Phenome-wide analysis of genome-wide polygenic scores

Examining intergenerational risk factors for conduct problems using polygenic scores in the Norwegian Mother, Father and Child Cohort Study

The aetiology of conduct problems involves a combination of genetic and environmental factors, many of which are inherently linked to parental characteristics given parents' central role in children's lives across development. It is important to disentangle to what extent links between parental heritable characteristics and children's behaviour are due to transmission of genetic risk or due to parental indirect genetic influences via the environment (i.e., genetic nurture). We used 31,290 genotyped mother-father-child trios from the Norwegian Mother, Father and Child Cohort Study (MoBa), testing genetic transmission and genetic nurture effects on conduct problems using 13 polygenic scores (PGS) spanning psychiatric conditions, substance use, education-related factors, and other risk factors. Maternal or self-reports of conduct problems at ages 8 and 14 years were available for up to 15,477 children. We found significant genetic transmission effects on conduct problems for 12 out of 13 PGS at age 8 years (strongest association: PGS for smoking, β = 0.07, 95% confidence interval = [0.05, 0.08]) and for 4 out of 13 PGS at age 14 years (strongest association: PGS for externalising problems, β = 0.08, 95% confidence interval = [0.05, 0.11]). Conversely, we did not find genetic nurture effects for conduct problems using our selection of PGS. Our findings provide evidence for genetic transmission in the association between parental characteristics and child conduct problems. Our results may also indicate that genetic nurture via traits indexed by our polygenic scores is of limited aetiological importance for conduct problems-though effects of small magnitude or effects via parental traits not captured by the included PGS remain a possibility.

Well-being spectrum traits are associated with polygenic scores for autism

Individuals with autism spectrum disorder (ASD) tend to experience lower well-being as demonstrated mostly for children and adolescents in epidemiological studies. A further investigation of inclusive well-being, in terms of five well-being spectrum (5-WBS) traits including neuroticism, depression, loneliness, life satisfaction, and positive affect, among adults with ASD may deepen our understanding of their well-being, and lead to the possibility to further modify societal supportive mechanisms for individuals with ASD. This study aims to investigate if a genetic predisposition for ASD is associated with 5-WBS traits using polygenic risk score (PRS) analysis. PRS for ASD were calculated based on the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and were created in the independent cohort UK Biobank. Regression analyses were performed to investigate the association between ASD PRS and 5-WBS traits in the UK Biobank population including 337,423 individuals. ASD PRS were significantly associated with all 5-WBS traits, showing a positive association with the negative WBS traits, neuroticism (max R2  = 0.04%, p < 1 × 10-4 ), depression (max R2  = 0.06%, p < 1 × 10-4 ), loneliness (max R2  = 0.04%, p < 1 × 10-4 ), and a negative association with the positive WBS traits, life satisfaction (max R2  = 0.08%, p < 1 × 10-4 ), positive affect (max R2  = 0.10%, p < 1 × 10-4 ). The findings suggest that adults carrying a high load of risk single nucleotide peptides (SNPs) for ASD are more likely to report decreased well-being. The study demonstrates a considerable connection between susceptibility to ASD, its underlying genetic etiology and well-being.

Dissecting Schizotypy and Its Association With Cognition and Polygenic Risk for Schizophrenia in a Nonclinical Sample

Schizotypy is a multidimensional construct that captures a continuum of risk for developing schizophrenia-spectrum psychopathology. Existing 3-factor models of schizotypy, consisting of positive, negative, and disorganized dimensions have yielded mixed evidence of genetic continuity with schizophrenia using polygenic risk scores. Here, we propose an approach that involves splitting positive and negative schizotypy into more specific subdimensions that are phenotypically continuous with distinct positive symptoms and negative symptoms recognized in clinical schizophrenia. We used item response theory to derive high-precision estimates of psychometric schizotypy using 251 self-report items obtained from a non-clinical sample of 727 (424 females) adults. These subdimensions were organized hierarchically using structural equation modeling into 3 empirically independent higher-order dimensions enabling associations with polygenic risk for schizophrenia to be examined at different levels of phenotypic generality and specificity. Results revealed that polygenic risk for schizophrenia was associated with variance specific to delusional experiences (γ = 0.093, P = .001) and reduced social interest and engagement (γ = 0.076, P = .020), and these effects were not mediated via the higher-order general, positive, or negative schizotypy factors. We further fractionated general intellectual functioning into fluid and crystallized intelligence in 446 (246 females) participants that underwent onsite cognitive assessment. Polygenic risk scores explained 3.6% of the variance in crystallized intelligence. Our precision phenotyping approach could be used to enhance the etiologic signal in future genetic association studies and improve the detection and prevention of schizophrenia-spectrum psychopathology.

General v. specific vulnerabilities: polygenic risk scores and higher-order psychopathology dimensions in the Adolescent Brain Cognitive Development (ABCD) Study

BACKGROUND

Polygenic risk scores (PRSs) capture genetic vulnerability to psychiatric conditions. However, PRSs are often associated with multiple mental health problems in children, complicating their use in research and clinical practice. The current study is the first to systematically test which PRSs associate broadly with all forms of childhood psychopathology, and which PRSs are more specific to one or a handful of forms of psychopathology.

METHODS

The sample consisted of 4717 unrelated children (mean age = 9.92, s.d. = 0.62; 47.1% female; all European ancestry). Psychopathology was conceptualized hierarchically as empirically derived general factor (p-factor) and five specific factors: externalizing, internalizing, neurodevelopmental, somatoform, and detachment. Partial correlations explored associations between psychopathology factors and 22 psychopathology-related PRSs. Regressions tested which level of the psychopathology hierarchy was most strongly associated with each PRS.

RESULTS

Thirteen PRSs were significantly associated with the general factor, most prominently Chronic Multisite Pain-PRS (r = 0.098), ADHD-PRS (r = 0.079), and Depression-PRS (r = 0.078). After adjusting for the general factor, Depression-PRS, Neuroticism-PRS, PTSD-PRS, Insomnia-PRS, Chronic Back Pain-PRS, and Autism-PRS were not associated with lower order factors. Conversely, several externalizing PRSs, including Adventurousness-PRS and Disinhibition-PRS, remained associated with the externalizing factor (|r| = 0.040-0.058). The ADHD-PRS remained uniquely associated with the neurodevelopmental factor (r = 062).

CONCLUSIONS

PRSs developed to predict vulnerability to emotional difficulties and chronic pain generally captured genetic risk for all forms of childhood psychopathology. PRSs developed to predict vulnerability to externalizing difficulties, e.g. disinhibition, tended to be more specific in predicting behavioral problems. The results may inform translation of existing PRSs to pediatric research and future clinical practice.

Polygenic risk associations with developmental and mental health outcomes in childhood and adolescence: A systematic review

Background

Neurodevelopmental and mental health disorders in childhood constitute an emerging global concern, with adverse sequelae which span children's physical, psychological and social well-being. The aetiology of these disorders is likely complex, multifactorial and polygenic. Polygenic risk scores (PRS), an estimate of an individual's genetic liability toward a disorder, have been increasingly used in psychiatric research to explore genetic associations with disorders of interest. However, limited work delineates polygenic associations with development and mental health in childhood populations.We aimed to systematically review existing literature on associations between genetic risk (as measured by PRS) and neurodevelopmental and mental health outcomes in childhood and adolescence.

Methods

Following the recommended Preferred Reporting Items for Meta-Analyses (PRISMA) guidelines, databases were searched using key search terms. The search commenced in March 2021 and concluded in June 2021. The studies eligible for inclusion were full-text articles investigating polygenic risk associations with neurodevelopmental and/or mental health outcomes in childhood or adolescence.

