PTGER4 expression-modulating polymorphisms in the 5p13.1 region predispose to Crohn's disease and affect NF-κB and XBP1 binding sites.

Genome-wide association studies identified a PTGER4 expression-modulating region on chromosome 5p13.1 as Crohn's disease (CD) susceptibility region. The study aim was to test this association in a large cohort of patients with inflammatory bowel disease (IBD) and to elucidate genotypic and phenotypic interactions with other IBD genes. A total of 7073 patients and controls were genotyped: 844 CD and 471 patients with ulcerative colitis and 1488 controls were analyzed for the single nucleotide polymorphisms (SNPs) rs4495224 and rs7720838 on chromosome 5p13.1. The study included two replication cohorts of North American (CD: n = 684; controls: n = 1440) and of German origin (CD: n = 1098; controls: n = 1048). Genotype-phenotype, epistasis and transcription factor binding analyses were performed. In the discovery cohort, an association of rs4495224 (p = 4.10×10⁻⁵; 0.76 [0.67-0.87]) and of rs7720838 (p = 6.91×10⁻⁴; 0.81 [0.71-0.91]) with susceptibility to CD was demonstrated. These associations were confirmed in both replication cohorts. In silico analysis predicted rs4495224 and rs7720838 as essential parts of binding sites for the transcription factors NF-κB and XBP1 with higher binding scores for carriers of the CD risk alleles, providing an explanation of how these SNPs might contribute to increased PTGER4 expression. There was no association of the PTGER4 SNPs with IBD phenotypes. Epistasis detected between 5p13.1 and ATG16L1 for CD susceptibility in the discovery cohort (p = 5.99×10⁻⁷ for rs7720838 and rs2241880) could not be replicated in both replication cohorts arguing against a major role of this gene-gene interaction in the susceptibility to CD. We confirmed 5p13.1 as a major CD susceptibility locus and demonstrate by in silico analysis rs4495224 and rs7720838 as part of binding sites for NF-κB and XBP1. Further functional studies are necessary to confirm the results of our in silico analysis and to analyze if changes in PTGER4 expression modulate CD susceptibility

Dissecting the Association Between Inflammation, Metabolic Dysregulation, and Specific Depressive Symptoms: A Genetic Correlation and 2-Sample Mendelian Randomization Study.

IMPORTANCE: Observational studies highlight associations of C-reactive protein (CRP), a general marker of inflammation, and interleukin 6 (IL-6), a cytokine-stimulating CRP production, with individual depressive symptoms. However, it is unclear whether inflammatory activity is associated with individual depressive symptoms and to what extent metabolic dysregulation underlies the reported associations. OBJECTIVE: To explore the genetic overlap and associations between inflammatory activity, metabolic dysregulation, and individual depressive symptoms. GWAS DATA SOURCES: Genome-wide association study (GWAS) summary data of European individuals, including the following: CRP levels (204 402 individuals); 9 individual depressive symptoms (3 of which did not differentiate between underlying diametrically opposite symptoms [eg, insomnia and hypersomnia]) as measured with the Patient Health Questionnaire 9 (up to 117 907 individuals); summary statistics for major depression, including and excluding UK Biobank participants, resulting in sample sizes of 500 199 and up to 230 214 individuals, respectively; insomnia (up to 386 533 individuals); body mass index (BMI) (up to 322 154 individuals); and height (up to 253 280 individuals). DESIGN: In this genetic correlation and 2-sample mendelian randomization (MR) study, linkage disequilibrium score (LDSC) regression was applied to infer single-nucleotide variant-based heritability and genetic correlation estimates. Two-sample MR tested potential causal associations of genetic variants associated with CRP levels, IL-6 signaling, and BMI with depressive symptoms. The study dates were November 2019 to April 2020. RESULTS: Based on large GWAS data sources, genetic correlation analyses revealed consistent false discovery rate (FDR)-controlled associations (genetic correlation range, 0.152-0.362; FDR P = .006 to P < .001) between CRP levels and depressive symptoms that were similar in size to genetic correlations of BMI with depressive symptoms. Two-sample MR analyses suggested that genetic upregulation of IL-6 signaling was associated with suicidality (estimate [SE], 0.035 [0.010]; FDR plus Bonferroni correction P = .01), a finding that remained stable across statistical models and sensitivity analyses using alternative instrument selection strategies. Mendelian randomization analyses did not consistently show associations of higher CRP levels or IL-6 signaling with other depressive symptoms, but higher BMI was associated with anhedonia, tiredness, changes in appetite, and feelings of inadequacy. CONCLUSIONS AND RELEVANCE: This study reports coheritability between CRP levels and individual depressive symptoms, which may result from the potentially causal association of metabolic dysregulation with anhedonia, tiredness, changes in appetite, and feelings of inadequacy. The study also found that IL-6 signaling is associated with suicidality. These findings may have clinical implications, highlighting the potential of anti-inflammatory approaches, especially IL-6 blockade, as a putative strategy for suicide prevention.Wellcome Trust (grant code: 201486/Z/16/Z

Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models

The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical baseline items. We analyzed data from an observational MDD cohort (Munich Antidepressant Response Signature [MARS] study, N = 1017), treated individually by psychopharmacological and psychotherapeutic means, and a multicenter, partially randomized clinical/pharmacogenomic study (Genome-based Therapeutic Drugs for Depression [GENDEP], N = 809). Symptoms were evaluated up to week 16 (or discharge) in MARS and week 12 in GENDEP. Clustering was performed on 809 MARS patients (discovery sample) using a mixed model with the integrated completed likelihood criterion for the assessment of cluster stability, and validated through a distinct MARS validation sample and GENDEP. A random forest algorithm was used to identify prediction patterns based on 50 clinical baseline items. From the clustering of the MARS discovery sample, seven TRCs emerged ranging from fast and complete response (average 4.9 weeks until discharge, 94% remitted patients) to slow and incomplete response (10% remitted patients at week 16). These proved stable representations of treatment response dynamics in both the MARS and the GENDEP validation sample. TRCs were strongly associated with established response markers, particularly the rate of remitted patients at discharge. TRCs were predictable from clinical items, particularly personality items, life events, episode duration, and specific psychopathological features. Prediction accuracy improved significantly when cluster-derived slopes were modelled instead of individual slopes. In conclusion, model-based clustering identified distinct and clinically meaningful treatment response classes in MDD that proved robust with regard to capturing response profiles of differently designed studies. Response classes were predictable from clinical baseline characteristics. Conceptually, model-based clustering is translatable to any outcome measure and could advance the large-scale integration of studies on treatment efficacy or the neurobiology of treatment response

The epigenetic clock and pubertal, neuroendocrine, psychiatric, and cognitive outcomes in adolescents

Abstract Background Molecular aging biomarkers, such as epigenetic age predictors, predict risk factors of premature aging, and morbidity/mortality more accurately than chronological age in middle-aged and elderly populations. Yet, it remains elusive if such biomarkers are associated with aging-related outcomes earlier in life when individuals begin to diverge in aging trajectories. We tested if the Horvath epigenetic age predictor is associated with pubertal, neuroendocrine, psychiatric, and cognitive aging-related outcomes in a sample of 239 adolescents, 11.0–13.2 years-old. Results Each year increase in epigenetic age acceleration (AA) was associated with 0.06 SD units higher weight-for-age, 0.08 SD units taller height-for-age, -0.09 SD units less missed from the expected adult height, 13 and 16% higher odds, respectively, for each stage increase in breast/genitals development on the Tanner Staging Questionnaire and pubertal stage on the Pubertal Development Scale, 4.2% higher salivary cortisol upon awakening, and 18 to 34% higher odds for internalizing and thought problems on the Child Behavior Checklist (p values <  0.045). AA was not significantly associated with cognition. Conclusions Our findings suggest that already in adolescence, AA is associated with physiological age acceleration, which may index risk of earlier aging. AA may identify individuals for preventive interventions decades before aging-related diseases become manifest

The Epigenetic Clock at Birth : Associations With Maternal Antenatal Depression and Child Psychiatric Problems

Objective: Maternal antenatal depression may compromise the fetal developmental milieu and contribute to individual differences in aging and disease trajectories in later life. We evaluated the association between maternal antenatal depression and a novel biomarker of aging at birth, namely epigenetic gestational age (GA) based on fetal cord blood methylation data. We also examined whether this biomarker prospectively predicts and mediates maternal effects on early childhood psychiatric problems. Method: A total of 694 mothers from the Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) Study provided information on history of depression diagnosed before pregnancy; 581 completed the Center for Epidemiological Studies Depression Scale throughout pregnancy, and 407 completed the Child Behavior Checklist at child's age 3.7 years (SD = 0.75 year). DNA methylation (DNAm) GA of fetal cord blood DNA was based on the methylation profile of 148 selected cytosine linked to guanine by phosphate (CpG) sites. Epigenetic GA was calculated as the arithmetic difference between DNAm GA and chronological GA and adjusted for chronological GA. Results: Maternal history of depression diagnosed before pregnancy (mean difference = -0.25 SD units, 95% CI = -0.46 to -0.03) and greater antenatal depressive symptoms (-0.08 SD unit per I-SD unit increase, 95% CI = -0.16 to -0.004) were associated with child's lower epigenetic GA. Child's lower epigenetic GA, in turn, prospectively predicted total and internalizing problems and partially mediated the effects of maternal antenatal depression on internalizing problems in boys. Conclusion: Maternal antenatal depression is associated with lower epigenetic GA in offspring. This lower epigenetic GA seems to be associated with a developmental disadvantage for boys, who, in early childhood, show greater psychiatric problems.Peer reviewe

Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation

We conducted a genome-wide association study (GWAS) on multiple sclerosis (MS) susceptibility in German cohorts with 4888 cases and 10,395 controls. In addition to associations within the major histocompatibility complex (MHC) region, 15 non-MHC loci reached genome-wide significance. Four of these loci are novel MS susceptibility loci. They map to the genes L3MBTL3, MAZ, ERG, and SHMT1. The lead variant at SHMT1 was replicated in an independent Sardinian cohort. Products of the genes L3MBTL3, MAZ, and ERG play important roles in immune cell regulation. SHMT1 encodes a serine hydroxymethyltransferase catalyzing the transfer of a carbon unit to the folate cycle. This reaction is required for regulation of methylation homeostasis, which is important for establishment and maintenance of epigenetic signatures. Our GWAS approach in a defined population with limited genetic substructure detected associations not found in larger, more heterogeneous cohorts, thus providing new clues regarding MS pathogenesis

MEIS1 and BTBD9: genetic association with restless leg syndrome in end stage renal disease

Background: Restless legs syndrome (RLS) is a sleep related movement disorder that occurs both in an idiopathic form and in symptomatic varieties. RLS is a frequent and distressing comorbidity in end stage renal disease (ESRD). For idiopathic RLS (iRLS), genetic risk factors have been identified, but their role in RLS in ESRD has not been investigated yet. Therefore, a caseecontrol association study of these variants in ESRD patients was performed. Methods: The study genotyped 10 iRLS associated variants at four loci encompassing the genes MEIS1, BTBD9, MAP2K5/SKOR1, and PTPRD, in two independent caseecontrol samples from Germany and Greece using multiplex PCR and MALDI-TOF (matrix assisted laser desorption/ionisation time-of-flight) mass spectrometry. Statistical analysis was performed as logistic regression with age and gender as covariates. For the combined analysis a CochraneManteleHaenszel test was applied. Results: The study included 200 RLS-positive and 443 RLS-negative ESRD patients in the German sample, and 141 and 393 patients, respectively, in the Greek sample. In the German sample, variants in MEIS1 and BTBD9 were associated with RLS in ESRD (Pnom≤0.004, ORs 1.52 and 1.55), whereas, in the Greek sample, there was a trend for association to MAP2K5/SKOR1 and BTBD9 (Pnom≤0.08, ORs 1.41 and 1.33). In the combined analysis including all samples, BTBD9 was associated after correction for multiple testing (Pcorrected=0.0013, OR 1.47). Conclusions: This is the first demonstration of a genetic influence on RLS in ESRD patients with BTBD9 being significantly associated. The extent of the genetic predisposition could vary between different subgroups of RLS in ESRD

An EPIC predictor of gestational age and its application to newborns conceived by assisted reproductive technologies

Background Gestational age is a useful proxy for assessing developmental maturity, but correct estimation of gestational age is difficult using clinical measures. DNA methylation at birth has proven to be an accurate predictor of gestational age. Previous predictors of epigenetic gestational age were based on DNA methylation data from the Illumina HumanMethylation 27 K or 450 K array, which have subsequently been replaced by the Illumina MethylationEPIC 850 K array (EPIC). Our aims here were to build an epigenetic gestational age clock specific for the EPIC array and to evaluate its precision and accuracy using the embryo transfer date of newborns from the largest EPIC-derived dataset to date on assisted reproductive technologies (ART). Methods We built an epigenetic gestational age clock using Lasso regression trained on 755 randomly selected non-ART newborns from the Norwegian Study of Assisted Reproductive Technologies (START)-a substudy of the Norwegian Mother, Father, and Child Cohort Study (MoBa). For the ART-conceived newborns, the START dataset had detailed information on the embryo transfer date and the specific ART procedure used for conception. The predicted gestational age was compared to clinically estimated gestational age in 200 non-ART and 838 ART newborns using MM-type robust regression. The performance of the clock was compared to previously published gestational age clocks in an independent replication sample of 148 newborns from the Prediction and Prevention of Preeclampsia and Intrauterine Growth Restrictions (PREDO) study-a prospective pregnancy cohort of Finnish women. Results Our new epigenetic gestational age clock showed higher precision and accuracy in predicting gestational age than previous gestational age clocks (R-2 = 0.724, median absolute deviation (MAD) = 3.14 days). Restricting the analysis to CpGs shared between 450 K and EPIC did not reduce the precision of the clock. Furthermore, validating the clock on ART newborns with known embryo transfer date confirmed that DNA methylation is an accurate predictor of gestational age (R-2 = 0.767, MAD = 3.7 days). Conclusions We present the first EPIC-based predictor of gestational age and demonstrate its robustness and precision in ART and non-ART newborns. As more datasets are being generated on the EPIC platform, this clock will be valuable in studies using gestational age to assess neonatal development.Peer reviewe