This chapter surveys current major approaches to mapping genes for multifactorial human diseases using either family-based or population-based samples. Complex, multifactorial diseases are generally hypothesized to be attributable to the effects of multiple genes with incomplete penetrance. Gene-gene and gene-environment interactions are also expected to play a role in the disease etiology.
Disease gene mapping strategies may be divided into two categories that are not mutually exclusive: linkage based and association based. Linkage mapping relies on the co-segregation of marker genotypes with phenotype within families, thereby detecting genetic loci that affect disease risk. Association mapping methods are designed to detect loci for which genotype or allelic status is correlated with phenotypic status on a population level, and can be carried out using either family samples or unrelated individuals. Association-based mapping has gained popularity in recent years due to technological advances that allow large-scale genotyping of single nucleotide polymorphism (SNP) markers, enabling genome-wide association studies (GWAS) of human diseases. This popularity also stems from earlier observations of Risch and Merikangas [1], who noted that association designs promise greater power to detect genes for complex traits, compared with sib-pair linkage approaches. This chapter will begin with an emphasis on association mapping, and in particular, large-scale, GWAS, as several high-profile association-based studies are currently making headlines. Large-scale association studies of unrelated cases and controls have reported and replicated gene discoveries for important diseases such as diabetes [2-4], breast cancer [5], smoking [6-9] and lung cancer [8,[10][11][12].
Genomics: Essential M ethods
Edite d by Mike S ta r ke y a nd R a mna th Ela swa r a pu