Results

Fourteen studies were eligible for inclusion in this systematic review. The association between higher PRS for attention-deficit/hyperactivity disorder (ADHD) and adverse developmental/mental health outcomes in childhood and adolescence was reported by five studies. Additionally, associations between PRS for bipolar disorder or major depressive disorder and adverse outcomes of interest were also described by two studies; and two studies highlighted associations between schizophrenia PRS and mental health disorders in childhood. The remaining studies highlighted shared polygenic contributions between and within NDDs and mental health disorders in children.

Conclusion

The findings of this systematic review suggest that PRS for neurodevelopmental and mental health disorders may associate with adverse neurodevelopmental and mental health outcomes from early childhood to adolescence. In addition, these associations seemed not to be phenotype-specific, suggesting potential shared genetic variation across the phenotypes of interest.

Polygenic Liability to Depression Is Associated With Multiple Medical Conditions in the Electronic Health Record: Phenome-wide Association Study of 46,782 Individuals

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disease-associated disability, with much of the increased burden due to psychiatric and medical comorbidity. This comorbidity partly reflects common genetic influences across conditions. Integrating molecular-genetic tools with health records enables tests of association with the broad range of physiological and clinical phenotypes. However, standard phenome-wide association studies analyze associations with individual genetic variants. For polygenic traits such as MDD, aggregate measures of genetic risk may yield greater insight into associations across the clinical phenome.

METHODS

We tested for associations between a genome-wide polygenic risk score for MDD and medical and psychiatric traits in a phenome-wide association study of 46,782 unrelated, European-ancestry participants from the Michigan Genomics Initiative.

RESULTS

The MDD polygenic risk score was associated with 211 traits from 15 medical and psychiatric disease categories at the phenome-wide significance threshold. After excluding patients with depression, continued associations were observed with respiratory, digestive, neurological, and genitourinary conditions; neoplasms; and mental disorders. Associations with tobacco use disorder, respiratory conditions, and genitourinary conditions persisted after accounting for genetic overlap between depression and other psychiatric traits. Temporal analyses of time-at-first-diagnosis indicated that depression disproportionately preceded chronic pain and substance-related disorders, while asthma disproportionately preceded depression.

CONCLUSIONS

The present results can inform the biological links between depression and both mental and systemic diseases. Although MDD polygenic risk scores cannot currently forecast health outcomes with precision at the individual level, as molecular-genetic discoveries for depression increase, these tools may augment risk prediction for medical and psychiatric conditions.

Age-dependent patterns of schizophrenia genetic risk affect cognition

Cognition shares substantial genetic overlap with schizophrenia, yet it remains unclear whether such genetic effects become significant during developmental periods of elevated risk for schizophrenia, such as the peak age of onset. We introduce an investigative framework integrating epidemiological, developmental, and genetic approaches to determine whether genetic effects shared between schizophrenia and cognition are significant across periods of differing risk for schizophrenia onset, and whether these effects are shared with depression. 771 European-American participants, including 636 (ages 15-84 years) from families with at least two first-degree relatives with schizophrenia and 135 unrelated controls, were divided into three age-risk groups based on ages relative to epidemiological age of onset patterns for schizophrenia: Pre-Peak (before peak age-of-onset: 15 to 22 years), Post-Peak (after peak age-of-onset: 23-42 years), and Plateau (during plateau of age-of-onset: over 42 years). For general cognition and 11 specific cognitive traits, we estimated genetic correlations with schizophrenia and with depression within each age-risk group. Genetic effects shared between deficits in general cognition and schizophrenia were nonsignificant before peak age of onset, yet were high and significant after peak age of onset and during the plateau of onset. These age-dependent genetic effects were largely consistent across specific cognitive traits and not transdiagnostically shared with depression. Schizophrenia genetic effects appear to influence cognitive traits in an age-dependent manner, supporting late developmental and perhaps neurodegenerative models that hypothesize increased expression of schizophrenia risk genes during and after the peak age of risk. Our findings underscore the utility of cognitive traits for tracking schizophrenia genetic effects across the lifespan.

Gene-Environment Correlation over Time: A Longitudinal Analysis of Polygenic Risk Scores for Schizophrenia and Major Depression in Three British Cohorts Studies

Research suggests that both genetic and environmental risk factors are involved in the aetiology of schizophrenia (SCZ) and major depressive disorder (MDD). Importantly, environmental and genetic risk factors are often related as evidenced in gene-environment correlation (rGE), which describes the observation that genetic and environmental factors are associated with each other. It is understood that rGE gets stronger over time as individuals select their environments more actively based on their genetic propensities. However, little is known whether rGEs remain stable over time or change across different development periods. Using data from three British longitudinal cohorts, we investigated whether rGE patterns of polygenic risk scores (PRS) for SCZ and MDD changed over time across childhood and adulthood, as well as across both from birth to age 55 and whether results differed between SCZ and MDD. Overall, the majority of rGEs remained stable across the investigated development periods. Furthermore, the few detected rGE changes which did differ between SCZ and MDD, could not be explained by the confounding of clinical cases and are therefore likely the result of actual changes in environmental and cultural risk factors with genetic susceptibility to SCZ and MDD likely playing a less significant role.

Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants With Treatment Resistance in Schizophrenia

IMPORTANCE

About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts.

OBJECTIVE

To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples.

DESIGN, SETTING, AND PARTICIPANTS

Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10 501) and individuals with non-TRS (n = 20 325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]).

MAIN OUTCOMES AND MEASURES

GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition.

RESULTS

The study included a total of 85 490 participants (48 635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P = .001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P = .04).

CONCLUSIONS AND RELEVANCE

In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance.

Causal Inference with Genetic Data: Past, Present, and Future

The set of methods discussed in this collection has emerged from the convergence of two scientific fields-genetics and causal inference. In this introduction, we discuss relevant aspects of each field and show how their convergence arises from the natural experiments that genetics offer. We present introductory concepts useful to readers unfamiliar with genetically informed methods for causal inference. We conclude that existing applications and foreseeable developments should ensure that we rapidly reap the rewards of this relatively new field, not only in terms of our understanding of human disease and development, but also in terms of tangible translational applications.

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.

From Genome-Wide to Environment-Wide: Capturing the Environome

Genome-wide association (GWA) studies have shown that genetic influences on individual differences in affect, behavior, and cognition are driven by thousands of DNA variants, each with very small effect sizes. Here, we propose taking inspiration from GWA studies for understanding and modeling the influence of the environment on complex phenotypes. We argue that the availability of DNA microarrays in genetic research is comparable with the advent of digital technologies in psychological science that enable collecting rich, naturalistic observations in real time of the environome, akin to the genome. These data can capture many thousand environmental elements, which we speculate each influence individual differences in affect, behavior, and cognition with very small effect sizes, akin to findings from GWA studies about DNA variants. We outline how the principles and mechanisms of genetic influences on psychological traits can be applied to improve the understanding and models of the environome.

The prospects of precision psychiatry

Since the turn of the twenty-first century, biomedical psychiatry around the globe has embraced the so-called precision medicine paradigm, a model for medical research that uses innovative techniques for data collection and analysis to reevaluate traditional theories of disease. The goal of precision medicine is to improve diagnostics by restratifying the patient population on the basis of a deeper understanding of disease processes. This paper argues that precision is ill-fitting for psychiatry for two reasons. First, in psychiatry, unlike in fields like oncology, precision medicine has been understood as an attempt to improve medicine by casting out, rather than merely revising, traditional taxonomic tools. Second, in psychiatry the term "biomarker" is often used in reference to signs or symptoms that allow patients to be classified and then matched with treatments; however, in oncology "biomarker" usually refers to a disease mechanism that is useful not only for diagnostics, but also for discovering causal pathways that drug therapies can target. Given these differences between how the precision medicine paradigm operates in psychiatry and in other medical fields like oncology, while precision psychiatry may offer successful rhetoric, it is not a promising paradigm.

