M18 - Breast cancer post GWAS

Investigator Names and Contact Information

Sara Lindstroem (saralind@uw.edu)

Formerly David Hunter

Introduction/Intent

This post-GWA project "Discovery, Biology and Risk of Inherited Variants in Breast Cancer" is designed to systematically discover and fine map new genetic loci that influence breast cancer risk, explore their biological impact, and to begin to translate these findings into clinically useful tools to assess the risk associated with inheritance of multiple alleles and their potential interaction with each other and with established non-genetic risk factors. To do this we have assembled a multi-disciplinary team that includes the largest pre-existing Consortia researching the genetics of breast cancer, and computational biologists and basic scientists with a strong track record in the investigation of mechanisms by which genetic loci may influence breast cancer risk.

Overall Goals of the RFA:

Goal 1. To pursue promising scientific leads from initial Genome Wide Association Studies (GWAS) of cancer (exploiting previously generated "initial scan" GWAS data).

Goal 2. To accelerate and coordinate integrative post-GWAS discovery research, which could provide the basis for expediting clinical translation and public health dissemination of the findings.

Modification 1: Use leftover DNA samples for Oncochip at CIDR

Specific Aims for Mod 1:

  1. Genotype approximately 600,000 variants in approximately 100,000 breast cancer cases and controls using a custom Illumina OncoChip. This OncoChip will include additional markers chosen because of the relevance to breast cancer, notably 38,000 markers chosen from the NHBGRI catalog of SNPs and for replication of cross-site meta-analysis. We will be able to complete this aim quickly (Years 1 and 2) because extracted DNA samples on these participants have already been assembled, and we have already established a pipeline for running Illumina Beadchips at CIDR and performing centralized genotype calling and quality control and assurance.

  2. Conduct GWAS of breast cancer incidence. We aim to identify individual variants that are associated with risk of cancer generally, and with risk of particular subtypes (estrogen-receptor negative and triple-negative breast cancer).

  3. Conduct GWAS of breast cancer survival. We aim to identify individual variants that are associated with cancer prognosis.

  4. Validate proposed breast cancer markers. We aim to replicate all markers that have been suggestively or definitively associated with breast cancer incidence from the NHGRI catalog and for cross-site meta-analysis.

  5. Fine-map known breast cancer loci. The OncoChip will contain many independent SNPs near all of the known breast cancer loci. Using these SNPs, we aim to carry out fine-mapping of known or suggestive breast cancer associated loci.

  6. Conduct exploratory gene-environment, and gene-gene interaction analyses. We aim to identify additional variants associated with breast cancer by incorporating effect modification by established risk factors, including age at diagnosis and hormone therapy. We also aim to characterize the joint effects of newly identified risk variants and known risk factors by testing for gene-gene and gene-environment interaction.​

Related Papers

Large-scale case-control analyses of rare variants in BRCA1 and BRCA2

Approved Manuscript, Michailidou, Kyriaki et al., 2024/6 MSID: 5132
Keywords: Brca1; Brca2; Vus; Rare Variant; Case-Control; Likelihood Ratio; Odds Ratio
Related Studies: M18

Gene-gene interactions in breast cancer

Approved Proposal, Lindstrom, Sara et al., 2013/2 MSID: 2046
Related Studies: M18

Risk prediction in breast cancer

Approved Proposal, Lindstrom, Sara et al., 2013/2 MSID: 2047
Related Studies: M18

Mendelian randomization analysis with survival outcomes

Youngjoo Cho et al., 2020/9 PubMed #32918779 MSID: 4000
Mendelian randomization (MR) is an established approach for assessing the causal effects of heritable exposures on outcomes. Outcomes of interest often include binary clinical endpoints, but may also include censored survival times. We explore the implications of both the Cox proportional hazard model and the additive hazard model in the context of MR, with a specific emphasis on two-stage methods. We show that naive application of standard MR approaches to censored survival times may induce sig...
Keywords: Breast Cancer; Survival; Mendelian Randomization; Genetics; Causal Inference
Related Studies: M18

