A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer

Clinical correlates of low-risk variants in FGFR2, TNRC9, MAP3K1, LSP1 and 8q24 in a Dutch cohort of incident breast cancer cases

Introduction

Seven SNPs in five genomic loci were recently found to confer a mildly increased risk of breast cancer.

Methods

We have investigated the correlations between disease characteristics and the patient genotypes of these SNPs in an unselected prospective cohort of 1,267 consecutive patients with primary breast cancer.

Results

Heterozygote carriers and minor allele homozygote carriers for SNP rs889312 in the MAP3K1 gene were less likely to be lymph node positive at breast cancer diagnosis (P = 0.044) relative to major allele homozygote carriers. Heterozygote carriers and minor allele homozygote carriers for SNP rs3803662 near the TNCR9 gene were more likely to be diagnosed before the age of 60 years (P = 0.025) relative to major allele homozygote carriers. We also noted a correlation between the number of minor alleles of rs2981582 in FGFR2 and the average number of first-degree and second-degree relatives with breast cancer and/or ovarian cancer (P = 0.05). All other disease characteristics, including tumour size and grade, and oestrogen or progesterone receptor status, were not significantly associated with any of these variants.

Conclusion

Some recently discovered genomic variants associated with a mildly increased risk of breast cancer are also associated with breast cancer characteristics or family history of breast cancer and ovarian cancer. These findings provide interesting new clues for further research on these low-risk susceptibility alleles.

Efficient approximation of P-value of the maximum of correlated tests, with applications to genome-wide association studies

Genome-wide association study (GWAS), typically involving 100,000 to 500,000 single-nucleotide polymorphisms (SNPs), is a powerful approach to identify disease susceptibility loci. In a GWAS, single-marker analysis, which tests one SNP at a time, is usually used as the first stage to screen SNPs across the genome in order to identify a small fraction of promising SNPs with relatively low p-values for further and more focused studies. For single-marker analysis, the trend test derived for an additive genetic model is often used. This may not be robust when the additive assumption is not appropriate for the true underlying disease model. A robust test, MAX, based on the maximum of three trend test statistics derived for recessive, additive, and dominant models, has been proposed recently for GWAS. But its p-value has to be evaluated through a resampling-based procedure, which is computationally challenging for the analysis of GWAS. Obtaining the p-value for MAX with adjustment for the covariates can be even more time-consuming. In this article, we provide a simple approximation for the p-value of the MAX test with or without adjusting for the covariates. The new method avoids resampling steps and thus makes the MAX test readily applicable to GWAS. We use simulation studies as well as real datasets on 17 confirmed disease-associated SNPs to assess the accuracy of the proposed method. We also apply the method to the GWAS of coronary artery disease.

Estimation and interpretation of models of absolute risk from epidemiologic data, including family-based studies

Absolute risk is the chance that a person with given risk factors and free of the disease of interest at age a will be diagnosed with that disease in the interval (a, a + tau]. Absolute risk is sometimes called cumulative incidence. Absolute risk is a "crude" risk because it is reduced by the chance that the person will die of competing causes of death before developing the disease of interest. Cohort studies admit flexibility in modeling absolute risk, either by allowing covariates to affect the cause-specific relative hazards or to affect the absolute risk itself. An advantage of cause-specific relative risk models is that various data sources can be used to fit the required components. For example, case-control data can be used to estimate relative risk and attributable risk, and these can be combined with registry data on age-specific composite hazard rates for the disease of interest and with national data on competing hazards of mortality to estimate absolute risk. Family-based designs, such as the kin-cohort design and collections of pedigrees with multiple affected individuals can be used to estimate the genotype-specific hazard of disease. Such analyses must be adjusted for ascertainment, and failure to take into account residual familial risk, such as might be induced by unmeasured genetic variants or by unmeasured behavioral or environmental exposures that are correlated within families, can lead to overestimates of mutation-specific absolute risk in the general population.

Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies

Genome-wide association studies (GWAS) provide an important approach to identifying common genetic variants that predispose to human disease. A typical GWAS may genotype hundreds of thousands of single nucleotide polymorphisms (SNPs) located throughout the human genome in a set of cases and controls. Logistic regression is often used to test for association between a SNP genotype and case versus control status, with corresponding odds ratios (ORs) typically reported only for those SNPs meeting selection criteria. However, when these estimates are based on the original data used to detect the variant, the results are affected by a selection bias sometimes referred to the "winner's curse" (Capen and others, 1971). The actual genetic association is typically overestimated. We show that such selection bias may be severe in the sense that the conditional expectation of the standard OR estimator may be quite far away from the underlying parameter. Also standard confidence intervals (CIs) may have far from the desired coverage rate for the selected ORs. We propose and evaluate 3 bias-reduced estimators, and also corresponding weighted estimators that combine corrected and uncorrected estimators, to reduce selection bias. Their corresponding CIs are also proposed. We study the performance of these estimators using simulated data sets and show that they reduce the bias and give CI coverage close to the desired level under various scenarios, even for associations having only small statistical power.

Multiple alternative splicing markers for ovarian cancer

Intense efforts are currently being directed toward profiling gene expression in the hope of developing better cancer markers and identifying potential drug targets. Here, we present a sensitive new approach for the identification of cancer signatures based on direct high-throughput reverse transcription-PCR validation of alternative splicing events. This layered and integrated system for splicing annotation (LISA) fills a gap between high-throughput microarray studies and high-sensitivity individual gene investigations, and was created to monitor the splicing of 600 cancer-associated genes in 25 normal and 21 serous ovarian cancer tissues. Out of >4,700 alternative splicing events screened, the LISA identified 48 events that were significantly associated with serous ovarian tumor tissues. In a further screen directed at 39 ovarian tissues containing cancer pathologies of various origins, our ovarian cancer splicing signature successfully distinguished all normal tissues from cancer. High-volume identification of cancer-associated splice forms by the LISA paves the way for the use of alternative splicing profiling to diagnose subtypes of cancer.

