAS) results for psychiatric outcomes in this sample. The PrOMIS cohort (N = 3308) was recruited from prenatal care clinics at the Instituto Nacional Materno Perinatal (INMP) in Lima, Peru. Three major psychiatric outcomes (depression, PTSD, and suicidal ideation and/or self-harm) were scored by interviewers using valid Spanish questionnaires. Illumina Multi-Ethnic Global chip was used for genotyping. Standard procedures for PRSs and GWAS were used along with extra steps to rule out confounding due to ancestry. Depression PRSs significantly predicted depression, PTSD, and suicidal ideation/self-harm and explained up to 0.6% of phenotypic variation (minimum p = 3.9 × 10−6). The associations were robust to sensitivity analyses using more homogeneous subgroups of participants and alternative choices of principal components. Successful polygenic prediction of three psychiatric phenotypes in this Peruvian cohort suggests that genetic influences on depression, PTSD, and suicidal ideation/self-harm are at least partially shared across global populations. These PRS and GWAS results from this large Peruvian cohort advance genetic research (and the potential for improved treatments) for diverse global populations. / National Institutes of Health / Revisión por pares

The Spl polymorphism of the Collagen I alpha 1 gene has previously been associated with low bone density and increased risk of fracture in a number of clinical studies. In chapter 3 the association with fracture was shown to be driven by the Spl polymorphism rather than other single nucleotide polymorphisms located in and around the collagen I alpha 1 gene. In chapter 4, the relationship between the Spl polymorphism and osteoporotic fracture was determined in a prospective population study of men and women. This study confirmed the association between "s" alleles and fracture and showed that COLIA1 genotyping interacted significantly with bone density measurements to enhance prediction of individuals at risk of osteoporotic fracture. In chapter 5, the "s" allele was found to be associated with body size in a population study of young adults. Although there was no association with BMD, individuals who carried the "s" allele were lighter at birth and this trend continued through adolescence and into young adulthood. This suggests that "s" individuals are at increased risk of osteoporosis from an early age, since body size is, in itself a risk factor for osteoporosis. In chapter 6, the effect of Spl alleles on quantitative ultrasound (QUS) was determined in a young post-menopausal population. It was found that there were no significant genotype related differences in broadband ultrasound attenuation (BUA). In chapter 7, family studies were conducted using the quantitative transmission disequilibrium test (qTDT). This showed evidence of a polygenic effect on BMD at the spine and hip and confirmed evidence of an association between Spl "s" alleles and BMD at the femoral neck. The data suggests that the previous associations of Spl alleles and BMD are genuine and not due to population admixture.

Obesity is a major risk factor for multiple common chronic diseases. The prevalence in European countries is high and a significant public health concern. This thesis aims to explore the obesity landscape in the Cooperative Health Research in South Tyrol (CHRIS) study. The first step was to characterise the obese CHRIS population, taking into account the established body mass index (BMI) classification from the World Health Organization (WHO) and looking at metabolically healthy and unhealthy obesity. We investigated the familial aggregation of these traits. We identified several families with significant familial aggregation and observed varying degrees of overlap for these traits in different families. The focus was then on implementing and applying a Genome-Wide Polygenic Score for obese participants. These scores were computed for individuals based on the presence of different genetic variants weighted according to their measured effects in genome-wide association studies (GWAS). We then paid attention to the targeted metabolomics data of the CHRIS study, to identify different serum metabolites associated with metabolically healthy/unhealthy obesity, using logistic regression and random forest methods to explore metabolic signatures to distinguish obesity into metabolically healthy and metabolically unhealthy obesity. Several biomarkers were shown to be related to obesity, many of which confirmed by existing evidence (such as BCAAs, tyrosine, and lysophosphatidylcholines).

Polygenic risk scores (PRS) are relatively novel tools for risk prediction, serving as a quantitative singular value which depicts a patient’s genetic disposition for a certain disease. Given that many current clinical risk predictors do not address heritability within their calculations, PRS are likely to improve prediction, especially in the case of complex diseases which are influenced by a combination of genetic, environmental and lifestyle factors. Altogether, PRS studies have been pursued for their abilities in trait detection, therapeutic intervention and disease protection, with much potential in personalized/precision medicine where each interpretation is unique and based on a patient’s genotype. However, despite the numerous advances over years, PRS have yet to reach the level where they can be implemented into standard clinical practices as originally intended. The goal is to develop PRS which are applicable to global populations, which has yet to be achieved due to the inconsistency and general skepticism regarding the method. Furthermore, PRS have yet to reach the upper threshold for risk prediction, as indicated by the heritability that remains unaccounted for with PRS calculations. Thus, this thesis addresses how PRS can inform and guide clinical decision-making for complex decisions with strong, genetic dispositions. It also presents novel approach to PRS aimed at mitigating some of its current limitations. / Thesis / Doctor of Philosophy (PhD) / Many common diseases, like coronary artery disease (CAD) and diabetes, are influenced not only by lifestyle and environmental factors, but also by genetics. Therefore, incorporating genetic information into disease risk prediction for patients in clinical settings would be logical, especially since genetic data can be obtained early in life. One tool for quantifying risk based on genetics is the polygenic risk score (PRS). PRS assigns a numerical value based on an individual’s genetic profile, calculated by summing up risk variants in their DNA. The risk level corresponds to the variant’s association with the trait, as determined by genome-wide association studies (GWAS). PRS have become increasingly popular for guiding disease treatment and personalized medicine. However, there’s still work to be done to make PRS suitable for clinical use. Many methods have attempted to enhance the predictive ability of PRS, but there’s still room for improvement. This thesis introduces various applications for PRS, along with a novel prediction method that potentially addresses some limitations and explores the applications of PRS in common diseases.

