title: Polygenic inheritance of breast cancer: Implications for design of association studies.
abstract: Susceptibility to breast cancer is likely to be the result of susceptibility alleles in many different genes. In particular, one segregation analysis of breast cancer suggested that disease susceptibility in noncarriers of BRCA1/2 mutations may be explicable in terms of a polygenic model, with large numbers of susceptibility polymorphisms acting multiplicatively on risk. We considered the implications for such a model on the design of association studies to detect susceptibility polymorphisms, in particular the efficacy of utilizing cases with a family history of the disease, together with unrelated controls. Relative to a standard case-control association study with cases unselected for family history, the sample size required to detect a common disease susceptibility allele was typically reduced by more than twofold if cases with an affected first-degree relative were selected, and by more than fourfold if cases with two affected first-degree relatives were utilized. The relative efficiency obtained by using familial cases was greater for rarer alleles. Analysis of extended families indicated that the power was most dependent on the immediate (first-degree) family history. Bilateral cases may offer a similar gain in power to cases with two affected first-degree relatives. In contrast to the strong effect of family history, varying the ages at diagnosis of the cases across the range of 35-65 years did not strongly affect the power to detect association. These results indicate that association studies based on cases with a strong family history, identified for example through cancer genetics clinics, may be substantially more efficient than population-based studies.
authors.affiliation: Cancer Research UK, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
authors.forename: Antonis C
authors.initials: AC
authors.lastname: Antoniou
authors.forename: Douglas F
authors.initials: DF
authors.lastname: Easton
mesh_terms: D000367:Age Factors; D000465:Algorithms; D001943:Breast Neoplasms / Q000453:epidemiology / Q000235:genetics*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D020412:Multifactorial Inheritance; D010375:Pedigree; D012107:Research Design; D012307:Risk Factors