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      Genetic evidence for causal association between migraine and dementia: a mendelian randomization study

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          Abstract

          Background

          There is an association between migraine and dementia, however, their causal relationship remains unclear. This study employed bidirectional two-sample Mendelian randomization (MR) to investigate the potential causal relationship between migraine and dementia and its subtypes: Alzheimer’s disease (AD), vascular dementia (VaD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB).

          Methods

          Summary-level statistics data were obtained from publicly available genome-wide association studies (GWAS) for both migraine and five types of dementia. Single nucleotide polymorphisms (SNPs) associated with migraine and each dementia subtype were selected. MR analysis was conducted using inverse variance weighting (IVW) and weighted median (WM) methods. Sensitivity analyses included Cochran’s Q test, MR pleiotropy residual sum and outlier (MR-PRESSO) analysis, the intercept of MR-Egger, and leave-one-out analysis.

          Results

          Migraine showed a significant causal relationship with AD and VaD, whereas no causal relationship was observed with all-cause dementia, FTD, or DLB. Migraine may be a potential risk factor for AD (odds ratio [OR]: 1.09; 95% confidence interval [CI]: 0.02–0.14; P = 0.007), while VaD may be a potential risk factor for migraine (OR: 1.04; 95% CI: 0.02–0.06; P = 7.760E-5). Sensitivity analyses demonstrated the robustness of our findings.

          Conclusion

          Our study suggest that migraine may have potential causal relationships with AD and VaD. Migraine may be a risk factor for AD, and VaD may be a risk factor for migraine. Our study contributes to unraveling the comprehensive genetic associations between migraine and various types of dementia, and our findings will enhance the academic understanding of the comorbidity between migraine and dementia.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12920-024-01956-x.

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          Most cited references33

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          A global reference for human genetic variation

          The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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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
                liuguiyou1981@163.com
                lululalavictor1985@126.com
                Journal
                BMC Med Genomics
                BMC Med Genomics
                BMC Medical Genomics
                BioMed Central (London )
                1755-8794
                5 July 2024
                5 July 2024
                2024
                : 17
                : 180
                Affiliations
                [1 ]GRID grid.24696.3f, ISNI 0000 0004 0369 153X, Beijing Key Laboratory of Acupuncture Neuromodulation, Department of Acupuncture and Moxibustion, Beijing Hospital of Traditional Chinese Medicine, , Capital Medical University, ; Beijing, 100010 China
                [2 ]Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Ministry of Science and Technology, Capital Medical University, Beijing, 100069 China
                [3 ]GRID grid.31880.32, ISNI 0000 0000 8780 1230, State Key Laboratory of Networking and Switching Technology, , Beijing University of Posts and Telecommunications, ; Beijing, 100876 China
                Article
                1956
                10.1186/s12920-024-01956-x
                11229492
                38970023
                770ccc2b-338c-426c-a3ae-74a7357bdbfa
                © 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 13 March 2024
                : 28 June 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 82374575
                Funded by: FundRef http://dx.doi.org/10.13039/501100004826, Natural Science Foundation of Beijing Municipality;
                Award ID: 7232270
                Funded by: FundRef http://dx.doi.org/10.13039/501100010270, Capital Health Research and Development of Special Fund;
                Award ID: Capital Development 2024-2-2235
                Funded by: FundRef http://dx.doi.org/10.13039/501100017665, Academic Fund for Outstanding Talents in Universities;
                Award ID: B2207
                Funded by: FundRef http://dx.doi.org/10.13039/501100002888, Beijing Municipal Commission of Education;
                Award ID: KM202110025005
                Funded by: FundRef http://dx.doi.org/10.13039/100010097, China Association for Science and Technology;
                Award ID: 2019-2021ZGZJXH-QNRC001
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Genetics
                migraine,dementia,alzheimer’s disease,vascular dementia,frontotemporal dementia,dementia with lewy bodies,mendelian randomization

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