Genetic Insights Into Hypertension and Breast Cancer Risk in African Women: A Mendelian Randomization and Colocalization Analyses.

IF 1.2 4区 生物学 Q4 GENETICS & HEREDITY
Emmanuel Owusu Ansah, Emmanuel Boateng Agyenim, Andrews Danquah, Stephen Kumi Addo, Daniel Sakyi Agyirifo, Kwadwo Fosu, Foster Kyei
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引用次数: 0

Abstract

Background: Breast cancer (BC) is a major global health concern. Although observational studies have reported an association between hypertension and BC risk, the causal nature of this relationship remains unclear.

Methods: We performed a univariate two-sample Mendelian randomization (MR) analysis to investigate the causal effect of genetically predicted hypertension on overall BC risk and its molecular subtypes. Summary-level genetic data for hypertension were obtained from the Africa Wits-INDEPTH Partnership for Genomic Research (AWI-Gen) study conducted in sub-Saharan Africa (n = 10,775). Single-nucleotide polymorphisms (SNPs) for exposure and outcome were harmonized before analysis. Causal estimates were primarily derived using the inverse-variance weighted (IVW) method with random effects, complemented by MR-Egger, weighted median, weighted mode, simple mode, and MR-robust adjusted profile score (RAPS) approaches. Sensitivity analyses assessed heterogeneity and horizontal pleiotropy, and reverse MR analyses evaluated potential causal effects of overall BC on hypertension. Additionally, colocalization analyses were conducted to determine whether hypertension- and BC-associated variants share common causal signals across genomic loci.

Results: After Bonferroni correction for multiple testing (p < 0.003), no statistically significant associations were observed between genetically predicted hypertension and overall BC or its molecular subtypes: overall BC (odds ratio [OR] = 1.26, 95% confidence interval [CI]: 0.86-1.83, p = 0.23), estrogen receptor-positive BC (OR = 1.29, 95% CI: 0.81-2.06, p = 0.28), estrogen receptor-negative (ER-) BC (OR = 1.90, 95% CI: 1.06-3.41, p = 0.03), and triple-negative BC (OR = 1.98, 95% CI: 1.95-4.14, p = 0.07). The nominal association observed for ER- BC did not withstand correction for multiple testing. Colocalization analyses revealed generally low posterior probabilities for shared causal variants (PP.H4 < 0.5) across all BC subtypes. Findings were consistent across sensitivity analyses, and reverse MR provided no evidence of causality in the opposite direction.

Impact: This study represents the first MR and colocalization investigation of hypertension and BC risk in a sub-Saharan African population. Although no evidence of a direct genetic causal relationship was identified, the combined MR and colocalization findings suggest that previously reported associations may be driven by nongenetic metabolic or vascular mechanisms rather than shared inherited genetic determinants. Further studies in larger and more diverse populations are warranted to confirm these findings and explore underlying biological pathways.

非洲妇女高血压和乳腺癌风险的遗传洞察:孟德尔随机化和共定位分析。
背景:乳腺癌(BC)是一个主要的全球健康问题。尽管观察性研究报道了高血压与BC风险之间的关联,但这种关系的因果性质尚不清楚。方法:我们进行了单变量双样本孟德尔随机化(MR)分析,以研究遗传预测的高血压对总体BC风险及其分子亚型的因果关系。高血压的概要遗传数据来自在撒哈拉以南非洲进行的非洲智慧- indepth基因组研究伙伴关系(AWI-Gen)研究(n = 10,775)。分析前对暴露和结果的单核苷酸多态性(snp)进行了协调。因果估计主要采用随机效应的反方差加权(IVW)方法,辅以MR-Egger、加权中位数、加权模式、简单模式和mr稳健调整轮廓评分(RAPS)方法。敏感性分析评估了异质性和水平多效性,反向磁共振分析评估了总体BC对高血压的潜在因果影响。此外,还进行了共定位分析,以确定高血压和bc相关变异是否在基因组位点上共享共同的因果信号。结果:经Bonferroni校正多重检验(p -) BC (OR = 1.90, 95% CI: 1.06-3.41, p = 0.03)和三阴性BC (OR = 1.98, 95% CI: 1.95-4.14, p = 0.07)。在ER- BC中观察到的名义关联不能经受多重测试的修正。共定位分析显示,共同因果变异的后验概率普遍较低(PP.H4影响:本研究首次对撒哈拉以南非洲人群的高血压和BC风险进行MR和共定位调查。虽然没有直接遗传因果关系的证据被确定,但MR和共定位的综合研究结果表明,先前报道的关联可能是由非遗传代谢或血管机制驱动的,而不是共同的遗传遗传决定因素。有必要在更大、更多样化的人群中进行进一步的研究,以证实这些发现,并探索潜在的生物学途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Annals of Human Genetics
Annals of Human Genetics 生物-遗传学
CiteScore
4.20
自引率
0.00%
发文量
34
审稿时长
3 months
期刊介绍: Annals of Human Genetics publishes material directly concerned with human genetics or the application of scientific principles and techniques to any aspect of human inheritance. Papers that describe work on other species that may be relevant to human genetics will also be considered. Mathematical models should include examples of application to data where possible. Authors are welcome to submit Supporting Information, such as data sets or additional figures or tables, that will not be published in the print edition of the journal, but which will be viewable via the online edition and stored on the website.
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