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Local genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions. However, accurate estimation of local genetic correlation remains challenging, due to linkage disequilibrium in local genomic regions and sample overlap across studies. We introduce SUPERGNOVA, a statistical framework to estimate local genetic correlations using summary statistics from genome-wide association studies. We demonstrate that SUPERGNOVA outperforms existing methods through simulations and analyses of 30 complex traits. In particular, we show that the positive yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically distinct genetic signatures with bidirectional local genetic correlations. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-021-02478-w.

International audience; We 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 analytical framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate of 0.1 or less. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained to have severe neurodevelopmental delay, whereas 53 show higher frequencies in individuals ascertained to have ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most 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 cells from the human cortex, expression of risk genes is enriched in excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory-inhibitory imbalance underlying ASD.

BackgroundGenetic variants in the voltage-gated sodium channelsSCN1A,SCN2A,SCN3A, andSCN8Aare leading causes of epilepsy, developmental delay, and autism spectrum disorder. The mRNA splicing patterns of all four genes vary across development in the rodent brain, including mutually exclusive copies of the fifth protein-coding exon detected in the neonate (5N) and adult (5A). A second pair of mutually exclusive exons is reported inSCN8Aonly (18N and 18A). We aimed to quantify the expression of individual exons in the developing human brain.MethodsRNA-seq data from 783 human brain samples across development were analyzed to estimate exon-level expression. Developmental changes in exon utilization were validated by assessing intron splicing. Exon expression was also estimated in RNA-seq data from 58 developing mouse neocortical samples.ResultsIn the mature human neocortex, exon 5A is consistently expressed at least 4-fold higher than exon 5N in all four genes. ForSCN2A,SCN3A, andSCN8A, a brain-wide synchronized 5N to 5A transition occurs between 24 post-conceptual weeks (2nd trimester) and 6 years of age. In mice, the equivalent 5N to 5A transition begins at or before embryonic day 15.5. InSCN8A, over 90% of transcripts in the mature human cortex include exon 18A. Early in fetal development, most transcripts include 18N or skip both 18N and 18A, with a transition to 18A inclusion occurring from 13 post-conceptual weeks to 6 months of age. No other protein-coding exons showed comparably dynamic developmental trajectories.ConclusionsExon usage inSCN1A,SCN2A,SCN3A, andSCN8Achanges dramatically during human brain development. These splice isoforms, which alter the biophysical properties of the encoded channels, may account for some of the observed phenotypic differences across development and between specific variants. Manipulation of the proportion of splicing isoforms at appropriate stages of development may act as a therapeutic strategy for specific mutations or even epilepsy in general.

Autism spectrum disorder (ASD) is consistently diagnosed 3 to 5 times more frequently in males than females, a dramatically sex-biased prevalence that suggests the involvement of sex-differential biological factors in modulating risk. The genomic scale of transcriptomic analyses of human brain tissue can provide an unbiased approach for identifying genes and associated functional processes at the intersection of sex-differential and ASD-impacted neurobiology. Several studies characterizing gene expression changes in the ASD brain have been published in recent years with increasing sample size and cellular resolution. These studies report several convergent patterns across data sets and genetically heterogeneous samples in the ASD brain, including elevated expression of gene sets associated with glial and immune function, and reduced expression of gene sets associated with neuronal and synaptic functions. Assessment of neurotypical cortex tissue has reported parallel patterns by sex, with male-elevated expression of overlapping sets of glial/immune-related genes and female-biased expression of neuron-associated genes, suggesting potential roles for these cell types in sex-differential ASD risk mechanisms. However, validating and further exploring these mechanisms is challenged by the available data, as existing studies of ASD brain include a limited number of female ASD donors and focus predominantly on cortex regions not known to show pronounced sex-differential morphology or function. With this review, we summarize convergent findings from several landmark studies of the transcriptome in ASD brain and their relationship to sex-differential gene expression, and we discuss limitations and remaining questions regarding transcriptomic analysis of sex differences in ASD.