Genetic overlap and causal associations between smoking behaviours and mental health

Cigarette smoking is a modifiable behaviour associated with mental health. We investigated the degree of genetic overlap between smoking behaviours and psychiatric traits and disorders, and whether genetic associations exist beyond genetic influences shared with confounding variables (cannabis and alcohol use, risk-taking and insomnia). Second, we investigated the presence of causal associations between smoking initiation and psychiatric traits and disorders. We found significant genetic correlations between smoking and psychiatric disorders and adult psychotic experiences. When genetic influences on known covariates were controlled for, genetic associations between most smoking behaviours and schizophrenia and depression endured (but not with bipolar disorder or most psychotic experiences). Mendelian randomization results supported a causal role of smoking initiation on psychiatric disorders and adolescent cognitive and negative psychotic experiences, although not consistently across all sensitivity analyses. In conclusion, smoking and psychiatric disorders share genetic influences that cannot be attributed to covariates such as risk-taking, insomnia or other substance use. As such, there may be some common genetic pathways underlying smoking and psychiatric disorders. In addition, smoking may play a causal role in vulnerability for mental illness.

Greater genetic risk for adult psychiatric diseases increases vulnerability to adverse outcome after preterm birth

Preterm birth is an extreme environmental stress associated with an increased risk of later cognitive dysfunction and mental health problems. However, the extent to which preterm birth is modulated by genetic variation remains largely unclear. Here, we test for an interaction effect between psychiatric polygenic risk and gestational age at birth on cognition at age four. Our sample comprises 4934 unrelated individuals (2066 individuals born < 37 weeks, 918 born <  = 34 weeks). Genome-wide polygenic scores (GPS's) were calculated for each individual for five different psychiatric pathologies: Schizophrenia, Bipolar Disorder, Major Depressive Disorder, Attention Deficit Hyperactivity Disorder and Autism Spectrum Disorder. Linear regression modelling was used to estimate the interaction effect between psychiatric GPS and gestational age at birth (GA) on cognitive outcome for the five psychiatric disorders. We found a significant interaction effect between Schizophrenia GPS and GA (β = 0.038 ± 0.013, p = 6.85 × 10-3) and Bipolar Disorder GPS and GA (β = 0.038 ± 0.014, p = 6.61 × 10-3) on cognitive outcome. Individuals with greater genetic risk for Schizophrenia or Bipolar Disorder are more vulnerable to the adverse effects of birth at early gestational age on brain development, as assessed by cognition at age four. Better understanding of gene-environment interactions will inform more effective risk-reducing interventions for this vulnerable population.

The practical utility of genetic screening in school settings

Can genetic screening be used to personalize education for students? Genome-wide association studies (GWAS) screen an individual's DNA for specific variations in their genome, and how said variations relate to specific traits. The variations can then be assigned a corresponding weight and summed to produce polygenic scores (PGS) for given traits. Though first developed for disease risk, PGS is now used to predict educational achievement. Using a novel simulation method, this paper examines if PGS could advance screening in schools, a goal of personalized education. Results show limited potential benefits for using PGS to personalize education for individual students. However, further analysis shows PGS can be effectively used alongside progress monitoring measures to screen for learning disability risk. Altogether, PGS is not useful in personalizing education for every child but has potential utility when used simultaneously with additional screening tools to help determine which children may struggle academically.

Fate or coincidence: do COPD and major depression share genetic risk factors?

Major depressive disorder (MDD) is a common comorbidity in chronic obstructive pulmonary disease (COPD), affecting up to 57% of patients with COPD. Although the comorbidity of COPD and MDD is well established, the causal relationship between these two diseases is unclear. A large-scale electronic health record clinical biobank and genome-wide association study summary statistics for MDD and lung function traits were used to investigate potential shared underlying genetic susceptibility between COPD and MDD. Linkage disequilibrium score regression was used to estimate genetic correlation between phenotypes. Polygenic risk scores (PRS) for MDD and lung function traits were developed and used to perform a phenome-wide association study (PheWAS). Multi-trait-based conditional and joint analysis identified single-nucleotide polymorphisms (SNPs) influencing both lung function and MDD. We found genetic correlations between MDD and all lung function traits were small and not statistically significant. A PRS-MDD was significantly associated with an increased risk of COPD in a PheWAS [odds ratio (OR) = 1.12, 95% confidence interval (CI): 1.09-1.16] when adjusting for age, sex and genetic ancestry, but this relationship became attenuated when controlling for smoking history (OR = 1.08, 95% CI: 1.04-1.13). No significant associations were found between the lung function PRS and MDD. Multi-trait-based conditional and joint analysis identified three SNPs that may contribute to both traits, two of which were previously associated with mood disorders and COPD. Our findings suggest that the observed relationship between COPD and MDD may not be driven by a strong shared genetic architecture.

Evidence for specificity of polygenic contributions to attainment in English, maths and science during adolescence

How well one does at school is predictive of a wide range of important cognitive, socioeconomic, and health outcomes. The last few years have shown marked advancement in our understanding of the genetic contributions to, and correlations with, academic attainment. However, there exists a gap in our understanding of the specificity of genetic associations with performance in academic subjects during adolescence, a critical developmental period. To address this, the Avon Longitudinal Study of Parents and Children was used to conduct genome-wide association studies of standardised national English (N = 5983), maths (N = 6017) and science (N = 6089) tests. High SNP-based heritabilities (h2SNP) for all subjects were found (41-53%). Further, h2SNP for maths and science remained after removing shared variance between subjects or IQ (N = 3197-5895). One genome-wide significant single nucleotide polymorphism (rs952964, p = 4.86 × 10-8) and four gene-level associations with science attainment (MEF2C, BRINP1, S100A1 and S100A13) were identified. Rs952964 remained significant after removing the variance shared between academic subjects. The findings highlight the benefits of using environmentally homogeneous samples for genetic analyses and indicate that finer-grained phenotyping will help build more specific biological models of variance in learning processes and abilities.

Polygenic Scores for ADHD: A Meta-Analysis

Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder that is known to have a polygenic (i.e., many genes of individually small effects) architecture. Polygenic scores (PGS), which characterize this polygenicity as a single score for a given individual, are considered the state-of-the-art in psychiatric genetics research. Despite the proliferation of ADHD studies adopting this approach and its clinical implications, remarkably little is known about the predictive utility of PGS in ADHD research to date, given that there have not yet been any systematic or meta-analytic reviews of this rapidly developing literature. We meta-analyzed 12 unique effect sizes from ADHD PGS studies, yielding an N = 40,088. These studies, which included a mixture of large population-based cohorts and case-control samples of predominantly European ancestry, yielded a pooled ADHD PGS effect size of r_random = 0.201 (95% CI = [0.144, 0.288]) and an r_fixed = 0.190 (95% CI = [0.180, 0.199]) in predicting ADHD. In other words, ADHD PGS reliably account for between 3.6% (in the fixed effects model) to 4.0% (in the random effects model) of the variance in broadly defined phenotypic ADHD. Findings provide important insights into the genetics of psychiatric outcomes and raise several key questions about the impact of PGS on psychiatric research moving forward. Our review concludes by providing recommendations for future research directions in the use of PGS, including new methods to account for comorbidities, integrating bioinformatics to elucidate biological pathways, and leveraging PGS to test mechanistic models of ADHD.

