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Genomic analyses applied to 14 childhood- and adult-onset psychiatric disorders identifies five underlying genomic factors that explain the majority of the genetic variance of the individual disorders.

This Perspective argues that diagnoses derived from self-reports, electronic health records and self-administered questionnaires introduce heritable bias that confounds the interpretation of data from genome-wide association studies, resulting in false-positive and disease-irrelevant genetic signals.

Different functional variants in ADH1B (and elsewhere) in Europeans and Africans strongly affect risk for alcohol dependence. Dependence only partly genetically correlates with consumption, with strong correlations to other psychiatric disorders.

A genome-wide association study including over 76,000 individuals with schizophrenia and over 243,000 control individuals identifies common variant associations at 287 genomic loci, and further fine-mapping analyses highlight the importance of genes involved in synaptic processes.

Multi-ancestry genome-wide association meta-analysis of major depression identifies new risk loci, assesses the transferability of risk loci across ancestry groups, and improves fine-mapping resolution and prioritization of candidate effector genes.

Patrick Sullivan and colleagues report a multi-stage genome-wide association study for schizophrenia in a Swedish population. They identify 13 loci newly associated with schizophrenia.

Sexual dimorphism in genetic vulnerability to schizophrenia, systemic lupus erythematosus and Sjögren’s syndrome is linked to differential protein abundance from alleles of complement component 4.

A genome-wide association meta-analysis of individuals with clinically assessed or self-reported depression identifies 44 independent risk loci. Comparisons with other psychiatric disorders and candidate gene analyses provide new insights into major depressive disorder.

The CNV analysis group of the Psychiatric Genomic Consortium analyzes a large schizophrenia cohort to examine genomic copy number variants (CNVs) and disease risk. They find an enrichment of CNV burden in cases versus controls and identify 8 genome-wide significant loci as well as novel suggestive loci conferring either risk or protection to schizophrenia.

Genomic analysis of 5,303 Chinese women with recurrent major depressive disorder (MDD) enables the identification and replication of two genome-wide significant loci contributing to risk of MDD on chromosome 10: one near the SIRT1 gene; the other in an intron of the LHPP gene.

Phenotype imputation increases the effective sample size of major depressive disorder cases in UK Biobank, enhancing study power and polygenic risk score (PRS) accuracy. A new pleiotropy metric enables assessment of PRS specificity and comparison among different PRS models.

The largest genetic study of educational attainment (EA) so far combines gene mapping and family analyses to show that genetic associations with EA and its health benefits may be mostly indirect. As such, future genetic studies of human social and behavioral traits must include diversity in population, demographic and environmental contexts.

Defining the molecular nature of the genetic risk of psychiatric disorders has been notoriously difficult. Now, a flurry of genome-wide association studies for schizophrenia and bipolar disorder provides preliminary evidence for replicated signals.

Stratified medicine promises to tailor treatment for individual patients, however it remains a major challenge to leverage genetic risk data to aid patient stratification. Here the authors introduce an approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue-specific gene expression levels, and highlight its ability to identify biologically meaningful and clinically actionable patient subgroups, supporting the notion of different patient ‘biotypes’ characterized by partially distinct disease mechanisms.

In the third of three papers looking at the genetics of schizophrenia, a genome-wide association study using the Molecular Genetics of Schizophrenia case-control data set followed by a meta-analysis further implicates the major histocompatibility complex. The study also reveals that although common schizophrenia susceptibility alleles can be detected, there are probably few or no single common loci with large effects.

Genome-wide analyses yield insights into the polygenic effects contributing to clinical heterogeneity in attention deficit hyperactivity disorder, advancing understanding of its genetic etiology and serving as a model for future studies in other complex disorders.

Joint analysis of 11 major psychiatric disorders identifies four broad factor underlying genetic correlations among the disorders. Association analyses detect 152 loci acting on these factors and identify 9 loci that act heterogeneously across disorders.

Better analytical methods are needed to extract biological meaning from genome-wide association studies (GWAS) of psychiatric disorders. Here the authors take GWAS data from over 60,000 subjects, including patients with schizophrenia, bipolar disorder and major depression, and identify common etiological pathways shared amongst them.

This study uses gene expression predictors for the dorsolateral prefrontal cortex and other brain regions to perform a transcriptomic imputation analysis of schizophrenia, identifying 413 genic associations across 13 brain regions and 36 significantly enriched pathways.

Using a sample of more than 1 million individuals, Hatoum et al. identify genomic loci associated with general and substance-specific addiction risk.

Relatives of patients with amyotrophic lateral sclerosis have an unexpectedly high incidence of schizophrenia. Here, the authors show a genetic link between the two conditions, suggesting shared neurobiological mechanisms.

The authors defined a roadmap for investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders. Their proof-of-concept study using the largest available common variant data sets for schizophrenia and volumes of several (mainly subcortical) brain structures did not find evidence of genetic overlap.

Schizophrenia is a highly heritable genetic disorder, however, identification of specific genetic risk variants has proven difficult because of its complex polygenic nature—a large multi-stage genome-wide association study identifies 128 independent associations in over 100 loci (83 of which are new); key findings include identification of genes involved in glutamergic neurotransmission and support for a link between the immune system and schizophrenia.

AutoComplete is a deep learning-based method that imputes missing phenotypes in population-scale biobank datasets, increasing effective sample sizes and improving power for genetic discoveries in genome-wide association studies.

Meta-analysis of genome-wide association studies for cognitive ability identifies 190 new loci and implicates 939 new genes related to neurogenesis, neuron differentiation and synaptic structure.

Environmental influences during prenatal development may have implications for health and disease later in life. Here, Czamara et al. assess DNA methylation in cord blood from new-born under various models including environmental and genetic effects individually and their additive or interaction effects.

Depression is correlated with many brain-related traits. Here, Shen et al. perform phenome-wide association studies of a depression polygenic risk score (PRS) and find associations with 51 behavioural and 26 neuroimaging traits which are further followed up on using Mendelian randomization and mediation analyses.

Genetic analyses of depression based on minimal phenotyping identify nonspecific genetic risk factors shared between major depressive disorder (MDD) and other psychiatric conditions, suggesting that this approach may have limited ability to identify pathways specific to MDD.

A genome-wide-association meta-analysis of 18,381 austim spectrum disorder (ASD) cases and 27,969 controls identifies five risk loci. The authors find quantitative and qualitative polygenic heterogeneity across ASD subtypes.

Naomi Wray and colleagues report an analysis of genome-wide association data sets from the Psychiatric Genomics Consortium for five psychiatric disorders. They find that common variation explains 17–29% of the variance in liability and provide further support for a shared genetic etiology for these related psychiatric disorders.