Local genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions, which could shed unique light on etiologic sharing and provide additional mechanistic insights into the genetic basis of complex traits compared to global genetic correlation. However, accurate estimation of local genetic correlation remains challenging, in part due to extensive linkage disequilibrium in local genomic regions and pervasive sample overlap across studies. We introduce SUPERGNOVA, a unified framework to estimate both global and local genetic correlations using summary statistics from genome-wide association studies. Through extensive simulations and analyses of 30 complex traits, we demonstrate that SUPERGNOVA substantially outperforms existing methods and identifies 150 trait pairs with significant local genetic correlations. In particular, we show that the positive, consistently-identified, yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically-distinct genetic signatures with bidirectional local genetic correlations. We believe that statistically-rigorous local genetic correlation analysis could accelerate progress in complex trait genetics research.

As technology advances, whole genome sequencing (WGS) is likely to supersede other genotyping technologies. The rate of this change depends on its relative cost and utility. Variants identified uniquely through WGS may reveal novel biological pathways underlying complex disorders and provide high-resolution insight into when, where, and in which cell type these pathways are affected. Alternatively, cheaper and less computationally intensive approaches may yield equivalent insights. Understanding the role of rare variants in the noncoding gene-regulating genome, through pilot WGS projects, will be critical to determine which of these two extremes best represents reality. With large cohorts, well-defined risk loci, and a compelling need to understand the underlying biology, psychiatric disorders have a role to play in this preliminary WGS assessment. The WGSPD consortium will integrate data for 18,000 individuals with psychiatric disorders, beginning with autism spectrum disorder, schizophrenia, bipolar disorder, and major depressive disorder, along with over 150,000 controls.

Literature indicates that some children with ASD may show behavioral improvements during fever; however, little is known about the behavioral profiles of these children. This study aims to (a) investigate the subset of children who show parent-reported behavioral improvements associated with fever and (b) compare the demographic, behavioral, and genetic characteristics of this subset of children to children whose parents report no change during fever. Parents of 2,152 children from the Simons Simplex Collection provided information about whether and in which areas their child improved during fever. Children were randomly assigned into discovery or replication samples. In discovery analyses, children who reportedly improved with fever (Improve Group) were compared to those who reportedly did not improve (No Improve Group) on demographics, medical history, ASD symptoms, adaptive skills, and presence of de novo ASD-associated mutations. Significant and marginal results from discovery analyses were tested in the replication sample. Parent reports of 17% of children indicated improvements during fever across a range of domains. Discovery and replication analyses revealed that the Improve Group had significantly lower non-verbal cognitive skills (NVIQ) and language levels and more repetitive behaviors. Groups did not differ on demographic variables, parent-report of current ASD symptoms or the presence of de novo mutations. Understanding the profiles of children who improve during episodes of fever may provide insights into innovative treatments for ASD. Autism Res 2018, 11: 175-184. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Lay summary This study explored characteristics of children with ASD who are reported to improve during fever. Parents of 17% of children with ASD report improvements across a range of domains during fever including cognition, communication, repetitive behaviors, social interaction, and behavior. Children who are reported to improve during fever have significantly lower non-verbal cognitive skills and language levels and more repetitive behaviors. Understanding the profiles of children who improve during episodes of fever may provide insights into new treatments for ASD.

SUMMARY Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and postnatal development. Here we identify common variants that alter gene expression (expression quantitative trait loci [eQTLs]) constantly across development or predominantly during prenatal or postnatal stages. Both “constant” and “temporal-predominant” eQTLs are enriched for loci associated with neuropsychiatric traits and disorders and colocalize with specific variants. Expression levels of more than 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell-type-specific genes and neuropsychiatric risk loci, underscoring the importance of cataloging developmental trajectories in understanding cortical physiology and pathology., In Brief Werling et al. analyze gene expression across the span of human cerebral cortical development and profile the trajectories of individual genes, coordinated groups of genes, and their relationships to disorders. Integration of genetic variation identifies quantitative trait loci that implicate specific genes in loci associated with neuropsychiatric traits and disorders., Graphical Abstract

We 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.

