Research Review: Polygenic methods and their application to psychiatric traits

Despite evidence from twin and family studies for an important contribution of genetic factors to both childhood and adult onset psychiatric disorders, identifying robustly associated specific DNA variants has proved challenging. In the pregenomics era the genetic architecture (number, frequency and effect size of risk variants) of complex genetic disorders was unknown. Empirical evidence for the genetic architecture of psychiatric disorders is emerging from the genetic studies of the last 5 years. Methods and scope: We review the methods investigating the polygenic nature of complex disorders. We provide mini-guides to genomic profile (or polygenic) risk scoring and to estimation of variance (or heritability) from common SNPs; a glossary of key terms is also provided. We review results of applications of the methods to psychiatric disorders and related traits and consider how these methods inform on missing heritability, hidden heritability and still-missing heritability. Findings: Genome-wide genotyping and sequencing studies are providing evidence that psychiatric disorders are truly polygenic, that is they have a genetic architecture of many genetic variants, including risk variants that are both common and rare in the population. Sample sizes published to date are mostly underpowered to detect effect sizes of the magnitude presented by nature, and these effect sizes may be constrained by the biological validity of the diagnostic constructs. Conclusions: Increasing the sample size for genome wide association studies of psychiatric disorders will lead to the identification of more associated genetic variants, as already found for schizophrenia. These loci provide the starting point of functional analyses that might eventually lead to new prevention and treatment options and to improved biological validity of diagnostic constructs. Polygenic analyses will contribute further to our understanding of complex genetic traits as sample sizes increase and as sample resources become richer in phenotypic descriptors, both in terms of clinical symptoms and of nongenetic risk factors

Anxiety at age 15 predicts psychiatric diagnoses and suicidal ideation in late adolescence and young adulthood: results from two longitudinal studies

Background: Anxiety disorders in adolescence have been associated with several psychiatric outcomes. We sought to describe the prospective relationship between various levels of adolescent anxiety and psychiatric diagnoses (anxiety-, bipolar/psychotic-, depressive-, and alcohol and drug misuse disorders) and suicidal ideation in early adulthood while adjusting for childhood attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and developmental coordination disorder (DCD). Furthermore, we aimed to estimate the proportion attributable to the various anxiety levels for the outcomes. Methods: We used a nation-wide population-based Swedish twin study comprising 14,106 fifteen-year-old twins born in Sweden between 1994 and 2002 and a replication sample consisting of 9211 Dutch twins, born between 1985 and 1999. Adolescent anxiety was measured with parental and self-report. Psychiatric diagnoses and suicidal ideation were retrieved from the Swedish National Patient Register and via self-report. Results: Adolescent anxiety, of various levels, predicted, in the Swedish National Patient Register, anxiety disorders: hazard ratio (HR) = 4.92 (CI 3.33–7.28); depressive disorders: HR = 4.79 (3.23–7.08), and any psychiatric outcome: HR = 3.40 (2.58–4.48), when adjusting for ADHD, ASD, and DCD. The results were replicated in the Dutch data. The proportion of psychiatric outcome attributable to adolescent anxiety over time (age 15–21) was 29% for any psychiatric outcome, 43–40% for anxiety disorders, and 39–38% for depressive disorders. Conclusion: Anxiety in adolescence constitutes an important risk factor in the development of psychiatric outcomes, revealing unique predictions for the different levels of anxiety, and beyond the risk conferred by childhood ADHD, ASD, and DCD. Developmental trajectories leading into psychiatric outcomes should further empirically investigated

Do parental psychiatric symptoms predict outcome in children with psychiatric disorders? A naturalistic clinical study

