Dataset Information

Interactions between a Polygenic Risk Score and Non-genetic Risk Factors in Young-Onset Breast Cancer.

ABSTRACT: Most gene-environmental studies have focused on breast cancers generally, the preponderance of which occur after age 50. Young-onset breast cancers (YOBC) tend to be aggressive and may be etiologically different. The goal of this analysis was to assess interactions between an established 77-SNP polygenic risk score (PRS) and non-genetic risk factors for YOBC. We constructed the PRS using a family-based study of 1,291 women diagnosed with breast cancer before age 50 and their parents and unaffected sisters. We used conditional logistic regression to analyze interactions between the PRS and 14 established risk factors. In further analyses we assessed the same interactions, but for invasive cancer, estrogen receptor (ER) positive cancer and with broader inclusion of racial/ethnic groups. Results showed a decreased association between the PRS and YOBC risk for women who had ever used hormonal birth control (odds ratio [OR]?=?2.20 versus 3.89) and a stronger association between the PRS and YOBC risk in pre-menopausal women (OR?=?2.46 versus 1.23). Restricting the analysis to ER+ cancers or invasive cancers or using samples from all ethnic groups produced similar results. In conclusion, the PRS may interact with hormonal birth control use and with menopausal status on risk of YOBC.

SUBMITTER: Shi M

PROVIDER: S-EPMC7039983 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Interactions between a Polygenic Risk Score and Non-genetic Risk Factors in Young-Onset Breast Cancer.

Shi M M O'Brien K M KM Weinberg C R CR

Scientific reports 20200224 1

Most gene-environmental studies have focused on breast cancers generally, the preponderance of which occur after age 50. Young-onset breast cancers (YOBC) tend to be aggressive and may be etiologically different. The goal of this analysis was to assess interactions between an established 77-SNP polygenic risk score (PRS) and non-genetic risk factors for YOBC. We constructed the PRS using a family-based study of 1,291 women diagnosed with breast cancer before age 50 and their parents and unaffect ...[more]

PMID: 32094468

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Project description:Background:Polygenic risk scores (PRS) for breast cancer can be used to stratify the population into groups at substantially different levels of risk. Combining PRS and environmental risk factors will improve risk prediction; however, integrating PRS into risk prediction models requires evaluation of their joint association with known environmental risk factors. Methods:Analyses were based on data from 20 studies; datasets analysed ranged from 3453 to 23 104 invasive breast cancer cases and similar numbers of controls, depending on the analysed environmental risk factor. We evaluated joint associations of a 77-single nucleotide polymorphism (SNP) PRS with reproductive history, alcohol consumption, menopausal hormone therapy (MHT), height and body mass index (BMI). We tested the null hypothesis of multiplicative joint associations for PRS and each of the environmental factors, and performed global and tail-based goodness-of-fit tests in logistic regression models. The outcomes were breast cancer overall and by estrogen receptor (ER) status. Results:The strongest evidence for a non-multiplicative joint associations with the 77-SNP PRS was for alcohol consumption (P-interaction?=?0.009), adult height (P-interaction?=?0.025) and current use of combined MHT (P-interaction?=?0.038) in ER-positive disease. Risk associations for these factors by percentiles of PRS did not follow a clear dose-response. In addition, global and tail-based goodness of fit tests showed little evidence for departures from a multiplicative risk model, with alcohol consumption showing the strongest evidence for ER-positive disease (P?=?0.013 for global and 0.18 for tail-based tests). Conclusions:The combined effects of the 77-SNP PRS and environmental risk factors for breast cancer are generally well described by a multiplicative model. Larger studies are required to confirm possible departures from the multiplicative model for individual risk factors, and assess models specific for ER-negative disease.

Dataset Information

Interactions between a Polygenic Risk Score and Non-genetic Risk Factors in Young-Onset Breast Cancer.

Publications

Interactions between a Polygenic Risk Score and Non-genetic Risk Factors in Young-Onset Breast Cancer.

Similar Datasets

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