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The recent successful genome-wide association studies (GWASs) for depression have yielded more than 80 replicated loci and brought back the excitement that had evaporated during the years of negative GWAS findings. The identified loci provide anchors to explore their relevance for depression, but this comes with new challenges. Using the watershed model of genotype–phenotype relationships as a conceptual aid and recent genetic findings on other complex phenotypes, we discuss why it took so long and identify seven future challenges. The biggest challenge involves the identification of causal mechanisms since GWAS associations merely flag genomic regions without a direct link to underlying biological function. Furthermore, the genetic association with the index phenotype may also be part of a more extensive causal pathway (e.g., from variant to comorbid condition) or be due to indirect influences via intermediate traits located in the causal pathways to the final outcome. This challenge is highly relevant for depression because even its narrow definition of major depressive disorder captures a heterogeneous set of phenotypes which are often measured by even more broadly defined operational definitions consisting of a few questions (minimal phenotyping). Here, Mendelian randomization and future discovery of additional genetic variants for depression and related phenotypes will be of great help. In addition, reduction of phenotypic heterogeneity may also be worthwhile. Other challenges include detecting rare variants, determining the genetic architecture of depression, closing the “heritability gap”, and realizing the potential for personalized treatment. Along the way, we identify pertinent open questions that, when addressed, will advance the field.

Host genetics are known to influence the gut microbiome, yet their role remains poorly understood. To robustly characterize these effects, we performed a genome-wide association study of 207 taxa and 205 pathways representing microbial composition and function in 7,738 participants of the Dutch Microbiome Project. Two robust, study-wide significant ( P  < 1.89 × 10 −10 ) signals near the LCT and ABO genes were found to be associated with multiple microbial taxa and pathways and were replicated in two independent cohorts. The LCT locus associations seemed modulated by lactose intake, whereas those at ABO could be explained by participant secretor status determined by their FUT2 genotype. Twenty-two other loci showed suggestive evidence ( P  < 5 × 10 −8 ) of association with microbial taxa and pathways. At a more lenient threshold, the number of loci we identified strongly correlated with trait heritability, suggesting that much larger sample sizes are needed to elucidate the remaining effects of host genetics on the gut microbiome. A genome-wide association study of 207 taxa and 205 pathways representing gut microbial composition and function from 7,738 individuals of the Dutch Microbiome Project identifies genetic associations at the LCT and ABO loci.

Depression, anxiety, obesity and substance use are heritable and often co-occur. However, the mechanisms underlying this co-occurrence are not fully understood. We estimated their familial aggregation and co-aggregation as well as heritabilities and genetic correlations to improve etiological understanding. Data came from the multi-generational population-based Lifelines Cohort Study ( n  = 162,439). Current depression and anxiety were determined using the MINI International Neuropsychiatric Interview. Smoking, alcohol and drug use were assessed by self-report questionnaires. Body mass index (BMI) and obesity were calculated by measured height and weight. Modified Cox proportional hazards models estimated recurrence risk ratios (λ R ), and restricted maximum likelihood variance decomposition methods estimated heritabilities (h 2 ) and genetic correlations (r G ). All analyses were adjusted for age, age 2 , and sex. Depression, anxiety, obesity and substance use aggregated within families (λ R first-degree relative  = 1.08–2.74) as well as between spouses (λ R  = 1.11–6.60). All phenotypes were moderately heritable (from h 2 depression  = 0.25 to h 2 BMI  = 0.53). Depression, anxiety, obesity and smoking showed positive familial co-aggregation. That is, each of these traits confers increased risk on the other ones within families, consistent with the positive genetic correlations between these phenotypes (r G  = 0.16–0.94). The exception was obesity, which showed a negative co-aggregation with alcohol and drug use and vice versa, consistent with the negative genetic correlations of BMI with alcohol (r G  = −0.14) and soft drug use (r G  = −0.10). Patterns of cross-phenotype recurrence risk highlight the co-occurrence among depression, anxiety, obesity and substance use within families. Patterns of genetic overlap between these phenotypes provide clues to uncovering the mechanisms underlying familial co-aggregation.

Although a genetic basis of depression has been well established in twin studies, identification of genome-wide significant loci has been difficult. We hypothesized that bivariate analyses of findings from a meta-analysis of genome-wide association studies (meta-GWASs) of the broad depression phenotype with those from meta-GWASs of self-reported and recurrent major depressive disorder (MDD), bipolar disorder and schizophrenia would enhance statistical power to identify novel genetic loci for depression. LD score regression analyses were first used to estimate the genetic correlations of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia. Then, we performed four bivariate GWAS analyses. The genetic correlations (rg ± SE) of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia were 0.79 ± 0.07, 0.24 ± 0.08, 0.53 ± 0.09 and 0.57 ± 0.05, respectively. From a total of 20 independent genome-wide significant loci, 13 loci replicated of which 8 were novel for depression. These were MUC21 for the broad depression phenotype with self-reported MDD and ZNF804A, MIR3143, PSORS1C2, STK19, SPATA31D1, RTN1 and TCF4 for the broad depression phenotype with schizophrenia. Post-GWAS functional analyses of these loci revealed their potential biological involvement in psychiatric disorders. Our results emphasize the genetic similarities among different psychiatric disorders and indicate that cross-disorder analyses may be the best way forward to accelerate gene finding for depression, or psychiatric disorders in general.

