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

Longitudinal associations of lifetime adiposity with leukocyte telomere length and mitochondrial DNA copy number

Adiposity may cause adverse health outcomes by increasing oxidative stress and systemic inflammation, which can be reflected by altered telomere length (TL) and mitochondrial DNA copy number (mtCN) in peripheral blood leukocytes. However, little is known about the influence of lifetime adiposity on TL and mtCN in later life. This study was performed to investigate the associations of lifetime adiposity with leukocyte TL and mtCN in 9613 participants from the Nurses' Health Study. A group-based trajectory modelling approach was used to create trajectories of body shape from age 5 through 60 years, and a genetic risk score (GRS) was created based on 97 known adiposity susceptibility variants. Associations of body shape trajectories and GRS with dichotomized TL and mtCN were assessed by logistic regression models. After adjustment for lifestyle and dietary factors, compared with the lean-stable group, the lean-marked increase group had higher odds of having below-median TL (OR = 1.18, 95% CI 1.04, 1.35; P = 0.01), and the medium-marked increase group had higher odds of having below-median mtCN (OR = 1.28, 95% CI 1.00, 1.64; P = 0.047). There was a suggestive trend toward lower mtCN across the GRS quartiles (P for trend = 0.07). In conclusion, telomere attrition may be accelerated by marked weight gain in middle life, whereas mtCN is likely to be reduced persistently by adiposity over the life course. The findings indicate the importance of lifetime weight management to preserve functional telomeres and mitochondria.

Genetic susceptibility markers for a breast-colorectal cancer phenotype: Exploratory results from genome-wide association studies

BACKGROUND

Clustering of breast and colorectal cancer has been observed within some families and cannot be explained by chance or known high-risk mutations in major susceptibility genes. Potential shared genetic susceptibility between breast and colorectal cancer, not explained by high-penetrance genes, has been postulated. We hypothesized that yet undiscovered genetic variants predispose to a breast-colorectal cancer phenotype.

METHODS

To identify variants associated with a breast-colorectal cancer phenotype, we analyzed genome-wide association study (GWAS) data from cases and controls that met the following criteria: cases (n = 985) were women with breast cancer who had one or more first- or second-degree relatives with colorectal cancer, men/women with colorectal cancer who had one or more first- or second-degree relatives with breast cancer, and women diagnosed with both breast and colorectal cancer. Controls (n = 1769), were unrelated, breast and colorectal cancer-free, and age- and sex- frequency-matched to cases. After imputation, 6,220,060 variants were analyzed using the discovery set and variants associated with the breast-colorectal cancer phenotype at P<5.0E-04 (n = 549, at 60 loci) were analyzed for replication (n = 293 cases and 2,103 controls).

RESULTS

Multiple correlated SNPs in intron 1 of the ROBO1 gene were suggestively associated with the breast-colorectal cancer phenotype in the discovery and replication data (most significant; rs7430339, Pdiscovery = 1.2E-04; rs7429100, Preplication = 2.8E-03). In meta-analysis of the discovery and replication data, the most significant association remained at rs7429100 (P = 1.84E-06).

CONCLUSION

The results of this exploratory analysis did not find clear evidence for a susceptibility locus with a pleiotropic effect on hereditary breast and colorectal cancer risk, although the suggestive association of genetic variation in the region of ROBO1, a potential tumor suppressor gene, merits further investigation.

Emerging applications of genome-editing technology to examine functionality of GWAS-associated variants for complex traits

Over the last decade, genome-wide association studies (GWAS) have propelled the discovery of thousands of loci associated with complex diseases. The focus is now turning toward the function of these association signals, determining the causal variant(s) among those in strong linkage disequilibrium, and identifying their underlying mechanisms, such as long-range gene regulation. Genome-editing techniques utilizing zinc-finger nucleases (ZFN), transcription activator-like effector nucleases (TALENs), and clustered regularly-interspaced short palindromic repeats with Cas9 nuclease (CRISPR-Cas9) are becoming the tools of choice to establish functionality for these variants, due to the ability to assess effects of single variants in vivo. This review will discuss examples of how these technologies have begun to aid functional analysis of GWAS loci for complex traits such as cardiovascular disease, Type 2 diabetes, cancer, obesity, and autoimmune disease. We focus on analysis of variants occurring within noncoding genomic regions, as these comprise the majority of GWAS variants, providing the greatest challenges to determining functionality, and compare editing strategies that provide different levels of evidence for variant functionality. The review describes molecular insights into some of these potentially causal variants and how these may relate to the pathology of the trait and look toward future directions for these technologies in post-GWAS analysis, such as base-editing.

XPAT: a toolkit to conduct cross-platform association studies with heterogeneous sequencing datasets

High-throughput sequencing data are increasingly being made available to the research community for secondary analyses, providing new opportunities for large-scale association studies. However, heterogeneity in target capture and sequencing technologies often introduce strong technological stratification biases that overwhelm subtle signals of association in studies of complex traits. Here, we introduce the Cross-Platform Association Toolkit, XPAT, which provides a suite of tools designed to support and conduct large-scale association studies with heterogeneous sequencing datasets. XPAT includes tools to support cross-platform aware variant calling, quality control filtering, gene-based association testing and rare variant effect size estimation. To evaluate the performance of XPAT, we conducted case-control association studies for three diseases, including 783 breast cancer cases, 272 ovarian cancer cases, 205 Crohn disease cases and 3507 shared controls (including 1722 females) using sequencing data from multiple sources. XPAT greatly reduced Type I error inflation in the case-control analyses, while replicating many previously identified disease-gene associations. We also show that association tests conducted with XPAT using cross-platform data have comparable performance to tests using matched platform data. XPAT enables new association studies that combine existing sequencing datasets to identify genetic loci associated with common diseases and other complex traits.

Breast Cancer Family History and Contralateral Breast Cancer Risk in Young Women: An Update From the Women's Environmental Cancer and Radiation Epidemiology Study

Purpose The Women's Environmental Cancer and Radiation Epidemiology (WECARE) study demonstrated the importance of breast cancer family history on contralateral breast cancer (CBC) risk, even for noncarriers of deleterious BRCA1/2 mutations. With the completion of WECARE II, updated risk estimates are reported. Additional analyses that exclude women negative for deleterious mutations in ATM, CHEK2*1100delC, and PALB2 were performed. Patients and Methods The WECARE Study is a population-based case-control study that compared 1,521 CBC cases with 2,212 individually matched unilateral breast cancer (UBC) controls. Participants were younger than age 55 years when diagnosed with a first invasive breast cancer between 1985 and 2008. Women were interviewed about breast cancer risk factors, including family history. A subset of women was screened for deleterious mutations in BRCA1/2, ATM, CHEK2*1100delC, and PALB2. Rate ratios (RRs) were estimated using multivariable conditional logistic regression. Cumulative absolute risks (ARs) were estimated by combining RRs from the WECARE Study and population-based SEER*Stat cancer incidence data. Results Women with any first-degree relative with breast cancer had a 10-year AR of 8.1% for CBC (95% CI, 6.7% to 9.8%). Risks also were increased if the relative was diagnosed at an age younger than 40 years (10-year AR, 13.5%; 95% CI, 8.8% to 20.8%) or with CBC (10-year AR, 14.1%; 95% CI, 9.5% to 20.7%). These risks are comparable with those seen in BRCA1/2 deleterious mutation carriers (10-year AR, 18.4%; 95% CI, 16.0% to 21.3%). In the subset of women who tested negative for deleterious mutations in BRCA1/2, ATM, CHEK2*1100delC, and PALB2, estimates were unchanged. Adjustment for known breast cancer single-nucleotide polymorphisms did not affect estimates. Conclusion Breast cancer family history confers a high CBC risk, even after excluding women with deleterious mutations. Clinicians are urged to use detailed family histories to guide treatment and future screening decisions for young women with breast cancer.

