Genetic epidemiology of breast cancer: Segregation analysis of 389 Icelandic pedigrees

Genetic Epidemiology 18:81–94 (2000)

© 2000 Wiley-Liss, Inc.

Genetic Epidemiology of Breast Cancer:Segregation Analysis of 389 IcelandicPedigrees

Agnes B. Baffoe-Bonnie, 2,3 Terri H. Beaty, 1* Joan E. Bailey-Wilson, 3

Lambertus A.L.M. Kiemeney, 4 Helgi Sigvaldason, 5 Gud–rid–ur Ólafsdóttir, 5

Laufey Tryggvadóttir, 5 and Hrafn Tulinius 5

1Department of Epidemiology, The Johns Hopkins School of Hygiene and PublicHealth, Baltimore, Maryland

2Division of Population Science, Fox Chase Cancer Center, Philadelphia,Pennsylvania

3Division of Statistical Genetics, National Human Genome Research Institute ofthe National Institutes of Health, Bethesda, Maryland

4Departments of Epidemiology and Urology, University of Nijmegen, TheNetherlands

5Cancer Registry, Icelandic Cancer Society, Reykjavik, Iceland

A genetic epidemiologic investigation of breast cancer involving 389 breast cancerpedigrees including information on 14,721 individuals from the Icelandic popula-tion-based cancer registry is presented. Probands were women born in or after 1920and reported to have breast cancer in the cancer registry. The average age of the 389probands was 45.5 years (SD 8.92). Segregation analyses was performed evaluatingresidual maternal effects, a dichotomous cohort effect, and assuming the age at diag-nosis followed a logistic distribution after log-transformation. Familial aggregationcould be best explained by the inheritance of a high-risk allele leading to early onsetbreast cancer among the homozygotes, which represent approximately 2.6% of thepopulation. A Mendelian codominant model was selected as the best fitting model,with an estimated age at diagnosis of 51.8 years among these high-risk homozy-gotes, 64.0 years for heterozygotes and 76.3 years for the low-risk genotype. The

Contract grant sponsor: National Institutes of Health; Contract grant numbers: 5R25CA57708,5T32CA09314.

*Correspondence to: Terri H. Beaty, Ph.D., Dept. of Epidemiology, Rm. 6507, The Johns HopkinsSchool of Hygiene and Public Health, 615 N. Wolfe Street, Baltimore, MD 21205. E-mail:[email protected]

Received for publication 10 February 1998; revision accepted 28 February 1999

82 Baffoe-Bonnie et al.

predicted cumulative risk for homozygote carriers of the high-risk allele is 32.2% byage 60, compared to 16.4% for heterozygotes and 5.0% for non-carriers of the sameage. These predicted age profiles in the current study complement recent reportsfrom Iceland of a majority of BRCA2 mutation carriers being diagnosed with breastcancer below the age of 50 years, and 60 years being the mean age at diagnosis fornon-carriers. This model also predicted a high background risk of breast cancer forwomen in this population (estimated susceptibility γ = 0.44 ± 0.08). This impliesthat if carriers and non-carriers did not die of competing causes, the estimated risk ofbeing diagnosed with breast cancer by age 80 years irrespective of carrier status is11.4%. Genet. Epidemiol. 18:81–94, 2000.© 2000 Wiley-Liss, Inc.

Key words: Mendelian inheritance; BRCA2; segregation analyses; population-based study; Iceland

INTRODUCTION

Given that a positive family history is a major risk factor, several studies haveattempted to identify the mode of genetic transmission in breast cancer. However,different study designs, different phenotypic definitions, and/or different populationshave yielded different results among the published segregation analyses of breastcancer. Many of the studies have concluded that an autosomal dominant allele withhigh penetrance could best explain the clustering of breast cancer in families [Bishopand Gardner, 1980; Go et al., 1983; Williams and Anderson, 1984; Bishop et al.,1988; Newman et al., 1988; Claus et al., 1991; Iselius et al., 1991; Eccles et al.,1994; Essioux et al., 1995]. A recessive pattern of transmission has also been re-ported [Goldstein et al., 1987, 1988; Goldstein and Amos, 1990; Amos et al., 1991].A more recent study using post-menopausal breast cancer probands reported Mende-lian codominant inheritance [Chen et al., 1995].

Breast cancer is heterogeneous, the age of onset is late and variable, so thatwomen are only informative if they are affected, or unaffected and elderly [Bailey-Wilson et al., 1986]. Currently, at least three major loci are known to cause breastcancer (p53 at 17p13, BRCA1 at 17q21, and BRCA2 at 13q12-q13). In Western Eu-rope and the United States, approximately one in 12 women develop breast cancer[Wooster et al., 1995]. An estimated 5–10% of all breast cancer is attributable toinherited mutations in two genes, BRCA1 and BRCA2 [Skolnick et al., 1997]. BRCA2carries a risk of breast cancer similar to BRCA1, but is associated with a lower riskof ovarian cancer and a higher risk of male breast cancer [Stratton and Wooster,1996]. The p53 gene is involved in breast cancer development as part of the Li-Fraumeni syndrome. Two other loci that may influence risk are germline mutationsin the androgen receptor gene (AR), which is present in a subset of male breast can-cers, and the ataxia telangiectasia (AT) gene for which obligate female carriers havea four- to 12-fold risk of developing breast cancer compared with the general femalepopulation [Andersen, 1996].

