FGFR2 variants and breast cancer risk: fine-scale mapping using African American studies and analysis of chromatin conformation

FGFR2 genetic variants in women with breast cancer

Black African populations are more genetically diverse than others, but genetic variants have been studied primarily in European populations. The present study examined the association of four single nucleotide polymorphisms (SNPs) of the fibroblast growth factor receptor 2, associated with breast cancer in non‑African populations, with breast cancer in Black, southern African women. Genomic DNA was extracted from whole blood samples of 1,001 patients with breast cancer and 1,006 controls (without breast cancer), and the rs2981582, rs35054928, rs2981578, and rs11200014 polymorphisms were analyzed using allele‑specific Kompetitive allele‑specific PCR™, and the χ2 or Fisher's exact tests were used to compare the genotype frequencies. There was no association between those SNPs and breast cancer in the studied cohort, although an association was identified between the C/C homozygote genotype for rs2981578 and invasive lobular carcinoma. These results show that genetic biomarkers of breast cancer risk in European populations are not necessarily associated with risk in sub‑Saharan African populations. African populations are more heterogenous than other populations, and the information from this population can help focus genetic risks of cancer in this understudied population.

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure

Background

Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging.

Results

In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants.

Conclusions

Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-01021-x.

Methods for statistical fine-mapping and their applications to auto-immune diseases

Although genome-wide association studies (GWAS) have identified thousands of loci in the human genome that are associated with different traits, understanding the biological mechanisms underlying the association signals identified in GWAS remains challenging. Statistical fine-mapping is a method aiming to refine GWAS signals by evaluating which variant(s) are truly causal to the phenotype. Here, we review the types of statistical fine-mapping methods that have been widely used to date, with a focus on recently developed functionally informed fine-mapping (FIFM) methods that utilize functional annotations. We then systematically review the applications of statistical fine-mapping in autoimmune disease studies to highlight the value of statistical fine-mapping in biological contexts.

A Personal Breast Cancer Risk Stratification Model Using Common Variants and Environmental Risk Factors in Japanese Females

Simple Summary

Breast cancer remains the most common cancer in females, warranting the development of new approaches to prevention. One such approach is personalized prevention using genetic risk models. Here, we developed a risk model using both genetic and environmental risk factors. Results showed that a genetic risk score defined by the number of risk alleles for 14 breast cancer risk SNPs clearly stratified breast cancer risk. Moreover, the combination of this genetic risk score model with an environmental risk model which included established environmental risk factors showed significantly better C-statistics than the environmental risk model alone. This genetic risk score model in combination with the environmental model may be suitable for stratifying individual breast cancer risk, and may form the basis for a new personalized approach to breast cancer prevention.

Abstract

Personalized approaches to prevention based on genetic risk models have been anticipated, and many models for the prediction of individual breast cancer risk have been developed. However, few studies have evaluated personalized risk using both genetic and environmental factors. We developed a risk model using genetic and environmental risk factors using 1319 breast cancer cases and 2094 controls from three case–control studies in Japan. Risk groups were defined based on the number of risk alleles for 14 breast cancer susceptibility loci, namely low (0–10 alleles), moderate (11–16) and high (17+). Environmental risk factors were collected using a self-administered questionnaire and implemented with harmonization. Odds ratio (OR) and C-statistics, calculated using a logistic regression model, were used to evaluate breast cancer susceptibility and model performance. Respective breast cancer ORs in the moderate- and high-risk groups were 1.69 (95% confidence interval, 1.39–2.04) and 3.27 (2.46–4.34) compared with the low-risk group. The C-statistic for the environmental model of 0.616 (0.596–0.636) was significantly improved by combination with the genetic model, to 0.659 (0.640–0.678). This combined genetic and environmental risk model may be suitable for the stratification of individuals by breast cancer risk. New approaches to breast cancer prevention using the model are warranted.

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure.. [DOI: 10.1101/2020.10.06.328567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging. In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants. Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

Fine-mapping of a novel premenopausal breast cancer susceptibility locus at Chr4q31.22 in Caucasian women and validation in African and Chinese women

We previously identified a novel breast cancer susceptibility variant on chromosome 4q31.22 locus (rs1429142) conferring risk among women of European ancestry. Here, we report replication of findings, validation of the variant in diverse populations and fine-mapping of the associated locus in Caucasian population. The SNP rs1429142 (C/T, minor allele frequency 18%) showed association for the overall breast cancer risk in Stages 1-4 (n = 4,331 cases/4271 controls; p = 4.35 × 10-8 ; odds ratio, ORC-allele ,1.25), and an elevated risk among premenopausal women (n = 1,503 cases/4271 controls; p = 5.81 × 10-10 ; ORC-allele 1.40) in European populations. SNP rs1429142 was associated with premenopausal breast cancer risk in women of African (T/C; p-value 1.45 × 10-02 ; ORC-allele 1.2) but not from Chinese ancestry. Fine-mapping of the locus revealed several potential causal variants which are present within a single association signal, revealed from the conditional regression analysis. Functional annotation of the potential causal variants revealed three putative SNPs rs1366691, rs1429139 and rs7667633 with active enhancer functions inferred based on histone marks, DNase hypersensitive sites in breast cell line data. These putative variants were bound by transcription factors (C-FOS, STAT1/3 and POL2/3) with known roles in inflammatory pathways. Furthermore, Hi-C data revealed several short-range interactions in the fine-mapped locus harboring the putative variants. The fine mapped locus was predicted to be within a single topologically associated domain, potentially facilitating enhancer-promoter interactions possibly leading to the regulation of nearby genes.

Tumor microenvironment: Challenges and opportunities in targeting metastasis of triple negative breast cancer

Triple negative breast cancer (TNBC) is most aggressive subtype of breast cancers with high probability of metastasis as well as lack of specific targets and targeted therapeutics. TNBC is characterized with unique tumor microenvironment (TME), which differs from other subtypes. TME is associated with induction of proliferation, angiogenesis, inhibition of apoptosis and immune system suppression, and drug resistance. Exosomes are promising nanovesicles, which orchestrate the TME by communicating with different cells within TME. The components of TME including transformed ECM, soluble factors, immune suppressive cells, epigenetic modifications and re-programmed fibroblasts together hamper antitumor response and helps progression and metastasis of TNBCs. Therefore, TME could be a therapeutic target of TNBC. The current review presents latest updates on the role of exosomes in modulation of TME, approaches for targeting TME and combination of immune checkpoint inhibitors and target chemotherapeutics. Finally, we also discussed various phytochemicals that alter genetic, transcriptomic and proteomic profiles of TME along with current challenges and future implications. Thus, as TME is associated with the hallmarks of TNBC, the understanding of the impact of different components can improve the clinical benefits of TNBC patients.

Copy number alternations of the 17q23-rs6504950 locus are associated with advanced breast cancers in Taiwanese women

Objective:

Breast cancer is one of the most common malignancies and a leading cause of cancer-related death in women worldwide. Both hormone-related factors and genetic aberrations could cause breast cancer. We investigated copy number alternations (CNAs) on four breast cancer-susceptible loci, namely 2q35-rs13387042, 3p24-rs4973768, 17q23-rs6504950, and fibroblast growth factor receptor 2 (FGFR2)-rs2981578, in Taiwanese women.

Patients and Methods:

Breast cancer tissues and blood samples from 66 patients and their clinical data were collected from a human biobank. The copy numbers of the germline samples (from blood) and cancer tissues from each patient on the susceptible loci – 2q35, 3p24, 17q23, and FGFR2 – were obtained using TaqMan probes in the Applied Biosystems Inc., (ABI) StepOnePlus Real-Time Polymerase Chain Reaction instrument and CopyCaller^® Software v1.0 (ABI, CA, USA).

