Genetic Epidemiology 7:47-55 (1990)
Genetic Epidemiology of Bilateral Breast Cancer: A Linkage Analysis Using the Affected-Pedigree-Member Method Robert W. Haile, Alisa M. Goldstein, Daniel E. Weeks, Robert S. Sparkes, and Annlia Paganini-Hill Department of Epidemiology, School of Public Health (R. W.H., A. M.G.), and Departments of Biomathematics (0.E. W.) and Medicine (R.S.S.), School of Medicine, University of California, and Department of Preventive Medicine, University of Southern California School of Medicine (A.P.-H.), Los Angeles We used the affected-pedigree-member (APM) method to conduct linkage analyses on 19 pedigrees in which the probands had premenopausal bilateral breast cancer. This method analyzes all affected pairs of relatives, as opposed to siblings only, and incorporates into the analyses information on the frequency of marker alleles. Fourteen codominant marker systems were evaluated in two separate analyses. In the first, only premenopausal cases of breast cancer were coded as affected because we assumed that postmenopausal cases were due to a different etiology. In the second analysis, all cases of breast cancer were coded as affected, irrespective of menopausal status. In the premenopausal-cases-only analysis, we observed evidence suggestive of nonindependent segregation for C3 and ESD. In the all-cases analysis, we observed much weaker evidence for C3 and ESD and noted a suggestion of nonindependent segregation for AMY2 and PGMl . Key words: affected relatives, affected pairs, cosegregation, nonindependentsegregation
INTRODUCTION
Previous analyses have not yielded significant evidence of linkage between a breast cancer susceptibility (BCS) locus and a genetic marker. Although several markers (ABO, Received for publication March 2, 1989; revision accepted October 4, 1989. Address reprint requests to Robert W. Haile, Department of Epidemiology, School of Public Health, University of California, Los Angeles, CA 90024- 1772. Current institutions for A.M.G. and D.E.W. are Family Studies Section, National Cancer Institute, and Columbia University, respectively.
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BF/HLA, GPT, RH) have been implicated with various subtypes of breast cancer, statistical significance in favor of linkage to a BCS locus has not been reached [Bishop et al., 19861. One reason for the inconsistent results may be unresolved etiological heterogeneity. Several studies have refined the definition of breast cancer using epidemiological and clinical criteria to investigate a presumably more homogeneous sample of breast cancer pedigrees [Goldstein et al., 19891. The present study continues this approach by restricting pedigrees to those in which the proband had bilateral breast cancer diagnosed before 50 years of age. These families manifest a very high familial risk of breast cancer [Anderson, 1976; Anderson, 1977; Kelsey, 19791, and they represent a group that is homogeneous with respect to certain clinical characteristics and, therefore, presumably more homogeneous with respect to etiology. Most previous linkage analyses of breast cancer were based on the lod score method [King et a]., 1980, 1983; Anderson et al., 1985; Bishop et al., 1988; Goldstein et al., 19891. In principle, this is a powerful technique, but the validity of results depends in part on specifying the correct model of inheritance. Typically, model parameters are taken from segregation analyses of the same data; however, given etiological heterogeneity, confounding by environmental factors, and other challenges to segregation analysis, there is no consensus about the exact models of inheritance for breast cancer. The affected-pedigree-member (APM) method is attractive in the face of this uncertainty because it does not require assumptions about the underlying model of inheritance. We present here results of APM analyses of breast cancer in 19 pedigrees. MATERIALS AND METHODS FamiI ies
Families were ascertained through at least one female member affected with bilateral breast cancer diagnosed before the age of 50 years and registered by the University of Southern California Cancer Surveillance Program, a population-based cancer registry for Los Angeles County [Mack, 19771. Since multiplex families are the most informative for linkage analysis, they were sought preferentially. Twenty Caucasian families (1 1 multigenerational and 9 nuclear) with an average family size of 8 comprise the data set. One of these was eliminated from this analysis because the proband had postmenopausal breast cancer. Approximately 5 individuals per family provided blood for marker typing. Fourteen pedigrees contained at least two cases of premenopausal breast cancer. Data on these pedigrees are available on request. Genetic Marker Typing
Family members were typed for the following 23 red blood cell antigens, red blood cell enzymes, and serum protein markers: ABO, ACP1, ADA, AK1, AMY2, BF, C3, ESD, FY, GALT, GC, GLO1, GPT, HP, JK, KEL, MNS, P, 6PGD, PGM1, PGP, RH, and TF. Gene symbols are those used by the Human Gene Mapping conference 9 (1987). All marker typing was done by one of us (R.S.S.) using standard laboratory techniques. The analytical method employed in this investigation requires the marker phenotypes to be entirely codominant. Therefore, our analysis was limited to 14 codominant marker systems: ACP1, ADA, AK1, AMY2, BF, C3, ESD, GALT, GLO1, GPT, HP, PGD, PGMl, and PGP.
Linkage Analyses of Affected Breast Cancer Pairs
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Analytical Methods
We used the APM method to test for departures from independent segregation of breast cancer and the 14 codominant marker phenotypes [Weeks and Lange, 19881. The APM method is an extension of the sib-pair approach that uses marker information on all pairs of affected members of a pedigree but not on unaffected members. Families with only one affected person or with only one affected person or with only one affected person on whom we have necessary data are not used in this analysis. Since it is more striking for distantly affected relatives to share a rare marker allele than a common marker allele, the test statistic includes a weighting factor f(p) based on allele frequency p [Weeks and Lange, 19881. Examples of this weighting factor are The function f(p) = 1/ may represent a reasonable compromise 1, Up, or 1/ between the extremes of ignoring allele frequency [f(p) = 11 and of so strongly weighting by allele frequency [f(p) = Up] as to disturb the asymptotic normality of the test statistic, so we present results using only this weighting function (we obtained similar results with other weights; markers positive for cosegregation remained positive). Allele frequencies were taken or estimated from published frequencies of markers in Caucasians. We conducted two analyses: “premenopausal-cases-only’’ and ‘ ‘all-cases. Affected women were classified with respect to menopausal status (premenopausal or postmenopausal) based on known age at menopause and age at diagnosis. For the premenopausal-cases-only analysis, we considered only cases of premenopausal breast cancer to be affected. All postmenopausal cases of breast cancer were coded as unaffected, since we were assuming that their disease involved etiologic pathways different from those of premenopausal breast cancer. For the all-cases analysis, all breast cancer cases were coded as affected irrespective of menopausal status. We considered no other partitions of our data (e.g., synchronous vs. asynchronous, presence or not of lobular or ductal breast cancer or fibrocystic disease) as we had in previous linkage analyses [Goldstein et al., 19891 because of the small number of pedigrees and cases of breast cancer available for an APM analysis.