INTRODUCTION The DNA of protein-coding genes is transcribed into mRNA, which is translated into proteins. The “coding genome” describes the DNA that contains the information to make these proteins and represents ~1.5% of the human genome. Newly arising de novo mutations (variants observed in a child but not in either parent) in the coding genome contribute to numerous childhood developmental disorders, including autism spectrum disorder (ASD). Discovery of these effects is aided by the triplet code that enables the functional impact of many mutations to be readily deciphered. In contrast, the “noncoding genome” covers the remaining ~98.5% and includes elements that regulate when, where, and to what degree protein-coding genes are transcribed. Understanding this noncoding sequence could provide insights into human disorders and refined control of emerging genetic therapies. Yet little is known about the role of mutations in noncoding regions, including whether they contribute to childhood developmental disorders, which noncoding elements are most vulnerable to disruption, and the manner in which information is encoded in the noncoding genome. RATIONALE Whole-genome sequencing (WGS) provides the opportunity to identify the majority of genetic variation in each individual. By performing WGS on 1902 quartet families including a child affected with ASD, one unaffected sibling control, and their parents, we identified ~67 de novo mutations across each child’s genome. To characterize the functional role of these mutations, we integrated multiple datasets relating to gene function, genes implicated in neurodevelopmental disorders, conservation across species, and epigenetic markers, thereby combinatorially defining 55,143 categories. The scope of the problem—testing for an excess of de novo mutations in cases relative to controls for each category—is challenging because there are more categories than families. RESULTS Comparing cases to controls, we observed an excess of de novo mutations in cases in individual categories in the coding genome but not in the noncoding genome. To overcome the challenge of detecting noncoding association, we used machine learning tools to develop a de novo risk score to look for an excess of de novo mutations across multiple categories. This score demonstrated a contribution to ASD risk from coding mutations and a weaker, but significant, contribution from noncoding mutations. This noncoding signal was driven by mutations in the promoter region, defined as the 2000 nucleotides upstream of the transcription start site (TSS) where mRNA synthesis starts. The strongest promoter signals were defined by conservation across species and transcription factor binding sites. Well-defined promoter elements (e.g., TATA-box) are usually observed within 80 nucleotides of the TSS; however, the strongest ASD association was observed distally, 750 to 2000 nucleotides upstream of the TSS. CONCLUSION We conclude that de novo mutations in the noncoding genome contribute to ASD. The clearest evidence of noncoding ASD association came from mutations at evolutionarily conserved nucleotides in the promoter region. The enrichment for transcription factor binding sites, primarily in the distal promoter, suggests that these mutations may disrupt gene transcription via their interaction with enhancer elements in the promoter region, rather than interfering with transcriptional initiation directly. Promoter regions in autism. De novo mutations from 1902 quartet families are assigned to 55,143 annotation categories, which are each assessed for autism spectrum disorder (ASD) association by comparing mutation counts in cases and sibling controls. A de novo risk score demonstrated a noncoding contribution to ASD driven by promoter mutations, especially at sites conserved across species, in the distal promoter or targeted by transcription factors.

INTRODUCTION The brain is responsible for cognition, behavior, and much of what makes us uniquely human. The development of the brain is a highly complex process, and this process is reliant on precise regulation of molecular and cellular events grounded in the spatiotemporal regulation of the transcriptome. Disruption of this regulation can lead to neuropsychiatric disorders. RATIONALE The regulatory, epigenomic, and transcriptomic features of the human brain have not been comprehensively compiled across time, regions, or cell types. Understanding the etiology of neuropsychiatric disorders requires knowledge not just of endpoint differences between healthy and diseased brains but also of the developmental and cellular contexts in which these differences arise. Moreover, an emerging body of research indicates that many aspects of the development and physiology of the human brain are not well recapitulated in model organisms, and therefore it is necessary that neuropsychiatric disorders be understood in the broader context of the developing and adult human brain. RESULTS Here we describe the generation and analysis of a variety of genomic data modalities at the tissue and single-cell levels, including transcriptome, DNA methylation, and histone modifications across multiple brain regions ranging in age from embryonic development through adulthood. We observed a widespread transcriptomic transition beginning during late fetal development and consisting of sharply decreased regional differences. This reduction coincided with increases in the transcriptional signatures of mature neurons and the expression of genes associated with dendrite development, synapse development, and neuronal activity, all of which were temporally synchronous across neocortical areas, as well as myelination and oligodendrocytes, which were asynchronous. Moreover, genes including MEF2C , SATB2 , and TCF4 , with genetic associations to multiple brain-related traits and disorders, converged in a small number of modules exhibiting spatial or spatiotemporal specificity. CONCLUSION We generated and applied our dataset to document transcriptomic and epigenetic changes across human development and then related those changes to major neuropsychiatric disorders. These data allowed us to identify genes, cell types, gene coexpression modules, and spatiotemporal loci where disease risk might converge, demonstrating the utility of the dataset and providing new insights into human development and disease. Spatiotemporal dynamics of human brain development and neuropsychiatric risks. Human brain development begins during embryonic development and continues through adulthood (top). Integrating data modalities (bottom left) revealed age- and cell type–specific properties and global patterns of transcriptional dynamics, including a late fetal transition (bottom middle). We related the variation in gene expression (brown, high; purple, low) to regulatory elements in the fetal and adult brains, cell type–specific signatures, and genetic loci associated with neuropsychiatric disorders (bottom right; gray circles indicate enrichment for corresponding features among module genes). Relationships depicted in this panel do not correspond to specific observations. CBC, cerebellar cortex; STR, striatum; HIP, hippocampus; MD, mediodorsal nucleus of thalamus; AMY, amygdala.