Results:

The mean copy numbers output by CopyCaller^® Software v1.0 of the cancer tissues on these susceptible loci (2q35, 3p24, 17q23, and FGFR2) from the 66 patients were higher than those of the blood samples (2.0 vs. 1.9); however, significantly higher copy numbers for cancer tissues compared with germline samples were discovered only on 2q35-rs13387042 (P = 0.035). In addition, patients with advanced breast cancers had relatively many CNAs between their cancer tissues and germline samples on 17q23-rs6504950 (P = 0.008). Multivariate analysis revealed that the risk factor for patients with advanced breast cancers was CNAs between cancer tissues and germline samples on 17q23-rs6504950 (odds ratio = 13.337, 95% confidence interval: 1.525–122.468).

Conclusions:

CNAs on 17q23-rs6504950 between cancer tissues and germline samples could affect cancer progression in Taiwanese women with breast cancer. Further investigations regarding the role of CNAs on 17q23-rs6504950 in cancer progression are necessary to elucidate the pathogenesis of breast cancer.

A genome wide SNP genotyping study in the Tunisian population: specific reporting on a subset of common breast cancer risk loci

Background

Breast cancer is the most common cancer in women worldwide. Around 50% of breast cancer familial risk has been so far explained by known susceptibility alleles with variable levels of risk and prevalence. The vast majority of these breast cancer associated variations reported to date are from populations of European ancestry. In spite of its heterogeneity and genetic wealth, North-African populations have not been studied by the HapMap and the 1000Genomes projects. Thus, very little is known about the genetic architecture of these populations.

Methods

This study aimed to investigate a subset of common breast cancer loci in the general Tunisian population and to compare their genetic composition to those of other ethnic groups. We undertook a genome-wide haplotype study by genotyping 135 Tunisian subjects using the Affymetrix 6.0-Array. We compared Tunisian allele frequencies and linkage disequilibrium patterns to those of HapMap populations and we performed a comprehensive assessment of the functional effects of several selected variants.

Results

Haplotype analyses showed that at risk haplotypes on 2p24, 4q21, 6q25, 9q31, 10q26, 11p15, 11q13 and 14q32 loci are considerably frequent in the Tunisian population (> 20%). Allele frequency comparison showed that the frequency of rs13329835 is significantly different between Tunisian and all other HapMap populations. LD-blocks and Principle Component Analysis revealed that the genetic characteristics of breast cancer variants in the Tunisian, and so probably the North-African populations, are more similar to those of Europeans than Africans.

Using eQTl analysis, we characterized rs9911630 as the most strongly expression-associated SNP that seems to affect the expression levels of BRCA1 and two long non coding RNAs (NBR2 and LINC008854). Additional in-silico analysis also suggested a potential functional significance of this variant.

Conclusions

We illustrated the utility of combining haplotype analysis in diverse ethnic groups with functional analysis to explore breast cancer genetic architecture in Tunisia. Results presented in this study provide the first report on a large number of common breast cancer genetic polymorphisms in the Tunisian population which may establish a baseline database to guide future association studies in North Africa.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-5133-8) contains supplementary material, which is available to authorized users.

Elucidating the Underlying Functional Mechanisms of Breast Cancer Susceptibility Through Post-GWAS Analyses

Genome-wide association studies (GWAS) have identified more than 170 single nucleotide polymorphisms (SNPs) associated with the susceptibility to breast cancer. Together, these SNPs explain 18% of the familial relative risk, which is estimated to be nearly half of the total familial breast cancer risk that is collectively explained by low-risk susceptibility alleles. An important aspect of this success has been the access to large sample sizes through collaborative efforts within the Breast Cancer Association Consortium (BCAC), but also collaborations between cancer association consortia. Despite these achievements, however, understanding of each variant's underlying mechanism and how these SNPs predispose women to breast cancer remains limited and represents a major challenge in the field, particularly since the vast majority of the GWAS-identified SNPs are located in non-coding regions of the genome and are merely tags for the causal variants. In recent years, fine-scale mapping studies followed by functional evaluation of putative causal variants have begun to elucidate the biological function of several GWAS-identified variants. In this review, we discuss the findings and lessons learned from these post-GWAS analyses of 22 risk loci. Identifying the true causal variants underlying breast cancer susceptibility and their function not only provides better estimates of the explained familial relative risk thereby improving polygenetic risk scores (PRSs), it also increases our understanding of the biological mechanisms responsible for causing susceptibility to breast cancer. This will facilitate the identification of further breast cancer risk alleles and the development of preventive medicine for those women at increased risk for developing the disease.

Genome-wide mapping of quantitative trait loci in admixed populations using mixed linear model and Bayesian multiple regression analysis

Background

Population stratification and cryptic relationships have been the main sources of excessive false-positives and false-negatives in population-based association studies. Many methods have been developed to model these confounding factors and minimize their impact on the results of genome-wide association studies. In most of these methods, a two-stage approach is applied where: (1) methods are used to determine if there is a population structure in the sample dataset and (2) the effects of population structure are corrected either by modeling it or by running a separate analysis within each sub-population. The objective of this study was to evaluate the impact of population structure on the accuracy and power of genome-wide association studies using a Bayesian multiple regression method.

Methods

We conducted a genome-wide association study in a stochastically simulated admixed population. The genome was composed of six chromosomes, each with 1000 markers. Fifteen segregating quantitative trait loci contributed to the genetic variation of a quantitative trait with heritability of 0.30. The impact of genetic relationships and breed composition (BC) on three analysis methods were evaluated: single marker simple regression (SMR), single marker mixed linear model (MLM) and Bayesian multiple-regression analysis (BMR). Each method was fitted with and without BC. Accuracy, power, false-positive rate and the positive predictive value of each method were calculated and used for comparison.

Results

SMR and BMR, both without BC, were ranked as the worst and the best performing approaches, respectively. Our results showed that, while explicit modeling of genetic relationships and BC is essential for models SMR and MLM, BMR can disregard them and yet result in a higher power without compromising its false-positive rate.

Conclusions

This study showed that the Bayesian multiple-regression analysis is robust to population structure and to relationships among study subjects and performs better than a single marker mixed linear model approach.

Genetic Modifiers of the Breast Tumor Microenvironment

Multiple nonmalignant cell types in the tumor microenvironment (TME) impact breast cancer risk, metastasis, and response to therapy, yet most heritable mechanisms that influence TME cell function and breast cancer outcomes are largely unknown. Breast cancer risk is ∼30% heritable and >170 genetic loci have been associated with breast cancer traits. However, the majority of candidate genes have poorly defined mechanistic roles in breast cancer biology. Research indicates that breast cancer risk modifiers directly impact cancer cells, yet it is equally plausible that some modifier alleles impact the nonmalignant TME. The objective of this review is to examine the list of current breast cancer candidate genes that may modify breast cancer risk and outcome through the TME.

Dissecting the genetics of complex traits using summary association statistics

During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyse summary association statistics. Here, we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.

Trans-ethnic follow-up of breast cancer GWAS hits using the preferential linkage disequilibrium approach

Leveraging population-distinct linkage equilibrium (LD) patterns, trans-ethnic follow-up of variants discovered from genome-wide association studies (GWAS) has proved to be useful in facilitating the identification of bona fide causal variants. We previously developed the preferential LD approach, a novel method that successfully identified causal variants driving the GWAS signals within European-descent populations even when the causal variants were only weakly linked with the GWAS-discovered variants. To evaluate the performance of our approach in a trans-ethnic setting, we applied it to follow up breast cancer GWAS hits identified mostly from populations of European ancestry in African Americans (AA). We evaluated 74 breast cancer GWAS variants in 8,315 AA women from the African American Breast Cancer Epidemiology and Risk (AMBER) consortium. Only 27% of them were associated with breast cancer risk at significance level α=0.05, suggesting race-specificity of the identified breast cancer risk loci. We followed up on those replicated GWAS hits in the AMBER consortium utilizing the preferential LD approach, to search for causal variants or better breast cancer markers from the 1000 Genomes variant catalog. Our approach identified stronger breast cancer markers for 80% of the GWAS hits with at least nominal breast cancer association, and in 81% of these cases, the marker identified was among the top 10 of all 1000 Genomes variants in the corresponding locus. The results support trans-ethnic application of the preferential LD approach in search for candidate causal variants, and may have implications for future genetic research of breast cancer in AA women.

