Abstract
Candidate polymorphism-based genetic epidemiological studies have yielded little success in the search for low-penetrance breast cancer susceptibility genes. The lack of progress is partially due to insufficient coverage of genomic regions with genetic markers, as well as economic constraints, limiting both the number of genetic targets and the number of individuals being studied. Recent rapid advances in high-throughput genotyping technology and our understanding of genetic variation patterns across the human genome are now revolutionizing the way in which genetic epidemiological studies are being designed and conducted. Genetic epidemiological studies are quickly progressing from candidate gene studies to comprehensive pathway investigation and, further, to genomic epidemiological studies where the whole human genome is being interrogated to identify susceptibility alleles. This paper reviews the evolving approaches in the search for low-penetrance breast cancer susceptibility gene variants and discusses their potential promises and pitfalls.
Highlights
History is a well-established risk factor for breast cancer
The remaining familial risk is likely to be explained by a polygenic model where breast cancer susceptibility is conferred by a large number of lowpenetrance alleles
Further progress in the search for genetic determinants of breast cancer likely lies in the identification of Candidate polymorphism analysis
Summary
History is a well-established risk factor for breast cancer. Breast cancer risk is typically increased by two- to three-fold in first-degree relatives of affected individuals. Limited progress has been made by such candidate polymorphism-based genetic epidemiological studies in identifying low-penetrance risk alleles for breast cancer. Since breast carcinogenesis is a complex and still only partially understood process, it is likely that many important genes are overlooked in candidate gene studies Such a limitation can only be overcome by genomic epidemiological studies where no prior biological hypotheses are assumed and the entire human genome is targeted for identifying genetic variation associated with breast cancer susceptibility. Despite all the promises held by genome-wide association studies, if such studies are not handled properly, large numbers of false positive results will be generated and published This will result in a significant drain in resources invested in studies with slim prior probabilities of yielding significant findings, which would slow down the search for breast cancer susceptibility genes. Such study samples will be derived from large prospective studies
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