Background

In spite of intensive efforts, understanding of the genetic aspects of familial prostate cancer remains largely incomplete. In a previous microsatellite-based linkage scan of 1233 prostate cancer (PC) families, we identified suggestive evidence for linkage (i.e. LOD≥1.86) at 5q12, 15q11, 17q21, 22q12, and two loci on 8p, with additional regions implicated in subsets of families defined by age at diagnosis, disease aggressiveness, or number of affected members.

Methods

In an attempt to replicate these findings and increase linkage resolution, we used the Illumina 6000 SNP linkage panel to perform a genome-wide linkage scan of an independent set of 762 multiplex PC families, collected by 11 ICPCG groups.

Results

Of the regions identified previously, modest evidence of replication was observed only on the short arm of chromosome 8, where HLOD scores of 1.63 and 3.60 were observed in the complete set of families and families with young average age at diagnosis, respectively. The most significant linkage signals found in the complete set of families were observed across a broad, 37 cM interval on 4q13-25, with LOD scores ranging from 2.02 to 2.62, increasing to 4.50 in families with older average age at diagnosis. In families with multiple cases presenting with more aggressive disease, LOD scores over 3.0 were observed at 8q24 in the vicinity of previously identified common PC risk variants, as well as MYC, an important gene in PC biology.

Conclusions

These results will be useful in prioritizing future susceptibility gene discovery efforts in this common cancer.

Multiple prostate cancer (PCa) risk-related loci have been discovered by genome-wide association studies (GWAS) based on case–control designs. However, GWAS findings may be confounded by population stratification if cases and controls are inadvertently drawn from different genetic backgrounds. In addition, since these loci were identified in cases with predominantly sporadic disease, little is known about their relationships with hereditary prostate cancer (HPC). The association between seventeen reported PCa susceptibility loci was evaluated with a family-based association test using 1,979 hereditary PCa families of European descent collected by members of the International Consortium for Prostate Cancer Genetics, with a total of 5,730 affected men. The risk alleles for 8 of the 17 loci were significantly over-transmitted from parents to affected offspring, including SNPs residing in 8q24 (regions 1, 2 and 3), 10q11, 11q13, 17q12 (region 1), 17q24 and Xp11. In subgroup analyses, three loci, at 8q24 (regions 1 and 2) plus 17q12, were significantly over-transmitted in hereditary PCa families with five or more affected members, while loci at 3p12, 8q24 (region 2), 11q13, 17q12 (region 1), 17q24 and Xp11 were significantly over-transmitted in HPC families with an average age of diagnosis at 65 years or less. Our results indicate that at least a subset of PCa risk-related loci identified by case–control GWAS are also associated with disease risk in HPC families.

Purpose

Women with germline BRCA1 and BRCA2 mutations have five- to 20-fold increased risks of developing breast and ovarian cancer. A recent study claimed that women testing negative for their family-specific BRCA1 or BRCA2 mutation (noncarriers) have a five-fold increased risk of breast cancer. We estimated breast cancer risks for noncarriers by using a population-based sample of patients with breast cancer and their female first-degree relatives (FDRs).

Patients and Methods

Patients were women with breast cancer and their FDRs enrolled in the population-based component of the Breast Cancer Family Registry; patients with breast cancer were tested for BRCA1 and BRCA2 mutations, as were FDRs of identified mutation carriers. We used segregation analysis to fit a model that accommodates familial correlation in breast cancer risk due to unobserved shared risk factors.

Results

We studied 3,047 families; 160 had BRCA1 and 132 had BRCA2 mutations. There was no evidence of increased breast cancer risk for noncarriers of identified mutations compared with FDRs from families without BRCA1 or BRCA2 mutations: relative risk was 0.39 (95% CI, 0.04 to 3.81). Residual breast cancer correlation within families was strong, suggesting substantial risk heterogeneity in women without BRCA1 or BRCA2 mutations, with some 3.4% of them accounting for roughly one third of breast cancer cases.

