Embracing polygenicity: a review of methods and tools for psychiatric genetics research

Genomic structural equation modeling reveals latent phenotypes in the human cortex with distinct genetic architecture

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs, although sensitivity analyses indicated that other structures were plausible. The multivariate GWASs of the GIBNs identified 74 genome-wide significant (GWS) loci (p < 5 × 10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed between attention deficit hyperactivity disorder (ADHD) and major depressive disorder (MDD). CT GIBNs displayed a negative genetic correlation with alcohol dependence. Even though we observed model instability in our application of genomic SEM to high-dimensional data, jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across neuroimaging phenotypes offers new insights into the genetics of cortical structure and relationships to psychopathology.

Exploring the design of clinical research studies on the efficacy mechanisms in type 2 diabetes mellitus

This review examines the complexities of Type 2 Diabetes Mellitus (T2DM), focusing on the critical role of integrating omics technologies with traditional experimental methods. It underscores the advancements in understanding the genetic diversity of T2DM and emphasizes the evolution towards personalized treatment modalities. The paper analyzes a variety of omics approaches, including genomics, methylation, transcriptomics, proteomics, metabolomics, and intestinal microbiomics, delineating their substantial contributions to deciphering the multifaceted mechanisms underlying T2DM. Furthermore, the review highlights the indispensable role of non-omics experimental techniques in comprehending and managing T2DM, advocating for their integration in the development of tailored medicine and precision treatment strategies. By identifying existing research gaps and suggesting future research trajectories, the review underscores the necessity for a comprehensive, multidisciplinary approach. This approach synergistically combines clinical insights with cutting-edge biotechnologies, aiming to refine the management and therapeutic interventions of T2DM, and ultimately enhancing patient outcomes. This synthesis of knowledge and methodologies paves the way for innovative advancements in T2DM research, fostering a deeper understanding and more effective treatment of this complex condition.

Investigating Genetic Overlap between Alzheimer's Disease, Lipids, and Coronary Artery Disease: A Large-Scale Genome-Wide Cross Trait Analysis

There is evidence to support a link between abnormal lipid metabolism and Alzheimer's disease (AD) risk. Similarly, observational studies suggest a comorbid relationship between AD and coronary artery disease (CAD). However, the intricate biological mechanisms of AD are poorly understood, and its relationship with lipids and CAD traits remains unresolved. Conflicting evidence further underscores the ongoing investigation into this research area. Here, we systematically assess the cross-trait genetic overlap of AD with 13 representative lipids (from eight classes) and seven CAD traits, leveraging robust analytical methods, well-powered large-scale genetic data, and rigorous replication testing. Our main analysis demonstrates a significant positive global genetic correlation of AD with triglycerides and all seven CAD traits assessed-angina pectoris, cardiac dysrhythmias, coronary arteriosclerosis, ischemic heart disease, myocardial infarction, non-specific chest pain, and coronary artery disease. Gene-level analyses largely reinforce these findings and highlight the genetic overlap between AD and three additional lipids: high-density lipoproteins (HDLs), low-density lipoproteins (LDLs), and total cholesterol. Moreover, we identify genome-wide significant genes (Fisher's combined p value [FCPgene] < 2.60 × 10-6) shared across AD, several lipids, and CAD traits, including WDR12, BAG6, HLA-DRA, PHB, ZNF652, APOE, APOC4, PVRL2, and TOMM40. Mendelian randomisation analysis found no evidence of a significant causal relationship between AD, lipids, and CAD traits. However, local genetic correlation analysis identifies several local pleiotropic hotspots contributing to the relationship of AD with lipids and CAD traits across chromosomes 6, 8, 17, and 19. Completing a three-way analysis, we confirm a strong genetic correlation between lipids and CAD traits-HDL and sphingomyelin demonstrate negative correlations, while LDL, triglycerides, and total cholesterol show positive correlations. These findings support genetic overlap between AD, specific lipids, and CAD traits, implicating shared but non-causal genetic susceptibility. The identified shared genes and pleiotropic hotspots are valuable targets for further investigation into AD and, potentially, its comorbidity with CAD traits.

Causal relationship between dyslipidemia and risk of facial aging: Insights from Mendelian randomization in East Asian populations

BACKGROUND

Emerging observational studies showed an association between dyslipidemia and aging. However, it remains unclear whether this association is causal, particularly in the case of Asians, which are aging more rapidly than other continents. Given the visible manifestations of aging often include changes in facial appearance, the objective of this study is to assess the causal relationship between dyslipidemia and facial aging in East Asian populations.

METHODS

SNPs related to dyslipidemia in East Asian people such as Total cholesterol (TC), High-density-lipoprotein cholesterol (HDL), Low-density-lipoprotein cholesterol (LDL), and Triglyceride (TG) along with outcomes data on facial aging, were extracted from public genome-wide association studies (GWAS). A two-sample Mendelian randomization (MR) analysis was then performed using publicly available GWAS data to investigate the potential causal relationship. The effect estimates were primarily calculated using the fixed-effects inverse variance weighted (IVW) method.

RESULTS

Totally, 88 SNPs related to HDL among 70657 East Asian participants in GWAS. Based on the primary causal effects model using MR analyses with the IVW method, high HDL level was demonstrated as significantly related to the risk of facial aging (OR, 1.060; 95% CI, 1.005-1.119, p = 0.034), while high TC level (OR, 0.995; 95% CI, 0.920-1.076, p = 0.903), high LDL level (OR, 0.980, 95% CI, 0.924-1.041, p = 0.515), as well as high TG level (OR, 0.999, 95% CI, 0.932-1.071, p = 0.974), showed no significant correlation with facial aging.

CONCLUSIONS

The two-sample MR analysis conducted in this study revealed a positive causal relationship between high HDL levels and facial aging. In contrast, facial aging demonstrated no significant correlation with high levels of TC, LDL, or TG. Further large-sample prospective studies are needed to validate these findings and to provide appropriate recommendations regarding nutrition management to delay the aging process among old patients in East Asia.

Interactions Between Genetic, Prenatal Substance Use, Puberty, and Parenting are Less Important for Understanding Adolescents' Internalizing, Externalizing, and Substance Use than Developmental Cascades in Multifactorial Models

This study tested interactions among puberty-related genetic risk, prenatal substance use, harsh discipline, and pubertal timing for the severity and directionality (i.e., differentiation) of externalizing and internalizing problems and adolescent substance use. This is a companion paper to Marceau et al. (2021) which examined the same influences in developmental cascade models. Data were from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (n = 4504 White boys, n = 4287 White girls assessed from the prenatal period through 18.5 years). We hypothesized generally that later predictors would strengthen the influence of puberty-related genetic risk, prenatal substance use exposure, and pubertal risk on psychopathology and substance use (two-way interactions), and that later predictors would strengthen the interactions of earlier influences on psychopathology and substance use (three-way interactions). Interactions were sparse. Although all fourteen interactions showed that later influences can exacerbate or trigger the effects of earlier ones, they often were not in the expected direction. The most robust moderator was parental discipline, and differing and synergistic effects of biological and socially-relevant aspects of puberty were found. In all, the influences examined here operate more robustly in developmental cascades than in interaction with each other for the development of psychopathology and transitions to substance use.

Causal relationship between lung diseases and risk of esophageal cancer: insights from Mendelian randomization

BACKGROUND

An increasing number of cohort studies have indicated a correlation between lung diseases and esophageal cancer, but the exact causal relationship has not been definitively established. Therefore, the objective of this study is to assess the causal relationship between lung diseases and esophageal cancer.

METHODS

Single-nucleotide polymorphisms (SNPs) related to lung diseases such as asthma, chronic obstructive pulmonary disease (COPD), lung cancer, and idiopathic pulmonary fibrosis (IPF), along with outcomes data on esophageal cancer, were extracted from public genome-wide association studies (GWAS). A two-sample Mendelian randomization (MR) analysis was then performed using publicly available GWAS data to investigate the potential causal relationship. The effect estimates were primarily calculated using the fixed-effects inverse-variance-weighted method.

