[Show abstract][Hide abstract] ABSTRACT: Mean telomere length in blood cells (TL) is heritable and has been reported to be associated with risks of several diseases, including cancer. We conducted a meta-analysis of three GWAS for TL (total n=2,240) and selected 1,629 variants for replication via the "iCOGS" custom genotyping array. All ∼200,000 iCOGS variants were analysed with TL and those displaying associations in healthy controls (n=15,065) were further tested in breast cancer cases (n=11,024). We found a novel TL association (P-trend<4×10(-10)) at 3p14.4 close to PXK and evidence (P-trend<7×10(-7)) for TL loci at 6p22.1 (ZNF311) and 20q11.2 (BCL2L1). We additionally confirmed (P-trend<5×10(-14)) the previously-reported loci at 3q26.2 (TERC), 5p15.3 (TERT) and 10q24.3 (OBFC1) and found supportive evidence (P-trend<5×10(-4)) for the published loci at 2p16.2 (ACYP2), 4q32.2 (NAF1) and 20q13.3 (RTEL1). SNPs tagging these loci explain TL differences of up to 731 bp (corresponding to 18% of total TL in healthy individuals), however, they display little direct evidence for association with breast, ovarian or prostate cancer risks.
Human Molecular Genetics 07/2013; · 7.69 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: BACKGROUND: Mammographic breast density and endogenous sex-hormone levels are both strong risk factors for breast cancer. This study investigated whether there is evidence for a shared genetic basis between these risk factors. METHODS: Using data on 1286 women from 617 families we estimated the heritabilities of serum estradiol, testosterone and sex-hormone binding globulin (SHBG) levels and of three measures of breast density (dense area, non-dense area and percent density). We tested for associations between hormone levels and density measures, and estimated the genetic and environmental correlations between pairs of traits using variance and co-variance components models and pedigree-based maximum likelihood methods. RESULTS: We found no significant associations between estradiol, testosterone or SHBG levels and any of the three density measures, after adjusting for BMI. The estimated heritabilities were 63%, 66% and 65% for square-root transformed adjusted percent-density, dense area and non-dense area respectively, and 40%, 25% and 58% for log-transformed adjusted estradiol, testosterone and SHBG. We found no evidence of a shared genetic basis between any hormone levels and any measure of density, after adjusting for BMI. The negative genetic correlation between dense and non-dense areas remained significant even after adjustment for BMI and other covariates (p=-0.34, se=0.08, P=0.0005). Conclusions and Impact: Breast density and sex hormones can thus be considered as independent sets of traits, each of which can be used as intermediate phenotypes in the search for breast cancer susceptibility loci.
Cancer Epidemiology Biomarkers & Prevention 10/2012; · 4.56 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: 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 nucleotide polymorphisms). We attempted replication of these loci in 7018 women from seven additional studies (stage 2). The meta-analysis of stage 1 and 2 data identified a novel locus, rs1265507 on 12q24, associated with percent density, adjusting for age and BMI (P = 4.43 × 10(-8)). We refined the 12q24 locus with 459 additional variants (stage 3) in a combined analysis of all three stages (n = 10 377) and confirmed that rs1265507 has the strongest association in the 12q24 region (P = 1.03 × 10(-8)). Rs1265507 is located between the genes TBX5 and TBX3, which are members of the phylogenetically conserved T-box gene family and encode transcription factors involved in developmental regulation. Understanding the mechanism underlying this association will provide insight into the genetics of breast tissue composition.
Human Molecular Genetics 04/2012; 21(14):3299-305. · 7.69 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Mammographic density adjusted for age and body mass index (BMI) is a heritable marker of breast cancer susceptibility. Little is known about the biologic mechanisms underlying the association between mammographic density and breast cancer risk. We examined whether common low-penetrance breast cancer susceptibility variants contribute to interindividual differences in mammographic density measures.
We established an international consortium (DENSNP) of 19 studies from 10 countries, comprising 16,895 Caucasian women, to conduct a pooled cross-sectional analysis of common breast cancer susceptibility variants in 14 independent loci and mammographic density measures. Dense and nondense areas, and percent density, were measured using interactive-thresholding techniques. Mixed linear models were used to assess the association between genetic variants and the square roots of mammographic density measures adjusted for study, age, case status, BMI, and menopausal status.
Consistent with their breast cancer associations, the C-allele of rs3817198 in LSP1 was positively associated with both adjusted dense area (P = 0.00005) and adjusted percent density (P = 0.001), whereas the A-allele of rs10483813 in RAD51L1 was inversely associated with adjusted percent density (P = 0.003), but not with adjusted dense area (P = 0.07).
We identified two common breast cancer susceptibility variants associated with mammographic measures of radiodense tissue in the breast gland.
We examined the association of 14 established breast cancer susceptibility loci with mammographic density phenotypes within a large genetic consortium and identified two breast cancer susceptibility variants, LSP1-rs3817198 and RAD51L1-rs10483813, associated with mammographic measures and in the same direction as the breast cancer association.
