Browsing by Author "Lindgren, Cecilia M"

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Imprinted genes and imprinting control regions show predominant intermediate methylation in adult somatic tissues
(Epigenomics, 2016-03) Pervjakova, Natalia; Kasela, Silva; Morris, Andrew P; Kals, Mart; Metspalu, Andres; Lindgren, Cecilia M; Salumets, Andres; Mägi, Reedik
Genomic imprinting is an epigenetic feature characterized by parent-specific monoallelic gene expression. The aim of this study was to compare the DNA methylation status of imprinted genes and imprinting control regions (ICRs), harboring differentially methylated regions (DMRs) in a comprehensive panel of 18 somatic tissues. The germline DMRs analyzed were divided into ubiquitously imprinted and placenta-specific DMRs, which show identical and different methylation imprints in adult somatic and placental tissues, respectively. We showed that imprinted genes and ICR DMRs maintain methylation patterns characterized by intermediate methylation levels in somatic tissues, which are pronounced in a specific region of the promoter area, located 200–1500 bp from the transcription start site. This intermediate methylation is concordant with gene expression from a single unmethylated allele and silencing of a reciprocal parental allele through DNA methylation. The only exceptions were seen for ICR DMRs of placenta-specific imprinted genes, which showed low levels of methylation, suggesting that these genes escape parent-specific epigenetic regulation in somatic tissues.
Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria
(2018) Day, Felix; Karaderi, Tugce; Jones, Michelle R; Meun, Cindy; He, Chunyan; Drong, Alex; Kraft, Peter; Lin, Nan; Huang, Hongyan; Broer, Linda; Magi, Reedik; Saxena, Richa; Laisk-Podar, Triin; Urbanek, Margrit; Welt, Corrine K; Lindgren, Cecilia M; Franks, Steve; Thorsteinsdottir, Unnur; Stefansson, Kari; Ong, Ken; Anttila, Verneri; Neale, Benjamin M; Jarvelin, Marjo-Riitta; Hayes, Geoffrey M; Thorleifsson, Gudmar; Fernandez-Tajes, Juan; Mahajan, Anubha; Mullin, Benjamin H; Stuckey, Bronwyn G.A; Spector, Timothy D.; Wilson, Scott G; Goodarzi, Mark O; Davis, Lea; Obermeyer-Pietsch, Barbara; Uitterlinden, Andre G; Fauser, Bart; Kowalska, Irina; Visser, Jenny A; Anderson, Marianne; McCarthy, Mark I; Morin-Papunen, Laure; Salumets, Andres; Stener-Victorin, Elisabet; Ehrmann, David; Legro, Richard S; Styrkarsdottir, Unnur; Perry, John; Dunaif, Andrea; Laven, Joop S.E.
Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. Affected women frequently have metabolic disturbances including insulin resistance and dysregulation of glucose homeostasis. PCOS is diagnosed with two different sets of diagnostic criteria, resulting in a phenotypic spectrum of PCOS cases. The genetic similarities between cases diagnosed based on the two criteria have been largely unknown. Previous studies in Chinese and European subjects have identified 16 loci associated with risk of PCOS. We report a fixed-effect, inverse-weighted-variance meta-analysis from 10,074 PCOS cases and 103,164 controls of European ancestry and characterisation of PCOS related traits. We identified 3 novel loci (near PLGRKT, ZBTB16 and MAPRE1), and provide replication of 11 previously reported loci. Only one locus differed significantly in its association by diagnostic criteria; otherwise the genetic architecture was similar between PCOS diagnosed by self-report and PCOS diagnosed by NIH or non-NIH Rotterdam criteria across common variants at 13 loci. Identified variants were associated with hyperandrogenism, gonadotropin regulation and testosterone levels in affected women. Linkage disequilibrium score regression analysis revealed genetic correlations with obesity, fasting insulin, type 2 diabetes, lipid levels and coronary artery disease, indicating shared genetic architecture between metabolic traits and PCOS. Mendelian randomization analyses suggested variants associated with body mass index, fasting insulin, menopause timing, depression and male-pattern balding play a causal role in PCOS. The data thus demonstrate 3 novel loci associated with PCOS and similar genetic architecture for all diagnostic criteria. The data also provide the first genetic evidence for a male phenotype for PCOS and a causal link to depression, a previously hypothesized comorbid disease. Thus, the genetics provide a comprehensive view of PCOS that encompasses multiple diagnostic criteria, gender, reproductive potential and mental health.
