Genetic variants in lncRNA SRA and risk of breast cancer.
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ABSTRACT:
SUBMITTER: Yan R
PROVIDER: S-EPMC5008375 | biostudies-literature | 2016 Apr
REPOSITORIES: biostudies-literature
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Project description:BackgroundWhile some factors of breast morphology, such as density, are directly implicated in breast cancer, the relationship between breast size and cancer is less clear. Breast size is moderately heritable, yet the genetic variants leading to differences in breast size have not been identified.MethodsTo investigate the genetic factors underlying breast size, we conducted a genome-wide association study (GWAS) of self-reported bra cup size, controlling for age, genetic ancestry, breast surgeries, pregnancy history and bra band size, in a cohort of 16,175 women of European ancestry.ResultsWe identified seven single-nucleotide polymorphisms (SNPs) significantly associated with breast size (p<5.10(-8)): rs7816345 near ZNF703, rs4849887 and (independently) rs17625845 flanking INHBB, rs12173570 near ESR1, rs7089814 in ZNF365, rs12371778 near PTHLH, and rs62314947 near AREG. Two of these seven SNPs are in linkage disequilibrium (LD) with SNPs associated with breast cancer (those near ESR1 and PTHLH), and a third (ZNF365) is near, but not in LD with, a breast cancer SNP. The other three loci (ZNF703, INHBB, and AREG) have strong links to breast cancer, estrogen regulation, and breast development.ConclusionsThese results provide insight into the genetic factors underlying normal breast development and show that some of these factors are shared with breast cancer. While these results do not directly support any possible epidemiological relationships between breast size and cancer, this study may contribute to a better understanding of the subtle interactions between breast morphology and breast cancer risk.
| S-EPMC3483246 | biostudies-literature
Project description:Several recent genome-wide association studies have identified genetic variants associated with breast cancer. However, how much these genetic variants may help advance breast cancer risk prediction based on other clinical features, like mammographic findings, is unknown. We conducted a retrospective case-control study, collecting mammographic findings and high-frequency/low-penetrance genetic variants from an existing personalized medicine data repository. A Bayesian network was developed using Tree Augmented Naive Bayes (TAN) by training on the mammographic findings, with and without the 22 genetic variants collected. We analyzed the predictive performance using the area under the ROC curve, and found that the genetic variants significantly improved breast cancer risk prediction on mammograms. We also identified the interaction effect between the genetic variants and collected mammographic findings in an attempt to link genotype to mammographic phenotype to better understand disease patterns, mechanisms, and/or natural history.
| S-EPMC3900221 | biostudies-literature
Project description:IntroductionBreast cancer in situ (BCIS) diagnoses, a precursor lesion for invasive breast cancer, comprise about 20 % of all breast cancers (BC) in countries with screening programs. Family history of BC is considered one of the strongest risk factors for BCIS.MethodsTo evaluate the association of BC susceptibility loci with BCIS risk, we genotyped 39 single nucleotide polymorphisms (SNPs), associated with risk of invasive BC, in 1317 BCIS cases, 10,645 invasive BC cases, and 14,006 healthy controls in the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3). Using unconditional logistic regression models adjusted for age and study, we estimated the association of SNPs with BCIS using two different comparison groups: healthy controls and invasive BC subjects to investigate whether BCIS and BC share a common genetic profile.ResultsWe found that five SNPs (CDKN2BAS-rs1011970, FGFR2-rs3750817, FGFR2-rs2981582, TNRC9-rs3803662, 5p12-rs10941679) were significantly associated with BCIS risk (P value adjusted for multiple comparisons <0.0016). Comparing invasive BC and BCIS, the largest difference was for CDKN2BAS-rs1011970, which showed a positive association with BCIS (OR = 1.24, 95 % CI: 1.11-1.38, P = 1.27 x 10(-4)) and no association with invasive BC (OR = 1.03, 95 % CI: 0.99-1.07, P = 0.06), with a P value for case-case comparison of 0.006. Subgroup analyses investigating associations with ductal carcinoma in situ (DCIS) found similar associations, albeit less significant (OR = 1.25, 95 % CI: 1.09-1.42, P = 1.07 x 10(-3)). Additional risk analyses showed significant associations with invasive disease at the 0.05 level for 28 of the alleles and the OR estimates were consistent with those reported by other studies.ConclusionsOur study adds to the knowledge that several of the known BC susceptibility loci are risk factors for both BCIS and invasive BC, with the possible exception of rs1011970, a putatively functional SNP situated in the CDKN2BAS gene that may be a specific BCIS susceptibility locus.