RESULTS

Totally, 81 SNPs related to asthma among 218,792 participants in GWAS. Based on the primary causal effects model using MR analyses with the inverse variance weighted (IVW) method, asthma was demonstrated a significantly related to the risk of esophageal cancer (OR 1.0006; 95% CI 1.0003-1.0010, p = 0.001), while COPD (OR 1.0306; 95% CI 0.9504-1.1176, p = 0.466), lung cancer (OR 1.0003, 95% CI 0.9998-1.0008, p = 0.305), as well as IPF (OR 0.9999, 95% CI 0.9998-1.0000, p = 0.147), showed no significant correlation with esophageal cancer.

CONCLUSIONS

The two-sample MR analysis conducted in this study revealed a positive causal relationship between asthma and esophageal cancer. In contrast, esophageal cancer demonstrated no significant correlation with COPD, lung cancer, or IPF. Further large-sample prospective studies are needed to validate these findings and to provide appropriate recommendations regarding esophageal cancer screening among patients with asthma.

Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for the 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs. The multivariate GWASs of these GIBNs identified 74 genome-wide significant (GWS) loci (p<5×10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed with attention deficit hyperactivity disorder (ADHD), major depressive disorder (MDD), and insomnia, indicating genetic predisposition to a larger SA in the specific GIBN is associated with lower genetic risk of these disorders. CT GIBNs displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping the cortex into genetically informed networks.

The collaborative study on the genetics of alcoholism: Genetics

This review describes the genetic approaches and results from the family-based Collaborative Study on the Genetics of Alcoholism (COGA). COGA was designed during the linkage era to identify genes affecting the risk for alcohol use disorder (AUD) and related problems, and was among the first AUD-focused studies to subsequently adopt a genome-wide association (GWAS) approach. COGA's family-based structure, multimodal assessment with gold-standard clinical and neurophysiological data, and the availability of prospective longitudinal phenotyping continues to provide insights into the etiology of AUD and related disorders. These include investigations of genetic risk and trajectories of substance use and use disorders, phenome-wide association studies of loci of interest, and investigations of pleiotropy, social genomics, genetic nurture, and within-family comparisons. COGA is one of the few AUD genetics projects that includes a substantial number of participants of African ancestry. The sharing of data and biospecimens has been a cornerstone of the COGA project, and COGA is a key contributor to large-scale GWAS consortia. COGA's wealth of publicly available genetic and extensive phenotyping data continues to provide a unique and adaptable resource for our understanding of the genetic etiology of AUD and related traits.

Brain structure, phenotypic and genetic correlates of reading performance

Reading is an evolutionarily recent development that recruits and tunes brain circuitry connecting primary- and language-processing regions. We investigated whether metrics of the brain's physical structure correlate with reading performance and whether genetic variants affect this relationship. To this aim, we used the Adolescent Brain Cognitive Development dataset (n = 9,013) of 9-10-year-olds and focused on 150 measures of cortical surface area (CSA) and thickness. Our results reveal that reading performance is associated with nine measures of brain structure including relevant regions of the reading network. Furthermore, we show that this relationship is partially mediated by genetic factors for two of these measures: the CSA of the entire left hemisphere and, specifically, of the left superior temporal gyrus CSA. These effects emphasize the complex and subtle interplay between genes, brain and reading, which is a partly heritable polygenic skill that relies on a distributed network.

Pervasive Downward Bias in Estimates of Liability-Scale Heritability in Genome-wide Association Study Meta-analysis: A Simple Solution

BACKGROUND

Single nucleotide polymorphism-based heritability is a fundamental quantity in the genetic analysis of complex traits. For case-control phenotypes, for which the continuous distribution of risk in the population is unobserved, observed-scale heritability estimates must be transformed to the more interpretable liability scale. This article describes how the field standard approach incorrectly performs the liability correction in that it does not appropriately account for variation in the proportion of cases across the cohorts comprising the meta-analysis. We propose a simple solution that incorporates cohort-specific ascertainment using the summation of effective sample sizes across cohorts. This solution is applied at the stage of single nucleotide polymorphism-based heritability estimation and does not require generating updated meta-analytic genome-wide association study summary statistics.

METHODS

We began by performing a series of simulations to examine the ability of the standard approach and our proposed approach to recapture liability-scale heritability in the population. We went on to examine the differences in estimates obtained from these 2 approaches for real data for 12 major case-control genome-wide association studies of psychiatric and neurologic traits.

RESULTS

We found that the field standard approach for performing the liability conversion can downwardly bias estimates by as much as approximately 50% in simulation and approximately 30% in real data.

CONCLUSIONS

Prior estimates of liability-scale heritability for genome-wide association study meta-analysis may be drastically underestimated. To this end, we strongly recommend using our proposed approach of using the sum of effective sample sizes across contributing cohorts to obtain unbiased estimates.

The intergenerational transmission of educational attainment: A closer look at the (interrelated) roles of paternal involvement and genetic inheritance

Numerous studies have documented a strong intergenerational transmission of educational attainment. In explaining this transmission, separate fields of research have studied separate mechanisms. To obtain a more complete understanding, the current study integrates insights from the fields of behavioural sciences and genetics and examines the extent to which paternal involvement and children's polygenic score (PGS) are unique underlying mechanisms, correlate with each other, and/or act as important confounders in the intergenerational transmission of fathers' educational attainment. To answer our research questions, we use rich data from The National Longitudinal Study of Adolescent to Adult Health (n = 4,579). Firstly, results from our mediation analyses showed a significant association between fathers' educational attainment and children's educational attainment (0.303). This association is for about 4 per cent accounted for by paternal involvement, whereas a much larger share, 21 per cent, is accounted for by children's education PGS. Secondly, our results showed that these genetic and behavioural factors are significantly correlated with each other (correlations between 0.06 and 0.09). Thirdly, we found support for genetic confounding, as adding children's education PGS to the model reduced the association between paternal involvement and children's educational attainment by 11 per cent. Fourthly, evidence for social confounding was almost negligible (the association between child's education PGS and educational attainment was only reduced by half of a per cent). Our findings highlight the importance of integrating insights and data from multiple disciplines in understanding the mechanisms underlying the intergenerational transmission of inequality, as our study reveals that behavioural and genetic influences overlap, correlate, and confound each other as mechanisms underlying this transmission.

Mapping, clustering, and analysis of research in psychiatric genomics

INTRODUCTION

The distribution pattern and knowledge structure of psychiatric genomics were surveyed based on literature dealing with both psychiatry and genomics/genetics. Coword analysis and bibliographic coupling of the records retrieved from Scopus and PubMed for 2016-2020 revealed the subsurface research aspects.

METHOD

The data were analyzed using coword analysis and clustering methods using Sci2 and VOSviewer.

RESULT

Analysis of ~3800 records showed that psychiatric genomics is, as expectedly, covered largely under biomedical subjects with a visible interest in other disciplines such as humanities and ethics. A coword analysis was done for all the years, followed by a year-wise analysis based on the keywords, and then a bibliographic coupling based on the cited references. This led to the generation of different clusters of prevalent research areas. The centrality values described the position of each component.

DISCUSSION

'Schizophrenia', 'depression', 'pharmacogenomics', and 'immunopathogenesis' were the research topics of overarching interest. 'Gut-brain axis' and 'gene-environment interaction' were the emerging topics, whereas certain topics such as 'child and adolescent psychiatry' remained priorities when compared to earlier studies. The keywords and research focus were diverse. They ranged from genetics to transcriptomics and epigenetics to proteomics of psychiatric disorders. We found a stagnation of science communication in the field with only 0.2% of the articles from the entire corpus relevant to it. The research categories identified in this study reflect the current publication and research trends in psychiatric genomics.