[Show abstract][Hide abstract] ABSTRACT: Genome-wide association studies (GWAS) have successfully identified common genetic variants that contribute to breast cancer risk. Discovering additional variants has become difficult, as power to detect variants of weaker effect with present sample sizes is limited. An alternative approach is to look for variants associated with quantitative traits that in turn affect disease risk. As exposure to high circulating estradiol and testosterone, and low sex hormone-binding globulin (SHBG) levels is implicated in breast cancer etiology, we conducted GWAS analyses of plasma estradiol, testosterone, and SHBG to identify new susceptibility alleles. Cancer Genetic Markers of Susceptibility (CGEMS) data from the Nurses' Health Study (NHS), and Sisters in Breast Cancer Screening data were used to carry out primary meta-analyses among ~1600 postmenopausal women who were not taking postmenopausal hormones at blood draw. We observed a genome-wide significant association between SHBG levels and rs727428 (joint β = -0.126; joint P = 2.09 × 10(-16)), downstream of the SHBG gene. No genome-wide significant associations were observed with estradiol or testosterone levels. Among variants that were suggestively associated with estradiol (P<10(-5)), several were located at the CYP19A1 gene locus. Overall results were similar in secondary meta-analyses that included ~900 NHS current postmenopausal hormone users. No variant associated with estradiol, testosterone, or SHBG at P<10(-5) was associated with postmenopausal breast cancer risk among CGEMS participants. Our results suggest that the small magnitude of difference in hormone levels associated with common genetic variants is likely insufficient to detectably contribute to breast cancer risk.
PLoS ONE 01/2012; 7(6):e37815. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: High-percent mammographic density adjusted for age and body mass index is one of the strongest risk factors for breast cancer. We conducted a meta analysis of five genome-wide association studies of percent mammographic density and report an association with rs10995190 in ZNF365 (combined P = 9.6 × 10(-10)). Common variants in ZNF365 have also recently been associated with susceptibility to breast cancer.
[Show abstract][Hide abstract] ABSTRACT: A recent study reported genetic variants in the TERT-CLPTM1L locus that were associated with mean telomere length, and with risk of multiple cancers.
We evaluated the association between single nucleotide polymorphism (SNP) rs401681 (C > T) and mean telomere length, using quantitative real-time PCR, in blood-extracted DNA collected from 11,314 cancer-free participants from the Sisters in Breast Screening study, the Melanoma and Pigmented Lesions Evaluative Study melanoma family study, and the SEARCH Breast, Colorectal, Melanoma studies. We also examined the relationship between rs401618 genotype and susceptibility to breast cancer (6,800 cases and 6,608 controls), colorectal cancer (2,259 cases and 2,181 controls), and melanoma (787 cases and 999 controls).
The "per T allele" change in mean telomere length (DeltaCt), adjusted for age, study plate, gender, and family was 0.001 [95% confidence intervals (CI), 0.01-0.02; P trend = 0.61]. The "per T allele" odds ratio for each cancer was 1.01 for breast cancer (95% CI, 0.96-1.06; P trend = 0.64), 1.02 for colorectal cancer (95% CI, 0.94-1.11; P trend = 0.66), and 0.99 for melanoma (95% CI, 0.84-1.15; P trend = 0.87).
We found no evidence that this SNP was associated with mean telomere length, or with risk of breast cancer, colorectal cancer, or melanoma.
Our results indicate that the observed associations between rs401681 and several cancer types might be weaker than previously described. The lack of an association in our study between this SNP and mean telomere length suggests that any association with cancer risk at this locus is not mediated through TERT.
[Show abstract][Hide abstract] ABSTRACT: Mammographic breast density (MBD) has a strong genetic component. Investigating the genetic models for mammographic density may provide further insights into the genetic factors affecting breast cancer risk.
To evaluate the familial aggregation of MBD and investigate the genetic models of susceptibility.
We used data on 746 women from 305 families participating in the Sisters in Breast Screening study. Retrieved mammograms were digitized, and percent mammographic density was determined using the Cumulus software. Linear regression analysis was done to identify the factors that are associated with mammographic density and a multivariate regression model was constructed. Familial correlations between relative pairs were calculated using the residuals from these models. Genetic models of susceptibility were investigated using segregation analysis.
After adjusting for covariates, the intraclass correlation coefficient among the residuals was 0.26 (95% confidence interval, 0.16-0.36) in sister-sister pairs and 0.67 (0.27-1.00) among the monozygotic twin pairs. The most parsimonious model was a Mendelian single major gene model in which an allele with population frequency 0.39 (95% confidence interval, 0.33-0.46) influenced mammographic density in an additive fashion. This model explained 66% of the residual variance.
These results confirm that MBD has a strong heritable basis, and suggest that major genes may explain some of the familial aggregation. These results may have implications for the search of genes that control mammographic density.