Investigating the effects of genetic risk of schizophrenia on behavioural traits

To characterise the trait-effects of increased genetic risk for schizophrenia, and highlight potential risk mediators, we test the association between schizophrenia polygenic risk scores (PRSs) and 529 behavioural traits (personality, psychological, lifestyle, nutritional) in the UK Biobank. Our primary analysis is performed on individuals aged 38–71 with no history of schizophrenia or related disorders, allowing us to report the effects of schizophrenia genetic risk in the sub-clinical general population. Higher schizophrenia PRSs were associated with a range of traits, including lower verbal-numerical reasoning (P = 6 × 10^–61), higher nervous feelings (P = 1 × 10⁻⁴⁶) and higher self-reported risk-taking (P = 3 × 10⁻³⁸). We follow-up the risk-taking association, hypothesising that the association may be due to a genetic propensity for risk-taking leading to greater migration, urbanicity or drug-taking — reported environmental risk factors for schizophrenia, and all positively associated with risk-taking in these data. Next, to identify potential disorder or medication effects, we compare the PRS–trait associations in the general population to the trait values in 599 medicated and non-medicated individuals diagnosed with schizophrenia in the biobank. This analysis highlights, for example, levels of BMI, physical activity and risk-taking in cases in the opposite directions than expected from the PRS–trait associations in the general population. Our analyses offer simple yet potentially revealing insights into the possible causes of observed trait–disorder associations, which can complement approaches such as Mendelian Randomisation. While we urge caution in causal interpretations in PRS cross-trait studies that are highly powered to detect weak horizontal pleiotropy or population structure, we propose that well-designed polygenic score analyses have the potential to highlight modifiable risk factors that lie on the path between genetic risk and disorder.

Meta-analysis of sample-level dbGaP data reveals novel shared genetic link between body height and Crohn's disease

To further explore genetic links between complex traits, we developed a comprehensive framework to harmonize and integrate extensive genotype and phenotype data from the four well-characterized cohorts with the focus on cardiometabolic diseases deposited to the database of Genotypes and Phenotypes (dbGaP). We generated a series of polygenic risk scores (PRS) to investigate pleiotropic effects of loci that confer genetic risk for 19 common diseases and traits on body height, type 2 diabetes (T2D), and myocardial infarction (MI). In a meta-analysis of 20,021 subjects, we identified shared genetic determinants of Crohn's Disease (CD), a type of inflammatory bowel disease, and body height (p = 5.5 × 10^-5). The association of PRS-CD with height was replicated in UK Biobank (p = 1.1 × 10^-5) and an independent cohort of 510 CD cases and controls (1.57 cm shorter height per PRS-CD interquartile increase, p = 5.0 × 10^-3 and a 28% reduction in CD risk per interquartile increase in PRS-height, p = 1.1 × 10^-3, with the effect independent of CD diagnosis). A pathway analysis of the variants overlapping between PRS-height and PRS-CD detected significant enrichment of genes from the inflammatory, immune-mediated and growth factor regulation pathways. This finding supports the clinical observation of growth failure in patients with childhood-onset CD and demonstrates the value of using individual-level data from dbGaP in searching for shared genetic determinants. This information can help provide a refined insight into disease pathogenesis and may have major implications for novel therapies and drug repurposing.

The positive end of the polygenic score distribution for ADHD: a low risk or a protective factor?

BACKGROUND

Polygenic scores (PGS) are widely used to characterize genetic liability for heritable mental disorders, including attention-deficit/hyperactivity disorder (ADHD). However, little is known about the effects of a low burden of genetic liability for ADHD, including whether this functions as a low risk or protective factor for ADHD and related functional outcomes in later life. The current study examines the association of low ADHD PGS and functional outcomes in adulthood.

METHODS

Participants were from Wave IV of the National Longitudinal Study of Adolescent to Adult Health (Add Health) (N = 7088; mean age = 29, s.d. = 1.74). ADHD PGS was computed from an existing genome-wide association study, and adult functional outcomes, including cognition, educational attainment, mental health, and physical health were assessed during in-home interviews.

RESULTS

Individuals at the lowest end of the ADHD PGS distribution (i.e. lowest 20th percentile) had the lowest probabilities of ADHD, exhibiting a 17-19% reduction in risk for ADHD relative to the observed 8.3% prevalence rate of ADHD in Add Health. Furthermore, individuals with low ADHD PGS had higher cognitive performance, greater levels of educational attainment, and lower BMI relative to individuals representing the rest of the ADHD PGS distribution, including those who were in the medium and high-PGS groups.

CONCLUSIONS

Findings indicate that psychiatric PGS likely capture far more than just the risk and the absence of risk for a psychiatric outcome; where one lies along the PGS distribution may predict diverging functional consequences, for better and for worse.

Multivariable G-E interplay in the prediction of educational achievement

Polygenic scores are increasingly powerful predictors of educational achievement. It is unclear, however, how sets of polygenic scores, which partly capture environmental effects, perform jointly with sets of environmental measures, which are themselves heritable, in prediction models of educational achievement. Here, for the first time, we systematically investigate gene-environment correlation (rGE) and interaction (GxE) in the joint analysis of multiple genome-wide polygenic scores (GPS) and multiple environmental measures as they predict tested educational achievement (EA). We predict EA in a representative sample of 7,026 16-year-olds, with 20 GPS for psychiatric, cognitive and anthropometric traits, and 13 environments (including life events, home environment, and SES) measured earlier in life. Environmental and GPS predictors were modelled, separately and jointly, in penalized regression models with out-of-sample comparisons of prediction accuracy, considering the implications that their interplay had on model performance. Jointly modelling multiple GPS and environmental factors significantly improved prediction of EA, with cognitive-related GPS adding unique independent information beyond SES, home environment and life events. We found evidence for rGE underlying variation in EA (rGE = .38; 95% CIs = .30, .45). We estimated that 40% (95% CIs = 31%, 50%) of the polygenic scores effects on EA were mediated by environmental effects, and in turn that 18% (95% CIs = 12%, 25%) of environmental effects were accounted for by the polygenic model, indicating genetic confounding. Lastly, we did not find evidence that GxE effects significantly contributed to multivariable prediction. Our multivariable polygenic and environmental prediction model suggests widespread rGE and unsystematic GxE contributions to EA in adolescence.

Our study investigates the complex interplay between genetic and environmental contributions underlying educational achievement (EA). Polygenic scores are becoming increasingly powerful predictors of EA. While emerging evidence indicates that polygenic scores are not pure measures of genetic predisposition, previous quantitative genetics findings indicate that measures of the environment are themselves heritable. In this regard it is unclear how such measures of individual predisposition jointly combine to predict EA. We investigate this question in a representative UK sample of 7,026 16-year-olds where we provide substantive results on gene-environment correlation and interaction underlying variation in EA. We show that polygenic score and environmental prediction models of EA overlap substantially. Polygenic scores effects on EA are partly accounted for by their correlation with environmental effects; similarly, environmental effects on EA are linked to polygenic scores effects. Nonetheless, jointly considering polygenic scores and measured environments significantly improves prediction of EA. We also find that, although correlation between polygenic scores and measured environments is substantial, interactions between them do not play a significant role in the prediction of EA. Our findings have relevance for genomic and environmental prediction models alike, as they show the way in which individuals’ genetic predispositions and environmental effects are intertwined. This suggests that both genetic and environmental effects must be taken into account in prediction models of complex behavioral traits such as EA.