6.
6
”
RESULTS
Table I presents data on each case in the 19 pedigrees. The average age of the proband at diagnosis of the second primary is 44 years. The majority (14/25 = 56%) of nonproband relatives are sisters of the proband. A majority of cases (14/24 = 58%) were premenopausal at time of diagnosis. Results for the premenopausal-cases-only analysis are presented in Table 11. The results suggest nonindependent segregation of breast cancer with C3 ( P = 0.03 1) and less so with ESD ( P = 0.085), which are on different chromosomes. Evidence of nonindependence for C3 was generated from 9 of 1 I families where all cases within each family shared both alleles. One family contained two affected sisters who shared one allele and the remaining family contained an affected mother-daughter pair who shared one allele. We evaluated the possibility of an association between C3 markers and breast cancer by computing the observed frequency of genotypes 1 / 1 , 1/2, and 212 among our cases (using one proband per family) and comparing them with the expected frequencies assuming Hardy-Weinberg equilibrium and no association. The observed/ expected frequencies for 1/1, 1/2, and 2/2 were .09/.022, .273/.255, and .637/.723, respectively, suggesting no association within our data. Evidence of nonindependence
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Haile et al.
TABLE I. Descriptionsof Breast Cancer Cases in 19 Families
Family ID
Position in family
Age at diagnosis
BCSOOI
Proband Sistei" Sister Proband Sister DZ twin Proband Sister Proband
41/48' 55 43 48/48 55 34 49149 52 45148
Proband (sister) Mother Cousin (maternal) Proband Sister Aunt (maternal) Proband Mother Proband Mother Proband
46/46
Sister
26
Proband Sister Aunt (maternal) Proband Sister Sister Proband Sister Proband Sister Proband Aunt (maternal) Proband Father Proband Cousin (maternal) Proband Daughter Proband Sister
35/31 26 41 49/49 49 68 41/44 48 34/35
Proband Sister Proband Mother
48/49 52 27/28
BCS002
BCS003 BCSOO4
BCSO05
BCS006 BCS007 BCSOOS
BCSOlO
BCSOII
BCS012 BCSO 13 BCSO14 BCSO 15 BCS016 BCS017 BCS018
BCS019 BCS020
"Relationship to proband. 'Ages at diagnoses of the two primaries
64 56 46/46 43 58 42/48 48 49149 86 30130
26 48/49 50 44/44 55 45/49 41 38148 35 43/44 31
46 .I
Menopause status (pre or post)
Pathology Ductal, lobular CIS Lobular, ductal Intraductal, intralobular Ductal, lobular CIS Ductal Ductal Ductal Lobular CIS Ductal, mucinous adenocarcinoma Ductal Lobular Ductal Ductal Lobular CIS Intraductal adenocarcinoma Medullary, Fibroadenocarcinoma Ductal Ductal Ductal Ductal Intraductal Ductal Ductal Scirrhous Ductal Lobu I ar Ductal Ductal Ductal Intraductal Intraductal, Comedocarcinoma Ductal, tubular Ductal -
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TABLE 11. Results for Premenopausal-Cases-OnlyAnalysis Using the Affected-PedigreeMember Method Marker
Chromosomal location
ACP ADA AK I AMY2 BF c3 ESD GALT GLO 1 GFT HP 6PGD PGM 1 PGP
2p25 or 2p23 (C) 20q13.2-qter (C) 9q34 (C) lP21 (C) 6 ~ 2 1 . 3(C) 19pter-q 13.2 (C) 1 3 ~ 1 4 . 1(C) 9p2 1-p 13 (C) 6~21.3-21.2(C) LG2 16q21-q22 (C) lpter-p36.13 (C) lp22.1 (C) 16~13-pl2(C)
Number of families"
Test statistic (2)
P value
13 13 13 13 13 11 13 13 13 13 13 13 13 13
-0.35 0.88 -0.22 1.09 -1.67 1.86 1.37 -0.44 0.03 -0.44 0.37 -1.23 -0.20 1.22
0.637 0.189 0.587 0.138 0.953 0.031 0.085 0.670 0.512 0.670 0.644 0.891 0.579 0.111
"The number of families may differ slightly from marker to marker because we were unable to type all affected members for each marker.
of ESD was generated from 13 of 13 families, where every case within each family shared both ESD alleles. The observed/expected frequencies for the ESD genotypes 1/1, 1/2, and 2/2 were .846/.810, .154/. 180, and .OOO/.OlO, respectively, again suggesting no association. Results for the all-cases analysis are presented in Table 111. The previous suggestions of nonindependent segregation for C3 and ESD are now much weaker (there were numerous instances where postmenopausal cases did not share both alleles with other cases in the family). In fact, in this analysis, PGMl ( P = 0.084) and AMY2 ( P = 0.069), which are on the same chromosome, appear to be segregating nonindepenTABLE 111. Results for All-Cases Analysis Using the Affected-Pedigree-MemberMethod Marker
Chromosomal location
ACP ADA AK 1 AMY2 BF c3 ESD GALT GLO 1 GPT HP 6PGD PGM 1 PGP
2p25 or 2p23 (C) 20q13.2-qter (C) 9q34 (C) 1P21 (C) 6 ~ 2 1 . 3(C) 19pter-q13.2 (C) 1 3 ~ 1 4 . 1(C) 9p21-pI3 (C) 6~21.3-21.2(C) LG2 16q21-q22 (C) lpter-p36.13 (C) lp22.1 (C) 16p13-pl2 (C)
Number of families"
Test statistic (z)
P value
19 19 19 19 19 16 19 19 19 19 19 19 19 19
-0.08 0.71 - 1.08 1.48 0.40 0.46 1.19 -0.78 0.74 -0.69 0.27 0.87 1.38 0.64
0.532 0.239 0.860 0.069 0.345 0.323 0.117 0.782 0.230 0.755 0.394 0. I92 0.084 0.261
"The number of families may differ slightly from marker to marker because we were unable to type all affected members for each marker.
