Risk of psychiatric disorders among the surviving twins after a co-twin loss

Publisher's version (útgefin grein)Losing a co-twin by death is a severely stressful event yet with unknown impact on the surviving twin’s risk of psychiatric disorders. We identified all Swedish-born twins who lost a cotwin by death between 1973 and 2013 (n = 4,528), their 4939 non-twin full siblings, together with 22,640 age-and sex-matched non-bereaved twins. Compared to the non-bereaved twins, exposed twins were at increased risk of receiving a first diagnosis of psychiatric disorders (hazard ratio = 1.65, 95% confidence interval1.48–1.83), particularly during the first month after loss. Similarly, compared to non-twin full siblings, the relative risks were significantly increased after loss of monozygotic co-twin (2.45-fold), and loss of a dizygotic co-twin (1.29-fold), with higher HR observed with greater age gaps between twins and non-twin siblings. As dizygotic twins share equal genetic relatedness to the deceased twin as their full siblings, this pattern suggests that beyond the contribution of genetic factors, shared early life experiences and attachment contribute to the risk of psychiatric disorders among surviving twins after co-twin loss.The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council.Peer Reviewe

Selective serotonin re-uptake inhibitor use during pregnancy: association with offspring birth size and gestational age.

BACKGROUND: Depression around the time of pregnancy affects at least 1 in 8 women and treatment with selective serotonin re-uptake inhibitors (SSRIs) in pregnant women has been increasing, but research on adverse effects on the fetus have so far commonly used designs unable to account for confounding. We aimed to examine the effects of prenatal SSRI exposure on offspring size outcomes and gestational age, and disentangle whether associations observed were due to the medication or other factors. METHODS: We used a Swedish population-based cohort of 392,029 children and national registers to estimate the associations between prenatal exposure to SSRIs and depression on the outcomes birthweight, birth length, birth head circumference, gestational age at birth and preterm birth. A sub-sample of 1007 children was analysed in a within-family design that accounts for unmeasured parental genetic and environmental confounders. RESULTS: Crude analyses revealed associations between prenatal SSRI exposure, and offspring birth size and gestational age. However, in the within-family analyses, only the association between SSRI exposure and reduced gestational age (-2.3 days; 95% confidence interval -3.8 to -0.8) was observed. CONCLUSIONS: This study indicates that prenatal SSRI exposure may not be causally related to offspring birth size. Rather, our analyses suggest that the association could be caused by other underlying differences instead of the medication per se. A small reduction of gestational age was associated with SSRI exposure in the within-family analysis and could be due to either the exposure, or other factors changing between pregnancies.The Swedish Research CouncilSwedish Initiative for Research on Microdata in the Social and Medical Sciences (SIMSAM).The Swedish Medical Research CouncilThe Swedish foundation for Strategic ResearchThe Swedish Brain FoundationManuscrip

Heritability of perinatal depression and genetic overlap with nonperinatal depression.

OBJECTIVE: The authors investigated the relative importance of genetic and environmental influences on perinatal depression, and the genetic overlap between perinatal depression and nonperinatal depression. METHOD: Analyses were conducted using structural equation modeling for 1) the lifetime version of the Edinburgh Postnatal Depression Scale in 3,427 Swedish female twins and 2) clinical diagnoses of depression separated into perinatal depression and nonperinatal depression in a Swedish population-based cohort of 580,006 sisters. RESULTS: In the twin study, the heritability of perinatal depression was estimated at 54% (95% CI=35%-70%), with the remaining variance attributable to nonshared environment (46%; 95% CI=31%-65%). In the sibling design, the heritability of perinatal depression was estimated at 44% (95% CI=35%-52%) and the heritability of nonperinatal depression at 32% (95% CI=24%-41%). Bivariate analysis showed that 14% of the total variance (or 33% of the genetic variance) in perinatal depression was unique for perinatal depression. CONCLUSIONS: The heritability of perinatal depression was estimated at 54% and 44%, respectively, in separate samples, and the heritability of nonperinatal depression at 32%. One-third of the genetic contribution was unique to perinatal depression and not shared with nonperinatal depression, suggesting only partially overlapping genetic etiologies for perinatal depression and nonperinatal depression. The authors suggest that perinatal depression constitutes a subset of depression that could be prioritized for genomic discovery efforts. The study findings have direct translational impact that can assist clinicians in the counseling of their patients regarding risk and prognosis of perinatal depression.The Swedish Research CouncilThe Swedish foundation for Strategic ResearchThe Swedish Brain foundationThe National Institute of Mental HealthAccepte

MethylPCA: a toolkit to control for confounders in methylome-wide association studies

