Optimal designs for estimating penetrance of rare mutations of a disease-susceptibility gene.
Many clinical decisions require accurate estimates of disease risks associated with mutations of known disease-susceptibility genes. Such risk estimation is difficult when the mutations are rare. We used computer simulations to compare the performance of estimates obtained from two types of designs based on family data. In the first (clinic-based designs), families are ascertained because they meet certain criteria concerning multiple disease occurrences among family members. In the second (population-based designs), families are sampled through a population-based registry of affected individuals called probands, with oversampling of probands whose families are more likely to segregate mutations. We generated family structures, genotypes, and phenotypes using models that reflect the frequencies and penetrances of mutations of the BRCA1/2 genes. We studied the effects of risk heterogeneity due to unmeasured, shared risk factors by including risk variation due to unmeasured genotypes of another gene. The simulations were chosen to mimic the ascertainment and selection processes commonly used in the two types of designs. We found that penetrance estimates from both designs are nearly unbiased in the absence of unmeasured shared risk factors, but are biased upward in the presence of such factors. The bias increases with increasing variation in risks across genotypes of the second gene. However, it is small compared to the standard error of the estimates. Standard errors from population-based designs are roughly twice those from clinic-based designs with the same number of families. Using the root-mean-square error as a measure of performance, we found that in all instances, the clinic-based designs gave more accurate estimates than did the population-based designs with the same numbers of families. Rough variance calculations suggest that clinic-based designs give more accurate estimates because they include more identified mutation carriers.
Gong
Gail
G
Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California 94305-5405, USA.
Whittemore
Alice S
AS
D000328:Adult; D000368:Aged; D015982:Bias; D001943:Breast Neoplasms / Q000453:epidemiology / Q000235:genetics*; D005260:Female; D019398:Genes, BRCA1; D024522:Genes, BRCA2; D020022:Genetic Predisposition to Disease; D005838:Genotype; D006801:Humans; D008875:Middle Aged; D009154:Mutation; D010375:Pedigree; D019683:Penetrance; D010641:Phenotype; D015995:Prevalence; D018570:Risk Assessment; D012307:Risk Factors