Conclusion

These results support the practice of advising noncarriers that they do not have any increase in breast cancer risk attributable to the family-specific BRCA1 or BRCA2 mutation.

Meta-analysis of genome-wide association studies involves testing single nucleotide polymorphisms (SNPs) using summary statistics that are weighted sums of site-specific score or Wald statistics. This approach avoids having to pool individual-level data. We describe the weights that maximize the power of the summary statistics. For small effect-sizes, any choice of weights yields summary Wald and score statistics with the same power, and the optimal weights are proportional to the square roots of the sites' Fisher information for the SNP's regression coefficient. When SNP effect size is constant across sites, the optimal summary Wald statistic is the well-known inverse-variance-weighted combination of estimated regression coefficients, divided by its standard deviation. We give simple approximations to the optimal weights for various phenotypes, and show that weights proportional to the square roots of study sizes are suboptimal for data from case-control studies with varying case-control ratios, for quantitative trait data when the trait variance differs across sites, for count data when the site-specific mean counts differ, and for survival data with different proportions of failing subjects. Simulations suggest that weights that accommodate inter-site variation in imputation error give little power gain compared to those obtained ignoring imputation uncertainties. We note advantages to combining site-specific score statistics, and we show how they can be used to assess effect-size heterogeneity across sites. The utility of the summary score statistic is illustrated by application to a meta-analysis of schizophrenia data in which only site-specific p-values and directions of association are available.

Prostate cancer has a strong familial component but uncovering the molecular basis for inherited susceptibility for this disease has been challenging. Recently, a rare, recurrent mutation (G84E) in HOXB13 was reported to be associated with prostate cancer risk. Confirmation and characterization of this finding is necessary to potentially translate this information to the clinic. To examine this finding in a large international sample of prostate cancer families, we genotyped this mutation and 14 other SNPs in or flanking HOXB13 in 2,443 prostate cancer families recruited by the International Consortium for Prostate Cancer Genetics (ICPCG). At least one mutation carrier was found in 112 prostate cancer families (4.6 %), all of European descent. Within carrier families, the G84E mutation was more common in men with a diagnosis of prostate cancer (194 of 382, 51 %) than those without (42 of 137, 30 %), P = 9.9 × 10−8 [odds ratio 4.42 (95 % confidence interval 2.56–7.64)]. A family-based association test found G84E to be significantly over-transmitted from parents to affected offspring (P = 6.5 × 10−6). Analysis of markers flanking the G84E mutation indicates that it resides in the same haplotype in 95 % of carriers, consistent with a founder effect. Clinical characteristics of cancers in mutation carriers included features of high-risk disease. These findings demonstrate that the HOXB13 G84E mutation is present in ~5 % of prostate cancer families, predominantly of European descent, and confirm its association with prostate cancer risk. While future studies are needed to more fully define the clinical utility of this observation, this allele and others like it could form the basis for early, targeted screening of men at elevated risk for this common, clinically heterogeneous cancer.

Electronic supplementary material

The online version of this article (doi:10.1007/s00439-012-1229-4) contains supplementary material, which is available to authorized users.

Background A previous Australian population-based breast cancer case-control study found indirect evidence that control participation, although high, was not random. We hypothesized that unaffected sisters may provide a more appropriate comparison group than unrelated population controls.

Methods Three population-based case-control-family studies of breast cancer in women of white European origin were carried out by the Australian, Ontario and Northern California sites of the Breast Cancer Family Registry. We compared risk factors between 3643 cases, 2444 of their unaffected sisters and 2877 population controls and conducted separate case-control analyses based on population and sister controls using unconditional multivariable logistic regression.

Results Compared with sister controls, population controls were more highly educated, had an earlier age at menarche, fewer births, their first birth at a later age and their last birth more recently. The established breast cancer associations detected using sister controls, but not detected using population controls, were decreasing risk with each of later age at menarche, more births, younger age at first birth and greater time since last birth.