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Haile et al.
dently with breast cancer. For AMY2,41 of 44 affected were 1/1; the other three cases, all from the same family, were 1/2. The population frequencies of 1/1 and 1/2 are .895 and.O51,respectively.ForPGMl, 1 2 o f 4 4 h a d l / l ; 8 o f 4 4 h a d 1/2; llof44had113; 4 of 44 had 2/2; 3 of 44 had 2/3; 2 of 44 had 3/3, and 2 of 44 had 414. The corresponding population frequencies are .384, .105, .087, .029, .024, .020, and ,005,respectively. Because our data set consists of a rather modest number of small pedigrees, we thought it prudent to carry out simulations to check the asymptotic normality of our statistic for those markers where there was a suggestion of nonindependent segregation (AMY2, C3, ESD, and PGM1). Using the appropriate data set (pre- or all-cases), we simulated the segregation of the appropriate marker down through the pedigrees independently of disease status. The random number generator we used is described in Wickman and Hill 119821. We calculated the statistics for each of 5,000 simulated data sets; these statistics had a near-normal distribution with positive skewness and kurtosis (Table IV). While 5,000 trials may not be enough to give an accurate estimate of small P values, the “empirical” P values agreed well with the theoretical P values, except for the marker ESD on the premenopausal data. In this simulation for the marker ESD, a statistic at least as extreme as 1.37 was observed in 10.9% of the 5,000 trials. This empirical P value of .lo9 is larger than the theoretical P value of 0.085 obtained under a normal (0,l) distribution; this may be due to a combination of a small number of families and a nonpolymorphic marker. DISCUSSION
Nonindependent segregation between 14 standard genetic markers and breast cancer was evaluated in 19 pedigrees identified through a bilateral breast cancer case diagnosed before 50 years of age. Evidence suggestive of nonindependence was observed for C3 and ESD in our premenopausal-cases-only analysis. Four genetic markers (ABO, BF, GPT, and RH) have been implicated in previous linkage analyses of breast cancer families. King et al. [1980] reported evidence of possible linkage to GPT, at a recombination fraction (0)of 0.0 assuming a dominant model of inheritance, for 11 families with breast cancer only or breast and ovarian cancer with an average age at diagnosis of 48 years. In a subsequent paper [King et al., 19831, evidence of linkage to GPT was reported for seven families with primarily premenopausal breast cancer plus ovarian cancer. Three other groups have reported either no evidence of linkage to GPT [Anderson et al., 19851 or evidence against linkage TABLE IV. Simulation Results Based on 5,000 Trials for Selected Markers Marker
Mean
Variance
Skewness
Kurtosis
Empirical P value
Theoretical P value
,082 ,226
0.369 1.188
0.034 0.109
0.031
0.448 0.780 1.330 0.437
0.323 0.117 0.064
0.323 0. I I7
Premenopausal-cases-only data c3
ESD All-cases data c3 ESD AMY2 PGM 1
,014 ,014
1.028 1.053
,022
1.008
,123
,009 - ,008 ,014
1.004 0.989 1.009
,138
,286 .317
0.086
0.085
0.069 0.084
Linkage Analyses of Affected Breast Cancer Pairs
53
to GPT [Bishop et al., 1988; McLellan et al., 19841. In a previous lod score analysis of our data [Goldstein et al., 19891, no positive lod scores were observed for GPT generally. In the present analysis, we observe no evidence of cosegregation with breast cancer. Bishop et al. [1988] reported positive lod scores for BF/HLA. We previously reported no informative lod scores with BF for these pedigrees, and in the present analysis we observe no evidence of nonindependent segregation. We were unable to evaluate RH and ABO since the APM method requires marker phenotypes that are codominant. Generally, the observation of nonsignificant cosegregation between a disease and putative genetic markers (e.g., breast cancer and BF and GPT in our analyses) does not provide strong evidence against linkage since APM methods are less powerful than lod score methods when the correct model parameters are specified. In addition, this particular analysis was based on a small number of pedigrees (affected pairs), and a number of markers were not very polymorphic, further decreasing power. In the face of this low power, positive results become even more noteworthy. We observed evidence of nonindependent segregation for C3 in the premenopausal-casesonly analysis. In previous linkage analyses of these families, Goldstein [ 19881reported a maximum lod score of 1.25 at 8 = .001 for C3 under a premenopausal-cases-only model with no sporadics and model parameters based on previously published literature. When we assume model parameters taken from our own segregation analyses [Goldstein et al., 19891 we observe small positive lod scores (z 0.4) only in the recessive-like premenopausal-cases-only analysis. Bishop et al. [ 19881 reported lod scores for C3 that are essentially uninformative for their families. If this suggestion of nonindependent segregation is sustained with more data (we will have completed data collection on 61 families by the end of 1989), and we continue to observe relatively uninformative lod scores, we may be misspecifying model parameters in our lod score linkage analyses. There was also a suggestion of nonindependent segregation with ESD in the premenopausal-cases-only analysis. This is consistent with positive Iod scores that we have previously reported for these data [Goldstein et al., 19891. The maximum lod score for ESD of 1.42 at 8 = 0.001 was observed under a recessive model with a gene frequency of 0.0259 and a maximum penetrance of 95%. This result is also consistent with the work of Lundberg et al. [1987], who reported a loss of heterozygosity for chromosome 13 markers in human ductal breast cancers, which suggests a somatic cell recessive mechanism and the possible involvement of a chromosome 13 locus in some forms of breast cancer. For both C3 and ESD, positive evidence of nonindependent segregation was observed in the premenopausal-cases-only analysis but not in the all-cases analysis. This may be due to random variation; however, it is also consistent with the hypothesis that the etiology of premenopausal breast cancer is different from the etiology of postmenopausal breast cancer. In the all-cases analysis, there was a suggestion of nonindependent segregation with AMY2 and PGMl , but the evidence is weaker than with C3. All cases shared the same marker genotype for AMY2, but the population frequency of the particular genotype is very high (>.90). The collection of more data should help us evaluate the validity of these initial results. To investigate whether monomorphic markers generate spurious evidence of cosegregation, we ran the APM method on the premenopausal data set with two alleles,
-
54
Haile et al.