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.

Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism, the contribution of de novo noncoding variation is probably modest in comparison to that of de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple-testing burden.

Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism, the contribution of de novo noncoding variation is probably modest in comparison to that of de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple-testing burden.

Background Genomic studies in Autism Spectrum Disorder (ASD) have largely focused on high impact de novo mutations that disrupt protein-coding sequence. Given the size of the noncoding genome and its regulatory role in gene function, it is likely that rare, noncoding variation represents an important component of the genetic architecture of ASD. Characterization of noncoding mutations may also provide insights into the cell types and critical developmental stages involved in ASD. However, there is no obvious noncoding equivalent to protein-truncating variants, thus necessitating an unbiased and statistically rigorous association strategy for noncoding variation that parallels standards set by common variant analyses. Methods Using whole genome sequencing (WGS) of 2,076 individuals (519 cases, unaffected sibling controls, and both parents), we evaluated genomic variation across the spectrum of variant size and frequency. The results of 12 variant discovery algorithms were integrated to identify de novo SNVs, indels, and structural variants (SVs); cross-site validation exceeded 93% for all variant classes. Variants were annotated using an extensive series of noncoding functional annotations and gene sets, including gene-defined regions (e.g. promoters) and regulatory regions (e.g. histone marks), conservation, proximity to gene sets of interest (e.g. FMRP targets), and variant type (e.g. SNVs, indels), resulting in 51,801 combinations of annotation categories. ASD association within each category was assessed using a binomial test to compare variant counts in cases and controls in a Category-Wide Association Study (CWAS). To account for multiple testing, correlations of p-values were assessed between the 51,801 categories from 20,000 sets of simulated variants. Eigenvalue decomposition estimated that 4,211 effective tests explained 99% of the variation. Results A small excess of de novo noncoding variants was observed in cases (1.02-fold), which was not significant after correcting for paternal age. While we identified several variants that were likely causal for an individual, no annotation category was significant after correcting for 4,211 tests in the CWAS. The lowest p-values were observed in coding regions, including missense variation and SVs not detected by previous technologies. Similarly, no category of rare inherited variants demonstrated parental transmission bias or ASD association. Discussion Our results suggest that there is no clear category of rare noncoding variation with equivalent impact on ASD risk as large SVs or protein-disrupting mutations. Furthermore, given the multiple categories of noncoding mutations that may potentially confer ASD risk, identifying noncoding disease associations and quantifying this risk will require a statistically rigorous approach that includes stringent multiple testing correction for this multitude of plausible hypotheses. Analogous to genome-wide association studies of common variation, this approach is likely to identify sound and replicable noncoding associations, but will require substantially larger sample sizes, likely in excess of 5,000 cases.

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). Through analysis of de novo mutations in autism spectrum disorder (ASD), Sanders et al. find that small deletions, but not large deletions/duplications, contain one critical gene. Combining CNV and sequencing data, they identify 6 loci and 65 genes associated with ASD.

