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The genetic correlation describes the genetic relationship between two traits and can contribute to a better understanding of the shared biological pathways and/or the causality relationships between them. The rarity of large family cohorts with recorded instances of two traits, particularly disease traits, has made it difficult to estimate genetic correlations using traditional epidemiological approaches. However, advances in genomic methodologies, such as genome-wide association studies, and widespread sharing of data now allow genetic correlations to be estimated for virtually any trait pair. Here, we review the definition, estimation, interpretation and uses of genetic correlations, with a focus on applications to human disease.In this Review, van Rheenen et al. outline how improved methodologies have enabled genetic correlations to be estimated for almost any trait pair. Genetic correlations can improve our understanding of the shared biology and causal relationships between traits.

The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R 2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks. Polygenic scores (PGS) have high potential for clinical use but are currently underpowered for many applications. Here, the authors develop an approach that leverages an agnostic library of hundreds of PGS to increase prediction of complex diseases and other traits. This multi-PGS framework is ideal for emerging biobank data.

Recent results indicate that genome-wide association studies (GWAS) have the potential to explain much of the heritability of common complex phenotypes, but methods are lacking to reliably identify the remaining associated single nucleotide polymorphisms (SNPs). We applied stratified False Discovery Rate (sFDR) methods to leverage genic enrichment in GWAS summary statistics data to uncover new loci likely to replicate in independent samples. Specifically, we use linkage disequilibrium-weighted annotations for each SNP in combination with nominal p-values to estimate the True Discovery Rate (TDR = 1-FDR) for strata determined by different genic categories. We show a consistent pattern of enrichment of polygenic effects in specific annotation categories across diverse phenotypes, with the greatest enrichment for SNPs tagging regulatory and coding genic elements, little enrichment in introns, and negative enrichment for intergenic SNPs. Stratified enrichment directly leads to increased TDR for a given p-value, mirrored by increased replication rates in independent samples. We show this in independent Crohn's disease GWAS, where we find a hundredfold variation in replication rate across genic categories. Applying a well-established sFDR methodology we demonstrate the utility of stratification for improving power of GWAS in complex phenotypes, with increased rejection rates from 20% in height to 300% in schizophrenia with traditional FDR and sFDR both fixed at 0.05. Our analyses demonstrate an inherent stratification among GWAS SNPs with important conceptual implications that can be leveraged by statistical methods to improve the discovery of loci.

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.

Proportional hazards models have been proposed to analyse time-to-event phenotypes in genome-wide association studies (GWAS). However, little is known about the ability of proportional hazards models to identify genetic associations under different generative models and when ascertainment is present. Here we propose the age-dependent liability threshold (ADuLT) model as an alternative to a Cox regression based GWAS, here represented by SPACox. We compare ADuLT, SPACox, and standard case-control GWAS in simulations under two generative models and with varying degrees of ascertainment as well as in the iPSYCH cohort. We find Cox regression GWAS to be underpowered when cases are strongly ascertained (cases are oversampled by a factor 5), regardless of the generative model used. ADuLT is robust to ascertainment in all simulated scenarios. Then, we analyse four psychiatric disorders in iPSYCH, ADHD, Autism, Depression, and Schizophrenia, with a strong case-ascertainment. Across these psychiatric disorders, ADuLT identifies 20 independent genome-wide significant associations, case-control GWAS finds 17, and SPACox finds 8, which is consistent with simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information will help increase power in analyses for common health outcomes. Robust genome-wide association study (GWAS) methods that can utilise time-to-event information such as age-of-onset will help increase power in analyses for common health outcomes. Here, the authors propose a computationally efficient time-to-event model for GWAS.

The impact of social restrictions during the COVID-19 pandemic on social isolation and loneliness has been widely debated, yet little attention has been given to identifying particularly vulnerable groups. In this study, we analysed data from 8,042 participants of the Danish Blood Donor Study (DBDS) through a prospective design with multiple follow-ups, integrating genetic, health, and socioeconomic information to identify distinct loneliness trajectories during the pandemic. Using the 3-item UCLA Loneliness Scale (UCLA-3), we found that self-reported loneliness increased in parallel with social restriction index, with women being particularly affected. We identified three distinct loneliness trajectories: high loneliness, pandemic loneliness, and low loneliness. Individuals in the high and pandemic loneliness trajectories both had higher polygenic scores (PGS) for loneliness and for the personality trait neuroticism compared to the low loneliness trajectory. The high loneliness trajectory was additionally associated with high PGS for psychiatric disorders and low PGS for the personality trait extraversion in addition to a higher proportion of pre-pandemic psychiatric disorder diagnoses. In contrast, the pandemic loneliness trajectory was linked to low PGS for the personality traits agreeableness and conscientiousness, as well as higher PGS for religious participation. These findings highlight the need for tailored interventions targeting individuals with poor mental well-being.