where the allele frequencies were 0.001 and 0.999. We made every affected individual homozygous for the common allele. The APM method tended to give a test statistic close to zero for all weighting functions. This is because as the marker becomes more and more monomorphic, there is only one possible value that the similarity measure can take on. So when one forms the statistic by substracting the mean of the similarity measure from the observed similarity measure, one will get zero (or close to it). So we do not believe that a monomorphic marker will tend to provide spurious evidence for linkage any more than will a more polymorphic marker. The power of a monomorphic marker will, of course, be much less than that of a polymorphic marker. There have been few other studies of breast cancer pedigrees using the APM method. Kammerer [ 19861 studied several pedigrees from Utah and reported significant differences ( P < 0.01) between the mean proportion of genes identical by descent among sib-pairs concordant and discordant for breast cancer for BF, when parental information was not used, and for JK and KELL when parental information was used. Kammerer [ 19861 also analyzed pedigrees from the Netherlands and reported significant ( P < 0.05) sib-pair linkage for GLO 1. We observed no evidence of cosegregation for BF or GLO 1. We were unable to evaluate data on JK and KELL because of the requirement of codominance of the marker phenotypes. We believe that unresolved etiological heterogeneity and random variation (with multiple comparisons) are likely explanations of the conflicting results. The collection and analysis of more data by our group and others, with an attention to defining more homogeneous subgroups, should help elucidate the underlying reason(s) for these apparent inconsistencies. The APM method is attractive for the analysis of breast cancer pedigrees for a number of reasons. First, as we noted earlier, we are not confident about segregationbased parameters used to define a model of inheritance that are required for lod score calculations. In contrast, the APM method circumvents this problem of unknown mode of inheritance by making no assumptions about the underlying model of inheritance. As an extension of this first point, is is very plausible, based on epidemiological and experimental evidence, that environmental factors also are involved in the etiology of breast cancer, perhaps via the same etiological pathway of the gene(s) we are trying to map. Currently, most linkage programs do not incorporate environmental exposures into the analysis. In preliminary results from our group (V. Cortessis, personal comunder some models munication) this limitation may produce biased estimates of where an environmental factor is necessary for the gene to be penetrant. An advantage of the APM method is that it obviates the need to incorporate environmental exposures into the analysis since it is limited to affected members whose exposures, whatever they were, were sufficient to produce disease. Finally, as argued above, although we regard our preliminary findings as interesting and worthy of further research, one must be careful in interpreting the results. When cosegregation of a disease is investigated with each of several markers on different chromosomes, “significant” results can arise owing to random variation (chance) alone. The extreme practice of multiplying the observed P value (e.g., P = 0.03 1 for C3 in the premenopausal-cases-only analysis) by the number of multiple comparisons (14 in this case, giving rise to an adjusted P = 0.434) is highly conservative and would necessarily destroy all evidence in favor of nonrandom segregation. Only additional analyses of much larger data sets can resolve the uncertainty surrounding our preliminary findings.
(e)
Linkage Analyses of Affected Breast Cancer Pairs
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ACKNOWLEDGMENTS This work was supported by Public Health Service grant CA36387, National Cancer Institute. D.E.W. was supported in part by USPHS National Research Award GM-08 185. REFERENCES Anderson DE (1976): Genetic predisposition to breast cancer. In St-Ameault G, Band G, Israel L (eds): “Recent Results in Cancer Research, Breast Cancer: A Multidisciplinary Approach,” Vol. 57. Berlin: Springer-Verlag, pp 10-20. Anderson DE (1977): Breast cancer in families. Cancer 40: 1855-1 860. Anderson DE, Ferrell RE, Williams WR (1985): A linkage study of human breast cancer: Human gene mapping. Cytogenet Cell Genet 40:568. Bishop DT, Falk CT, MacCluer JW (eds) (1986): Proceedings of the Genetic Analysis Workshop IV, held at Snowbird, Utah, October 5-7, 1985. ‘‘Genetic Epidemiology: Applications and Comparisons of Methods: Supplement 1 .” New York Alan R. Liss, Inc. Bishop DT, Cannon-Albright L, McLellan T, Gardner EJ, Skolnick MH (1988): Segregation and linkage analysis of nine Utah breast cancer pedigrees. Genet Epidemiol5: 15 1- 169. Goldstein AM (1988): A Genetic Epidemiologic Investigation of Breast Cancer in Families with Bilateral Breast Cancer. Doctoral Dissertation, University of California, Los Angeles. Goldstein AM, Haile RW, Spence MA, Sparkes RS, Paganini-Hill A (1989): A genetic epidemiologic investigation of breast cancer in families with bilateral breast cancer. 11: Linkage analysis. Clin Genet 36(2): 100- 106. Kammerer CM (1986): Linkage analysis of breast cancer among Utah and Dutch families using the sibpair test. Genet Epidemiol (Suppl) 1:83-86. Kelsey JL (1979): A review of the epidemiology of human breast cancer Epidemiol Rev 1:74-109. King MC, Go RC, Elston RC, Lynch HT, Petrakis NL (1980): Allele increasing susceptibility to human breast cancer may be linked to the glutamate-pyruvate transaminase locus. Science 208:406-408. King MC, Go RCP, Lynch HT, Elson RC, Terasaki PI, Petrakis NL, Rodgers GC, Lattanzio D, BaileyWilson J (1983): Genetic epidemiology of breast cancer and associated cancers in high-risk families. 11: Linkage analysis. JNCI 71:463-467. Lundberg C, Lambert S, Cavenee WK, Nordenskjold M (1987): Loss of heterozygosity in human ductal breast tumors indicates a recessive mutation on chromosome 13. Proc Natl Acad Sci USA 84:2372-2376. Mack T (1 977): Cancer Surveillance Program in Los Angeles County. Natl Cancer Inst Monogr 47:99- 10 I . McLellan T , Cannon LA, Bishop DT, Skolnick MH (1984): The cumulative LOD score between a breast cancer susceptibility locus and GPT is - 3.86. Cytogenet Cell Genet 37:536-537. Weeks DE, Lange K (1988): The affected-pedigree-member method of linkage analysis. Am J Hum Genet 42:3 15-326. Wickman BA, and Hill ID (1982): Algorithm AS 183: An efficient and portable pseudo-random number generator. Appl Stat 31:188-190.
