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      Epidemiologic and Genetic Associations of Endometriosis With Depression, Anxiety, and Eating Disorders

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          Key Points

          Question

          What biological mechanisms are associated with endometriosis with mental health comorbidities?

          Findings

          This genetic association study of 8276 women with endometriosis and 194 000 female controls found that pleiotropy is likely associated with the comorbid depression, anxiety, and eating disorders observed in patients with endometriosis.

          Meaning

          This study highlights the importance of considering endometriosis pathogenesis from a more comprehensive perspective, including both mental and physical health.

          Abstract

          This genetic association study uses phenotypic and genetic association analyses of unrelated women to test the comorbidity of endometriosis with depression, anxiety, and eating disorders.

          Abstract

          Importance

          Endometriosis is a common chronic gynecologic pathology with a large negative impact on women’s health. Beyond severe physical symptoms, endometriosis is also associated with several psychiatric comorbidities, including depression and anxiety.

          Objective

          To investigate whether pleiotropy contributes to the association of endometriosis with depression, anxiety, and eating disorders.

          Design, Setting, and Participants

          This genetic association study was performed between September 13, 2021, and June 24, 2022, in 202 276 unrelated female participants. Genotypic and phenotypic information from the UK Biobank was combined with genome-wide association statistics available from the Psychiatric Genomics Consortium (11 countries), the Million Veteran Program (US), the FinnGen study (Finland), and the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium (5 countries).

          Main Outcomes and Measures

          The main outcomes were the phenotypic and genetic associations of endometriosis with anxiety, depression, and eating disorders.

          Results

          A total of 8276 women with endometriosis (mean [SD] age, 53.1 [7.9] years) and 194 000 female controls (mean [SD] age, 56.7 [7.9] years) were included in the study. In a multivariate regression analysis accounting for age, body mass index, socioeconomic status, chronic pain–related phenotypes, irritable bowel syndrome, and psychiatric comorbidities, endometriosis was associated with increased odds of depression (odds ratio [OR], 3.61; 95% CI, 3.32-3.92), eating disorders (OR, 2.94; 95% CI, 1.96-4.41), and anxiety (OR, 2.61; 95% CI, 2.30-2.97). These associations were supported by consistent genetic correlations (rg) (depression rg, 0.36, P = 1.5 × 10 −9; anxiety rg, 0.33, P = 1.17 × 10 −5; and eating disorders rg, 0.61, P = .02). With the application of a 1-sample mendelian randomization, the genetic liabilities to depression and anxiety were associated with increased odds of endometriosis (depression: OR, 1.09; 95% CI, 1.08-1.11; anxiety: OR, 1.39; 95% CI, 1.13-1.65). A genome-wide analysis of pleiotropic associations shared between endometriosis and psychiatric disorders identified 1 locus, DGKB rs12666606, with evidence of pleiotropy between endometriosis and depression after multiple testing correction ( z = −9.46 for endometriosis, z = 8.10 for depression, P = 5.56 × 10 −8; false discovery rate q = 4.95 × 10 −4).

          Conclusions and Relevance

          These findings highlight that endometriosis is associated with women’s mental health through pleiotropic mechanisms. To our knowledge, this is the first large-scale study to provide genetic and phenotypic evidence of the processes underlying the psychiatric comorbidities of endometriosis.

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          Most cited references50

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          Second-generation PLINK: rising to the challenge of larger and richer datasets

          PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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            The UK Biobank resource with deep phenotyping and genomic data

            The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
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              LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

              Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                18 January 2023
                January 2023
                18 January 2023
                : 6
                : 1
                : e2251214
                Affiliations
                [1 ]Department of Psychiatry, Yale School of Medicine, West Haven, Connecticut
                [2 ]Veterans Affairs Connecticut Healthcare Center, West Haven
                [3 ]Department of Genetics, Microbiology, and Statistics, Faculty of Biology, University of Barcelona, Catalonia, Spain
                [4 ]Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, New Haven, Connecticut
                Author notes
                Article Information
                Accepted for Publication: November 20, 2022.
                Published: January 18, 2023. doi:10.1001/jamanetworkopen.2022.51214
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2023 Koller D et al. JAMA Network Open.
                Corresponding Author: Dora Koller, PhD, MSc ( dora.koller@ 123456yale.edu ) and Renato Polimanti, PhD, MSc ( renato.polimanti@ 123456yale.edu ), Veterans Affairs Healthcare Center, 950 Campbell Ave, Mailing Code 116A2, West Haven, CT 06516.
                Author Contributions: Drs Koller and Polimanti had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Koller, Wendt, Polimanti.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Koller, Pathak, Taylor, Polimanti.
                Critical revision of the manuscript for important intellectual content: Koller, Wendt, Tylee, Levey, Overstreet, Gelernter, Taylor, Polimanti.
                Statistical analysis: Koller, Pathak, Wendt, Levey.
                Obtained funding: Koller, Polimanti.
                Administrative, technical, or material support: Koller, Pathak, Wendt, Tylee, Levey, Gelernter.
                Supervision: Polimanti.
                Conflict of Interest Disclosures: Dr Gelernter reported receiving grants from the Department of Veterans Affairs during the conduct of the study and grants from the National Institutes of Health and the Department of Veterans Affairs Medical Research Program outside the submitted work; in addition, Dr Gelernter had a patent issued (Genotype-Guided Dosing of Opioid Agonists) and is paid for editorial work for the journal Complex Psychiatry. Dr Taylor reported receiving grants from AbbVie outside the submitted work. Dr Polimanti reported receiving personal fees from Karger Publishers and grants from Alkermes outside the submitted work. No other disclosures were reported.
                Funding/Support: This study was supported by grant 101028810 from the Horizon 2020 Marie Sklodowska-Curie Individual Fellowship from the European Commission (Dr Koller), grants R33 DA047527 and R21 DC018098 from the National Institutes of Health (Dr Polimanti), and One Mind (Dr Polimanti).
                Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Data Sharing Statement: See Supplement 2.
                Additional Contributions: We thank the participants and the investigators involved in the UK Biobank, Million Veteran Program, FinnGen, and the Psychiatric Genomics Consortium for making their data publicly available.
                Additional Information: This research was conducted using the UK Biobank Resource (application No. 58146).
                Article
                zoi221460
                10.1001/jamanetworkopen.2022.51214
                9856929
                36652249
                2de73e89-fdf9-495b-8285-0df26700db14
                Copyright 2023 Koller D et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 18 July 2022
                : 20 November 2022
                Categories
                Research
                Original Investigation
                Online Only
                Genetics and Genomics

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