Statistical Methods For Detecting Genetic Risk Factors of a Disease with Applications to Genome-Wide Association Studies

This thesis aims to develop various statistical methods for analysing the data derived from genome wide association studies (GWAS). The GWAS often involves genotyping individual human genetic variation, using high-throughput genome-wide single nucleotide polymorphism (SNP) arrays, in thousands of individuals and testing for association between those variants and a given disease under the assumption of common disease/common variant. Although GWAS have identified many potential genetic factors in the genome that affect the risks to complex diseases, there is still much of the genetic heritability that remains unexplained. The power of detecting new genetic risk variants can be improved by considering multiple genetic variants simultaneously with novel statistical methods. Improving the analysis of the GWAS data has received much attention from statisticians and other scientific researchers over the past decade. There are several challenges arising in analysing the GWAS data. First, determining the risk SNPs might be difficult due to non-random correlation between SNPs that can inflate type I and II errors in statistical inference. When a group of SNPs are considered together in the context of haplotypes/genotypes, the distribution of the haplotypes/genotypes is sparse, which makes it difficult to detect risk haplotypes/genotypes in terms of disease penetrance. In this work, we proposed four new methods to identify risk haplotypes/genotypes based on their frequency differences between cases and controls. To evaluate the performances of our methods, we simulated datasets under wide range of scenarios according to both retrospective and prospective designs. In the first method, we first reconstruct haplotypes by using unphased genotypes, followed by clustering and thresholding the inferred haplotypes into risk and non-risk groups with a two-component binomial-mixture model. In the method, the parameters were estimated by using the modified Expectation-Maximization algorithm, where the maximisation step was replaced the posterior sampling of the component parameters. We also elucidated the relationships between risk and non-risk haplotypes under different modes of inheritance and genotypic relative risk. In the second method, we fitted a three-component mixture model to genotype data directly, followed by an odds-ratio thresholding. In the third method, we combined the existing haplotype reconstruction software PHASE and permutation method to infer risk haplotypes. In the fourth method, we proposed a new way to score the genotypes by clustering and combined it with a logistic regression approach to infer risk haplotypes. The simulation studies showed that the first three methods outperformed the multiple testing method of (Zhu, 2010) in terms of average specificity and sensitivity (AVSS) in all scenarios considered. The logistic regression methods also outperformed the standard logistic regression method. We applied our methods to two GWAS datasets on coronary artery disease (CAD) and hypertension (HT), detecting several new risk haplotypes and recovering a number of the existing disease-associated genetic variants in the literature

Adult skills: their use and usefulness in Estonia. Summaries of thematic reports on the PIAAC study

Between these covers you will find summaries of the seven thematic reports published in Estonian on the PIAAC (Programme for the International Assessment of Adult Competencies) study. The reports were completed as part of the PIAAC Estonia programme in 2014 and 2015. This publication is a précis of the two years of analysis conducted after the study, which was carried out in the country for the first time and whose main focus was key themes in education and the labour market – areas of importance to Estonia. The full versions of the reports, as well as the initial results of the study published in 2013 in the report ‘Adult skills in Estonia and the world: Initial results of the PIAAC study’, are available on the website of the Ministry of Education and Research (MER). Details of completed international reports and those in progress can be found on the websites of both the OECD and the Nordic Council of Ministers – the latter of which has also published the Nordic PIAAC report, which analysed the results of five countries, including Estonia1 . In addition, short reports, scientific articles and a number of graduation theses have been published on the basis of PIAAC data (the reports being available on the website of the MER). The data can be downloaded from the OECD website for further analysis.https://www.hm.ee/sites/default/files/inglise_sisu_veebi_14okt.pd