Edited by D.C. Rao and G.P. Vogler
Genetic Epidemiology of Bilateral Breast Cancer: A Linkage Analysis Using the Affected-Pedigree-Member Method Robert W. Haile, Alisa M. Goldstein, Daniel E. Weeks, Robert S. Sparkes, and Annlia Paganini-Hill Department of Epidemiology, School of Public Health (R. W.H., A. M.G.), and Departments of Biomathematics (0.E. W.) and Medicine (R.S.S.), School of Medicine, University of California, and Department of Preventive Medicine, University of Southern California School of Medicine (A.P.-H.), Los Angeles We used the affected-pedigree-member (APM) method to conduct linkage analyses on 19 pedigrees in which the probands had premenopausal bilateral breast cancer. This method analyzes all affected pairs of relatives, as opposed to siblings only, and incorporates into the analyses information on the frequency of marker alleles. Fourteen codominant marker systems were evaluated in two separate analyses. In the first, only premenopausal cases of breast cancer were coded as affected because we assumed that postmenopausal cases were due to a different etiology. In the second analysis, all cases of breast cancer were coded as affected, irrespective of menopausal status. In the premenopausal-cases-only analysis, we observed evidence suggestive of nonindependent segregation for C3 and ESD. In the all-cases analysis, we observed much weaker evidence for C3 and ESD and noted a suggestion of nonindependent segregation for AMY2 and PGMl . Key words: affected relatives, affected pairs, cosegregation, nonindependentsegregation
INTRODUCTION
Previous analyses have not yielded significant evidence of linkage between a breast cancer susceptibility (BCS) locus and a genetic marker. Although several markers (ABO, Received for publication March 2, 1989; revision accepted October 4, 1989. Address reprint requests to Robert W. Haile, Department of Epidemiology, School of Public Health, University of California, Los Angeles, CA 90024- 1772. Current institutions for A.M.G. and D.E.W. are Family Studies Section, National Cancer Institute, and Columbia University, respectively.
01990 Wiley-Liss, Inc.
48
Haile et al.
BF/HLA, GPT, RH) have been implicated with various subtypes of breast cancer, statistical significance in favor of linkage to a BCS locus has not been reached [Bishop et al., 19861. One reason for the inconsistent results may be unresolved etiological heterogeneity. Several studies have refined the definition of breast cancer using epidemiological and clinical criteria to investigate a presumably more homogeneous sample of breast cancer pedigrees [Goldstein et al., 19891. The present study continues this approach by restricting pedigrees to those in which the proband had bilateral breast cancer diagnosed before 50 years of age. These families manifest a very high familial risk of breast cancer [Anderson, 1976; Anderson, 1977; Kelsey, 19791, and they represent a group that is homogeneous with respect to certain clinical characteristics and, therefore, presumably more homogeneous with respect to etiology. Most previous linkage analyses of breast cancer were based on the lod score method [King et a]., 1980, 1983; Anderson et al., 1985; Bishop et al., 1988; Goldstein et al., 19891. In principle, this is a powerful technique, but the validity of results depends in part on specifying the correct model of inheritance. Typically, model parameters are taken from segregation analyses of the same data; however, given etiological heterogeneity, confounding by environmental factors, and other challenges to segregation analysis, there is no consensus about the exact models of inheritance for breast cancer. The affected-pedigree-member (APM) method is attractive in the face of this uncertainty because it does not require assumptions about the underlying model of inheritance. We present here results of APM analyses of breast cancer in 19 pedigrees. MATERIALS AND METHODS FamiI ies
Families were ascertained through at least one female member affected with bilateral breast cancer diagnosed before the age of 50 years and registered by the University of Southern California Cancer Surveillance Program, a population-based cancer registry for Los Angeles County [Mack, 19771. Since multiplex families are the most informative for linkage analysis, they were sought preferentially. Twenty Caucasian families (1 1 multigenerational and 9 nuclear) with an average family size of 8 comprise the data set. One of these was eliminated from this analysis because the proband had postmenopausal breast cancer. Approximately 5 individuals per family provided blood for marker typing. Fourteen pedigrees contained at least two cases of premenopausal breast cancer. Data on these pedigrees are available on request. Genetic Marker Typing
Family members were typed for the following 23 red blood cell antigens, red blood cell enzymes, and serum protein markers: ABO, ACP1, ADA, AK1, AMY2, BF, C3, ESD, FY, GALT, GC, GLO1, GPT, HP, JK, KEL, MNS, P, 6PGD, PGM1, PGP, RH, and TF. Gene symbols are those used by the Human Gene Mapping conference 9 (1987). All marker typing was done by one of us (R.S.S.) using standard laboratory techniques. The analytical method employed in this investigation requires the marker phenotypes to be entirely codominant. Therefore, our analysis was limited to 14 codominant marker systems: ACP1, ADA, AK1, AMY2, BF, C3, ESD, GALT, GLO1, GPT, HP, PGD, PGMl, and PGP.
Linkage Analyses of Affected Breast Cancer Pairs
49
Analytical Methods
We used the APM method to test for departures from independent segregation of breast cancer and the 14 codominant marker phenotypes [Weeks and Lange, 19881. The APM method is an extension of the sib-pair approach that uses marker information on all pairs of affected members of a pedigree but not on unaffected members. Families with only one affected person or with only one affected person or with only one affected person on whom we have necessary data are not used in this analysis. Since it is more striking for distantly affected relatives to share a rare marker allele than a common marker allele, the test statistic includes a weighting factor f(p) based on allele frequency p [Weeks and Lange, 19881. Examples of this weighting factor are The function f(p) = 1/ may represent a reasonable compromise 1, Up, or 1/ between the extremes of ignoring allele frequency [f(p) = 11 and of so strongly weighting by allele frequency [f(p) = Up] as to disturb the asymptotic normality of the test statistic, so we present results using only this weighting function (we obtained similar results with other weights; markers positive for cosegregation remained positive). Allele frequencies were taken or estimated from published frequencies of markers in Caucasians. We conducted two analyses: “premenopausal-cases-only’’ and ‘ ‘all-cases. Affected women were classified with respect to menopausal status (premenopausal or postmenopausal) based on known age at menopause and age at diagnosis. For the premenopausal-cases-only analysis, we considered only cases of premenopausal breast cancer to be affected. All postmenopausal cases of breast cancer were coded as unaffected, since we were assuming that their disease involved etiologic pathways different from those of premenopausal breast cancer. For the all-cases analysis, all breast cancer cases were coded as affected irrespective of menopausal status. We considered no other partitions of our data (e.g., synchronous vs. asynchronous, presence or not of lobular or ductal breast cancer or fibrocystic disease) as we had in previous linkage analyses [Goldstein et al., 19891 because of the small number of pedigrees and cases of breast cancer available for an APM analysis.
