Large-scale genomics unveils the genetic architecture of psychiatric disorders

Family study results are consistent with genetic effects making substantial contributions to risk of psychiatric disorders such as schizophrenia, yet robust identification of specific genetic variants that explain variation in population risk had been disappointing until the advent of technologies that assay the entire genome in large samples. We highlight recent progress that has led to a better understanding of the number of risk variants in the population and the interaction of allele frequency and effect size. The emerging genetic architecture implies a large number of contributing loci (that is, a high genome-wide mutational target) and suggests that genetic risk of psychiatric disorders involves the combined effects of many common variants of small effect, as well as rare and de novo variants of large effect. The capture of a substantial proportion of genetic risk facilitates new study designs to investigate the combined effects of genes and the environment

Interpreting the role of de novo protein-coding mutations in neuropsychiatric disease

Pedigree, linkage and association studies are consistent with heritable variation for complex disease due to the segregation of genetic factors in families and in the population. In contrast, de novo mutations make only minor contributions to heritability estimates for complex traits. Nonetheless, some de novo variants are known to be important in disease etiology. The identification of risk-conferring de novo variants will contribute to the discovery of etiologically relevant genes and pathways and may help in genetic counseling. There is considerable interest in the role of such mutations in complex neuropsychiatric disease, largely driven by new genotyping and sequencing technologies. An important role for large de novo copy number variations has been established. Recently, whole-exome sequencing has been used to extend the investigation of de novo variation to point mutations in protein-coding regions. Here, we consider several challenges for the interpretation of such mutations in the context of their role in neuropsychiatric disease

The genetic relationship between female reproductive traits and six psychiatric disorders

Female reproductive behaviours have important implications for evolutionary fitness and health of offspring. Here we used the second release of UK Biobank data (N = 220,685) to evaluate the association between five female reproductive traits and polygenic risk scores (PRS) projected from genome-wide association study summary statistics of six psychiatric disorders (N = 429,178). We found that the PRS of attention-deficit/hyperactivity disorder (ADHD) were strongly associated with age at first birth (AFB) (genetic correlation of -0.68 ± 0.03), age at first sexual intercourse (AFS) (-0.56 ± 0.03), number of live births (NLB) (0.36 ± 0.04) and age at menopause (-0.27 ± 0.04). There were also robustly significant associations between the PRS of eating disorder (ED) and AFB (0.35 ± 0.06), ED and AFS (0.19 ± 0.06), major depressive disorder (MDD) and AFB (-0.27 ± 0.07), MDD and AFS (-0.27 ± 0.03) and schizophrenia and AFS (-0.10 ± 0.03). These associations were mostly explained by pleiotropic effects and there was little evidence of causal relationships. Our findings can potentially help improve reproductive health in women, hence better child outcomes. Our findings also lend partial support to the evolutionary hypothesis that causal mutations underlying psychiatric disorders have positive effects on reproductive success

Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses

Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (|ρ^| ≈ 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.</p

Examining the Impact of Imputation Errors on Fine-Mapping Using DNA Methylation QTL as a Model Trait

Genetic variants disrupting DNA methylation at CpG dinucleotides (CpG-SNP) provide a set of known causal variants to serve as models for testing fine-mapping methodology. We use 1716 CpG-SNPs to test three fine-mapping approaches (BIMBAM, BSLMM, and the J-test), assessing the impact of imputation errors and the choice of reference panel by using both whole-genome sequence (WGS), and genotype array data on the same individuals (n=1166). The choice of imputation reference panel had a strong effect on imputation accuracy, with the 1000 Genomes Phase 3 (1000G) reference panel (n=2504 from 26 populations) giving a mean non-reference discordance rate between imputed and sequenced genotypes of 3.2% compared to 1.6% when using the Haplotype Reference Consortium (HRC) reference panel (n=32470 Europeans). These imputation errors impacted on whether the CpG-SNP was included in the 95% credible set, with a difference of ∼ 23% and ∼ 7% between the WGS and the 1000G and HRC imputed datasets respectively. All of the fine-mapping methods failed to reach the expected 95% coverage of the CpG-SNP. This is attributed to secondary cis genetic effects that are unable to be statistically separated from the CpG-SNP, and through a masking mechanism where the effect of the methylation disrupting allele at the CpG-SNP is hidden by the effect of a nearby SNP that has strong LD with the CpG-SNP. The reduced accuracy in fine-mapping a known causal variant in a low level biological trait with imputed genetic data has implications for the study of higher order complex traits and disease

Study protocol for the Australian autism biobank: an international resource to advance autism discovery research