OBJECTIVE: Aside from features associated with risk of neurogenetic syndromes in general (e.g., cognitive impairment), limited progress has been made in identifying phenotype-genotype relationships in autism spectrum disorder (ASD). The objective of this study was to extend work in the Simons Simplex Collection by comparing the phenotypic profiles of ASD probands with or without identified de novo loss of function mutations or copy number variants in high-confidence ASD-associated genes or loci.METHOD: Analyses preemptively accounted for documented differences in sex and IQ in affected individuals with de novo mutations by matching probands with and without these genetic events on sex, IQ, and age before comparing them on multiple behavioral domains.RESULTS: Children with de novo mutations (N=112) had a greater likelihood of motor delay during early development (later age at walking), but they were less impaired on certain measures of ASD core symptoms (parent-rated social communication abnormalities and clinician-rated diagnostic certainty about ASD) in later childhood. These children also showed relative strengths in verbal and language abilities, including a smaller discrepancy between nonverbal and verbal IQ and a greater likelihood of having achieved fluent language (i.e., regular use of complex sentences).CONCLUSIONS: Children with ASD with de novo mutations may exhibit a "muted" symptom profile with respect to social communication and language deficits relative to those with ASD with no identified genetic abnormalities. Such findings suggest that examining early milestone differences and standardized testing results may be helpful in etiologic efforts, and potentially in clinical differentiation of various subtypes of ASD, but only if developmental and demographic variables are properly accounted for first.

SummaryGenomic studies to date in autism spectrum disorder (ASD) have largely focused on newly arising mutations that disrupt protein coding sequence and strongly influence risk. We evaluate the contribution of noncoding regulatory variation across the size and frequency spectrum through whole genome sequencing of 519 ASD cases, their unaffected sibling controls, and parents. Cases carry a small excess of de novo (1.02-fold) noncoding variants, which is not significant after correcting for paternal age. Assessing 51,801 regulatory classes, no category is significantly associated with ASD after correction for multiple testing. The strongest signals are observed in coding regions, including structural variation not detected by previous technologies and missense variation. While rare noncoding variation likely contributes to risk in neurodevelopmental disorders, no category of variation has impact equivalent to loss-of-function mutations. Average effect sizes are likely to be smaller than that for coding variation, requiring substantially larger samples to quantify this risk.

A strong male bias in autism spectrum disorder (ASD) prevalence has been observed with striking consistency, but no mechanism has yet to definitively account for this sex difference. This review explores the current status of epidemiological, genetic, and neuroendocrinological work addressing ASD prevalence and liability in males and females, so as to frame the major issues necessary to pursue a more complete understanding of the biological basis for sex-differential risk.Recent studies continue to report a male bias in ASD prevalence, but also suggest that sex differences in phenotypic presentation, including fewer restricted and repetitive behaviors and externalizing behavioral problems in females, may contribute to this bias. Genetic studies demonstrate that females are protected from the effects of heritable and de-novo ASD risk variants, and compelling work suggests that sex chromosomal genes and/or sex hormones, especially testosterone, may modulate the effects of genetic variation on the presentation of an autistic phenotype.ASDs affect females less frequently than males, and several sex-differential genetic and hormonal factors may contribute. Future work to determine the mechanisms by which these factors confer risk and protection to males and females is essential.

Autism spectrum disorder (ASD) is a developmental condition that affects approximately four times as many males as females, a strong sex bias that has not yet been fully explained. Understanding the causes of this biased prevalence may highlight novel avenues for treatment development that could benefit patients with diverse genetic backgrounds, and the expertise of sex differences researchers will be invaluable in this endeavor. In this review, I aim to assess current evidence pertaining to the sex difference in ASD prevalence and to identify outstanding questions and remaining gaps in our understanding of how males come to be more frequently affected and/or diagnosed with ASD. Though males consistently outnumber females in ASD prevalence studies, prevalence estimates generated using different approaches report male/female ratios of variable magnitude that suggest that ascertainment or diagnostic biases may contribute to the male skew in ASD. Here, I present the different methods applied and implications of their findings. Additionally, even as prevalence estimations challenge the degree of male bias in ASD, support is growing for the long-proposed female protective effect model of ASD risk, and I review the relevant results from recurrence rate, quantitative trait, and genetic analyses. Lastly, I describe work investigating several sex-differential biological factors and pathways that may be responsible for females' protection and/or males' increased risk predicted by the female protective effect model, including sex steroid hormone exposure and regulation and sex-differential activity of certain neural cell types. However, much future work from both the ASD and sex differences research communities will be required to flesh out our understanding of how these factors act to influence the developing brain and modulate ASD risk.