| S-EPMC4487950 | biostudies-literature
Project description:IntroductionKnown prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population.MethodsWe estimated the risk of breast cancer using a prospective cohort of 24,161 women aged 50 to 64 from Singapore with available mammograms and known risk factors for breast cancer who were recruited between 1994 and 1997. We measured mammographic density using the medio-lateral oblique views of both breasts. Each woman's genotype for 75 SNPs was simulated based on the genotype frequency obtained from the Breast Cancer Association Consortium data and the cumulative effect was summarized by a genetic risk score (GRS). Any improvement in the performance of our proposed prediction model versus one containing only variables from the Gail model was assessed by changes in receiver-operating characteristic and predictive values.ResultsDuring 17 years of follow-up, 680 breast cancer cases were diagnosed. The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22-2.10), 2.20 (1.65-2.92), 2.33 (1.71-3.20), 2.12 (1.43-3.14), and 3.27 (2.24-4.76) for the corresponding mammographic density categories: 11-20cm2, 21-30cm2, 31-40cm2, 41-50cm2, 51-60cm2, and 1.10 (1.03-1.16) for GRS. At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively.ConclusionMammographic density and common genetic variants can improve the discriminatory power of an established breast cancer risk prediction model among females in Singapore.
| S-EPMC4581713 | biostudies-literature
Project description:BackgroundGenomewide association studies have identified multiple genetic variants associated with breast cancer. The extent to which these variants add to existing risk-assessment models is unknown.MethodsWe used information on traditional risk factors and 10 common genetic variants associated with breast cancer in 5590 case subjects and 5998 control subjects, 50 to 79 years of age, from four U.S. cohort studies and one case-control study from Poland to fit models of the absolute risk of breast cancer. With the use of receiver-operating-characteristic curve analysis, we calculated the area under the curve (AUC) as a measure of discrimination. By definition, random classification of case and control subjects provides an AUC of 50%; perfect classification provides an AUC of 100%. We calculated the fraction of case subjects in quintiles of estimated absolute risk after the addition of genetic variants to the traditional risk model.ResultsThe AUC for a risk model with age, study and entry year, and four traditional risk factors was 58.0%; with the addition of 10 genetic variants, the AUC was 61.8%. About half the case subjects (47.2%) were in the same quintile of risk as in a model without genetic variants; 32.5% were in a higher quintile, and 20.4% were in a lower quintile.ConclusionsThe inclusion of newly discovered genetic factors modestly improved the performance of risk models for breast cancer. The level of predicted breast-cancer risk among most women changed little after the addition of currently available genetic information.
| S-EPMC2921181 | biostudies-literature
Project description:Ovarian cancer is one of the common female malignant tumors globally. However, exactly mechanism of ovarian cancer remained unknown. HOTAIR, a lncRNA in the mammalian HOXC locus, has been fully explored for its genetic variants, expression level and carcinogenesis, development and progression of multiple cancers, except for ovarian cancer. In this study, we hypothesized that abnormal expression of HOTAIR and common variants of HOTAIR are associated with risk of Epithelial ovarian cancer (EOC). We first evaluated the HOTAIR levels in 100 paired tissues of EOC patients and corresponding normal tissues. Results showed that the expression level of HOTAIR in EOC tissues was significantly higher than that in corresponding normal tissues. Then the genotyping analyses of HOTAIR gene was conducted in a Chinese population. The results indicated that rs4759314 and rs7958904 were significantly associated with EOC susceptibility. For rs4759314, the difference between the G allele (as the reference) and the A allele was statistically significant (adjusted OR, 1.34; 95% CI: 1.08-1.65; P = 6.8 × 10-3). For rs7958904, C allele was associated a significantly decreased EOC risk when compared with G allele (OR: 0.77; 95% CI: 0.67-0.89; P = 4.2 × 10-4). The study identified that HOTAIR variants could be a useful biomarker for the predisposition to EOC and for the early diagnosis of the disease.
| S-EPMC5173041 | biostudies-literature
Project description:BACKGROUND:Genome-wide association studies have identified several genomic regions that are associated with breast cancer risk, but these provide an explanation for only a small fraction of familial breast cancer aggregation. Genotype by environment interactions may contribute further to such explanation, and may help to refine the genomic regions of interest. METHODS:We examined genotypes for 4,988 SNPs, selected from recent genome-wide studies, and four randomized hormonal and dietary interventions among 2,166 women who developed invasive breast cancer during the intervention phase of the Women's Health Initiative (WHI) clinical trial (1993 to 2005), and one-to-one matched controls. These SNPs derive from 3,224 genomic regions having pairwise squared correlation (r2) between adjacent regions less than 0.2. Breast cancer and SNP associations were identified using a test statistic that combined evidence of overall association with evidence for SNPs by intervention interaction. RESULTS:The combined 'main effect' and interaction test led to a focus on two genomic regions, the fibroblast growth factor receptor two (FGFR2) and the mitochondrial ribosomal protein S30 (MRPS30) regions. The ranking of SNPs by significance level, based on this combined test, was rather different from that based on the main effect alone, and drew attention to the vicinities of rs3750817 in FGFR2 and rs7705343 in MRPS30. Specifically, rs7705343 was included with several FGFR2 SNPs in a group of SNPs having an estimated false discovery rate < 0.05. In further analyses, there were suggestions (nominal P < 0.05) that hormonal and dietary intervention hazard ratios varied with the number of minor alleles of rs7705343. CONCLUSIONS:Genotype by environment interaction information may help to define genomic regions relevant to disease risk. Combined main effect and intervention interaction analyses raise novel hypotheses concerning the MRPS30 genomic region and the effects of hormonal and dietary exposures on postmenopausal breast cancer risk.