The precision relationships between eight GWAS-identified genetic variants and breast cancer in a Chinese population

Some of the new breast cancer susceptibility loci discovered in recent Genome-wide association studies (GWASs) have not been confirmed in Chinese populations. To determine whether eight novel Single-Nucleotide Polymorphisms (SNPs) have associations with breast cancer risk in women from southeast China, we conducted a case-control study of 1,156 breast cancer patients and 1,256 healthy controls. We first validated that the SNPs rs12922061, rs2290203, and rs2981578 were associated with overall breast cancer risk in southeast Chinese women, with the per-allele OR of 1.209 (95%CI: 1.064-1.372), 1.176 (95%CI: 1.048-1.320), and 0.852 (95%CI: 0.759-0.956), respectively. Rs12922061 and rs2290203 even passed the threshold for Bonferroni correction (P value: 0.00625). In stratified analysis, we found another three SNPs were significantly associated within different subgroups. However, after Bonferroni correction (P value: 0.000446), there were no statistically significant was observed. In gene-environment interaction analysis, we observed gene-environment interactions played a potential role of in the risk of breast cancer. These findings provide new insight into the associations between the genetic susceptibility and fine classifications of breast cancer. Based on these results, we encourage further large series studies and functional research to confirm these finding.

Management of women with a hereditary predisposition for breast cancer

Women with a hereditary breast cancer predisposition have three management options: screening, chemoprevention (risk-reducing medication) and risk-reducing surgery. However, no randomized trials have addressed the effect of these strategies in mutation carriers. In the general population, randomized trials failed to demonstrate a benefit for screening in premenopausal women. Moreover, although chemoprevention reduces breast cancer incidence in high-risk populations, this benefit is potentially confined to estrogen receptor-positive tumors. Finally, observational studies suggest that prophylactic mastectomy and even prophylactic salpingo-ophorectomy reduces breast cancer risk in BRCA mutation carriers, but there are systematic biases associated with such studies. Therefore, women with a hereditary predisposition for breast cancer should be informed of the three risk-reducing strategies, and that their benefits are not fully understood.

Association of FGFR3 and FGFR4 gene polymorphisms with breast cancer in Chinese women of Heilongjiang province

BACKGROUND

The fibroblast growth factor (FGF) receptor pathway is activated in many tumors. FGFR2 has been identified as a breast cancer susceptibility gene. Common variation in other FGF receptors might also affect breast cancer risk. We carried out a case-control study to investigate associations of variants in FGFR3 and FGFR4 with breast cancer in women from Heilongjiang Province.

METHODS

SNP rs2234909 and rs3135848 in FGFR3 and rs1966265 and rs351855 in FGFR4 were successfully genotyped in 747 breast cancer patients and 716 healthy controls using the SNaPshot method. The associations between SNPs and breast cancer were examined by logistic regression. The associations between SNPs and disease characteristics were examined by chi-square tests or one-way ANOVA as needed.

RESULTS

The minor alleles of rs1966265 and rs351855 in FGFR4 were strongly associated with breast cancer in the population, with odds ratios of 1.335 (95%CI = 1.154-1.545) and 1.364 (95%CI = 1.177-1.580), respectively. However, no significant associations were detected between other SNPs and breast cancer. Analyses of the disease characteristics showed that SNP rs351855 was associated with lymph-node-positive breast cancer with a dose-dependent effect of the minor allele (P = 0.008).

CONCLUSIONS

SNPs rs1966265 and rs351855 in FGFR4 were associated with breast cancer in a northern Chinese population.

FGFR2 risk SNPs confer breast cancer risk by augmenting oestrogen responsiveness

The fibroblast growth factor receptor 2 (FGFR2) locus is consistently the top hit in genome-wide association studies for oestrogen receptor-positive (ER(+)) breast cancer. Yet, its mode of action continues to be controversial. Here, we employ a systems biology approach to demonstrate that signalling via FGFR2 counteracts cell activation by oestrogen. In the presence of oestrogen, the oestrogen receptor (ESR1) regulon (set of ESR1 target genes) is in an active state. However, signalling by FGFR2 is able to reverse the activity of the ESR1 regulon. This effect is seen in multiple distinct FGFR2 signalling model systems, across multiple cells lines and is dependent on the presence of FGFR2. Increased oestrogen exposure has long been associated with an increased risk of breast cancer. We therefore hypothesized that risk variants should reduce FGFR2 expression and subsequent signalling. Indeed, transient transfection experiments assaying the three independent variants of the FGFR2 risk locus (rs2981578, rs35054928 and rs45631563) in their normal chromosomal context show that these single-nucleotide polymorphisms (SNPs) map to transcriptional silencer elements and that, compared with wild type, the risk alleles augment silencer activity. The presence of risk variants results in lower FGFR2 expression and increased oestrogen responsiveness. We thus propose a molecular mechanism by which FGFR2 can confer increased breast cancer risk that is consistent with oestrogen exposure as a major driver of breast cancer risk. Our findings may have implications for the clinical use of FGFR2 inhibitors.

Local Joint Testing Improves Power and Identifies Hidden Heritability in Association Studies

There is mounting evidence that complex human phenotypes are highly polygenic, with many loci harboring multiple causal variants, yet most genetic association studies examine each SNP in isolation. While this has led to the discovery of thousands of disease associations, discovered variants account for only a small fraction of disease heritability. Alternative multi-SNP methods have been proposed, but issues such as multiple-testing correction, sensitivity to genotyping error, and optimization for the underlying genetic architectures remain. Here we describe a local joint-testing procedure, complete with multiple-testing correction, that leverages a genetic phenomenon we call linkage masking wherein linkage disequilibrium between SNPs hides their signal under standard association methods. We show that local joint testing on the original Wellcome Trust Case Control Consortium (WTCCC) data set leads to the discovery of 22 associated loci, 5 more than the marginal approach. These loci were later found in follow-up studies containing thousands of additional individuals. We find that these loci significantly increase the heritability explained by genome-wide significant associations in the WTCCC data set. Furthermore, we show that local joint testing in a cis-expression QTL (eQTL) study of the gEUVADIS data set increases the number of genes containing significant eQTL by 10.7% over marginal analyses. Our multiple-hypothesis correction and joint-testing framework are available in a python software package called Jester, available at github.com/brielin/Jester.

Variants of FGFR2 and their associations with breast cancer risk: a HUGE systematic review and meta-analysis

Extensive epidemiological studies have demonstrated that there are associations between variants in intron 2 of FGFR2 and the breast cancer risk in various populations; however, the relationships are not yet conclusively established. To comprehensively review the epidemiological studies showing associations between the variants of FGFR2 and the breast cancer risk, and to establish correlations via a meta-analysis. The PubMed and MEDLINE databases were searched for eligible studies. The associations between the variants and breast cancer risk were evaluated using a random-effects model. The heterogeneity among the studies and the potential publication bias were also evaluated. Fifty-three studies with a total of 121,740 cases and 198,549 controls have examined the associations between 23 variants in intron 2 of FGFR2 and the breast cancer risk. The relationships for the 10 most frequently evaluated variants-rs1078806, rs11200014, rs1219648, rs2420946, rs2981578, rs2981579, rs2981582, rs3135718, rs10736303, and rs3750817-were synthesized based on a meta-analysis. Interestingly, we found that all 10 variants were significantly associated with the risk of breast cancer. In studies stratified by ethnicity, we found that the associations were more notable in Caucasians and Asians compared to Africans. Similar pooled results were found in population-based and hospital-based case-control studies and in studies with small and large sample sizes. FGFR2 is a breast cancer susceptibility gene, and various variants of FGFR2 are significantly associated with the breast cancer risk. However, the biological mechanisms underlying the associations need to be elucidated in future studies.