6.
6
”
RESULTS
Table I presents data on each case in the 19 pedigrees. The average age of the proband at diagnosis of the second primary is 44 years. The majority (14/25 = 56%) of nonproband relatives are sisters of the proband. A majority of cases (14/24 = 58%) were premenopausal at time of diagnosis. Results for the premenopausal-cases-only analysis are presented in Table 11. The results suggest nonindependent segregation of breast cancer with C3 ( P = 0.03 1) and less so with ESD ( P = 0.085), which are on different chromosomes. Evidence of nonindependence for C3 was generated from 9 of 1 I families where all cases within each family shared both alleles. One family contained two affected sisters who shared one allele and the remaining family contained an affected mother-daughter pair who shared one allele. We evaluated the possibility of an association between C3 markers and breast cancer by computing the observed frequency of genotypes 1 / 1 , 1/2, and 212 among our cases (using one proband per family) and comparing them with the expected frequencies assuming Hardy-Weinberg equilibrium and no association. The observed/ expected frequencies for 1/1, 1/2, and 2/2 were .09/.022, .273/.255, and .637/.723, respectively, suggesting no association within our data. Evidence of nonindependence
50
Haile et al.
TABLE I. Descriptionsof Breast Cancer Cases in 19 Families
Family ID
Position in family
Age at diagnosis
BCSOOI
Proband Sistei" Sister Proband Sister DZ twin Proband Sister Proband
41/48' 55 43 48/48 55 34 49149 52 45148
Proband (sister) Mother Cousin (maternal) Proband Sister Aunt (maternal) Proband Mother Proband Mother Proband
46/46
Sister
26
Proband Sister Aunt (maternal) Proband Sister Sister Proband Sister Proband Sister Proband Aunt (maternal) Proband Father Proband Cousin (maternal) Proband Daughter Proband Sister
35/31 26 41 49/49 49 68 41/44 48 34/35
Proband Sister Proband Mother
48/49 52 27/28
BCS002
BCS003 BCSOO4
BCSO05
BCS006 BCS007 BCSOOS
BCSOlO
BCSOII
BCS012 BCSO 13 BCSO14 BCSO 15 BCS016 BCS017 BCS018
BCS019 BCS020
"Relationship to proband. 'Ages at diagnoses of the two primaries
64 56 46/46 43 58 42/48 48 49149 86 30130
26 48/49 50 44/44 55 45/49 41 38148 35 43/44 31
46 .I
Menopause status (pre or post)
Pathology Ductal, lobular CIS Lobular, ductal Intraductal, intralobular Ductal, lobular CIS Ductal Ductal Ductal Lobular CIS Ductal, mucinous adenocarcinoma Ductal Lobular Ductal Ductal Lobular CIS Intraductal adenocarcinoma Medullary, Fibroadenocarcinoma Ductal Ductal Ductal Ductal Intraductal Ductal Ductal Scirrhous Ductal Lobu I ar Ductal Ductal Ductal Intraductal Intraductal, Comedocarcinoma Ductal, tubular Ductal -
Linkage Analyses of Affected Breast Cancer Pairs
51
TABLE 11. Results for Premenopausal-Cases-OnlyAnalysis Using the Affected-PedigreeMember Method Marker
Chromosomal location
ACP ADA AK I AMY2 BF c3 ESD GALT GLO 1 GFT HP 6PGD PGM 1 PGP
2p25 or 2p23 (C) 20q13.2-qter (C) 9q34 (C) lP21 (C) 6 ~ 2 1 . 3(C) 19pter-q 13.2 (C) 1 3 ~ 1 4 . 1(C) 9p2 1-p 13 (C) 6~21.3-21.2(C) LG2 16q21-q22 (C) lpter-p36.13 (C) lp22.1 (C) 16~13-pl2(C)
Number of families"
Test statistic (2)
P value
13 13 13 13 13 11 13 13 13 13 13 13 13 13
-0.35 0.88 -0.22 1.09 -1.67 1.86 1.37 -0.44 0.03 -0.44 0.37 -1.23 -0.20 1.22
0.637 0.189 0.587 0.138 0.953 0.031 0.085 0.670 0.512 0.670 0.644 0.891 0.579 0.111
"The number of families may differ slightly from marker to marker because we were unable to type all affected members for each marker.
of ESD was generated from 13 of 13 families, where every case within each family shared both ESD alleles. The observed/expected frequencies for the ESD genotypes 1/1, 1/2, and 2/2 were .846/.810, .154/. 180, and .OOO/.OlO, respectively, again suggesting no association. Results for the all-cases analysis are presented in Table 111. The previous suggestions of nonindependent segregation for C3 and ESD are now much weaker (there were numerous instances where postmenopausal cases did not share both alleles with other cases in the family). In fact, in this analysis, PGMl ( P = 0.084) and AMY2 ( P = 0.069), which are on the same chromosome, appear to be segregating nonindepenTABLE 111. Results for All-Cases Analysis Using the Affected-Pedigree-MemberMethod Marker
Chromosomal location
ACP ADA AK 1 AMY2 BF c3 ESD GALT GLO 1 GPT HP 6PGD PGM 1 PGP
2p25 or 2p23 (C) 20q13.2-qter (C) 9q34 (C) 1P21 (C) 6 ~ 2 1 . 3(C) 19pter-q13.2 (C) 1 3 ~ 1 4 . 1(C) 9p21-pI3 (C) 6~21.3-21.2(C) LG2 16q21-q22 (C) lpter-p36.13 (C) lp22.1 (C) 16p13-pl2 (C)
Number of families"
Test statistic (z)
P value
19 19 19 19 19 16 19 19 19 19 19 19 19 19
-0.08 0.71 - 1.08 1.48 0.40 0.46 1.19 -0.78 0.74 -0.69 0.27 0.87 1.38 0.64
0.532 0.239 0.860 0.069 0.345 0.323 0.117 0.782 0.230 0.755 0.394 0. I92 0.084 0.261
"The number of families may differ slightly from marker to marker because we were unable to type all affected members for each marker.