Novel functional genomics approaches bridging neuroscience and psychiatry

The possibility of establishing a metric of individual genetic risk for a particular disease or trait has sparked the interest of the clinical and research communities, with many groups developing and validating genomic profiling methodologies for their potential application in clinical care. Current approaches for calculating genetic risk to specific psychiatric conditions consist of aggregating genome-wide association studies-derived estimates into polygenic risk scores, which broadly represent the number of inherited risk alleles for an individual. While the traditional approach for polygenic risk score calculation aggregates estimates of gene-disease associations, novel alternative approaches have started to consider functional molecular phenotypes that are closer to genetic variation and are less penalized by the multiple testing required in genome-wide association studies. Moving the focus from genotype-disease to genotype-gene regulation frameworks, these novel approaches incorporate prior knowledge regarding biological processes involved in disease and aggregate estimates for the association of genotypes and phenotypes using multi-omics data modalities. In this review, we discuss and list different functional genomics tools that can be used and integrated to inform researchers and clinicians for a better understanding and diagnosis of psychopathology. We suggest that these novel approaches can help generate biologically driven hypotheses for polygenic signals that can ultimately serve the clinical community as potential biomarkers of psychiatric disease susceptibility.

MicroRNA and MicroRNA-Target Variants Associated with Autism Spectrum Disorder and Related Disorders

Autism spectrum disorder (ASD) is a childhood neurodevelopmental disorder with a complex and heterogeneous genetic etiology. MicroRNA (miRNA), a class of small non-coding RNAs, could regulate ASD risk genes post-transcriptionally and affect broad molecular pathways related to ASD and associated disorders. Using whole-genome sequencing, we analyzed 272 samples in 73 families in the New Jersey Language and Autism Genetics Study (NJLAGS) cohort. Families with at least one ASD patient were recruited and were further assessed for language impairment, reading impairment, and other associated phenotypes. A total of 5104 miRNA variants and 1,181,148 3' untranslated region (3' UTR) variants were identified in the dataset. After applying several filtering criteria, including population allele frequency, brain expression, miRNA functional regions, and inheritance patterns, we identified high-confidence variants in five brain-expressed miRNAs (targeting 326 genes) and 3' UTR miRNA target regions of 152 genes. Some genes, such as SCP2 and UCGC, were identified in multiple families. Using Gene Ontology overrepresentation analysis and protein-protein interaction network analysis, we identified clusters of genes and pathways that are important for neurodevelopment. The miRNAs and miRNA target genes identified in this study are potentially involved in neurodevelopmental disorders and should be considered for further functional studies.

Overview of CAPICE-Childhood and Adolescence Psychopathology: unravelling the complex etiology by a large Interdisciplinary Collaboration in Europe-an EU Marie Skłodowska-Curie International Training Network

The Roadmap for Mental Health and Wellbeing Research in Europe (ROAMER) identified child and adolescent mental illness as a priority area for research. CAPICE (Childhood and Adolescence Psychopathology: unravelling the complex etiology by a large Interdisciplinary Collaboration in Europe) is a European Union (EU) funded training network aimed at investigating the causes of individual differences in common childhood and adolescent psychopathology, especially depression, anxiety, and attention deficit hyperactivity disorder. CAPICE brings together eight birth and childhood cohorts as well as other cohorts from the EArly Genetics and Life course Epidemiology (EAGLE) consortium, including twin cohorts, with unique longitudinal data on environmental exposures and mental health problems, and genetic data on participants. Here we describe the objectives, summarize the methodological approaches and initial results, and present the dissemination strategy of the CAPICE network. Besides identifying genetic and epigenetic variants associated with these phenotypes, analyses have been performed to shed light on the role of genetic factors and the interplay with the environment in influencing the persistence of symptoms across the lifespan. Data harmonization and building an advanced data catalogue are also part of the work plan. Findings will be disseminated to non-academic parties, in close collaboration with the Global Alliance of Mental Illness Advocacy Networks-Europe (GAMIAN-Europe).

Multi-type childhood maltreatment exposure and substance use development from adolescence to early adulthood: A GxE study

BACKGROUND

Childhood maltreatment types can co-occur and are associated with increased substance use during adolescence and early adulthood. There is also a strong genetic basis for substance use which interacts with environmental factors (e.g., childhood maltreatment) to influence substance use phenotype.

OBJECTIVE

This research aimed to identify childhood maltreatment sub-groups based on type and chronicity, and their association with substance use change from adolescence to early adulthood, while accounting for the influence of substance use polygenic risk (i.e., genetic risk based on the combined effects of multiple genes).

PARTICIPANTS

We used a sample of unrelated European-origin Americans with genetic and childhood maltreatment data (n = 2,664) from the National Longitudinal Study of Adolescent to Adult Health.

METHODS

Latent profile analysis was used for sub-group identification and direct and interaction effects were tested for longitudinal trajectories of substance use utilizing generalized estimating equations.

RESULTS

Three sub-groups with co-occurring childhood maltreatment exposures were identified: a high sexual abuse sub-group, a high physical abuse sub-group, and a normative sub-group (with low maltreatment exposure). At high polygenic risk, the high physical abuse sub-group had faster increases in substance use over time. In comparison, the high sexual abuse sub-group had faster progression in substance use only at low and medium polygenic risk.

CONCLUSIONS

Findings provide initial evidence for biological and environmental differences among maltreatment sub-groups on trajectories of substance use.

Predicting disordered gambling across adolescence and young adulthood from polygenic contributions to Big 5 personality traits in a UK birth cohort

BACKGROUND AND AIMS

Previous research has demonstrated phenotypical associations between disordered gambling (DG) and Big 5 personality traits, and a twin study suggested that shared genetic influences accounted for a substantial portion of this relation. The present study examined associations between DG and polygenic scores (PSs) for Big 5 traits to measure the shared genetic underpinnings of Big 5 personality traits and DG.

DESIGN

Zero-inflated negative binomial regression models estimated associations between Big 5 PSs and past-year and life-time assessments of DG in a longitudinally assessed population-based birth cohort.

SETTING

United Kingdom.

PARTICIPANTS

A total of 4729 unrelated children of European ancestry from the Avon Longitudinal Study of Parents and Children (ALSPAC) with both phenotypical and genetic data.

MEASUREMENTS

Phenotypical outcomes included past-year assessment of DG using the problem gambling severity index (PGSI) and life-time assessment of DSM-IV pathological gambling symptoms (DPG) across the ages of 17, 20 and 24 years. Polygenic scores were derived for the Big 5 personality traits of agreeableness, extraversion, conscientiousness, openness and neuroticism using summary statistics from genome-wide association studies (GWAS).

FINDINGS

PSs for agreeableness [β= - 0.25, standard error (SE) = 0.054, P = 3.031e-6, ΔR2  = 0.008] and neuroticism (β=0.14, SE = 0.046, P = 0.0017, ΔR2  = 0.002) significantly predicted PGSI scores over and above included covariates (i.e. sex and first five ancestral principal components). PSs for agreeableness (β= - 0.20, SE = 0.056, P = 0.00036, ΔR2  = 0.003) and neuroticism, when interactions with age were taken into account (β = 0.29, SE = 0.090, P = 0.002, ΔR2  = 0.004), also predicted DPG scores.

CONCLUSIONS

Polygenic contributions to low agreeableness and high neuroticism appear to predict two measures of disordered gambling (problem gambling severity index and life-time assessment of DSM-IV pathological gambling symptoms). Polygenic scores for neuroticism interact with age to suggest that the positive association becomes stronger from adolescence through young adulthood.

The "Golden Age" of Behavior Genetics?