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."

Applications of RNA Indexes for Precision Oncology in Breast Cancer

Precision oncology aims to offer the most appropriate treatments to cancer patients mainly based on their individual genetic information. Genomics has provided numerous valuable data on driver mutations and risk loci; however, it remains a formidable challenge to transform these data into therapeutic agents. Transcriptomics describes the multifarious expression patterns of both mRNAs and non-coding RNAs (ncRNAs), which facilitates the deciphering of genomic codes. In this review, we take breast cancer as an example to demonstrate the applications of these rich RNA resources in precision medicine exploration. These include the use of mRNA profiles in triple-negative breast cancer (TNBC) subtyping to inform corresponding candidate targeted therapies; current advancements and achievements of high-throughput RNA interference (RNAi) screening technologies in breast cancer; and microRNAs as functional signatures for defining cell identities and regulating the biological activities of breast cancer cells. We summarize the benefits of transcriptomic analyses in breast cancer management and propose that unscrambling the core signaling networks of cancer may be an important task of multiple-omic data integration for precision oncology.

Facilitating Anti-Cancer Combinatorial Drug Discovery by Targeting Epistatic Disease Genes

Due to synergistic effects, combinatorial drugs are widely used for treating complex diseases. However, combining drugs and making them synergetic remains a challenge. Genetic disease genes are considered a promising source of drug targets with important implications for navigating the drug space. Most diseases are not caused by a single pathogenic factor, but by multiple disease genes, in particular, interacting disease genes. Thus, it is reasonable to consider that targeting epistatic disease genes may enhance the therapeutic effects of combinatorial drugs. In this study, synthetic lethality gene pairs of tumors, similar to epistatic disease genes, were first targeted by combinatorial drugs, resulting in the enrichment of the combinatorial drugs with cancer treatment, which verified our hypothesis. Then, conventional epistasis detection software was used to identify epistatic disease genes from the genome wide association studies (GWAS) dataset. Furthermore, combinatorial drugs were predicted by targeting these epistatic disease genes, and five combinations were proven to have synergistic anti-cancer effects on MCF-7 cells through cell cytotoxicity assay. Combined with the three-dimensional (3D) genome-based method, the epistatic disease genes were filtered and were more closely related to disease. By targeting the filtered gene pairs, the efficiency of combinatorial drug discovery has been further improved.

A comprehensive analysis of polymorphic variants in steroid hormone and insulin-like growth factor-1 metabolism and risk of in situ breast cancer: Results from the Breast and Prostate Cancer Cohort Consortium

We assessed the association between 1,414 single nucleotide polymorphisms (SNPs) in genes involved in synthesis and metabolism of steroid hormones and insulin-like growth factor 1, and risk of breast cancer in situ (BCIS), with the aim of determining whether any of these were disease specific. This was carried out using 1,062 BCIS cases and 10,126 controls as well as 6,113 invasive breast cancer cases from the Breast and Prostate Cancer Cohort Consortium (BPC3). Three SNPs showed at least one nominally significant association in homozygous minor versus homozygous major models. ACVR2A-rs2382112 (OR_hom  = 3.05, 95%CI = 1.72-5.44, P_hom  = 1.47 × 10^-4 ), MAST2-rs12124649 (OR_hom  = 1.73, 95% CI =1.18-2.54, P_hom  = 5.24 × 10^-3 ), and INSR-rs10500204 (OR_hom  = 1.96, 95% CI = 1.44-2.67, P_hom =1.68 × 10^-5 ) were associated with increased risk of BCIS; however, only the latter association was significant after correcting for multiple testing. Furthermore, INSR-rs10500204 was more strongly associated with the risk of BCIS than invasive disease in case-only analyses using the homozygous minor versus homozygous major model (OR_hom  = 1.78, 95% CI = 1.30-2.44, P_hom  = 3.23 × 10^-4 ). The SNP INSR-rs10500204 is located in an intron of the INSR gene and is likely to affect binding of the promyelocytic leukemia (PML) protein. The PML gene is known as a tumor suppressor and growth regulator in cancer. However, it is not clear on what pathway the A-allele of rs10500204 could operate to influence the binding of the protein. Hence, functional studies are warranted to investigate this further.

Capture Hi-C identifies putative target genes at 33 breast cancer risk loci

Genome-wide association studies (GWAS) have identified approximately 100 breast cancer risk loci. Translating these findings into a greater understanding of the mechanisms that influence disease risk requires identification of the genes or non-coding RNAs that mediate these associations. Here, we use Capture Hi-C (CHi-C) to annotate 63 loci; we identify 110 putative target genes at 33 loci. To assess the support for these target genes in other data sources we test for associations between levels of expression and SNP genotype (eQTLs), disease-specific survival (DSS), and compare them with somatically mutated cancer genes. 22 putative target genes are eQTLs, 32 are associated with DSS and 14 are somatically mutated in breast, or other, cancers. Identifying the target genes at GWAS risk loci will lead to a greater understanding of the mechanisms that influence breast cancer risk and prognosis.

Association between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and breast cancer susceptibility: a meta-analysis

The association between fibroblast growth factor receptor 2 (FGFR2) polymorphism and breast cancer (BC) susceptibility remains inconclusive. The purpose of this systematic review was to evaluate the relationship between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk. PubMed, Web of science and the Cochrane Library databases were searched before October 11, 2015 to identify relevant studies. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to estimate the strength of associations. Sensitivity and subgroup analyses were conducted. Thirty-five studies published from 2007 to 2015 were included in this meta-analysis. The pooled results showed that there was significant association between all the 3 variants and BC risk in any genetic model. Subgroup analysis was performed on rs2981582 and rs2420946 by ethnicity and Source of controls, the effects remained in Asians, Caucasians, population-based and hospital-based groups. We did not carryout subgroup analysis on rs2981578 for the variant included only 3 articles. This meta-analysis of case-control studies provides strong evidence that FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphisms were significantly associated with the BC risk. For rs2981582 and rs2420946, the association remained significant in Asians, Caucasians, general populations and hospital populations. However, further large scale multicenter epidemiological studies are warranted to confirm this finding and the molecular mechanism for the association need to be elucidated further.

Telomerase Reverse Transcriptase Polymorphism rs2736100: A Balancing Act between Cancer and Non-Cancer Disease, a Meta-Analysis

The enzyme telomerase reverse transcriptase (TERT) is essential for telomere maintenance. In replicating cells, maintenance of telomere length is important for the preservation of vital genetic information and prevention of genomic instability. A common genetic variant in TERT, rs2736100 C/A, is associated with both telomere length and multiple diseases. Carriage of the C allele is associated with longer telomere length, while carriage of the A allele is associated with shorter telomere length. Furthermore, some diseases have a positive association with the C and some with the A allele. In this study, meta-analyses were performed for two groups of diseases, cancerous diseases, e.g., lung cancer and non-cancerous diseases, e.g., pulmonary fibrosis, using data from genome-wide association studies and case-control studies. In the meta-analysis it was found that cancer positively associated with the C allele (pooled OR 1.16 [95% CI 1.09–1.23]) and non-cancerous diseases negatively associated with the C allele (pooled OR 0.81 [95% CI 0.65–0.99]). This observation illustrates that the ambiguous role of telomere maintenance in disease hinges, at least in part, on a single locus in telomerase genes. The dual role of this single nucleotide polymorphism also emphasizes that therapeutic agents aimed at influencing telomere maintenance should be used with caution.

