Identification of a novel percent mammographic density locus at 12q24.
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ABSTRACT:
SUBMITTER: Stevens KN
PROVIDER: S-EPMC3384385 | biostudies-literature | 2012 Jul
REPOSITORIES: biostudies-literature
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Identification of a novel percent mammographic density locus at 12q24.
Stevens Kristen N KN Lindstrom Sara S Scott Christopher G CG Thompson Deborah D Sellers Thomas A TA Wang Xianshu X Wang Alice A Atkinson Elizabeth E Rider David N DN Eckel-Passow Jeanette E JE Varghese Jajini S JS Audley Tina T Brown Judith J Leyland Jean J Luben Robert N RN Warren Ruth M L RM Loos Ruth J F RJ Wareham Nicholas J NJ Li Jingmei J Hall Per P Liu Jianjun J Eriksson Louise L Czene Kamila K Olson Janet E JE Pankratz V Shane VS Fredericksen Zachary Z Diasio Robert B RB Lee Adam M AM Heit John A JA DeAndrade Mariza M Goode Ellen L EL Vierkant Robert A RA Cunningham Julie M JM Armasu Sebastian M SM Weinshilboum Richard R Fridley Brooke L BL Batzler Anthony A Ingle James N JN Boyd Norman F NF Paterson Andrew D AD Rommens Johanna J Martin Lisa J LJ Hopper John L JL Southey Melissa C MC Stone Jennifer J Apicella Carmel C Kraft Peter P Hankinson Susan E SE Hazra Aditi A Hunter David J DJ Easton Douglas F DF Couch Fergus J FJ Tamimi Rulla M RM Vachon Celine M CM
Human molecular genetics 20120424 14
Percent mammographic density adjusted for age and body mass index (BMI) is one of the strongest risk factors for breast cancer and has a heritable component that remains largely unidentified. We performed a three-stage genome-wide association study (GWAS) of percent mammographic density to identify novel genetic loci associated with this trait. In stage 1, we combined three GWASs of percent density comprised of 1241 women from studies at the Mayo Clinic and identified the top 48 loci (99 single ...[more]
Similar Datasets
Project description:BACKGROUND:Percent mammographic density (PD) estimates the proportion of stromal, fat, and epithelial breast tissues on the mammogram image. Adjusted for age and body mass index (BMI), PD is one of the strongest risk factors for breast cancer. Inherited factors are hypothesized to explain between 30 and 60% of the variance in this trait. However, previously identified common genetic variants account for less than 6% of the variance in PD, leaving much of the genetic contribution to this trait unexplained. We performed the first study to examine whether germline copy number variation (CNV) are associated with PD. Two genome-wide association studies (GWAS) of percent density conducted on the Illumina 660W-Quad were used to identify and replicate the association between candidate CNVs and PD: the Minnesota Breast Cancer Family Study (MBCFS) and controls from the Mayo Venous Thromboembolism (Mayo VTE) Case-Control Study, with 585 and 328 women, respectively. Linear models were utilized to examine the association of each probe with PD, adjusted for age, menopausal status and BMI. Segmentation was subsequently performed on the probe-level test statistics to identify candidate CNV regions that were associated with PD. RESULTS:Sixty-one probes from five chromosomal regions [3q26.1 (2 regions), 8q24.22, 11p15.3, and 17q22] were significantly associated with PD in MBCFS (p-values <0.0001). A CNV at 3q26.1 showed the greatest evidence for association with PD; a region without any known SNPs. Conversely, the CNV at 17q22 was largely due to the association between SNPs and PD in the region. SNPs in the 8q24.22 region have been shown to be associated with risk of many cancers; however, SNPs in this region were not responsible for the observed CNV association. While we were unable to replicate the associations with PD, two of the five CNVs (3q26.1 and 11p15.3) were also observed in the Mayo VTE controls. CONCLUSIONS:CNVs may help to explain some of the variability in PD that is currently unexplained by SNPs. While we were able to replicate the existence of two CNVs across the two GWAS studies, we were unable to replicate the associations with PD. Even so, the proximity of the identified CNV regions to loci known to be associated with breast cancer risk suggests further investigation and potentially shared genetic mechanisms underlying the PD and breast cancer association.