The search for genetic risk factors underlying the presumed heritability of all human behavior has unfolded in two phases. The first phase, characterized by candidate-gene-association (CGA) studies, has fallen out of favor in the behavior-genetics community, so much so that it has been referred to as a "cautionary tale." The second and current iteration is characterized by genome-wide association studies (GWASs), single-nucleotide polymorphism (SNP) heritability estimates, and polygenic risk scores. This research is guided by the resurrection of, or reemphasis on, Fisher's "infinite infinitesimal allele" model of the heritability of complex phenotypes, first proposed over 100 years ago. Despite seemingly significant differences between the two iterations, they are united in viewing the discovery of risk alleles underlying heritability as a matter of finding differences in allele frequencies. Many of the infirmities that beset CGA studies persist in the era of GWASs, accompanied by a host of new difficulties due to the human genome's underlying complexities and the limitations of Fisher's model in the postgenomics era.

Polygenic association with severity and long-term outcome in eating disorder cases

About 20% of individuals with anorexia nervosa (AN) remain chronically ill. Therefore, early identification of poor outcome could improve care. Genetic research has identified regions of the genome associated with AN. Patients with anorexia nervosa were identified via the Swedish eating disorder quality registers Stepwise and Riksät and invited to participate in the Anorexia Nervosa Genetics Initiative. First, we associated genetic information longitudinally with eating disorder severity indexed by scores on the Clinical Impairment Assessment (CIA) in 2843 patients with lifetime AN with or without diagnostic migration to other forms of eating disorders followed for up to 16 years (mean = 5.3 years). Second, we indexed the development of a severe and enduring eating disorder (SEED) by a high CIA score plus a follow-up time ≥5 years. We associated individual polygenic scores (PGSs) indexing polygenic liability for AN, schizophrenia, and body mass index (BMI) with severity and SEED. After multiple testing correction, only the BMI PGS when calculated with traditional clumping and p value thresholding was robustly associated with disorder severity (β_PGS = 1.30; 95% CI: 0.72, 1.88; p = 1.2 × 10^-5) across all p value thresholds at which we generated the PGS. However, using the alternative PGS calculation method PRS-CS yielded inconsistent results for all PGS. The positive association stands in contrast to the negative genetic correlation between BMI and AN. Larger discovery GWASs to calculate PGS will increase power, and it is essential to increase sample sizes of the AN GWASs to generate clinically meaningful PGS as adjunct risk prediction variables. Nevertheless, this study provides the first evidence of potential clinical utility of PGSs for eating disorders.

Systematic Review: Molecular Studies of Common Genetic Variation in Child and Adolescent Psychiatric Disorders

OBJECTIVE

A systematic review of studies using molecular genetics and statistical approaches to investigate the role of common genetic variation in the development, persistence, and comorbidity of childhood psychiatric traits was conducted.

METHOD

A literature review was performed using the PubMed database, following PRISMA guidelines. There were 131 studies meeting inclusion criteria, having investigated at least one type of childhood-onset or childhood-measured psychiatric disorder or trait with the aim of identifying trait-associated common genetic variants, estimating the contribution of single nucleotide polymorphisms (SNPs) to the amount of variance explained (SNP-based heritability), investigating genetic overlap between psychiatric traits, or investigating whether the stability in traits or the association with adult traits is explained by genetic factors.

RESULTS

The first robustly associated genetic variants have started to be identified for childhood psychiatric traits. There were substantial contributions of common genetic variants to many traits, with variation in single nucleotide polymorphism heritability estimates depending on age and raters. Moreover, genetic variants also appeared to explain comorbidity as well as stability across a range of psychiatric traits in childhood and across the life span.

CONCLUSION

Common genetic variation plays a substantial role in childhood psychiatric traits. Increased sample sizes will lead to increased power to identify genetic variants and to understand genetic architecture, which will ultimately be beneficial to targeted and prevention strategies. This can be achieved by harmonizing phenotype measurements, as is already proposed by large international consortia and by including the collection of genetic material in every study.

A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

BACKGROUND

Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear.

METHODS

We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders.

RESULTS

We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders.

CONCLUSIONS

Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

Post-partum psychosis and its association with bipolar disorder in the UK: a case-control study using polygenic risk scores

BACKGROUND

For more than 150 years, controversy over the status of post-partum psychosis has hindered research and caused considerable confusion for clinicians and women, with potentially negative consequences. We aimed to explore the hypothesis that genetic vulnerability differs between women with first-onset post-partum psychosis and those with bipolar disorder more generally.

METHODS

In this case-control study on first-onset post-partum psychosis and bipolar disorder in the UK, we included 203 women with first-onset post-partum psychosis (defined as a manic, mixed, or psychotic depression episode within 6 weeks of delivery without a psychiatric history) and 1225 parous women with a history of bipolar disorder. Information on women with bipolar disorder was obtained from the Bipolar Disorder Research Network database, and participants were recruited through screening community mental health teams across the UK and via the media and patient support organisations. All were assessed using a semistructured face-to-face psychiatric interview and psychiatric case note review. 2809 women from the general population were recruited via the national UK Blood Services and the 1958 Birth Cohort (UK National Child Development Study) as controls and matched to cases according to genetic ancestry. All self-reported their ethnicity as White and were recruited from across the UK. Polygenic risk scores (PRSs) were generated from discovery genome-wide association studies of schizophrenia, bipolar disorder, and major depression. Logistic regression was used to model the effect of each PRS on diagnosis, and the RRs and ORs presented were adjusted for ten principal components of genetic variation to account for population stratification.

FINDINGS

203 women with first-onset post-partum psychosis (median age at interview: 46 years [IQR 37-55]) and 1225 women with bipolar disorder (49 years [41-58]) were recruited between September, 1991, and May, 2013, as well as 2809 controls. Women with first-onset post-partum psychosis had similar bipolar disorder and schizophrenia PRSs to women with bipolar disorder, which were significantly higher than those of controls. When compared with controls, women with first-onset post-partum psychosis had an adjusted relative risk ratio (RR) for bipolar disorder PRSs of 1·71 (95% CI 1·56-1·86, p<0·0001) and for schizophrenia PRSs of 1·82 (1·66-1·97, p<0·0001). The effect sizes were similar when comparing women with bipolar disorder to controls (adjusted RR 1·77 [1·69-1·84], p<0·0001 for bipolar disorder PRSs; 2·00 (1·92-2·08), p<0·0001 for schizophrenia PRSs). Although women with bipolar disorder also had higher major depression PRSs than did controls (1·24 [1·17-1·31], p<0·0001), women with first-onset post-partum psychosis did not differ from controls in their polygenic liability to major depression (0·97 (0·82-1·11), p=0·63).

INTERPRETATION

Our study supports the recognition of first-onset post-partum psychosis as a separate nosological entity within the bipolar disorder spectrum both in research and clinical settings.

FUNDING

Wellcome Trust and Medical Research Council.

Twin studies to GWAS: there and back again

The field of human behavioral genetics has come full circle. It began by using twin/family studies to estimate the relative importance of genetic and environmental influences. As large-scale genotyping became cost-effective, genome-wide association studies (GWASs) yielded insights about the nature of genetic influences and new methods that use GWAS data to estimate heritability and genetic correlations invigorated the field. Yet these newer GWAS methods have not replaced twin/family studies. In this review, we discuss the strengths and weaknesses of the two approaches with respect to characterizing genetic and environmental influences, measurement of behavioral phenotypes, and evaluation of causal models, with a particular focus on cognitive neuroscience. This discussion highlights how twin/family studies and GWAS complement and mutually reinforce one another.