In order to investigate the applicability of routine 10s electrocardiogram (ECG) recordings for time-domain heart rate variability (HRV) calculation we explored to what extent these (ultra-)short recordings capture the \"actual\" HRV. The standard deviation of normal-to-normal intervals (SDNN) and the root mean square of successive differences (RMSSD) were measured in 3,387 adults. SDNN and RMSSD were assessed from (ultra)short recordings of 10s(3x), 30s, and 120s and compared to 240s-300s (gold standard) measurements. Pearson's correlation coefficients (r), Bland-Altman 95% limits of agreement and Cohen's d statistics were used as agreement analysis techniques. Agreement between the separate 10s recordings and the 240s-300s recording was already substantial (r = 0.758-0.764/Bias = 0.398-0.416/d = 0.855-0.894 for SDNN; r = 0.853-0.862/Bias = 0.079-0.096/d = 0.150-0.171 for RMSSD), and improved further when three 10s periods were averaged (r = 0.863/Bias = 0.406/d = 0.874 for SDNN; r = 0.941/Bias = 0.088/d = 0.167 for RMSSD). Agreement increased with recording length and reached near perfect agreement at 120s (r = 0.956/Bias = 0.064/d = 0.137 for SDNN; r = 0.986/Bias = 0.014/d = 0.027 for RMSSD). For all recording lengths and agreement measures, RMSSD outperformed SDNN. Our results confirm that it is unnecessary to use recordings longer than 120s to obtain accurate measures of RMSSD and SDNN in the time domain. Even a single 10s (standard ECG) recording yields a valid RMSSD measurement, although an average over multiple 10s ECGs is preferable. For SDNN we would recommend either 30s or multiple 10s ECGs. Future research projects using time-domain HRV parameters, e.g. genetic epidemiological studies, could calculate HRV from (ultra-)short ECGs enabling such projects to be performed at a large scale.

The acute heart rate response to exercise, i.e., heart rate increase during and heart rate recovery after exercise, has often been associated with all-cause and cardiovascular mortality. The long-term response of heart rate to exercise results in favourable changes in chronotropic function, including decreased resting and submaximal heart rate as well as increased heart rate recovery. Both the acute and long-term heart rate response to exercise have been shown to be heritable. Advances in genetic analysis enable researchers to investigate this hereditary component to gain insights in possible molecular mechanisms underlying interindividual differences in the heart rate response to exercise. In this review, we comprehensively searched candidate gene, linkage, and genome-wide association studies that investigated the heart rate response to exercise. A total of ten genes were associated with the acute heart rate response to exercise in candidate gene studies. Only one gene ( CHRM2 ), related to heart rate recovery, was replicated in recent genome-wide association studies (GWASs). Additional 17 candidate causal genes were identified for heart rate increase and 26 for heart rate recovery in these GWASs. Nine of these genes were associated with both acute increase and recovery of the heart rate during exercise. These genes can be broadly categorized into four categories: (1) development of the nervous system ( CCDC141 , PAX2 , SOX5, and CAV2 ); (2) prolongation of neuronal life span ( SYT10 ); (3) cardiac development ( RNF220 and MCTP2 ); (4) cardiac rhythm ( SCN10A and RGS6 ). Additional 10 genes were linked to long-term modification of the heart rate response to exercise, nine with heart rate increase and one with heart rate recovery. Follow-up will be essential to get functional insights in how candidate causal genes affect the heart rate response to exercise. Future work will be required to translate these findings to preventive and therapeutic applications.

It is unclear whether and to what extent stress-related exposures moderate the effects of polygenic risk scores (PRSs) on depression and anxiety. We aimed to examine such moderation effects for a variety of stress-related exposures on depression and anxiety. We included 41,810 participants with both genome-wide genetic data and measurements of depression and anxiety in the Lifelines Cohort Study. Current depression and anxiety were measured by the MINI International Neuropsychiatric Interview. Stress-related exposures included long-term difficulties, stressful life events, reduced social support, childhood trauma, and loneliness, which were measured by self-report questionnaires. PRSs were calculated based on recent large genome-wide association studies for depression and anxiety. We used linear mixed models adjusting for family relationships to estimate the interactions between PRSs and stress-related exposures. Nine of the ten investigated interactions between the five stress-related exposures and the two PRSs for depression and anxiety were significant (Ps < 0.001). Reduced social support, and higher exposure to long-term difficulties, stressful life events, and loneliness amplified the genetic effects on both depression and anxiety. As for childhood trauma exposure, its interaction with the PRS was significant for depression (P = 1.78 × 10–05) but not for anxiety (P = 0.32). Higher levels of stress-related exposures significantly amplify the effects of genetic susceptibility on depression and anxiety. With a large sample size and a comprehensive set of stress-related exposures, our study provides powerful evidence on the presence of polygenic risk-by-environment interactions in relation to depression and anxiety.

