Using Genomic Data to Find Disease-Modifying Loci in Huntington's Disease (HD).

In this chapter, genetic modifiers are defined, and the rationale for investigating them in HD explained. Issues involved in modeling the phenotype are discussed, using age at motor onset as an example. The statistical methods for analyzing genetic data (linkage and association) are discussed, along with the advantages and disadvantages of each. In particular, the advantage of a genome-wide approach over one based on candidate genes is stressed. Genome-wide association studies (GWAS) are current method of choice to detect genetic modifiers. The power of GWAS is discussed, along with sources of error, and how these might be detected and corrected. Extensions to GWAS, such as gene- and pathway-wide analyses, are discussed, and also how GWAS may be used to estimate genetic risks and trait heritability. Since GWAS are most effective to detect common genetic variants, methods for analyzing rare variation are also discussed. The uses of other types of genomic data (notably, expression) are discussed, and how they might be integrated with genetic data to find causal genes and variants. The chapter ends with a short overview of future prospects for detecting genetic modifiers of HD.