Assessing Environmental Modifiers of Disease Risk Associated with Rare Mutations

Abstract

Objective: As disease-predisposing mutations are increasingly identified, there is growing need to assess the effects of lifestyle and environmental factors on disease risks in mutation carriers. Such assessment is difficult when the mutations are rare and evaluating them in large population samples is costly. Methods: This paper describes four study designs for evaluating the effects of environmental exposures in carriers of rare disease-predisposing mutations. Results: The strengths and weaknesses of the designs are assessed, and strategies for analyzing the data obtained from such designs are considered. Conclusions: When exposure effects in noncarriers are well-established and exposure is independent of carrier status in the population of disease-free controls, the case-only design provides a feasible and efficient method for inferring effects in carriers. When exposure effects in noncarriers are not well established, the most feasible design options are those that compare exposures in carrier cases to either untyped controls or to carrier controls. These two designs have complementary strengths and weaknesses; thus inferences are stronger when measures of association estimated using the two designs are consistent.

Modifiers of breast and ovarian cancer risks for BRCA1 and BRCA2 mutation carriers

“…In line with the established associations for the general population, breast feeding for at least 1 year has been found to be protective (RR: 0.50-0.68) (Jernstrom et al 2004, Andrieu et al 2006, Gronwald et al 2006a, Kotsopoulos et al 2012a, as has later age at menarche (Chang-Claude et al 2007, Kotsopoulos et al 2005, 2012a, (RR: 0.91 per year; Kotsopoulos et al 2012a). Later age at first fullterm pregnancy has also consistently been reported to be associated with reduced risk of breast cancer for BRCA1 mutation carriers (Andrieu et al 2006, Milne et al 2010b, Lecarpentier et al 2012 (RR: 0.65 for age ≥30 years vs <30 years; Friebel et al 2014), which is in contrast to what is known about the association with risk for overall breast cancer in the general population.…”

“…The optimal study design for studying factors that modify cancer risks is a prospective cohort in which unaffected mutation carriers are followed up over time to observe prospectively who goes on to develop cancer. This study design overcomes issues of ascertainment, recall and testing bias (Antoniou et al 2005a, Whittemore 2007, Heemskerk-Gerritsen et al 2015b, but many years of follow-up are required for a sufficient number of incident cancer cases to occur to obtain adequately precise risk estimates. Furthermore, a large fraction of mutation carriers opt for risk-reducing surgery and hence are removed from the 'at-risk' cohort.…”

“…Therefore, the sampling of mutation carriers is not random with respect to their disease status and standard methods of analysis can yield biased relative risk estimates (Antoniou et al 2005a, Barnes et al 2012. A number of different analytical approaches have been proposed to adjust for the sampling frame (Antoniou et al 2005a, Whittemore 2007, Barnes et al 2012, and these have been applied in several, but not all, large-scale association analyses.…”

Common genetic variants and cancer risk in Mendelian cancer syndromes

“…Study designs for evaluating environmental exposures have been extensively reviewed elsewhere [54]. Although some of the issues are also applied in the study of modifier genes, these pose additional problems.…”

Genes, Environment, Health, and Disease: Facing up to Complexity

“…authors invited to contribute to this issue, including: strategies for detecting associations with interacting genes [17] or interacting genetic and environmental factors [18] that have minimal apparent influence on their own; pursuit of gene ! environment interactions to detect marginal genetic associations [19] ; study designs for identifying environmental modifiers of rare mutations [20] ; impact of genotype misclassification on detection of gene ! environment interactions [21] ; assumption-free analytic methods for detecting gene !…”