Department of Breast, Fujian Maternity and Child Health Hospital, Fuzhou, China.
Department of Pathology, University of Otago, Dunedin, 9016, New Zealand.
Sci Rep. 2023 Oct 9;13(1):16979. doi: 10.1038/s41598-023-44397-9.
Exploring the potential association between peripheral blood leukocyte counts and breast cancer risk by Mendelian randomization (MR) analysis methods. Genetic data related to peripheral blood sorting counts of leukocytes were collected from a genome-wide association study by Blood Cell Consortium (BCX). Single nucleotide polymorphic loci predicting peripheral blood sorting counts of these leukocytes were selected as instrumental variables according to the correlation assumption, independence assumption and exclusivity assumption of MR. The data on breast cancer and its subtypes were obtained from Breast Cancer Association Consortium (BCAC) and FinnGen Consortium. In this study, the Inverse-Variance Weighted (IVW), Weighted Median, MR-Egger, Maximum Likelihood (ML), MR-PRESSO and Constrained Maximum Likelihood and Model Averaging (cML-MA) methods of random effects models were used for MR analysis. Cochran's Q analysis, and MR-Egger intercept analysis were applied for sensitivity analysis. IVW and cML-MA were considered the primary analytical tools, and the results of the other 4 MRs were used as complementary and validation. The results suggest that there is no significant causal relationship between leukocyte count and breast cancer risk (IVW OR = 0.98 [95% CI: 0.93-1.03], p-value = 0.35; CML-MA OR = 1.01 [95% CI: 0.98-1.05], p-value = 0.51). In addition, we analyzed whether there was a potential correlation between the five main types of categorized leukocyte counts and different breast cancer subtypes. We did not find significant evidence to support a significant correlation between leukocyte counts and breast cancer subtypes.
采用 Mendelian 随机化（MR）分析方法探究外周血白细胞计数与乳腺癌风险之间的潜在关联。从 Blood Cell Consortium（BCX）的全基因组关联研究中收集了与外周血白细胞分类计数相关的遗传数据。根据 MR 的相关性假设、独立性假设和排他性假设，选择预测这些白细胞外周血分类计数的单核苷酸多态性位点作为工具变量。乳腺癌及其亚型的数据来自 Breast Cancer Association Consortium（BCAC）和 FinnGen 联盟。在本研究中，采用随机效应模型的Inverse-Variance Weighted（IVW）、Weighted Median、MR-Egger、Maximum Likelihood（ML）、MR-PRESSO 和 Constrained Maximum Likelihood and Model Averaging（cML-MA）方法进行 MR 分析。应用 Cochran's Q 分析和 MR-Egger 截距分析进行敏感性分析。IVW 和 cML-MA 被认为是主要的分析工具，其他 4 种 MR 的结果被用作补充和验证。结果表明，白细胞计数与乳腺癌风险之间没有显著的因果关系（IVW OR=0.98[95% CI：0.93-1.03]，p 值=0.35；cML-MA OR=1.01[95% CI：0.98-1.05]，p 值=0.51）。此外，我们分析了五类分类白细胞计数与不同乳腺癌亚型之间是否存在潜在相关性。我们没有发现有意义的证据支持白细胞计数与乳腺癌亚型之间存在显著相关性。