A systematic review of gene-by-intervention studies of alcohol and other substance use

Alcohol and other substance use problems are common, and the efficacy of current prevention and intervention programs is limited. Genetics may contribute to differential effectiveness of psychosocial prevention and intervention programs. This paper reviews gene-by-intervention (G×I) studies of alcohol and other substance use, and implications for integrating genetics into prevention science. Systematic review yielded 17 studies for inclusion. Most studies focused on youth substance prevention, alcohol was the most common outcome, and measures of genotype were heterogeneous. All studies reported at least one significant G×I interaction. We discuss these findings in the context of the history and current state of genetics, and provide recommendations for future G×I research. These include the integration of genome-wide polygenic scores into prevention studies, broad outcome measurement, recruitment of underrepresented populations, testing mediators of G×I effects, and addressing ethical implications. Integrating genetic research into prevention science, and training researchers to work fluidly across these fields, will enhance our ability to determine the best intervention for each individual across development. With growing public interest in obtaining personalized genetic information, we anticipate that the integration of genetics and prevention science will become increasingly important as we move into the era of precision medicine.

Developmental Cascades from Polygenic and Prenatal Substance Use to Adolescent Substance Use: Leveraging Severity and Directionality of Externalizing and Internalizing Problems to Understand Pubertal and Harsh Discipline-Related Risk

The current study leveraged the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (n = 4504 White boys, n = 4287 White girls assessed from the prenatal period through 18.5 years of age) to test a developmental cascade from genetic and prenatal substance use through pubertal timing and parenting to the severity of (regardless of type) and directionality (i.e., differentiation) of externalizing and internalizing problems to adolescent substance use. Limited associations of early pubertal timing with substance use outcomes were only observable via symptom directionality, differently for girls and boys. For boys, more severe exposure to prenatal substance use influenced adolescent substance use progression via differentiation towards relatively more pure externalizing problems, but in girls the associations were largely direct. Severity and especially directionality (i.e., differentiation towards relatively more pure externalizing problems) were key intermediaries in developmental cascades from parental harsh discipline with substance use progressions for girls and boys.

Using a developmental perspective to examine the moderating effects of marriage on heavy episodic drinking in a young adult sample enriched for risk

Many studies demonstrate that marriage protects against risky alcohol use and moderates genetic influences on alcohol outcomes; however, previous work has not considered these effects from a developmental perspective or in high-risk individuals. These represent important gaps, as it cannot be assumed that marriage has uniform effects across development or in high-risk samples. We took a longitudinal developmental approach to examine whether marital status was associated with heavy episodic drinking (HED), and whether marital status moderated polygenic influences on HED. Our sample included 937 individuals (53.25% female) from the Collaborative Study on the Genetics of Alcoholism who reported their HED and marital status biennially between the ages of 21 and 25. Polygenic risk scores (PRS) were derived from a genome-wide association study of alcohol consumption. Marital status was not associated with HED; however, we observed pathogenic gene-by-environment effects that changed across young adulthood. Among those who married young (age 21), individuals with higher PRS reported more HED; however, these effects decayed over time. The same pattern was found in supplementary analyses using parental history of alcohol use disorder as the index of genetic liability. Our findings indicate that early marriage may exacerbate risk for those with higher polygenic load.

Identification of pleiotropy at the gene level between psychiatric disorders and related traits

Major mental disorders are highly prevalent and make a substantial contribution to the global disease burden. It is known that mental disorders share clinical characteristics, and genome-wide association studies (GWASs) have recently provided evidence for shared genetic factors as well. Genetic overlaps are usually identified at the single-marker level. Here, we aimed to identify genetic overlaps at the gene level between 7 mental disorders (schizophrenia, autism spectrum disorder, major depressive disorder, anorexia nervosa, ADHD, bipolar disorder and anxiety), 8 brain morphometric traits, 2 cognitive traits (educational attainment and general cognitive function) and 9 personality traits (subjective well-being, depressive symptoms, neuroticism, extraversion, openness to experience, agreeableness and conscientiousness, children's aggressive behaviour, loneliness) based on publicly available GWASs. We performed systematic conditional regression analyses to identify independent signals and select loci associated with more than one trait. We identified 48 genes containing independent markers associated with several traits (pleiotropy at the gene level). We also report 9 genes with different markers that show independent associations with single traits (allelic heterogeneity). This study demonstrates that mental disorders and related traits do show pleiotropy at the gene level as well as the single-marker level. The identification of these genes might be important for prioritizing further deep genotyping, functional studies, or drug targeting.

Polygenic Risk Scores

As genome-wide association studies have continued to identify loci associated with complex traits, the implications of and necessity for proper use of these findings, including prediction of disease risk, have become apparent. Many complex diseases have numerous associated loci with detectable effects implicating risk for or protection from disease. A common contemporary approach to using this information for disease prediction is through the application of genetic risk scores. These scores estimate an individual's liability for a specific outcome by aggregating the effects of associated loci into a single measure as described in the previous version of this article. Although genetic risk scores have traditionally included variants that meet criteria for genome-wide significance, an extension known as the polygenic risk score has been developed to include the effects of more variants across the entire genome. Here, we describe common methods and software packages for calculating and interpreting polygenic risk scores. In this revised version of the article, we detail information that is needed to perform a polygenic risk score analysis, considerations for planning the analysis and interpreting results, as well as discussion of the limitations based on the choices made. We also provide simulated sample data and a walkthrough for four different polygenic risk score software. © 2021 Wiley Periodicals LLC.

Attachment Style Moderates Polygenic Risk for Posttraumatic Stress in United States Military Veterans: Results From the National Health and Resilience in Veterans Study

BACKGROUND

A polygenic risk score (PRS) derived from genome-wide association studies of posttraumatic stress disorder (PTSD) may inform risk for this disorder. To date, however, no known study has examined whether social environmental factors such as attachment style may moderate the relation between PRS and PTSD.

METHODS

We evaluated main and interactive effects of PRS and attachment style on PTSD symptoms in a nationally representative sample of trauma-exposed European-American U.S. military veterans (N = 2030). PRS was derived from a genome-wide association study of PTSD re-experiencing symptoms (N = 146,660) in the Million Veteran Program cohort. Using one-sample Mendelian randomization with data from the UK Biobank (N = 115,099), we evaluated the effects of re-experiencing PRS and attachment style on PTSD symptoms.

RESULTS

Higher re-experiencing PRS and secure attachment style were independently associated with PTSD symptoms. A significant PRS-by-attachment style interaction was also observed (β = -.11, p = .006), with a positive association between re-experiencing PRS and PTSD symptoms observed only among veterans with an insecure attachment style. One-sample Mendelian randomization analyses suggested that the association between PTSD symptoms and attachment style is bidirectional. PRS enrichment analyses revealed a significant interaction between attachment style and a variant mapping to the IGSF11 gene (rs151177743, p = 2.1 × 10^-7), which is implicated in regulating excitatory synaptic transmission and plasticity.

CONCLUSIONS

Attachment style may moderate polygenic risk for PTSD symptoms, and a novel locus implicated in synaptic transmission and plasticity may serve as a possible biological mediator of this association. These findings may help inform interpersonally oriented treatments for PTSD for individuals with high polygenic risk for this disorder.

Phenotypic and genetic markers of psychopathology in a population-based sample of older adults

Although psychiatric phenotypes are hypothesized to organize into a two-factor internalizing-externalizing structure, few studies have evaluated the structure of psychopathology in older adults, nor explored whether genome-wide polygenic scores (PGSs) are associated with psychopathology in a domain-specific manner. We used data from 6003 individuals of European ancestry from the Health and Retirement Study, a large population-based sample of older adults in the United States. Confirmatory factor analyses were applied to validated measures of psychopathology and PGSs were derived from well-powered genome-wide association studies (GWAS). Genomic SEM was implemented to construct latent PGSs for internalizing, externalizing, and general psychopathology. Phenotypically, the data were best characterized by a single general factor of psychopathology, a factor structure that was replicated across genders and age groups. Although externalizing PGSs (cannabis use, antisocial behavior, alcohol dependence, attention deficit hyperactivity disorder) were not associated with any phenotypes, PGSs for major depressive disorder, neuroticism, and anxiety disorders were associated with both internalizing and externalizing phenotypes. Moreover, the variance explained in the general factor of psychopathology increased by twofold (from 1% to 2%) using the latent internalizing or latent one-factor PGSs, derived using weights from Genomic Structural Equation Modeling (SEM), compared with any of the individual PGSs. Collectively, results suggest that genetic risk factors for and phenotypic markers of psychiatric disorders are transdiagnostic in older adults of European ancestry. Alternative explanations are discussed, including methodological limitations of GWAS and phenotypic measurement of psychiatric outcome in large-scale population-based studies.