Surveying germline genomic landscape of breast cancer

The recent large genotyping studies have identified a new repertoire of breast cancer susceptibility genes and loci which are characterized by common risk alleles and low relative risks. Because of these properties, these loci explain a much larger proportion of the etiology of the particular cancers, described by the population attributable fraction (PAF), than of their familial risks. PAF is particularly suitable for 'genomic landscaping' because it defines the proportion of breast cancer explained by the variant under study. The joint PAF for the previously described high-penetrance alleles is about 1%, for moderate-penetrance alleles it is 1.5% and for low-penetrance susceptibility alleles it is 58%. The evidence appears compelling in pointing to the remarkably high population impacts of the recently described heritable loci compared to the 'classical' high-penetrance genes.

Terminal end bud maintenance in mammary gland is dependent upon FGFR2b signaling

We previously demonstrated that Fibroblast Growth Factor 10 (FGF10) and its receptor FGFR2b play a key role in controlling the very early stages of mammary gland development during embryogenesis [Mailleux, A.A., Spencer-Dene, B., Dillon, C., Ndiaye, D., Savona-Baron, C., Itoh, N., Kato, S., Dickson, C., Thiery, J.P., and Bellusci, S. (2002). Role of FGF10/FGFR2b signaling during mammary gland development in the mouse embryo. Development 129, 53-60. Veltmaat, J. M., Relaix, F., Le, L.T., Kratochwil, K., Sala, F.G., van Veelen, W., Rice, R., Spencer-Dene, B., Mailleux, A.A., Rice, D.P., Thiery, J.P., and Bellusci, S. (2006). Gli3-mediated somitic Fgf10 expression gradients are required for the induction and patterning of mammary epithelium along the embryonic axes. Development 133, 2325-35.]. However, the role of FGFR2b signaling in postnatal mammary gland development is still elusive. We show that FGF10 is expressed at high level throughout the adipose tissue in the mammary gland of young virgin female mice whereas its main receptor FGFR2 is found mostly in the epithelium. Using a rtTA transactivator/tetracycline promoter approach allowing inducible and reversible attenuation of the FGFR2b signaling throughout the adult mouse, we are now reporting that FGFR2b signaling is also critical during postnatal mammary gland development. Ubiquitous attenuation of FGFR2b signaling in the postnatal mouse for 6 weeks starting immediately after birth is not lethal and leads to minor defects in the animal. Upon dissection of the mammary glands, a 40% reduction in size compared to the WT control is observed. Further examination shows a rudimentary mammary epithelial tree with completely absent terminal end buds (TEBs), compared to a well-branched structure observed in wild type. Transplantation of mammary gland explants into cleared fat pad of wild type mouse recipients indicates that the observed abnormal branching results from defective FGFR2b signaling in the epithelium. We also demonstrate that this rudimentary tree reforms TEBs and resumes branching upon removal of doxycycline suggesting that the regenerative capacities of the mammary epithelial progenitor cells were still functional despite long-term inactivation of the FGFR2b pathway. At the cellular level, upon FGFR2b attenuation, we show an increase in apoptosis associated with a decrease in the proliferation of the mammary luminal epithelium. We conclude that during puberty, there is a differential requirement for FGFR2b signaling in ductal vs. TEBs epithelium. FGFR2b signaling is crucial for the survival and proliferation of the mammary luminal epithelial cells, but does not affect the regenerative potential of the mammary epithelial progenitor cells.

The success of the genome-wide association approach: a brief story of a long struggle

The genome-wide association approach has been the most powerful and efficient study design thus far in identifying genetic variants that are associated with complex human diseases. This approach became feasible as the result of several key advancements in genetic knowledge, genotyping technologies, statistical analysis algorithms and the availability of large collections of cases and controls. With all these necessary tools in hand, many genome-wide association studies were recently completed, and many more studies which will explore the genetic basis of various complex diseases and quantitative traits are soon to come. This approach has started to reap the fruits of its labor over the past several months. Publications of genome-wide association studies in several complex diseases such as inflammatory bowel disease, type-2 diabetes, breast cancer and prostate cancer have been abundant in the first half of this year. The aims of this review are firstly, to provide a timely summary for most of the genome-wide association studies that have been published until June/July 2007 and secondly, to evaluate to what extent these results have been validated in subsequent replication studies.

Prospective surveillance of women with a family history of breast cancer: auditing the risk threshold

To evaluate current guidelines criteria for inclusion of women in special ‘breast cancer family history’ surveillance programmes, records were reviewed of women referred to Scottish breast cancer family clinics between January 1994 and December 2003 but discharged as at ‘less than ‘moderate’ familial risk’. The Scottish Cancer Registry was then interrogated to determine subsequent age-specific incidence of breast cancer in this cohort and corresponding Scottish population figures. Among 2074 women, with an average follow-up of 4.0 years, 28 invasive breast cancers were recorded up to December 2003, where 14.4 were expected, a relative risk (RR) of 1.94. Eleven further breast cancers were recorded between January 2004 and February 2006 (ascertainment incomplete for this period). The overall RR for women in the study cohort exceeded the accepted ‘cutoff’ level (RR=1.7) for provision of special counselling and surveillance. The highest RR was found for the age group 45–59 years and this group also generated the majority of breast cancers. The National Institute for Clinical Excellence (‘NICE’) guidelines appear to be more accurate than those of the Scottish Intercollegiate Guidelines Network (‘SIGN’) in defining ‘moderate’ familial risk, and longer follow-up of this cohort could generate an evidence base for further modification of familial breast cancer services.

Common variation in the fibroblast growth factor receptor 2 gene is not associated with endometriosis risk

BACKGROUND

Endometriosis is a polygenic disease with a complex and multifactorial aetiology that affects 8-10% of women of reproductive age. Epidemiological data support a link between endometriosis and cancers of the reproductive tract. Fibroblast growth factor receptor 2 (FGFR2) has recently been implicated in both endometrial and breast cancer. Our previous studies on endometriosis identified significant linkage to a novel susceptibility locus on chromosome 10q26 and the FGFR2 gene maps within this linkage region. We therefore hypothesized that variation in FGFR2 may contribute to the risk of endometriosis.