52
Haile et al.
dently with breast cancer. For AMY2,41 of 44 affected were 1/1; the other three cases, all from the same family, were 1/2. The population frequencies of 1/1 and 1/2 are .895 and.O51,respectively.ForPGMl, 1 2 o f 4 4 h a d l / l ; 8 o f 4 4 h a d 1/2; llof44had113; 4 of 44 had 2/2; 3 of 44 had 2/3; 2 of 44 had 3/3, and 2 of 44 had 414. The corresponding population frequencies are .384, .105, .087, .029, .024, .020, and ,005,respectively. Because our data set consists of a rather modest number of small pedigrees, we thought it prudent to carry out simulations to check the asymptotic normality of our statistic for those markers where there was a suggestion of nonindependent segregation (AMY2, C3, ESD, and PGM1). Using the appropriate data set (pre- or all-cases), we simulated the segregation of the appropriate marker down through the pedigrees independently of disease status. The random number generator we used is described in Wickman and Hill 119821. We calculated the statistics for each of 5,000 simulated data sets; these statistics had a near-normal distribution with positive skewness and kurtosis (Table IV). While 5,000 trials may not be enough to give an accurate estimate of small P values, the “empirical” P values agreed well with the theoretical P values, except for the marker ESD on the premenopausal data. In this simulation for the marker ESD, a statistic at least as extreme as 1.37 was observed in 10.9% of the 5,000 trials. This empirical P value of .lo9 is larger than the theoretical P value of 0.085 obtained under a normal (0,l) distribution; this may be due to a combination of a small number of families and a nonpolymorphic marker. DISCUSSION
Nonindependent segregation between 14 standard genetic markers and breast cancer was evaluated in 19 pedigrees identified through a bilateral breast cancer case diagnosed before 50 years of age. Evidence suggestive of nonindependence was observed for C3 and ESD in our premenopausal-cases-only analysis. Four genetic markers (ABO, BF, GPT, and RH) have been implicated in previous linkage analyses of breast cancer families. King et al. [1980] reported evidence of possible linkage to GPT, at a recombination fraction (0)of 0.0 assuming a dominant model of inheritance, for 11 families with breast cancer only or breast and ovarian cancer with an average age at diagnosis of 48 years. In a subsequent paper [King et al., 19831, evidence of linkage to GPT was reported for seven families with primarily premenopausal breast cancer plus ovarian cancer. Three other groups have reported either no evidence of linkage to GPT [Anderson et al., 19851 or evidence against linkage TABLE IV. Simulation Results Based on 5,000 Trials for Selected Markers Marker
Mean
Variance
Skewness
Kurtosis
Empirical P value
Theoretical P value
,082 ,226
0.369 1.188
0.034 0.109
0.031
0.448 0.780 1.330 0.437
0.323 0.117 0.064
0.323 0. I I7
Premenopausal-cases-only data c3
ESD All-cases data c3 ESD AMY2 PGM 1
,014 ,014
1.028 1.053
,022
1.008
,123
,009 - ,008 ,014
1.004 0.989 1.009
,138
,286 .317
0.086
0.085
0.069 0.084
Linkage Analyses of Affected Breast Cancer Pairs
53
to GPT [Bishop et al., 1988; McLellan et al., 19841. In a previous lod score analysis of our data [Goldstein et al., 19891, no positive lod scores were observed for GPT generally. In the present analysis, we observe no evidence of cosegregation with breast cancer. Bishop et al. [1988] reported positive lod scores for BF/HLA. We previously reported no informative lod scores with BF for these pedigrees, and in the present analysis we observe no evidence of nonindependent segregation. We were unable to evaluate RH and ABO since the APM method requires marker phenotypes that are codominant. Generally, the observation of nonsignificant cosegregation between a disease and putative genetic markers (e.g., breast cancer and BF and GPT in our analyses) does not provide strong evidence against linkage since APM methods are less powerful than lod score methods when the correct model parameters are specified. In addition, this particular analysis was based on a small number of pedigrees (affected pairs), and a number of markers were not very polymorphic, further decreasing power. In the face of this low power, positive results become even more noteworthy. We observed evidence of nonindependent segregation for C3 in the premenopausal-casesonly analysis. In previous linkage analyses of these families, Goldstein [ 19881reported a maximum lod score of 1.25 at 8 = .001 for C3 under a premenopausal-cases-only model with no sporadics and model parameters based on previously published literature. When we assume model parameters taken from our own segregation analyses [Goldstein et al., 19891 we observe small positive lod scores (z 0.4) only in the recessive-like premenopausal-cases-only analysis. Bishop et al. [ 19881 reported lod scores for C3 that are essentially uninformative for their families. If this suggestion of nonindependent segregation is sustained with more data (we will have completed data collection on 61 families by the end of 1989), and we continue to observe relatively uninformative lod scores, we may be misspecifying model parameters in our lod score linkage analyses. There was also a suggestion of nonindependent segregation with ESD in the premenopausal-cases-only analysis. This is consistent with positive Iod scores that we have previously reported for these data [Goldstein et al., 19891. The maximum lod score for ESD of 1.42 at 8 = 0.001 was observed under a recessive model with a gene frequency of 0.0259 and a maximum penetrance of 95%. This result is also consistent with the work of Lundberg et al. [1987], who reported a loss of heterozygosity for chromosome 13 markers in human ductal breast cancers, which suggests a somatic cell recessive mechanism and the possible involvement of a chromosome 13 locus in some forms of breast cancer. For both C3 and ESD, positive evidence of nonindependent segregation was observed in the premenopausal-cases-only analysis but not in the all-cases analysis. This may be due to random variation; however, it is also consistent with the hypothesis that the etiology of premenopausal breast cancer is different from the etiology of postmenopausal breast cancer. In the all-cases analysis, there was a suggestion of nonindependent segregation with AMY2 and PGMl , but the evidence is weaker than with C3. All cases shared the same marker genotype for AMY2, but the population frequency of the particular genotype is very high (>.90). The collection of more data should help us evaluate the validity of these initial results. To investigate whether monomorphic markers generate spurious evidence of cosegregation, we ran the APM method on the premenopausal data set with two alleles,
-
54
Haile et al.