Support for the presence of a genetic factor in the etiology of familial breastcancer in Iceland was reported previously [Tulinius et al., 1982, 1992a,b; Tryggvadóttiret al., 1988]. The report on the epidemiology of breast cancer [Tulinius et al., 1992b]indicates an increased relative risk among first-degree relatives of patients with thedisease, especially among sisters, suggesting the importance of shared environmen-tal factors at young ages [Thorlacius et al., 1997].

Genetic Epidemiology of Breast Cancer in Iceland 83

It has been suggested that BRCA1 mutations seem to be a minor explanation offamilial risk of breast cancer in Iceland [Arason et al., 1993; Barkardottir et al.,1995; Thorlacius et al., 1997]. Linkage to the BRCA2 region has been demonstratedin families with multiple cases of male breast cancer, and all the affected men sharethe same haplotype for the BRCA2 markers and loss of the other alleles in theirtumors [Thorlacius et al., 1995, 1997]. The most recent report from Iceland suggeststhat the BRCA2 mutation 999del5 is found in families with both female and malebreast cancers [Thorlacius et al., 1997].

The present analysis of 389 pedigrees from Iceland presents a segregation analy-ses designed to test genetic and non-genetic models controlling age at diagnosis forbreast cancer, assuming that age at diagnosis follows a logistic distribution after log-transformation. A population-based cancer registry was used to identify probandsborn in or after 1920, and extensive family data were available including vital status,cancer status, and age of diagnosis.

METHODSSelection of Pedigrees

The pedigrees used for the current investigation were drawn from the compre-hensive population-based Icelandic Cancer Registry, which has been in operationsince 1954 [Tulinius et al., 1987]. The families were ascertained through 389 probandswho formed part of the cohort of 456 probands born in or after 1920. Family mem-bers were crosslinked with a register of all Icelandic people with cancer using theunique personal identification number available in this population. Duplication offamilies was eliminated by choosing the oldest affected sibling as the proband. Theinformation for constructing family trees was obtained from records of the GeneticsCommittee of the University of Iceland or from published genealogies. Thus, familymembers were not asked about the structure of their family or about cancer in rela-tives directly. This is a weakness in confirming the pedigree structures, but repre-sents a strength in identifying affected individuals among more distant relatives.Information on the diagnosis of breast cancer was obtained by comparing familymembers in these pedigrees with the list of all breast cancer patients entered in thecancer registry between 1910 and 1988. Recognized breast cancer, both in situ andinvasive, is recorded in the cancer registry.

Rejected Versus Accepted Pedigrees

Due to the computational limitations in the computer software, in particular theFamily Structure Program of the SAGE package [SAGE, 1997], and/or to difficul-ties with some of the pedigrees (often due to their size and/or structure, e.g., multiplemates and complicated marriage or inbreeding loops), only 85% of the 456 pedi-grees identified through a proband born in or after 1920 (i.e., 389) could be includedin the current sample. The mean age of the 67 probands in the excluded pedigreeswas 46.4 years (SD 9.1), which was comparable to the 45.5 years (SD 8.9) of the389 accepted probands. The mean age of the 22 affected non-probands in the ex-cluded pedigrees was 56.0 years (SD 9.1) compared to a mean of 60.0 years (SD14.5) of the 309 affected non-probands in the accepted samples. About 10% (7/67)of the rejected pedigrees were simplex families, i.e., only the proband was affected,


and in five of these seven, the entire pedigree consisted of only the proband with noother relatives. The average size of the rejected pedigrees was 24 people rangingfrom one to 93.

Segregation Analyses

To test specifically for Mendelian inheritance of breast cancer in these Icelandicpedigrees, maximum likelihood segregation analyses were performed on the age atdiagnosis of breast cancer expressed as a censored trait using the SAGE programREGTL [SAGE, 1997]. Under model 1 of this program, employing the class A re-gressive models [Bonney, 1986], the “type,” or “ousiotype” [Go et al., 1978; Canningset al., 1978], influences age at diagnosis of breast cancer through the location andscale parameters of the logistic distribution, but does not influence susceptibility.Specifically, some constant proportion (γ) of the population is assumed to be at riskfor the disease (here breast cancer). As only female breast cancer was considered inthis study, this susceptibility γ was fixed at zero for all males. Breast cancer is de-fined as a dichotomous variable (Y), where Y = 1 if affected and Y = 0 if unaffected(censored). Parameters usually estimated in the analyses include: qA, the frequencyof the putative high risk allele; βi, baseline parameters, where i represents anindividual’s type (AA, AB, BB); αi the age coefficients; γi the susceptibilities; ξk

covariate coefficients, for covariates k = 1 to N; δj(Yj) coefficients measuring theresidual effects of having an affected (Yj = 1) or unaffected (Yj = 0) spouse (j = S),mother (j = M), or father (j = F) [Elston and George, 1989]. A simplified formula ismodeled in the current analysis in that only one covariate is considered, and spouse/father effects are not estimated. The logistic function describes the probability thatan individual is affected by age a as: γi[1/1 + e–Φ)], where

Φ = βi + αi (a) + δM(YM) + ξ1(x1) (1)