| S-EPMC4494822 | biostudies-literature
Project description:Percent mammographic density (PMD) is a strong breast cancer risk factor, however, other mammographic features, such as V, the standard deviation (SD) of pixel intensity, may be associated with risk. We assessed whether PMD, automated PMD (APD), and V, yielded independent associations with breast cancer risk. We included 1900 breast cancer cases and 3921 matched controls from the Nurses' Health Study (NHS) and the NHSII. Using digitized film mammograms, we estimated PMD using a computer-assisted thresholding technique. APD and V were determined using an automated computer algorithm. We used logistic regression to generate odds ratios (ORs) and 95% confidence intervals (CIs). Median time from mammogram to diagnosis was 4.1 years (interquartile range: 1.6-6.8 years). PMD (OR per SD:1.52, 95% CI: 1.42, 1.63), APD (OR per SD:1.32, 95% CI: 1.24, 1.41), and V (OR per SD:1.32, 95% CI: 1.24, 1.40) were positively associated with breast cancer risk. Associations for APD were attenuated but remained statistically significant after mutual adjustment for PMD or V. Women in the highest quartile of both APD and V (OR vs Q1/Q1: 2.49, 95% CI: 2.02, 3.06), or PMD and V (OR vs Q1/Q1: 3.57, 95% CI: 2.79, 4.58) had increased breast cancer risk. An automated method of PMD assessment is feasible and yields similar, but somewhat weaker, estimates to a manual measure. PMD, APD and V are each independently, positively associated with breast cancer risk. Women with dense breasts and greater texture variation are at the highest relative risk of breast cancer.
| S-EPMC8166859 | biostudies-literature
Project description:INTRODUCTION:Mammographic density (MD) is a strong heritable and intermediate phenotype for breast cancer, but much of its genetic variation remains unexplained. We performed a large-scale genetic association study including 8,419 women of European ancestry to identify MD loci. METHODS:Participants of three Swedish studies were genotyped on a custom Illumina iSelect genotyping array and percent and absolute mammographic density were ascertained using semiautomated and fully automated methods from film and digital mammograms. Linear regression analysis was used to test for SNP-MD associations, adjusting for age, body mass index, menopausal status and six principal components. Meta-analyses were performed by combining P values taking sample size, study-specific inflation factor and direction of effect into account. RESULTS:Genome-wide significant associations were observed for two previously identified loci: ZNF365 (rs10995194, P = 2.3 × 10(-8) for percent MD and P = 8.7 × 10(-9) for absolute MD) and AREG (rs10034692, P = 6.7 × 10(-9) for absolute MD). In addition, we found evidence of association for two variants at 6q25.1, both of which are known breast cancer susceptibility loci: rs9485370 in the TAB2 gene (P = 4.8 × 10(-9) for percent MD and P = 2.5 × 10(-8) for absolute MD) and rs60705924 in the CCDC170/ESR1 region (P = 2.2 × 10(-8) for absolute MD). Both regions have been implicated in estrogen receptor signaling with TAB2 being a potential regulator of tamoxifen response. CONCLUSIONS:We identified two novel MD loci at 6q25.1. These findings underscore the importance of 6q25.1 as a susceptibility region and provide more insight into the mechanisms through which MD influences breast cancer risk.