Analysis of human hippocampus identifies two modules of coexpressed genes that are conserved throughout the human cortex and in mouse hippocampi. These modules are enriched for genetic variants associated with both cognitive phenotypes and neuropsychiatric disorders.

In the version of this article initially published, the consortium authorship and corresponding authors were not presented correctly. In the PDF and print versions, the Whole Genome Sequencing for Psychiatric Disorders (WGSPD) consortium was missing from the author list at the beginning of the paper, where it should have appeared as the seventh author; it was present in the author list at the end of the paper, but the footnote directing readers to the Supplementary Note for a list of members was missing. In the HTML version, the consortium was listed as the last author instead of as the seventh, and the line directing readers to the Supplementary Note for a list of members appeared at the end of the paper under Author Information but not in association with the consortium name itself. Also, this line stated that both member names and affiliations could be found in the Supplementary Note; in fact, only names are given. In all versions of the paper, the corresponding author symbols were attached to A. Jeremy Willsey, Steven E. Hyman, Anjene M. Addington and Thomas Lehner; they should have been attached, respectively, to Steven E. Hyman, Anjene M. Addington, Thomas Lehner and Nelson B. Freimer. As a result of this shift, the respective contact links in the HTML version did not lead to the indicated individuals. The errors have been corrected in the HTML and PDF versions of the article.

Recent research on disparate psychiatric disorders has implicated rare variants in genes involved in global gene regulation and chromatin modification, as well as many common variants located primarily in regulatory regions of the genome. Understanding precisely how these variants contribute to disease will require a deeper appreciation for the mechanisms of gene regulation in the developing and adult human brain. The PsychENCODE project aims to produce a public resource of multidimensional genomic data using tissue- and cell type–specific samples from approximately 1,000 phenotypically well-characterized, high-quality healthy and disease-affected human post-mortem brains, as well as functionally characterize disease-associated regulatory elements and variants in model systems. We are beginning with a focus on autism spectrum disorder, bipolar disorder and schizophrenia, and expect that this knowledge will apply to a wide variety of psychiatric disorders. This paper outlines the motivation and design of PsychENCODE.

Rare maternally inherited duplications at 15q11-13 are observed in ~1% of individuals with an autism spectrum disorder (ASD), making it among the most common causes of ASD. 15q11-13 comprises a complex region, and as this copy number variation encompasses many genes, it is important to explore individual genotype-phenotype relationships. Cytoplasmic FMR1-interacting protein 1 (CYFIP1) is of particular interest because of its interaction with Fragile X mental retardation protein (FMRP), its upregulation in transformed lymphoblastoid cell lines from patients with duplications at 15q11-13 and ASD and the presence of smaller overlapping deletions of CYFIP1 in patients with schizophrenia and intellectual disability. Here, we confirm that CYFIP1 is upregulated in transformed lymphoblastoid cell lines and demonstrate its upregulation in the post-mortem brain from 15q11-13 duplication patients for the first time. To investigate how increased CYFIP1 dosage might predispose to neurodevelopmental disease, we studied the consequence of its overexpression in multiple systems. We show that overexpression of CYFIP1 results in morphological abnormalities including cellular hypertrophy in SY5Y cells and differentiated mouse neuronal progenitors. We validate these results in vivo by generating a BAC transgenic mouse, which overexpresses Cyfip1 under the endogenous promotor, observing an increase in the proportion of mature dendritic spines and dendritic spine density. Gene expression profiling on embryonic day 15 suggested the dysregulation of mammalian target of rapamycin (mTOR) signaling, which was confirmed at the protein level. Importantly, similar evidence of mTOR-related dysregulation was seen in brains from 15q11-13 duplication patients with ASD. Finally, treatment of differentiated mouse neuronal progenitors with an mTOR inhibitor (rapamycin) rescued the morphological abnormalities resulting from CYFIP1 overexpression. Together, these data show that CYFIP1 overexpression results in specific cellular phenotypes and implicate modulation by mTOR signaling, further emphasizing its role as a potential convergent pathway in some forms of ASD.