Conclusions Since participation of population controls might be unintentionally related to some risk factors, we hypothesize that sister controls could provide more valid relative risk estimates and be recruited at lower cost. Given declining study participation by population controls, this contention is highly relevant to epidemiologic research.

Background

Prostate cancer is generally believed to have a strong inherited component, but the search for susceptibility genes has been hindered by the effects of genetic heterogeneity. The recently developed sumLINK and sumLOD statistics are powerful tools for linkage analysis in the presence of heterogeneity.

Methods

We performed a secondary analysis of 1233 prostate cancer pedigrees from the International Consortium for Prostate Cancer Genetics (ICPCG) using two novel statistics, the sumLINK and sumLOD. For both statistics, dominant and recessive genetic models were considered. False discovery rate (FDR) analysis was conducted to assess the effects of multiple testing.

Results

Our analysis identified significant linkage evidence at chromosome 22q12, confirming previous findings by the initial conventional analyses of the same ICPCG data. Twelve other regions were identified with genomewide suggestive evidence for linkage. Seven regions (1q23, 5q11, 5q35, 6p21, 8q12, 11q13, 20p11-q11) are near loci previously identified in the initial ICPCG pooled data analysis or the subset of aggressive prostate cancer (PC) pedigrees. Three other regions (1p12, 8p23, 19q13) confirm loci reported by others, and two (2p24, 6q27) are novel susceptibility loci. FDR testing indicates that over 70% of these results are likely true positive findings. Statistical recombinant mapping narrowed regions to an average of 9 cM.

Conclusions

Our results represent genomic regions with the greatest consistency of positive linkage evidence across a very large collection of high-risk prostate cancer pedigrees using new statistical tests that deal powerfully with heterogeneity. These regions are excellent candidates for further study to identify prostate cancer predisposition genes.

Background

Genetic variants are likely to contribute to a portion of prostate cancer risk. Full elucidation of the genetic etiology of prostate cancer is difficult because of incomplete penetrance and genetic and phenotypic heterogeneity. Current evidence suggests that genetic linkage to prostate cancer has been found on several chromosomes including the X; however, identification of causative genes has been elusive.

Methods

Parametric and non-parametric linkage analyses were performed using 26 microsatellite markers in each of 11 groups of multiple-case prostate cancer families from the International Consortium for Prostate Cancer Genetics (ICPCG). Meta-analyses of the resultant family-specific linkage statistics across the entire 1,323 families and in several predefined subsets were then performed.

Results

Meta-analyses of linkage statistics resulted in a maximum parametric heterogeneity lod score (HLOD) of 1.28, and an allele-sharing lod score (LOD) of 2.0 in favor of linkage to Xq27-q28 at 138 cM. In subset analyses, families with average age at onset less than 65 years exhibited a maximum HLOD of 1.8 (at 138 cM) versus a maximum regional HLOD of only 0.32 in families with average age at onset of 65 years or older. Surprisingly, the subset of families with only 2–3 affected men and some evidence of male-to-male transmission of prostate cancer gave the strongest evidence of linkage to the region (HLOD = 3.24, 134 cM). For this subset, the HLOD was slightly increased (HLOD = 3.47 at 134 cM) when families used in the original published report of linkage to Xq27-28 were excluded.

Conclusions

Although there was not strong support for linkage to the Xq27-28 region in the complete set of families, the subset of families with earlier age at onset exhibited more evidence of linkage than families with later onset of disease. A subset of families with 2–3 affected individuals and with some evidence of male to male disease transmission showed stronger linkage signals. Our results suggest that the genetic basis for prostate cancer in our families is much more complex than a single susceptibility locus on the X chromosome, and that future explorations of the Xq27-28 region should focus on the subset of families identified here with the strongest evidence of linkage to this region.