Heterogeneity of Breast Cancer Associations with Common Genetic Variants in FGFR2 according to the Intrinsic Subtypes in Southern Han Chinese Women

GWAS have identified variation in the FGFR2 locus as risk factors for breast cancer. Validation studies, however, have shown inconsistent results by ethnics and pathological characteristics. To further explore this inconsistency and investigate the associations of FGFR2 variants with breast cancer according to intrinsic subtype (Luminal-A, Luminal-B, ER-&PR-&HER2+, and triple negative) among Southern Han Chinese women, we genotyped rs1078806, rs1219648, rs2420946, rs2981579, and rs2981582 polymorphisms in 609 patients and 882 controls. Significant associations with breast cancer risk were observed for rs2420946, rs2981579, and rs2981582 with OR (95% CI) per risk allele of 1.19 (1.03-1.39), 1.24 (1.07-1.43), and 1.17 (1.01-1.36), respectively. In subtype specific analysis, above three SNPs were significantly associated with increased Luminal-A risk in a dose-dependent manner (P trend < 0.01); however, only rs2981579 was associated with Luminal-B, and none were linked to ER-&PR- subtypes (ER-&PR-&HER2+ and triple negative). Haplotype analyses also identified common haplotypes significantly associated with luminal-like subtypes (Luminal-A and Luminal-B), but not with ER-&PR- subtypes. Our results suggest that associations of FGFR2 SNPs with breast cancer were heterogeneous according to intrinsic subtype. Future studies stratifying patients by their intrinsic subtypes will provide new insights into the complex genetic mechanisms underlying breast cancer.

Leveraging Functional-Annotation Data in Trans-ethnic Fine-Mapping Studies

Localization of causal variants underlying known risk loci is one of the main research challenges following genome-wide association studies. Risk loci are typically dissected through fine-mapping experiments in trans-ethnic cohorts for leveraging the variability in the local genetic structure across populations. More recent works have shown that genomic functional annotations (i.e., localization of tissue-specific regulatory marks) can be integrated for increasing fine-mapping performance within single-population studies. Here, we introduce methods that integrate the strength of association between genotype and phenotype, the variability in the genetic backgrounds across populations, and the genomic map of tissue-specific functional elements to increase trans-ethnic fine-mapping accuracy. Through extensive simulations and empirical data, we have demonstrated that our approach increases fine-mapping resolution over existing methods. We analyzed empirical data from a large-scale trans-ethnic rheumatoid arthritis (RA) study and showed that the functional genetic architecture of RA is consistent across European and Asian ancestries. In these data, we used our proposed methods to reduce the average size of the 90% credible set from 29 variants per locus for standard non-integrative approaches to 22 variants.

Polymorphisms of FGFR1 in HBV-related hepatocellular carcinoma

Hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is one of the most commonly diagnosed cancers in China. It is important to understand the genetic mechanisms underlying the development and progression of HBV-related HCC and to identify new biomarkers for clinical treatment. The important role of fibroblast growth factor receptors (FGFRs) has been widely recognized in many types of cancers, but the association between FGFR polymorphisms and HCC carcinogenesis has been rarely reported. In this study, 199 patients with HBV-associated cirrhosis, 203 with HBV-associated HCC, and 184 healthy controls with no liver diseases were enrolled as participants. Using SNaPshot assays, five SNPs (rs13317, rs7825208, rs1047057, rs1047111, and rs1966265) of growth factor receptor genes were genotyped. Our results showed that the G/A and G/G genotypes at rs7825208 of FGFR1 were negatively correlated with HBV-related HCC (odds ratio (OR) = 0.45, 95% confidence interval (CI) = 0.22-0.93, P = 0.027). However, after Bonferroni correction, these significant differences no longer existed (P > 0.05). Our results indicated that these five polymorphisms of fibroblast growth factor receptor genes do not play any independent roles in the tumorigenesis and progression of HBV-related HCC in Han Chinese patients.

Novel bayes factors that capture expert uncertainty in prior density specification in genetic association studies

Bayes factors (BFs) are becoming increasingly important tools in genetic association studies, partly because they provide a natural framework for including prior information. The Wakefield BF (WBF) approximation is easy to calculate and assumes a normal prior on the log odds ratio (logOR) with a mean of zero. However, the prior variance (W) must be specified. Because of the potentially high sensitivity of the WBF to the choice of W, we propose several new BF approximations with logOR ∼N(0,W), but allow W to take a probability distribution rather than a fixed value. We provide several prior distributions for W which lead to BFs that can be calculated easily in freely available software packages. These priors allow a wide range of densities for W and provide considerable flexibility. We examine some properties of the priors and BFs and show how to determine the most appropriate prior based on elicited quantiles of the prior odds ratio (OR). We show by simulation that our novel BFs have superior true-positive rates at low false-positive rates compared to those from both P-value and WBF analyses across a range of sample sizes and ORs. We give an example of utilizing our BFs to fine-map the CASP8 region using genotype data on approximately 46,000 breast cancer case and 43,000 healthy control samples from the Collaborative Oncological Gene-environment Study (COGS) Consortium, and compare the single-nucleotide polymorphism ranks to those obtained using WBFs and P-values from univariate logistic regression.

From candidate gene studies to GWAS and post-GWAS analyses in breast cancer

There are now more than 90 established breast cancer risk loci, with 57 new ones, revealed through genome-wide-association studies (GWAS) during the last two years. Established high, moderate and low penetrance genetic variants currently explain ∼49% of familial breast cancer risk. GWAS-discovered variants account for 14%, and it is estimated that another 1000 yet-to-be-discovered loci could contribute an additional ∼14% of familial risk. Polygenic risk scores can already be used to stratify breast cancer risk in the female population and could improve the targeting of mammographic screening programmes, which are at present largely based on age-specific risks. Fine-scale mapping and functional analyses are revealing candidate causal variants and the molecular mechanisms by which GWAS-hits may act. Better-powered GWAS and genome-wide sequencing projects are likely to continue identifying new breast cancer causal variants.

A study on genetic variants of Fibroblast growth factor receptor 2 (FGFR2) and the risk of breast cancer from North India

Genome-Wide Association Studies (GWAS) have identified Fibroblast growth factor receptor 2 (FGFR2) as a candidate gene for breast cancer with single nucleotide polymorphisms (SNPs) located in intron 2 region as the susceptibility loci strongly associated with the risk. However, replicate studies have often failed to extrapolate the association to diverse ethnic regions. This hints towards the existing heterogeneity among different populations, arising due to differential linkage disequilibrium (LD) structures and frequencies of SNPs within the associated regions of the genome. It is therefore important to revisit the previously linked candidates in varied population groups to unravel the extent of heterogeneity. In an attempt to investigate the role of FGFR2 polymorphisms in susceptibility to the risk of breast cancer among North Indian women, we genotyped rs2981582, rs1219648, rs2981578 and rs7895676 polymorphisms in 368 breast cancer patients and 484 healthy controls by Polymerase chain reaction-Restriction fragment length polymorphism (PCR-RFLP) assay. We observed a statistically significant association with breast cancer risk for all the four genetic variants (P<0.05). In per-allele model for rs2981582, rs1219648, rs7895676 and in dominant model for rs2981578, association remained significant after bonferroni correction (P<0.0125). On performing stratified analysis, significant correlations with various clinicopathological as well as environmental and lifestyle characteristics were observed. It was evident that rs1219648 and rs2981578 interacted with exogenous hormone use and advanced clinical stage III (after Bonferroni correction, P<0.000694), respectively. Furthermore, combined analysis on these four loci revealed that compared to women with 0–1 risk loci, those with 2–4 risk loci had increased risk (OR = 1.645, 95%CI = 1.152–2.347, P = 0.006). In haplotype analysis, for rs2981578, rs2981582 and rs1219648, risk haplotype (GTG) was associated with a significantly increased risk compared to the common (ACA) haplotype (OR = 1.365, 95% CI = 1.086–1.717, P = 0.008). Our results suggest that intron 2 SNPs of FGFR2 may contribute to genetic susceptibility of breast cancer in North India population.