BackgroundAutism spectrum disorders (ASDs) are male-biased and genetically heterogeneous. While sequencing of sporadic cases has identifiedde novorisk variants, the heritable genetic contribution and mechanisms driving the male bias are less understood. Here, we aimed to identify familial and sex-differential risk loci in the largest available, uniformly ascertained, densely genotyped sample of multiplex ASD families from the Autism Genetics Resource Exchange (AGRE), and to compare results with earlier findings from AGRE.MethodsFrom a total sample of 1,008 multiplex families, we performed genome-wide, non-parametric linkage analysis in a discovery sample of 847 families, and separately on subsets of families with only male, affected children (male-only, MO) or with at least one female, affected child (female-containing, FC). Loci showing evidence for suggestive linkage (logarithm of odds ≥2.2) in this discovery sample, or in previous AGRE samples, were re-evaluated in an extension study utilizing all 1,008 available families. For regions with genome-wide significant linkage signal in the discovery stage, those families not included in the corresponding discovery sample were then evaluated for independent replication of linkage. Association testing of common single nucleotide polymorphisms (SNPs) was also performed within suggestive linkage regions.ResultsWe observed an independent replication of previously observed linkage at chromosome 20p13 (P ConclusionsWith few exceptions, analyses of subsets of families from the AGRE cohort identify different risk loci, consistent with extreme locus heterogeneity in ASD. Large samples appear to yield more consistent results, and sex-stratified analyses facilitate the identification of sex-differential risk loci, suggesting that linkage analyses in large cohorts are useful for identifying heritable risk loci. Additional work, such as targeted re-sequencing, is needed to identify the specific variants within these loci that are responsible for increasing ASD risk.

Male preponderance in autistic behavioral impairment has been explained in terms of a hypothetical protective effect of female sex, yet little research has tested this hypothesis empirically. If females are protected, they should require greater etiologic load to manifest the same degree of impairment as males. The objective of this analysis was to examine whether greater familial etiologic load was associated with quantitative autistic impairments in females compared with males. Subjects included 3,842 dizygotic twin pairs from the Twins Early Development Study (TEDS) and 6,040 dizygotic twin pairs from the Child and Adolescent Twin Study of Sweden (CATSS). In both samples, we compared sibling autistic traits between female and male probands, who were identified as children scoring in the top 90th and 95th percentiles of the population autistic trait distributions. In both TEDS and CATSS, siblings of female probands above the 90th percentile had significantly more autistic impairments than the siblings of male probands above the 90th percentile. The siblings of female probands above the 90th percentile also had greater categorical recurrence risk in both TEDS and CATSS. Results were similar in probands above the 95th percentile. This finding, replicated across two nationally-representative samples, suggests that female sex protects girls from autistic impairments and that girls may require greater familial etiologic load to manifest the phenotype. It provides empirical support for the hypothesis of a female protective effect against autistic behavior and can be used to inform and interpret future gene finding efforts in autism spectrum disorders.

Dysfunctional social reward and social orienting attend a variety of neuropsychiatric disorders including autism, schizophrenia, social anxiety, and psychopathy. Here we show that similar social reward and attention dysfunction attend anorexia nervosa, a disorder defined by avoidance of food and extreme weight loss. We measured the implicit reward value of social stimuli for female participants with (n=11) and without (n=11) anorexia nervosa using an econometric choice task and also tracked gaze patterns during free viewing of images of female faces and bodies. As predicted, the reward value of viewing bodies varied inversely with observed body weight for women with anorexia but not neurotypical women, in contrast with their explicit ratings of attractiveness. Surprisingly, women with anorexia nervosa, unlike neurotypical women, did not find female faces rewarding and avoided looking at both the face and eyes—independent of observed body weight. These findings demonstrate comorbid dysfunction in the neural circuits mediating gustatory and social reward in anorexia nervosa.