This thesis examines various methods for generating multi-trait polygenic risk scores (PRS). The primary objective is to see which multi-trait method performs best and are there any simpler methods that can perform as well. The thesis evaluates each method by comparing the weighted-average multi-trait PRS with true phenotype values (target traits), using the correlation coefficient (ρ) for continuous traits and the area under the receiver operating characteristic curve (AUC) for binary traits. It also investigates how different simulation parameters influence performance. Two additional novel multi-trait PRS methods are introduced in this work: mt-lm and mt-CVb. mt-lm is essentially a multiple linear regression for a continuous focal trait and logistic regression for a binary focal trait, while mt-CVb combines cross-validation and bagging techniques in a hybrid approach to improve model performance. The existing multi-trait method wMT-SBLUP consistently achieves the best performance, outperforming all other methods in most scenarios. While the two novel methods are not the top performers, they demonstrate better results compared to other methods (excluding wMT-SBLUP) for both continuous and binary focal traits across various parameter settings. Moreover, mt-lm offers the additional advantage of being faster than wMT-SBLUP. / Thesis / Master of Science (MSc)

The aim of this thesis is to explore the role of common genetic variation, identified through genome-wide association (GWA) studies, in human traits and diseases, using height as a model polygenic trait, type 2 diabetes as a model common polygenic disease, and maturity onset diabetes of the young (MODY) as a model monogenic disease. The wave of the initial GWA studies, such as the Wellcome Trust Case-Control Consortium (WTCCC) study of seven common diseases, substantially increased the number of common variants associated with a range of different multifactorial traits and diseases. The initial excitement, however, seems to have been followed by some disappointment that the identified variants explain a relatively small proportion of the genetic variance of the studied trait, and that only few large effect or causal variants have been identified. Inevitably, this has led to criticism of the GWA studies, mainly that the findings are of limited clinical, or indeed scientific, benefit. Using height as a model, Chapter 2 explores the utility of GWA studies in terms of identifying regions that contain relevant genes, and in answering some general questions about the genetic architecture of highly polygenic traits. Chapter 3 takes this further into a large collaborative study and the largest sample size in a GWA study to date, mainly focusing on demonstrating the biological relevance of the identified variants, even when a large number of associated regions throughout the genome is implicated by these associations. Furthermore, it shows examples of different features of the genetic architecture, such as allelic heterogeneity and pleiotropy. Chapter 4 looks at the predictive value and, therefore, clinical utility, of variants found to associate with type 2 diabetes, a common multifactorial disease that is increasing in prevalence despite known environmental risk factors. This is a disease where knowledge of the genetic risk has potentially substantial clinical relevance. Finally, Chapter 5 approaches the monogenic-polygenic disease bridge in the direction opposite to that approached in the past: most studies have investigated genes mutated in monogenic diseases as candidates for harboring common variants predisposing to related polygenic diseases. This chapter looks at the common type 2 diabetes variants as modifiers of disease onset in patients with a monogenic but clinically heterogeneous disease, maturity onset diabetes of the young (MODY).