Identifying causal variants at loci with multiple signals of association

Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20-50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/.

Integrating functional data to prioritize causal variants in statistical fine-mapping studies

Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further increase performance. Using simulations starting from the 1000 Genomes data, we find that our framework consistently outperforms the current state-of-the-art fine-mapping methods, reducing the number of variants that need to be selected to capture 90% of the causal variants from an average of 13.3 to 10.4 SNPs per locus (as compared to the next-best performing strategy). Furthermore, we introduce a cost-to-benefit optimization framework for determining the number of variants to be followed up in functional assays and assess its performance using real and simulation data. We validate our findings using a large scale meta-analysis of four blood lipids traits and find that the relative probability for causality is increased for variants in exons and transcription start sites and decreased in repressed genomic regions at the risk loci of these traits. Using these highly predictive, trait-specific functional annotations, we estimate causality probabilities across all traits and variants, reducing the size of the 90% confidence set from an average of 17.5 to 13.5 variants per locus in this data.

The role of local ancestry adjustment in association studies using admixed populations

Association analysis using admixed populations imposes challenges and opportunities for disease mapping. By developing some explicit results for the variance of an allele of interest conditional on either local or global ancestry and by simulation of recently admixed genomes we evaluate power and false-positive rates under a variety of scenarios concerning linkage disequilibrium (LD) and the presence of unmeasured variants. Pairwise LD patterns were compared between admixed and nonadmixed populations using the HapMap phase 3 data. Based on the above, we showed that as follows:

For causal variants with similar effect size in all populations, power is generally higher in a study using admixed population than using nonadmixed population, especially for highly differentiated SNPs. This gain of power is achieved with adjustment of global ancestry, which completely removes any cross-chromosome inflation of type I error rates, and addresses much of the intrachromosome inflation.

If reliably estimated, adjusting for local ancestry precisely recovers the localization that could have been achieved in a stratified analysis of source populations. Improved localization is most evident for highly differentiated SNPs; however, the advantage of higher power is lost on exactly the same differentiated SNPs.

In the real admixed populations such as African Americans and Latinos, the expansion of LD is not as dramatic as in our simulation.

While adjustment for global ancestry is required prior to announcing a novel association seen in an admixed population, local ancestry adjustment may best be regarded as a localization tool not strictly required for discovery purposes.

Ethnic background and genetic variation in the evaluation of cancer risk: a systematic review

The clinical use of genetic variation in the evaluation of cancer risk is expanding, and thus understanding how determinants of cancer susceptibility identified in one population can be applied to another is of growing importance. However there is considerable debate on the relevance of ethnic background in clinical genetics, reflecting both the significance and complexity of genetic heritage. We address this via a systematic review of reported associations with cancer risk for 82 markers in 68 studies across six different cancer types, comparing association results between ethnic groups and examining linkage disequilibrium between risk alleles and nearby genetic loci. We find that the relevance of ethnic background depends on the question. If asked whether the association of variants with disease risk is conserved across ethnic boundaries, we find that the answer is yes, the majority of markers show insignificant variability in association with cancer risk across ethnic groups. However if the question is whether a significant association between a variant and cancer risk is likely to reproduce, the answer is no, most markers do not validate in an ethnic group other than the discovery cohort's ancestry. This lack of reproducibility is not attributable to studies being inadequately populated due to low allele frequency in other ethnic groups. Instead, differences in local genomic structure between ethnic groups are associated with the strength of association with cancer risk and therefore confound interpretation of the implied physiologic association tracked by the disease allele. This suggest that a biological association for cancer risk alleles may be broadly consistent across ethnic boundaries, but reproduction of a clinical study in another ethnic group is uncommon, in part due to confounding genomic architecture. As clinical studies are increasingly performed globally this has important implications for how cancer risk stratifiers should be studied and employed.

A genome-wide association study of early-onset breast cancer identifies PFKM as a novel breast cancer gene and supports a common genetic spectrum for breast cancer at any age

Early-onset breast cancer (EOBC) causes substantial loss of life and productivity, creating a major burden among women worldwide. We analyzed 1,265,548 Hapmap3 single-nucleotide polymorphisms (SNP) among a discovery set of 3,523 EOBC incident cases and 2,702 population control women ages ≤ 51 years. The SNPs with smallest P values were examined in a replication set of 3,470 EOBC cases and 5,475 control women. We also tested EOBC association with 19,684 genes by annotating each gene with putative functional SNPs, and then combining their P values to obtain a gene-based P value. We examined the gene with smallest P value for replication in 1,145 breast cancer cases and 1,142 control women. The combined discovery and replication sets identified 72 new SNPs associated with EOBC (P < 4 × 10(-8)) located in six genomic regions previously reported to contain SNPs associated largely with later-onset breast cancer (LOBC). SNP rs2229882 and 10 other SNPs on chromosome 5q11.2 remained associated (P < 6 × 10(-4)) after adjustment for the strongest published SNPs in the region. Thirty-two of the 82 currently known LOBC SNPs were associated with EOBC (P < 0.05). Low power is likely responsible for the remaining 50 unassociated known LOBC SNPs. The gene-based analysis identified an association between breast cancer and the phosphofructokinase-muscle (PFKM) gene on chromosome 12q13.11 that met the genome-wide gene-based threshold of 2.5 × 10(-6). In conclusion, EOBC and LOBC seem to have similar genetic etiologies; the 5q11.2 region may contain multiple distinct breast cancer loci; and the PFKM gene region is worthy of further investigation. These findings should enhance our understanding of the etiology of breast cancer.

Genome-based approaches for the diagnosis of breast cancer: a review with perspective

SUMMARY Despite progress with microarray-based gene expression profiling of multiple genes concurrently, solid biomarkers or molecular classification have not been established as a result of Phase III randomized trials. Conventional clinicopathological characteristics and single-gene defect-based molecular tools based on the old dogma of reductionist approaches and linear experimentation that have created our knowledge in biology over the past century, and still today represent the basis for the prevention, diagnosis and treatment of all diseases in clinical medicine, are saving the lives of tens of thousands of patients with breast cancer. Almost 5000 manuscripts have been published on next-generation technologies in MEDLINE in the last 3 years, with 100 of them regarding breast cancer. This review considers evidence published after 2010 and up until October 2013 of the latest studies published using high-throughput next-generation techniques in significant numbers of samples from patients with breast cancer and data from trials enrolled on ClinicalTrials.gov website. A perspective estimation of the potential and challenges of modern approaches are also explained in detail.

FGF receptor genes and breast cancer susceptibility: results from the Breast Cancer Association Consortium

BACKGROUND

Breast cancer is one of the most common malignancies in women. Genome-wide association studies have identified FGFR2 as a breast cancer susceptibility gene. Common variation in other fibroblast growth factor (FGF) receptors might also modify risk. We tested this hypothesis by studying genotyped single-nucleotide polymorphisms (SNPs) and imputed SNPs in FGFR1, FGFR3, FGFR4 and FGFRL1 in the Breast Cancer Association Consortium.

