ABSTRACT
Most complex diseases including obesity, diabetes, cardiovascular disease, hypertension, schizophrenia, Alzheimer’s disease, and cancer are common diseases and pose great public health concerns. Clinical manifestations arise from integrated actions of multiple genetic and environmental factors. In the past decades, linkage analyses have been the primary method for genetic studies of diseases. However, the fact that many diseases are caused by multiple mutations and genes that individually contribute only modestly to disease risk limits the power of linkage studies. The rapid development in next-generation sequencing technologies that are generating unprecedentedly high-dimensional genetic variation data change the paradigm of genetic studies of complex diseases from linkage analysis to association analysis and from single marker analysis to the joint analysis of multiple variants in a genomic region.
This chapter begins with an introduction of the Hardy–Weinberg equilibrium and genetic models that are the basis of underlying test statistics and then covers multivariate group tests: collapsing method, combined multivariate and collapsing (CMC) method, weighted sum method, score test and logistic regression, and sequencing kernel association test (SKAT). Finally, functional association analysis, including function principal component analysis for association tests and smoothed functional principal component analysis for association tests, are presented in this chapter.
