There is considerable evidence indicating that disease risk in carriers of high-risk mutations (e.g. BRCA1 and BRCA2) varies by other genetic factors. Such mutations tend to be rare in the population and studies of genetic modifiers of risk have focused on sampling mutation carriers through clinical genetics centres. Genetic testing targets affected individuals from high-risk families, making ascertainment of mutation carriers non-random with respect to disease phenotype. Standard analytical methods can lead to biased estimates of associations. Methods proposed to address this problem include a weighted-cohort (WC) and retrospective likelihood (RL) approach. Their performance has not been evaluated systematically. We evaluate these methods by simulation and extend the RL to analysing associations of two diseases simultaneously (competing risks RL-CRRL). The standard cohort approach (Cox regression) yielded the most biased risk ratio (RR) estimates (relative bias-RB: -25% to -17%) and had the lowest power. The WC and RL approaches provided similar RR estimates, were least biased (RB: -2.6% to 2.5%), and had the lowest mean-squared errors. The RL method generally had more power than WC. When analysing associations with two diseases, ignoring a potential association with one disease leads to inflated type I errors for inferences with respect to the second disease and biased RR estimates. The CRRL generally gave unbiased RR estimates for both disease risks and had correct nominal type I errors. These methods are illustrated by analyses of genetic modifiers of breast and ovarian cancer risk for BRCA1 and BRCA2 mutation carriers.
© 2012 Wiley Periodicals, Inc.