METHODS

Data were combined from 49 studies, including 53 835 cases and 50 156 controls, of which 89 050 (46 450 cases and 42 600 controls) were of European ancestry, 12 893 (6269 cases and 6624 controls) of Asian and 2048 (1116 cases and 932 controls) of African ancestry. Associations with risk of breast cancer, overall and by disease sub-type, were assessed using unconditional logistic regression.

RESULTS

Little evidence of association with breast cancer risk was observed for SNPs in the FGF receptor genes. The strongest evidence in European women was for rs743682 in FGFR3; the estimated per-allele odds ratio was 1.05 (95% confidence interval=1.02-1.09, P=0.0020), which is substantially lower than that observed for SNPs in FGFR2.

CONCLUSION

Our results suggest that common variants in the other FGF receptors are not associated with risk of breast cancer to the degree observed for FGFR2.

Replication of breast cancer susceptibility loci in whites and African Americans using a Bayesian approach

Genome-wide association studies (GWAS) and candidate gene analyses have led to the discovery of several dozen genetic polymorphisms associated with breast cancer susceptibility, many of which are considered well-established risk factors for the disease. Despite attempts to replicate these same variant-disease associations in African Americans, the evaluable populations are often too small to produce precise or consistent results. We estimated the associations between 83 previously identified single nucleotide polymorphisms (SNPs) and breast cancer among Carolina Breast Cancer Study (1993-2001) participants using maximum likelihood, Bayesian, and hierarchical methods. The selected SNPs were previous GWAS hits (n = 22), near-hits (n = 19), otherwise well-established risk loci (n = 5), or located in the same genes as selected variants (n = 37). We successfully replicated 18 GWAS-identified SNPs in whites (n = 2,352) and 10 in African Americans (n = 1,447). SNPs in the fibroblast growth factor receptor 2 gene (FGFR2) and the TOC high mobility group box family member 3 gene (TOX3) were strongly associated with breast cancer in both races. SNPs in the mitochondrial ribosomal protein S30 gene (MRPS30), mitogen-activated protein kinase kinase kinase 1 gene (MAP3K1), zinc finger, MIZ-type containing 1 gene (ZMIZ1), and H19, imprinted maternally expressed transcript gene (H19) were associated with breast cancer in whites, and SNPs in the estrogen receptor 1 gene (ESR1) and H19 gene were associated with breast cancer in African Americans. We provide precise and well-informed race-stratified odds ratios for key breast cancer-related SNPs. Our results demonstrate the utility of Bayesian methods in genetic epidemiology and provide support for their application in small, etiologically driven investigations.

Fine-scale mapping of the FGFR2 breast cancer risk locus: putative functional variants differentially bind FOXA1 and E2F1

The 10q26 locus in the second intron of FGFR2 is the locus most strongly associated with estrogen-receptor-positive breast cancer in genome-wide association studies. We conducted fine-scale mapping in case-control studies genotyped with a custom chip (iCOGS), comprising 41 studies (n = 89,050) of European ancestry, 9 Asian ancestry studies (n = 13,983), and 2 African ancestry studies (n = 2,028) from the Breast Cancer Association Consortium. We identified three statistically independent risk signals within the locus. Within risk signals 1 and 3, genetic analysis identified five and two variants, respectively, highly correlated with the most strongly associated SNPs. By using a combination of genetic fine mapping, data on DNase hypersensitivity, and electrophoretic mobility shift assays to study protein-DNA binding, we identified rs35054928, rs2981578, and rs45631563 as putative functional SNPs. Chromatin immunoprecipitation showed that FOXA1 preferentially bound to the risk-associated allele (C) of rs2981578 and was able to recruit ERα to this site in an allele-specific manner, whereas E2F1 preferentially bound the risk variant of rs35054928. The risk alleles were preferentially found in open chromatin and bound by Ser5 phosphorylated RNA polymerase II, suggesting that the risk alleles are associated with changes in transcription. Chromatin conformation capture demonstrated that the risk region was able to interact with the promoter of FGFR2, the likely target gene of this risk region. A role for FOXA1 in mediating breast cancer susceptibility at this locus is consistent with the finding that the FGFR2 risk locus primarily predisposes to estrogen-receptor-positive disease.

Comparing the efficacy of SNP filtering methods for identifying a single causal SNP in a known association region

Genome-wide association studies have successfully identified associations between common diseases and a large number of single nucleotide polymorphisms (SNPs) across the genome. We investigate the effectiveness of several statistics, including p-values, likelihoods, genetic map distance and linkage disequilibrium between SNPs, in filtering SNPs in several disease-associated regions. We use simulated data to compare the efficacy of filters with different sample sizes and for causal SNPs with different minor allele frequencies (MAFs) and effect sizes, focusing on the small effect sizes and MAFs likely to represent the majority of unidentified causal SNPs. In our analyses, of all the methods investigated, filtering on the ranked likelihoods consistently retains the true causal SNP with the highest probability for a given false positive rate. This was the case for all the local linkage disequilibrium patterns investigated. Our results indicate that when using this method to retain only the top 5% of SNPs, even a causal SNP with an odds ratio of 1.1 and MAF of 0.08 can be retained with a probability exceeding 0.9 using an overall sample size of 50,000.

Beyond GWASs: illuminating the dark road from association to function

Genome-wide association studies (GWASs) have enabled the discovery of common genetic variation contributing to normal and pathological traits and clinical drug responses, but recognizing the precise targets of these associations is now the major challenge. Here, we review recent approaches to the functional follow-up of GWAS loci, including fine mapping of GWAS signal(s), prioritization of putative functional SNPs by the integration of genetic epidemiological and bioinformatic methods, and in vitro and in vivo experimental verification of predicted molecular mechanisms for identifying the targeted genes. The majority of GWAS-identified variants fall in noncoding regions of the genome. Therefore, this review focuses on strategies for assessing likely mechanisms affected by noncoding variants; such mechanisms include transcriptional regulation, noncoding RNA function, and epigenetic regulation. These approaches have already accelerated progress from genetic studies to biological knowledge and might ultimately guide the development of prognostic, preventive, and therapeutic measures.

Breast cancer subtypes and previously established genetic risk factors: a bayesian approach

BACKGROUND

Gene expression analyses indicate that breast cancer is a heterogeneous disease with at least five immunohistologic subtypes. Despite growing evidence that these subtypes are etiologically and prognostically distinct, few studies have investigated whether they have divergent genetic risk factors. To help fill in this gap in our understanding, we examined associations between breast cancer subtypes and previously established susceptibility loci among white and African-American women in the Carolina Breast Cancer Study.

METHODS

We used Bayesian polytomous logistic regression to estimate ORs and 95% posterior intervals for the association between each of 78 single nucleotide polymorphisms (SNP) and five breast cancer subtypes. Subtypes were defined using five immunohistochemical markers: estrogen receptors (ER), progesterone receptors (PR), human epidermal growth factor receptors 1 and 2 (HER1/2), and cytokeratin (CK) 5/6.

RESULTS

Several SNPs in TNRC9/TOX3 were associated with luminal A (ER/PR+, HER2-) or basal-like breast cancer (ER-, PR-, HER2-, HER1, or CK 5/6+), and one SNP (rs3104746) was associated with both. SNPs in FGFR2 were associated with luminal A, luminal B (ER/PR+, HER2+), or HER2+/ER- disease, but none were associated with basal-like disease. We also observed subtype differences in the effects of SNPs in 2q35, 4p, TLR1, MAP3K1, ESR1, CDKN2A/B, ANKRD16, and ZM1Z1.