Aims/hypothesisType 2 diabetes is a complex metabolic disease with increasing prevalence worldwide. Improving the prediction of incident type 2 diabetes using epigenetic markers could help tailor prevention efforts to those at the highest risk. The aim of this study was to identify predictive methylation markers for incident type 2 diabetes by combining epigenome-wide association study (EWAS) results from five prospective European cohorts.MethodsWe conducted a meta-analysis of EWASs in blood collected 7–10 years prior to type 2 diabetes diagnosis. DNA methylation was measured with Illumina Infinium Methylation arrays. A total of 1250 cases and 1950 controls from five longitudinal cohorts were included: Doetinchem, ESTHER, KORA1, KORA2 and EPIC-Norfolk. Associations between DNA methylation and incident type 2 diabetes were examined using robust linear regression with adjustment for potential confounders. Inverse-variance fixed-effects meta-analysis of cohort-level individual CpG EWAS estimates was performed using METAL. The methylGSA R package was used for gene set enrichment analysis. Confirmation of genome-wide significant CpG sites was performed in a cohort of Indian Asians (LOLIPOP, UK).ResultsThe meta-analysis identified 76 CpG sites that were differentially methylated in individuals with incident type 2 diabetes compared with control individuals (p values <1.1 × 10−7). Sixty-four out of 76 (84.2%) CpG sites were confirmed by directionally consistent effects and p values <0.05 in an independent cohort of Indian Asians. However, on adjustment for baseline BMI only four CpG sites remained genome-wide significant, and addition of the 76 CpG methylation risk score to a prediction model including established predictors of type 2 diabetes (age, sex, BMI and HbA1c) showed no improvement (AUC 0.757 vs 0.753). Gene set enrichment analysis of the full epigenome-wide results clearly showed enrichment of processes linked to insulin signalling, lipid homeostasis and inflammation.Conclusions/interpretationBy combining results from five European cohorts, and thus significantly increasing study sample size, we identified 76 CpG sites associated with incident type 2 diabetes. Replication of 64 CpGs in an independent cohort of Indian Asians suggests that the association between DNA methylation levels and incident type 2 diabetes is robust and independent of ethnicity. Our data also indicate that BMI partly explains the association between DNA methylation and incident type 2 diabetes. Further studies are required to elucidate the underlying biological mechanisms and to determine potential causal roles of the differentially methylated CpG sites in type 2 diabetes development.

Matthew Robinson, Peter Visscher and colleagues use phenotypic data on 172,000 sibling pairs and phenotypic and SNP data on 150,832 unrelated individuals to estimate the heritability of body mass index across a range of experimental designs. They conclude that substantially larger sample sizes across ages and lifestyle factors will be required to understand the full genetic architecture of this trait. Obesity is a worldwide epidemic, with major health and economic costs. Here we estimate heritability for body mass index (BMI) in 172,000 sibling pairs and 150,832 unrelated individuals and explore the contribution of genotype–covariate interaction effects at common SNP loci. We find evidence for genotype–age interaction (likelihood ratio test (LRT) = 73.58, degrees of freedom (df) = 1, P = 4.83 × 10 −18 ), which contributed 8.1% (1.4% s.e.) to BMI variation. Across eight self-reported lifestyle factors, including diet and exercise, we find genotype–environment interaction only for smoking behavior (LRT = 19.70, P = 5.03 × 10 −5 and LRT = 30.80, P = 1.42 × 10 −8 ), which contributed 4.0% (0.8% s.e.) to BMI variation. Bayesian association analysis suggests that BMI is highly polygenic, with 75% of the SNP heritability attributable to loci that each explain <0.01% of the phenotypic variance. Our findings imply that substantially larger sample sizes across ages and lifestyles are required to understand the full genetic architecture of BMI.

Increased arterial stiffness measured by pulse wave velocity (PWV) is an important parameter in the assessment of cardiovascular risk. Our previous longitudinal study has demonstrated that carotid-distal PWV showed reasonable stability throughout youth and young adulthood. This stability might be driven by genetic factors that are expressed consistently over time. We aimed to illustrate the relative contributions of genetic and environmental factors to the stability of carotid-distal PWV from youth to young adulthood. We also examined potential ethnic differences. For this purpose, carotid-distal PWV was measured twice in 497 European American (EA) and African American (AA) twins, with an average interval time of 3 years. Twin modelling on PWV showed that heritability decreased over time (62–35%), with the nonshared environmental influences becoming larger. There was no correlation between the nonshared environmental factors on PWV measured at visit 1 and visit 2, with the phenotypic tracking correlation (r = 0.32) completely explained by shared genetic factors over time. Novel genetic influences were identified accounting for a significant part of the variance (19%) at the second measurement occasion. There was no evidence for ethnic differences. In summary, novel genetic effects appear during development into young adulthood and account for a considerable part of the variation in PWV. Environmental influences become larger with age for PWV.