Genetic and clinical analyses of psychosis spectrum symptoms in a large multiethnic youth cohort reveal significant link with ADHD

Psychotic symptoms are not only an important feature of severe neuropsychiatric disorders, but are also common in the general population, especially in youth. The genetic etiology of psychosis symptoms in youth remains poorly understood. To characterize genetic risk for psychosis spectrum symptoms (PS), we leverage a community-based multiethnic sample of children and adolescents aged 8-22 years, the Philadelphia Neurodevelopmental Cohort (n = 7225, 20% PS). Using an elastic net regression model, we aim to classify PS status using polygenic scores (PGS) based on a range of heritable psychiatric and brain-related traits in a multi-PGS model. We also perform univariate PGS associations and evaluate age-specific effects. The multi-PGS analyses do not improve prediction of PS status over univariate models, but reveal that the attention deficit hyperactivity disorder (ADHD) PGS is robustly and uniquely associated with PS (OR 1.12 (1.05, 1.18) P = 0.0003). This association is driven by subjects of European ancestry (OR = 1.23 (1.14, 1.34), P = 4.15 × 10^-7) but is not observed in African American subjects (P = 0.65). We find a significant interaction of ADHD PGS with age (P = 0.01), with a stronger association in younger children. The association is independent of phenotypic overlap between ADHD and PS, not indirectly driven by substance use or childhood trauma, and appears to be specific to PS rather than reflecting general psychopathology in youth. In an independent sample, we replicate an increased ADHD PGS in 328 youth at clinical high risk for psychosis, compared to 216 unaffected controls (OR 1.06, CI(1.01, 1.11), P = 0.02). Our findings suggest that PS in youth may reflect a different genetic etiology than psychotic symptoms in adulthood, one more akin to ADHD, and shed light on how genetic risk can be investigated across early disease trajectories.

Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics

Depression is a heterogeneous and etiologically complex psychiatric syndrome, not a unitary disease entity, encompassing a broad spectrum of psychopathology arising from distinct pathophysiological mechanisms. Motivated by a need to advance our understanding of these mechanisms and develop new treatment strategies, there is a renewed interest in investigating the neurobiological basis of heterogeneity in depression and rethinking our approach to diagnosis for research purposes. Large-scale genome-wide association studies have now identified multiple genetic risk variants implicating excitatory neurotransmission and synapse function and underscoring a highly polygenic inheritance pattern that may be another important contributor to heterogeneity in depression. Here, we review various sources of phenotypic heterogeneity and approaches to defining and studying depression subtypes, including symptom-based subtypes and biology-based approaches to decomposing the depression syndrome. We review "dimensional," "categorical," and "hybrid" approaches to parsing phenotypic heterogeneity in depression and defining subtypes using functional neuroimaging. Next, we review recent progress in neuroimaging genetics (correlating neuroimaging patterns of brain function with genetic data) and its potential utility for generating testable hypotheses concerning molecular and circuit-level mechanisms. We discuss how genetic variants and transcriptomic profiles may confer risk for depression by modulating brain structure and function. We conclude by highlighting several promising areas for future research into the neurobiological underpinnings of heterogeneity, including efforts to understand sexually dimorphic mechanisms, the longitudinal dynamics of depressive episodes, and strategies for developing personalized treatments and facilitating clinical decision-making.

Genetic Architecture of Depression: Where Do We Stand Now?

The research of depression genetics has been occupied by historical candidate genes which were tested by candidate gene association studies. However, these studies were mostly not replicable. Thus, genetics of depression have remained elusive for a long time. As research moves from candidate gene association studies to GWAS, the hypothesis-free non-candidate gene association studies in genome-wide level, this trend will likely change. Despite the fact that the earlier GWAS of depression were not successful, the recent GWAS suggest robust findings for depression genetics. These altogether will catalyze a new wave of multidisciplinary research to pin down the neurobiology of depression.

The Role of Eating Behaviours in Genetic Susceptibility to Obesity

PURPOSE OF REVIEW

Eating behaviours are hypothesised to be the behavioural expression of genetic risk of obesity. In this review, we summarise findings from behavioural genetic research on the association between genetic risk for obesity and validated psychometrics measures of eating behaviours in children and adults (published in the past 10 years).

RECENT FINDINGS

Twin studies have produced some evidence for a shared genetic aetiology underlying body mass index and eating behaviours. Studies using measured genetic susceptibility to obesity have suggested that increased genetic liability for obesity is associated with variation in obesogenic eating behaviours such as emotional and uncontrolled eating. More research on this topic is needed. Especially longitudinal studies using genetically sensitive designs to investigate the direction of genetic pathways between genetic liability of eating behaviours to weight and vice versa, as well as the potential subsequent link to eating disorders.

Genetically determined intelligence and coronary artery disease risk

BACKGROUND

Epidemiological studies have shown inverse association between intelligence and coronary artery disease (CAD) risk, but the underlying mechanisms remain unclear.

METHODS

Based on 242 SNPs independently associated with intelligence, we calculated the genetic intelligence score (gIQ) for participants from 10 CAD case-control studies (n = 34,083) and UK Biobank (n = 427,306). From UK Biobank, we extracted phenotypes including body mass index (BMI), type 2 diabetes (T2D), smoking, hypertension, HDL cholesterol, LDL cholesterol, measured intelligence score, and education attainment. To estimate the effects of gIQ on CAD and its related risk factors, regression analyses was applied. Next, we studied the mediatory roles of measured intelligence and educational attainment. Lastly, Mendelian randomization was performed to validate the findings.

RESULTS

In CAD case-control studies, one standard deviation (SD) increase of gIQ was related to a 5% decrease of CAD risk (odds ratio [OR] of 0.95; 95% confidence interval [CI] 0.93 to 0.98; P = 4.93e-5), which was validated in UK Biobank (OR = 0.97; 95% CI 0.96 to 0.99; P = 6.4e-4). In UK Biobank, we also found significant inverse correlations between gIQ and risk factors of CAD including smoking, BMI, T2D, hypertension, and a positive correlation with HDL cholesterol. The association signals between gIQ and CAD as well as its risk factors got largely attenuated after the adjustment of measured intelligence and educational attainment. The causal role of intelligence in mediating CAD risk was confirmed by Mendelian randomization analyses.

CONCLUSION

Genetic components of intelligence affect measured intelligence and educational attainment, which subsequently affect the prevalence of CAD via a series of unfavorable risk factor profiles.

Using the tools of genetic epidemiology to understand sex differences in neuropsychiatric disorders

Many neuropsychiatric disorders exhibit differences in prevalence, age of onset, symptoms or course of illness between males and females. For the most part, the origins of these differences are not well understood. In this article, we provide an overview of sex differences in psychiatric disorders including autism spectrum disorder (ASD), attention deficit/hyperactivity disorder (ADHD), anxiety, depression, alcohol and substance abuse, schizophrenia, eating disorders and risk of suicide. We discuss both genetic and nongenetic mechanisms that have been hypothesized to underlie these differences, including ascertainment bias, environmental stressors, X‐ or Y‐linked risk loci, and differential liability thresholds in males and females. We then review the use of twin, family and genome‐wide association approaches to study potential genetic mechanisms of sex differences and the extent to which these designs have been employed in studies of psychiatric disorders. We describe the utility of genetic epidemiologic study designs, including classical twin and family studies, large‐scale studies of population registries, derived recurrence risks, and molecular genetic analyses of genome‐wide variation that may enhance our understanding sex differences in neuropsychiatric disorders.

