Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses.
Zhang H, Ahearn TU, Lecarpentier J et al.
Publication Details
Comprehensive information about this research publication
Authors
Abstract
Summary of the research findings
Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype1-3. To identify novel loci, we performed a genome-wide association study including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status and tumor grade. We identified 32 novel susceptibility loci (P < 5.0 × 10-8), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate < 0.05). Five loci showed associations (P < 0.05) in opposite directions between luminal and non-luminal subtypes. In silico analyses showed that these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 54.2% for luminal A-like disease and 37.6% for triple-negative disease. The odds ratios of polygenic risk scores, which included 330 variants, for the highest 1% of quantiles compared with middle quantiles were 5.63 and 3.02 for luminal A-like and triple-negative disease, respectively. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores.
133,384 European ancestry female cases, 113,789 European ancestry female controls
Study Statistics
Key metrics and study information
AI-Generated Summary
AI-generated by DNAGENICSIndependent AI summary of health and genetic findings from the published study
Important: This summary is AI-generated by DNAGENICS for informational purposes only. It was not created by, affiliated with, or endorsed by the researchers behind the original publication, and is based solely on that published research. It may contain errors or omissions. DNAGENICS disclaims all liability for any inaccuracies or consequences arising from use of this information. Verify all information against the original publication. This is not professional scientific review or medical advice.
AI Summary In Progress
Our AI-generated summary of this publication is being prepared. Please check back soon.
Summary
Key Findings
Health Insights
Disease Analysis
Genetic Trait Analysis
Clinical Relevance
Related Publications
Other publications that may be of interest
Genome-wide analysis of a model-derived binge eating disorder phenotype identifies risk loci and implicates iron metabolism.
Burstein D
Nat Genet
Binge-eating disorder (BMI-adjusted)
Multitrait analyses identify genetic variants associated with aortic valve function and aortic stenosis risk.
Kany S
Nat Genet
Aortic stenosis (MTAG)
Genome-wide association study meta-analysis provides insights into the etiology of heart failure and its subtypes.
Henry A
Nat Genet
Heart failure
Global multi-ancestry genome-wide analyses identify genes and biological pathways associated with thyroid cancer and benign thyroid diseases.
White SL
Nat Genet
Thyroid cancer
Multi-trait and multi-ancestry genetic analysis of comorbid lung diseases and traits improves genetic discovery and polygenic risk prediction.
He Y
Nat Genet
Forced expiratory volume in 1 second (FEV1)
Explore More Research
Discover the latest findings in health and genetic research