Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism.

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.

Rare coding variants in ten genes confer substantial risk for schizophrenia.

Rare coding variation has historically provided the most direct connections between gene function and disease pathogenesis. By meta-analysing the whole exomes of 24,248 schizophrenia cases and 97,322 controls, we implicate ultra-rare coding variants (URVs) in 10 genes as conferring substantial risk for schizophrenia (odds ratios of 3-50, P < 2.14 × 10-6) and 32 genes at a false discovery rate of <5%. These genes have the greatest expression in central nervous system neurons and have diverse molecular functions that include the formation, structure and function of the synapse. The associations of the NMDA (N-methyl-D-aspartate) receptor subunit GRIN2A and AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid) receptor subunit GRIA3 provide support for dysfunction of the glutamatergic system as a mechanistic hypothesis in the pathogenesis of schizophrenia. We observe an overlap of rare variant risk among schizophrenia, autism spectrum disorders1, epilepsy and severe neurodevelopmental disorders2, although different mutation types are implicated in some shared genes. Most genes described here, however, are not implicated in neurodevelopment. We demonstrate that genes prioritized from common variant analyses of schizophrenia are enriched in rare variant risk3, suggesting that common and rare genetic risk factors converge at least partially on the same underlying pathogenic biological processes. Even after excluding significantly associated genes, schizophrenia cases still carry a substantial excess of URVs, which indicates that more risk genes await discovery using this approach.

Mapping genomic loci implicates genes and synaptic biology in schizophrenia.

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.

Accounting for age of onset and family history improves power in genome-wide association studies.

Genome-wide association studies (GWASs) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age of onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Here, we present LT-FH++, an extension of the liability threshold model conditioned on family history (LT-FH), which jointly accounts for age of onset and sex as well as family history. Using simulations, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields statistically significant power gains over LT-FH and large power gains over genome-wide association study by proxy (GWAX). We applied our method to four psychiatric disorders available in the iPSYCH data and to mortality in the UK Biobank and found 20 genome-wide significant associations with LT-FH++, compared to ten for LT-FH and eight for a standard case-control GWAS. As more genetic data with linked electronic health records become available to researchers, we expect methods that account for additional health information, such as LT-FH++, to become even more beneficial.

Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction.

The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWASs). However, it is now common for researchers to have access to large individual-level data as well, such as the UK Biobank data. To the best of our knowledge, it has not yet been explored how best to combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using 12 real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and meta-PRS. We find that, when large individual-level data are available, the linear combination of PRSs (meta-PRS) is both a simple alternative to meta-GWAS and often more accurate.

Unraveling the metabolomic architecture of autism in a large Danish population-based cohort.

BACKGROUND: The prevalence of autism in Denmark has been increasing, reaching 1.65% among 10-year-old children, and similar trends are seen elsewhere. Although there are several factors associated with autism, including genetic, environmental, and prenatal factors, the molecular etiology of autism is largely unknown. Here, we use untargeted metabolomics to characterize the neonatal metabolome from dried blood spots collected shortly after birth. METHODS: We analyze the metabolomic profiles of a subset of a large Danish population-based cohort (iPSYCH2015) consisting of over 1400 newborns, who later are diagnosed with autism and matching controls and in two Swedish population-based cohorts comprising over 7000 adult participants. Mass spectrometry analysis was performed by a timsTOF Pro operated in QTOF mode, using data-dependent acquisition. By applying an untargeted metabolomics approach, we could reproducibly measure over 800 metabolite features. RESULTS: We detected underlying molecular perturbations across several metabolite classes that precede autism. In particular, the cyclic dipeptide cyclo-leucine-proline (FDR-adjusted p = 0.003) and the carnitine-related 5-aminovaleric acid betaine (5-AVAB) (FDR-adjusted p = 0.03), were associated with an increased probability for autism, independently of known prenatal and genetic risk factors. Analysis of genetic and dietary data in adults revealed that 5-AVAB was associated with increased habitual dietary intake of dairy (FDR-adjusted p < 0.05) and with variants near SLC22A4 and SLC22A5 (p < 5.0e - 8), coding for a transmembrane carnitine transporter protein involved in controlling intracellular carnitine levels. CONCLUSIONS: Cyclo-leucine-proline and 5-AVAB are associated with future diagnosis of autism in Danish neonates, both representing novel early biomarkers for autism. 5-AVAB is potentially modifiable and may influence carnitine homeostasis.