Harnessing Progress in Psychiatric Genetics to Advance Population Mental Health

Advances in genomics and neuroscience have ushered in unprecedented opportunities to increase our understanding of the biological underpinnings of mental disorders, yet there has been limited progress in translating knowledge on genetic risk factors to reduce the burden of these conditions in the population. We describe the challenges and opportunities afforded by the growth of large-scale population health databases, progress in genomics, and collaborative efforts in epidemiology and neuroscience to develop informed population-wide interventions for mental disorders. Future progress is likely to benefit from the following efforts: expansion of large collaborative studies of mental disorders to include more systematically ascertained multiethnic samples from biobanks and registries, harmonization of phenotypic characterization in registry and population samples to extend clinical diagnosis to transdiagnostic concepts, systematic investigation of the influences of both specific and nonspecific environmental factors that may combine with genetic susceptibility to confer increased risk of specific mental disorders, and implementation of study designs that can inform gene-environment interactions. Such data can ultimately be combined to develop comprehensive models of risks of, interventions for, and outcomes of mental disorders. With its focus on phenotypic characterization, sampling, study designs, and analytic methods, epidemiology will be central to progress in translating genomics to public health.

Polygenic Influences on Pubertal Timing and Tempo and Depressive Symptoms in Boys and Girls

This study used polygenic scoring (PGS) to test whether puberty-related genes were correlated with depressive symptoms, and whether there were indirect effects through pubertal maturation. The sample included 8,795 adolescents from the Avon Longitudinal Study of Parents and Children (measures of puberty drawn ages 8-17 years; of depressive symptoms at age 16.5 years). The PGS (derived from a genome-wide meta-analysis of later age at menarche) predicted boys' and girls' later pubertal timing, boys' slower gonadal development, and girls' faster breast development. Earlier perceived breast development timing predicted more depressive symptoms in girls. Findings support shared genetic underpinnings for boys' and girls' puberty, contributing to multiple pubertal phenotypes with differences in how these genetic variants affect boys' and girls' development.

Measurement and genetic architecture of lifetime depression in the Netherlands as assessed by LIDAS (Lifetime Depression Assessment Self-report)

BACKGROUND

Major depressive disorder (MDD) is a common mood disorder, with a heritability of around 34%. Molecular genetic studies made significant progress and identified genetic markers associated with the risk of MDD; however, progress is slowed down by substantial heterogeneity as MDD is assessed differently across international cohorts. Here, we used a standardized online approach to measure MDD in multiple cohorts in the Netherlands and evaluated whether this approach can be used in epidemiological and genetic association studies of depression.

METHODS

Within the Biobank Netherlands Internet Collaboration (BIONIC) project, we collected MDD data in eight cohorts involving 31 936 participants, using the online Lifetime Depression Assessment Self-report (LIDAS), and estimated the prevalence of current and lifetime MDD in 22 623 unrelated individuals. In a large Netherlands Twin Register (NTR) twin-family dataset (n ≈ 18 000), we estimated the heritability of MDD, and the prediction of MDD in a subset (n = 4782) through Polygenic Risk Score (PRS).

RESULTS

Estimates of current and lifetime MDD prevalence were 6.7% and 18.1%, respectively, in line with population estimates based on validated psychiatric interviews. In the NTR heritability estimates were 0.34/0.30 (s.e. = 0.02/0.02) for current/lifetime MDD, respectively, showing that the LIDAS gives similar heritability rates for MDD as reported in the literature. The PRS predicted risk of MDD (OR 1.23, 95% CI 1.15-1.32, R2 = 1.47%).

CONCLUSIONS

By assessing MDD status in the Netherlands using the LIDAS instrument, we were able to confirm previously reported MDD prevalence and heritability estimates, which suggests that this instrument can be used in epidemiological and genetic association studies of depression.

Sibling comparisons elucidate the associations between educational attainment polygenic scores and alcohol, nicotine and cannabis

BACKGROUND AND AIMS

The associations between low educational attainment and substance use disorders (SUDs) may be related to a common genetic vulnerability. We aimed to elucidate the associations between polygenic scores for educational attainment and clinical criterion counts for three SUDs (alcohol, nicotine and cannabis).

DESIGN

Polygenic association and sibling comparison methods. The latter strengthens inferences in observational research by controlling for confounding factors that differ between families.

SETTING

Six sites in the United States.

PARTICIPANTS

European ancestry participants aged 25 years and older from the Collaborative Study on the Genetics of Alcoholism (COGA). Polygenic association analyses included 5582 (54% female) participants. Sibling comparisons included 3098 (52% female) participants from 1226 sibling groups nested within the overall sample.

MEASUREMENTS

Outcomes included criterion counts for DSM-5 alcohol use disorder (AUDSX), Fagerström nicotine dependence (NDSX) and DSM-5 cannabis use disorder (CUDSX). We derived polygenic scores for educational attainment (EduYears-GPS) using summary statistics from a large (> 1 million) genome-wide association study of educational attainment.

FINDINGS

In polygenic association analyses, higher EduYears-GPS predicted lower AUDSX, NDSX and CUDSX [P < 0.01, effect sizes (R2 ) ranging from 0.30 to 1.84%]. These effects were robust in sibling comparisons, where sibling differences in EduYears-GPS predicted all three SUDs (P < 0.05, R2 0.13-0.20%).

CONCLUSIONS

Individuals who carry more alleles associated with educational attainment tend to meet fewer clinical criteria for alcohol, nicotine and cannabis use disorders, and these effects are robust to rigorous controls for potentially confounding factors that differ between families (e.g. socio-economic status, urban-rural residency and parental education).

Genetic correlations of psychiatric traits with body composition and glycemic traits are sex- and age-dependent

Body composition is often altered in psychiatric disorders. Using genome-wide common genetic variation data, we calculate sex-specific genetic correlations amongst body fat %, fat mass, fat-free mass, physical activity, glycemic traits and 17 psychiatric traits (up to N = 217,568). Two patterns emerge: (1) anorexia nervosa, schizophrenia, obsessive-compulsive disorder, and education years are negatively genetically correlated with body fat % and fat-free mass, whereas (2) attention-deficit/hyperactivity disorder (ADHD), alcohol dependence, insomnia, and heavy smoking are positively correlated. Anorexia nervosa shows a stronger genetic correlation with body fat % in females, whereas education years is more strongly correlated with fat mass in males. Education years and ADHD show genetic overlap with childhood obesity. Mendelian randomization identifies schizophrenia, anorexia nervosa, and higher education as causal for decreased fat mass, with higher body fat % possibly being a causal risk factor for ADHD and heavy smoking. These results suggest new possibilities for targeted preventive strategies.

Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies

To identify genetic risk loci for major depressive disorder (MDD), two broad study design approaches have been applied: (1) to maximize sample size by combining data from different phenotype assessment modalities (e.g. clinical interview, self-report questionnaires) and (2) to reduce phenotypic heterogeneity through selecting more homogenous MDD subtypes. The value of these strategies has been debated. In this review, we summarize the most recent findings of large genomic studies that applied these approaches, and we highlight the merits and pitfalls of both approaches with particular attention to methodological and psychometric issues. We also discuss the results of analyses that investigated the heterogeneity of MDD. We conclude that both study designs are essential for further research. So far, increasing sample size has led to the identification of a relatively high number of genomic loci linked to depression. However, part of the identified variants may be related to a phenotype common to internalizing disorders and related traits. As such, samples containing detailed clinical information are needed to dissect depression heterogeneity and enable the potential identification of variants specific to a more restricted MDD phenotype. A balanced portfolio reconciling both study design approaches is the optimal approach to progress further in unraveling the genetic architecture of depression.