The extracellular matrix phenome across species

Extracellular matrices are essential for cellular and organismal function. Recent genome-wide and phenome-wide association studies started to reveal a broad spectrum of phenotypes associated with genetic variants. However, the phenome or spectrum of all phenotypes associated with genetic variants in extracellular matrix genes is unknown. Here, we analyzed over two million recorded genotype-to-phenotype relationships across multiple species to define their extracellular matrix phenomes. By using the previously defined matrisomes of humans, mice, zebrafish, Drosophila, and C. elegans, we found that the extracellular matrix phenome comprises of 3-10% of the entire phenome. Collagens (COL1A1, COL2A1) and fibrillin (FBN1) are each associated with >150 distinct phenotypes in humans, whereas collagen COL4A1, Wnt- and sonic hedgehog (shh) signaling are predominantly associated in other species. We determined the phenotypic fingerprints of matrisome genes and found that MSTN, CTSD, LAMB2, HSPG2, and COL11A2 and their corresponding orthologues have the most phenotypes across species. Out of the 42,551 unique matrisome genotype-to-phenotype relationships across the five species with defined matrisomes, we have constructed interaction networks to identify the underlying molecular components connecting with orthologues phenotypes and with novel phenotypes. Thus, our networks provide a framework to predict unassessed phenotypes and their potential underlying molecular interactions. These frameworks inform on matrisome genotype-to-phenotype relationships and potentially provide a sophisticated choice of biological model system to study human phenotypes and diseases.

Tutorial: a guide to performing polygenic risk score analyses

A polygenic score (PGS) or polygenic risk score (PRS) is an estimate of an individual's genetic liability to a trait or disease, calculated according to their genotype profile and relevant genome-wide association study (GWAS) data. While present PRSs typically explain only a small fraction of trait variance, their correlation with the single largest contributor to phenotypic variation-genetic liability-has led to the routine application of PRSs across biomedical research. Among a range of applications, PRSs are exploited to assess shared etiology between phenotypes, to evaluate the clinical utility of genetic data for complex disease and as part of experimental studies in which, for example, experiments are performed that compare outcomes (e.g., gene expression and cellular response to treatment) between individuals with low and high PRS values. As GWAS sample sizes increase and PRSs become more powerful, PRSs are set to play a key role in research and stratified medicine. However, despite the importance and growing application of PRSs, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here, we provide detailed guidelines for performing and interpreting PRS analyses. We outline standard quality control steps, discuss different methods for the calculation of PRSs, provide an introductory online tutorial, highlight common misconceptions relating to PRS results, offer recommendations for best practice and discuss future challenges.

The contribution of common genetic risk variants for ADHD to a general factor of childhood psychopathology

Common genetic risk variants have been implicated in the etiology of clinical attention-deficit/hyperactivity disorder (ADHD) diagnoses and symptoms in the general population. However, given the extensive comorbidity across ADHD and other psychiatric conditions, the extent to which genetic variants associated with ADHD also influence broader psychopathology dimensions remains unclear. The aim of this study was to evaluate the associations between ADHD polygenic risk scores (PRS) and a broad range of childhood psychiatric symptoms, and to quantify the extent to which such associations can be attributed to a general factor of childhood psychopathology. We derived ADHD PRS for 13,457 children aged 9 or 12 from the Child and Adolescent Twin Study in Sweden, using results from an independent meta-analysis of genome-wide association studies of ADHD diagnosis and symptoms. We estimated associations between ADHD PRS, a general psychopathology factor, and several dimensions of neurodevelopmental, externalizing, and internalizing symptoms, using structural equation modeling. Higher ADHD PRS were statistically significantly associated with elevated neurodevelopmental, externalizing, and depressive symptoms (R2 = 0.26-1.69%), but not with anxiety. After accounting for a general psychopathology factor, on which all symptoms loaded positively (mean loading = 0.50, range = 0.09-0.91), an association with specific hyperactivity/impulsivity remained significant. ADHD PRS explained ~ 1% (p value < 0.0001) of the variance in the general psychopathology factor and ~ 0.50% (p value < 0.0001) in specific hyperactivity/impulsivity. Our results suggest that common genetic risk variants associated with ADHD, and captured by PRS, also influence a general genetic liability towards broad childhood psychopathology in the general population, in addition to a specific association with hyperactivity/impulsivity symptoms.

Developing and testing high‐efficacy patient subgroups within a clinical trial using risk scores

There is the potential for high-dimensional information about patients collected in clinical trials (such as genomic, imaging, and data from wearable technologies) to be informative for the efficacy of a new treatment in situations where only a subset of patients benefits from the treatment. The adaptive signature design (ASD) method has been proposed for developing and testing the efficacy of a treatment in a high-efficacy patient group (the sensitive group) using genetic data. The method requires selection of three tuning parameters which may be highly computationally expensive. We propose a variation to the ASD method, the cross-validated risk scores (CVRS) design method, that does not require selection of any tuning parameters. The method is based on computing a risk score for each patient and dividing them into clusters using a nonparametric clustering procedure.We assess the properties of CVRS against the originally proposed cross-validated ASD using simulation data and a real psychiatry trial. CVRS, as assessed for various sample sizes and response rates, has a substantial reduction in the computational time required. In many simulation scenarios, there is a substantial improvement in the ability to correctly identify the sensitive group and the power of the design to detect a treatment effect in the sensitive group.We illustrate the application of the CVRS method on the psychiatry trial.

Genetic Associations Between Childhood Psychopathology and Adult Depression and Associated Traits in 42 998 Individuals: A Meta-analysis

Importance

Adult mood disorders are often preceded by behavioral and emotional problems in childhood. It is yet unclear what explains the associations between childhood psychopathology and adult traits.

Objective

To investigate whether genetic risk for adult mood disorders and associated traits is associated with childhood disorders.

Design, Setting, and Participants

This meta-analysis examined data from 7 ongoing longitudinal birth and childhood cohorts from the UK, the Netherlands, Sweden, Norway, and Finland. Starting points of data collection ranged from July 1985 to April 2002. Participants were repeatedly assessed for childhood psychopathology from ages 6 to 17 years. Data analysis occurred from September 2017 to May 2019.

Exposures

Individual polygenic scores (PGS) were constructed in children based on genome-wide association studies of adult major depression, bipolar disorder, subjective well-being, neuroticism, insomnia, educational attainment, and body mass index (BMI).

Main Outcomes and Measures

Regression meta-analyses were used to test associations between PGS and attention-deficit/hyperactivity disorder (ADHD) symptoms and internalizing and social problems measured repeatedly across childhood and adolescence and whether these associations depended on childhood phenotype, age, and rater.

Results

The sample included 42 998 participants aged 6 to 17 years. Male participants varied from 43.0% (1040 of 2417 participants) to 53.1% (2434 of 4583 participants) by age and across all cohorts. The PGS of adult major depression, neuroticism, BMI, and insomnia were positively associated with childhood psychopathology (β estimate range, 0.023-0.042 [95% CI, 0.017-0.049]), while associations with PGS of subjective well-being and educational attainment were negative (β, -0.026 to -0.046 [95% CI, -0.020 to -0.057]). There was no moderation of age, type of childhood phenotype, or rater with the associations. The exceptions were stronger associations between educational attainment PGS and ADHD compared with internalizing problems (Δβ, 0.0561 [Δ95% CI, 0.0318-0.0804]; ΔSE, 0.0124) and social problems (Δβ, 0.0528 [Δ95% CI, 0.0282-0.0775]; ΔSE, 0.0126), and between BMI PGS and ADHD and social problems (Δβ, -0.0001 [Δ95% CI, -0.0102 to 0.0100]; ΔSE, 0.0052), compared with internalizing problems (Δβ, -0.0310 [Δ95% CI, -0.0456 to -0.0164]; ΔSE, 0.0074). Furthermore, the association between educational attainment PGS and ADHD increased with age (Δβ, -0.0032 [Δ 95% CI, -0.0048 to -0.0017]; ΔSE, 0.0008).