CONCLUSION AND IMPACT

We found evidence that genetic risk factors for breast cancer vary by subtype and further clarified the role of several key susceptibility genes. .

Inherited genetic susceptibility to breast cancer: the beginning of the end or the end of the beginning?

Genome-wide association studies have identified 72 loci associated with breast cancer susceptibility. Seventeen of these are known to predispose to other cancers. High-penetrance susceptibility loci for breast cancer usually result from coding alterations, principally in genes involved in DNA repair, whereas almost all of the associations identified through genome-wide association studies are found in noncoding regions of the genome and are likely to involve regulation of genes in multiple pathways. However, the genes underlying most associations are not yet known. In this review, we summarize the findings from genome-wide association studies in breast cancer and describe the genes and mechanisms that are likely to be involved in the tumorigenesis process. We also discuss approaches to fine-scale mapping of susceptibility regions used to identify the likely causal variant(s) underlying the associations, a major challenge in genetic epidemiology. Finally, we discuss the potential impact of such findings on personalized medicine and future avenues for screening, prediction, and prevention programs.

Breast cancer susceptibility associated with rs1219648 (fibroblast growth factor receptor 2) and postmenopausal hormone therapy use in a population-based United States study

OBJECTIVE

Genomewide association studies have consistently found variants in fibroblast growth factor receptor 2 (FGFR2) to be associated with breast cancer. Recent reports suggest that postmenopausal hormone therapy (HT) use may modify the association between single nucleotide polymorphisms (SNPs) in FGFR2 and breast cancer risk. We assessed the hypothesis that the association between rs1219648 (FGFR2) SNP and breast cancer risk is modified by postmenopausal HT use in a population-based case-control study.

METHODS

We evaluated rs1219648 SNP for an association with breast cancer risk using data obtained from 869 postmenopausal breast cancer cases diagnosed between 1995 and 2000 and from 808 postmenopausal community controls who participated in a study conducted in three US states. Detailed postmenopausal HT information was collected through a structured telephone interview, and DNA samples were collected by mail using an established mouthwash protocol. Odds ratios and 95% confidence intervals (CIs) were calculated using logistic regression models adjusted for age and state of residence.

RESULTS

We observed a significant association between rs1219648 and breast cancer risk (per-allele odds ratio, 1.22; 95% CI, 1.06-1.41; P = 0.007), which did not vary significantly by ever use of estrogen plus progestogen therapy (interaction P = 0.48). There was stronger evidence of an interaction between ever use of estrogen-only HT and increasing number of rs1219648 risk alleles to increase breast cancer risk (interaction P = 0.08).

CONCLUSIONS

Our results are consistent with a risk association with FGFR2 but provide limited support for interaction with HT use. The study raises the possibility that the FGFR2 rs1219648 variant is more strongly associated with risk in estrogen-only hormone users, although this observation needs to be examined in larger studies.

FGF receptors: cancer biology and therapeutics

Fibroblast growth factors (FGFs) are involved in a variety of cellular processes, such as stemness, proliferation, anti-apoptosis, drug resistance, and angiogenesis. Here, FGF signaling network, cancer genetics/genomics of FGF receptors (FGFRs), and FGFR-targeted therapeutics will be reviewed. FGF signaling to RAS-MAPK branch and canonical WNT signaling cascade mutually regulate transcription programming. FGF signaling to PI3K-AKT branch and Hedgehog, Notch, TGFβ, and noncanonical WNT signaling cascades regulate epithelial-to-mesenchymal transition (EMT) and invasion. Gene amplification of FGFR1 occurs in lung cancer and estrogen receptor (ER)-positive breast cancer, and that of FGFR2 in diffuse-type gastric cancer and triple-negative breast cancer. Chromosomal translocation of FGFR1 occurs in the 8p11 myeloproliferative syndrome and alveolar rhabdomyosarcoma, as with FGFR3 in multiple myeloma and peripheral T-cell lymphoma. FGFR1 and FGFR3 genes are fused to neighboring TACC1 and TACC3 genes, respectively, due to interstitial deletions in glioblastoma multiforme. Missense mutations of FGFR2 are found in endometrial uterine cancer and melanoma, and similar FGFR3 mutations in invasive bladder tumors, and FGFR4 mutations in rhabdomyosarcoma. Dovitinib, Ki23057, ponatinib, and AZD4547 are orally bioavailable FGFR inhibitors, which have demonstrated striking effects in preclinical model experiments. Dovitinib, ponatinib, and AZD4547 are currently in clinical trial as anticancer drugs. Because there are multiple mechanisms of actions for FGFR inhibitors to overcome drug resistance, FGFR-targeted therapy is a promising strategy for the treatment of refractory cancer. Whole exome/transcriptome sequencing will be introduced to the clinical laboratory as the companion diagnostic platform facilitating patient selection for FGFR-targeted therapeutics in the era of personalized medicine.

Evaluating genome-wide association study-identified breast cancer risk variants in African-American women

Genome-wide association studies (GWAS), conducted mostly in European or Asian descendants, have identified approximately 67 genetic susceptibility loci for breast cancer. Given the large differences in genetic architecture between the African-ancestry genome and genomes of Asians and Europeans, it is important to investigate these loci in African-ancestry populations. We evaluated index SNPs in all 67 breast cancer susceptibility loci identified to date in our study including up to 3,300 African-American women (1,231 cases and 2,069 controls), recruited in the Southern Community Cohort Study (SCCS) and the Nashville Breast Health Study (NBHS). Seven SNPs were statistically significant (P ≤ 0.05) with the risk of overall breast cancer in the same direction as previously reported: rs10069690 (5p15/TERT), rs999737 (14q24/RAD51L1), rs13387042 (2q35/TNP1), rs1219648 (10q26/FGFR2), rs8170 (19p13/BABAM1), rs17817449 (16q12/FTO), and rs13329835 (16q23/DYL2). A marginally significant association (P<0.10) was found for three additional SNPs: rs1045485 (2q33/CASP8), rs4849887 (2q14/INHBB), and rs4808801 (19p13/ELL). Three additional SNPs, including rs1011970 (9p21/CDKN2A/2B), rs941764 (14q32/CCDC88C), and rs17529111 (6q14/FAM46A), showed a significant association in analyses conducted by breast cancer subtype. The risk of breast cancer was elevated with an increasing number of risk variants, as measured by quintile of the genetic risk score, from 1.00 (reference), to 1.75 (1.30-2.37), 1.56 (1.15-2.11), 2.02 (1.50-2.74) and 2.63 (1.96-3.52), respectively, (P = 7.8 × 10(-10)). Results from this study highlight the need for large genetic studies in AAs to identify risk variants impacting this population.

Fine mapping of breast cancer genome-wide association studies loci in women of African ancestry identifies novel susceptibility markers

Numerous single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility have been identified by genome-wide association studies (GWAS). However, these SNPs were primarily discovered and validated in women of European and Asian ancestry. Because linkage disequilibrium is ancestry-dependent and heterogeneous among racial/ethnic populations, we evaluated common genetic variants at 22 GWAS-identified breast cancer susceptibility loci in a pooled sample of 1502 breast cancer cases and 1378 controls of African ancestry. None of the 22 GWAS index SNPs could be validated, challenging the direct generalizability of breast cancer risk variants identified in Caucasians or Asians to other populations. Novel breast cancer risk variants for women of African ancestry were identified in regions including 5p12 (odds ratio [OR] = 1.40, 95% confidence interval [CI] = 1.11-1.76; P = 0.004), 5q11.2 (OR = 1.22, 95% CI = 1.09-1.36; P = 0.00053) and 10p15.1 (OR = 1.22, 95% CI = 1.08-1.38; P = 0.0015). We also found positive association signals in three regions (6q25.1, 10q26.13 and 16q12.1-q12.2) previously confirmed by fine mapping in women of African ancestry. In addition, polygenic model indicated that eight best markers in this study, compared with 22 GWAS-identified SNPs, could better predict breast cancer risk in women of African ancestry (per-allele OR = 1.21, 95% CI = 1.16-1.27; P = 9.7 × 10(-16)). Our results demonstrate that fine mapping is a powerful approach to better characterize the breast cancer risk alleles in diverse populations. Future studies and new GWAS in women of African ancestry hold promise to discover additional variants for breast cancer susceptibility with clinical implications throughout the African diaspora.

