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Journal of Clinical Oncology, Vol 24, No 6 (February 20), 2006: pp. 863-871 © 2006 American Society of Clinical Oncology. DOI: 10.1200/JCO.2005.03.6772 Characterization of BRCA1 and BRCA2 Mutations in a Large United States SampleFrom the Departments of Environmental Health Sciences and Biostatistics, Johns Hopkins Bloomberg School of Public Health; The Sidney Kimmel Comprehensive Cancer Center and the Department of Biostatistics, Johns Hopkins University, Baltimore, MD; Departments of Biostatistics and Bioinformatics and Community and Family Medicine, Duke University, Durham, NC; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA; The Massachusetts General Hospital, Boston, MA; Translational Medicine and Genetics, GlaxoSmithKline, West Chester, PA; Preventive Oncology and Medical Oncologist, Toronto Sunnybrook Regional Cancer Centre, Toronto, Ontario, Canada; Departments of Medicine and Molecular and Human Genetics, Baylor College of Medicine; Departments of Epidemiology, Molecular Genetics, and Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston; Departments of Pediatrics and Surgery, University of Texas Southwestern, Dallas, TX; Georgetown University Lombardi Cancer Center, Washington, DC; and Huntsman Cancer Center, University of Utah, Salt Lake City, UT Address reprint requests to Sining Chen, MD, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, W7041, Baltimore, MD 21205; e-mail: sichen{at}jhsph.edu
PURPOSE: An accurate evaluation of the penetrance of BRCA1 and BRCA2 mutations is essential to the identification and clinical management of families at high risk of breast and ovarian cancer. Existing studies have focused on Ashkenazi Jews (AJ) or on families from outside the United States. In this article, we consider the US population using the largest US-based cohort to date of both AJ and non-AJ families. METHODS: We collected 676 AJ families and 1,272 families of other ethnicities through the Cancer Genetics Network. Two hundred eighty-two AJ families were population based, whereas the remainder was collected through counseling clinics. We used a retrospective likelihood approach to correct for bias induced by oversampling of participants with a positive family history. Our approach takes full advantage of detailed family history information and the Mendelian transmission of mutated alleles in the family. RESULTS: In the US population, the estimated cumulative breast cancer risk at age 70 years was 0.46 (95% CI, 0.39 to 0.54) in BRCA1 carriers and 0.43 (95% CI, 0.36 to 0.51) in BRCA2 carriers, whereas ovarian cancer risk was 0.39 (95% CI, 0.30 to 0.50) in BRCA1 carriers and 0.22 (95% CI, 0.14 to 0.32) in BRCA2 carriers. We also reported the prospective risks of developing cancer for cancer-free carriers in 10-year age intervals. We noted a rapid decrease in the relative risk of breast cancer with age and derived its implication for genetic counseling. CONCLUSION: The penetrance of BRCA mutations in the United States is largely consistent with previous studies on Western populations given the large CIs on existing estimates. However, the absolute cumulative risks are on the lower end of the spectrum.
