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      Epidemiologic and genetic associations of female reproductive disorders with depression or dysthymia: a Mendelian randomization study

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          Abstract

          Observational studies have previously reported an association between depression and certain female reproductive disorders. However, the causal relationships between depression and different types of female reproductive disorders remain unclear in terms of direction and magnitude. We conducted a comprehensive investigation using a two-sample bi-directional Mendelian randomization analysis, incorporating publicly available GWAS summary statistics. Our aim was to establish a causal relationship between genetically predicted depression and the risk of various female reproductive pathological conditions, such as ovarian dysfunction, polycystic ovary syndrome(PCOS), ovarian cysts, abnormal uterine and vaginal bleeding(AUB), endometriosis, leiomyoma of the uterus, female infertility, spontaneous abortion, eclampsia, pregnancy hypertension, gestational diabetes, excessive vomiting in pregnancy, cervical cancer, and uterine/endometrial cancer. We analyzed a substantial sample size, ranging from 111,831 to 210,870 individuals, and employed robust statistical methods, including inverse variance weighted, MR-Egger, weighted median, and MR-PRESSO, to estimate causal effects. Sensitivity analyses, such as Cochran's Q test, MR-Egger intercept test, MR-PRESSO, leave-one-out analysis, and funnel plots, were also conducted to ensure the validity of our results. Furthermore, risk factor analyses were performed to investigate potential mediators associated with these observed relationships. Our results demonstrated that genetic predisposition to depression or dysthymia was associated with an increased risk of developing PCOS (OR = 1.43, 95% CI 1.28–1.59; P = 6.66 × 10 –11), ovarian cysts (OR = 1.36, 95% CI 1.20–1.55; P = 1.57 × 10 –6), AUB (OR = 1.41, 95% CI 1.20–1.66; P = 3.01 × 10 –5), and endometriosis (OR = 1.43, 95% CI 1.27–1.70; P = 2.21 × 10 –7) after Bonferroni correction, but no evidence for reverse causality. Our study did not find any evidence supporting a causal or reverse causal relationship between depression/dysthymia and other types of female reproductive disorders. In summary, our study provides evidence for a causal relationship between genetically predicted depression and specific types of female reproductive disorders. Our findings emphasize the importance of depression management in the prevention and treatment of female reproductive disorders, notably including PCOS, ovarian cysts, AUB, and endometriosis.

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          Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

          Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
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            Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

            ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.
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              Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases

              Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the ‘no horizontal pleiotropy’ assumption can cause severe bias in MR. We developed the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that MR-PRESSO is best suited when horizontal pleiotropy occurs in <50% of instruments. Next, we applied MR-PRESSO, along with several other MR tests to complex traits and diseases, and found that horizontal pleiotropy: (i) was detectable in over 48% of significant causal relationships in MR; (ii) introduced distortions in the causal estimates in MR that ranged on average from −131% to 201%; (iii) induced false positive causal relationships in up to 10% of relationships; and (iv) can be corrected in some but not all instances.
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                Author and article information

                Contributors
                zhong_zhisheng@sina.com
                yuehuizheng@163.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                12 March 2024
                12 March 2024
                2024
                : 14
                : 5984
                Affiliations
                GRID grid.411866.c, ISNI 0000 0000 8848 7685, Reproductive Health Department, Shenzhen Traditional Chinese Medicine Hospital, , The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, ; Shenzhen, 518000 Guangdong China
                Article
                55993
                10.1038/s41598-024-55993-8
                10933377
                38472314
                3180ceb8-2682-464d-abac-84942951252c
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 September 2023
                : 29 February 2024
                Funding
                Funded by: The National Nature Science Foundation of China
                Award ID: No. 81671455
                Funded by: Basic Research Scheme of Shenzhen Science and Technology Innovation Commission
                Award ID: JCYJ20230807094815031
                Award ID: JCYJ20220531092208018
                Categories
                Article
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                © Springer Nature Limited 2024

                Uncategorized
                depression or dysthymia,female reproductive disorders,mendelian randomization,causality,gwas,risk factors,urogenital reproductive disorders

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