Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate diseases variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards premarital genetic screening by simulating a population of children and analyze the diseases they might be carrying, based on the genetic factors of their parents taking into consideration the recombination hotspots. VSIM supports output formats based on Ideograms that are easy to interpret and understand, which makes it a biologist-friendly powerful tool for data visualization, and interpretation of personal genomic data. Our results show that VSIM can efficiently identify the causative variants by referencing well-known databases for variants in whole genomes associated with different kind of diseases. Moreover, it can be used for premarital genetic screening by simulating a population of offspring and analyze the disorders they might be carrying. The output format provides a better understanding of such large genomics data. VSIM thus helps biologists and marriage counsellor to visualize a variety of genomic variants associated with diseases seamlessly.

Human Mendelian disease in Saudi Arabia is both significant and challenging. Next-generation sequencing (NGS) has resulted in important discoveries of the genetic variants responsible for inherited disease. However, the success of clinical genomics using NGS requires accurate and consistent identification of rare genome variants. Rarity is one very important criterion for pathogenicity. Here we describe a model to detect variants by analyzing allele frequencies of a Saudi population. This work will enhance the opportunity to improve variant calling workflow to gain robust frequency estimates in order to better detect rare and unusual variants which are frequently associated with inherited disease.

Many phenotypic traits are heritable, but the exact genetic causes are difficult to determine. A common approach for disentangling the different genetic factors is to conduct a "genome-wide association study" (GWAS), where each single nucleotide variant (SNV) is tested for association with a trait of interest. Many SNVs for complex traits have been found by GWAS, but to date they explain only a fraction of heritability of complex traits. In this dissertation, we propose novel optimization-based and multiple testing procedures for variant set tests. In the second chapter, we propose a novel variant set test, convex-optimized SKAT (cSKAT), that leverages multiple SNV annotations. The test generalizes SKAT to convex combinations of SKAT statistics constructed from functional genomic annotations. We differ from previous approaches by optimizing kernel weights with a multiple kernel learning algorithm. In cSKAT, the contribution of each variant to the overall statistic is a product of annotation values and kernel weights for annotation classes. We demonstrate the utility of our biologically-informed SNV weights in a rare-variant analysis of fasting glucose in the FHS. In the third chapter, we propose a sequential testing procedure for GWAS that joins tests of single SNVs and groups of SNVs (SNV-sets) with common biological function. The proposed procedure differs from previous procedures by testing genes and sliding 4kb intergenic windows rather than chromosomes or the whole genome. We also sharpen an existing tree-based multiple testing correction by incorporating correlation between SNVs, which is present in any SNV-set containing contiguous regions (such as genes). In the fourth chapter, we present a sequential testing procedure for SNV-sets that incorporates correlation between test statistics of the SNV-sets. At each step of the procedure, the multiplicity correction is the number of remaining independent tests, making no assumption about the null distribution of tests. We provide an estimator for the number of remaining independent tests based on previous work in single-SNV GWAS and demonstrate the estimator is valid for sequential procedures. We implement the proposed method for GWAS by sequentially testing chromosomes, genes, 4kb windows, and SNVs.

Genetic association analyses have successfully identified thousands of genetic variants contributing to complex disease susceptibility. However, these discoveries do not explain the full heritability of many diseases, due to the limited statistical power to detect loci with small effects, especially in regions with rare variants. The development of new and powerful methods is necessary to fully characterize the underlying genetic basis of complex diseases. Family history (FH) contains information on the disease status of un-genotyped relatives, which is related to the genotypes of probands at disease loci. Exploiting available FH in relatives could potentially enhance the ability to identify associations by increasing sample size. Many studies have very low power for genetic research in late-onset diseases because younger participants do not contribute a sufficient number of cases and older patients are more likely deceased without genotypes. Genetic association studies relying on cases and controls need to progress by incorporating additional information from FH to expand genetic research. This dissertation overcomes these challenges and opens up a new paradigm in genetic research. The first chapter summarizes relevant methods used in this dissertation. In the second chapter, we develop novel methods to exploit the availability of FH in aggregation unit-based test, which have greater power than other existing methods that do not incorporate FH, while maintaining a correct type I error. In the third chapter, we develop methods to exploit FH while adjusting for relatedness using the generalized linear mixed effect models. Such adjustment allows the methods to have well-controlled type I error and maintain the highest sample size because there is no need to restrict the analysis to an unrelated subset in family studies. We demonstrate the flexibility and validity of the methods to incorporate FH from various relatives. The methods presented in the fourth chapter overcome the issue of inflated type I error caused by extremely unbalanced case-control ratio. We propose robust versions of the methods developed in the second and third chapters, which can provide more accurate results for unbalanced study designs. Availability of these novel methods will facilitate the identification of rare variants associated with complex traits.