Germline deleterious mutations in the BRCA1 (MIM 113705 [OMIM] ) and BRCA2 (MIM 600185 [OMIM] ) genes convey a significantly elevated risk of developing breast and ovarian cancer and of developing these cancers at earlier ages. Such mutations are considered to be responsible for approximately 40% of familial breast cancer and for the majority of familial ovarian cancers1-3 and account for 5% to 20% of the total percentage of breast and ovarian cancers.4-6 The screening, counseling, testing, and clinical management of families at high risk for these mutations relies on an accurate characterization of the BRCA1 and BRCA2 genes and particularly on the evaluation of the penetrance (the risk of developing cancer in carriers) and prevalence of mutations in these genes.7-9 Earlier studies from the Breast Cancer Linkage Consortium were conducted on families with four or more members having cancer.10-13 These studies reported a breast cancer risk of approximately 80% at age 70 years for mutation carriers. There is a potential for these estimates to be biased upwards because of the use of a logarithm of the odds (LOD) score-maximization approach while ascertaining families with high LOD scores. Later studies have focused attention on population-based samples or case-series cohorts.14-18 Struewing et al14 led a population-based study that obtained participants through Ashkenazi Jewish (AJ) volunteers in the Washington, DC, area. Hopper et al15 estimated breast cancer risk for carriers of a predefined set of mutations through early-onset patients from a population-based Australian cancer registry. Satagopan et al16,19 conducted two New York hospital–based case-control studies that estimated breast and ovarian cancer risks from AJ patients. Antoniou et al17 assembled information from 22 studies that were based on unselected index cases (ie, the family was ascertained through an individual with breast or ovarian cancer, who was sampled independently of family history). This also included data from the United Kingdom–based Anglian Breast Cancer Study.20 More recently, King et al18 derived penetrance by genotyping 1,008 New York hospital–based female AJ breast cancer patients and all of their female relatives. Risk estimates from these studies have a broad range. For example, Hopper et al15 found a cumulative breast cancer risk for BRCA1 or BRCA2 carriers at age 70 years of 36%, whereas King et al18 reported a risk of 71%. To obtain a sufficient number of carriers with such population-based designs, studies are usually carried out on subpopulations that segregate founder mutations at higher frequencies, such as the AJ population. In the general population, where mutation prevalence is approximately 10% of that in AJs, it would be much more costly to arrive at estimates with similar precision using population-based designs. In this article, we study the largest set to date of detailed family histories from US families, including both AJ and non-AJ families, and provide estimates of breast and ovarian cancer risk in BRCA1 and BRCA2 mutation carriers. Families were assembled through the National Cancer Institute's Cancer Genetics Network (CGN) in eight centers across the United States.
Families Using the CGN, we created a multicenter database for the study of the BRCA genes in the US population. Eight centers within the CGN (Table 1) provided detailed family history information. There are a total of 1,948 families in the database. Of these, 676 are AJs, and 1,272 are not. Most families were acquired through high-risk counseling clinics. Although criteria for inclusion varied across centers, most families have a positive family history of breast or ovarian cancer. On average, there were more than three diagnoses of breast or ovarian cancer per family. As an exception to the retrospective family history–based sampling scheme, the 282 AJ families recruited by the Baylor College of Medicine were population based.
Family history information included whether the family was of AJ origin and, for the counselee and his or her first- and second-degree relatives, the following variables: affection status (cancer of breasts and/or ovaries), age at onset if affected, and age of last follow-up or death if not affected. For each family, genetic test results were reported to us for one individual, who was called the counselee. Table 2 lists the characteristics of the counselees.
Mutation Analysis Mutation analysis had been previously performed on all 1,948 counselees on one or both of the genes. A total of 283 BRCA1 mutations carriers and 143 BRCA2 mutations carriers were identified. An array of testing techniques were used, including direct sequencing, targeted sequencing for AJ founder mutations, allele-specific oligohybridization, denaturing gradient gel electrophoresis, denaturing high-pressure liquid chromatography, and protein truncation test. The number of families tested with each technique or combination of techniques are listed in Table 3. Because different germline testing techniques have different sensitivities, we accounted for mutation analysis errors using the sensitivity estimates listed in Table 3. We discuss in the Appendix how those estimates were derived and used.
Statistical Methods We use a Mendelian retrospective likelihood approach21,22 that takes advantage of all available family history information while correcting for ascertainment bias from oversampling families with multiple patients. This is similar in spirit to the maximum LOD score approach, whereas the likelihood considers joint estimation of the risk of breast and ovarian cancers in both BRCA1 and BRCA2 carriers as well as relative prevalence parameters.