Broad clinical manifestations of polygenic risk for coronary artery disease in the Women’s Health Initiative

Shoa Clarke et al., 2022/8 PubMed #36034645 MSID: 3914
Background: The genetic basis for coronary artery disease (CAD) risk is highly complex. Genome-wide polygenic risk scores (PRS) can help to quantify that risk, but the broader impacts of polygenic risk for CAD are not well characterized. Methods: We measured polygenic risk for CAD using the meta genomic risk score, a previously validated genome-wide PRS, in a subset of genotyped participants from the Women's Health Initiative and applied a phenome-wide association study framework to assess assoc...
Keywords: Polygenic Risk Score; Genetic Risk Score; Phenome-Wide Association Study; Coronary Artery Disease; Cardiovascular Disease; Myocardial Infarction; Coronary Revascularization
Related Studies: 224, 264, 349, 564, BA3, M5, M13, M18, W63, W66

Population risks of breast cancer associated with pathogenic variants in 28 cancer predisposition genes from 64,791 women in the U.S.-based CARRIERS study

Approved Manuscript, Couch, Fergus et al., 2020/5 MSID: 4153
Related Studies: M6, M18

Association analysis identifies 65 new breast cancer risk loci

Kyriaki Michailidou et al., 2017/10 PubMed #29059683 MSID: 3054
Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk ...
Related Studies: M18

A type 1 diabetes genetic risk score is not associated with type 2 diabetes in adults from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium

Approved Manuscript, Srinivasan, Shylaja et al., 2022/7 MSID: 3779
Keywords: Type 1 Diabetes; Genetics; Type 2 Diabetes; Insulin; Hla
Related Studies: 349, M18, W68

Interactions between breast cancer susceptibility loci and menopausal hormone therapy in relationship to breast cancer in the Breast and Prostate Cancer Cohort Consortium

Mia Gaudet et al., 2016/1 PubMed #26802016 MSID: 2725
Current use of menopausal hormone therapy (MHT) has important implications for postmenopausal breast cancer risk, and observed associations might be modified by known breast cancer susceptibility loci. To provide the most comprehensive assessment of interactions of prospectively collected data on MHT and 17 confirmed susceptibility loci with invasive breast cancer risk, a nested case-control design among eight cohorts within the NCI Breast and Prostate Cancer Cohort Consortium was used. Based on...
Related Studies: M18

Breast cancer risk from modifiable and nonmodifiable risk factors among white women in the United States

Paige Maas et al., 2016/10 PubMed #27228256 MSID: 2757
IMPORTANCE: An improved model for risk stratification can be useful for guiding public health strategies of breast cancer prevention. OBJECTIVE: To evaluate combined risk stratification utility of common low penetrant single nucleotide polymorphisms (SNPs) and epidemiologic risk factors. DESIGN, SETTING, AND PARTICIPANTS: Using a total of 17 171 cases and 19 862 controls sampled from the Breast and Prostate Cancer Cohort Consortium (BPC3) and 5879 women participating in the 2010 National Health ...
Related Studies: M18

Post-GWAS gene-environment interplay in breast cancer: results from the Breast and Prostate Cancer Cohort Consortium and a meta-analysis on 79,000 women

Myrto Barrdahl et al., 2014/5 PubMed #24895409 MSID: 2045
We studied the interplay between 39 breast cancer (BC) risk SNPs and established BC risk (body mass index, height, age at menarche, parity, age at menopause, smoking, alcohol and family history of BC) and prognostic factors (TNM stage, tumor grade, tumor size, age at diagnosis, estrogen receptor status and progesterone receptor status) as joint determinants of BC risk. We used a nested case-control design within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3),...
Related Studies: M18

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

Roger Milne et al., 2017/10 PubMed #29058716 MSID: 3055
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 p...
Related Studies: M18

A genome-wide gene-environment interaction study of breast cancer risk in European ancestry women

Approved Proposal, Middha-Kapoor, Pooja et al., MSID: 4701
Related Studies: M18