GWAS in the SIGNAL/PHARE clinical cohort restricts the association between the FGFR2 locus and estrogen receptor status to HER2-negative breast cancer patients

Genetic polymorphisms are associated with breast cancer risk. Clinical and epidemiological observations suggest that clinical characteristics of breast cancer, such as estrogen receptor or HER2 status, are also influenced by hereditary factors. To identify genetic variants associated with pathological characteristics of breast cancer patients, a Genome Wide Association Study was performed in a cohort of 9365 women from the French nationwide SIGNAL/PHARE studies (NCT00381901/RECF1098). Strong association between the FGFR2 locus and ER status of breast cancer patients was observed (ER-positive n=6211, ER-negative n=2516; rs3135718 OR=1.34 p=5.46×10-12). This association was limited to patients with HER2-negative tumors (ER-positive n=4267, ER-negative n=1185; rs3135724 OR=1.85 p=1.16×10-11). The FGFR2 locus is known to be associated with breast cancer risk. This study provides sound evidence for an association between variants in the FGFR2 locus and ER status among breast cancer patients, particularly among patients with HER2-negative disease. This refinement of the association between FGFR2 variants and ER-status to HER2-negative disease provides novel insight to potential biological and clinical influence of genetic polymorphisms on breast tumors.

Binding of human recombinant mutant soluble ectodomain of FGFR2IIIc to c subtype of FGFRs: implications for anticancer activity

FGFRs are considered essential targets for cancer therapy. We previously reported that msFGFR2c, a Ser252Trp mutant soluble ectodomain of FGFR2IIIc, inhibited tumor growth by blocking FGF signaling pathway. However, the underlying molecular mechanism is still obscure. In this study, we reported that msFGFR2c but not wild-type soluble ectodomain of FGFR2IIIc (wsFGFR2c) could selectively bind to c subtype of FGFRs in the presence of FGF-2. Thermodynamic analysis demonstrated that msFGFR2c bound to wsFGFR2c in the presence of FGF-2 with a K value of 6.61 × 10⁵ M⁻¹. Molecular dynamics simulations revealed that the mutated residue Trp252 of msFGFR2c preferred a π-π interaction with His254 of wsFGFR2c. Concomitantly, Arg255 of msFGFR2c and Glu250 of wsFGFR2c adjusted their conformations and formed three H-bonds. These two interactions therefore stabilized the final structure of wsFGFR2c and msFGFR2c heterocomplex. In FGFR2IIIc-positive/high FGF-2-secreted BT-549 cells, msFGFR2c significantly inhibited the proliferation and induced apoptosis by the blockage of FGF-2-activated FGFRs phosphorylation, also the growth and angiogenesis of its xenograft tumors implanted in chick embryo chorioallantoic membrane model. While weaker the above inhibitory effects of msFGFR2c were observed on FGFR2IIIc-negative/low FGF-2-secreted MCF-7 and MDA-MB-231 cell lines in vitro and in vivo. Moreover, msFGFR2c significantly inhibited the proliferation of FGFR1IIIc-positive NCI-H1299 lung cancer cells by the suppression of FGF-2-induced FGFR1 activation and suppressed the growth of NCI-H1299 transplanted tumors in nude mice. In sum, msFGFR2c is a potential anti-tumor agent targeting FGFR2c/FGFR1c-positive tumor cells. These findings also provide a molecular basis for msFGFR2c to disrupt the activation of FGF signaling.

Fibroblast growth factor signalling induces loss of progesterone receptor in breast cancer cells

We have recently demonstrated that, fibroblast growth factor 2 (FGFR2), signalling via ribosomal S6 kinase 2 (RSK2), promotes progression of breast cancer (BCa). Loss of progesterone receptor (PR), whose activity in BCa cells can be stimulated by growth factor receptors (GFRs), is associated with poor patient outcome. Here we showed that FGF7/FGFR2 triggered phosphorylation of PR at Ser294, PR ubiquitination and subsequent receptor`s degradation via the 26S proteasome pathway in BCa cells. We further demonstrated that RSK2 mediated FGF7/FGFR2-induced PR downregulation. In addition, a strong synergistic effect of FGF7 and progesterone (Pg), reflected in the enhanced anchorage-independent growth and cell migration, was observed. Analysis of clinical material demonstrated that expression of PR inversely correlated with activated RSK (RSK-P) (p = 0.016). Patients with RSK-P(+)/PR(–) tumours had 3.629-fold higher risk of recurrence (p = 0.002), when compared with the rest of the cohort. Moreover, RSK-P(+)/PR(–) phenotype was shown as an independent prognostic factor (p = 0.006). These results indicate that the FGF7/FGFR2-RSK2 axis promotes PR turnover and activity, which may sensitize BCa cells to stromal stimuli and contribute to the progression toward steroid hormone negative BCa.

Receptor tyrosine kinases (RTKs) in breast cancer: signaling, therapeutic implications and challenges

Breast cancer is a multifactorial disease and driven by aberrant regulation of cell signaling pathways due to the acquisition of genetic and epigenetic changes. An array of growth factors and their receptors is involved in cancer development and metastasis. Receptor Tyrosine Kinases (RTKs) constitute a class of receptors that play important role in cancer progression. RTKs are cell surface receptors with specialized structural and biological features which respond to environmental cues by initiating appropriate signaling cascades in tumor cells. RTKs are known to regulate various downstream signaling pathways such as MAPK, PI3K/Akt and JAK/STAT. These pathways have a pivotal role in the regulation of cancer stemness, angiogenesis and metastasis. These pathways are also imperative for a reciprocal interaction of tumor and stromal cells. Multi-faceted role of RTKs renders them amenable to therapy in breast cancer. However, structural mutations, gene amplification and alternate pathway activation pose challenges to anti-RTK therapy.

Association study of genetic variation in the autophagy lysosome pathway genes and risk of eight kinds of cancers

The autophagy lysosome pathway is essential to maintain cell viability and homeostasis in response to many stressful environments, which is reported to play a vital role in cancer development and therapy. However, the association of genetic alterations of this pathway with risk of cancer remains unclear. Based on genome-wide association study data of eight kinds of cancers, we used an adaptive rank truncated product approach to perform a pathway-level and gene-level analysis, and used a logistic model to calculate SNP-level associations to examine whether an altered autophagy lysosome pathway contributes to cancer susceptibility. Among eight kinds of cancers, four of them showed significant statistics in the pathway-level analysis, including breast cancer (p = 0.00705), gastric cancer (p = 0.00880), lung cancer (p = 0.000100) and renal cell carcinoma (p = 0.00190). We also found that some autophagy lysosome genes had signals of association with cancer risk. Our results demonstrated that inherited genetic variants in the overall autophagy lysosome pathway and certain associated genes might contribute to cancer susceptibility, which warrant further evaluation in other independent datasets.

A test for gene-environment interaction in the presence of measurement error in the environmental variable

The identification of gene-environment interactions in relation to risk of human diseases has been challenging. One difficulty has been that measurement error in the exposure can lead to massive reductions in the power of the test, as well as in bias toward the null in the interaction effect estimates. Leveraging previous work on linear discriminant analysis, we develop a new test of interaction between genetic variants and a continuous exposure that mitigates these detrimental impacts of exposure measurement error in ExG testing by reversing the role of exposure and the diseases status in the fitted model, thus transforming the analysis to standard linear regression. Through simulation studies, we show that the proposed approach is valid in the presence of classical exposure measurement error as well as when there is correlation between the exposure and the genetic variant. Simulations also demonstrated that the reverse test has greater power compared to logistic regression. Finally, we confirmed that our approach eliminates bias from exposure measurement error in estimation. Computing times are reduced by as much as fivefold in this new approach. For illustrative purposes, we applied the new approach to an ExGWAS study of interactions with alcohol and body mass index among 1,145 cases with invasive breast cancer and 1,142 controls from the Cancer Genetic Markers of Susceptibility study.