Major Depressive Disorder is a devastating psychiatric illness with a complex genetic and environmental component that affects 10% of the UK population. Previous studies have shown that that individuals with depression show poorer performance on measures of cognitive domains such as memory, attention, language and executive functioning. A major risk factor for depression is a higher level of neuroticism, which has been shown to be associated with depression throughout life. Understanding cognitive performance in depression and neuroticism could lead to a better understanding of the aetiology of depression. The first aim of this thesis focused on assessing phenotypic and genetic differences in cognitive performance between healthy controls and depressed individuals and also between single episode and recurrent depression. A second aim was determining the capability of two decision-tree based methods to detect simulated gene-gene interactions. The third aim was to develop a novel statistical methodology for simultaneously analysing single SNP, additive and interacting genetic components associated with neuroticism using machine leaning. To assess the phenotypic and genetic differences in depression, 7,012 unrelated Generation Scotland participants (of which 1,042 were clinically diagnosed with depression) were analysed. Significant differences in cognitive performance were observed in two domains: processing speed and vocabulary. Individuals with recurrent depression showed lower processing speed scores compared to both controls and individuals with single episode depression. Higher vocabulary scores were observed in depressed individuals compared to controls and in individuals with recurrent depression compared to controls. These significant differences could not be tied to significant single locus associations. Derived polygenic scores using the large CHARGE processing speed GWAS explained up to 1% of variation in processing speed performance among individuals with single episode and recurrent depression. Two greedy non-parametric decision-tree based methods - C5.0 and logic regression - were applied to simulated gene-gene interaction data from Generation Scotland. Several gene-gene interactions were simulated under multiple scenarios (e.g. size, strength of association levels and the presence of a polygenic component) to assess the power and type I error. C5.0 was found to have an increased power with a conservative type I error using simulated data. C5.0 was applied to years of education as a proxy of educational attainment in 6,765 Generation Scotland participants. Multiple interacting loci were detected that were associated with years of education, some most notably located in genes known to be associated with reading and spelling (RCAN3) and neurodevelopmental traits (NPAS3). C5.0 was incorporated in a novel methodology called Machine-learning for Additive and Interaction Combined Analysis (MAICA). MAICA allows for a simultaneous analysis of single locus, polygenic components, and gene-gene interaction risk factors by means of a machine learning implementation. MAICA was applied on neuroticism scores in both Generation Scotland and UK Biobank. The MAICA model in Generation Scotland included 151 single loci and 11 gene-gene interaction sets, and explained ~6.5% of variation in neuroticism scores. Applying the same model to UK Biobank did not lead to a statistically significant prediction of neuroticism scores. The results presented in this thesis showed that individuals with depression performed significantly lower on the processing speed tests but higher on vocabulary test and that 1% of variation in processing speed can be explained by using a large processing speed GWAS. Evidence was provided that C5.0 had increased power and acceptable type I error rates versus logic regression when epistatic models exist - even with a strong underlying polygenic component, and that MAICA is an efficient tool to assess single locus, polygenic and epistatic components simultaneously. MAICA is open-source, and will provide a useful tool for other researchers of complex human traits who are interested in exploring the relative contributions of these different genomic architectures.

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京都大学 / 新制・課程博士 / 博士(医学) / 甲第25001号 / 医博第5035号 / 新制||医||1070(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 近藤 尚己, 教授 古川 壽亮, 教授 森田 智視 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Various models of the genetic architecture of quantitative traits have been considered to provide the basis for increased genetic progress. The finite polygenic model (FPM), which contains a finite number of unlinked polygenic loci, is proposed as an improvement to the infinitesimal model (IM) for estimating both additive and dominance variance for a wide range of genetic models. Analysis under an additive five-loci FPM by either a deterministic Maximum Likelihood (DML) or a Markov chain Monte Carlo (MCMC) Bayesian method (BGS) produced accurate estimates of narrow-sense heritability (0.48 to 0.50 with true values of h2 = 0.50) for phenotypic data from a five-generation, 6300-member pedigree simulated without selection under either an IM, FPMs containing five or forty loci with equal homozygote difference, or a FPM with eighteen loci of diminishing homozygote difference. However, reducing the analysis to a three- or four-loci FPM resulted in some biased estimates of heritability (0.53 to 0.55 across all genetic models for the 3-loci BGS analysis and 0.47 to 0.48 for the 40-loci FPM and the infinitesimal model for both the 3- and 4-loci DML analyses). The practice of cutting marriage and inbreeding loops utilized by the DML method expectedly produced overestimates of additive genetic variance (55.4 to 66.6 with a true value of sigma squared sub a = 50.0 across all four genetic models) for the same pedigree structure under selection, while the BGS method was mostly unaffected by selection, except for slight overestimates of additive variance (55.0 and 58.8) when analyzing the 40-loci FPM and the infinitesimal model, the two models with the largest numbers of loci. Changes to the BGS method to accommodate estimation of dominance variance by sampling genotypes at individual loci are explored. Analyzing the additive data sets with the BGS method, assuming a five-loci FPM including both additive and dominance effects, resulted in accurate estimates of additive genetic variance (50.8 to 52.2 for true sigma squared sub a = 50.0) and no significant dominance variance (3.7 to 3.9) being detected where none existed. The FPM has the potential to produce accurate estimates of dominance variance for large, complex pedigrees containing inbreeding, whereas the IM suffers severe limitations under inbreeding. Inclusion of dominance effects into the genetic evaluations of livestock, with the potential increase in accuracy of additive breeding values and added ability to exploit specific combining abilities, is the ultimate goal. / Master of Science