The insulin-like growth factor (IGF) signaling axis plays an important role in cancer biology. We hypothesized that genetic variation in this pathway may influence risk of ovarian cancer. A three-center study of non-Hispanic whites including 1880 control women, 1135 women with invasive epithelial ovarian cancer and 321 women with borderline epithelial ovarian tumors was carried out to test the association between tag single-nucleotide polymorphisms (tSNPs) (n=58) in this pathway and risk of ovarian cancer. We found no association between variation in IGF1, IGFBP1 or IGFBP3 and risk of invasive disease, whereas five tSNPs in IGF2 were associated with risk of invasive epithelial ovarian cancer at P<0.05 and followed-up one of the associated SNPs. We conducted genotyping in 3216 additional non-Hispanic white cases and 5382 additional controls and were able to independently replicate our initial findings. In the combined set of studies, rs4320932 was associated with a 13% decreased risk of ovarian cancer per copy of the minor allele carried (95% confidence interval 0.81–0.93, P-trend=7.4 × 10−5). No heterogeneity of effect across study centers was observed (phet=0.25). IGF2 is emerging as an important gene for ovarian cancer; additional genotyping is warranted to further confirm these associations with IGF2 and to narrow down the region harboring the causal SNP.

Background

We previously reported an association between rs2660753, a prostate cancer susceptibility polymorphism, and invasive epithelial ovarian cancer (EOC) [odds ratio (OR)=1.2, 95% confidence interval (CI)=1.0-1.4, Ptrend=0.01] that showed a stronger association with the serous histological subtype (OR=1.3, 95% CI=1.1-1.5, Ptrend=0.003).

Methods

We sought to replicate this association in 12 other studies comprising 4,482 cases and 6,894 controls of white non-Hispanic ancestry in the Ovarian Cancer Association Consortium.

Results

No evidence for an association with all cancers or serous cancers was observed in a combined analysis of data from the replication studies (all: OR=1.0, 95% CI=0.9-1.1, Ptrend=0.61; serous: OR=1.0, 95% CI=0.9-1.1, Ptrend=0.85) or from the combined analysis of discovery and replication studies (all: OR=1.0, 95% CI=1.0-1.1, Ptrend= 0.28; serous: OR=1.1, 95% CI=1.0-1.2, Ptrend=0.11). There was no evidence for statistical heterogeneity in ORs across the studies.

Conclusions

Although rs2660753 is a strong a prostate cancer susceptibility polymorphism, the association with another hormonally related cancer, invasive EOC, is not supported by this replication study.

Impact

Our findings, based on a larger sample size, emphasize the importance of replicating potentially promising genetic risk associations.

The association of invasive ovarian carcinoma risk with the functional polymorphism rs2228570 (aka rs10735810; FokI polymorphism) in the vitamin D receptor (VDR) gene was examined in 1820 white non-Hispanic cases and 3479 controls in a pooled analysis of five population-based case-control studies within the Ovarian Cancer Association Consortium. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using unconditional logistic regression. Carriers of the rare T allele were at increased risk of ovarian carcinoma compared to women with the CC genotype in all studies combined; each copy of the T allele was associated with a modest 9% increased risk (OR=1.09; 95% CI:1.01–1.19; p=0.04). No significant heterogeneity among studies was observed (p=0.37) and, after excluding the dataset from the Hawaii study, the risk association for rs2228570 among replication studies was unchanged (OR=1.09; 95% CI: 1.00–1.19; p=0.06). A stronger association of rs2228570 with risk was observed among younger women (aged < 50 years versus 50 years or older) (p=0.04). In all studies combined, the increased risk per copy of the T allele among younger women was 24% (OR=1.24; 95% CI: 1.04–1.47; p=0.02). This association remained statistically significant after excluding the Hawaii data (OR= 1.20; 95% CI: 1.01–1.43; p=0.04). No heterogeneity of the association was observed by stage (p= 0.46), tumor histology (p=0.98), or time between diagnosis and interview (p=0.94). This pooled analysis provides further evidence that the VDR rs2228570 polymorphism might influence ovarian cancer susceptibility.