Longitudinal Analysis of Genetic Susceptibility and BMI Throughout Adult Life

Little is known about the genetic influence on BMI trajectory throughout adulthood. We created a genetic risk score (GRS) comprising 97 adult BMI-associated variants among 9,971 women and 6,405 men of European ancestry. Serial measures of BMI were assessed from 18 (women) or 21 (men) years to 85 years of age. We also examined BMI change in early (from 18 or 21 to 45 years of age), middle (from 45 to 65 years of age), and late adulthood (from 65 to 80 years of age). GRS was positively associated with BMI across all ages, with stronger associations in women than in men. The associations increased from early to middle adulthood, peaked at 45 years of age in men and at 60 years of age in women (0.91 and 1.35 kg/m² per 10-allele increment, respectively) and subsequently declined in late adulthood. For women, each 10-allele increment in the GRS was associated with an average BMI gain of 0.54 kg/m² in early adulthood, whereas no statistically significant association was found for BMI change in middle or late adulthood or for BMI change in any life period in men. Our findings indicate that genetic predisposition exerts a persistent effect on adiposity throughout adult life and increases early adulthood weight gain in women.

Evaluation of three polygenic risk score models for the prediction of breast cancer risk in Singapore Chinese

Genome-wide association studies (GWAS) have proven highly successful in identifying single nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk. The majority of these studies are on European populations, with limited SNP association data in other populations. We genotyped 51 GWAS-identified SNPs in two independent cohorts of Singaporean Chinese. Cohort 1 comprised 1294 BC cases and 885 controls and was used to determine odds ratios (ORs); Cohort 2 had 301 BC cases and 243 controls for deriving polygenic risk scores (PRS). After age-adjustment, 11 SNPs were found to be significantly associated with BC risk. Five SNPs were present in <1% of Cohort 1 and were excluded from further PRS analysis. To assess the cumulative effect of the remaining 46 SNPs on BC risk, we generated three PRS models: Model-1 included 46 SNPs; Model-2 included 11 statistically significant SNPs; and Model-3 included the SNPs in Model-2 but excluded SNPs that were in strong linkage disequilibrium with the others. Across Models-1, -2 and -3, women in the highest PRS quartile had the greatest ORs of 1.894 (95% CI = 1.157–3.100), 2.013 (95% CI = 1.227–3.302) and 1.751 (95% CI = 1.073–2.856) respectively, suggesting a direct correlation between PRS and BC risk. Given the potential of PRS in BC risk stratification, our findings suggest the need to tailor the selection of SNPs to be included in an ethnic-specific PRS model.

Genome-wide association studies in women of African ancestry identified 3q26.21 as a novel susceptibility locus for oestrogen receptor negative breast cancer

Multiple breast cancer loci have been identified in previous genome-wide association studies, but they were mainly conducted in populations of European ancestry. Women of African ancestry are more likely to have young-onset and oestrogen receptor (ER) negative breast cancer for reasons that are unknown and understudied. To identify genetic risk factors for breast cancer in women of African descent, we conducted a meta-analysis of two genome-wide association studies of breast cancer; one study consists of 1,657 cases and 2,029 controls genotyped with Illumina’s HumanOmni2.5 BeadChip and the other study included 3,016 cases and 2,745 controls genotyped using Illumina Human1M-Duo BeadChip. The top 18,376 single nucleotide polymorphisms (SNP) from the meta-analysis were replicated in the third study that consists of 1,984 African Americans cases and 2,939 controls. We found that SNP rs13074711, 26.5 Kb upstream of TNFSF10 at 3q26.21, was significantly associated with risk of oestrogen receptor (ER)-negative breast cancer (odds ratio [OR]=1.29, 95% CI: 1.18-1.40; P = 1.8 × 10 − 8). Functional annotations suggest that the TNFSF10 gene may be involved in breast cancer aetiology, but further functional experiments are needed. In addition, we confirmed SNP rs10069690 was the best indicator for ER-negative breast cancer at 5p15.33 (OR = 1.30; P = 2.4 × 10 − 10) and identified rs12998806 as the best indicator for ER-positive breast cancer at 2q35 (OR = 1.34; P = 2.2 × 10 − 8) for women of African ancestry. These findings demonstrated additional susceptibility alleles for breast cancer can be revealed in diverse populations and have important public health implications in building race/ethnicity-specific risk prediction model for breast cancer.

The effect of risk factor misclassification on the partial population attributable risk

The partial population attributable risk (pPAR) is used to quantify the population-level impact of preventive interventions in a multifactorial disease setting. In this paper, we consider the effect of nondifferential risk factor misclassification on the direction and magnitude of bias of pPAR estimands and related quantities. We found that the bias in the uncorrected pPAR depends nonlinearly and nonmonotonically on the sensitivities, specificities, relative risks, and joint prevalence of the exposure of interest and background risk factors, as well as the associations between these factors. The bias in the uncorrected pPAR is most dependent on the sensitivity of the exposure. The magnitude of bias varies over a large range, and in a small region of the parameter space determining the pPAR, the direction of bias is away from the null. In contrast, the crude PAR can only be unbiased or biased towards the null by risk factor misclassification. The semiadjusted PAR is calculated using the formula for the crude PAR but plugs in the multivariate-adjusted relative risk. Because the crude and semiadjusted PARs continue to be used in public health research, we also investigated the magnitude and direction of the bias that may arise when using these formulae instead of the pPAR. These PAR estimators and their uncorrected counterparts were calculated in a study of risk factors for colorectal cancer in the Health Professionals Follow-up Study, where it was found that because of misclassification, the pPAR for low folate intake was overestimated with a relative bias of 48%, when red meat and alcohol intake were treated as misclassified risk factors that are not modified, and when red meat was treated as the modifiable risk factor, the estimated value of the pPAR went from 14% to 60%, further illustrating the extent to which misclassification can bias estimates of the pPAR.

Pathway analysis of expression-related SNPs on genome-wide association study of basal cell carcinoma

Genome-wide association studies (GWASs) have primarily focused on the association between individual genetic markers and risk of disease. We applied a novel approach that integrates skin expression-related single-nucleotide polymorphisms (eSNPs) and pathway analysis for GWAS of basal cell carcinoma (BCC) to identify potential novel biological pathways. We evaluated the associations between 70,932 skin eSNPs and risk of BCC among 2,323 cases and 7,275 controls of European ancestry, and then assigned them to the pathways defined by KEGG, GO, and BioCarta databases. Three KEGG pathways (colorectal cancer, actin cytoskeleton, and BCC), two GO pathways (cellular component disassembly in apoptosis, and nucleus organization), and four BioCarta pathways (Ras signaling, T cell receptor signaling, natural killer cell-mediated cytotoxicity, and links between Pyk2 and Map Kinases) showed significant association with BCC risk with p-value<0.05 and FDR<0.2. These pathways also ranked at top in sensitivity analyses. Two positive controls in KEGG, the hedgehog pathway and the BCC pathway, showed significant association with BCC risk in both main and sensitivity analyses. Our results indicate that SNPs that are undetectable by conventional GWASs are significantly associated with BCC when tested as pathways. Biological studies of these gene groups suggest their potential roles in the etiology of BCC.