| S-EPMC4501298 | biostudies-literature
Project description:Purpose: Mammographic characteristics are known to be correlated to breast cancer risk. Percent mammographic density (PMD), as assessed by computer-assisted methods, is an established risk factor for breast cancer. Along with this assessment the absolute dense area (DA) of the breast is reported as well. Aim of this study was to assess the predictive value of DA concerning breast cancer risk in addition to other risk factors and in addition to PMD. Methods: We conducted a case control study with hospital-based patients with a diagnosis of invasive breast cancer and healthy women as controls. A total of 561 patients and 376 controls with available mammographic density were included into this study. We describe the differences concerning the common risk factors BMI, parital status, use of hormone replacement therapy (HRT) and menopause between cases and controls and estimate the odds ratios for PMD and DA, adjusted for the mentioned risk factors. Furthermore we compare the prediction models with each other to find out whether the addition of DA improves the model. Results: Mammographic density and DA were highly correlated with each other. Both variables were as well correlated to the commonly known risk factors with an expected direction and strength, however PMD (??=?-0.56) was stronger correlated to BMI than DA (??=?-0.11). The group of women within the highest quartil of PMD had an OR of 2.12 (95?% CI: 1.25-3.62). This could not be seen for the fourth quartile concerning DA. However the assessment of breast cancer risk could be improved by including DA in a prediction model in addition to common risk factors and PMD. Conclusions: The inclusion of the parameter DA into a prediction model for breast cancer in addition to established risk factors and PMD could improve the breast cancer risk assessment. As DA is measured together with PMD in the process of computer-assisted assessment of PMD it might be considered to include it as one additional breast cancer risk factor that is obtained from breast imaging.
| S-EPMC4168400 | biostudies-other
Project description:To develop a model to predict percent mammographic density (MD) using questionnaire data and mammograms from controls in the Nurses' Health Studies' nested breast cancer case-control studies. Further, we assessed the association between both measured and predicted percent MD and breast cancer risk.Using data from 2,955 controls, we assessed several variables as potential predictors. We randomly divided our dataset into a training dataset (two-thirds of the dataset) and a testing dataset (one-third of the dataset). We used stepwise linear regression to identify the subset of variables that were most predictive. Next, we examined the correlation between measured and predicted percent MD in the testing dataset and computed the r 2 in the total dataset. We used logistic regression to examine the association between measured and predicted percent MD and breast cancer risk.In the training dataset, several variables were selected for inclusion, including age, body mass index, and parity, among others. In the testing dataset, the Spearman correlation coefficient between predicted and measured percent MD was 0.61. As the prediction model performed well in the testing dataset, we developed the final model in the total dataset. The final prediction model explained 41% of the variability in percent MD. Both measured and predicted percent MD were similarly associated with breast cancer risk adjusting for age, menopausal status, and hormone use (OR per five unit increase = 1.09 for both).These results suggest that predicted percent MD may be useful for research studies in which mammograms are unavailable.
| S-EPMC5568000 | biostudies-other
Project description:INTRODUCTION:Increased mammographic breast density is one of the strongest risk factors for breast cancer. While two-thirds of the variation in mammographic density appears to be genetically influenced, few variants have been identified. We examined the association of inherited variation in genes from pathways that mediate cell division with percent mammographic density (PMD) adjusted for age, body mass index (BMI) and postmenopausal hormones, in two studies of healthy postmenopausal women. METHODS:We investigated 2,058 single nucleotide polymorphisms (SNPs) in 378 genes involved in regulation of mitosis for associations with adjusted PMD among 484 unaffected postmenopausal controls (without breast cancer) from the Mayo Clinic Breast Cancer Study (MCBCS) and replicated the findings in postmenopausal controls (n = 726) from the Singapore and Sweden Breast Cancer Study (SASBAC) study. PMD was assessed in both studies by a computer-thresholding method (Cumulus) and linear regression approaches were used to assess the association of SNPs and PMD, adjusted for age, BMI and postmenopausal hormones. A P-value threshold of 4.2 × 10-5 based on a Bonferroni correction of effective number of independent tests was used for statistical significance. Further, a pathway-level analysis was conducted of all 378 genes using the self-contained gene-set analysis method GLOSSI. RESULTS:A variant in PRPF4, rs10733604, was significantly associated with adjusted PMD in the MCBCS (P = 2.7 × 10-7), otherwise, no single SNP was associated with PMD. Additionally, the pathway analysis provided no evidence of enrichment in the number of associations observed between SNPs in the mitotic genes and PMD (P = 0.60). We evaluated rs10733604 (PRPF4), and 73 other SNPs at P < 0.05 from 51 genes in the SASBAC study. There was no evidence of an association of rs10733604 (PRPF4) with adjusted PMD in SASBAC (P = 0.23). There were, however, consistent associations (P < 0.05) of variants at the putative locus, LOC375190, Aurora B kinase (AURKB), and Mini-chromosome maintenance complex component 3 (MCM3) with adjusted PMD, although these were not statistically significant. CONCLUSIONS:Our findings do not support a role of inherited variation in genes involved in regulation of cell division and adjusted percent mammographic density in postmenopausal women.
