A genome-wide scan for breast cancer risk haplotypes among African American women

Fyn and Lyn gene polymorphisms impact the risk of thyroid cancer

Thyroid cancer is the most common malignancy of the endocrine glands, and during last couple of decades, its incidence has risen alarmingly, across the globe. Etiology of thyroid cancer is still debatable. There are a few worth mentioning risk factors which contribute to initiation of abnormalities in thyroid gland leading to cancer. Genetic instability is major risk factors in thyroid carcinogenesis. Among the genetic factors, the Src family of genes (Src, Yes1, Fyn and Lyn) have been implicated in many cancers but there is little data regarding the association of these (Src, Yes1, Fyn and Lyn) genes with thyroid carcinogenesis. Fyn and Lyn genes of Src family found engaged in proliferation, migration, invasion, angiogenesis, and metastasis in different cancers. This study was planned to examine the effect of Fyn and Lyn SNPs on thyroid cancer risk in Pakistani population in 500 patients and 500 controls. Three polymorphisms of Fyn gene (rs6916861, rs2182644 and rs12910) and three polymorphisms of Lyn gene (rs2668011, rs45587541 and rs45489500) were analyzed using Tetra-primer ARMS-PCR followed by DNA sequencing. SNP rs6916861 of Fyn gene mutant genotype (CC) showed statistically significant threefold increased risk of thyroid cancer (P < 0.0001). In case of rs2182644 of Fyn gene, mutant genotype (AA) indicated statistically significant 17-fold increased risk of thyroid cancer (P < 0.0001). Statistically significant threefold increased risk of thyroid cancer was observed in genotype AC (P < 0.0001) of Fyn gene polymorphism rs12910. In SNP rs2668011 of Lyn gene, TT genotype showed statistically significant threefold increased risk of thyroid cancer (P < 0.0001). In case of rs45587541 of Lyn gene, GA genotypes showed statistically significant 11-fold increased risk in thyroid cancer (P < 0.0001). Haplotype analysis revealed that AAATAG*, AGACAG*, AGCCAA*, AGCCAG*, CAATAG*, CGCCAG* and CGCCGA* haplotypes of Fyn and Lyn polymorphisms are associated with increased thyroid cancer risk. These results showed that genotypes and allele distribution of Fyn and Lyn are significantly linked with increased thyroid cancer risk and could be genetic adjuster for said disease.

Identifying the Potential Mechanism of Action of SNPs Associated With Breast Cancer Susceptibility With GVITamIN

In the last decade, a large number of genome-wide association studies have uncovered many single-nucleotide polymorphisms (SNPs) that are associated with complex traits and confer susceptibility to diseases, such as cancer. However, so far only a few heritable traits with medium-to-high penetrance have been identified. The vast majority of the discovered variants only leads to disease in combination with other still unknown factors. Furthermore, while many studies aimed to link the effect of SNPs to changes in molecular phenotypes, the analysis has been often focused on testing associations between a single SNP and a transcript, hence disregarding the dysregulation of gene regulatory networks that has been shown to play an essential role in disease onset, notably in cancer. Here we take a systems biology approach and develop GVITamIN (Genetic VarIaTIoN functional analysis tool), a new statistical and computational approach to characterize the effect of a SNP on both genes and transcriptional regulatory programs. GVITamIN exploits a novel statistical approach to combine the usually small effect of disease-susceptibility SNPs, and reveals important potential oncogenic mechanisms, hence taking one step further in the direction of understanding the SNP mechanism of action. We apply GVITamIN on a breast cancer cohort and identify well-known cancer-related transcription factors, such as CTCF, LEF1, and FOXA1, as TFs dysregulated by breast cancer-associated SNPs. Furthermore, our results reveal that SNPs located on the RAD51B gene are significantly associated with an abnormal regulatory activity, suggesting a pivotal role for homologous recombination repair mechanisms in breast cancer.

Integrating biology and access to care in addressing breast cancer disparities: 25 years' research experience in the Carolina Breast Cancer Study

Purpose of Review

To review research on breast cancer mortality disparities, emphasizing research conducted in the Carolina Breast Cancer Study, with a focus on challenges and opportunities for integration of tumor biology and access characteristics across the cancer care continuum.