From equation (1) above, two coefficients are estimated: 1) the coefficient δM re-flecting familial influence on risk corresponding to having an affected mother, and2) the coefficient ξ1 representing a birth cohort effect. Positive values of δM meanthat the individual with an affected mother is more likely to have an earlier age atdiagnosis, while negative values mean that the individual with an affected mother ismore likely to have a later age at diagnosis. A dichotomous covariate representingtwo birth cohorts was created: born before or in/after 1900. Records on breast canceravailable in the Icelandic Cancer Registry date back to 1910 [Thorlacius et al., 1997].Even though the mean age at diagnosis of the 698 with breast cancer was 51.9 years,a simple plot of age at diagnosis of breast cancer by year of birth indicated thataffected individuals born before 1900 tended to have higher ages of onset (range,50–93) compared to individuals born in or after 1900 (range, 20–90). Those bornbefore 1900 were assigned 0 while those born in or after 1900 were assigned 1.Similarly, positive values of ξ1 mean the individuals born in the later cohort have ahigher risk of developing early onset breast cancer than the earlier cohort. Otherresearchers have identified 1930 or 1934 as distinct cohorts among BRCA1 mutationcarriers [Narod et al., 1995; Chang-Claude et al., 1997].

Age at diagnosis for breast cancer is assumed to follow a logistic distribution


described by two parameters α and β, with the probability distribution function (p.d.f.)according to [Elston and George, 1989].

f(age) = [αeβi+α(age)]/(1 + eβi+α(age))2 (2)

This symmetric distribution is similar to a normal distribution and has a mean, –β/α,and variance, π2/3α2, where π has the value of 3.1416.

Based on this logistic distribution, the cumulative distribution function (c.d.f.)is given by

F(age) = γ[αeβi+α(age)]/[1 + eβi+α(age)] (3)

and represents the probability that a susceptible person will be affected by a givenage. Age-specific penetrances were calculated for each genotype as:

P(Y|genotype i, age) = γ{ eβi+α(age)}/(1 + eβi+α(age)) (4)

If the observed age at diagnosis itself does not follow a logistic distribution,this model may still be appropriate after transformation. A transformation equationequivalent to: aG1 × ln(age) was considered here, where aG1 is the geometric meanage at diagnosis, computed from the observed ages at diagnosis among the 309 af-fected non-probands.

Tests for genetic control were implemented by postulating three types of indi-viduals (AA, AB, and BB) with three corresponding transmission parameters (τAA,τAB, and τBB) describing the probability that a parent of a given type transmits thedisease-producing factor A to his/her offspring [Elston and Stewart, 1971; Elstonand Yelverton, 1975]. Under the hypotheses of genetic transmission, these τ param-eters are constrained to the Mendelian values of τAA = 1.0, τAB = 0.5, and τBB = 0.0,respectively. Five sub-models of disease transmission were tested against the generalunrestricted model to identify the best model for these data. The “no major gene”model assumes that baseline risk is not influenced by “type”; therefore, all personswould have the same age-specific risk of breast cancer. Single locus Mendelian modelsassume a major locus with two alleles should act either in a codominant, dominant,or recessive fashion. The dominant and recessive models are special cases of thecodominant model, where each genotype has a distinct age at diagnosis distribution.An environmental model with potentially distinct types of individuals was also tested,but here the transmission probability was held constant for all individuals.

Hypothesis Testing

The likelihood ratio test (LRT) was used to test each sub-model against thegeneral, unrestricted model and was computed as minus twice the natural log likeli-hood (–2ln(L)) of the general model subtracted from that for a restricted sub-model.This difference is asymptotically distributed as a χ2 distribution with degrees of free-dom equal to the difference in the number of parameters estimated in the two mod-els. Another method to compare models uses Akaike’s information criteria (AIC),defined as AIC = –2ln(L) + 2 (number of parameters estimated). The most parsimo-nious model has the minimum AIC value [Akaike, 1974].


To correct for ascertainment bias, the likelihood of each pedigree was condi-tioned on the proband’s affection status using her age at diagnosis as recorded in thecancer registry [Cannings and Thompson, 1977; Elston and Sobel, 1979].

Assessing the Fit of the Best Mendelian Model

The adequacy of the fit of the best model was assessed graphically in two ways.First, the genotype-specific cumulative probability curves were superimposed on thecumulative age at diagnosis curve of the observed data. Second, the probability dis-tribution functions were computed and superimposed on a histogram of the observedages of onset among affected non-probands.

RESULTSDescriptive Analyses

The 389 pedigrees include 14,721 individuals: 7,554 males and 7,167 females.Pedigree sizes ranged between 13 and 180 persons with an average of 38. The modaland median sizes coincide at 28 persons. Pedigrees with >40 individuals form 26%of the data. Only 3.3% of pedigrees had >100 persons. Relationships between the14,721 individuals in the 389 pedigrees, some of which span five generations, in-clude 5,751 mother-daughter pairs and 7,485 sister-sister pairs, regardless of affec-tion status.

The observed age at diagnosis data are positively skewed (g1 = +0.571 ± 0.093),where g1 is a sample statistic for measuring skewness, and hence lend themselves to log-transformation [Sokal and Rohlf, 1966]. Forty-five percent of the 389 probands wereunder 45 years of age. For the 309 affected non-probands, the mean age at diagnosis was60 years (SD ± 14.5), with a range of diagnosis ages from 22 to 93 years.