where the allele frequencies were 0.001 and 0.999. We made every affected individual homozygous for the common allele. The APM method tended to give a test statistic close to zero for all weighting functions. This is because as the marker becomes more and more monomorphic, there is only one possible value that the similarity measure can take on. So when one forms the statistic by substracting the mean of the similarity measure from the observed similarity measure, one will get zero (or close to it). So we do not believe that a monomorphic marker will tend to provide spurious evidence for linkage any more than will a more polymorphic marker. The power of a monomorphic marker will, of course, be much less than that of a polymorphic marker. There have been few other studies of breast cancer pedigrees using the APM method. Kammerer [ 19861 studied several pedigrees from Utah and reported significant differences ( P < 0.01) between the mean proportion of genes identical by descent among sib-pairs concordant and discordant for breast cancer for BF, when parental information was not used, and for JK and KELL when parental information was used. Kammerer [ 19861 also analyzed pedigrees from the Netherlands and reported significant ( P < 0.05) sib-pair linkage for GLO 1. We observed no evidence of cosegregation for BF or GLO 1. We were unable to evaluate data on JK and KELL because of the requirement of codominance of the marker phenotypes. We believe that unresolved etiological heterogeneity and random variation (with multiple comparisons) are likely explanations of the conflicting results. The collection and analysis of more data by our group and others, with an attention to defining more homogeneous subgroups, should help elucidate the underlying reason(s) for these apparent inconsistencies. The APM method is attractive for the analysis of breast cancer pedigrees for a number of reasons. First, as we noted earlier, we are not confident about segregationbased parameters used to define a model of inheritance that are required for lod score calculations. In contrast, the APM method circumvents this problem of unknown mode of inheritance by making no assumptions about the underlying model of inheritance. As an extension of this first point, is is very plausible, based on epidemiological and experimental evidence, that environmental factors also are involved in the etiology of breast cancer, perhaps via the same etiological pathway of the gene(s) we are trying to map. Currently, most linkage programs do not incorporate environmental exposures into the analysis. In preliminary results from our group (V. Cortessis, personal comunder some models munication) this limitation may produce biased estimates of where an environmental factor is necessary for the gene to be penetrant. An advantage of the APM method is that it obviates the need to incorporate environmental exposures into the analysis since it is limited to affected members whose exposures, whatever they were, were sufficient to produce disease. Finally, as argued above, although we regard our preliminary findings as interesting and worthy of further research, one must be careful in interpreting the results. When cosegregation of a disease is investigated with each of several markers on different chromosomes, “significant” results can arise owing to random variation (chance) alone. The extreme practice of multiplying the observed P value (e.g., P = 0.03 1 for C3 in the premenopausal-cases-only analysis) by the number of multiple comparisons (14 in this case, giving rise to an adjusted P = 0.434) is highly conservative and would necessarily destroy all evidence in favor of nonrandom segregation. Only additional analyses of much larger data sets can resolve the uncertainty surrounding our preliminary findings.
(e)
Linkage Analyses of Affected Breast Cancer Pairs
55
ACKNOWLEDGMENTS This work was supported by Public Health Service grant CA36387, National Cancer Institute. D.E.W. was supported in part by USPHS National Research Award GM-08 185. REFERENCES Anderson DE (1976): Genetic predisposition to breast cancer. In St-Ameault G, Band G, Israel L (eds): “Recent Results in Cancer Research, Breast Cancer: A Multidisciplinary Approach,” Vol. 57. Berlin: Springer-Verlag, pp 10-20. Anderson DE (1977): Breast cancer in families. Cancer 40: 1855-1 860. Anderson DE, Ferrell RE, Williams WR (1985): A linkage study of human breast cancer: Human gene mapping. Cytogenet Cell Genet 40:568. Bishop DT, Falk CT, MacCluer JW (eds) (1986): Proceedings of the Genetic Analysis Workshop IV, held at Snowbird, Utah, October 5-7, 1985. ‘‘Genetic Epidemiology: Applications and Comparisons of Methods: Supplement 1 .” New York Alan R. Liss, Inc. Bishop DT, Cannon-Albright L, McLellan T, Gardner EJ, Skolnick MH (1988): Segregation and linkage analysis of nine Utah breast cancer pedigrees. Genet Epidemiol5: 15 1- 169. Goldstein AM (1988): A Genetic Epidemiologic Investigation of Breast Cancer in Families with Bilateral Breast Cancer. Doctoral Dissertation, University of California, Los Angeles. Goldstein AM, Haile RW, Spence MA, Sparkes RS, Paganini-Hill A (1989): A genetic epidemiologic investigation of breast cancer in families with bilateral breast cancer. 11: Linkage analysis. Clin Genet 36(2): 100- 106. Kammerer CM (1986): Linkage analysis of breast cancer among Utah and Dutch families using the sibpair test. Genet Epidemiol (Suppl) 1:83-86. Kelsey JL (1979): A review of the epidemiology of human breast cancer Epidemiol Rev 1:74-109. King MC, Go RC, Elston RC, Lynch HT, Petrakis NL (1980): Allele increasing susceptibility to human breast cancer may be linked to the glutamate-pyruvate transaminase locus. Science 208:406-408. King MC, Go RCP, Lynch HT, Elson RC, Terasaki PI, Petrakis NL, Rodgers GC, Lattanzio D, BaileyWilson J (1983): Genetic epidemiology of breast cancer and associated cancers in high-risk families. 11: Linkage analysis. JNCI 71:463-467. Lundberg C, Lambert S, Cavenee WK, Nordenskjold M (1987): Loss of heterozygosity in human ductal breast tumors indicates a recessive mutation on chromosome 13. Proc Natl Acad Sci USA 84:2372-2376. Mack T (1 977): Cancer Surveillance Program in Los Angeles County. Natl Cancer Inst Monogr 47:99- 10 I . McLellan T , Cannon LA, Bishop DT, Skolnick MH (1984): The cumulative LOD score between a breast cancer susceptibility locus and GPT is - 3.86. Cytogenet Cell Genet 37:536-537. Weeks DE, Lange K (1988): The affected-pedigree-member method of linkage analysis. Am J Hum Genet 42:3 15-326. Wickman BA, and Hill ID (1982): Algorithm AS 183: An efficient and portable pseudo-random number generator. Appl Stat 31:188-190.
Edited by D.C. Rao and G.P. Vogler