SUMMARY

Interest in targeted disease prevention has stimulated development of models that assign risks to individuals, using their personal covariates. We need to evaluate these models and quantify the gains achieved by expanding a model to include additional covariates. This paper reviews several performance measures and shows how they are related. Examples are used to show that appropriate performance criteria for a risk model depend upon how the model is used. Application of the performance measures to risk models for hypothetical populations and for US women at risk of breast cancer illustrate two additional points. First, model performance is constrained by the distribution of risk-determining covariates in the population. This complicates the comparison of two models when applied to populations with different covariate distributions. Second, all summary performance measures obscure model features of relevance to its utility for the application at hand, such as performance in specific subgroups of the population. In particular, the precision gained by adding covariates to a model can be small overall, but large in certain subgroups. We propose new ways to identify these subgroups and to quantify how much they gain by measuring the additional covariates. Those with largest gains could be targeted for cost-efficient covariate assessment.

Ovarian cancer (OC) accounts for more deaths than all other gynecological cancers combined. To identify common low-penetrance OC susceptibility genes, we conducted a genome-wide association study (GWAS) of 507,094 SNPs in 1,768 cases and 2,354 controls, with follow-up of 21,955 SNPs in 4,162 cases and 4,810 controls, leading to the identification of a confirmed susceptibility locus at 9p22 (BNC2)1. Here, we report on nine additional candidate loci (p≤10-4), identified after stratifying cases by histology, genotyped in an additional 4,353 cases and 6,021 controls. Two novel susceptibility loci with p≤5×10-8 were confirmed (8q24, p=8.0×10-15 and 2q31, p=3.8×10-14); two additional loci were also identified that approached genome-wide significance (3q25, p=7.1×10-8 and 17q21, p=1.4×10-7). The associations with serous OC were generally stronger than other subtypes. Analysis of HOXD1, MYC, TiPARP, and SKAP1 at these loci, and BNC2 at 9p22, supports a functional role for these genes in OC development.

Few studies have considered the joint association of body mass index (BMI) and physical activity, two modifiable factors, with all-cause mortality after breast cancer diagnosis. Women diagnosed with invasive breast cancer (n=4,153) between 1991 and 2000 were enrolled in the Breast Cancer Family Registry through population-based sampling in Northern California, USA; Ontario, Canada; and Melbourne and Sydney, Australia. During a median follow-up of 7.8 years, 725 deaths occurred. Baseline questionnaires assessed moderate and vigorous recreational physical activity and BMI prior to diagnosis. Associations with all-cause mortality were assessed using Cox proportional hazards regression, adjusting for established prognostic factors. Compared with no physical activity, any recreational activity during the three years prior to diagnosis was associated with a 34% lower risk of death (hazard ratio (HR) = 0.66, 95% confidence interval (CI): 0.51-0.85) for women with estrogen receptor (ER)-positive tumors, but not those with ER-negative tumors; this association did not appear to differ by race/ethnicity or BMI. Lifetime physical activity was not associated with all-cause mortality. BMI was positively associated with all-cause mortality for women diagnosed at age ≥50 years with ER-positive tumors (compared with normal-weight women, HR for overweight = 1.39, 95% CI: 0.90-2.15; HR for obese = 1.77, 95% CI: 1.11-2.82). BMI associations did not appear to differ by race/ethnicity. Our findings suggest that physical activity and BMI exert independent effects on overall mortality after breast cancer.

Epithelial ovarian cancer (EOC) is the leading cause of death from gynecological malignancy in the developed world accounting for 4 percent of deaths from cancer in women1. We performed a three-phase genome-wide association study of EOC survival in 8,951 EOC cases with available survival time data, and a parallel association analysis of EOC susceptibility. Two SNPs at 19p13.11, rs8170 and rs2363956, showed evidence of association with survival (overall P=5×10−4 and 6×10−4), but did not replicate in phase 3. However, the same two SNPs demonstrated genome-wide significance for risk of serous EOC (P=3×10−9 and 4×10−11 respectively). Expression analysis of candidate genes at this locus in ovarian tumors supported a role for the BRCA1 interacting gene C19orf62, also known as MERIT40, which contains rs8170, in EOC development.