Conclusions and Relevance

Results from this study suggest the existence of a set of genetic factors influencing a range of traits across the life span with stable associations present throughout childhood. Knowledge of underlying mechanisms may affect treatment and long-term outcomes of individuals with psychopathology.

The Contribution of Cognitive and Noncognitive Skills to Intergenerational Social Mobility

We investigated intergenerational educational and occupational mobility in a sample of 2,594 adult offspring and 2,530 of their parents. Participants completed assessments of general cognitive ability and five noncognitive factors related to social achievement; 88% were also genotyped, allowing computation of educational-attainment polygenic scores. Most offspring were socially mobile. Offspring who scored at least 1 standard deviation higher than their parents on both cognitive and noncognitive measures rarely moved down and frequently moved up. Polygenic scores were also associated with social mobility. Inheritance of a favorable subset of parent alleles was associated with moving up, and inheritance of an unfavorable subset was associated with moving down. Parents' education did not moderate the association of offspring's skill with mobility, suggesting that low-skilled offspring from advantaged homes were not protected from downward mobility. These data suggest that cognitive and noncognitive skills as well as genetic factors contribute to the reordering of social standing that takes place across generations.

A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies

CONTEXT

As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice.

OBJECTIVE

Utilizing polygenic risk prediction, we aim to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment.

DESIGN, PATIENTS, AND METHODS

Leveraging the electronic health records (EHRs) of 124 852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS.

RESULTS

The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity", "type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension", and "sleep apnea" reaching phenome-wide significance.

CONCLUSIONS

Our study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome-phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.

Pleiotropy of polygenic factors associated with focal and generalized epilepsy in the general population

Epilepsy is clinically heterogeneous, and neurological or psychiatric comorbidities are frequently observed in patients. It has not been tested whether common risk variants for generalized or focal epilepsy are enriched in people with other disorders or traits related to brain or cognitive function. Here, we perform two brain-focused phenome association studies of polygenic risk scores (PRS) for generalized epilepsy (GE-PRS) or focal epilepsy (FE-PRS) with all binary brain or cognitive function-related traits available for 334,310 European-ancestry individuals of the UK Biobank. Higher GE-PRS were associated with not having a college or university degree (P = 3.00x10-4), five neuroticism-related personality traits (P<2.51x10-4), and having ever smoked (P = 1.27x10-6). Higher FE-PRS were associated with several measures of low educational attainment (P<4.87x10-5), one neuroticism-related personality trait (P = 2.33x10-4), having ever smoked (P = 1.71x10-4), and having experienced events of anxiety or depression (P = 2.83x10-4). GE- and FE-PRS had the same direction of effect for each of the associated traits. Genetic factors associated with GE or FE showed similar patterns of correlation with genetic factors associated with cortical morphology in a subset of the UKB with 16,612 individuals and T1 magnetic resonance imaging data. In summary, our results suggest that genetic factors associated with epilepsies may confer risk for other neurological and psychiatric disorders in a population sample not enriched for epilepsy.

Distinct Polygenic Score Profiles in Schizophrenia Subgroups With Different Trajectories of Cognitive Development

OBJECTIVE

Different cognitive development histories in schizophrenia may reflect variation across dimensions of genetic influence. The authors derived and characterized cognitive development trajectory subgroups within a schizophrenia sample and profiled the subgroups across polygenic scores (PGSs) for schizophrenia, cognition, educational attainment, and attention deficit hyperactivity disorder (ADHD).

METHODS

Demographic, clinical, and genetic data were collected at the National Institute of Mental Health from 540 schizophrenia patients, 247 unaffected siblings, and 844 control subjects. Cognitive trajectory subgroups were derived through cluster analysis using estimates of premorbid and current IQ. PGSs were generated using standard methods. Associations were tested using general linear models and logistic regression.

RESULTS

Cluster analyses identified three cognitive trajectory subgroups in the schizophrenia group: preadolescent cognitive impairment (19%), adolescent disruption of cognitive development (44%), and cognitively stable adolescent development (37%). Together, the four PGSs significantly predicted 7.9% of the variance in subgroup membership. Subgroup characteristics converged with genetic patterns. Cognitively stable individuals had the best adult clinical outcomes and differed from control subjects only in schizophrenia PGSs. Those with adolescent disruption of cognitive development showed the most severe symptoms after diagnosis and were cognitively impaired. This subgroup had the highest schizophrenia PGSs and had disadvantageous cognitive PGSs relative to control subjects and cognitively stable individuals. Individuals showing preadolescent impairment in cognitive and academic performance and poor adult outcome exhibited a generalized PGS disadvantage relative to control subjects and were the only subgroup to differ significantly in education and ADHD PGSs.

CONCLUSIONS

Subgroups derived from patterns of premorbid and current IQ showed different premorbid and clinical characteristics, which converged with broad genetic profiles. Simultaneous analysis of multiple PGSs may contribute to useful clinical stratification in schizophrenia.

Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models

Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRSs). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications, including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed-model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work, we present a novel reference-free method to produce a PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial overfitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues and can produce informative in-sample PRSs over a single cohort without overfitting. We then demonstrate several novel applications of reference-free PRSs, including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.

Genetic overlap between psychotic experiences in the community across age and with psychiatric disorders

This study explores the degree to which genetic influences on psychotic experiences are stable across adolescence and adulthood, and their overlap with psychiatric disorders. Genome-wide association results were obtained for adolescent psychotic experiences and negative symptom traits (N = 6297-10,098), schizotypy (N = 3967-4057) and positive psychotic experiences in adulthood (N = 116,787-117,794), schizophrenia (N = 150,064), bipolar disorder (N = 41,653), and depression (N = 173,005). Linkage disequilibrium score regression was used to estimate genetic correlations. Implicated genes from functional and gene-based analyses were compared. Mendelian randomization was performed on trait pairs with significant genetic correlations. Results indicated that subclinical auditory and visual hallucinations and delusions of persecution during adulthood were significantly genetically correlated with schizophrenia (rg = 0.27-0.67) and major depression (rg = 0.41-96) after correction for multiple testing. Auditory and visual subclinical hallucinations were highly genetically correlated (rg = 0.95). Cross-age genetic correlations for psychotic experiences were not significant. Gene mapping and association analyses revealed 14 possible genes associated with psychotic experiences that overlapped across age for psychotic experiences or between psychotic experiences and psychiatric disorders. Mendelian randomization indicated bidirectional associations between auditory and visual hallucinations in adults but did not support causal relationships between psychotic experiences and psychiatric disorders. These findings indicate that psychotic experiences in adulthood may be more linked genetically to schizophrenia and major depression than psychotic experiences in adolescence. Our study implicated specific genes that are associated with psychotic experiences across development, as well as genes shared between psychotic experiences and psychiatric disorders.

Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts

Understanding the impact of rare variants is essential to understanding human health. We analyze rare (MAF < 0.1%) variants against 4264 phenotypes in 49,960 exome-sequenced individuals from the UK Biobank and 1934 phenotypes (1821 overlapping with UK Biobank) in 21,866 members of the Healthy Nevada Project (HNP) cohort who underwent Exome + sequencing at Helix. After using our rare-variant-tailored methodology to reduce test statistic inflation, we identify 64 statistically significant gene-based associations in our meta-analysis of the two cohorts and 37 for phenotypes available in only one cohort. Singletons make significant contributions to our results, and the vast majority of the associations could not have been identified with a genotyping chip. Our results are available for interactive browsing in a webapp (https://ukb.research.helix.com). This comprehensive analysis illustrates the biological value of large, deeply phenotyped cohorts of unselected populations coupled with NGS data.