Real-life helping behaviours in North America: A genome-wide association approach

In humans, prosocial behaviour is essential for social functioning. Twin studies suggest this distinct human trait to be partly hardwired. In the last decade research on the genetics of prosocial behaviour focused on neurotransmitters and neuropeptides, such as oxytocin, dopamine, and their respective pathways. Recent trends towards large scale medical studies targeting the genetic basis of complex diseases such as Alzheimer’s disease and schizophrenia pave the way for new directions also in behavioural genetics.

Based on data from 10,713 participants of the American Health and Retirement Study we estimated heritability of helping behaviour–its total variance explained by 1.2 million single nucleotide polymorphisms–to be 11%. Both, fixed models and mixed linear models identified rs11697300, an intergene variant on chromosome 20, as a candidate variant moderating this particular helping behaviour. We assume that this so far undescribed area is worth further investigation in association with human prosocial behaviour.

Improving adherence to healthy dietary patterns, genetic risk, and long term weight gain: gene-diet interaction analysis in two prospective cohort studies

OBJECTIVE

To investigate whether improving adherence to healthy dietary patterns interacts with the genetic predisposition to obesity in relation to long term changes in body mass index and body weight.

DESIGN

Prospective cohort study.

SETTING

Health professionals in the United States.

PARTICIPANTS

8828 women from the Nurses' Health Study and 5218 men from the Health Professionals Follow-up Study.

EXPOSURE

Genetic predisposition score was calculated on the basis of 77 variants associated with body mass index. Dietary patterns were assessed by the Alternate Healthy Eating Index 2010 (AHEI-2010), Dietary Approach to Stop Hypertension (DASH), and Alternate Mediterranean Diet (AMED).

MAIN OUTCOME MEASURES

Five repeated measurements of four year changes in body mass index and body weight over follow-up (1986 to 2006).

RESULTS

During a 20 year follow-up, genetic association with change in body mass index was significantly attenuated with increasing adherence to the AHEI-2010 in the Nurses' Health Study (P=0.001 for interaction) and Health Professionals Follow-up Study (P=0.005 for interaction). In the combined cohorts, four year changes in body mass index per 10 risk allele increment were 0.07 (SE 0.02) among participants with decreased AHEI-2010 score and -0.01 (0.02) among those with increased AHEI-2010 score, corresponding to 0.16 (0.05) kg versus -0.02 (0.05) kg weight change every four years (P<0.001 for interaction). Viewed differently, changes in body mass index per 1 SD increment of AHEI-2010 score were -0.12 (0.01), -0.14 (0.01), and -0.18 (0.01) (weight change: -0.35 (0.03), -0.36 (0.04), and -0.50 (0.04) kg) among participants with low, intermediate, and high genetic risk, respectively. Similar interaction was also found for DASH but not for AMED.

CONCLUSIONS

These data indicate that improving adherence to healthy dietary patterns could attenuate the genetic association with weight gain. Moreover, the beneficial effect of improved diet quality on weight management was particularly pronounced in people at high genetic risk for obesity.

Polymorphisms of long non-coding RNA HOTAIR with breast cancer susceptibility and clinical outcomes for a southeast Chinese Han population

Hox transcript antisense intergenic RNA (HOTAIR) is a well-known long non-coding RNA (lncRNA) which participates in tumorigenesis and progress of multiple cancers. However, the associations among polymorphisms on HOTAIR, breast cancer (BC) susceptibility and clinical outcomes have remained obscure. In this case-control study, we assessed the interaction between three lncRNA HOTAIR single nucleotide polymorphisms (SNPs) (rs1899663, rs4759314 and rs7958904) on the risk and clinical outcome of breast cancer in a Chinese Han population. In total, 969 breast cancer cases and 970 healthy controls were enrolled in this study. Associations among genotypes, BC risk and survival were evaluated by univariate and multivariate logistic regression to estimate the odds ratio (OR), hazard ratio (HR) and its 95% confidence interval (CI). The disease-free survival (DFS) and overall survival (OS) was calculated by the Kaplan–Meier method. We found that the T allele of rs1899663 and C allele of rs7958904 both achieved significant differences between cases and controls in the single locus analyses (P = 0.017 and 0.010, respectively). Multivariate analyses also revealed the rs1899663 TT genotype and rs7958904 CC genotype were both at higher risk of breast cancer compared with the GG homozygotes (OR = 2.08, 95% CI = 1.20–3.60 and OR = 1.45, 95% CI = 1.01–2.08, respectively). In survival analysis, we observed that the T allele of rs1899663 presented significant differences for both DFS (HR = 1.64, 95% CI = 1.12–2.40) and OS (HR = 2.10, 95% CI = 1.29–3.42) in younger subjects (age ≤ 40). Our findings may provide new insights into the associations among the genetic susceptibility, the fine classifications and the prognosis of breast cancer. Further studies with larger sample size and functional research should also be conducted to validate our findings and better elucidate the underlying biological mechanisms.

Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer

Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10-8 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 16% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.

Association between rs11200014, rs2981579, and rs1219648 polymorphism and breast cancer susceptibility: A meta-analysis

BACKGROUND

Research on the polymorphism of breast cancer (BC) helps to search the BC susceptibility gene for mass screening, early diagnosis, and gene therapy, which has become a hotspot in BC research field. Previous studies have suggested associations between rs11200014, rs2981579, and rs1219648 polymorphisms and cancer risk. The aim of this study was to evaluate the relationship between rs11200014, rs2981579, and rs1219648 polymorphism and BC risk.

METHODS

PubMed, Web of science, and the Cochrane Library databases were searched before October 11, 2015, to identify relevant studies. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to estimate the strength of associations. Sensitivity and subgroup analyses were conducted. All included cases should have been diagnosed by a pathological examination.

RESULTS

Twenty-six studies published from 2007 to 2015 were included in this meta-analysis. The pooled results showed that there was a significant association between all the 3 variants and BC risk in any genetic model. When stratified by Source of controls, the results showed the same association between rs2981579 polymorphism and BC susceptibility in hospital-based (HB) group, although there was not any genetic model attained statistical correlation in population-based (PB) group. Subgroup analysis was performed on rs1219648 by ethnicity and Source of controls, and the effects remained in Asians, Caucasians, HB, and PB groups.

CONCLUSION

This meta-analysis of case-control studies provides strong evidence that fibroblast growth factor 2 (FGFR2; rs11200014, rs2981579, and rs1219648) polymorphisms are significantly associated with the BC risk. For rs2981579, the association remained in hospital populations, while not in general populations. For rs1219648, the association remained in Asians, Caucasians, hospital populations, and general populations. However, further large-scale multicenter epidemiological studies are warranted to confirm this finding and the molecular mechanism for the associations need to be elucidated in future studies.

Genome-Wide Association Studies: Progress in Identifying Genetic Biomarkers in Common, Complex Diseases

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

FGFR2 regulation by picrasidine Q inhibits the cell growth and induces apoptosis in esophageal squamous cell carcinoma

Fibroblast growth factor receptor (FGFR) 2 and its downstream signaling cascades, PI3 K/AKT/mTOR is playing an important role in cell survival and proliferations. In this study, we firstly found that picrasidine Q (PQ), an alkaloid component extracted from Angelica keiskei species, has the capacity of anti-cell transformation and anti-cancer. After ligand shape similarity approach of PQ, we found that PQ targeted FGFR 2 and verified by FGFR2 kinase assay as well as computational docking model. FGFR2 highly expressed in esophageal cancer tissues and PQ inhibited fibroblast growth factor (FGF)-induced cell transformation. Furthermore, PQ inhibited cell proliferation and induced cell cycle arrest and apoptosis in KYSE30, KYSE410, and KYSE450 esophageal squamous cell carcinoma (ESCC) cells. It was confirmed by detecting of biological markers such as cyclinD1, cyclinD3 and cyclinB1 for cell cycle or cleaved caspase-7, caspase-3, and PARP for apoptosis. PQ targeting of FGFR2 kinase activities suppressed downstream target proteins including phosphorylation of AKT and mTOR but not MEK/ERK signaling pathways. Taken together, our results are the first to identify that PQ might be a chemopreventive and chemotherapeutic agent by direct targeting FGFR2 and inhibiting cell proliferation of ESCC cells.