Recent Findings

Black women experience higher mortality following breast cancer diagnosis, despite lower incidence compared to white women. Biological factors, such as stage at diagnosis and breast cancer subtypes, play a role in these disparities. Simultaneously, social, behavioral, environmental, and access to care factors are important. However, integrated studies of biology and access are challenging and it is uncommon to have both data types available in the same study population. The central emphasis of Phase 3 of the Carolina Breast Cancer Study, initiated in 2008, was to collect rich data on biology (including germline and tumor genomics and pathology) and health care access in a diverse study population, with the long term goal of defining intervention opportunities to reduce disparities across the cancer care continuum.

Summary

Early and ongoing research from CBCS has identified important interactions between biology and access, leading to opportunities to build greater equity. However, sample size, population-specific relationships among variables, and complexities of treatment paths along the care continuum pose important research challenges. Interdisciplinary teams, including experts in novel data integration and causal inference, are needed to address gaps in our understanding of breast cancer disparities.

Bridging the gap by discerning SNPs in linkage disequilibrium and their role in breast cancer

Breast Cancer is the most common cancer among women with several genes involved in disease susceptibility. As majority of genome-wide significant variants fall outside the coding region, it is likely that some of them alter specific gene functions. GWAS database was used to interpret the regulatory functions of these genetic variants. A total of 320 SNPs for breast cancer were selected via GWAS, which were entered into the SNAP web portal tool, to determine the one's found to be in Linkage Disequilibrium (r² < 0.80). The resulting 2024 proxy SNP's were processed in RegulomeDB to predict their regulatory role. Of these, 1440 produced a score ranging from 1-6, whereas the remaining produced no data. Only the variants under score 4 (cut-off value) in RegulomeDB has been studied further. From these variants, 221 had scores of less than 4, indicating a high degree of potential regulatory role associated with them. Further study revealed that 61 of the 221 SNPs were reported to be genome-wide significant for breast cancer, 52 to be associated with other diseases, 99 as unconfirmed for association with breast cancer, leaving only 9 to be novel proxy SNPs linked to breast cancer. Therefore, the study further confirmed postulation of non-coding variants being linked to disease risk thereby, requiring additional validation through genome-wide association studies to substantiate their underlying mechanism.

Common Genetic Variation and Breast Cancer Risk-Past, Present, and Future

Breast cancer is the most common cancer among women in the United States, with up to 30% of those diagnosed displaying a family history of breast cancer. To date, 18% of the familial risk of breast cancer can be explained by SNPs. This review summarizes the discovery of risk-associated SNPs using candidate gene and genome-wide association studies (GWAS), including discovery and replication in large collaborative efforts such as The Collaborative Oncologic Gene-environment Study and OncoArray. We discuss the evolution of GWAS studies, efforts to discover additional SNPs, and methods for identifying causal variants. We summarize findings associated with overall breast cancer, pathologic subtypes, and mutation carriers (BRCA1, BRCA2, and CHEK2). In addition, we summarize the development of polygenic risk scores (PRS) using the risk-associated SNPs and show how PRS can contribute to estimation of individual risks for developing breast cancer. Cancer Epidemiol Biomarkers Prev; 27(4); 380-94. ©2018 AACRSee all articles in this CEBP Focus section, "Genome-Wide Association Studies in Cancer."

Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit

Forward Wright–Fisher simulations are powerful in their ability to model complex demography and selection scenarios, but suffer from slow execution on the Central Processor Unit (CPU), thus limiting their usefulness. However, the single-locus Wright–Fisher forward algorithm is exceedingly parallelizable, with many steps that are so-called “embarrassingly parallel,” consisting of a vast number of individual computations that are all independent of each other and thus capable of being performed concurrently. The rise of modern Graphics Processing Units (GPUs) and programming languages designed to leverage the inherent parallel nature of these processors have allowed researchers to dramatically speed up many programs that have such high arithmetic intensity and intrinsic concurrency. The presented GPU Optimized Wright–Fisher simulation, or “GO Fish” for short, can be used to simulate arbitrary selection and demographic scenarios while running over 250-fold faster than its serial counterpart on the CPU. Even modest GPU hardware can achieve an impressive speedup of over two orders of magnitude. With simulations so accelerated, one can not only do quick parametric bootstrapping of previously estimated parameters, but also use simulated results to calculate the likelihoods and summary statistics of demographic and selection models against real polymorphism data, all without restricting the demographic and selection scenarios that can be modeled or requiring approximations to the single-locus forward algorithm for efficiency. Further, as many of the parallel programming techniques used in this simulation can be applied to other computationally intensive algorithms important in population genetics, GO Fish serves as an exciting template for future research into accelerating computation in evolution. GO Fish is part of the Parallel PopGen Package available at: http://dl42.github.io/ParallelPopGen/.