Population-based association analyses of rare genetic variants with complex traits are limited by the availability of data from sufficiently large cohorts. Here, Cirulli et al. report gene-based collapsing analysis of exomes from 49,960 participants of the UK Biobank and 21,866 participants of the Healthy Nevada Project over a total of 4377 traits.

Polygenic Risk Scores Shed Light on the Relationship between Schizophrenia and Cognitive Functioning: Review and Meta-Analysis

Schizophrenia is a multifactorial disease associated with widespread cognitive impairment. Although cognitive deficits are one of the factors most strongly associated with functional impairment in schizophrenia (SZ), current treatment strategies hardly tackle these impairments. To develop more efficient treatment strategies in patients, a better understanding of their pathogenesis is needed. Recent progress in genetics, driven by large genome-wide association studies (GWAS) and the use of polygenic risk scores (PRS), has provided new insights about the genetic architecture of complex human traits, including cognition and SZ. Here, we review the recent findings examining the genetic links between SZ and cognitive functions in population-based samples as well as in participants with SZ. The performed meta-analysis showed a negative correlation between the polygenetic risk score of schizophrenia and global cognition (p < 0.001) when the samples rely on general and healthy participants, while no significant correlation was detected when the three studies devoted to schizophrenia patients were meta-analysed (p > 0.05). Our review and meta-analysis therefore argues against universal pleiotropy for schizophrenia alleles and cognition, since cognition in SZ patients would be underpinned by the same genetic factors than in the general population, and substantially independent of common variant liability to the disorder.

Maternal environmental risk factors and the development of internalizing and externalizing problems in childhood: The complex role of genetic factors

The development of problem behavior in children is associated with exposure to environmental factors, including the maternal environment. Both are influenced by genetic factors, which may also be correlated, that is, environmental risk and problem behavior in children might be influenced by partly the same genetic factors. In addition, environmental and genetic factors could interact with each other increasing the risk of problem behavior in children. To date, limited research investigated these mechanisms in a genome-wide approach. Therefore, the goal of this study was to investigate the association between genetic risk for psychiatric and related traits, as indicated by polygenetic risk scores (PRSs), exposure to previously identified maternal risk factors, and problem behavior in a sample of 1,154 children from the Amsterdam Born Children and their Development study at ages 5-6 and 11-12 years old. The PRSs were derived from genome-wide association studies (GWASs) on schizophrenia, major depressive disorder, neuroticism, and wellbeing. Regression analysis showed that the PRSs were associated with exposure to multiple environmental risk factors, suggesting passive gene-environment correlation. In addition, the PRS based on the schizophrenia GWAS was associated with externalizing behavior problems in children at age 5-6. We did not find any association with problem behavior for the other PRSs. Our results indicate that genetic predispositions for psychiatric disorders and wellbeing are associated with early environmental risk factors for children's problem behavior.

Genomics and psychological resilience: a research agenda

Although exposure to adversity increases risk for poor mental health outcomes, many people exposed to adversity do not develop such outcomes. Psychological resilience, defined broadly as positive emotional and/or behavioral adaptation to adversity, may be influenced by genetic factors that have remained largely unexplored in the era of large-scale genome-wide studies. In this perspective, we provide an integrative framework for studying human genome-wide variation underlying resilience. We first outline three complementary working definitions of psychological resilience-as a capacity, process, and outcome. For each definition, we review emerging empirical evidence, including findings from positive psychology, to illustrate how a resilience-based framework can guide novel and fruitful directions for the field of psychiatric genomics, distinct from the ongoing study of psychiatric risk and related traits. Finally, we provide practical recommendations for future genomic research on resilience, highlighting a need to augment cross-sectional findings with prospective designs that include detailed measurement of adversities and outcomes. A research framework that explicitly addresses resilience could help us to probe biological mechanisms of stress adaptation, identify individuals who may benefit the most from prevention and early intervention, and ascertain modifiable protective factors that mitigate negative outcomes even for those at high genetic risk.

Polygenic risk scores for Alzheimer's disease, and academic achievement, cognitive and behavioural measures in children from the general population

OBJECTIVE

Several studies report a polygenic component of risk for Alzheimer's disease. Understanding whether this polygenic signal is associated with educational, cognitive and behavioural outcomes in children could provide an earlier window for intervention.

METHODS

We examined whether polygenic risk scores (PRS) at varying P-value thresholds in children from the Avon Longitudinal Study of Parents and Children were associated with academic achievement, cognitive and behavioural measures in childhood and adolescence.

RESULTS

We did not detect any evidence that the genome-wide significant PRS (5x10-8) were associated with these outcomes. PRS at the highest P-value threshold examined (P ≤ 5x10-1) were associated with lower academic achievement in adolescents (Key Stage 3; β: -0.03; 95% confidence interval: -0.05, -0.003) but the effect was attenuated when single nucleotide polymorphisms (SNPs) associated with educational attainment were removed. These PRS were associated with lower IQ (β: -0.04; 95% CI: -0.07, -0.02) at age 8 years with the effect remaining after removing SNPs associated with educational attainment.

CONCLUSIONS

SNPs mediating the biological effects of Alzheimer's disease are unlikely to operate early in life. The evidence of association between PRS for Alzheimer's disease at liberal thresholds and cognitive measures suggest shared genetic pathways between Alzheimer's disease, academic achievement and cognition.

Evaluating statistical approaches to leverage large clinical datasets for uncovering therapeutic and adverse medication effects

Motivation

Phenome-wide association studies (PheWAS) have been used to discover many genotype-phenotype relationships and have the potential to identify therapeutic and adverse drug outcomes using longitudinal data within electronic health records (EHRs). However, the statistical methods for PheWAS applied to longitudinal EHR medication data have not been established.

Results

In this study, we developed methods to address two challenges faced with reuse of EHR for this purpose: confounding by indication, and low exposure and event rates. We used Monte Carlo simulation to assess propensity score (PS) methods, focusing on two of the most commonly used methods, PS matching and PS adjustment, to address confounding by indication. We also compared two logistic regression approaches (the default of Wald versus Firth's penalized maximum likelihood, PML) to address complete separation due to sparse data with low exposure and event rates. PS adjustment resulted in greater power than PS matching, while controlling Type I error at 0.05. The PML method provided reasonable P-values, even in cases with complete separation, with well controlled Type I error rates. Using PS adjustment and the PML method, we identify novel latent drug effects in pediatric patients exposed to two common antibiotic drugs, ampicillin and gentamicin.

Availability and implementation

R packages PheWAS and EHR are available at https://github.com/PheWAS/PheWAS and at CRAN (https://www.r-project.org/), respectively. The R script for data processing and the main analysis is available at https://github.com/choileena/EHR.

Supplementary information

Supplementary data are available at Bioinformatics online.

Gene set enrichment analysis to create polygenic scores: a developmental examination of aggression

Previous approaches for creating polygenic risk scores (PRSs) do not explicitly consider the biological or developmental relevance of the genetic variants selected for inclusion. We applied gene set enrichment analysis to meta-GWAS data to create developmentally targeted, functionally informed PRSs. Using two developmentally matched meta-GWAS discovery samples, separate PRSs were formed, then examined in time-varying effect models of aggression in a second, longitudinal sample of children (n = 515, 49% female) in early childhood (2-5 years old), and middle childhood (7.5-10.5 years old). Functional PRSs were associated with aggression in both the early and middle childhood models.

Genetic risk for schizophrenia is associated with substance use in emerging adulthood: an event-level polygenic prediction model

BACKGROUND

Emerging adulthood is a peak period of risk for alcohol and illicit drug use. Recent advances in psychiatric genetics suggest that the co-occurrence of substance use and psychopathology arises, in part, from a shared genetic etiology. We sought to extend this research by investigating the influence of genetic risk for schizophrenia on trajectories of four substance use behaviors as they occurred across emerging adulthood.

