Effects of violations of assumptions on likelihood methods for estimating the penetrance of an autosomal dominant mutation from kin-cohort studies
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abstract
Struewing et al. (1997) used the kin-cohort design to estimate the risk of breast cancer in women with autosomal dominant mutations in the genes BRCA1 and BRCA2. In this design, a proband volunteers to be genotyped and then reports the disease history (phenotype) of his or her first-degree relatives. Gail et al. (1999) developed maximum likelihood estimation of parameters for autosomal dominant genes with the kin-cohort design. In this paper we examine the effects of violations of key assumptions on likelihood-based inference. Serious overestimates of disease risk (penetrance) and allele frequency result if people with affected relatives tend to volunteer to be probands more readily than people without affected relatives. Penetrance will be underestimated if probands fail to report all the disease present among their relatives, and serious overestimates of penetrance and allele frequency can result if probands give false positive reports of disease. Sources of familial disease aggregation other than the gene under study result in overestimates of the penetrance in mutation carriers, underestimates of penetrance in non-carriers, and overestimates of allele frequency. Unless sample sizes are quite large, confidence intervals based on the Wald procedure can have subnominal coverage; limited numerical studies indicate that likelihood ratio-based confidence intervals perform better.