Segregation Analyses: Choice of REGTL Model

Since the segregation analyses using log-transformed ages at diagnosis of breastcancer resulted in significant improvement to the fit of the models compared to ob-served data [Baffoe-Bonnie, 1997], we present here results from the log-transformedanalyses. The fit of models with genotype-specific susceptibilities (Model 2 REGTL)was worse than that for genotype-specific ages of onset (Model 1 REGTL); thus,model 2 was abandoned. Incorporation of a non-genetic covariate, birth cohort, re-sulted in significant improvement of the likelihood estimates from model 1 and thusthe final model presented here is for genotype-specific ages of onset, with log-trans-formation, controlling for birth cohort, and residual maternal affection status.

Segregation Analyses Results

Log-Transformed Ages of Onset With Residual Maternal and Cohort Effects

As shown in Table I, the no major gene model gave a very poor fit to the dataand is thus rejected against the general model (model 1 vs. model 6; χ2 = 38.64; P <0.001 for 6 df). The final general model reported is almost identical to the codomi-nant model even though both τAA and τAB went to a bound. τAA going to 1 sug-gests a tendency toward Mendelian τs. It was extremely difficult to obtain a generalmodel in which all τs were estimated, and so the degrees of freedom are reduced to 9

Genetic E

pidemiology of B

reast Cancer in Iceland

87

TABLE I. Segregation Analysis of Age-at-Diagnosis in 14,721 Members of 389 Icelandic Breast Cancer Pedigrees*

Model q(A) β(AA) β(AB) β(BB) τ(AA) τ(AB) τ(BB) M(aff) Cov α γ –2ln(L) AIC df Chi square

1. No major gene [1] –24.00 =β(AA) =β(AA) — — — 2.76 0.63 0.092 0.402 4,336.59 4,346.59 6 38.64(1.15) (0.24) (0.16) (0.005) (0.080) P < 0.001

2. Dominant 0.389 –24.28 =β(AA) –31.01 [1] [0.5] [0.0] 2.67 0.65 0.094 0.482 4,332.66 4,346.66 4 34.71(0.23) (1.29) (1.19) (0.26) (0.16) (0.007) (0.139) P < 0.001

3. Recessive 0.174 –26.75 =β(BB) –33.48 [1] [0.5] [0.0] 3.57 0.63 0.117 0.424 4,313.55 4,327.50 4 15.55(0.04) (2.24) (2.46) (0.44) (0.19) (0.011) (0.081) P < 0.001

4. Codominant 0.160 –28.43 –35.16 –41.89 [1] [0.5] [0.0] 4.09 0.51 0.126 0.436 4,299.75 4,315.75 3 1.80(0.03) (2.66) (2.59) (2.75) (0.49) (0.22) (0.012) (0.081) P = 0.60

5. Environmental 0.199 –34.51 –38.59 –41.22 0.27 =τ(AA) =τ(AA) 4.74 0.78 0.155 0.379 4,320.59 4,338.59 2 22.64 (taus equal) (0.04) (3.95) (4.41) (3.13) (0.04) (0.68) (0.26) (0.019) (0.077) P < 0.0016. General 0.044 –46.81 –32.58 –37.09 [1.0]a [1.0]a 0.07 4.86 0.62 0.143 0.409 4,297.95 4,315.95 — —

(0.01) (1.10) (2.35) (2.68) (0.49) (0.26) (0.113) (0.057)

*q(A) - frequency of the risk allele A; β(AA) = baseline parameter for homozygote with high-risk allele; β(AB) = baseline parameter for heterozygote with onecopy of high-risk allele; β(BB) = baseline parameter for homozygote with no copy of high-risk alleles; τ(AA) = is the probability that a parent of type AAtransmits allele A to offspring; τ(AB) = is the probability that a parent of type AB transmits allele A to offspring; τ(BB) = is the probability that a parent of typeBB transmits allele A to offspring; M(aff) = effects of mother under the regressive familial effects; α = age coefficient representing variance; γ = susceptibilityparameter denotes the probability that an individual is susceptible; –2ln(L) = minus twice the natural log-likelihood; LRT = likelihood ratio test; AIC = AkaikeInformation Criteria; df = degrees of freedom; P = P value.a[1.0], went to a bound.


instead of 11 to take care of boundary problems. The dominant, the recessive and theenvironmental models were all rejected compared to the general model (P < 0.001).Based on the LRT, there was no significant difference between the codominant modeland the general model (model 4 vs. model 6; χ2 = 1.80; P < 0.60 for 3 df). The AICconfirmed that this codominant model was the most parsimonious model.

Figure 1 shows the predicted cumulative distribution function curves for log-transformed ages at diagnosis with residual maternal and birth cohort effects. High-risk AA individuals have predicted age-specific cumulative probabilities greater thanheterozygotes and non-carriers. The predicted mean age at diagnosis is 51.8 yearsfor homozygous carriers of the high-risk allele, 64.0 years for the heterozygotes, and76.3 years for the non-carriers. The susceptibility parameter γ was estimated at 0.436± 0.081, suggesting that 43.6% of the female population would express breast cancerif they lived to infinity and did not die of competing causes. Under the Mendeliancodominant hypothesis obtained, the cumulative probability that a woman was af-fected with breast cancer by age 80 was 0.424 for homozygous carriers of the riskallele, 0.385 for heterozygotes, and 0.268 for noncarriers. The age at diagnosis of thenon-carriers was 76.3 years (SD ± 8.3), close to 80 years; therefore, the parametersfor the BB genotype were dropped from the estimation of the lifetime probability ofdeveloping breast cancer. Thus, conditioning on the homozygote and heterozygotecarriers of the high-risk allele, these cumulative probabilities were multiplied by the

Fig. 1. Cumulative age at diagnosis distribution of breast cancer in Iceland. Observed and predictedfor AA, AB, and BB genotypes. AA = carriers of high-risk allele; AB = heterozygotes; BB = non-carriers or low-risk genotypes; Data = cumulative distribution function of 14,271 ages.


genotypic frequencies to obtain an estimated lifetime risk of being diagnosed withbreast cancer of 11.4% by age 80 years among a hypothetical population of 100,000women. Including a residual maternal regressive coefficient and a birth cohort sub-stantially improves the fit of these models. The impact of genotype alone vs. residualeffect of having an affected mother can be measured by computing log odds of vari-ous combinations.

