Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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.

Original publication

DOI

10.1002/gepi.21620

Type

Journal article

Journal

Genetic epidemiology

Publication Date

04/2012

Volume

36

Pages

274 - 291

Addresses

Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom.

Keywords

EMBRACE Investigators, kConFab Investigators, Humans, Breast Neoplasms, Ovarian Neoplasms, BRCA1 Protein, BRCA2 Protein, Likelihood Functions, Risk Assessment, Cohort Studies, Heterozygote, Mutation, Polymorphism, Single Nucleotide, Models, Genetic, Computer Simulation, Adult, Aged, Middle Aged, Female, Young Adult, Genetic Testing