METHODS

We genotyped 13 single nucleotide polymorphisms (SNPs) densely covering a 27 kb region within intron 2 of FGFR2 including two SNPs (rs2981582 and rs1219648) significantly associated with breast cancer and a total 40 tagSNPs across 150 kb of the FGFR2 gene. SNPs were genotyped in 958 endometriosis cases and 959 unrelated controls.

RESULTS

We found no evidence for association between endometriosis and FGFR2 intron 2 SNPs or SNP haplotypes and no evidence for association between endometriosis and variation across the FGFR2 gene.

CONCLUSIONS

Common variation in the breast-cancer implicated intron 2 and other highly plausible causative candidate regions of FGFR2 do not appear to be a major contributor to endometriosis susceptibility in our large Australian sample.

Gene expression analyses in breast cancer epidemiology: the Norwegian Women and Cancer postgenome cohort study

Introduction

The introduction of high-throughput technologies, also called -omics technologies, into epidemiology has raised the need for high-quality observational studies to reduce several sources of error and bias.

Methods

The Norwegian Women and Cancer (NOWAC) postgenome cohort study consists of approximately 50,000 women born between 1943 and 1957 who gave blood samples between 2003 and 2006 and filled out a two-page questionnaire. Blood was collected in such a way that RNA is preserved and can be used for gene expression analyses. The women are part of the NOWAC study consisting of 172,471 women 30 to 70 years of age at recruitment from 1991 to 2006 who answered one to three questionnaires on diet, medication use, and lifestyle. In collaboration with the Norwegian Breast Cancer Group, every NOWAC participant born between 1943 and 1957 who is admitted to a collaborating hospital for a diagnostic biopsy or for surgery of breast cancer will be asked to donate a tumor biopsy and two blood samples. In parallel, at least three controls are approached for each breast cancer case in order to obtain blood samples from at least two controls per case. The controls are drawn at random from NOWAC matched by time of follow-up and age. In addition, 400 normal breast tissues as well as blood samples will be collected among healthy women participating at the Norwegian Mammography Screening program at the Breast Imaging Center at the University Hospital of North-Norway, Tromsø.

Results

The NOWAC postgenome cohort offers a unique opportunity (a) to study blood-derived gene expression profiles as a diagnostic test for breast cancer in a nested case-control design with adjustment for confounding factors related to different exposures, (b) to improve the reliability and accuracy of this approach by adjusting for an individual's genotype (for example, variants in genes coding for hormone and drug-metabolizing and detoxifying enzymes), (c) to study gene expression profiles from peripheral blood as surrogate tissue to biomonitor defined exposure (for example, hormone) and its association with disease risk (that is, breast cancer), and (d) to study gene variants (single nucleotide polymorphisms and copy number variations) and environmental exposure (endogenous and exogenous hormones) and their influence on the incidence of different molecular subtypes of breast cancer.

Conclusion

The NOWAC postgenome cohort combining a valid epidemiological approach with richness of biological samples should make an important contribution to the study of the etiology and system biology of breast cancer.

Analysis of genetic variation in Ashkenazi Jews by high density SNP genotyping

BACKGROUND

Genetic isolates such as the Ashkenazi Jews (AJ) potentially offer advantages in mapping novel loci in whole genome disease association studies. To analyze patterns of genetic variation in AJ, genotypes of 101 healthy individuals were determined using the Affymetrix EAv3 500 K SNP array and compared to 60 CEPH-derived HapMap (CEU) individuals. 435,632 SNPs overlapped and met annotation criteria in the two groups.

RESULTS

A small but significant global difference in allele frequencies between AJ and CEU was demonstrated by a mean FST of 0.009 (P < 0.001); large regions that differed were found on chromosomes 2 and 6. Haplotype blocks inferred from pairwise linkage disequilibrium (LD) statistics (Haploview) as well as by expectation-maximization haplotype phase inference (HAP) showed a greater number of haplotype blocks in AJ compared to CEU by Haploview (50,397 vs. 44,169) or by HAP (59,269 vs. 54,457). Average haplotype blocks were smaller in AJ compared to CEU (e.g., 36.8 kb vs. 40.5 kb HAP). Analysis of global patterns of local LD decay for closely-spaced SNPs in CEU demonstrated more LD, while for SNPs further apart, LD was slightly greater in the AJ. A likelihood ratio approach showed that runs of homozygous SNPs were approximately 20% longer in AJ. A principal components analysis was sufficient to completely resolve the CEU from the AJ.

CONCLUSION

LD in the AJ versus was lower than expected by some measures and higher by others. Any putative advantage in whole genome association mapping using the AJ population will be highly dependent on regional LD structure.

Early life events and conditions and breast cancer risk: from epidemiology to etiology

Risk factors for breast cancer--documented by intensive epidemiological investigations and viewed in the context of general principles of carcinogenesis--can be integrated to an etiologic model comprising 3 principal components: the likelihood of breast cancer occurrence depends on the number of mammary tissue-specific stem cells, which is determined in early life; all growth-enhancing mammotropic hormones affect the rate of expansion of initiated clones; and while a pregnancy stimulates the replication of already initiated cells, it conveys long-term protection through differentiation of mammary tissue-specific stem cells. This perspective accommodates much of what is known about the epidemiology and natural history of breast cancer and highlights the role of early life in the origin of this cancer.

The role of single nucleotide polymorphisms in breast cancer metastasis

Our understanding of many aspects of cancer biology has been advanced through the use of modern genetics. These studies have already shown that germ line polymorphisms play a significant role in disease initiation and response to therapy. However, what is less well studied is the role of germ line polymorphisms in cancer progression. Studies in rodents indicate that differential susceptibility to cancer metastasis can be heritable; thus, the search for the genes that control cancer metastasis is underway. Although some provocative studies suggest potential candidates for metastasis regulating genes, the conclusive identification of a specific inherited genetic variant that alters metastatic potential awaits further studies.