Resequencing and fine-mapping of the chromosome 12q13-14 locus associated with multiple sclerosis refines the number of implicated genes

Multiple sclerosis (MS) is a common chronic inflammatory disease of the central nervous system. Susceptibility to the disease is affected by both environmental and genetic factors. Genetic factors include haplotypes in the histocompatibility complex (MHC) and over 50 non-MHC loci reported by genome-wide association studies. Amongst these, we previously reported polymorphisms in chromosome 12q13-14 with a protective effect in individuals of European descent. This locus spans 288 kb and contains 17 genes, including several candidate genes which have potentially significant pathogenic and therapeutic implications. In this study, we aimed to fine-map this locus. We have implemented a two-phase study: a variant discovery phase where we have used next-generation sequencing and two target-enrichment strategies [long-range polymerase chain reaction (PCR) and Nimblegen's solution phase hybridization capture] in pools of 25 samples; and a genotyping phase where we genotyped 712 variants in 3577 healthy controls and 3269 MS patients. This study confirmed the association (rs2069502, P = 9.9 × 10(-11), OR = 0.787) and narrowed down the locus of association to an 86.5 kb region. Although the study was unable to pinpoint the key-associated variant, we have identified a 42 (genotyped and imputed) single-nucleotide polymorphism haplotype block likely to harbour the causal variant. No evidence of association at previously reported low-frequency variants in CYP27B1 was observed. As part of the study we compared variant discovery performance using two target-enrichment strategies. We concluded that our pools enriched with Nimblegen's solution phase hybridization capture had better sensitivity to detect true variants than the pools enriched with long-range PCR, whilst specificity was better in the long-range PCR-enriched pools compared with solution phase hybridization capture enriched pools; this result has important implications for the design of future fine-mapping studies.

A genome-wide association study of breast cancer in women of African ancestry

Genome-wide association studies (GWAS) in diverse populations are needed to reveal variants that are more common and/or limited to defined populations. We conducted a GWAS of breast cancer in women of African ancestry, with genotyping of >1,000,000 SNPs in 3,153 African American cases and 2,831 controls, and replication testing of the top 66 associations in an additional 3,607 breast cancer cases and 11,330 controls of African ancestry. Two of the 66 SNPs replicated (p < 0.05) in stage 2, which reached statistical significance levels of 10(-6) and 10(-5) in the stage 1 and 2 combined analysis (rs4322600 at chromosome 14q31: OR = 1.18, p = 4.3 × 10(-6); rs10510333 at chromosome 3p26: OR = 1.15, p = 1.5 × 10(-5)). These suggestive risk loci have not been identified in previous GWAS in other populations and will need to be examined in additional samples. Identification of novel risk variants for breast cancer in women of African ancestry will demand testing of a substantially larger set of markers from stage 1 in a larger replication sample.

Master regulators of FGFR2 signalling and breast cancer risk

The fibroblast growth factor receptor 2 (FGFR2) locus has been consistently identified as a breast cancer risk locus in independent genome-wide association studies. However, the molecular mechanisms underlying FGFR2-mediated risk are still unknown. Using model systems we show that FGFR2-regulated genes are preferentially linked to breast cancer risk loci in expression quantitative trait loci analysis, supporting the concept that risk genes cluster in pathways. Using a network derived from 2,000 transcriptional profiles we identify SPDEF, ERα, FOXA1, GATA3 and PTTG1 as master regulators of fibroblast growth factor receptor 2 signalling, and show that ERα occupancy responds to fibroblast growth factor receptor 2 signalling. Our results indicate that ERα, FOXA1 and GATA3 contribute to the regulation of breast cancer susceptibility genes, which is consistent with the effects of anti-oestrogen treatment in breast cancer prevention, and suggest that fibroblast growth factor receptor 2 signalling has an important role in mediating breast cancer risk.

Genetic variants in FGFR2 and MAP3K1 are associated with the risk of familial and early-onset breast cancer in a South-American population

Genome-Wide Association Studies have identified several loci associated with breast cancer (BC) in populations of different ethnic origins. One of the strongest associations was found in the FGFR2 gene, and MAP3K1 has been proposed as a low-penetrance BC risk factor. In this study, we evaluated the associations among FGFR2 SNPs rs2981582, rs2420946, and rs1219648; and MAP3K1 rs889312, with BC risk in 351 BRCA1/2-negative Chilean BC cases and 802 controls. All the SNPs studied were significantly associated with increased BC risk in familial BC and in non-familial early-onset BC, in a dose-dependent manner. Subjects with 3 risk alleles were at a significantly increased risk of BC compared with subjects with 0-2 risk alleles, in both familial BC and early-onset non-familial BC (OR = 1.47, 95 % CI 1.04-2.07, P = 0.026 and OR = 2.04 95 % CI 1.32-3.24, P < 0.001, respectively). In the haplotype analysis, the FGFR2 rs2981582 T / rs2420946 T / rs1219648 G haplotype (ht2) was associated with a significantly increased BC risk compared with the rs2981582 C / rs2420946 C / rs1219648 A haplotype in familial BC and in non-familial early-onset BC (OR = 1.32, 95 % CI 1.06-1.65, P = 0.012; OR = 1.46, 95 % CI 1.11-1.91, P = 0.004, respectively). When the FGFR2 ht2 and MAP3K1 rs889312 were evaluated as risk alleles, the risk of BC increased in a dose-dependent manner as the number of risk alleles increased (P trend <0.0001), indicating an additive effect. Nevertheless, there is no evidence of an interaction between FGFR2 ht2 and the MAP3K1 rs889312 C allele. These findings suggest that genetic variants in the FGFR2 and MAP3K1 genes may contribute to genetic susceptibility to BC.

Assessing interactions between the associations of fibroblast growth factor receptor 2 common genetic variants and hormone receptor status with breast cancer risk

Fibroblast growth factor receptor 2 (FGFR2) is a member of a receptor tyrosine kinase gene superfamily, involved in cell growth, invasiveness, motility, and angiogenesis, which has attracted considerable attention as a candidate gene for breast cancer (BC) since it was first identified through genome-wide association approach. In the past few years, the relationship between FGFR2 and BC has been reported in various ethnic groups; however, these studies have yielded contradictory results. To investigate this inconsistency, we performed a meta-analysis of 37 studies involving a total of 288,142 subjects for rs2981582, rs1219648, and rs2420946 polymorphism of the FGFR2 gene to evaluate the effect of FGFR2 on genetic susceptibility for BC. Overall, significantly increased BC risk was associated with these polymorphisms when all studies were pooled into the meta-analysis. In addition, our data indicate that FGFR2 is involved in BC susceptibility and confer its effect primarily in estrogen receptor-positive and progesterone receptor-positive tumors. When stratified by ethnicity, significantly increased risks were found in Caucasian and East Asian populations. However, no significant associations were detected among African descent populations. There was strong evidence of heterogeneity (P < 0.05), which largely disappeared after stratification by ethnicity. This meta-analysis demonstrated that FGFR2 polymorphism is a risk factor associated with increased BC susceptibility, but these associations vary in different ethnic populations.