Genetic variation of clock genes and cancer risk: a field synopsis and meta-analysis

BACKGROUND

The number of studies on the association between clock genes’ polymorphisms and cancer susceptibility has increased over the last years but the results are often conflicting and no comprehensive overview and quantitative summary of the evidence in this field is available.

RESULTS

Literature search identified 27 eligible studies comprising 96756 subjects (cases: 38231) and investigating 687 polymorphisms involving 14 clock genes. Overall, 1025 primary and subgroup meta-analyses on 366 gene variants were performed. Study distribution by tumor was as follows: breast cancer (n=15), prostate cancer (n=3), pancreatic cancer (n=2), non-Hodgkin's lymphoma (n=2), glioma (n=1), chronic lymphocytic leukemia (n=1), colorectal cancer (n=1), non-small cell lung cancer (n=1) and ovarian cancer (n=1).

We identified 10 single nucleotide polymorphisms (SNPs) significantly associated with cancer risk: NPAS2 rs10165970 (mixed and breast cancer shiftworkers), rs895520 (mixed), rs17024869 (breast) and rs7581886 (breast); CLOCK rs3749474 (breast) and rs11943456 (breast); RORA rs7164773 (breast and breast cancer postmenopausal), rs10519097 (breast); RORB rs7867494 (breast cancer postmenopausal), PER3 rs1012477 (breast cancer subgroups) and assessed the level of quality evidence to be intermediate. We also identified polymorphisms with lower quality statistically significant associations (n=30).

CONCLUSIONS

Our work supports the hypothesis that genetic variation of clock genes might affect cancer risk. These findings also highlight the need for more efforts in this research field in order to fully establish the contribution of clock gene variants to the risk of developing cancer.

METHODS

We conducted a systematic review and meta-analysis of the evidence on the association between clock genes’ germline variants and the risk of developing cancer. To assess result credibility, summary evidence was graded according to the Venice criteria and false positive report probability (FPRP) was calculated to further validate result noteworthiness. Subgroup meta-analysis was also performed based on participant features and tumor type. The breast cancer subgroup was further stratified by work conditions, estrogen receptor/progesterone receptor status and menopausal status, conditions associated with the risk of breast cancer in different studies.

Genetic variation at the microRNA binding site of CAV1 gene is associated with lung cancer susceptibility

Single nucleotide polymorphism (SNP) may influence the genesis and development of cancer in a variety of ways depending on their location. Here we conducted a study in Chinese female non-smokers to investigate the relationship between rs1049337, rs926198 and the risk or survival of lung cancer. Further, we explored whether rs1049337 could alter the binding affinity between the mRNA of CAV1 and the corresponding microRNAs. Finally, we evaluated the relationship between expression level of CAV1 and prognosis of lung cancer. The results showed that the rs1049337-C allele and rs926198-C allele were the protective alleles of lung cancer risk. Haplotype analysis indicated that the C-C haplotype (constructed by rs1049337 and rs926198) was a protective haplotype for lung cancer risk. The result of luciferase reporter assay showed that rs1049337 can affect the binding affinity of CAV1 mRNA to the corresponding microRNAs both in A549 cell line and H1299 cell line. Compared with C allele, T allele had a relatively decreased luciferase activity. Compared with paired normal adjacent tissue or normal lung tissue, lung cancer tissue showed a relatively low level of CAV1. Refer to those patients at early stage of lung cancer, the expression level of CAV1 in patients at late stage of lung cancer was relatively low. In conclusion, the results indicated that rs1049337, it's a SNP located at 3′UTR region of CAV1 may affect lung cancer risk by altering the binding affinity between the mRNA of CAV1 and the corresponding microRNAs.

Genetic Susceptibility, Change in Physical Activity, and Long-term Weight Gain

Whether change in physical activity over time modifies the genetic susceptibility to long-term weight gain is unknown. We calculated a BMI-genetic risk score (GRS) based on 77 BMI-associated single nucleotide polymorphisms (SNPs) and a body fat percentage (BF%)-GRS based on 12 BF%-associated SNPs in 9,390 women from the Nurses' Health Study (NHS) and 5,291 men from the Health Professionals Follow-Up Study (HPFS). We analyzed the interactions between each GRS and change in physical activity on BMI/body weight change within five 4-year intervals from 1986 to 2006 using multivariable generalized linear models with repeated-measures analyses. Both the BMI-GRS and the BF%-GRS were associated with long-term increases in BMI/weight, and change in physical activity consistently interacted with the BF%-GRS on BMI change in the NHS (P for interaction = 0.025) and HPFS (P for interaction = 0.001). In the combined cohorts, 4-year BMI change per 10-risk allele increment was -0.02 kg/m² among participants with greatest increase in physical activity and 0.24 kg/m² among those with greatest decrease in physical activity (P for interaction < 0.001), corresponding to 0.01 kg versus 0.63 kg weight changes every 4 years (P for interaction = 0.001). Similar but marginal interactions were observed for the BMI-GRS (P for interaction = 0.045). Our data indicate that the genetic susceptibility to weight gain may be diminished by increasing physical activity.

Trans-ethnic predicted expression genome-wide association analysis identifies a gene for estrogen receptor-negative breast cancer

Genome-wide association studies (GWAS) have identified more than 90 susceptibility loci for breast cancer, but the underlying biology of those associations needs to be further elucidated. More genetic factors for breast cancer are yet to be identified but sample size constraints preclude the identification of individual genetic variants with weak effects using traditional GWAS methods. To address this challenge, we utilized a gene-level expression-based method, implemented in the MetaXcan software, to predict gene expression levels for 11,536 genes using expression quantitative trait loci and examine the genetically-predicted expression of specific genes for association with overall breast cancer risk and estrogen receptor (ER)-negative breast cancer risk. Using GWAS datasets from a Challenge launched by National Cancer Institute, we identified TP53INP2 (tumor protein p53-inducible nuclear protein 2) at 20q11.22 to be significantly associated with ER-negative breast cancer (Z = -5.013, p = 5.35×10⁻⁷, Bonferroni threshold = 4.33×10⁻⁶). The association was consistent across four GWAS datasets, representing European, African and Asian ancestry populations. There are 6 single nucleotide polymorphisms (SNPs) included in the prediction of TP53INP2 expression and five of them were associated with estrogen-receptor negative breast cancer, although none of the SNP-level associations reached genome-wide significance. We conducted a replication study using a dataset outside of the Challenge, and found the association between TP53INP2 and ER-negative breast cancer was significant (p = 5.07x10^-3). Expression of HP (16q22.2) showed a suggestive association with ER-negative breast cancer in the discovery phase (Z = 4.30, p = 1.70x10^-5) although the association was not significant after Bonferroni adjustment. Of the 249 genes that are 250 kb within known breast cancer susceptibility loci identified from previous GWAS, 20 genes (8.0%) were statistically significant associated with ER-negative breast cancer (p<0.05), compared to 582 (5.2%) of 11,287 genes that are not close to previous GWAS loci. This study demonstrated that expression-based gene mapping is a promising approach for identifying cancer susceptibility genes.