For example, among women of the same age, born in the same cohort, andhaving the same affected mother status, the log odds for being a homozygous carrierof the high-risk allele A is computed as the difference between the genotypic baselinecoefficients compared with the heterozygote:

(βAA + αa¢ + δM(YM) + ξ1(x1)) – (βAB + αa¢ + δM (YM) + ξ1(x1))= βAA – βAB

= –28.97 – (–35.16) = 6.73.

The odds of the breast cancer in homozygous carriers of the A allele comparedto heterozygous carriers is 837.15, i.e., exp(6.73). The log odds due to an affectedmother between two people with the same genotype is 4.09 and the correspondingodds ratio is 59.74. The increase in log odds for a homozygote for the high-riskallele A with an affected mother compared to a heterozygote carrier of the same age,born in the same cohort, but having an unaffected mother would therefore be βAA –βAB + δM(YM) = 6.73 + 4.09 = 10.82. The increase in log odds due to a cohort effectis seen in those born in or after 1900. Predicted probability distribution functioncurves are also plotted for the log-transformed data under the codominant model inFig. 2 and are superimposed on the observed ages of onset among the 309 affectednon-probands. This demonstrates the three peaks of age at diagnosis of breast cancerassociated with each genotype.

DISCUSSION

Evidence from the present study suggests that the genetic control of the age atdiagnosis of breast cancer in Iceland appears to be due to the Mendelian codominantinheritance of a common major autosomal gene after controlling for a residual ma-ternal effect and a cohort effect. Codominance implies the full expression of bothalleles in the heterozygote such that the heterozygote is phenotypically distinct fromthe two homozygotes, leading to a three distribution model. Under Hardy-Weinbergequilibrium, the estimated allele frequency of 0.16 implies that 29.4% of the popula-tion would carry this putative high-risk allele.

In addition to a postulated major gene(s), socio-cultural and environmental fac-tors are of great interest in describing the epidemiology of breast cancer in Iceland,where the incidence of the disease has nearly doubled since 1954 to its 1990–1994value of 74.5/100,000 [Thorlacius et al., 1997]. This current study suggests that 43.6%of women live in an environment that makes them susceptible (estimated suscepti-bility γ = 0.0436 ± 0.08), such that irrespective of genetic susceptibility, if they livedto age infinity, one in every 8.7 women will develop breast cancer in their lifetime,an estimate close to the expected 12% lifetime risk of developing breast cancer inthe Western world [American Cancer Society, 1994]. The homozygous carriers of


the risk allele have a lower age at diagnosis compared to the heterozygotes whomaintain an age at diagnosis lower than the non-carriers of the gene. The mean ageat diagnosis of 51.8 years for the homozygous carriers seems to point to the inherit-ance of the BRCA2 mutation described in Iceland as explaining the majority of breastcancer [Eyfjord and Thorlacius, 1992; Tavtigian et al., 1996; Thorlacius et al., 1995,1996, 1997].

In a recent report on breast cancer in Iceland, the prevalence of the most com-mon mutation of BRCA2 999del5 was seen to have a very low frequency of 0.6% (3/520) among a screened population of controls [Thorlacius et al., 1997], comparedwith 7.7% (49/632) prevalence among a group of female breast cancer cases. Theproportion of cases with the mutation increased significantly with decreasing age,and the mutation was detected in 16% of cases diagnosed at age below 50 years. Theproportion dropped to 3.9% (17/439) among those aged 50 years and above. Themedian age of the 49 breast cancer cases in the BRCA2-positive group was 45 yearsand the non-carrier group 60 years. The penetrance of the BRCA2 mutation variesconsiderably and a number of cancer-free obligate carriers (age >70 years) have beenreported [Thorlacius et al., 1997]. In this segregation analysis, we have no way ofidentifying the nature of the codominant gene modeled, but we do know that both

Fig. 2. Probability density of age at diagnosis distribution of breast cancer in Iceland. Observedand predicted for AA, AB, and BB genotypes. AA = carriers of high-risk allele; AB = heterozy-gotes; BB = non-carriers or low-risk genotypes; Data = histogram of age at diagnosis of affectednon-probands.


BRCA1 and BRCA2 are known genes that control early onset breast cancer in Ice-land. There could still be yet unidentified genes controlling the age of diagnosis ofbreast cancer since BRCA1 and BRCA2 cannot account for all the familial aggrega-tion of breast cancer seen in the general population.