Penetrance Definition and Parameterization
Prospective and Retrospective Likelihood Components
Allele Frequencies The allele frequencies in the non-AJ population have been studied using case-series data or multiple-patient families. Ford et al13 gave a 95% CI of 0.0002 to 0.0010 in BRCA1; Andersen30 estimated BRCA2 mutated allele frequency at 0.0002; and Antoniou et al31 used segregation analysis to arrive at 0.00042 for BRCA1 and 0.00054 for BRCA2 under the major genes model, although estimates are slightly different under different models. We fixed the combined allele mutation frequency at 0.0008, which is the current default of the genetic counseling software BRCAPRO,22 whereas we estimated the proportion of BRCA1 mutations.
Statistical Computing
Genetic Counseling Implications: Bayes Factor
The other key element is the prior odds. These reflect the mutation prevalence, which, however, affects only weakly the BF. Thus, the same family history may yield a BF of approximately 50 for counselees in both AJ and non-AJ families. Although this is strong evidence towards a mutation in both types of counselees, the resulting carrier probability is 0.57 for the AJ counselee and 0.07 for the non-AJ counselee. This difference is a result of the different mutation prevalence, although the severity of family history is the same.
To illustrate the implications of penetrance in calculating the BF, we present the case in which the counselee seeks genetic counseling immediately after being diagnosed with cancer and is not aware of the disease history of any relatives. The BF in this case is as follows:
To counsel a person with a family history, we derive the BF based on her particular family history. For a simple example, a healthy counselee whose mother had a breast cancer diagnosis at age 35 years would be calculated as follows:
The BF is different from the RR in that it is a ratio of densities and not hazards. The relationship between BF and RR can be expressed as follows:
The age-specific cumulative risks, or penetrances, are listed in Table 4. The estimated breast cancer penetrances at age 70 years were 0.46 (95% CI, 0.39 to 0.54) in BRCA1 carriers and 0.43 (95% CI, 0.36 to 0.51) in BRCA2 carriers, whereas ovarian cancer penetrances at age 70 years were 0.39 (95% CI, 0.30 to 0.50) in BRCA1 carriers and 0.22 (95% CI, 0.14 to 0.32) in BRCA2 carriers. Cancer risks were close to those reported on an Italian population,35 similar to those reported by Satagopan et al16 and Antoniou et al,17 and generally consistent with most past findings given the statistical variances reported. Our study suggests lower absolute ovarian cancer risks among BRCA2 mutation carriers than among BRCA1 mutation carriers. This finding is consistent with other studies.17-19,35
At genetic counseling clinics, it is important to assess the future cancer risks for a cancer-free counselee. We report such risks Figure 1 and Table 5. Risks and associated CIs are provided for every 10-year interval from age 20 to 70 years. A counselee can directly read her prospective risks from one of the curves in this figure and from one row of the table depending on her current age and use them to make clinical decisions such as for prophylactic surgeries.
Table 6 and Figure 2 report our relative risk estimates. For breast cancer, the RRs were high in early ages and rapidly decreased. This trend is confirmed by Antoniou et al17 and partially confirmed by Satagopan et al.16 However, our reported RR was much higher in the 20- to 29-year-old age group, and the decreasing trend was steeper. The availability of a large number of families in our sample has allowed us to estimate this RR more precisely than other studies. For ovarian cancer, RRs were consistently high across age groups and did not have a clear increasing or decreasing trend. The RRs for BRCA2 carriers were lower than for BRCA1 carriers. This is consistent with Satagopan et al19 and Antoniou et al.17 There were too few participants of ages 20 to 29 to precisely estimate risk for that age interval.
In Table 7, we reported the Bayes Factor (BF), or the ratio of the age-specific risks, as evidence towards carrying a mutation given a diagnosis at various ages. By comparing Table 6 with Table 7, we see that the BF is smaller than the RR and increasingly so at higher ages. For breast cancer at age interval 60 to 69 years, the BFs are not distinguishable from 1. This has significant implications for genetic counseling and for current practices in selecting high-risk individuals and families.