| S-EPMC3496122 | biostudies-literature
Project description:We investigated if associations of breast density and breast cancer differ according to the level of other known breast cancer risk factors, including body mass index (BMI), age at menarche, parity, age at first child's birth, age at menopause, alcohol consumption, a family history of breast cancer, a history of benign breast disease, and physical activity. This study included 1,044 postmenopausal incident breast cancer cases diagnosed within the Nurses' Health Study cohort and 1,794 matched controls. Percent breast density, absolute dense, and non-dense areas were measured from digitized film images with computerized techniques. Information on breast cancer risk factors was obtained prospectively from biennial questionnaires. Percent breast density was more strongly associated with breast cancer risk in current postmenopausal hormone users (?50 vs. 10 %: OR 5.34, 95 % CI 3.36-8.49) as compared to women with past (OR 2.69, 95 % CI 1.32-5.49) or no hormone history (OR 2.57, 95 % CI 1.18-5.60, p-interaction = 0.03). Non-dense area was inversely associated with breast cancer risk in parous women, but not in women without children (p-interaction = 0.03). Associations of density with breast cancer risk did not differ by the levels of BMI, age at menarche, parity, age at first child's birth, age at menopause, alcohol consumption, a family history of breast cancer, a history of benign breast disease, and physical activity. Women with dense breasts, who currently use menopausal hormone therapy are at a particularly high risk of breast cancer. Most breast cancer risk factors do not modify the association between mammographic breast density and breast cancer risk.
| S-EPMC4372799 | biostudies-literature
Project description:Genetic linkage analyses, genome-wide association studies of single nucleotide polymorphisms, copy number variation surveys, and mutation screenings found the human chromosomal 12q24 locus, with the genes SH2B3 and ATXN2 in its core, to be associated with an exceptionally wide spectrum of disease susceptibilities. Hematopoietic traits of red and white blood cells (like erythrocytosis and myeloproliferative disease), autoimmune disorders (like type 1 diabetes, coeliac disease, juvenile idiopathic arthritis, rheumatoid arthritis, thrombotic antiphospholipid syndrome, lupus erythematosus, multiple sclerosis, hypothyroidism and vitiligo), also vascular pathology (like kidney glomerular filtration rate deficits, serum urate levels, plasma beta-2-microglobulin levels, retinal microcirculation problems, diastolic and systolic blood pressure and hypertension, cardiovascular infarction), furthermore obesity, neurodegenerative conditions (like the polyglutamine-expansion disorder spinocerebellar ataxia type 2, Parkinson's disease, the motor-neuron disease amyotrophic lateral sclerosis, and progressive supranuclear palsy), and finally longevity were reported. Now it is important to clarify, in which ways the loss or gain of function of the locally encoded proteins SH2B3/LNK and ataxin-2, respectively, contribute to these polygenic health problems. SH2B3/LNK is known to repress the JAK2/ABL1 dependent proliferation of white blood cells. Its null mutations in human and mouse are triggers of autoimmune traits and leukemia (acute lymphoblastic leukemia or chronic myeloid leukemia-like), while missense mutations were found in erythrocytosis-1 patients. Ataxin-2 is known to act on RNA-processing and trophic receptor internalization. While its polyglutamine-expansion mediated gain-of-function causes neuronal atrophy in human and mouse, its deletion leads to obesity and insulin resistance in mice. Thus, it is conceivable that the polygenic pathogenesis of type 1 diabetes is enhanced by an SH2B3-dysregulation-mediated predisposition to autoimmune diseases that conspires with an ATXN2-deficiency-mediated predisposition to lipid and glucose metabolism pathology.