Ford et al. [1995] suggest that the majority of breast cancer families with lessthan four cases and no ovarian cancer are not due to rare highly penetrant genes suchas BRCA1 but are more likely to be due either to chance or to more common genesof lower penetrance. Additional breast cancer susceptibility genes probably exist andare yet to be discovered [Bishop, 1994; Ford and Easton, 1995; Friedman et al.,1995; Rebbeck et al., 1996; Ford et al., 1995, 1998]. Seitz et al. [1997] publishedresults of linkage analysis in two German multiplex familes that considerablystrengthen the evidence for a third breast cancer susceptibility locus (BRCA3) map-ping to the short arm of human chromosome at 8p12-p22.

Several models in REGTL were tested in this study, but on the whole, our analysisof genotype-specific βs, a common age coefficient (α), and a single susceptibilityparameter γ, strongly suggest that estimation of residual maternal effects while con-trolling for a cohort effect after log-transformation of the age at diagnosis is the bestof the methods tried here for analyzing age at diagnosis for breast cancer. In thepresent study, as in Go et al. [1983] and Elston [1981], only females were consideredsusceptible to breast cancer, basing this assumption on the fact that male breast can-cer is very rare, and, therefore, their susceptibility to breast cancer was zero. In theoriginal data set of 45,425 individuals, only eight males had breast cancer. In Iceland, asin other Western countries, male breast carcinoma constitutes <1% of all breast malig-nancies and 0.25% of all malignant tumors in males [Jonasson et al., 1996].

The majority of inherited breast cancer in Iceland seems to be due to afounder mutation in the BRCA2 gene, a generalized tumor suppressor gene thatmay be involved in the tumorigenesis of several types of cancer, including pros-tate, pancreas, and ovarian cancers [Gudmundsson et al., 1996a,b; Thorlacius etal., 1997]. Due to the association of BRCA2 mutation with other cancer types inthis population, a broader and more complex cancer phenotype could also bedefined with all or some of the cancers co-aggregating in families [Bondy et al.,1991; Chen et al., 1995; Gudmundsson et al., 1996b; Baffoe-Bonnie, 1997;Thorlacius et al., 1997].

This study sought to characterize the mode of inheritance of female breastcancer susceptibility gene(s) in a population-based sample from Iceland and sug-gests that the BRCA2 or other still unidentified breast cancer gene(s) may betransmitted in Mendelian codominant fashion. Since it is not known that geneticfactors alone can explain the frequency and pattern of female breast cancer inthis population, future studies should seek to incorporate environmental risk fac-tors that may explain the overall predicted susceptibility (γ) of 43.6%. It hasbeen documented that the frequency of breast cancer in Iceland has increasedsignificantly during the past 50 years, and during this time there has been a greatchange in lifestyle, which might indicate the involvement of new contributingfactors, most likely environmental [Thorlacius et al., 1997]. This fact is furtherborne out by the decreasing age at diagnosis for female mutation carriers, whichsuggests that there could be some new environmental factors affecting the phe-notypic expression of the BRCA2 mutation.


ACKNOWLEDGMENTS

This work was completed in partial fulfillment of the Doctor of Philosophydegree requirements for the Johns Hopkins School of Hygiene and Public Health.The authors acknowledge the cooperation of the Genetical Committee of the Univer-sity of Iceland, which has developed the system allowing construction of pedigreesof Icelanders. The program package SAGE supported by the U.S. Public Health Ser-vice Resource grant RR03655 from the Division of Research Resources, was usedfor the complex segregation analyses. This work was partially supported by NationalInstitutes of Health Training grants 5R25CA57708 and 5T32CA09314.

REFERENCES

Akaike H. 1974. A new look at the statistical model identification. IEEE Trans Automatic Control. AC-19:716–723.

American Cancer Society. 1994. Cancer Facts and Figures. Atlanta: ACS.Amos CI, Goldstein AM, Harris EL. 1991. Familiality of breast cancer and socio-economic status in

blacks. Cancer Res 51:1793–1797.Andersen TI. 1996. Genetic heterogeneity in breast cancer susceptibility. Acta Oncol 35:407–410.Arason A, Barkardottir RB, Egilsson V. 1993. Linkage analysis of chromosome 17q markers and breast-

ovarian cancer in Icelandic families, and possible relationship to prostatic cancer. Am J HumGenet 52:711–717.

Baffoe-Bonnie AB. 1997. A genetic epidemiologic approach to studying the relationship between pros-tate cancer and breast cancer. Doctor of Philosophy Disseration, Johns Hopkins University, Bal-timore, MD.

Bailey-Wilson JE, Cannon LA, King M-C. 1986. Genetic analysis of human breast cancer: a synthesisof contribution of GAW IV. Genet Epidemiol Suppl 1:15–35.

Barkardottir RB, Arason A, Egilsson V, Gudmundsson V, Jonasdottir AS, Johannesdottir G. 1995. Chro-mosome 17q-linkage seems to be infrequent in Icelandic families at risk of breast cancer. ActaOncol 34:657–662.

Bishop DT. 1994. BRCA1, BRCA2, BRCA3....a myriad of breast cancer genes. Eur J Cancer 30A:1738–1739.

Bishop DT, Gardner E. 1980. Analysis of the genetic predisposition to cancer in individual pedigrees.In: Cairns J, Lyon JL, Skolnick M, editors. Cancer incidence in defined populations: BanburyReport 4. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press. p 389–408.

Bishop DT, Cannon-Albright L, McLellan T, Gardner EJ, Skolnick MH. 1988. Segregation and linkageanalysis of nine Utah breast cancer pedigrees. Genet Epidemiol 5:151–169.

