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Hilary Finucane, Brendan Bulik-Sullivan, Benjamin Neale, Alkes Price and colleagues introduce a new method, called stratified LD score regression, for partitioning heritability by functional category using genome-wide association study summary statistics. They observe a substantial enrichment of heritability in conserved regions and illustrate how this approach can provide insights into the biological basis of disease and direction for functional follow-up. Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here we analyze a broad set of functional elements, including cell type–specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits with an average sample size of 73,599. To enable this analysis, we introduce a new method, stratified LD score regression, for partitioning heritability from GWAS summary statistics while accounting for linked markers. This new method is computationally tractable at very large sample sizes and leverages genome-wide information. Our findings include a large enrichment of heritability in conserved regions across many traits, a very large immunological disease–specific enrichment of heritability in FANTOM5 enhancers and many cell type–specific enrichments, including significant enrichment of central nervous system cell types in the heritability of body mass index, age at menarche, educational attainment and smoking behavior.

A genome-wide association analysis using data from Chinese individuals combined with a transethnic meta-analysis of Psychiatry Genomics Consortium data identifies 30 new loci for schizophrenia. These analyses improve the fine-mapping of susceptibility loci and implicate multiple pathways in schizophrenia biology. We conducted a genome-wide association study (GWAS) with replication in 36,180 Chinese individuals and performed further transancestry meta-analyses with data from the Psychiatry Genomics Consortium (PGC2). Approximately 95% of the genome-wide significant (GWS) index alleles (or their proxies) from the PGC2 study were overrepresented in Chinese schizophrenia cases, including ∼50% that achieved nominal significance and ∼75% that continued to be GWS in the transancestry analysis. The Chinese-only analysis identified seven GWS loci; three of these also were GWS in the transancestry analyses, which identified 109 GWS loci, thus yielding a total of 113 GWS loci (30 novel) in at least one of these analyses. We observed improvements in the fine-mapping resolution at many susceptibility loci. Our results provide several lines of evidence supporting candidate genes at many loci and highlight some pathways for further research. Together, our findings provide novel insight into the genetic architecture and biological etiology of schizophrenia.

Naomi Wray, Peter Visscher and colleagues report analyses of the common variation that contributes to schizophrenia risk within three independent case-control datasets from the Psychiatric GWAS Consortium for schizophrenia. They estimate that 23% of the variation in liability to schizophrenia is captured by SNPs on current platforms. Schizophrenia is a complex disorder caused by both genetic and environmental factors. Using 9,087 affected individuals, 12,171 controls and 915,354 imputed SNPs from the Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium (PGC-SCZ), we estimate that 23% (s.e. = 1%) of variation in liability to schizophrenia is captured by SNPs. We show that a substantial proportion of this variation must be the result of common causal variants, that the variance explained by each chromosome is linearly related to its length ( r = 0.89, P = 2.6 × 10 −8 ), that the genetic basis of schizophrenia is the same in males and females, and that a disproportionate proportion of variation is attributable to a set of 2,725 genes expressed in the central nervous system (CNS; P = 7.6 × 10 −8 ). These results are consistent with a polygenic genetic architecture and imply more individual SNP associations will be detected for this disease as sample size increases.

Background Large-scale collaborative efforts in the field of psychiatric genetics have made substantial progress in unraveling the biological architecture of schizophrenia (SCZ). Although both genetic and environmental factors are known to play a role in schizophrenia etiology our mechanistic understanding of how they shape risk, resilience and disease trajectories remains limited. Methods Here, we present the study protocol of the Berlin Research Initiative for Diagnostics, Genetic and Environmental Factors of Schizophrenia (BRIDGE-S), which aims to collect a densely phenotyped genetic cohort of 1,000 schizophrenia cases and 1,000 controls. The study’s main objectives are to build a resource for i) promoting genetic discoveries and ii) genotype–phenotype associations to infer specific disease subtypes, and iii) exploring gene-environment interactions using polyrisk models. All subjects provide a biological sample for genotyping and complete a core questionnaire capturing a variety of environmental exposures, demographic, psychological and health data. Approximately 50% of individuals in the sample will further undergo a comprehensive clinical and neurocognitive assessment. Discussion With BRIDGE-S we created a valuable database to study genomic and environmental contributions to schizophrenia risk, onset, and outcomes. Results of the BRIDGE-S study could yield insights into the etiological mechanisms of schizophrenia that could ultimately inform risk prediction, and early intervention and treatment strategies.

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. Genetic prediction of complex traits so far has limited accuracy because of insufficient understanding of the genetic risk. Here, Maier et al. develop an improved method for trait prediction that makes use of genetic correlations between traits and apply it to summary statistics of psychiatric diseases.