Identification of breast cancer associated variants that modulate transcription factor binding

Genome-wide association studies (GWAS) have discovered thousands loci associated with disease risk and quantitative traits, yet most of the variants responsible for risk remain uncharacterized. The majority of GWAS-identified loci are enriched for non-coding single-nucleotide polymorphisms (SNPs) and defining the molecular mechanism of risk is challenging. Many non-coding causal SNPs are hypothesized to alter transcription factor (TF) binding sites as the mechanism by which they affect organismal phenotypes. We employed an integrative genomics approach to identify candidate TF binding motifs that confer breast cancer-specific phenotypes identified by GWAS. We performed de novo motif analysis of regulatory elements, analyzed evolutionary conservation of identified motifs, and assayed TF footprinting data to identify sequence elements that recruit TFs and maintain chromatin landscape in breast cancer-relevant tissue and cell lines. We identified candidate causal SNPs that are predicted to alter TF binding within breast cancer-relevant regulatory regions that are in strong linkage disequilibrium with significantly associated GWAS SNPs. We confirm that the TFs bind with predicted allele-specific preferences using CTCF ChIP-seq data. We used The Cancer Genome Atlas breast cancer patient data to identify ANKLE1 and ZNF404 as the target genes of candidate TF binding site SNPs in the 19p13.11 and 19q13.31 GWAS-identified loci. These SNPs are associated with the expression of ZNF404 and ANKLE1 in breast tissue. This integrative analysis pipeline is a general framework to identify candidate causal variants within regulatory regions and TF binding sites that confer phenotypic variation and disease risk.

Previous GWAS hits in relation to young-onset breast cancer

PURPOSE

Genome-wide association studies (GWAS) have identified dozens of single-nucleotide polymorphisms (SNPs) associated with breast cancer. Few studies focused on young-onset breast cancer, which exhibits etiologic and tumor-type differences from older-onset disease. Possible confounding by prenatal effects of the maternal genome has also not been considered.

METHODS

Using a family-based design for breast cancer before age 50, we assessed the relationship between breast cancer and 77 GWAS-identified breast cancer risk SNPs. We estimated relative risks (RR) for inherited and maternally mediated genetic effects. We also used published RR estimates to calculate genetic risk scores and model joint effects.

RESULTS

Seventeen of the candidate SNPs were nominally associated with young-onset breast cancer in our 1296 non-Hispanic white affected families (uncorrected p value <0.05). Top-ranked SNPs included rs3803662-A (TOX3, RR = 1.39; p = 7.0 × 10-6), rs12662670-G (ESR1, RR = 1.56; p = 5.7 × 10-4), rs2981579-A (FGFR2, RR = 1.24; p = 0.002), and rs999737-G (RAD51B, RR = 1.37; p = 0.003). No maternally mediated effects were found. A risk score based on all 77 SNPs indicated that their overall relationship to young-onset breast cancer risk was more than additive (additive-fit p = 2.2 × 10-7) and consistent with a multiplicative joint effect (multiplicative-fit p = 0.27). With the multiplicative formulation, the case sister's genetic risk score exceeded that of her unaffected sister in 59% of families.

CONCLUSIONS

The results of this family-based study indicate that no effects of previously identified risk SNPs were explained by prenatal effects of maternal variants. Many of the known breast cancer risk variants were associated with young-onset breast cancer, with evidence that TOX3, ESR1, FGFR2, and RAD51B are important for young-onset disease.

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.

Genetic variants in microRNA and microRNA biogenesis pathway genes and breast cancer risk among women of African ancestry

MicroRNAs (miRNA) regulate breast biology by binding to specific RNA sequences, leading to RNA degradation and inhibition of translation of their target genes. While germline genetic variations may disrupt some of these interactions between miRNAs and their targets, studies assessing the relationship between genetic variations in the miRNA network and breast cancer risk are still limited, particularly among women of African ancestry. We systematically put together a list of 822 and 10,468 genetic variants among primary miRNA sequences and 38 genes in the miRNA biogenesis pathway, respectively; and examined their association with breast cancer risk in the ROOT consortium which includes women of African ancestry. Findings were replicated in an independent consortium. Logistic regression was used to estimate the odds ratio (OR) and 95 % confidence intervals (CI). For overall breast cancer risk, three single-nucleotide polymorphisms (SNPs) in miRNA biogenesis genes DROSHA rs78393591 (OR = 0.69, 95 % CI: 0.55-0.88, P = 0.003), ESR1 rs523736 (OR = 0.88, 95 % CI: 0.82-0.95, P = 3.99 × 10(-4)), and ZCCHC11 rs114101502 (OR = 1.33, 95 % CI: 1.11-1.59, P = 0.002), and one SNP in primary miRNA sequence (rs116159732 in miR-6826, OR = 0.74, 95 % CI: 0.63-0.89, P = 0.001) were found to have significant associations in both discovery and validation phases. In a subgroup analysis, two SNPs were associated with risk of estrogen receptor (ER)-negative breast cancer, and three SNPs were associated with risk of ER-positive breast cancer. Several variants in miRNA and miRNA biogenesis pathway genes were associated with breast cancer risk. Risk associations varied by ER status, suggesting potential new mechanisms in etiology.