The association of ovarian carcinoma risk with the polymorphism rs1271572 in the estrogen receptor beta (ESR2) gene was examined in 4946 women with primary invasive ovarian carcinoma and 6582 controls in a pooled analysis of ten case-control studies within the Ovarian Cancer Association Consortium (OCAC). All participants were non-Hispanic white women. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using unconditional logistic regression adjusted for site and age. Women with the TT genotype were at increased risk of ovarian carcinoma compared to carriers of the G allele (OR = 1.10; 95%; CI: 1.01–1.21; p = 0.04); the OR was 1.09 (CI: 0.99–1.20; p = 0.07) after excluding data from the center (Hawaii) that nominated this SNP for OCAC genotyping A stronger association of rs1271572 TT versus GT/GG with risk was observed among women aged ≤50 years versus older women (OR = 1.35; CI: 1.12–1.62; p = 0.002; p for interaction = 0.02) that remained statistically significant after excluding Hawaii data (OR = 1.34; CI: 1.11–1.61; p = 0.009). No heterogeneity of the association was observed by study, menopausal status, gravidity, parity, use of contraceptive or menopausal hormones, tumor histological type, or stage at diagnosis. This pooled analysis suggests that rs1271572 might influence the risk of ovarian cancer, in particular among younger women.

Family data are useful for estimating disease risk in carriers of specific genotypes of a given gene (penetrance). Penetrance is frequently estimated assuming that relatives' phenotypes are independent, given their genotypes for the gene of interest. This assumption is unrealistic when multiple shared risk factors contribute to disease risk. In this setting, the phenotypes of relatives are correlated even after adjustment for the genotypes of any one gene (residual correlation). Many methods have been proposed to address this problem, but their performance has not been evaluated systematically. In simulations we generated genotypes for a rare (frequency 0.35%) allele of moderate penetrance, and a common (frequency 15%) allele of low penetrance, and then generated correlated disease survival times using the Clayton-Oakes copula model. We ascertained families using both population and clinic designs. We then compared the estimates of several methods to the optimal ones obtained from the model used to generate the data. We found that penetrance estimates for common low-risk genotypes were more robust to model misspecification than those for rare, moderate-risk genotypes. For the latter, penetrance estimates obtained ignoring residual disease correlation had large biases. Also biased were estimates based only on families that segregate the risk allele. In contrast, a method for accommodating phenotype correlation by assuming the presence of genetic heterogeneity performed nearly optimally, even when the survival data were coded as binary outcomes. We conclude that penetrance estimates that accommodate residual phenotype correlation (even only approximately) outperform those that ignore it, and that coding censored survival outcomes as binary does not substantially increase the mean-square errror of the estimates, provided the censoring is not extensive.

Aberrant glycosylation is a well-described hallmark of cancer. In a previous ovarian cancer case control study that examined polymorphisms in 26 glycosylation-associated genes, we found strong statistical evidence (P = 0.00017) that women who inherited two copies of a single-nucleotide polymorphism in the UDP-N-acetylgalactosamine:polypeptide N-acetylgalactosaminyltransferase, GALNT1, had decreased ovarian cancer risk. The current study attempted to replicate this observation. The GALNT1 single-nucleotide polymorphism rs17647532 was genotyped in 6,965 cases and 8,377 controls from 14 studies forming the Ovarian Cancer Association Consortium. The fixed effects estimate per rs17647532 allele was null (odds ratio, 0.99; 95% confidence interval, 0.92–1.07). When a recessive model was fit, the results were unchanged. Test for hetero geneity of the odds ratios revealed consistency across the 14 replication sites but significant differences compared with the original study population (P = 0.03). This study underscores the need for replication of putative findings in genetic association studies.