The emerging landscape of breast cancer susceptibility

The genetic basis of inherited predisposition to breast cancer has been assiduously investigated for the past two decades and has been the subject of several recent discoveries. Three reasonably well-defined classes of breast cancer susceptibility alleles with different levels of risk and prevalence in the population have become apparent: rare high-penetrance alleles, rare moderate-penetrance alleles and common low-penetrance alleles. The contribution of each component to breast cancer predisposition is still to be fully explored, as are the phenotypic characteristics of the cancers associated with them, the ways in which they interact, much of their biology and their clinical utility. These recent advances herald a new chapter in the exploration of susceptibility to breast cancer and are likely to provide insights relevant to other common, heterogeneous diseases.

In search of breast cancer culprits: suspecting the suspected and the unsuspected

I would like to welcome breast cancer research community to the first editorial of our newest journal "Breast Cancer: Basic and Clinical Research". In pursuit of breast cancer culprits, we have come a long way since the early 90's when the first breast cancer susceptibility gene BRCA1 was mapped and cloned. In the past few years, several new loci associated with the various degree of breast cancer risk have been identified using "Candidate Gene Association Study (CGAS) and Genome-Wide Association Study (GWAS)" approaches. This editorial is meant to quickly glance over recent findings of these population-based association studies.

Variation of breast cancer risk among BRCA1/2 carriers

CONTEXT

The risk of breast cancer in BRCA1 and BRCA2 mutation carriers has been examined in many studies, but relatively little attention has been paid to the degree to which the risk may vary among carriers.

OBJECTIVES

To determine the extent to which risks for BRCA1 and BRCA2 carriers vary with respect to observable and unobservable characteristics.

DESIGN, SETTING, AND PARTICIPANTS

Probands were identified from a population-based, case-control study (Women's Environmental Cancer and Radiation Epidemiology [WECARE]) of asynchronous contralateral breast cancer conducted during the period of January 2000 to July 2004. Participants previously diagnosed with contralateral breast cancer or unilateral breast cancer were genotyped for mutations in BRCA1 and BRCA2. All participants had their initial breast cancer diagnosed during the period of January 1985 to December 2000, before the age of 55 years.

MAIN OUTCOME MEASURE

Incidence of breast cancer in first-degree female relatives of the probands was examined and compared on the basis of proband characteristics and on the basis of variation between families.

RESULTS

Among the 1394 participants with unilateral breast cancer, 73 (5.2%) were identified as carriers of deleterious mutations (42 with BRCA1 and 31 with BRCA2). Among the 704 participants with contralateral breast cancer, 108 (15.3%) were identified as carriers of deleterious mutations (67 with BRCA1 and 41 with BRCA2). Among relatives of carriers, risk was significantly associated with younger age at diagnosis in the proband (P = .04), and there was a trend toward higher risk for relatives of contralateral breast cancer vs unilateral breast cancer participants (odds ratio, 1.4 [95% confidence interval, 0.8-2.4]; P = .28). In addition, there were significant differences in risk between carrier families after adjusting for these observed characteristics.

CONCLUSION

There exists broad variation in breast cancer risk among carriers of BRCA1 and BRCA2 mutations.

Association of single-nucleotide polymorphisms in the cell cycle genes with breast cancer in the British population

Using a large-scale case-control study, we examined whether common single-nucleotide polymorphisms (SNPs) within 13 genes involved in the cell cycle pathway are associated with breast cancer risk. Seventy-nine tag SNPs were used to evaluate 240 common SNPs found in the genes: CCND1, CCND2, CCND3, CCNE1, CDK2, CDK4, CDK6, CDKN1A, CDKNIB, CDKN2A/CDKN2B, CDKN2C and CDKN2D. These were genotyped in 2270 cases and 2280 controls from the Studies in Epidemiology and Risks of Cancer Heredity (SEARCH) study. Tag SNPs showing evidence of statistically significant differences between cases and controls (P < 0.1) were genotyped in a further 2200 cases and 2280 controls from the same population. This approach found evidence for breast cancer-associated SNPs in four of the cell cycle genes: the cyclin CCNE1 rs997669 had an odds ratio (OR) (GG/AA) of 1.18 [95% confidence interval (95% CI) 1.04-1.34] P = 0.003 and the cyclin-dependent kinase inhibitors-CDKN1A rs3176336: OR (TT/AA) = 1.25 (95% CI 1.11-1.42) P = 0.0026; CDKN1B rs34330: OR (TT/CC) = 1.22 (95% CI 1.02-1.47) P = 0.013 and the region of CDKN2A/2B rs3731239: OR (CC/TT) = 0.90 (95% CI 0.79-1.03) P = 0.013 and rs3218005 OR (GG/AA) = 1.55 (95% CI 1.02-2.37) P = 0.013 (P-values unadjusted for multiple testing). We were able to exclude the D-type cyclins, cyclin-dependent kinases, CDKN2C and CDKN2D from having any significantly associated risk with breast cancer in our study population. The combined effects of the cell cycle genes considered here provide evidence for a significant association with breast cancer risk in a global test (P-heterogeneity = 0.010, P-trend = 0.048). Further large-scale studies are needed to confirm these results.

In Search of Breast Cancer Culprits: Suspecting the Suspected and the Unsuspected

I would like to welcome breast cancer research community to the first editorial of our newest journal “Breast Cancer: Basic and Clinical Research”. In pursuit of breast cancer culprits, we have come a long way since the early 90's when the first breast cancer susceptibility gene BRCA1 was mapped and cloned. In the past few years, several new loci associated with the various degree of breast cancer risk have been identified using “Candidate Gene Association Study (CGAS) and Genome-Wide Association Study (GWAS)” approaches. This editorial is meant to quickly glance over recent findings of these population-based association studies.

Genetic predisposition to breast cancer: past, present, and future

In recent years, our understanding of genetic predisposition to breast cancer has advanced significantly. Three classes of predisposition factors, categorized by their associated risks of breast cancer, are currently known. BRCA1 and BRCA2 are high-penetrance breast cancer predisposition genes identified by genome-wide linkage analysis and positional cloning. Mutational screening of genes functionally related to BRCA1 and/or BRCA2 has revealed four genes, CHEK2, ATM, BRIP1, and PALB2; mutations in these genes are rare and confer an intermediate risk of breast cancer. Association studies have further identified eight common variants associated with low-penetrance breast cancer predisposition. Despite these discoveries, most of the familial risk of breast cancer remains unexplained. In this review, we describe the known genetic predisposition factors, expound on the methods by which they were identified, and consider how further technological and intellectual advances may assist in identifying the remaining genetic factors underlying breast cancer susceptibility.