Abstract Background In methylome-wide association studies (MWAS) there are many possible differences between cases and controls (e.g. related to life style, diet, and medication use) that may affect the methylome and produce false positive findings. An effective approach to control for these confounders is to first capture the major sources of variation in the methylation data and then regress out these components in the association analyses. This approach is, however, computationally very challenging due to the extremely large number of methylation sites in the human genome. Result We introduce MethylPCA that is specifically designed to control for potential confounders in studies where the number of methylation sites is extremely large. MethylPCA offers a complete and flexible data analysis including 1) an adaptive method that performs data reduction prior to PCA by empirically combining methylation data of neighboring sites, 2) an efficient algorithm that performs a principal component analysis (PCA) on the ultra high-dimensional data matrix, and 3) association tests. To accomplish this MethylPCA allows for parallel execution of tasks, uses C++ for CPU and I/O intensive calculations, and stores intermediate results to avoid computing the same statistics multiple times or keeping results in memory. Through simulations and an analysis of a real whole methylome MBD-seq study of 1,500 subjects we show that MethylPCA effectively controls for potential confounders. Conclusions MethylPCA provides users a convenient tool to perform MWAS. The software effectively handles the challenge in memory and speed to perform tasks that would be impossible to accomplish using existing software when millions of sites are interrogated with the sample sizes required for MWAS

Exome Sequencing Reveals Common and Rare Variants in F5 Associated With ACE Inhibitor and Angiotensin Receptor Blocker–Induced Angioedema

Angioedema occurring in the head and neck region is a rare and sometimes life‐threatening adverse reaction to angiotensin‐converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs). Few studies have investigated the association of common variants with this extreme reaction, but none have explored the combined influence of rare variants yet. Adjudicated cases of ACEI‐induced angioedema (ACEI‐AE) or ARB‐induced angioedema (ARB‐AE) and controls were recruited at five different centers. Sequencing of 1,066 samples (408 ACEI‐AE, ARB‐AE, and 658 controls) was performed using exome‐enriched sequence data. A common variant of the F5 gene that causes an increase in blood clotting (rs6025, p.Arg506Gln, also called factor V Leiden), was significantly associated with both ACEI‐AE and ARB‐AE (odds ratio: 2.85, 95% confidence interval (CI), 1.89–4.25). A burden test analysis of five rare missense variants in F5 was also found to be associated with ACEI‐AE or ARB‐AE, P = 2.09 × 10−3. A combined gene risk score of these variants, and the common variants rs6025 and rs6020, showed that individuals carrying at least one variant had 2.21 (95% CI, 1.49–3.27, P = 6.30 × 10−9) times the odds of having ACEI‐AE or ARB‐AE. The increased risk due to the common Leiden allele was confirmed in a genome‐wide association study from the United States. A high risk of angioedema was also observed for the rs6020 variant that is the main coagulation defect‐causing variant in black African and Asian populations. We found that deleterious missense variants in F5 are associated with an increased risk of ACEI‐AE or ARB‐AE

MBD-seq as a cost-effective approach for methylome-wide association studies: demonstration in 1500 case–control samples

We studied the use of methyl-CpG binding domain (MBD) protein-enriched genome sequencing (MBD-seq) as a cost-effective screening tool for methylome-wide association studies (MWAS)

Phenotype Harmonization in the GLIDE2 Oral Health Genomics Consortium

Genetic risk factors play important roles in the etiology of oral, dental, and craniofacial diseases. Identifying the relevant risk loci and understanding their molecular biology could highlight new prevention and management avenues. Our current understanding of oral health genomics suggests that dental caries and periodontitis are polygenic diseases, and very large sample sizes and informative phenotypic measures are required to discover signals and adequately map associations across the human genome. In this article, we introduce the second wave of the Gene-Lifestyle Interactions and Dental Endpoints consortium (GLIDE2) and discuss relevant data analytics challenges, opportunities, and applications. In this phase, the consortium comprises a diverse, multiethnic sample of over 700,000 participants from 21 studies contributing clinical data on dental caries experience and periodontitis. We outline the methodological challenges of combining data from heterogeneous populations, as well as the data reduction problem in resolving detailed clinical examination records into tractable phenotypes, and describe a strategy that addresses this. Specifically, we propose a 3-tiered phenotyping approach aimed at leveraging both the large sample size in the consortium and the detailed clinical information available in some studies, wherein binary, severity-encompassing, and "precision," data-driven clinical traits are employed. As an illustration of the use of data-driven traits across multiple cohorts, we present an application of dental caries experience data harmonization in 8 participating studies (N = 55,143) using previously developed permanent dentition tooth surface-level dental caries pattern traits. We demonstrate that these clinical patterns are transferable across multiple cohorts, have similar relative contributions within each study, and thus are prime targets for genetic interrogation in the expanded and diverse multiethnic sample of GLIDE2. We anticipate that results from GLIDE2 will decisively advance the knowledge base of mechanisms at play in oral, dental, and craniofacial health and disease and further catalyze international collaboration and data and resource sharing in genomics research.Peer reviewe