BACKGROUND: The phenotypic and genetic heterogeneity of autism spectrum disorder (ASD) presents considerable challenges in understanding etiological pathways, selecting effective therapies, providing genetic counselling, and predicting clinical outcomes. With advances in genetic and biological research alongside rapid-pace technological innovations, there is an increasing imperative to access large, representative, and diverse cohorts to advance knowledge of ASD. To date, there has not been any single collective effort towards a similar resource in Australia, which has its own unique ethnic and cultural diversity. The Australian Autism Biobank was initiated by the Cooperative Research Centre for Living with Autism (Autism CRC) to establish a large-scale repository of biological samples and detailed clinical information about children diagnosed with ASD to facilitate future discovery research. METHODS: The primary group of participants were children with a confirmed diagnosis of ASD, aged between 2 and 17 years, recruited through four sites in Australia. No exclusion criteria regarding language level, cognitive ability, or comorbid conditions were applied to ensure a representative cohort was recruited. Both biological parents and siblings were invited to participate, along with children without a diagnosis of ASD, and children who had been queried for an ASD diagnosis but did not meet diagnostic criteria. All children completed cognitive assessments, with probands and parents completing additional assessments measuring ASD symptomatology. Parents completed questionnaires about their child's medical history and early development. Physical measurements and biological samples (blood, stool, urine, and hair) were collected from children, and physical measurements and blood samples were collected from parents. Samples were sent to a central processing site and placed into long-term storage. DISCUSSION: The establishment of this biobank is a valuable international resource incorporating detailed clinical and biological information that will help accelerate the pace of ASD discovery research. Recruitment into this study has also supported the feasibility of large-scale biological sample collection in children diagnosed with ASD with comprehensive phenotyping across a wide range of ages, intellectual abilities, and levels of adaptive functioning. This biological and clinical resource will be open to data access requests from national and international researchers to support future discovery research that will benefit the autistic community

Correction: Corrigendum: Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses

In the version of this article initially published, one of the affiliations listed for author Maciej Trzaskowski, to the Department of Public Health, Faculty of Medicine, University of Split, Split, Croatia, was included in error. The correct affiliation for this author is the Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. The error has been corrected in the HTML and PDF versions of the article

Correction: Corrigendum: Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses

In the version of this article initially published, one of the affiliations listed for author Maciej Trzaskowski, to the Department of Public Health, Faculty of Medicine, University of Split, Split, Croatia, was included in error. The correct affiliation for this author is the Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. The error has been corrected in the HTML and PDF versions of the article

Evidence for Genetic Overlap Between Schizophrenia and Age at First Birth in Women

ImportanceA recently published study of national data by McGrath et al in 2014 showed increased risk of schizophrenia (SCZ) in offspring associated with both early and delayed parental age, consistent with a U-shaped relationship. However, it remains unclear if the risk to the child is due to psychosocial factors associated with parental age or if those at higher risk for SCZ tend to have children at an earlier or later age.ObjectiveTo determine if there is a genetic association between SCZ and age at first birth (AFB) using genetically informative but independently ascertained data sets.Design, setting, and participantsThis investigation used multiple independent genome-wide association study data sets. The SCZ sample comprised 18 957 SCZ cases and 22 673 controls in a genome-wide association study from the second phase of the Psychiatric Genomics Consortium, and the AFB sample comprised 12 247 genotyped women measured for AFB from the following 4 community cohorts: Estonia (Estonian Genome Center Biobank, University of Tartu), the Netherlands (LifeLines Cohort Study), Sweden (Swedish Twin Registry), and the United Kingdom (TwinsUK). Schizophrenia genetic risk for each woman in the AFB community sample was estimated using genetic effects inferred from the SCZ genome-wide association study.Main outcomes and measuresWe tested if SCZ genetic risk was a significant predictor of response variables based on published polynomial functions that described the relationship between maternal age and SCZ risk in offspring in Denmark. We substituted AFB for maternal age in these functions, one of which was corrected for the age of the father, and found that the fit was superior for the model without adjustment for the father's age.ResultsWe observed a U-shaped relationship between SCZ risk and AFB in the community cohorts, consistent with the previously reported relationship between SCZ risk in offspring and maternal age when not adjusted for the age of the father. We confirmed that SCZ risk profile scores significantly predicted the response variables (coefficient of determination R2 = 1.1E-03, P = 4.1E-04), reflecting the published relationship between maternal age and SCZ risk in offspring by McGrath et al in 2014.Conclusions and relevanceThis study provides evidence for a significant overlap between genetic factors associated with risk of SCZ and genetic factors associated with AFB. It has been reported that SCZ risk associated with increased maternal age is explained by the age of the father and that de novo mutations that occur more frequently in the germline of older men are the underlying causal mechanism. This explanation may need to be revised if, as suggested herein and if replicated in future studies, there is also increased genetic risk of SCZ in older mothers