Cancer risk assessment and the genetic counseling process: using hereditary breast and ovarian cancer as an example

While only a small proportion of cancers can be attributed to a hereditary susceptibility, identifying high-risk individuals plays an essential role in medical management and has a significant impact on the patient as well as their immediate and extended family members. This paper aims at increasing the medical professionals' knowledge of the components of a genetic counseling session, with particular attention toward identifying at-risk individuals and understanding the complexities of the testing process. In addition, tools are provided to assist in identifying these individuals in clinical practice and streamlining the referral process to a cancer genetics center.

From human genetics and genomics to pharmacogenetics and pharmacogenomics: past lessons, future directions

A brief history of human genetics and genomics is provided, comparing recent progress in those fields with that in pharmacogenetics and pharmacogenomics, which are subsets of genetics and genomics, respectively. Sequencing of the entire human genome, the mapping of common haplotypes of single-nucleotide polymorphisms (SNPs), and cost-effective genotyping technologies leading to genome-wide association (GWA) studies - have combined convincingly in the past several years to demonstrate the requirements needed to separate true associations from the plethora of false positives. While research in human genetics has moved from monogenic to oligogenic to complex diseases, its pharmacogenetics branch has followed, usually a few years behind. The continuous discoveries, even today, of new surprises about our genome cause us to question reviews declaring that "personalized medicine is almost here" or that "individualized drug therapy will soon be a reality." As summarized herein, numerous reasons exist to show that an "unequivocal genotype" or even an "unequivocal phenotype" is virtually impossible to achieve in current limited-size studies of human populations. This problem (of insufficiently stringent criteria) leads to a decrease in statistical power and, consequently, equivocal interpretation of most genotype-phenotype association studies. It remains unclear whether personalized medicine or individualized drug therapy will ever be achievable by means of DNA testing alone.

Copy number variants and common disorders: filling the gaps and exploring complexity in genome-wide association studies

Genome-wide association scans (GWASs) using single nucleotide polymorphisms (SNPs) have been completed successfully for several common disorders and have detected over 30 new associations. Considering the large sample sizes and genome-wide SNP coverage of the scans, one might have expected many of the common variants underpinning the genetic component of various disorders to have been identified by now. However, these studies have not evaluated the contribution of other forms of genetic variation, such as structural variation, mainly in the form of copy number variants (CNVs). Known CNVs account for over 15% of the assembled human genome sequence. Since CNVs are not easily tagged by SNPs, might have a wide range of copy number variability, and often fall in genomic regions not well covered by whole-genome arrays or not genotyped by the HapMap project, current GWASs have largely missed the contribution of CNVs to complex disorders. In fact, some CNVs have already been reported to show association with several complex disorders using candidate gene/region approaches, underpinning the importance of regions not investigated in current GWASs. This reveals the need for new generation arrays (some already in the market) and the use of tailored approaches to explore the full dimension of genome variability beyond the single nucleotide scale.

Hereditary breast cancer: part I. Diagnosing hereditary breast cancer syndromes

Hereditary breast cancer (HBC) accounts for as much as 10% of the total BC burden. Most of these cases will be found to be due to a BRCA germline mutation. An estimated additional 15-20% of those affected with BC will have one or more first- and/or second-degree relatives with BC. Therefore, when these numbers are combined, familial BC risk accounts for approximately 20-25% of the total BC burden. However, because of the often limited information on family history in the etiologic assessment of BC, this may be an underestimate. Confounding factors include its phenotypic and genotypic heterogeneity, given the association of HBC with a plethora of differing cancer syndromes. Its most common occurrence is its association with ovarian cancer in the so-called hereditary breast-ovarian cancer syndrome due to BRCA1 and BRCA2 mutations. More rarely, it occurs in the Li-Fraumeni syndrome, caused by a p53 germline mutation, in which markedly early-onset BC is found in association with brain tumors, sarcomas, leukemia, lymphoma, malignant melanoma, and adrenal cortical carcinoma. Importantly, the age-adjusted incidence of BC in women in the United States fell sharply, by 6.7%, in 2003, when compared with the rate identified in 2002. We postulate that increasing knowledge about the genetics of BC may have partially contributed to the identification of high-risk patients who thereby may have benefited significantly from early diagnosis.

The RAD51D E233G variant and breast cancer risk: population-based and clinic-based family studies of Australian women

RAD51D is a homolog of the RAD51 protein, which is known to be an important component of the DNA repair pathway. A rare missense variant in the RAD51D gene, E233G (c.A>G), has been reported to be more prevalent in breast cancer cases from specific multiple-case breast cancer families, with an odds ratio of 2.6 (95% confidence interval (CI): 1.12-6.03). We assessed whether this variant was associated with breast cancer risk using two studies: a population-based case-control-family study based on 1,110 cases and 629 controls, and a clinic-based study based on 390 cases from multiple-case breast cancer families. We conducted case-control analyses and modified segregation analyses of carrier families. The carrier frequencies (95% CI) of the RAD51D variant were 4.1% (2.4-6.6) for clinic-based cases, 3.9% (2.8-5.2) for population-based cases, and 3.7% (2.3-5.4) for population-based controls, and were not significantly higher in case groups than controls (P=0.7 and P=0.8, respectively). After genotyping the relatives of cases who carried the variant, modified segregation analyses of these families were conducted, and the estimated hazard ratio for breast cancer corresponding to the E233G variant was 1.30 (95% CI: 0.66-2.58; P=0.4) for familial breast cancer families and 1.28 (95% CI: 0.47-3.43; P=0.6) for families unselected for family history. Therefore, despite being well powered to detect moderate risks, no evidence for an association between the E233G variant and breast cancer risk was observed in any setting. Larger studies would be required to determine if this variant is associated with a smaller risk of breast cancer.