METHOD

Young adult participants of non-Hispanic European descent provided DNA samples and completed daily reports of substance use for 1 month per year across 4 years (N = 30 085 observations of N = 342 participants). A schizophrenia polygenic score was included in two-level hierarchical linear models designed to test associations between genetic risk for schizophrenia, participant age, and four substance use phenotypes.

RESULTS

Participants with a greater schizophrenia polygenic score experienced greater age-related increases in the likelihood of using substances across emerging adulthood (p < 0.005). Additionally, our results suggest that the polygenic score was positively associated with participants' overall likelihood to engage in illicit drug use but not alcohol-related substance use.

CONCLUSIONS

This study used a novel combination of polygenic prediction and intensive longitudinal methods to characterize the influence of genetic risk for schizophrenia on patterns of age-related change in substance use across emerging adulthood. Results suggest that genetic risk for schizophrenia has developmentally specific effects on substance use behaviors in a non-clinical population of young adults.

Comparing Within- and Between-Family Polygenic Score Prediction

Polygenic scores are a popular tool for prediction of complex traits. However, prediction estimates in samples of unrelated participants can include effects of population stratification, assortative mating, and environmentally mediated parental genetic effects, a form of genotype-environment correlation (rGE). Comparing genome-wide polygenic score (GPS) predictions in unrelated individuals with predictions between siblings in a within-family design is a powerful approach to identify these different sources of prediction. Here, we compared within- to between-family GPS predictions of eight outcomes (anthropometric, cognitive, personality, and health) for eight corresponding GPSs. The outcomes were assessed in up to 2,366 dizygotic (DZ) twin pairs from the Twins Early Development Study from age 12 to age 21. To account for family clustering, we used mixed-effects modeling, simultaneously estimating within- and between-family effects for target- and cross-trait GPS prediction of the outcomes. There were three main findings: (1) DZ twin GPS differences predicted DZ differences in height, BMI, intelligence, educational achievement, and ADHD symptoms; (2) target and cross-trait analyses indicated that GPS prediction estimates for cognitive traits (intelligence and educational achievement) were on average 60% greater between families than within families, but this was not the case for non-cognitive traits; and (3) much of this within- and between-family difference for cognitive traits disappeared after controlling for family socio-economic status (SES), suggesting that SES is a major source of between-family prediction through rGE mechanisms. These results provide insights into the patterns by which rGE contributes to GPS prediction, while ruling out confounding due to population stratification and assortative mating.

PRSice-2: Polygenic Risk Score software for biobank-scale data

BACKGROUND

Polygenic risk score (PRS) analyses have become an integral part of biomedical research, exploited to gain insights into shared aetiology among traits, to control for genomic profile in experimental studies, and to strengthen causal inference, among a range of applications. Substantial efforts are now devoted to biobank projects to collect large genetic and phenotypic data, providing unprecedented opportunity for genetic discovery and applications. To process the large-scale data provided by such biobank resources, highly efficient and scalable methods and software are required.

RESULTS

Here we introduce PRSice-2, an efficient and scalable software program for automating and simplifying PRS analyses on large-scale data. PRSice-2 handles both genotyped and imputed data, provides empirical association P-values free from inflation due to overfitting, supports different inheritance models, and can evaluate multiple continuous and binary target traits simultaneously. We demonstrate that PRSice-2 is dramatically faster and more memory-efficient than PRSice-1 and alternative PRS software, LDpred and lassosum, while having comparable predictive power.

CONCLUSION

PRSice-2's combination of efficiency and power will be increasingly important as data sizes grow and as the applications of PRS become more sophisticated, e.g., when incorporated into high-dimensional or gene set-based analyses. PRSice-2 is written in C++, with an R script for plotting, and is freely available for download from http://PRSice.info.

Polygenic risk associated with post-traumatic stress disorder onset and severity

Post-traumatic stress disorder (PTSD) is a psychiatric illness with a highly polygenic architecture without large effect-size common single-nucleotide polymorphisms (SNPs). Thus, to capture a substantial portion of the genetic contribution, effects from many variants need to be aggregated. We investigated various aspects of one such approach that has been successfully applied to many traits, polygenic risk score (PRS) for PTSD. Theoretical analyses indicate the potential prediction ability of PRS. We used the latest summary statistics from the largest published genome-wide association study (GWAS) conducted by Psychiatric Genomics Consortium for PTSD (PGC-PTSD). We found that the PRS constructed for a cohort comprising veterans of recent wars (n = 244) explains a considerable proportion of PTSD onset (Nagelkerke R2 = 4.68%, P = 0.003) and severity (R2 = 4.35%, P = 0.0008) variances. However, the performance on an African ancestry sub-cohort was minimal. A PRS constructed with schizophrenia GWAS also explained a significant fraction of PTSD diagnosis variance (Nagelkerke R2 = 2.96%, P = 0.0175), confirming previously reported genetic correlation between the two psychiatric ailments. Overall, these findings demonstrate the important role polygenic analyses of PTSD will play in risk prediction models as well as in elucidating the biology of the disorder.

Genomic prediction of cognitive traits in childhood and adolescence

Recent advances in genomics are producing powerful DNA predictors of complex traits, especially cognitive abilities. Here, we leveraged summary statistics from the most recent genome-wide association studies of intelligence and educational attainment, with highly genetically correlated traits, to build prediction models of general cognitive ability and educational achievement. To this end, we compared the performances of multi-trait genomic and polygenic scoring methods. In a representative UK sample of 7,026 children at ages 12 and 16, we show that we can now predict up to 11% of the variance in intelligence and 16% in educational achievement. We also show that predictive power increases from age 12 to age 16 and that genomic predictions do not differ for girls and boys. We found that multi-trait genomic methods were effective in boosting predictive power. Prediction accuracy varied across polygenic score approaches, however results were similar for different multi-trait and polygenic score methods. We discuss general caveats of multi-trait methods and polygenic score prediction, and conclude that polygenic scores for educational attainment and intelligence are currently the most powerful predictors in the behavioural sciences.

Genetic Risks for Chronic Conditions: Implications for Long-term Wellbeing

Background

Relationships between genetic risks for chronic diseases and long-run wellbeing are largely unexplored. We examined the associations between genetic predispositions to several chronic conditions and long-term functional health and socioeconomic status (SES).

Methods

We used data on a nationally representative sample of 9,317 adults aged 65 years or older from the 1992 to 2012 Health and Retirement Survey (HRS) in the US. Survey data were linked to genetic data on nearly 2 million single-nucleotide polymorphisms (SNPs). We measured individual-level genetic predispositions for coronary-artery disease, type 2 diabetes (T2D), obesity, rheumatoid arthritis (RA), Alzheimer's disease, and major depressive disorder (MDD) by polygenic risk scores (PRS) derived from genome-wide association studies (GWAS). The outcomes were self-rated health, depressive symptoms, cognitive ability, activities of everyday life, educational attainment, and wealth. We employed regression analyses for the outcomes including all polygenic scores and adjusting for gender, birth period, and genetic ancestry.

Results

The polygenic scores had important associations with functional health and SES. An increase in genetic risk for all conditions except T2D was significantly (p < .01) associated with reduced functional health and socioeconomic outcomes. The magnitudes of functional health declines were meaningful and in many cases equivalent in magnitude to several years of aging. These associations were robust to several sensitivity checks for ancestry and adjustment for parental educational attainment and age at death or the last interview if alive.

Conclusion

Stronger genetic predispositions for leading chronic conditions are related to worse long-run health and SES outcomes, likely reflecting the adverse effects of the onset of these conditions on one's wellbeing.