Bondy ML, Lustbader ED, Buffler PA, Schull WJ, Hardy RJ, Strong LC. 1991. Genetic epidemiologyof childhood brain tumors. Genet Epidemiol 8:253–267.

Bonney GE. 1986. Regressive logistic models for familial diseases and other binary traits. Biometrics42:611–625.

Cannings C, Thompson EA. 1977. Ascertainment in the sequential sampling of pedigrees. Clin Genet12:208–212.

Cannings C, Thompson EA, Skolnick MH. 1978. Probability functions on complex pedigrees. AdvAppl Prob 10:26–61.

Chang-Claude J, Becher H, Eby N, Bastert G, Wharendorf J, Hamann U. 1997. Modifying effect ofreproductive risk factors on the age at onset of breast cancer for German BRCA1 mutationcarriers. J Cancer Res Clin Oncol 123:272–279.

Chen PL, Sellers TA, Rich SS, Potter JD, Folsom AR. 1995. Segregation analysis of breast cancer in apopulation-based sample of postmenopausal probands: Iowa Women’s Health Study. GenetEpidemiol 12:401–415.

Claus EB, Risch N, Thompson WD. 1991. Genetic analysis of breast cancer in the cancer and steroidhormone study. Am J Hum Genet 48:232–242.

Eccles D, Marlow A, Royle G, Collins A, Morton NE. 1994. Genetic epidemiology of early onsetbreast cancer. J Med Genet 31:944–949.


Elson RC. 1981. Segregation analysis. Adv Human Genet 11:63–120.Elston RC, George VT. 1989. Age at diagnosis, age at examination, and other covariates in the analysis

of family data. Genet Epidemiol 6:217–220.Elston RC, Sobel E. 1979. Sampling considerations in the gathering and analyses of pedigree data. Am

J Hum Genet 27:31–45.Elston RC, Stewart J. 1971. A general model for the genetic analysis of pedigree data. Hum Hered

21:523–542.Elston RC, Yelverton KC. 1975. General models for segregation analysis of pedigree data. Am J Hum

Genet 31:62–69.Essioux L, Abel L, Bonaiti-Pellie C. 1995. Genetic epidemiology of breast cancer: interest in survival

analysis methods. Ann Hum Genet 3:271–82.Eyfjord JE, Thorlacius S. 1992. Genetic changes in breast carcinomas in an Icelandic population. Phar-

macogenetics 2:309–316.Ford D, Easton DF. 1995. The genetics of breast and ovarian cancer. Br J Cancer 72:805–812.Ford D, Easton DF, Peto J. 1995. Estimates of the gene frequency of BRCA1 and its contribution to

breast cancer incidence. Am J Hum Genet 57:1457–1462.Ford D, Easton DE, Stratton M, Narod S, Goldgar D, Devilee P, Bishop T, Weber B, et al. 1998.

Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancerfamilies. The Breast Cancer Linkage Consortium. Am J Hum Genet 62:676–689.

Friedman LS, Szabo CI, Ostermeyer EA, Dowd P, Butler L, Park T, Lee MK, Goode EL, Rowell SE,King MC. 1995. Novel inherited mutations and variable expressivity of BRCA1 alleles, includ-ing the founder mutation 185delAG in Ashkenazi Jewish families. Am J Hum Genet 57:1284–1297.

Go RCP, Elston RC, Kaplan EB. 1978. Efficiency and robustness of pedigree segregation analysis. AmJ Hum Genet 30:28–37.

Go RCP, King MC, Bailey-Wilson J, Elston RC, Lynch HT. 1983. Genetic epidemiology of breastcancer and associated cancers in high-risk families. I. Segregation analysis. J Natl Cancer Inst71:455–461.

Goldstein AM, Amos CI. 1990. Segregation analysis of breast cancer from the cancer and steroid hor-mone study histologic subtypes. J Natl Cancer Inst 82:1911–1917.

Goldstein AM, Haile RWC, Marazita ML, Paganini-Hill A, Spence MA. 1987. A genetic epidemiologicinvestigation of breast cancer in families with bilateral breast cancer. I. Segregation analysis. JNatl Cancer Inst 78:911–918.

Goldstein AM, Haile RWC, Hodge SE, Paganini-Hill A, Spence MA. 1988. Possible heterogeneity inthe segregation pattern of breast cancer. Genet Epidemiol 5:121–133.

Gudmundsson V, Johannesdottir G, Arason A, Bergthorsson JT, Ingvarsson S, Egilsson V, BarkardottirRB. 1996a. Different tumor types from BRCA2 carriers show wild-type chromosome deletionson 13q12q13. Cancer Res 55:4830–4832.

Gudmundsson V, Johannesdottir G, Arason A, Bergthorsson JT, Ingvarsson S, Egilsson V, BarkardottirRB. 1996b. Frequent occurrence of BRCA2 linkage in Icelandic breast cancer families and seg-regation of a common BRCA2 haplotype. Am J Hum Genet 58:749–756.

Iselius L, Slack J, Littler M, Morton NE. 1991. Genetic epidemiology of breast cancer in Britain. AnnHum Genet 2:151–159.

Jonasson JG, Agnarsson BA, Thorlacius S, Eyfjord JE, Tulinius H. 1996. Male breast cancer in Ice-land. Intl J Cancer 65:446–449.

Narod SA, Goldgar D, Cannon-Albright L, Weber B, Moslehi R, Ives E, Lenoir G, Lynch H. 1995.Risk modifiers in carriers of BRCA1 mutations. Intl J Cancer 64:394–398.