Method for Testing Etiologic Heterogeneity Among Non-Competing Diagnoses, Applied to Impact of Perinatal Exposures on Autism and Attention Deficit Hyperactivity Disorder.

BACKGROUND: Testing etiologic heterogeneity - whether a disorder subtype is more or less impacted by a risk factor, is important for understanding causal pathways and optimizing statistical power. The study of mental health disorders especially benefits from strategic sub-categorization because these disorders are heterogenous and frequently co-occur. Existing methods to quantify etiologic heterogeneity are not appropriate for non-competing events in an open cohort of variable-length follow-up. Thus, we developed a new method. METHODS: We estimated risks from urban residence, maternal smoking during pregnancy, and parental psychiatric history, with subtypes defined by the presence or absence of a co-diagnosis: autism alone, attention deficit hyperactivity disorder (ADHD) alone, and joint diagnoses of autism+ADHD. To calculate the risk of a single diagnosis (e.g. autism alone), we subtracted the risk for autism+ADHD from the risk for autism overall. We tested the equivalency of average risk ratios over time, using a Wald-type test and bootstrapped standard errors. RESULTS: Urban residence was most strongly linked with autism+ADHD and least with ADHD only; maternal smoking was associated with ADHD only but not autism only; and parental psychiatric history exhibited similar associations with all subgroups. CONCLUSIONS: Our method allowed the calculation of appropriate p values to test strength of association, informing etiologic heterogeneity wherein two of these three risk factors exhibited different impacts across diagnostic subtypes. The method used all available data, avoided neurodevelopmental outcome misclassification, exhibited robust statistical precision, and is applicable to similar heterogeneous complex conditions using common diagnostic data with variable follow-up.

Polygenic liability, stressful life events and risk for secondary-treated depression in early life: a nationwide register-based case-cohort study.

BACKGROUND: In this study, we examined the relationship between polygenic liability for depression and number of stressful life events (SLEs) as risk factors for early-onset depression treated in inpatient, outpatient or emergency room settings at psychiatric hospitals in Denmark. METHODS: Data were drawn from the iPSYCH2012 case-cohort sample, a population-based sample of individuals born in Denmark between 1981 and 2005. The sample included 18 532 individuals who were diagnosed with depression by a psychiatrist by age 31 years, and a comparison group of 20 184 individuals. Information on SLEs was obtained from nationwide registers and operationalized as a time-varying count variable. Hazard ratios and cumulative incidence rates were estimated using Cox regressions. RESULTS: Risk for depression increased by 35% with each standard deviation increase in polygenic liability (p < 0.0001), and 36% (p < 0.0001) with each additional SLE. There was a small interaction between polygenic liability and SLEs (ß = -0.04, p = 0.0009). The probability of being diagnosed with depression in a hospital-based setting between ages 15 and 31 years ranged from 1.5% among males in the lowest quartile of polygenic liability with 0 events by age 15, to 18.8% among females in the highest quartile of polygenic liability with 4+ events by age 15. CONCLUSIONS: These findings suggest that although there is minimal interaction between polygenic liability and SLEs as risk factors for hospital-treated depression, combining information on these two important risk factors could potentially be useful for identifying high-risk individuals.

Genome-wide association study of febrile seizures implicates fever response and neuronal excitability genes.

