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) are also available from the gap repository (see section A.2 above). This chapter is devoted to demonstrating how to integrate the genome wide association study (WAS) literature to analyze single nucleotide polymorphisms (SNPs) with the power of WAS data. Each of the examples will be based on several datasets (described in Appendix D) for different diseases with the aim of allowing a person to understand an appropriate analysis of a particular WAS dataset. The most important thing to note is that the techniques described here assume that one is familiar with the basic concepts of statistics. For those of you that are new to this discipline, the examples are presented as step-by-step examples that require a very brief review of relevant concepts. The examples presented in this chapter cover WAS data analyzes for Alzheimer's disease, stroke, and Huntington's disease. However, analysis tools from other disease areas should be available. WAS data in other diseases are not covered in more detail because, given the cost, this is not possible today. We welcome questions and suggestions from those of you that may have experience with other biological studies that were not addressed in this chapter. A.2: Developing a Model for Studying WAS Datasets Step 004: Selecting the dataset: This section is the initial step that we introduce each person to the method. This step will cover four scenarios (Fig. A.1) with different datasets that we have used in our research, which are explained in Appendix B.1. A.2.1: Selecting the Genome Wide Association Study (WAS) Dataset If you have only one WAS dataset that you are interested in, our preferred choice would be to choose a dataset that has sufficient sample size. Since there are currently around 15 000 WAS studies being carried out per year worldwide on various diseases, it is impossible to estimate whether there are enough samples to cover the whole genome. However, there is sufficient information available about WAS datasets to enable the reader to pick a dataset that has a sufficient size to ensure robust results if the genome is scanned. The two leading studies we have used for our examples in this chapter are the HumanMethylation18 study on the DNA methylation state in humans, and the European Prospective Investigation into Cancer and Nutrition (EPIC-Oxford study) on the DNA methylation state in Europeans.

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