Nicotine's rewarding effects are mediated through distinct subunits of nAChRs, encoded by different nicotinic cholinergic receptor (CHRN) genes and expressed in discrete regions in the brain. In the present study, we aimed to test the associations between rare variants at CHRN genes and nicotine dependence (ND), and alcohol dependence (AD). A total of 26,498 subjects with nine different neuropsychiatric disorders in 15 independent cohorts, which were genotyped on Illumina, Affymetrix, or PERLEGEN microarray platforms, were analyzed. Associations between rare variants (minor allele frequency (MAF) <0.05) at CHRN genes and nicotine dependence, and alcohol dependence were tested. The mRNA expression of all Chrn genes in whole mouse brain and 10 specific brain areas was investigated. All CHRN genes except the muscle-type CHRNB1, including eight genomic regions containing 11 neuronal CHRN genes and three genomic regions containing four muscle-type CHRN genes, were significantly associated with ND, and/or AD. All of these genes were expressed in the mouse brain. We conclude that CHRNs are associated with ND (mainly) and AD, supporting the hypothesis that the full catalog of ND/AD risk genes may contain most neuronal nAChRs-encoding genes.

We comprehensively examined the rare variants in the IPO11-HTR1A region to explore their roles in neuropsychiatric disorders. Five hundred seventy-three to 1,181 rare SNPs in subjects of European descent and 1,234-2,529 SNPs in subjects of African descent (0

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第20249号 / 医博第4208号 / 新制||医||1020(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 清水 章, 教授 松田 道行, 教授 武田 俊一 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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The analysis of real-world data and the development of mathematical models played a fundamental role in understanding the epidemiology of COVID-19 and informing public policies throughout the recent pandemic. This thesis presents a collection of modeling approaches and results addressing key questions that arose during the COVID-19 crisis, with a specific focus on the emergence and epidemiological features of SARS-CoV-2 variants of concern (VOC) in Italy and related public health implications. In the first chapter, conducted analyses suggest that the Alpha variant was approximately 50% more transmissible than historical lineages of SARS-CoV-2, and that this transmissibility advantage was enough to outcompete a variant associated with immune escape phenomena and circulating in central Italy in February 2021 (i.e., the Gamma variant). In the second chapter, by investigating the potential impact of new hypothetical VOCs in Italy in late 2021, modeling results highlighted that the emergence of variants associated with significant immune escape (i.e., with a rate at which vaccinated or recovered individuals from infection with pre-circulating lineages become infected being at least one-fifth that of unvaccinated individuals who never experienced SARS-CoV-2 infection) would have been able to replace pre-circulating lineages in a couple of weeks. Strict restrictions would have been required to prevent a new large epidemic wave. In the third chapter, the analysis of genomic and epidemiological data associated with the expansion of the Omicron variant over the Italian territory revealed that this variant was able to become dominant at the national level in less than a month, increasing the net reproduction number from 1.15 to 1.83. Despite the marked growth advantage of Omicron compared to the previously circulating Delta variant, a moderate impact on the number of severe cases was observed, likely due to the high proportion of vaccinated individuals in the country by the end of 2021. In the fourth chapter, the estimation of the intrinsic generation time of the Omicron variant (mean: 6.84 days) was found to be similar to that of previous lineages. Such estimates have been key to define adequate isolation, quarantine, surveillance, and contact tracing protocols in 2022. The prevention of SARS-CoV-2 transmission in educational settings represented a key challenge during the pandemic, due to the large proportion of asymptomatic carriers in young individuals. The last chapter presented in this thesis shows that, when the Alpha variant was circulating in Italy, almost half of positive students and school personnel ascertained during in-person education were likely infected by school contacts. The mean number of secondary cases caused at schools was found to be 0.33, with high heterogeneity in the chance of onward transmission. Provided estimates suggest that the timely identification of cases combined with reactive quarantine policies had the potential of reducing SARS-CoV-2 transmission in schools by at least 30%.

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