In our penetrance estimation, we pooled the AJ and non-AJ families. We also carried out separate analyses for these two subgroups. The two sets of results were close to each other at both cancer sites and all age intervals; none of the differences was close to being statistically significant. The only other study with both AJ and non-AJ participants17,36 also did not notice a significant difference in penetrance between the two groups. Without strong evidence of a difference, we can achieve better accuracy by pooling the two groups. As expected, the result of the pooled analysis is similar to both sets of estimates from the separate analyses, with improved accuracy.
In this article, we provided an analysis of the largest US collection of both AJ and non-AJ families who have been ascertained for mutation analysis of BRCA1 and BRCA2. Because of our large sample size, the estimates that we provide are more accurate than those provided by previous studies. Several findings have direct impact on counseling and screening. First, our study reported the future risks of developing cancer for cancer-free mutation carriers. Risks given in this form can be immediately used by a genotyped individual to make preventative decisions for herself and her relatives. They can also be used in establishing cancer screening recommendations at various ages for mutation carriers. Second, our study found the RRs and BFs of breast cancer to be high in early ages, and then they rapidly decrease. This decreasing trend is the result of both a decreasing risk for carriers and an increasing risk for noncarriers. It is widely believed and used informally in genetic counseling practice that early-onset breast cancers are a strong indicator for germline mutations. Our study provided concrete quantitative evidence for this hypothesis. The high BFs that we obtained for the age groups from 20 to 49 years clearly indicate the importance of considering mutation analysis in counselees presenting with breast cancer in this age range. In the age interval of 60 to 69 years, the BF for breast cancer cases decreased to nearly 1, suggesting only weak evidence in favor of a mutation for women diagnosed with cancer in this age range. Last, we found consistently high RRs and high BFs for ovarian cancer for all ages. This is in accordance with the literature and empirical observation that the presence of any ovarian cancer diagnosis is strong evidence towards an inherited mutation, regardless of the age of diagnosis.11 Prophylactic oophorectomy is widely accepted as a risk reduction procedure in women who are found to be BRCA1/2 mutation carriers. Bilateral prophylactic oophorectomy (BPO) is reported to cut the risk of ovarian cancer by 96% (95% CI, 84% to 99%) and the risk to breast cancer by 53% (95% CI, 23% to 71%) among carriers.37 In the CGN centers, 27% to more than 60% of unaffected women who received a positive mutation analysis result underwent prophylactic oophorectomy within a year after the mutation testing38,39; this includes centers where all carrier women received a BPO unless they were less than 30 years old and/or had a desire to have children. By combining the results of our study and the risk reduction estimates just given, we derived the future risks for an asymptomatic woman who underwent BPO, which are reported in Table 8.
Ignoring the status of BPO leads to the underestimation of penetrance in prospective studies.40 However, this is unlikely to affect our analysis because, at the time the family history was collected, the probands and their relatives had not yet been genotyped. The rate of oophorectomy is only high for those women who are known carriers; the rate is low among women who do not yet know their genotype.41 Although our study includes mostly families having a strong history of breast or ovarian cancer, by using a retrospective likelihood, we can appropriately account for this sampling design, and in fact, we obtain penetrance estimates that are similar to those obtained from population-based designs. In addition, this approach allows more powerful estimates for probabilities of events that are rare in population-based samples, such as early-onset breast cancers. With the use of a Mendelian likelihood, we take advantage of all biologic information in the family history and are able to obtain penetrance estimates with tight CIs. A limitation of the likelihood formulation that we used is the lack of a parameter to model residual genetic or familial factors beyond the risks conferred by BRCA1 or BRCA2. If these factors have a strong impact on familial risk of cancer, it is possible that our model could overestimate absolute risks for cancer as a result of BRCA1 or BRCA2. However, King et al18 suggested that heterogeneity in the penetrance among BRCA1/2 carriers is unlikely to be significant. We conducted a sensitivity analysis examining how our estimates change if we use different assumptions on the combined allele frequency of BRCA1 and BRCA2 deleterious variants. The combined frequency is strongly negatively correlated with breast cancer risk for BRCA1 mutation carriers. Our statistical methodology has taken extensive care in accounting for family ascertainment issues, site heterogeneity, and sensitivities of mutation analysis techniques (see Appendix). Yet, because of the scope