Newman B, Austin MA, Lee M, King MC. 1988. Inheritance of human breast cancer: evidence forautosomal dominant transmission in high-risk families. Proc Natl Acad Sci USA 85:1–5.

Rebbeck TR, Couch FJ, Kant J, Calzone K, DeShano M, Peng Y, Chen K, Garber JE, Weber BL. 1996.Genetic heterogeneity in hereditary breast cancer: role of BRCA1 and BRCA2. Am J Hum Genet59:547–553.

SAGE. 1997. Statistical analysis for genetic epidemiology, release 3.1. Computer program packageavailable from the Department of Epidemiology and Biostatistics, Rammelkamp Center for Edu-cation and Research, MetroHealth Campus, Case Western Reserve University, Cleveland, Ohio.

Seitz S, Rhode K, Bender E, Nothnagel A, Kolble K, Schlag PM, Scherneck S. 1997. Strong indication


for a breast cancer susceptibility gene on chromosome 8p12-22: linkage analysis in Germanbreast cancer families. Oncogene 14:714–743.

Skolnick MH, Frank T, Shattuck-Eidens D, Tavtigian S. 1997. Genetic susceptibility to breast andovarian cancer. Pathol Biol 45:245–249.

Sokal RR, Rohlf FJ. 1966. Biometry: principles and practice of statistics in biological research. SanFrancisco: WH Freeman and Company. p 112–172.

Stratton MR, Wooster R. 1996. Hereditary predisposition to breast cancer. Curr Opin Genet Dev 6:93–97.Tavtigian SV, Simard J, Rommens J, Couch F, Shattuck-Eidens D, Neuhausen S, Merajver S, Thorlacius S,

Offit K, Stoppa-Lyonnet D, Belanger C, Bell R, Berry S, Bogden R, Chen Q, Davis T, Dumont M,Frye C, Hattier T, Jammulapati S, Janecki T, Jiang P, Kehrer R, Leblanc JF, Mitchell JT, McArthur-Morrison J, Nguyen K, Peng Y, Samson C, Schroeder M, Snyder SC, Steele L, Stringfellow M,Stroup C, Swedlund B, Swenson J, Teng D, Thomas A, Tran T, Tran T, Tranchant M, Weaver-FeldhausJ, Wong AKC, Shizuya H, Eyfjord JE, Cannon-Albright L, Labrie F, Skolnick MH, Weber B, KambA, Goldgar DE. 1996. The complete BRCA2 gene and mutations in chromosome 13q-linked kindreds.Nature Genet 12:333–337.

Thorlacius S, Trygvadottir L, Olafsdottir GH, Jonasson JG, Ogmundsdottir HM, Tulinius H, Eyford JE.1995. Linkage to BRCA2 region in hereditary male breast cancer. Lancet 346:544–545.

Thorlacius S, Olafsdottir GH, Trygvadottir L, Neuhausen S, Jonasson JC, Tavtigian SV, Tulinius H,Ogmundsdottir HM, Eyford JE. 1996. A single BRCA2 mutation in male and female breast can-cer families from Iceland with varied cancer phenotypes. Nature Genet 13:117–119.

Thorlacius S, Sigurdsson S, Bjarnadottir H, Olafsdottir GH, Jonasson JG, Trygvadottir L, Tulinius H,Eyford JE. 1997. Study of a single BRCA2 mutation with high carrier frequency in a smallpopulation. Am J Hum Genet 60:1079–1084.

Tryggvadóttir L, Tulinius H, Robertson JM. 1988. Familial and sporadic breast cancer cases in Iceland:a comparison related to ABO blood groups and risk of bilateral breast cancer. Int J Cancer42:499–501.

Tulinius H, Day NE, Bjarnason O, Geirsson G, Licaega de Gonzalez MA, Sigvaldason H, BjarnadottirG, Grimsdottir K. 1982. Familial breast cancer in Iceland. Int J Cancer 29:365–371.

Tulinius H, Bjarnason O, Sigvaldason H, Bjarnadottir G, Olafsdottir G, Bjarnason T. 1987. Cancerincidence in Iceland 1973–82. In: Muir C, Waterhouse J, Mack T, Powel J, Whelan S, editors.Cancer incidence in five continents. Volume V. Lyon: IARC Scientific publication no. 88.

Tulinius H, Sigvaldalson H, Olafsdottir G, Tryggvadóttir L. 1992a. Epidemiology of breast cancer infamilies in Iceland. J Med Genet 29:158–164.

Tulinius H, Egilsson V, Olafsdottir GH, Sigvaldalson H. 1992b. Risk of prostate, ovarian and endome-trial cancer among relatives of women with breast cancer. Br Med J 305:855–857.

Williams WR, Anderson DE. 1984. Genetic epidemiology of breast cancer: segregation analysis of 200Danish pedigrees. Genet Epidemiol 1:7–20.

Wooster R, Bignell G, Lancaster J, Swift S, Seal S, Mangion J, Collins N, Rice C, Biggs P, Hashim Y,Smith A, Connor F, Arason A, Gudmundsson J, Ficenec D, Kelsell D, Ford D, Tonin P. 1995.Identification of the breast cancer susceptibility gene BRCA2. Nature 378:789–792.

Documents

Genetic epidemiology of breast cancer: Segregation analysis of 389 Icelandic pedigrees