Objective: Parental psychiatric symptoms can negatively affect the outcome of children's psychopathology. Studies thus far have mainly shown a negative effect of maternal depression. This study examined the associations between a broad range of psychiatric symptoms in mothers and fathers and the child's outcome. Method: Internalizing and externalizing psychiatric symptoms were assessed in 742 mothers, 440 fathers, and their 811 children at the first evaluation in 3 child and adolescent psychiatric outpatient clinics and at follow-up (on average 1.7 years later). Predictions of child's symptoms scores were tested at follow-up by parental symptom scores at baseline, parental scores at follow-up, and offspring scores at baseline. Results: Children whose mother or father scored above the (sub)clinical threshold for psychiatric symptoms at baseline had higher symptom scores at baseline and at follow-up. Offspring follow-up scores were most strongly predicted by offspring baseline scores, in addition to parental psychiatric symptoms at follow-up. Offspring symptom scores at follow-up generally were not predicted by parental scores at baseline. Maternal and paternal associations were of similar magnitude. Conclusion: Higher symptom scores at follow-up in children of parents with psychopathology were mainly explained by higher symptom scores at baseline. Continuing parent–offspring associations could be a result of reciprocal effects, ie, parental symptoms influencing offspring symptoms and offspring symptoms influencing parental symptoms. Nevertheless, the results show that these children are at risk for persisting symptoms, possibly indicating the need to treat maternal and paternal psychopathology

Estimation of Genetic Relationships Between Individuals Across Cohorts and Platforms:Application to Childhood Height

Combining genotype data across cohorts increases power to estimate the heritability due to common single nucleotide polymorphisms (SNPs), based on analyzing a Genetic Relationship Matrix (GRM). However, the combination of SNP data across multiple cohorts may lead to stratification, when for example, different genotyping platforms are used. In the current study, we address issues of combining SNP data from different cohorts, the Netherlands Twin Register (NTR) and the Generation R (GENR) study. Both cohorts include children of Northern European Dutch background (N = 3102 + 2826, respectively) who were genotyped on different platforms. We explore imputation and phasing as a tool and compare three GRM-building strategies, when data from two cohorts are (1) just combined, (2) pre-combined and cross-platform imputed and (3) cross-platform imputed and post-combined. We test these three strategies with data on childhood height for unrelated individuals (N = 3124, average age 6.7 years) to explore their effect on SNP-heritability estimates and compare results to those obtained from the independent studies. All combination strategies result in SNP-heritability estimates with a standard error smaller than those of the independent studies. We did not observe significant difference in estimates of SNP-heritability based on various cross-platform imputed GRMs. SNP-heritability of childhood height was on average estimated as 0.50 (SE = 0.10). Introducing cohort as a covariate resulted in ≈2 % drop. Principal components (PCs) adjustment resulted in SNP-heritability estimates of about 0.39 (SE = 0.11). Strikingly, we did not find significant difference between cross-platform imputed and combined GRMs. All estimates were significant regardless the use of PCs adjustment. Based on these analyses we conclude that imputation with a reference set helps to increase power to estimate SNP-heritability by combining cohorts of the same ethnicity genotyped on different platforms. However, important factors should be taken into account such as remaining cohort stratification after imputation and/or phenotypic heterogeneity between and within cohorts. Whether one should use imputation, or just combine the genotype data, depends on the number of overlapping SNPs in relation to the total number of genotyped SNPs for both cohorts, and their ability to tag all the genetic variance related to the specific trait of interest

Assessment of bidirectional relationships between physical activity and depression among adults a 2-sample Mendelian randomization study