and computational complexity of the project, some simplifying assumptions had to be made. An important one is that testing results are conditionally independent of the family history and other parameters given the genotype. This could be violated in practice if there are additional sources of familial aggregation.42,43 Second, the chosen form of the penetrance function is based on an RR between carriers and noncarriers that is constant within 10-year age intervals. We have also explored more parsimonious parameterization, which gave similar results. We present the nonparametric results to avoid artifacts that may be introduced by specific parameterization. A limitation of our study is the lack of further ethnicity indicators beyond Ashkenazim. Minority groups, such as the African Americans, are often under-represented in breast cancer studies.44,45 Families of African ancestry have recently been shown to have a different mutation spectrum than that of well-studied populations.46 However, the performance of the carrier probability prediction software BRCAPRO is similar among families of different ethnic origins, suggesting similarity in genetic characteristics despite the difference in mutation spectra. In summary, we found that the penetrance of BRCA mutations in the United States is largely consistent with previous studies on Western populations given the large CIs on existing estimates. However, the absolute cumulative risks are on the lower end of the spectrum.13-19,35 Women newly diagnosed with breast or ovarian cancer can directly use our results to understand the implications for their risk of carrying a BRCA1/2 mutation. Asymptomatic women in whom mutations have been identified can use our results to understand their risk of developing breast and ovarian cancer, both before and after prophylactic oophorectomy.
Accounting for mutation analysis errors. Each counselee has been genotyped for carrier status at the BRCA1 and/or BRCA2 locus. Although the test results provide us with valuable information on genotype, we need to account for the fact that genotyping is not always accurate. We use g to denote the possible underlying carrier status, and we use G to denote the set of all possible carrier statuses. The carrier status g is two dimensional because we are concerned with both BRCA1 and BRCA2. We will not discuss individual variants in this article, so each dimension of g takes its value in the binary set G = {0:"Wild type", 1:"Mutated"}. One mild assumption made here is that the test result is conditionally independent of family history and penetrance, given the true genotype.
Now, the retrospective likelihood can be written as follows:
To account for the sensitivity of various testing modalities, the sensitivity component of each tested individual in Expression,1 The mutation analysis sensitivities are derived in a separate manuscript (Iversen et al, submitted for publication). We use the posterior mean from the study (Iversen et al, submitted for publication) for the test sensitivities ß, which combines experimental evidence with a superset of the present data from the CGN (Geisler J, Hatterman-Zogg M, Rathe J, et al: Ovarian cancer brca1 mutation detection: Protein truncation test (PTT) outperforms single strand conformation polymorphism analysis (SSCP). Hum Mutat 18:337-344, 2001; Eng C, Brody L, Wagner T, et al: Interpreting epidemiological research: Blinded comparison of methods used to estimate the prevalence of inherited mutations in BRCA1. J Med Genet 38:824-833, 2001; Andrulis I, Anton-Culver H, Beck J, et al: Comparison of DNA-and RNA-based methods for detection of truncating BRCA1 mutations. Hum Mutat 20:65-73, 2002).
We briefly summarize the combined analysis (Iversen et al, submitted for publication) as follows: for any subject, an array of predicted carrier probabilities (S) given by several available models is provided, in addition to the test result (T) and testing modality (M). The joint distribution of S and T was written in the following two parts: first, a model that predicts the underlying genotype g through a logistic regression on S, and second, a sensitivity component for the test result T given genotype g. The likelihood of the sensitivity parameters ß and logistic regression genotype model coefficients
In this article, we assume that the specificity of all modalities is 100%, meaning that if an allele is wild type, it will not test mutation positive using any of the techniques. This is highly plausible biologically. The values of sensitivities are shown in Table 3. These figures also roughly correspond to the 0.63 (95% CI, 0.51 to 0.77) sensitivity pooling of all testing techniques estimated by Ford et al.13
Mendelian calculations. The genotype component in Expression,1 For each gene, the model calculates the probability that the counselee is a carrier of a deleterious variant. We indicate the carrier status of the counselee by g0, with the subscript 0 signifying the counselee herself. Given a family with M members, we denote by gm, m = 0,...,M the genotype vector of relative m, and by hm m = 0,...,M the relevant phenotypes and ages of onset of relative m. For each relative, hm is a vector that includes information on affection status at relevant cancer sites with age of diagnosis, alive/dead status, and current age or age at death.
