Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique – cross-trait LD Score regression – for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among metaanalyses. We circumvent these difficulties by introducing a technique -cross-trait LD Score regression -for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use

DHD is a neurodevelopmental psychiatric disorder that affects around 5% of children and adolescents and 2.5% of adults worldwide 1. ADHD is often persistent and markedly impairing, with increased risk of harmful outcomes, such as injuries 2 , traffic accidents 3 , increased healthcare utilization 4,5 , substance abuse 6 , criminality 7 , unemployment 8 , divorce 4 , suicide 9 , AIDS risk behaviors 8 and premature mortality 10. Epidemiologic and clinical studies implicate genetic and environmental risk factors that affect the structure and functional capacity of brain networks involved in behavior and cognition 1 in the etiology of ADHD. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder Ditte Demontis

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 ASD cases and 27,969 controls that identifies five genome-wide significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), seven additional loci shared with other traits are identified at equally strict significance levels. Dissecting the polygenic architecture, we find both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis and establish that GWAS performed at scale will be much more productive in the near term in ASD.

SUMMARY Analysis of de novo CNVs (dnCNVs) from the full Simons Simplex Collection (SSC) (N = 2,591 families) replicates prior findings of strong association with autism spectrum disorders (ASDs) and confirms six risk loci (1q21.1, 3q29, 7q11.23, 16p11.2, 15q11.2-13, and 22q11.2). The addition of published CNV data from the Autism Genome Project (AGP) and exome sequencing data from the SSC and the Autism Sequencing Consortium (ASC) shows that genes within small de novo deletions, but not within large dnCNVs, significantly overlap the high-effect risk genes identified by sequencing. Alternatively, large dnCNVs are found likely to contain multiple modest-effect risk genes. Overall, we find strong evidence that de novo mutations are associated with ASD apart from the risk for intellectual disability. Extending the transmission and de novo association test (TADA) to include small de novo deletions reveals 71 ASD risk loci, including 6 CNV regions (noted above) and 65 risk genes (FDR ≤ 0.1).

Highlights d 102 genes implicated in risk for autism spectrum disorder (ASD genes, FDR % 0.1) d Most are expressed and enriched early in excitatory and inhibitory neuronal lineages d Most affect synapses or regulate other genes; how these roles dovetail is unknown d Some ASD genes alter early development broadly, others appear more specific to ASD

Spontaneously arising (‘de novo’) mutations play an important role in medical genetics. For diseases with extensive locus heterogeneity – such as autism spectrum disorders (ASDs) – the signal from de novo mutations (DNMs) is distributed across many genes, making it difficult to distinguish disease-relevant mutations from background variation. We provide a statistical framework for the analysis of DNM excesses per gene and gene set by calibrating a model of de novo mutation. We applied this framework to DNMs collected from 1,078 ASD trios and – while affirming a significant role for loss-of-function (LoF) mutations – found no excess of de novo LoF mutations in cases with IQ above 100, suggesting that the role of DNMs in ASD may reside in fundamental neurodevelopmental processes. We also used our model to identify ~1,000 genes that are significantly lacking functional coding variation in non-ASD samples and are enriched for de novo LoF mutations identified in ASD cases.

SummaryWe present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n=35,584 total samples, 11,986 with ASD). Using an enhanced Bayesian framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate ≤ 0.1. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained for severe neurodevelopmental delay, while 53 show higher frequencies in individuals ascertained for ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most of the risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In human cortex single-cell gene expression data, expression of risk genes is enriched in both excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory/inhibitory imbalance underlying ASD.