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© Research
Publication : Epidemiology (Cambridge, Mass.)

Variation in predictive ability of common genetic variants by established strata: the example of breast cancer and age

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in Epidemiology (Cambridge, Mass.) - 01 Jan 2015

Aschard H, Zaitlen N, Lindström S, Kraft P

Link to Pubmed [PMID] – 25380502

Epidemiology 2015 Jan;26(1):51-8

BACKGROUND: Recent studies of breast cancer and common genetic markers have failed to identify pervasive gene-gene and gene-environment interactions. Theoretical considerations also suggest that the contribution of modest interactions to risk discrimination in the general population is likely small. However, the clinical utility of common breast cancer risk markers may nonetheless differ across strata defined by known risk factors, such as age.

METHODS: We examined the age-specific per-allele odds ratios of 15 common single nucleotide polymorphisms (SNPs) found to be associated with breast cancer in 1142 breast cancer cases and 1145 controls from the Nurses’ Health Study. We calculated the age-specific discriminatory ability of risk models incorporating these SNPs. We then conducted simulation studies to explore how hypothetical underlying genetic models may fit the observed results.

RESULTS: Although all individual SNP-by-age interactions were modest, we found a negative interaction effect between age and a genetic risk score defined by the sum of risk alleles (P = 0.04). We also observed a decrease in discriminatory ability, as measured by the area under the curve (AUC), of the SNPs with age (P = 0.04). Simulation studies revealed models where the AUC can differ by strata defined by a risk factor without the presence of interactions; however, our study suggests that the observed differences in AUC are explained by the age-specific effect of the SNPs.

CONCLUSION: The identification of risk factors that alter the effect of multiple genetic variants can help to explain the genetic architecture of multifactorial diseases and identify subgroups of persons who may benefit from genetic screening.

https://www.ncbi.nlm.nih.gov/pubmed/25380502