Interaction between common breast cancer susceptibility variants, genetic ancestry, and nongenetic risk factors in Hispanic women

BACKGROUND

Most genetic variants associated with breast cancer risk have been discovered in women of European ancestry, and only a few genome-wide association studies (GWAS) have been conducted in minority groups. This research disparity persists in post-GWAS gene-environment interaction analyses. We tested the interaction between hormonal and lifestyle risk factors for breast cancer, and ten GWAS-identified SNPs among 2,107 Hispanic women with breast cancer and 2,587 unaffected controls, to gain insight into a previously reported gene by ancestry interaction in this population.

METHODS

We estimated genetic ancestry with a set of 104 ancestry-informative markers selected to discriminate between Indigenous American and European ancestry. We used logistic regression models to evaluate main effects and interactions.

RESULTS

We found that the rs13387042-2q35(G/A) SNP was associated with breast cancer risk only among postmenopausal women who never used hormone therapy [per A allele OR: 0.94 (95% confidence intervals, 0.74-1.20), 1.20 (0.94-1.53), and 1.49 (1.28-1.75) for current, former, and never hormone therapy users, respectively, Pinteraction 0.002] and premenopausal women who breastfed >12 months [OR: 1.01 (0.72-1.42), 1.19 (0.98-1.45), and 1.69 (1.26-2.26) for never, <12 months, and >12 months breastfeeding, respectively, Pinteraction 0.014].

CONCLUSIONS

The correlation between genetic ancestry, hormone replacement therapy use, and breastfeeding behavior partially explained a previously reported interaction between a breast cancer risk variant and genetic ancestry in Hispanic women.

IMPACT

These results highlight the importance of understanding the interplay between genetic ancestry, genetics, and nongenetic risk factors and their contribution to breast cancer risk.

Genome-wide association studies in Africans and African Americans: expanding the framework of the genomics of human traits and disease

Genomic research is one of the tools for elucidating the pathogenesis of diseases of global health relevance and paving the research dimension to clinical and public health translation. Recent advances in genomic research and technologies have increased our understanding of human diseases, genes associated with these disorders, and the relevant mechanisms. Genome-wide association studies (GWAS) have proliferated since the first studies were published several years ago and have become an important tool in helping researchers comprehend human variation and the role genetic variants play in disease. However, the need to expand the diversity of populations in GWAS has become increasingly apparent as new knowledge is gained about genetic variation. Inclusion of diverse populations in genomic studies is critical to a more complete understanding of human variation and elucidation of the underpinnings of complex diseases. In this review, we summarize the available data on GWAS in recent African ancestry populations within the western hemisphere (i.e. African Americans and peoples of the Caribbean) and continental African populations. Furthermore, we highlight ways in which genomic studies in populations of recent African ancestry have led to advances in the areas of malaria, HIV, prostate cancer, and other diseases. Finally, we discuss the advantages of conducting GWAS in recent African ancestry populations in the context of addressing existing and emerging global health conditions.

The role of environmental heterogeneity in meta-analysis of gene-environment interactions with quantitative traits

