independent case-control studies, limiting the eventual associations to those with the lowest p-values in the discovery study (Hoover, 2007) The selection of study participants is vital. Case misclassification can dilute the power of a study and prejudice the outcome toward no association. In addition, as shown in the large-scale GWAS of the highly heterogenous disease OA, the investigation of stratified, homogenous endophenotypes, are likely to lead to an increased number of genetic discoveries underpinning disease development (Panoutsopoulou and Zeggini, 2013). Selection of genotyping platform (technology used to assay the genotypes of the study participants) is influenced by; the number of SNPs to be examined, the available budget, resources, and sample size. The ideal platform combines high throughput and accuracy with low cost per SNP analysis (Kwok, 2001; Syvänen, 2001). Many platforms exist, with several being able to genotype millions of sites simultaneously, for example, the Illumina Inc. BeadArray technology (Shen et al. 2005). BeadArray technology combines a miniaturized array platform with a high level of assay multiplexing and scalable automation. 1.4.6: Meta-analysis and Replication Almost all of what we presently know about the genetics of human polygenic disorders and quantitative traits has derived from meta-analysis of genome-wide association data (Panagiotou et al. 2013). Meta-analysis is defined as a statistical method, which allows the quantitative synthesis of results from different studies in order to estimate a common summary effect (Lau et al. 1997). The need for large sample sizes meant that meta-analysis of GWAS data from different studies was recognized as the appropriate method to achieve adequate sample sizes and optimal power for the discovery of genetic associations with modest effect sizes (Evangelou et al. 2007; Skol et al. 2007). of multiple GWAS datasets is now a well-established and validated strategy for both discovery and replication of genetic association studies (Skol et al. 2007).
independent case-control studies, limiting the eventual associations to those with the lowest p-values in the discovery study (Hoover, 2007) The selection of study participants is vital. Case misclassification can dilute the power of a study and prejudice the outcome toward no association. In addition, as shown in the large-scale GWAS of the highly heterogenous disease OA, the investigation of stratified, homogenous endophenotypes, are likely to lead to an increased number of genetic discoveries underpinning disease development (Panoutsopoulou and Zeggini, 2013). Selection of genotyping platform (technology used to assay the genotypes of the study participants) is influenced by; the number of SNPs to be examined, the available budget, resources, and sample size. The ideal platform combines high throughput and accuracy with low cost per SNP analysis (Kwok, 2001; Syvänen, 2001). Many platforms exist, with several being able to genotype millions of sites simultaneously, for example, the Illumina Inc. BeadArray technology (Shen et al. 2005). BeadArray technology combines a miniaturized array platform with a high level of assay multiplexing and scalable automation. 1.4.6: Meta-analysis and Replication Almost all of what we presently know about the genetics of human polygenic disorders and quantitative traits has derived from meta-analysis of genome-wide association data (Panagiotou et al. 2013). Meta-analysis is defined as a statistical method, which allows the quantitative synthesis of results from different studies in order to estimate a common summary effect (Lau et al. 1997). The need for large sample sizes meant that meta-analysis of GWAS data from different studies was recognized as the appropriate method to achieve adequate sample sizes and optimal power for the discovery of genetic associations with modest effect sizes (Evangelou et al. 2007; Skol et al. 2007). of multiple GWAS datasets is now a well-established and validated strategy for both discovery and replication of genetic association studies (Skol et al. 2007).
Final-PhD-after-corrections-thesis-final.docx