title: Discovery of cancer susceptibility genes: study designs, analytic approaches, and trends in technology.
abstract: Determining the genetic causes of cancers has immense public health benefits, ranging from prevention to earlier detection and treatment of disease. Although a number of cancer susceptibility genes have been successfully identified, design and analytic issues remain that challenge the current paradigm of gene discovery. Some examples are the definition and measurement of cancer phenotype, the use of intermediate end points, the choice of sample (e.g., affected relative pairs versus large extended pedigrees), the choice of analytic method [e.g., parametric logarithm of the odds (LOD) score method versus model-free methods], and the influence of gene-environment interaction on linkage analysis. Furthermore, association methods, based on either the traditional case-control study design or family-based controls, are popular choices to evaluate candidate genes or screen for linkage disequilibrium. Finally, the study design and analytic methods for gene discovery are determined to some extent by what genomic technology is feasible within the laboratory. Many of the main issues related to gene discovery, as well as trends in genomic technology that will impact on gene discovery, are discussed from the perspective of their strengths and weaknesses, pointing to areas in need of further work.
authors.affiliation: Department of Health Sciences Research and Medical Genetics, Mayo Clinic/Mayo Foundation, Rochester, MN 55905, USA. schaid@mayo.edu
authors.forename: D J
authors.initials: DJ
authors.lastname: Schaid
authors.forename: K
authors.initials: K
authors.lastname: Buetow
authors.forename: D E
authors.initials: DE
authors.lastname: Weeks
authors.forename: E
authors.initials: E
authors.lastname: Wijsman
authors.forename: S W
authors.initials: SW
authors.lastname: Guo
authors.forename: J
authors.initials: J
authors.lastname: Ott
authors.forename: C
authors.initials: C
authors.lastname: Dahl
mesh_terms: D005796:Genes; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D016571:Neural Networks, Computer; D010641:Phenotype; D012107:Research Design