Comprehensive association testing of common genetic variation in DNA repair pathway genes in relationship with breast cancer risk in multiple populations

Genetic association studies of multiple populations investigate a wider range of risk alleles than studies of a single ethnic group. In this study, we developed a multiethnic tagging strategy, exploiting differences in linkage disequilibrium (LD) structure between populations, to comprehensively capture common genetic variation across 60 genes spanning multiple DNA repair pathways, in five racial/ethnic populations. Over 2600 SNPs were genotyped in each population and single- and multi-marker predictors of common alleles were selected to capture the LD patterns specific to each group. Coding variants (n = 211) were genotyped to test whether combinations of putative functional variants in DNA repair pathway genes could have cumulative effects on risk. Tests of association were conducted in a multiethnic breast cancer study (2093 cases and 2303 controls), with validation of the top allelic associations (P </= 0.01) performed in additional studies of 6483 cases and 7309 controls. A variant in the FANCA gene (rs1061646, 0.15-0.68 frequency across populations) was associated with risk in the initial study (P = 0.0052), and in the replication studies (P = 0.032). In a combined analysis (8556 cases and 9605 controls), this SNP yielded an 8% increase in risk per allele. Combinations of coding variants in these genes were not associated with breast cancer and together, these data suggest that common variation in these DNA repair pathway genes are not strongly associated with breast cancer risk. The methods utilized in this study, applied to multiple populations, provide a framework for testing in association studies in diverse populations.

New models of collaboration in genome-wide association studies: the Genetic Association Information Network

The Genetic Association Information Network (GAIN) is a public-private partnership established to investigate the genetic basis of common diseases through a series of collaborative genome-wide association studies. GAIN has used new approaches for project selection, data deposition and distribution, collaborative analysis, publication and protection from premature intellectual property claims. These demonstrate a new commitment to shared scientific knowledge that should facilitate rapid advances in understanding the genetics of complex diseases.

Breast cancer: origins and evolution

Breast cancer is not a single disease, but rather is composed of distinct subtypes associated with different clinical outcomes. Understanding this heterogeneity is key for the development of targeted cancer-preventative and -therapeutic interventions. Current models explaining inter- and intratumoral diversity are the cancer stem cell and the clonal evolution hypotheses. Although tumor initiation and progression are predominantly driven by acquired genetic alterations, recent data implicate a role for microenvironmental and epigenetic changes as well. Comprehensive unbiased studies of tumors and patient populations have significantly advanced our molecular understanding of breast cancer, but translating these findings into clinical practice remains a challenge.

Fine mapping versus replication in whole-genome association studies

Association replication studies have a poor track record and, even when successful, often claim association with different markers, alleles, and phenotypes than those reported in the primary study. It is unknown whether these outcomes reflect genuine associations or false-positive results. A greater understanding of these observations is essential for genomewide association (GWA) studies, since they have the potential to identify multiple new associations that that will require external validation. Theoretically, a repeat association with precisely the same variant in an independent sample is the gold standard for replication, but testing additional variants is commonplace in replication studies. Finding different associated SNPs within the same gene or region as that originally identified is often reported as confirmatory evidence. Here, we compare the probability of replicating a gene or region under two commonly used marker-selection strategies: an "exact" approach that involves only the originally significant markers and a "local" approach that involves both the originally significant markers and others in the same region. When a region of high intermarker linkage disequilibrium is tested to replicate an initial finding that is only weak association with disease, the local approach is a good strategy. Otherwise, the most powerful and efficient strategy for replication involves testing only the initially identified variants. Association with a marker other than that originally identified can occur frequently, even in the presence of real effects in a low-powered replication study, and instances of such association increase as the number of included variants increases. Our results provide a basis for the design and interpretation of GWA replication studies and point to the importance of a clear distinction between fine mapping and replication after GWA.

Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants

We have genotyped 14,436 nonsynonymous SNPs (nsSNPs) and 897 major histocompatibility complex (MHC) tag SNPs from 1,000 independent cases of ankylosing spondylitis (AS), autoimmune thyroid disease (AITD), multiple sclerosis (MS) and breast cancer (BC). Comparing these data against a common control dataset derived from 1,500 randomly selected healthy British individuals, we report initial association and independent replication in a North American sample of two new loci related to ankylosing spondylitis, ARTS1 and IL23R, and confirmation of the previously reported association of AITD with TSHR and FCRL3. These findings, enabled in part by increased statistical power resulting from the expansion of the control reference group to include individuals from the other disease groups, highlight notable new possibilities for autoimmune regulation and suggest that IL23R may be a common susceptibility factor for the major 'seronegative' diseases.

Genetic sequence variations and ADPRT haplotype analysis in French Canadian families with high risk of breast cancer

The poly(ADP-ribose) polymerase (PARP/ADPRT) protein family catalyzes the synthesis of cellular poly(ADP-ribose) following DNA damage and is involved in genomic integrity by regulating cellular responses to DNA damage and apoptosis. Moreover, ADPRT inhibition contributes to a protective effect against cancer development. These findings render ADPRT an attractive candidate susceptibility gene for breast cancer, and thus the goal of this study was to evaluate the possible involvement of ADPRT sequence variations in breast cancer susceptibility. The complete sequence of the 23 exons and flanking intronic sequences of the ADPRT gene was analyzed in 54 affected individuals from distinct high-risk non-BRCA1/2 French Canadian families. No deleterious truncating mutation was identified in the coding region. However, 34 sequence variations were identified, among which seven are coding variants and seven are novel changes. All coding variants and intronic changes located in the vicinity of the coding variants identified in the case series were also analyzed in a cohort of 73 unrelated healthy French Canadian individuals. Interestingly, one missense variant (Pro377Ser) was observed in three different breast cancer cases but was not present among unaffected individuals. We have conducted here an exhaustive detailed mutation and haplotype tagging analysis of the ADPRT gene with regard to breast cancer, providing useful data for other large-scale association studies. Additional studies in other cohorts and other populations are however needed to further evaluate the implication of the Pro377Ser missense variant with regard to breast cancer susceptibility.