 and of the probability of the phenotypes for the whole pedigree given the genotype of the counselee. The latter is then evaluated using the law of total probability by considering explicitly the unobserved genotypes of the relatives:
 is either the probability of being diagnosed at that age or 1 minus the cumulative probability of being diagnosed by that age. This way, both affected and unaffected relatives contribute to the likelihood of the penetrance parameter and that contribution depends on the exact form of the penetrance.
The joint genotype distribution of all relatives conditional on the counselee's genotype The above Mendelian likelihood allows us to make full use of the detailed phenotypic information in the family history through our knowledge about the inheritance of the alleles of interest. The calculation integrates over all possible genotypes of the relatives, so that each relative, whether affected or not, contributes to the likelihood of the penetrance. This makes the Mendelian likelihood a natural and efficient choice for estimating age-specific penetrance at a high resolution.
The authors indicated no potential conflicts of interest.
Supported in part by the National Cancer Institute Cancer Genetics Network. Also supported in part by National Cancer Institute grant Nos. P50CA88843, P50CA62924-05, 5P30 CA06973-39, and R01CA105090 and National Institutes of Health grant No. HL 99-024 and the Hecht Fund (G.P., S.C., T.F.). Work of investigators from Georgetown University was partly supported by Cancer Genetics Network grant No. CA78146-01 and by the Familial Cancer Registry Shared Resource of the Lombardi Comprehensive Cancer Center, which receives partial support from National Institutes of Health grant No. P30-CA-51008. D.F. is supported by Cancer Genetics Network grant No. CA78284. Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.
1. Martin A, Blackwood M, Antin-Ozerkis D, et al: Germline mutations in BRCA1 and BRCA2 in breast-ovarian families from a breast cancer risk evaluation clinic. J Clin Oncol 19:2247-2253, 2001 2. Szabo CI, King MC: Population genetics of BRCA1 and BRCA2. Am J Hum Genet 60:1013-1020, 1997[Medline] 3. Newman B, Millikan RC, King MC: Genetic epidemiology of breast and ovarian cancers. Epidemiol Rev 19:69-79, 1997 4. Claus EB, Schildkraut JM, Thompson WD, et al: The genetic attributable risk of breast and ovarian cancer. Cancer 77:2318-2324, 1996[CrossRef][Medline] 5. Couch FJ, DeShano ML, Blackwood MA, et al: BRCA1 mutations in women attending clinics that evaluate the risk of breast cancer. N Engl J Med 336:1409-1415, 1997 6. Spitzer E, Abbaszadegan M, Schmidt F, et al: Detection of BRCA1 and BRCA2 mutations in breast cancer families by a comprehensive two-stage screening procedure. Int J Cancer 85:474-481, 2000[CrossRef][Medline] 7. Chang-Claude J, Becher H, Caligo M, et al: Risk estimation as a decision-making tool for genetic analysis of the breast cancer susceptibility genes: EC demonstration project on familial breast cancer. Dis Markers 15:53-65, 1999[Medline] 8. Karp S: Clinical management of BRCA1-and BRCA2-associated breast cancer. Semin Surg Oncol 18:296-304, 2000[CrossRef][Medline] 9. Burke W, Daly M, Garber J, et al: Recommendations for follow-up care of individuals with an inherited predisposition to cancer: II. BRCA1 and BRCA2—Cancer Genetics Studies Consortium. JAMA 277:997-1003, 1997 10. Easton DF, Ford D, Bishop DT: Breast and ovarian cancer incidence in BRCA1-mutation carriers. Am J Hum Genet 