BackgroundOver the past decade genome-wide association studies (GWAS) have been applied to aid in the understanding of the biology of traits. The success of this approach is governed by the underlying effect sizes carried by the true risk variants and the corresponding statistical power to observe such effects given the study design and sample size under investigation. Previous ASD GWAS have identified genome-wide significant (GWS) risk loci; however, these studies were of only of low statistical power to identify GWS loci at the lower effect sizes (odds ratio (OR) <1.15).MethodsWe conducted a large-scale coordinated international collaboration to combine independent genotyping data to improve the statistical power and aid in robust discovery of GWS loci. This study uses genome-wide genotyping data from a discovery sample (7387 ASD cases and 8567 controls) followed by meta-analysis of summary statistics from two replication sets (7783 ASD cases and 11359 controls; and 1369 ASD cases and 137308 controls).ResultsWe observe a GWS locus at 10q24.32 that overlaps several genes including PITX3, which encodes a transcription factor identified as playing a role in neuronal differentiation and CUEDC2 previously reported to be associated with social skills in an independent population cohort. We also observe overlap with regions previously implicated in schizophrenia which was further supported by a strong genetic correlation between these disorders (Rg = 0.23; P = 9 × 10−6). We further combined these Psychiatric Genomics Consortium (PGC) ASD GWAS data with the recent PGC schizophrenia GWAS to identify additional regions which may be important in a common neurodevelopmental phenotype and identified 12 novel GWS loci. These include loci previously implicated in ASD such as FOXP1 at 3p13, ATP2B2 at 3p25.3, and a ‘neurodevelopmental hub’ on chromosome 8p11.23.ConclusionsThis study is an important step in the ongoing endeavour to identify the loci which underpin the common variant signal in ASD. In addition to novel GWS loci, we have identified a significant genetic correlation with schizophrenia and association of ASD with several neurodevelopmental-related genes such as EXT1, ASTN2, MACROD2, and HDAC4. Electronic supplementary materialThe online version of this article (doi:10.1186/s13229-017-0137-9) contains supplementary material, which is available to authorized users.

IMPORTANCE Biologic systems involved in the regulation of motor activity are intricately linked with other homeostatic systems such as sleep, feeding behavior, energy, and mood. Mobile monitoring technology (eg, actigraphy and ecological momentary assessment devices) allows the assessment of these multiple systems in real time. However, most clinical studies of mental disorders that use mobile devices have not focused on the dynamic associations between these systems. OBJECTIVES To examine the directional associations among motor activity, energy, mood, and sleep using mobile monitoring in a community-identified sample, and to evaluate whether these within-day associations differ between people with a history of bipolar or other mood disorders and controls without mood disorders.

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

Background There is growing evidence that neurocognitive function may be an endophenotype for mood disorders. The goal of this study is to examine the specificity and familiality of neurocognitive functioning across the full range of mood disorder subgroups, including Bipolar I (BP-I), Bipolar II (BP-II), Major Depressive Disorders (MDD), and controls in a community-based family study. Methods A total of 310 participants from 137 families with mood spectrum disorders (n=151) and controls (n=159) completed the University of Pennsylvania’s Computerized Neurocognitive Battery (CNB) that assessed the accuracy and speed of task performance across five domains. Mixed effects regression models tested association and familiality. Results Compared to those without mood disorders, participants with BP-I had increased accuracy in complex cognition, while participants with MDD were more accurate in emotion recognition. There was also a significant familial association for accuracy of complex cognition. Mood disorder subgroups did not differ in performance speed in any of the domains. Limitations The small number of BP-I cases, and family size limited statistical power of these analyses, and the cross-sectional assessment of neurocognitive function precluded our ability to determine whether performance precedes or post dates onset of disorder. Conclusions This is one of the few community-based family studies of potential neurocognitive endophenotypes that includes the full range of mood disorder subgroups. There were few differences in neurocognitive function except enhanced accuracy in specific domains among those with BP-I and MDD. The differential findings across specific mood disorder subgroups substantiate their heterogeneity in other biologic and endophenotypic domains.

IMPORTANCEPsychiatric and cognitive phenotypes have been associated with a range of specific, rare copy number variants (CNVs). Moreover, IQ is strongly associated with CNV risk scores that model the predicted risk of CNVs across the genome. But the utility of CNV risk scores for psychiatric phenotypes has been sparsely examined.OBJECTIVE To determine how CNV risk scores, common genetic variation indexed by polygenic scores (PGSs), and environmental factors combine to associate with cognition and psychopathology in a community sample. DESIGN, SETTING, AND PARTICIPANTSThe Philadelphia Neurodevelopmental Cohort is a community-based study examining genetics, psychopathology, neurocognition, and neuroimaging. Participants were recruited through the Children's Hospital of Philadelphia pediatric network. Participants with stable health and fluency in English underwent genotypic and phenotypic characterization from

Polygenic risk scores (PRS) represent an individual’s summed genetic risk for a trait and can serve as biomarkers for disease. Less is known about the utility of PRS as a means to quantify genetic risk for substance use disorders (SUDs) than for many other traits. Nonetheless, the growth of large, electronic health record-based biobanks makes it possible to evaluate the association of SUD PRS with other traits. We calculated PRS for smoking initiation, alcohol use disorder (AUD), and opioid use disorder (OUD) using summary statistics from the Million Veteran Program sample. We then tested the association of each PRS with its primary phenotype in the Penn Medicine BioBank (PMBB) using all available genotyped participants of African or European ancestry (AFR and EUR, respectively) (N=18,612). Finally, we conducted phenome-wide association analyses (PheWAS) separately by ancestry and sex to test for associations across disease categories. Tobacco use disorder was the most common SUD in the PMBB, followed by AUD and OUD, consistent with the population prevalence of these disorders. All PRS were associated with their primary phenotype in both ancestry groups. PheWAS results yielded cross-trait associations across multiple domains, including psychiatric disorders and medical conditions. SUD PRS were associated with their primary phenotypes, however they are not yet predictive enough to be useful diagnostically. The cross-trait associations of the SUD PRS are indicative of a broader genetic liability. Future work should extend findings to additional population groups and for other substances of abuse.

The field of psychiatric epidemiology has advanced both methodological and substantive knowledge in our understanding of mental disorders through the following contributions: (1) development of standardized tools that operationalize diagnostic criteria in order to obtain reliable estimates; (2) estimation of the magnitude, correlates and service patterns of mental disorders; (3) documentation of patterns of comorbidity; (4) quantification of disability attributable to mental disorders; and (5) identification of risk and protective factors for mental disorders and their core domains. Community surveys using standardized tools for ascertaining psychiatric disorders have shown that mental disorders are highly prevalent in the general population. With the growing success in identifying genetic risk factors for chronic human disorders, the field of epidemiology will play an important role in defining study designs, appropriate samples, population generalizability, and statistical tools that will facilitate our ability to identify the joint influence of genetic and environmental factors on the susceptibility to mental disorders.