ABSTRACT
There are two main aspects of statistical interest in genomic map construction: sampling variation and experimental error. Statistics provides a means to analyze data and to draw genetic inference. This chapter introduces some statistical methods commonly used in genomic mapping. It discusses sampling distributions, hypothesis tests and estimation theory. The chapter also introduces definitions of sample space, random variable, probability distribution and some standard distributions commonly used in genomic data analysis. The maximum likelihood (ML) method is the most widely used in genomic data analysis. The chapter also discusses sample size determination, which has gained a lot attention in statistics. The chapter presents the basic theory for determining sample sizes. It explains methods of statistical hypothesis testing, such as chi-square tests, log likelihood ratio tests, the lod score approach and nonparametric approaches. The chapter presents the concept of statistical power in the context of likelihood support limits and the probability distributions of the test statistics.