A role for biomarkers in the screening and diagnosis of breast cancer in younger women

The widespread usage of screening mammography has resulted in an increase in the detection of early-stage disease, particularly in situ (stage 0) and early-stage (stage 1) cancers. However, incidence of stage 2 and 3 disease has not fallen commensurately, suggesting a bias in the detection of indolent cancers rather than aggressive cancers. Improved screening and diagnosis of a broader range of cancers is therefore an important need. Although MRI is a very sensitive breast cancer detection tool that has become standard for women at very high risk, it lacks sufficient specificity and cost-effectiveness for use as a general screen. The greatest opportunity for molecular tools to improve breast cancer outcomes is to better discern biologically aggressive cancers, especially in women under the age of 50 years. In this age group, presentation in stage 2 or 3 is more common and mammographic screening is less efficacious. We propose a multi-tiered triage strategy that uses emerging markers of susceptibility to segment the population for more focused screening with imaging. In particular, it would be helpful to identify a subset of at-risk, younger women who would benefit from intensive surveillance or preventive interventions. It is likely that tests for susceptibility, unless they are highly specific, will need to be combined with indicators of short-term risk. Although the combined sensitivity and specificity of screening must be high, each individual test does not require high specificity. It is important, however, for the susceptibility tests and short-term risk markers to be highly sensitive. If the majority of women under 50 years of age who develop breast cancer are captured with this strategy, then mammography screening for the general population can start at age 50 years. Finally, and perhaps most importantly, biomarkers of susceptibility and short-term risk are likely to provide insight into the biology of tumors that develop, leading to new interventions to support prevention. The most effective preventive strategies will be those where a marker predicts risk for the disease, as well as the benefit from preventive interventions.

Genetic heterogeneity among Fanconi anemia heterozygotes and risk of cancer

Fanconi anemia (FA) is a rare autosomal recessive disease characterized by a greatly increased risk of cancer among those diagnosed with the syndrome. The question as to whether FA heterozygotes are at increased risk for cancer is of great importance to those at risk for being a carrier. To address this question, we formed a cohort of grandparents of probands identified through the International Fanconi Anemia Registry. We obtained informed consent, a short questionnaire, and either blood or buccal swab DNA. After diagnosis of the proband was confirmed and complementation studies or DNA sequencing on the proband were completed, mutation analyses of the putative carriers and noncarriers was carried out. Standardized incidence ratios (SIR) were calculated to compare the observed cancer incidence of the grandparents and other relatives with the expected rates of cancer, using the Surveillance, Epidemiology, and End Results registries and the Connecticut Cancer registry. In the 944 study subjects who participated (784 grandparents and 160 other relatives), there was no suggestion of an increase in overall cancer incidence. On the other hand, a significantly higher rate of breast cancer than expected was observed among carrier grandmothers [SIR, 1.7; 95% confidence interval (95% CI), 1.1-2.7]. Among the grandmothers, those who were carriers of FANCC mutations were found to be at highest risk (SIR, 2.4; 95% CI, 1.1-5.2). Overall, there was no increased risk for cancer among FA heterozygotes in this study of Fanconi relatives, although there is some evidence that FANCC mutations are possibly breast cancer susceptibility alleles.

Quantitative expression profiling guided by common retroviral insertion sites reveals novel and cell type specific cancer genes in leukemia

Proviral insertional mutagenesis is a powerful tool for the discovery of cancer-associated genes. The ability of integrated proviruses to affect gene expression over long distances combined with the lack of methods to determine the expression levels of large numbers of genes in a systematic and truly quantitative manner have limited the identification of cancer genes by proviral insertional mutagenesis. Here, we have characterized a new model of proviral insertional mutagenesis-induced lymphoid tumors derived from Eed Polycomb group gene mutant mice and quantitatively determined the expression levels of all genes within 100 kb of 20 different retroviral common insertion sites (CISs) identified in these tumors. Using high-throughput quantitative reverse transcription-polymerase chain reaction (Q-RT-PCR), we document an average of 13 CIS-associated genes deregulated per tumor, half of which are leukemia subtype-specific, while the others are coordinately deregulated in the majority of tumors analyzed. Interestingly, we find that genes located distantly from common proviral integration sites are as frequently deregulated as proximal genes, with multiple genes affected per integration. Our studies reveal an unsuspected conservation in the group of genes deregulated among phenotypically similar subtypes of lymphoid leukemias, and suggest that identification of common molecular determinants of this disease is within reach.

Copy number variants and genetic traits: closer to the resolution of phenotypic to genotypic variability

A considerable and unanticipated plasticity of the human genome, manifested as inter-individual copy number variation, has been discovered. These structural changes constitute a major source of inter-individual genetic variation that could explain variable penetrance of inherited (Mendelian and polygenic) diseases and variation in the phenotypic expression of aneuploidies and sporadic traits, and might represent a major factor in the aetiology of complex, multifactorial traits. For these reasons, an effort should be made to discover all common and rare copy number variants (CNVs) in the human population. This will also enable systematic exploration of both SNPs and CNVs in association studies to identify the genomic contributors to the common disorders and complex traits.

A genome-wide association study of breast and prostate cancer in the NHLBI's Framingham Heart Study

BACKGROUND

Breast and prostate cancer are two commonly diagnosed cancers in the United States. Prior work suggests that cancer causing genes and cancer susceptibility genes can be identified.

METHODS

We conducted a genome-wide association study (Affymetrix 100K SNP GeneChip) of cancer in the community-based Framingham Heart Study. We report on 2 cancer traits--prostate cancer and breast cancer--in up to 1335 participants from 330 families (54% women, mean entry age 33 years). Multivariable-adjusted residuals, computed using Cox proportional hazards models, were tested for association with qualifying SNPs (70, 987 autosomal SNPs with genotypic call rate > or =80%, minor allele frequency > or =10%, Hardy-Weinberg test p > or = 0.001) using generalized estimating equations (GEE) models and family based association tests (FBAT).