With challenges in data harmonization and environmental heterogeneity across various data sources, meta-analysis of gene-environment interaction studies can often involve subtle statistical issues. In this paper, we study the effect of environmental covariate heterogeneity (within and between cohorts) on two approaches for fixed-effect meta-analysis: the standard inverse-variance weighted meta-analysis and a meta-regression approach. Akin to the results in Simmonds and Higgins (), we obtain analytic efficiency results for both methods under certain assumptions. The relative efficiency of the two methods depends on the ratio of within versus between cohort variability of the environmental covariate. We propose to use an adaptively weighted estimator (AWE), between meta-analysis and meta-regression, for the interaction parameter. The AWE retains full efficiency of the joint analysis using individual level data under certain natural assumptions. Lin and Zeng (2010a, b) showed that a multivariate inverse-variance weighted estimator retains full efficiency as joint analysis using individual level data, if the estimates with full covariance matrices for all the common parameters are pooled across all studies. We show consistency of our work with Lin and Zeng (2010a, b). Without sacrificing much efficiency, the AWE uses only univariate summary statistics from each study, and bypasses issues with sharing individual level data or full covariance matrices across studies. We compare the performance of the methods both analytically and numerically. The methods are illustrated through meta-analysis of interaction between Single Nucleotide Polymorphisms in FTO gene and body mass index on high-density lipoprotein cholesterol data from a set of eight studies of type 2 diabetes.

Genome-wide association studies and the clinic: a focus on breast cancer

Breast cancer is the most frequently diagnosed cancer among women worldwide, and has long been considered to be a genetic disease. A wide range of genetic variants, both rare mutations and more common variants, have been shown to influence breast cancer risk. In particular, recent studies have identified a number of common genetic variants, or single nucleotide polymorphisms, that are associated with breast cancer risk. In this review, we will briefly present the genetic epidemiology of breast cancer, genome-wide association study technology and how this technology may influence breast cancer screening in the clinic.

Efficient haplotype block recognition of very long and dense genetic sequences

Background

The new sequencing technologies enable to scan very long and dense genetic sequences, obtaining datasets of genetic markers that are an order of magnitude larger than previously available. Such genetic sequences are characterized by common alleles interspersed with multiple rarer alleles. This situation has renewed the interest for the identification of haplotypes carrying the rare risk alleles. However, large scale explorations of the linkage-disequilibrium (LD) pattern to identify haplotype blocks are not easy to perform, because traditional algorithms have at least Θ(n²) time and memory complexity.

Results

We derived three incremental optimizations of the widely used haplotype block recognition algorithm proposed by Gabriel et al. in 2002. Our most efficient solution, called MIG ⁺⁺, has only Θ(n) memory complexity and, on a genome-wide scale, it omits >80% of the calculations, which makes it an order of magnitude faster than the original algorithm. Differently from the existing software, the MIG ⁺⁺ analyzes the LD between SNPs at any distance, avoiding restrictions on the maximal block length. The haplotype block partition of the entire HapMap II CEPH dataset was obtained in 457 hours. By replacing the standard likelihood-based D^′ variance estimator with an approximated estimator, the runtime was further improved. While producing a coarser partition, the approximate method allowed to obtain the full-genome haplotype block partition of the entire 1000 Genomes Project CEPH dataset in 44 hours, with no restrictions on allele frequency or long-range correlations. These experiments showed that LD-based haplotype blocks can span more than one million base-pairs in both HapMap II and 1000 Genomes datasets. An application to the North American Rheumatoid Arthritis Consortium (NARAC) dataset shows how the MIG ⁺⁺ can support genome-wide haplotype association studies.

Conclusions

The MIG ⁺⁺ enables to perform LD-based haplotype block recognition on genetic sequences of any length and density. In the new generation sequencing era, this can help identify haplotypes that carry rare variants of interest. The low computational requirements open the possibility to include the haplotype block structure into genome-wide association scans, downstream analyses, and visual interfaces for online genome browsers.

Short Reads Phasing to Construct Haplotypes in Genomic Regions That Are Associated with Body Mass Index in Korean Individuals

Genome-wide association (GWA) studies have found many important genetic variants that affect various traits. Since these studies are useful to investigate untyped but causal variants using linkage disequilibrium (LD), it would be useful to explore the haplotypes of single-nucleotide polymorphisms (SNPs) within the same LD block of significant associations based on high-density variants from population references. Here, we tried to make a haplotype catalog affecting body mass index (BMI) through an integrative analysis of previously published whole-genome next-generation sequencing (NGS) data of 7 representative Korean individuals and previously known Korean GWA signals. We selected 435 SNPs that were significantly associated with BMI from the GWA analysis and searched 53 LD ranges nearby those SNPs. With the NGS data, the haplotypes were phased within the LDs. A total of 44 possible haplotype blocks for Korean BMI were cataloged. Although the current result constitutes little data, this study provides new insights that may help to identify important haplotypes for traits and low variants nearby significant SNPs. Furthermore, we can build a more comprehensive catalog as a larger dataset becomes available.