56:265-271, 1995[Medline] 11. Easton DF, Bishop DT, Ford D, et al: Genetic linkage analysis in familial breast and ovarian cancer: Results from 214 families. Am J Hum Genet 52:678-701, 1993[Medline] 12. Narod S, Ford D, Devilee P, et al: An evaluation of genetic heterogeneity in 145 breast-ovarian cancer families: Breast Cancer Linkage Consortium. Am J Hum Genet 56:254-264, 1995[Medline] 13. Ford D, Easton DF, Stratton M, et al: Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families. Am J Hum Genet 62:676-689, 1998[CrossRef][Medline] 14. Struewing JP, Hartge P, Wacholder S, et al: The risk of cancer associated with specific mutations of BRCA1 and BRCA2 among Ashkenazi -Jews. N Engl J Med 336:1401-1408, 1997 15. Hopper J, Southey M, Dite G, et al: Population-based estimate of the average age-specific cumulative risk of breast cancer for a defined set of protein-truncating mutations in BRCA1 and BRCA2: Australian Breast Cancer Family Study. Cancer Epidemiol Biomarkers Prev 8:741-747, 1999 16. Satagopan J, Offit K, Foulkes W, et al: The lifetime risks of breast cancer in Ashkenazi Jewish carriers of BRCA1 and BRCA2 mutations. Cancer Epidemiol Biomarkers Prev 10:467-473, 2001 17. Antoniou A, Pharoah PDP, Narod S, et al: Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: A combined analysis of 22 studies. Am J Hum Genet 72:1117-1130, 2003[CrossRef][Medline] 18. King MC, Marks JH, Mandell JB: Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2. Science 302:643-646, 2003 19. Satagopan J, Boyd J, Kauff N, et al: Ovarian cancer risk in Ashkenazi Jewish carriers of BRCA1 and BRCA2 mutations. Clin Cancer Res 8:3776-3781, 2002 20. 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 83:1301-1308, 2000[CrossRef][Medline] 21. Kraft P, Thomas DC: Bias and efficiency in family-based gene-characterization studies: Conditional, prospective, retrospective, and joint likelihoods. Am J Hum Genet 66:1119-1131, 2000[CrossRef][Medline] 22. Parmigiani G, Berry DA, Aguilar O: Determining carrier probabilities for breast cancer susceptibility genes BRCA1 and BRCA2. Am J Hum Genet 62:145-158, 1998[CrossRef][Medline] 23. Fay MP, Pfeiffer R, Cronin KA, et al: Age-conditional probabilities of developing cancer. Stat Med 22:1837-1848, 2003[CrossRef][Medline] 24. National Cancer Institute: Surveillance, Epidemiology, and End Results (SEER) Program. http://www-seer.ims.nci.nih.gov 25. Whittemore A, Halpern J: Logistic regression of family data from retrospective study designs. Genet Epidemiol 25:177-189, 2003[CrossRef][Medline] 26. Iversen ES, Chen S: Population-calibrated gene characterization: Estimating age at onset distributions associated with cancer genes. J Am Stat Assoc 100:399-409, 2005[Medline] 27. Roa BB, Boyd AA, Volcik K, et al: Ashkenazi Jewish population frequencies for common mutations in BRCA1 and BRCA2. Nat Genet 14:185-187, 1996[CrossRef][Medline] 28. Oddoux C, Struewing JP, Clayton CM, et al: The carrier frequency of the BRCA2 6174delT mutation among Ashkenazi Jewish individuals is approximately 1%. Nat Genet 14:188-190, 1996[CrossRef][Medline] 29. Fodor FH, Weston A, Bleiweiss IJ, et al: Frequency and carrier risk associated with common BRCA1 and BRCA2 mutations in Ashkenazi Jewish breast cancer patients. Am J Hum Genet 63:45-51, 1998[CrossRef][Medline] 30. Andersen TI: Genetic heterogeneity in breast cancer susceptibility. Acta Oncol 35:407-410, 1996[Medline] 31. Antoniou AC, Pharoah PDP, McMullan G, et al: A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2 and