RESULTS

There were 58 women with breast cancer and 59 men with prostate cancer. No SNP associations attained genome-wide significance. The top SNP associations in GEE models for each trait were as follows: breast cancer, rs2075555, p = 8.0 x 10(-8) in COL1A1; and prostate cancer, rs9311171, p = 1.75 x 10(-6) in CTDSPL. In analysis of selected candidate cancer susceptibility genes, two MSR1 SNPs (rs9325782, GEE p = 0.008 and rs2410373, FBAT p = 0.021) were associated with prostate cancer and three ERBB4 SNPs (rs905883 GEE p = 0.0002, rs7564590 GEE p = 0.003, rs7558615 GEE p = 0.0078) were associated with breast cancer. The previously reported risk SNP for prostate cancer, rs1447295, was not included on the 100K chip. Results of cancer phenotype-genotype associations for all autosomal SNPs are web posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 webcite.

CONCLUSION

Although no association attained genome-wide significance, several interesting associations emerged for breast and prostate cancer. These findings can serve as a resource for replication in other populations to identify novel biologic pathways contributing to cancer susceptibility.

TGFB1 and TGFBR1 polymorphisms and breast cancer risk in the Nurses' Health Study

BACKGROUND

Transforming growth factor beta 1 (TGFB1) forms a signaling complex with transforming growth factor beta receptors 1 and 2 and has been described as both a tumor suppressor and tumor promoter. Single nucleotide polymorphisms in TGFB1 and a microsatellite in TGFBR1 have been investigated for association with risk of breast cancer, with conflicting results.

METHODS

We examined polymorphisms in the promoter region of the TGFB1 gene as well as the TGFBR1*6A microsatellite in the Nurses' Health Study cohort.

RESULTS

No overall associations between the L10P polymorphism of TGFB1 or the TGFBR1 microsatellite were detected. However, we observed an inverse association between the -509 C/T polymorphism of TGFB1 (p-trend = 0.04), which was stronger and more significant among women with estrogen receptor positive breast cancer.

CONCLUSION

Polymorphisms in the promoter region of TGFB1 are not likely to be associated with large increases in breast cancer risk overall among Caucasian women.

Power to detect risk alleles using genome-wide tag SNP panels

Advances in high-throughput genotyping and the International HapMap Project have enabled association studies at the whole-genome level. We have constructed whole-genome genotyping panels of over 550,000 (HumanHap550) and 650,000 (HumanHap650Y) SNP loci by choosing tag SNPs from all populations genotyped by the International HapMap Project. These panels also contain additional SNP content in regions that have historically been overrepresented in diseases, such as nonsynonymous sites, the MHC region, copy number variant regions and mitochondrial DNA. We estimate that the tag SNP loci in these panels cover the majority of all common variation in the genome as measured by coverage of both all common HapMap SNPs and an independent set of SNPs derived from complete resequencing of genes obtained from SeattleSNPs. We also estimate that, given a sample size of 1,000 cases and 1,000 controls, these panels have the power to detect single disease loci of moderate risk (λ ∼ 1.8–2.0). Relative risks as low as λ ∼ 1.1–1.3 can be detected using 10,000 cases and 10,000 controls depending on the sample population and disease model. If multiple loci are involved, the power increases significantly to detect at least one locus such that relative risks 20%–35% lower can be detected with 80% power if between two and four independent loci are involved. Although our SNP selection was based on HapMap data, which is a subset of all common SNPs, these panels effectively capture the majority of all common variation and provide high power to detect risk alleles that are not represented in the HapMap data.

Advances in high-throughput genotyping technology and the International HapMap Project have enabled genetic association studies at the whole-genome level. Our paper describes two genome-wide SNP panels that contain tag SNPs derived from the International HapMap Project. Tag SNPs are proxies for groups of highly correlated SNPs. Information can be captured for the entire group of correlated SNPs by genotyping only one representative SNP, the tag SNP. These whole-genome SNP panels also contain additional content thought to be overrepresented in disease, such as amino acid–changing nonsynonymous SNPs and mitochondrial SNPs. We show that these panels cover the genome with very high efficiency as measured by coverage of all HapMap SNPs and a set of SNPs derived from completely resequenced genes from the Seattle SNPs database. We also show that these panels have high power to detect disease risk alleles for both HapMap and non-HapMap SNPs. In complex disease where multiple risk alleles are believed to be involved, we show that the ability to detect at least one risk allele with the tag SNP panels is also high.

Genome-wide association studies: progress in identifying genetic biomarkers in common, complex diseases

Novel, comprehensive approaches for biomarker discovery and validation are urgently needed. One particular area of methodologic need is for discovery of novel genetic biomarkers in complex diseases and traits. Here, we review recent successes in the use of genome wide association (GWA) approaches to identify genetic biomarkers in common human diseases and traits. Such studies are yielding initial insights into the allelic architecture of complex traits. In general, it appears that complex diseases are associated with many common polymorphisms, implying profound genetic heterogeneity between affected individuals.

Etiologic impact of known cancer susceptibility genes

The impact of a gene variant on the population burden of cancer can be measured by the population attributable fraction (PAF), which depends on the risk conferred by the variant, genotype relative risk (GRR), the frequency of the variant in the population and the mode of inheritance. PAF defines the proportion of the disease in the study population due to a gene variant, hence the synonymic term, etiologic fraction. After a review of the literature, 27 confirmed cancer susceptibility genes, groups of genes and loci were selected for analysis on the basis of their prevalence and availability of validated GRR data. The covered variants represent the most common established cancer susceptibility genes; those not included have marginal PAFs on common cancers. The PAF due to known genes at the covered sites was highest for brain hemangioblastoma (19%), conferred by the VHL gene. For colorectal cancer, the PAF estimates amounted to 7.0%. Including genes and identified loci from whole genome scans, PAFs for both breast and prostate cancers summed up to 70%. The derived estimates should rectify common overstatements on the contribution of individual high penetrance genes on common cancers at the population level. More dramatically, the estimates show the large PAFs conferred by the recently discovered breast, prostate and colorectal cancer loci, most of which are not known to alter coding sequences or expression patterns and they thus act through yet unexplained mechanisms. Although of low risk, these common variants appear to explain large proportions of breast and prostate cancers in the population.