Although individual genetic variant-based genome-wide association studies have greatly increased our understanding of the genetic susceptibility to breast cancer, the genetic variants identified to date account for a relatively small proportion of the heritability. Shifting the focus of analysis from individual genetic variants to genes or gene sets could lead to the identification of novel genes involved in breast cancer risk. Here, we take advantage of a recently developed gene-level expression-based association method MetaXcan to examine the association of genetically-predicted expression levels for 11,536 genes across the human genome with breast cancer risk. The MetaXcan method uses external information on the effects of genetic variants on gene expression. We show that the TP53INP2 gene on human chromosome 20 is significantly associated with estrogen-receptor negative breast cancer (P = 5.35×10⁻⁷, Bonferroni threshold = 4.33×10⁻⁶). The association is consistent across analyses of four datasets, representing European, African and Asian ancestry populations. As a downstream gene of p53, TP53INP2 may affect breast cancer risk through p53 signaling pathway. Furthermore, TP53INP2, also known as DOR (Diabetes And Obesity-Regulated Gene), has been linked to obesity and diabetes, suggesting a novel biological pathway for the known association between obesity and breast cancer risk.

Six low-penetrance SNPs for the estimation of breast cancer heritability: A family-based study in Caucasian Italian patients

Breast cancer is a malignancy with a strong heritable component. Genetic counseling has been principally focused on families carrying high-penetrance breast cancer 1/2, early onset genes. Current modeling suggests that the majority of the unexplained fraction of familial risk is likely to be explained by a polygenic model. The aim of the present study was to estimate the heritability (h²) of breast cancer susceptibility through the analysis of 6 single nucleotide polymorphisms (SNPs), nuclear mitotic apparatus protein 1, cyclin D1, cytochrome C oxidase copper chaperone, fibroblast growth factor receptor 2, TOX high mobility group box family member 3 and solute carrier family 4 member 7. These 6 SNPs, previously identified by genome-wide association studies, were considered to evaluate the additive and common environmental components that contribute to the development of breast cancer in nuclear (pedigrees including only first degree relationships) and in extended families (with at most third degree relationships). A total of 22 extended pedigrees, subsequently split into 52 nuclear pedigrees were analyzed. An example of splitting process from extended to nuclear pedigree is shown in Fig. 1. Firstly, an underline latent continuous trait (Y*) using breast cancer status and information of 6 breast cancer-associated SNPs was calculated. This novel trait summarized the susceptibility of breast cancer in each individual. Secondly, the h² of Y* was estimated using an additive polygenic-common environment-unique error model. h² was evaluated in extended and immediate pedigrees, obtaining comparable results. h² accounts for ~40% of the total phenotypic variance, indicating a fairly strong additive genetic effect of breast cancer susceptibility. The present study indicated the importance of the evaluation and consideration of these six SNPs, which can be used as instrumental variables in order to obtain improved genetic models that are useful for h² analysis.

Genetic variants in long noncoding RNA H19 contribute to the risk of breast cancer in a southeast China Han population

The long noncoding RNA (lncRNA) H19 is a maternally expressed imprinted gene that plays important roles in tumorigenesis, progression, and metastasis. However, the association between polymorphisms on H19 and breast cancer (BC) susceptibility has remained obscure. In this case–control study, we assessed the interaction between two lncRNA H19 single-nucleotide polymorphisms (SNPs) (rs217727 C>T, rs2839698 C>T) and the risk of BC in a Chinese Han population. In total, 1,005 BC cases and 1,020 healthy controls were enrolled in this study. Correlations between genotypes and BC risk were evaluated by multivariate logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs). False-positive report probability calculation was also utilized to identify false-positive associations. We observed that the rs217727 T variant was consistently significantly associated with an increased risk of BC in both codominant and dominant models (CT vs CC, OR 1.25, 95% CI 1.03–1.51; TT vs CC, OR 1.56, 95% CI 1.15–2.09; CT + TT vs CC, OR 1.31, 95% CI 1.09–1.57), and all associations remained significant after Bonferroni correction (P<0.025). Subsequent stratified analyses also revealed that associations between BC risk and rs217727 genotypes were more profound in patients with estrogen receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative, and hormone receptor-positive–HER2-negative molecular subtypes (all passed the threshold for Bonferroni correction, P<0.005). These findings extend available data on the association of H19 polymorphisms and BC susceptibility. Based on these results, we encourage further large-scale studies and functional research to confirm our findings and better elucidate the underlying biological mechanisms.

Fibroblast Growth Factor Receptor 2 Signaling in Breast Cancer

Fibroblast growth factor receptor 2 (FGFR2) is a membrane-spanning tyrosine kinase that mediates signaling for FGFs. Recent studies detected various point mutations of FGFR2 in multiple types of cancers, including breast cancer, lung cancer, gastric cancer, uterine cancer and ovarian cancer, yet the casual relationship between these mutations and tumorigenesis is unclear. Here we will discuss possible interactions between FGFR2 signaling and several major pathways through which the aberrantly activated FGFR2 signaling may result in breast cancer development. We will also discuss some recent developments in the discovery and application of therapies and strategies for breast cancers by inhibiting FGFR2 activities.

Sequence robust association test for familial data

Genome-wide association studies (GWAS) and next generation sequencing studies (NGSS) are often performed in family studies to improve power in identifying genetic variants that are associated with clinical phenotypes. Efficient analysis of genome-wide studies with familial data is challenging due to the difficulty in modeling shared but unmeasured genetic and/or environmental factors that cause dependencies among family members. Existing genetic association testing procedures for family studies largely rely on generalized estimating equations (GEE) or linear mixed-effects (LME) models. These procedures may fail to properly control for type I errors when the imposed model assumptions fail. In this article, we propose the Sequence Robust Association Test (SRAT), a fully rank-based, flexible approach that tests for association between a set of genetic variants and an outcome, while accounting for within-family correlation and adjusting for covariates. Comparing to existing methods, SRAT has the advantages of allowing for unknown correlation structures and weaker assumptions about the outcome distribution. We provide theoretical justifications for SRAT and show that SRAT includes the well-known Wilcoxon rank sum test as a special case. Extensive simulation studies suggest that SRAT provides better protection against type I error rate inflation, and could be much more powerful for settings with skewed outcome distribution than existing methods. For illustration, we also apply SRAT to the familial data from the Framingham Heart Study and Offspring Study to examine the association between an inflammatory marker and a few sets of genetic variants.

Pathway-based discovery of genetic interactions in breast cancer

Breast cancer is the second largest cause of cancer death among U.S. women and the leading cause of cancer death among women worldwide. Genome-wide association studies (GWAS) have identified several genetic variants associated with susceptibility to breast cancer, but these still explain less than half of the estimated genetic contribution to the disease. Combinations of variants (i.e. genetic interactions) may play an important role in breast cancer susceptibility. However, due to a lack of statistical power, the current tests for genetic interactions from GWAS data mainly leverage prior knowledge to focus on small sets of genes or SNPs that are known to have an association with breast cancer. Thus, many genetic interactions, particularly among novel variants, remain understudied. Reverse-genetic interaction screens in model organisms have shown that genetic interactions frequently cluster into highly structured motifs, where members of the same pathway share similar patterns of genetic interactions. Based on this key observation, we recently developed a method called BridGE to search for such structured motifs in genetic networks derived from GWAS studies and identify pathway-level genetic interactions in human populations. We applied BridGE to six independent breast cancer cohorts and identified significant pathway-level interactions in five cohorts. Joint analysis across all five cohorts revealed a high confidence consensus set of genetic interactions with support in multiple cohorts. The discovered interactions implicated the glutathione conjugation, vitamin D receptor, purine metabolism, mitotic prometaphase, and steroid hormone biosynthesis pathways as major modifiers of breast cancer risk. Notably, while many of the pathways identified by BridGE show clear relevance to breast cancer, variants in these pathways had not been previously discovered by traditional single variant association tests, or single pathway enrichment analysis that does not consider SNP-SNP interactions.