Large-scale meta-analysis highlights the hypothalamic–pituitary–gonadal axis in the genetic regulation of menstrual cycle length
(Human Molecular Genetics, 2018) Laisk, Triin; Kukuškina, Viktorija; Palmer, Duncan; Laber, Samantha; Chen, Chia-Yen; Ferreira, Teresa; Rahmioglu, Nilufer; Zondervan, Krina; Becker, Christian; Smoller, Jordan W; Lippincott, Margaret; Salumets, Andres; Granne, Ingrid; Seminara, Stephanie; Neale, Benjamin; Mägi, Reedik; Lindgren, Cecilia M
The normal menstrual cycle requires a delicate interplay between the hypothalamus, pituitary and ovary. Therefore, its length is an important indicator of female reproductive health. Menstrual cycle length has been shown to be partially controlled by genetic factors, especially in the follicle-stimulating hormone beta-subunit (FSHB) locus. A genome-wide association study meta-analysis of menstrual cycle length in 44 871 women of European ancestry confirmed the previously observed association with the FSHB locus and identified four additional novel signals in, or near, the GNRH1, PGR, NR5A2 and INS-IGF2 genes. These findings not only confirm the role of the hypothalamic–pituitary–gonadal axis in the genetic regulation of menstrual cycle length but also highlight potential novel local regulatory mechanisms, such as those mediated by IGF2.
The influence of menstrual cycle and endometriosis on endometrial methylome
(Clin Epigenetics, 2016-01) Saare, Merli; Modhukur, Vijayachitra; Suhorutshenko, Marina; Rajashekar, Balaji; Rekker, Kadri; Sõritsa, Deniss; Karro, Helle; Soplepmann, Pille; Sõritsa, Andrei; Lindgren, Cecilia M; Rahmioglu, Nilufer; Drong, Alexander; Becker, Christian M; Zondervan, Krina T; Salumets, Andres; Peters, Maire
BACKGROUND: Alterations in endometrial DNA methylation profile have been proposed as one potential mechanism initiating the development of endometriosis. However, the normal endometrial methylome is influenced by the cyclic hormonal changes, and the menstrual cycle phase-dependent epigenetic signature should be considered when studying endometrial disorders. So far, no studies have been performed to evaluate the menstrual cycle influences and endometriosis-specific endometrial methylation pattern at the same time. RESULTS: Infinium HumanMethylation 450K BeadChip arrays were used to explore DNA methylation profiles of endometrial tissues from various menstrual cycle phases from 31 patients with endometriosis and 24 healthy women. The DNA methylation profile of patients and controls was highly similar and only 28 differentially methylated regions (DMRs) between patients and controls were found. However, the overall magnitude of the methylation differences between patients and controls was rather small (Δβ ranging from -0.01 to -0.16 and from 0.01 to 0.08, respectively, for hypo- and hypermethylated CpGs). Unsupervised hierarchical clustering of the methylation data divided endometrial samples based on the menstrual cycle phase rather than diseased/non-diseased status. Further analysis revealed a number of menstrual cycle phase-specific epigenetic changes with largest changes occurring during the late-secretory and menstrual phases when substantial rearrangements of endometrial tissue take place. Comparison of cycle phase- and endometriosis-specific methylation profile changes revealed that 13 out of 28 endometriosis-specific DMRs were present in both datasets. CONCLUSIONS: The results of our study accentuate the importance of considering normal cyclic epigenetic changes in studies investigating endometrium-related disease-specific methylation patterns.