IMPORTANCE Increasing evidence shows that physical activity is associated with reduced risk for depression, pointing to a potential modifiable target for prevention. However, the causality and direction of this association are not clear; physical activity may protect against depression, and/or depression may result in decreased physical activity. OBJECTIVE To examine bidirectional relationships between physical activity and depression using a genetically informed method for assessing potential causal inference. DESIGN, SETTING, AND PARTICIPANTS This 2-sample mendelian randomization (MR) used independent top genetic variants associated with 2 physical activity phenotypes-self-reported (n = 377 234) and objective accelerometer-based (n = 91 084)-and with major depressive disorder (MDD) (n = 143 265) as genetic instruments from the largest available, nonoverlapping genome-wide association studies (GWAS). GWAS were previously conducted in diverse observational cohorts, including the UK Biobank (for physical activity) and participating studies in the Psychiatric Genomics Consortium (for MDD) among adults of European ancestry. Mendelian randomization estimates from each genetic instrument were combined using inverse variance weighted meta-analysis, with alternate methods (eg, weighted median, MR Egger, MR-Pleiotropy Residual Sum and Outlier [PRESSO]) and multiple sensitivity analyses to assess horizontal pleiotropy and remove outliers. Data were analyzed from May 10 through July 31, 2018. MAIN OUTCOMES AND MEASURES MDD and physical activity. RESULTS GWAS summary data were available for a combined sample size of 611 583 adult participants. Mendelian randomization evidence suggested a protective relationship between accelerometer-based activity and MDD (odds ratio [OR], 0.74 for MDD per 1-SD increase in mean acceleration; 95% CI, 0.59-0.92; P =.006). In contrast, there was no statistically significant relationship between MDD and accelerometer-based activity (β = −0.08 in mean acceleration per MDD vs control status; 95% CI, −0.47 to 0.32; P =.70). Furthermore, there was no significant relationship between self-reported activity and MDD (OR, 1.28 for MDD per 1-SD increase in metabolic-equivalent minutes of reported moderate-to-vigorous activity; 95% CI, 0.57-3.37; P =.48), or between MDD and self-reported activity (β = 0.02 per MDD in standardized metabolic-equivalent minutes of reported moderate-to-vigorous activity per MDD vs control status; 95% CI, −0.008 to 0.05; P =.15). CONCLUSIONS AND RELEVANCE Using genetic instruments identified from large-scale GWAS, robust evidence supports a protective relationship between objectively assessed-but not self-reported-physical activity and the risk for MDD. Findings point to the importance of objective measurement of physical activity in epidemiologic studies of mental health and support the hypothesis that enhancing physical activity may be an effective prevention strategy for depression

A national harmonised data collection network for neurodevelopmental disorders: A transdiagnostic assessment protocol for neurodevelopment, mental health, functioning and well-being

BACKGROUND: Children with neurodevelopmental disorders share common phenotypes, support needs and comorbidities. Such overlap suggests the value of transdiagnostic assessment pathways that contribute to knowledge about research and clinical needs of these children and their families. Despite this, large transdiagnostic data collection networks for neurodevelopmental disorders are not well developed. This paper describes the development of a nationally supported transdiagnostic clinical and research assessment protocol across Australia. The vision is to establish a harmonised network for data collection and collaboration that promotes transdiagnostic clinical practice and research. METHODS: Clinicians, researchers and community groups across Australia were consulted using surveys and national summits to identify assessment instruments and unmet needs. A national research committee was formed and, using a consensus approach, selected assessment instruments according to pre-determined criteria to form a harmonised transdiagnostic assessment protocol. RESULTS: Identified assessment instruments were clustered into domains of transdiagnostic assessment needs, which included child functioning/quality of life, child mental health, caregiver mental health, and family background information. From this, the research committee identified a core set of nine measures and an extended set of 14 measures that capture these domains with potential for further modifications as recommended by clinicians, researchers and community members. CONCLUSION: The protocol proposed here was established through a strong partnership between clinicians, researchers and the community. It will enable (i) consensus driven transdiagnostic clinical assessments for children with neurodevelopmental disorders, and (ii) research studies that will inform large transdiagnostic datasets across neurodevelopmental disorders and that can be used to inform research and policy beyond narrow diagnostic groups. The long-term vision is to use this framework to facilitate collaboration across clinics to enable large-scale data collection and research. Ultimately, the transdiagnostic assessment data can be used to inform practice and improve the lives of children with neurodevelopmental disorders and their families