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JNCI | Editorials 973 The completion of nine large genome-wide association studies ( 1 – 9 ) has focused attention on single-nucleotide polymorphisms (SNPs) as risk factors for breast cancer. Despite considerable progress, the scientific yield, practical application, and commercial exploitation of SNP profiling for disease risk remain topics of debate ( 10 – 13 ). In this context, the study by Milne et al. ( 14 ) offers an excellent example of both the promise and challenges of current genetic epidemiological approaches to SNP genotyping for breast cancer risk. Drawing on an international consortium of 25 centers contributing DNA from more than 30 000 breast cancer case patients and 35 000 healthy control subjects, these investigators sought to confi rm if an SNP that was not detected in their initial scan was associated with risk for breast cancer. They confi rmed that this SNP on the long arm of chromosome 2 was associated with a 1.12-fold risk for invasive breast cancer in white women of European origin. Unlike the initial report ( 2 ), they found an association in both estrogen receptor – negative and estrogen receptor – positive breast cancer. Although highly statistically signifi cant ( P = 10 19 ), the clinical validity and biological implications of these fi ndings, and those of many genome-wide association studies, require additional research. For some readers, the fi rst hurdle in interpreting this, and other, genome-wide association studies relates to the statistical indexes of risk that are used. For example, the aggregate “per-allele” risk of 1.12 cited by Milne et al. ( 14 ) is derived from a log additive model. Although it is the best way to compare risks of different SNPs in different studies (see Table 1 ), per-allele risk is not the measure of greatest interest to patients or clinicians. For women homozygous for rs13387042, the risk of breast cancer increases 28% from baseline (odds ratio [OR] = 1.28). For heterozygotes, the risk increases only 9% (OR = 1.09). The discriminatory value of the SNP may therefore be limited because the “at-risk” allele frequency of rs13387042 is so high (51%). Other derived measures of risk in studies of SNPs are the “excess familial risk” and the “population attributable risk” (PAR%), also called the “population attributable fraction” ( 15 ). Both of these indexes refl ect measures of the population frequency of the variant, as well as the relative risk (or odds ratio). Excess familial risk is the amount of hereditary cancer risk in a fi rst-degree relative of a cancer patient that is due to the genetic variant in question. For very low penetrance SNPs, the amount of familial risk accounted for by the SNP is very low; only 0.6% for rs13387042 as reported by Milne et al. ( 14 ). Another measure of risk commonly used by epidemiologists, the PAR%, can be calculated to be 5.8% for Breast Cancer Single-Nucleotide Polymorphisms: Statistical Significance and Clinical Utility