African American women with breast cancer present more commonly with aggressive tumors that do not express the estrogen receptor (ER) and progesterone receptor (PR) compared with European American women. Whether this disparity is the result of inherited factors has not been established. We performed an admixture-based genome-wide scan to search for risk alleles for breast cancer that are highly differentiated in frequency between African American and European American women and may contribute to specific breast cancer phenotypes, such as ER negative (ER−) disease. African American women with invasive breast cancer (n=1,484) were pooled from 6 population-based studies and typed at ~1,500 ancestry informative markers (AIMs). We investigated global genetic ancestry and performed a whole genome admixture scan searching for breast cancer predisposing loci in association with disease phenotypes. We found a significant difference in ancestry between ER+PR+ and ER−PR− women, with higher European ancestry among ER+PR+ individuals, after controlling for possible confounders (OR for a 0 to 1 change in European ancestry proportion=2.84, 95% CI: 1.13–7.14, p=0.026). Women with localized tumors had higher European ancestry than women with non-localized tumors (OR=2.65, CI: 1.11–6.35, p=0.029). No genome-wide statistically significant associations were observed between European or African ancestry at any specific locus and breast cancer, or in analyses stratified by ER/PR status, stage or grade. In summary, in African American women genetic ancestry is associated with ER/PR status and disease stage. However, we found little evidence that genetic ancestry at any one region contributes significantly to breast cancer risk or hormone receptor status.

Background

Although having a family history of breast cancer is a well established breast cancer risk factor, it is not known whether it influences mortality after breast cancer diagnosis.

Methods

Subjects were 4,153 women with first primary incident invasive breast cancer diagnosed between 1991 and 2000, and enrolled in the Breast Cancer Family Registry through population-based sampling in Northern California, USA; Ontario, Canada; and Melbourne and Sydney, Australia. Cases were oversampled for younger age at diagnosis and/or family history of breast cancer. Carriers of germline mutations in BRCA1 or BRCA2 were excluded. Cases and their relatives completed structured questionnaires assessing breast cancer risk factors and family history of cancer. Cases were followed for a median of 6.5 years, during which 725 deaths occurred. Cox proportional hazards regression was used to evaluate associations between family history of breast cancer at the time of diagnosis and risk of all-cause mortality after breast cancer diagnosis, adjusting for established prognostic factors.

Results

The hazard ratios for all-cause mortality were 0.98 (95% confidence interval [CI]=0.84-1.15) for having at least one first- or second-degree relative with breast cancer, and 0.85 (95% CI=0.70-1.02) for having at least one first-degree relative with breast cancer, compared with having no such family history. Estimates did not vary appreciably when stratified by case or tumor characteristics.

Conclusions

Family history of breast cancer is not associated with all-cause mortality after breast cancer diagnosis for women without a known germline mutation in BRCA1 or BRCA2. Therefore, clinical management should not depend on family history of breast cancer.

Because both ovarian and breast cancer are hormone-related and are known to have some predisposition genes in common, we evaluated 11 of the most significant hits (six with confirmed associations with breast cancer) from the breast cancer genome-wide association study for association with invasive ovarian cancer. Eleven SNPs were initially genotyped in 2927 invasive ovarian cancer cases and 4143 controls from six ovarian cancer case–control studies. Genotype frequencies in cases and controls were compared using a likelihood ratio test in a logistic regression model stratified by study. Initially, three SNPs (rs2107425 in MRPL23, rs7313833 in PTHLH, rs3803662 in TNRC9) were weakly associated with ovarian cancer risk and one SNP (rs4954956 in NXPH2) was associated with serous ovarian cancer in non-Hispanic white subjects (P-trend < 0.1). These four SNPs were then genotyped in an additional 4060 cases and 6308 controls from eight independent studies. Only rs4954956 was significantly associated with ovarian cancer risk both in the replication study and in combined analyses. This association was stronger for the serous histological subtype [per minor allele odds ratio (OR) 1.07 95% CI 1.01–1.13, P-trend = 0.02 for all types of ovarian cancer and OR 1.14 95% CI 1.07–1.22, P-trend = 0.00017 for serous ovarian cancer]. In conclusion, we found that rs4954956 was associated with increased ovarian cancer risk, particularly for serous ovarian cancer. However, none of the six confirmed breast cancer susceptibility variants we tested was associated with ovarian cancer risk. Further work will be needed to identify the causal variant associated with rs4954956 or elucidate its function.