other genes. Br J Cancer 86:76-83, 2002[CrossRef][Medline] 32. Chen S, Wang W, Broman K, et al: BayesMendel: An R environment for Mendelian risk prediction. Stat Appl Genet Mol Biol 3:1-19, 2004 33. Parmigiani G: Modeling in Medical Decision Making. Chichester, United Kingdom, Wiley 2002 34. Murphy EA, Mutalik GS: The application of Bayesian methods in genetic counseling. Hum Hered 19:126-151, 1969 35. Marroni F, Aretini P, D'Andrea E, et al: Penetrances of breast and ovarian cancer in a large series of families tested for BRCA1/2 mutations. Eur J Hum Genet 12:899-906, 2004[CrossRef][Medline] 36. Antoniou AC, Pharoah PPD, Smith P, et al: The BOADICEA model of genetic susceptibility to breast and ovarian cancer. Br J Cancer 91:1580-1590, 2004[Medline] 37. Rebbeck TR, Lynch HT, Neuhausen SL, et al: Prophylactic oophorectomy in carriers of BRCA1 or BRCA2 mutations. N Engl J Med 346:1616-1622, 2002 38. Tomlinson GE, Schorge J, Robinson L, et al: Risk reduction practices after BRCA1/2 testing. Proc Am Soc Clin Oncol 22:97, 2003 (suppl: abstr 387) 39. Schwartz MD, Kaufman E, Peshkin BN, et al: Bilateral prophylactic oophorectomy and ovarian cancer screening following BRCA1/BRCA2 mutation testing. J Clin Oncol 21:4034-4041, 2003 40. Kramer J, Velazquez I, Chen B, et al: Prophylactic oophorectomy reduces breast cancer penetrance during prospective, long-term follow-up of brca1 mutation carriers. J Clin Oncol 23:8629-8635, 2005 41. Scheuer L, Kauff N, Robson M, et al: Outcome of preventive surgery and screening for breast and ovarian cancer in BRCA mutation carriers. J Clin Oncol 20:1260-1268, 2002 42. Claus E, Schildkraut J, Iversen EJ, et al: The effect of BRCA1 and BRCA2 on the association between breast cancer risk and family history. J Natl Cancer Inst 90:1824-1829, 1998 43. Antoniou AC, Gayther SA, Stratton JF, et al: Risk models for familial ovarian and breast cancer. Genet Epidemiol 18:173-190, 2000[CrossRef][Medline] 44. Hughes C, Peterson SK, Ramirez A, et al: Minority recruitment in hereditary breast cancer research. Cancer Epidemiol Biomarkers Prev 13:1146-1155, 2004 45. Olopade OI, Fackenthal JD, Dunston G, et al: Breast cancer genetics in African Americans. Cancer 97:236-245, 2003 (suppl 1)[CrossRef][Medline] 46. Nanda R, Schumm LP, Cummings S, et al: Genetic testing in an ethnically diverse cohort of high-risk women: A comparative analysis of BRCA1 and BRCA2 mutations in American families of European and African ancestry. JAMA 294:1925-1933, 2005 Submitted August 1, 2005; accepted December 6, 2005.
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Copyright © 2006 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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ABSTRACT
INTRODUCTION
and prevalence parameter 
. Each individual contributes differently to the likelihood function depending on how the data about him or her were collected. For a prospectively collected individual (ie, the 282 families from Baylor College of Medicine), we use the prospective likelihood, as follows:
(
, needs to be written as 
, where M stands for the testing modality used to obtain the test result T and ß stands for the test sensitivities for all modalities. 
was then written as follows:
, can be calculated based on Mendelian transmission of autosomal dominant alleles. This calculation is achieved by using the genetic epidemiology software BayesMendel,
is known from Mendel's law for the transmission of autosomal dominant alleles and is not dependent on the penetrance. 