| S-EPMC4058736 | biostudies-literature
Project description:BACKGROUND:Mammographic density (MD) is a strong and heritable intermediate phenotype of breast cancer, but much of its genetic variation remains unexplained. METHODS:We conducted a genetic association study of volumetric MD in a Swedish mammography screening cohort (n = 9498) to identify novel MD loci. Associations with volumetric MD phenotypes (percent dense volume, absolute dense volume, and absolute nondense volume) were estimated using linear regression adjusting for age, body mass index, menopausal status, and six principal components. We also estimated the proportion of MD variance explained by additive contributions from single-nucleotide polymorphisms (SNP-based heritability [h2SNP]) in 4948 participants of the cohort. RESULTS:In total, three novel MD loci were identified (at P < 5 × 10-?8): one for percent dense volume (HABP2) and two for the absolute dense volume (INHBB, LINC01483). INHBB is an established locus for ER-negative breast cancer, and HABP2 and LINC01483 represent putative new breast cancer susceptibility loci, because both loci were associated with breast cancer in available meta-analysis data including 122,977 breast cancer cases and 105,974 control subjects (P < 0.05). h2SNP (SE) estimates for percent dense, absolute dense, and nondense volume were 0.29 (0.07), 0.31 (0.07), and 0.25 (0.07), respectively. Corresponding ratios of h2SNP to previously observed narrow-sense h2 estimates in the same cohort were 0.46, 0.72, and 0.41, respectively. CONCLUSIONS:These findings provide new insights into the genetic basis of MD and biological mechanisms linking MD to breast cancer risk. Apart from identifying three novel loci, we demonstrate that at least 25% of the MD variance is explained by common genetic variation with h2SNP/h2 ratios varying between dense and nondense MD components.
| S-EPMC5904990 | biostudies-literature
Project description:Background:Japan is traditionally a country with one of the highest levels of fish consumption worldwide, although the westernization of the Japanese diet has resulted in the reduction of fish consumption. A recent meta-analysis of genome-wide association studies (GWASs) on Western populations has identified a single nucleotide polymorphism (SNP) associated with fish intake frequency. Here, we examined the genetic basis for fish intake frequency among Japanese individuals. Results:We conducted a meta-analysis of a GWAS including 12,603 Japanese individuals and identified a susceptibility locus for fish intake frequency at 12q24 (lead variant was rs11066015, P =?5.4?×?10-11). rs11066015 was in a strong linkage disequilibrium with rs671, a well-known SNP related to alcohol metabolism. When adjusted for alcohol drinking, the association between rs11066015 and fish intake frequency was substantially attenuated. Subgroup analysis revealed that the effect of the 12q24 variant on fish intake frequency was stronger in males than in females (P for interaction?=?0.007) and stronger in the older subgroup than in the younger subgroup (P for interaction?=?0.006). Conclusions:Our findings suggest that the 12q24 locus is associated with fish intake frequency via alcohol drinking. This study can help contribute to personalized nutrition information, suggesting that fish intake should be promoted to consumers who have the rs11066015 minor allele, which is genetically linked to low fish intake frequency, especially in male and older individuals.
| S-EPMC6612078 | biostudies-literature