Genetic Risk Scores

Genome-wide variation data with millions of genetic markers have become commonplace. However, the potential for interpretation and application of these data for clinical assessment of outcomes of interest, and prediction of disease risk, is currently not fully realized. Many common complex diseases now have numerous, well-established risk loci and likely harbor many genetic determinants with effects too small to be detected at genome-wide levels of statistical significance. A simple and intuitive approach for converting genetic data to a predictive measure of disease susceptibility is to aggregate the effects of these loci into a single measure, the genetic risk score. Here, we describe some common methods and software packages for calculating genetic risk scores and polygenic risk scores, with focus on studies of common complex diseases. We review the basic information needed, as well as important considerations for constructing genetic risk scores, including specific requirements for phenotypic and genetic data, and limitations in their application. © 2019 by John Wiley & Sons, Inc.

Polygenic Risk Scores for Psychiatric Disorders Reveal Novel Clues About the Genetics of Disordered Gambling

Disordered gambling (DG) is a rare but serious condition that results in considerable financial and interpersonal harms. Twin studies indicate that DG is heritable but are silent with respect to specific genes or pathways involved. Existing genomewide association studies (GWAS) of DG have been substantially underpowered. Larger GWAS of other psychiatric disorders now permit calculation of polygenic risk scores (PRSs) that reflect the aggregated effects of common genetic variants contributing risk for the target condition. The current study investigated whether gambling and DG are associated with PRSs for four psychiatric conditions found to be comorbid with DG in epidemiologic surveys: major depressive disorder (MDD), attention-deficit hyperactivity disorder (ADHD), bipolar disorder (BD) and schizophrenia (SCZ). Genotype data and survey responses were analyzed from the Wave IV assessment (conducted in 2008) of the National Longitudinal Study of Adolescent to Adult Health, a representative sample of adolescents recruited in 1994-1995 and followed into adulthood. Among participants classified as having European ancestry based on genetic analysis (N = 5215), 78.4% reported ever having gambled, and 1.3% reported lifetime DG. Polygenic risk for BD was associated with decreased odds of lifetime gambling, OR = 0.93 [0.87, 0.99], p = .045, pseudo-R2(%) = .12. The SCZ PRS was associated with increased odds of DG, OR = 1.54 [1.07, 2.21], p = .02, pseudo-R2(%) = .85. Polygenic risk scores for MDD and ADHD were not related to either gambling outcome. Investigating features common to both SCZ and DG might generate valuable clues about the genetically influenced liabilities to DG.

Genetics of Resilience: Gene-by-Environment Interaction Studies as a Tool to Dissect Mechanisms of Resilience

The identification and understanding of resilience mechanisms holds potential for the development of mechanistically informed prevention and interventions in psychiatry. However, investigating resilience mechanisms is conceptually and methodologically challenging because resilience does not merely constitute the absence of disease-specific risk but rather reflects active processes that aid in the maintenance of physiological and psychological homeostasis across a broad range of environmental circumstances. In this conceptual review, we argue that the principle used in gene-by-environment interaction studies may help to unravel resilience mechanisms on different investigation levels. We present how this could be achieved by top-down designs that start with gene-by-environment interaction effects on disease phenotypes as well as by bottom-up approaches that start at the molecular level. We also discuss how recent technological advances may improve both top-down and bottom-up strategies.

Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank

Genotype-by-environment interaction (GEI) is a fundamental component in understanding complex trait variation. However, it remains challenging to identify genetic variants with GEI effects in humans largely because of the small effect sizes and the difficulty of monitoring environmental fluctuations. Here, we demonstrate that GEI can be inferred from genetic variants associated with phenotypic variability in a large sample without the need of measuring environmental factors. We performed a genome-wide variance quantitative trait locus (vQTL) analysis of ~5.6 million variants on 348,501 unrelated individuals of European ancestry for 13 quantitative traits in the UK Biobank and identified 75 significant vQTLs with P < 2.0 × 10-9 for 9 traits, especially for those related to obesity. Direct GEI analysis with five environmental factors showed that the vQTLs were strongly enriched with GEI effects. Our results indicate pervasive GEI effects for obesity-related traits and demonstrate the detection of GEI without environmental data.

Genetic and environmental influences on gambling disorder liability: a replication and combined analysis of two twin studies

BACKGROUND

Gambling disorder (GD), recognized in Diagnostic and Statistical Manual of Mental Disorders, Version 5 (DSM-5) as a behavioral addiction, is associated with a range of adverse outcomes. However, there has been little research on the genetic and environmental influences on the development of this disorder. This study reports results from the largest twin study of GD conducted to date.

METHODS

Replication and combined analyses were based on samples of 3292 (mean age 31.8, born 1972-79) and 4764 (mean age 37.7, born 1964-71) male, female, and unlike-sex twin pairs from the Australian Twin Registry. Univariate biometric twin models estimated the proportion of variation in the latent GD liability that could be attributed to genetic, shared environmental, and unique environmental factors, and whether these differed quantitatively or qualitatively for men and women.

RESULTS

In the replication study, when using a lower GD threshold, there was evidence for significant genetic (60%; 95% confidence interval (CI) 45-76%) and unique environmental (40%; 95% CI 24-56%), but not shared environmental contributions (0%; 95% CI 0-0%) to GD liability; this did not significantly differ from the original study. In the combined analysis, higher GD thresholds (such as one consistent with DSM-5 GD) and a multiple threshold definitions of GD yielded similar results. There was no evidence for quantitative or qualitative sex differences in the liability for GD.

CONCLUSIONS

Twin studies of GD are few in number but they tell a remarkably similar story: substantial genetic and unique environmental influences, with no evidence for shared environmental contributions or sex differences in GD liability.

Uncovering the Genetic Architecture of Major Depression

There have been several recent studies addressing the genetic architecture of depression. This review serves to take stock of what is known now about the genetics of depression, how it has increased our knowledge and understanding of its mechanisms, and how the information and knowledge can be leveraged to improve the care of people affected. We identify four priorities for how the field of MD genetics research may move forward in future years, namely by increasing the sample sizes available for genome-wide association studies (GWASs), greater inclusion of diverse ancestries and low-income countries, the closer integration of psychiatric genetics with electronic medical records, and the development of the neuroscience toolkit for polygenic disorders.

Unraveling the association between depression and telomere length using genomics

OBJECTIVE

While there is robust evidence for a cross-sectional association between depression and shorter telomere length, suggestive of advanced biological aging, the nature of this association remains unclear. Here, we tested whether both traits share a common genetic liability with novel methods using genomics.

METHODS

Data were from 2032 participants of the Netherlands Study of Depression and Anxiety (NESDA) with genome-wide genetic information and multiple waves of data on DSM-IV lifetime depression diagnosis, depression severity, neuroticism and telomere length. Polygenic risk scores (PRS) for both traits were built using summary results from the largest genome-wide association studies (GWAS) on depression (59,851 cases and 113,154 controls) and telomere length (37,684 samples). Additionally, a PRS for neuroticism was built (337,000 samples). Genetic overlap between the traits was tested using PRS for same- and cross-trait associations. Furthermore, GWAS summary statistics were used to estimate the genome-wide genetic correlation between traits.

RESULTS

In NESDA data, the PRS for depression was associated with lifetime depression (odds ratio = 1.36; p = 6.49e-7) and depression severity level (β = 0.13; p = 1.24e-8), but not with telomere length. Similar results were found for the PRS for neuroticism. Conversely, the PRS for telomere length was associated with telomere length (β = 0.07; p = 8.42e-4) and 6-year telomere length attrition rate (β = 0.04; p = 2.15e-2), but not with depression variables. In summary-level analyses, the genetic correlation between the traits was small and not significant (rg=-0.08; p = .300).

CONCLUSION

The use of genetic methods in this paper indicated that the established phenotypic association between telomere length and depression is unlikely due to shared underlying genetic vulnerability. Our findings suggest that short telomeres in depressed patients may simply represent a generic marker of disease or may originate from non-genetic environmental factors.