White blood cell DNA methylation and risk of breast cancer in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO)

Background

Several studies have suggested that global DNA methylation in circulating white blood cells (WBC) is associated with breast cancer risk.

Methods

To address conflicting results and concerns that the findings for WBC DNA methylation in some prior studies may reflect disease effects, we evaluated the relationship between global levels of WBC DNA methylation in white blood cells and breast cancer risk in a case-control study nested within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) cohort. A total of 428 invasive breast cancer cases and 419 controls, frequency matched on age at entry (55–59, 60–64, 65–69, ≥70 years), year of entry (on/before September 30, 1997, on/after October 1, 1997) and period of DNA extraction (previously extracted, newly extracted) were included. The ratio of 5-methyl-2’ deoxycytidine [5-mdC] to 2’-deoxyguanine [dG], assuming [dG] = [5-mdC] + [2’-deoxycytidine [dC]] (%5-mdC), was determined by liquid chromatography-electrospray ionization-tandem mass spectrometry, an especially accurate method for assessing total genomic DNA methylation.

Results

Odds ratio (OR) estimates and 95% confidence intervals (CI) for breast cancer risk adjusted for age at entry, year of entry, and period of DNA extraction, were 1.0 (referent), 0.89 (95% CI, 0.6–1.3), 0.88 (95% CI, 0.6–1.3), and 0.84 (95% CI, 0.6–1.2) for women in the highest compared to lowest quartile levels of %5md-C (p for trend = .39). Effects did not meaningfully vary by time elapsed from WBC collection to diagnosis.

Discussion

These results do not support the hypothesis that global DNA hypomethylation in WBC DNA is associated with increased breast cancer risk prior to the appearance of clinical disease.

Association Analysis of Arsenic-Induced Straighthead in Rice (Oryza sativa L.) Based on the Selected Population with a Modified Model

A rice physiological disorder makes mature panicle keep erect with empty grains termed as "straighthead." Straighthead causes yield losses and is a serious threat to rice production worldwide. Here, a new study of association mapping was conducted to identify QTL involved in straighthead. A subset of 380 accessions was selected from the USDA rice core collection and genotyped with 72 genome-wide SSR markers. An optimal model implemented with principle components (PCs) was used in this association mapping. As a result, five markers were identified to be significantly associated with straighthead. Three of them, RM263, RM169, and RM224, were consistent with a previous study. Three markers, RM475, RM263, and RM19, had a resistant allele associated with a decrease in straighthead rating (straighthead rating ≤ 4.8). In contrast, the two other marker loci RM169 and RM224 had a few susceptible alleles associated with an increase in straighthead rating (straighthead rating ≥ 8.7). Interestingly, RM475 is close to QTL "qSH-2" and "AsS" with straighthead resistance, which was reported in two studies on linkage mapping of straighthead. This finding adds to previous work and is useful for further genetic study of straighthead.

Challenges and future of biomarker tests in the era of precision oncology: Can we rely on immunohistochemistry (IHC) or fluorescence in situ hybridization (FISH) to select the optimal patients for matched therapy?

Molecular techniques have improved our understanding of the pathogenesis of cancer development. These techniques have also fueled the rational development of targeted drugs for patient populations stratified by their genetic characteristics. These novel methods have changed the classic paradigm of diagnostic pathology; among them are IHC, FISH, polymerase chain reaction (PCR) and microarray technology. IHC and FISH detection methods for human epidermal growth factor receptor-2 (HER2), epidermal growth factor receptor (EGFR) and programmed death ligand-1 (PD-L1) were recently approved by the Food and Drug Administration (FDA) as routine clinical practice for cancer patients. Here, we discuss general challenges related to the predictive power of these molecular biomarkers for targeted therapy in cancer medicine. We will also discuss the prospects of utilizing new biomarkers for fibroblast growth factor receptor (FGFR) and hepatocyte growth factor receptor (cMET/MET) targeted therapies for developing new and robust predictive biomarkers in oncology.

Novel applications of next-generation sequencing in breast cancer research

With the rapid development of medicine, the studies of genes have become increasingly concerned by more people and being the contend of a great of researches. The next generation sequencing with its own advantages has been widely used in gene research nowadays. It has almost replaced the traditional sequencing methods (such as Sanger sequencing method), and played an important role in a variety of complex disease researches, including breast cancer. The next generation sequencing technology has the advantages of high speed, high throughput and high accuracy. It has been widely used in various cancers (such as prostate cancer, lung cancer, pancreatic cancer, liver cancer, etc.), especially in breast cancer. Moreover, the use of the next generation sequencing technology to make DNA sequence analysis and risk prediction has made a great contribution to the research of breast cancer. We will focus on the application of whole genome sequencing, exon sequencing and targeted gene sequencing in breast cancer gene research.

Improving the detection of pathways in genome-wide association studies by combined effects of SNPs from Linkage Disequilibrium blocks

Genome-wide association studies (GWAS) have successfully identified single variants associated with diseases. To increase the power of GWAS, gene-based and pathway-based tests are commonly employed to detect more risk factors. However, the gene- and pathway-based association tests may be biased towards genes or pathways containing a large number of single-nucleotide polymorphisms (SNPs) with small P-values caused by high linkage disequilibrium (LD) correlations. To address such bias, numerous pathway-based methods have been developed. Here we propose a novel method, DGAT-path, to divide all SNPs assigned to genes in each pathway into LD blocks, and to sum the chi-square statistics of LD blocks for assessing the significance of the pathway by permutation tests. The method was proven robust with the type I error rate >1.6 times lower than other methods. Meanwhile, the method displays a higher power and is not biased by the pathway size. The applications to the GWAS summary statistics for schizophrenia and breast cancer indicate that the detected top pathways contain more genes close to associated SNPs than other methods. As a result, the method identified 17 and 12 significant pathways containing 20 and 21 novel associated genes, respectively for two diseases. The method is available online by http://sparks-lab.org/server/DGAT-path.

Leveraging functional annotations in genetic risk prediction for human complex diseases

Genetic risk prediction is an important goal in human genetics research and precision medicine. Accurate prediction models will have great impacts on both disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome wide association studies (GWAS), genetic risk prediction accuracy remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes in the presence of linkage disequilibrium. In this paper, we introduce AnnoPred, a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases. AnnoPred is trained using GWAS summary statistics in a Bayesian framework in which we explicitly model various functional annotations and allow for linkage disequilibrium estimated from reference genotype data. Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and real data.

Fibroblast growth factor receptors in breast cancer

Fibroblast growth factor receptors are growth factor receptor tyrosine kinases, exerting their roles in embryogenesis, tissue homeostasis, and development of breast cancer. Recent genetic studies have identified some subtypes of fibroblast growth factor receptors as strong genetic loci associated with breast cancer. In this article, we review the recent epidemiological findings and experiment results of fibroblast growth factor receptors in breast cancer. First, we summarized the structure and physiological function of fibroblast growth factor receptors in humans. Then, we discussed the common genetic variations in fibroblast growth factor receptors that affect breast cancer risk. In addition, we also introduced the potential roles of each fibroblast growth factor receptors isoform in breast cancer. Finally, we explored the potential therapeutics targeting fibroblast growth factor receptors for breast cancer. Based on the biological mechanisms of fibroblast growth factor receptors leading to the pathogenesis in breast cancer, targeting fibroblast growth factor receptors may provide new opportunities for breast cancer therapeutic strategies.