Schizophrenia is a heterogeneous disorder, exhibiting variability in presentation and outcomes that complicate treatment and recovery. To explore this heterogeneity, we leverage the comprehensive Danish health registries to conduct a prospective, longitudinal study from birth of 5432 individuals who would ultimately be diagnosed with schizophrenia, building individual trajectories that represent sequences of comorbid diagnoses, and describing patterns in the individual-level variability. We show that psychiatric comorbidity is prevalent among individuals with schizophrenia (82%) and multi-morbidity occur more frequently in specific, time-ordered pairs. Three latent factors capture 79% of variation in longitudinal comorbidity and broadly relate to the number of co-occurring diagnoses, the presence of child versus adult comorbidities and substance abuse. Clustering of the factor scores revealed five stable clusters of individuals, associated with specific risk factors and outcomes. The presentation and course of schizophrenia may be associated with heterogeneity in etiological factors including family history of mental disorders. Schizophrenia is a complex disorder where individuals experience different symptoms and outcomes that can be captured by patterns in other diagnoses. Here the authors use computational approaches to summarize these patterns and suggest they are associated with genetic and environmental exposure.

Attention deficit/hyperactivity disorder (ADHD) prevalence has increased in the last 10 years, most likely due to increased recognition by clinicians. Even so, an issue with under-diagnostics may persist. Historically ADHD has been described as a male-dominant disorder. However, recent evidence shows that ADHD prevalence is similar between the sexes, but that the related impairment or symptomatology might vary. This study estimated the prevalence of undiagnosed ADHD symptoms (pADHD) and explored the sex-stratified symptomatology and associations with self-perceived health-related quality of life (HRQL) and experience of depressive symptoms. This was done in a unique cohort of 50,937 healthy blood donors - individuals who successfully maintain regular commitments despite potential ADHD symptoms. ADHD symptoms were estimated using the Adult ADHD Self-Report Scale (ASRS), health-related quality of life (HRQL) measured using mental and physical component scores (MCS/PCS) estimated based on a 12-item Short-Form Health Survey (SF-12) with a higher score indicating better HRQL, and depressive symptoms were measured using Major Depression Inventory (MDI) with higher score indicating more depressive symptoms. In total, 3% were classified with pADHD (sex ratio 1:1). pADHD was associated with reduced MCS and PCS, and increased MDI score. Males scored on average higher on inattentive symptoms compared to females, whereas females scored on average higher on hyperactive-impulsive symptoms. Individuals scoring high on the combined inattentive and hyperactive-impulsive ADHD symptom presentation were most likely to be impaired in terms of higher MDI scores and lower PCS when compared to non-ADHD controls. In conclusion, ADHD symptoms are common in this seemingly healthy and undiagnosed population. Symptom presentations differ between sexes and the type of presentation seems to impact the association with depressive symptoms and level of reduced HRQL.

Spatial mapping is a promising strategy to investigate the mechanisms underlying the incidence of psychosis. We analyzed a case-cohort study ( n  = 24,028), drawn from the 1.47 million Danish persons born between 1981 and 2005, using a novel framework for decomposing the geospatial risk for schizophrenia based on locale of upbringing and polygenic scores. Upbringing in a high environmental risk locale increases the risk for schizophrenia by 122%. Individuals living in a high gene-by-environmental risk locale have a 78% increased risk compared to those who have the same genetic liability but live in a low-risk locale. Effects of specific locales vary substantially within the most densely populated city of Denmark, with hazard ratios ranging from 0.26 to 9.26 for environment and from 0.20 to 5.95 for gene-by-environment. These findings indicate the critical synergism of gene and environment on the etiology of schizophrenia and demonstrate the potential of incorporating geolocation in genetic studies. Schizophrenia (SCZ) risk is influenced by genetic and environmental factors. Here, the authors develop a statistical method for analyzing gene-by-environment effects in SCZ risk across Denmark with fine spatial resolution.

Circulating levels of the astrocytic marker S100B have been associated with risk of neuropsychiatric or neurological disorders. However, reported effects have been inconsistent, and no causal relations have yet been established. We applied two-sample Mendelian Randomization (MR) on the association statistics from genome-wide association studies (GWAS) for circulating S100B levels measured 5-7 days after birth (the iPSYCH sample) and in an older adult sample (mean age, 72.5 years; the Lothian sample), upon those derived from major depression disorder (MDD), schizophrenia (SCZ), bipolar disorder (BIP), autism spectral disorder (ASD), Alzheimer’s disease (AD), and Parkinson’s disease (PD). We studied the causal relations in the two S100B datasets for risk of these six neuropsychiatric disorders. MR suggested increased S100B levels 5-7 days after birth to causally increase the risk of MDD (OR = 1.014; 95%CI = 1.007–1.022; FDR-corrected p = 6.43×10−4). In older adults, MR suggested increased S100B levels to have a causal relation to the risk of BIP (OR = 1.075; 95%CI = 1.026–1.127; FDR-corrected p = 1.35×10−2). No significant causal relations were found for the other five disorders. We did not observe any evidence for reverse causality of these neuropsychiatric or neurological disorders on altered S100B levels. Sensitivity analyses using more stringent SNP-selection criteria and three alternative MR models suggested the results are robust. Altogether, our findings imply a small cause-effect relation for the previously reported associations of S100B and mood disorders. Such findings may provide a novel avenue for the diagnosis and management of disorders.