| S-EPMC3218816 | biostudies-literature
Project description:Sex hormones play a key role in the development of breast cancer. Certain polymorphic variants (SNPs and repeat polymorphisms) in hormone-related genes are associated with sex hormone levels. However, the relationship observed between these genetic variants and breast cancer risk has been inconsistent. We conducted a case-control study nested within two prospective cohorts to assess the relationship between specific genetic variants in hormone-related genes and breast cancer risk. In total, 1164 cases and 2111 individually-matched controls were included in the study. We did not observe an association between potential functional genetic polymorphisms in the estrogen pathway, SHBG rs6259, ESR1 rs2234693, CYP19 rs10046 and rs4775936, and UGT1A1 rs8175347, or the progesterone pathway, PGR rs1042838, with the risk of breast cancer. Our results suggest that these genetic variants do not have a strong effect on breast cancer risk.
| S-EPMC3720532 | biostudies-literature
Project description:PurposeTo explore the etiologic role of genetic variants in telomere pathway genes and breast cancer risk.MethodsA population-based case-control study, the Long Island Breast Cancer Study Project, was conducted, and 1,067 cases and 1,110 controls were included in the present study. Fifty-two genetic variants of nine telomere-related genes were genotyped.ResultsSeven single nucleotide polymorphisms (SNP) showed significant case-control differences at the level of P < 0.05. The top three statistically significant SNPs under a dominant model were TERT-07 (rs2736109), TERT-54 (rs3816659), and POT1-03 (rs33964002). The odds ratios (OR) were 1.56 [95% confidence interval (95% CI), 1.22-1.99] for the TERT-07 G-allele, 1.27 (95% CI, 1.05-1.52) for the TERT-54 T-allele, and 0.79 (95% CI, 0.67-0.95) for the POT1-03 A-allele. TERT-67 (rs2853669) was statistically significant under a recessive model; the OR of the CC genotype was 0.69 (95% CI, 0.69-0.93) compared with the T-allele. However, none of the SNPs retained significance after Bonferroni adjustment for multiple testing at the level of P < 0.001 (0.05/52) except for TERT-07. When restricted to Caucasians (94% of the study subjects), a stronger association for the TERT-07 G-allele was observed with an OR of 1.60 (95% CI, 1.24-2.05; P = 0.0002). No effect modifications were found for variant alleles and menopausal status, telomere length, cigarette smoking, body mass index status, and family history of breast cancer risk.ConclusionsFour SNPs in the TERT and POT1 genes were significantly related with overall breast cancer risk. This initial analysis provides valuable clues for further exploration of the biological role of telomere pathway genes in breast cancer.
| S-EPMC2805470 | biostudies-literature
Project description:In previous studies among 1,144 cases and 1,256 controls recruited in stage 1 of the Shanghai Breast Cancer Study (SBCS I; 1996-1998), 18 known or potentially functional single nucleotide polymorphisms (SNPs) in 16 genes were found to be associated with breast cancer risk. The authors evaluated these associations among 1,918 cases and 1,819 controls recruited in stage 2 of the SBCS (SBCS II; 2002-2005) using genetic effect models and subgroup analyses predetermined from SBCS I results. Five SNPs (AHR rs2066853, ATM rs1003623, ESR1 rs2234693, GSTP1 rs1695, and SHBG rs6259) showed generally consistent results in SBCS I and SBCS II and statistically significant associations with breast cancer risk in combined analyses, mostly in subgroups defined by age or menopausal status. Further, the relation between breast cancer risk and SHBG rs6259 was found to vary by body mass index (weight (kg)/height (m)(2)) (P for interaction = 0.003). The strongest reduction in risk associated with SHBG rs6259 was found for lean (body mass index <23) postmenopausal minor allele carriers (odds ratio = 0.6, 95% confidence interval: 0.5, 0.8; P = 4.6 × 10(-4)). This biologically plausible and highly significant finding provides strong evidence for a true association among Asian women. This study also highlights the value of gene-environment interaction analyses in evaluating genetic factors for complex diseases.
| S-EPMC3121323 | biostudies-literature