Intrusive memories are a hallmark symptom of post-traumatic stress disorder (PTSD) and oxytocin has been implicated in the formation of intrusive memories. This study investigates how oxytocin influences the acquisition and consolidation of trauma-associated memories and whether these effects are influenced by individual neurobiological and genetic differences. In this randomized, double-blind, placebo-controlled study, 220 healthy women received either a single dose of intranasal 24IU oxytocin or a placebo before exposure to a trauma film paradigm that solicits intrusive memories. We used a “general random forest” machine learning approach to examine whether differences in the noradrenergic and hypothalamic-pituitary-adrenal axis activity, polygenic risk for psychiatric disorders, and genetic polymorphism of the oxytocin receptor influence the effect of oxytocin on the acquisition and consolidation of intrusive memories. Oxytocin induced significantly more intrusive memories than placebo did (t(188.33) = 2.12, p = 0.035, Cohen’s d = 0.30, 95% CI 0.16–0.44). As hypothesized, we found that the effect of oxytocin on intrusive memories was influenced by biological covariates, such as salivary cortisol, heart rate variability, and PTSD polygenic risk scores. The five factors that were most relevant to the oxytocin effect on intrusive memories were included in a Poisson regression, which showed that, besides oxytocin administration, higher polygenic loadings for PTSD and major depressive disorder were directly associated with a higher number of reported intrusions after exposure to the trauma film stressor. These results suggest that intranasal oxytocin amplifies the acquisition and consolidation of intrusive memories and that this effect is modulated by neurobiological and genetic factors. Trial registration: NCT03031405.

Soumya Raychaudhuri and Johanna Seddon and colleagues report the identification of a rare penetrant mutation in CFH that associates with increased risk of age-related macular degeneration. Two common variants in the gene encoding complement factor H ( CFH ), the Y402H substitution (rs1061170, c.1204C>T) 1 , 2 , 3 , 4 and the intronic rs1410996 SNP 5 , 6 , explain 17% of age-related macular degeneration (AMD) liability. However, proof for the involvement of CFH , as opposed to a neighboring transcript, and knowledge of the potential mechanism of susceptibility alleles are lacking. Assuming that rare functional variants might provide mechanistic insights, we used genotype data and high-throughput sequencing to discover a rare, high-risk CFH haplotype with a c.3628C>T mutation that resulted in an R1210C substitution. This allele has been implicated previously in atypical hemolytic uremic syndrome, and it abrogates C-terminal ligand binding 7 , 8 . Genotyping R1210C in 2,423 AMD cases and 1,122 controls demonstrated high penetrance (present in 40 cases versus 1 control, P = 7.0 × 10 −6 ) and an association with a 6-year-earlier onset of disease ( P = 2.3 × 10 −6 ). This result suggests that loss-of-function alleles at CFH are likely to drive AMD risk. This finding represents one of the first instances in which a common complex disease variant has led to the discovery of a rare penetrant mutation.

Several lines of evidence suggest that genome-wide association studies (GWAS) have the potential to explain more of the \"missing heritability\" of common complex phenotypes. However, reliable methods to identify a larger proportion of single nucleotide polymorphisms (SNPs) that impact disease risk are currently lacking. Here, we use a genetic pleiotropy-informed conditional false discovery rate (FDR) method on GWAS summary statistics data to identify new loci associated with schizophrenia (SCZ) and bipolar disorders (BD), two highly heritable disorders with significant missing heritability. Epidemiological and clinical evidence suggest similar disease characteristics and overlapping genes between SCZ and BD. Here, we computed conditional Q-Q curves of data from the Psychiatric Genome Consortium (SCZ; n = 9,379 cases and n = 7,736 controls; BD: n = 6,990 cases and n = 4,820 controls) to show enrichment of SNPs associated with SCZ as a function of association with BD and vice versa with a corresponding reduction in FDR. Applying the conditional FDR method, we identified 58 loci associated with SCZ and 35 loci associated with BD below the conditional FDR level of 0.05. Of these, 14 loci were associated with both SCZ and BD (conjunction FDR). Together, these findings show the feasibility of genetic pleiotropy-informed methods to improve gene discovery in SCZ and BD and indicate overlapping genetic mechanisms between these two disorders.

Longitudinal developmental fMRI studies just recently began to focus on within-subject reliability using the intraclass coefficient (ICC). It remains largely unclear which degree of reliability can be achieved in developmental studies and whether this depends on the type of task used. Therefore, we aimed to systematically investigate the reliability of three well-classified tasks: an emotional attention, a cognitive control, and an intertemporal choice paradigm. We hypothesized to find higher reliability in the cognitive task than in the emotional or reward-related task. 104 healthy mid-adolescents were scanned at age 14 and again at age 16 within M = 1.8 years using the same paradigms, scanner, and scanning protocols. Overall, we found both variability and stability (i.e. poor to excellent ICCs) depending largely on the region of interest (ROI) and task. Contrary to our hypothesis, whole brain reliability was fair for the cognitive control task but good for the emotional attention and intertemporal choice task. Subcortical ROIs (ventral striatum, amygdala) resulted in lower ICCs than visual ROIs. Current results add to the yet sparse overall ICC literature in both developing samples and adults. This study shows that analyses of stability, i.e. reliability, are helpful benchmarks for longitudinal studies and their implications for adolescent development.

Restless legs syndrome (RLS) is a frequent neurological disorder characterized by an imperative urge to move the legs during night, unpleasant sensation in the lower limbs, disturbed sleep and increased cardiovascular morbidity. In a genome-wide association study we found highly significant associations between RLS and intronic variants in the homeobox gene MEIS1 , the BTBD9 gene encoding a BTB(POZ) domain as well as variants in a third locus containing the genes encoding mitogen-activated protein kinase MAP2K5 and the transcription factor LBXCOR1 on chromosomes 2p, 6p and 15q, respectively. Two independent replications confirmed these association signals. Each genetic variant was associated with a more than 50% increase in risk for RLS, with the combined allelic variants conferring more than half of the risk. MEIS1 has been implicated in limb development, raising the possibility that RLS has components of a developmental disorder.