Purpose

Patients with early-onset breast and/or ovarian cancer frequently wish to know if they inherited a mutation in one of the cancer susceptibility genes, BRCA1 or BRCA2. Accurate carrier prediction models are needed to target costly testing. Two widely used models, BRCAPRO and BOADICEA, were developed using data from non-Hispanic Whites (NHW), but their accuracies have not been evaluated in other racial/ethnic populations.

Methods

We evaluated the BRCAPRO and BOADICEA models in a population-based series of African-American, Hispanic and NHW breast cancer patients tested for BRCA1 and BRCA2 mutations. We assessed model calibration by evaluating observed versus predicted mutations and attribute diagrams, and model discrimination using areas under receiver operating characteristic curves (AUCs).

Results

Both models were well-calibrated within each racial/ethnic group, with some exceptions. BOADICEA over-predicted mutations in African Americans and older NHWs, and BRCAPRO under-predicted in Hispanics. In all racial/ethnic groups, the models over-predicted in cases whose personal and family histories indicated greater than 80% probability of carriage. The two models showed similar discrimination in each racial/ethnic group, discriminating least well in Hispanics. For example, BRCAPRO’s AUCs were 83% (95% confidence interval 63–93%) for NHWs, compared to 74% (59–85%) for African Americans and 58% (45–70%) for Hispanics.

Conclusions

The models’ poor performance for Hispanics may be due to model misspecification in this racial/ethnic group. However, it also may reflect racial/ethnic differences in the distributions of personal and family histories among breast cancer cases in the Northern California population.

Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, ‘missing’ heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

Schizophrenia, a devastating psychiatric disorder, has a prevalence of 0.5–1%, with high heritability (80–85%) and complex transmission.1 Recent studies implicate rare, large, high-penetrance copy number variants (CNVs) in some cases2, but it is not known what genes or biological mechanisms underlie susceptibility. Here we show that schizophrenia is significantly associated with single nucleotide polymorphisms (SNPs) in the extended Major Histocompatibility Complex (MHC) region on chromosome 6. We carried out a genome-wide association study (GWAS) of common SNPs in the Molecular Genetics of Schizophrenia (MGS) case-control sample, and then a meta-analysis of data from the MGS, International Schizophrenia Consortium (ISC) and SGENE datasets. No MGS finding achieved genome-wide statistical significance. In the meta-analysis of European-ancestry subjects (8,008 cases, 19,077 controls), significant association with schizophrenia was observed in a region of linkage disequilibrium on chromosome 6p22.1 (P = 9.54 × 10−9). This region includes a histone gene cluster and several immunity-related genes, possibly implicating etiologic mechanisms involving chromatin modification, transcriptional regulation, auto-immunity and/or infection. These results demonstrate that common schizophrenia susceptibility alleles can be detected. The characterization of these signals will suggest important directions for research on susceptibility mechanisms.

Purpose

Animal experiments support the hypothesis that the metastatic potential of breast cancer is a heritable trait of the host. Our objective was to evaluate correlations in metastasis occurrence in large families with multiple cases of breast cancer.

Methods

We evaluated correlation among pairs of relatives in the occurrence and timing of distant metastasis using retrospective cohort data from 743 female breast cancer patients in 242 families. We adjusted for correlation in their age at diagnosis, year of diagnosis, educational level, lymph node involvement, and estrogen receptor status.

Results

Distant metastasis occurred in 255 patients (34.3%) mean followup of 11.7 years. None of the correlation coefficients for metastasis in blood relatives differed significantly from zero. The estimated correlation coefficient in first-degree relatives was -0.03 (95% confidence interval -0.11 to 0.06).

Conclusions

These findings suggest that a family history of metastatic breast cancer does not contribute substantially to risk of metastasis for breast cancer patients.