CORRESPONDENCE

Re: On the Use of Familial Aggregation in Population-Based Case Probands for Calculating Penetrance

Alice S. Whittemore, Gail Gong

Affiliation of authors: Stanford University School of Medicine, Stanford, CA.

Correspondence to: Alice S. Whittemore, Ph.D., Stanford University School of Medicine, Department of Health Research and Policy, Redwood Bldg., Rm. T204, Stanford, CA 94305–5405 (e-mail: alicesw{at}stanford.edu).

Breast cancer risks in BRCA1 and BRCA2 gene mutation carriers may vary with other modifying genes or personal attributes. Begg (1) noted that such heterogeneity causes upward bias in risk estimates based on cancer occurrence in families of population-based samples of case patients with breast cancer. We argue that this bias is small compared with the standard errors of the estimates. Thus, the large variability in risk estimates across studies does not appear to be caused by their biases but rather by the large standard errors of their estimates. This assertion is supported both by the data reviewed by Begg and by our computer simulations, as we discuss below. Our simulations also show that estimates from multiple-case families are more precise than those from families of population-based case patients.

Table 1Go shows results from the four studies reviewed by Begg that give estimates and 95% confidence intervals (CIs) for breast cancer risk among carriers of mutations in BRCA1 or BRCA2. The CIs are wide, reflecting the large standard errors of the risk estimates. We used the CIs in Table 1Go to calculate a variance for each risk estimate in the table and then used the estimates and their variances to test for differences in risk across the three studies with data for each gene. We found no evidence that risk differs across studies, which does not support Begg's hypothesis that some are more biased than others.


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Table 1. Population-based estimates and 95% confidence intervals (CIs) of breast cancer risk by age 70 years among carriers of BRCA1 and BRCA2 mutations
 
To investigate the relative magnitudes of bias and standard error in risk estimates from families of population-based samples of case patients with breast cancer, we generated data from 50 population-based synthetic studies. Each such study consisted of breast cancer data for the first-degree female relatives of 1600 population-based case patients with breast cancer who were typed for BRCA mutation status. To enrich the sample for carriers, we oversampled case patients with at least one affected first-degree relative. Specifically, we sampled case patients to obtain 800 with a family history of breast cancer and 800 without such a family history. We assumed various levels of heterogeneity in carrier risks from unmeasured genotypes of another modifying gene. We chose the parameters so that the mean risk by age 70 years among BRCA mutation carriers was 69%, regardless of the extent of heterogeneity among them. These findings, which are reported in detail elsewhere (6), provide surprising results about the extent of bias and standard error to be expected.

Table 2Go shows three risk ranges among BRCA mutation carriers corresponding to three sets of assumptions about risk heterogeneity among carriers as a result of the modifying gene. The lower and upper risks in each range are, respectively, the risks among noncarriers and carriers of the modifying gene. We approximated the bias in mean risk among relatives of case patients as half of the bias given by formula 2 of Begg (1), based on the arguments of Wacholder et al. (7). We then used this approximation to calculate the mean risk among the relatives. The calculated bias is small. Even when BRCA mutation penetrances in the population range from 52% to 98%, the bias is only 3.5%. In contrast, the standard deviations of risk estimates across the 50 simulated studies were much larger, giving 95% CIs that span some 25–30 percentage points.


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Table 2. Simulated breast cancer risk and 95% confidence intervals (CIs) by age 70 years in heterogenous groups of BRCA mutation carriers
 
The results in Tables 1 and 2GoGo indicate that the uncertainty in penetrance estimates obtained from families of population-based case patients substantially outweighs the bias. This uncertainty presents a formidable barrier to meaningful risk estimates as the basis for preventive decisions. Penetrance estimates obtained from multiple-case families ascertained in linkage studies or high-risk clinics also are biased upward. However, our simulations (6) suggest that this bias is no greater than that of population-based studies. More importantly, we found that the multiple-case families yield considerably more precise penetrance estimates than those obtained from population-based studies. The greater precision from multiple-case families reflects their greater number of BRCA mutation carriers per family. Thus, results from multiple-case families may be more informative than those from families of population-based case patients.

REFERENCES

1 Begg CB. On the use of familial aggregation in population-based case probands for calculating penetrance. J Natl Cancer Inst 2002;94:1221–6.[Abstract/Free Full Text]

2 Thorlacius S, Struewing JP, Hartge P, Olafsdottir GH, Sigvaldason H, Tryggvadottir L, et al. Population-based study of risk of breast cancer in carriers of BRCA2 mutation. Lancet 1998;352:1337–9.[CrossRef][Medline]

3 Anglian Breast Cancer Study Group. Prevalence and penetrance of BRCA1 and BRCA2 mutations in a population-based series of breast cancer cases. Br J Cancer 2000;83:1301–8.[CrossRef][Medline]

4 Antoniou AC, Gayther SA, Stratton JF, Ponder BA, Easton DF. Risk models for familial ovarian and breast cancer. Genet Epidemiol 2000;18:173–90.[CrossRef][Medline]

5 Satagopan JM, Offit K, Foulkes W, Robson ME, Wacholder S, Eng CM, et al. The lifetime risks of breast cancer in Ashkenazi Jewish carriers of BRCA1 and BRCA2 mutations. Cancer Epidemiol Biomarkers Prev 2001;10:467–73.[Abstract/Free Full Text]

6 Gong G, Whittemore AS. Optimal designs for estimating penetrance of rare mutations of disease-susceptibility genes. Genet Epidemiol. In press 2003.

7 Wacholder S, Hartge P, Struewing JP, Pee D, McAdams M, Brody L, et al. The kin-cohort study for estimating penetrance. Am J Epidemiol 1998;148:623–30.[Abstract]



             
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