Febrile seizures represent the most common type of pathological brain activity in young children and are influenced by genetic, environmental and developmental factors. In a minority of cases, febrile seizures precede later development of epilepsy. We conducted a genome-wide association study of febrile seizures in 7635 cases and 83 966 controls identifying and replicating seven new loci, all with P < 5 × 10-10. Variants at two loci were functionally related to altered expression of the fever response genes PTGER3 and IL10, and four other loci harboured genes (BSN, ERC2, GABRG2, HERC1) influencing neuronal excitability by regulating neurotransmitter release and binding, vesicular transport or membrane trafficking at the synapse. Four previously reported loci (SCN1A, SCN2A, ANO3 and 12q21.33) were all confirmed. Collectively, the seven novel and four previously reported loci explained 2.8% of the variance in liability to febrile seizures, and the single nucleotide polymorphism heritability based on all common autosomal single nucleotide polymorphisms was 10.8%. GABRG2, SCN1A and SCN2A are well-established epilepsy genes and, overall, we found positive genetic correlations with epilepsies (rg = 0.39, P = 1.68 × 10-4). Further, we found that higher polygenic risk scores for febrile seizures were associated with epilepsy and with history of hospital admission for febrile seizures. Finally, we found that polygenic risk of febrile seizures was lower in febrile seizure patients with neuropsychiatric disease compared to febrile seizure patients in a general population sample. In conclusion, this largest genetic investigation of febrile seizures to date implicates central fever response genes as well as genes affecting neuronal excitability, including several known epilepsy genes. Further functional and genetic studies based on these findings will provide important insights into the complex pathophysiological processes of seizures with and without fever.

Sequencing and de novo assembly of 150 genomes from Denmark as a population reference.

Hundreds of thousands of human genomes are now being sequenced to characterize genetic variation and use this information to augment association mapping studies of complex disorders and other phenotypic traits. Genetic variation is identified mainly by mapping short reads to the reference genome or by performing local assembly. However, these approaches are biased against discovery of structural variants and variation in the more complex parts of the genome. Hence, large-scale de novo assembly is needed. Here we show that it is possible to construct excellent de novo assemblies from high-coverage sequencing with mate-pair libraries extending up to 20 kilobases. We report de novo assemblies of 150 individuals (50 trios) from the GenomeDenmark project. The quality of these assemblies is similar to those obtained using the more expensive long-read technology. We use the assemblies to identify a rich set of structural variants including many novel insertions and demonstrate how this variant catalogue enables further deciphering of known association mapping signals. We leverage the assemblies to provide 100 completely resolved major histocompatibility complex haplotypes and to resolve major parts of the Y chromosome. Our study provides a regional reference genome that we expect will improve the power of future association mapping studies and hence pave the way for precision medicine initiatives, which now are being launched in many countries including Denmark.

A polygenic resilience score moderates the genetic risk for schizophrenia.

Based on the discovery by the Resilience Project (Chen R. et al. Nat Biotechnol 34:531-538, 2016) of rare variants that confer resistance to Mendelian disease, and protective alleles for some complex diseases, we posited the existence of genetic variants that promote resilience to highly heritable polygenic disorders1,0 such as schizophrenia. Resilience has been traditionally viewed as a psychological construct, although our use of the term resilience refers to a different construct that directly relates to the Resilience Project, namely: heritable variation that promotes resistance to disease by reducing the penetrance of risk loci, wherein resilience and risk loci operate orthogonal to one another. In this study, we established a procedure to identify unaffected individuals with relatively high polygenic risk for schizophrenia, and contrasted them with risk-matched schizophrenia cases to generate the first known "polygenic resilience score" that represents the additive contributions to SZ resistance by variants that are distinct from risk loci. The resilience score was derived from data compiled by the Psychiatric Genomics Consortium, and replicated in three independent samples. This work establishes a generalizable framework for finding resilience variants for any complex, heritable disorder.

Seasonal Variation and Risk of Febrile Seizures: A Danish Nationwide Cohort Study.

INTRODUCTION: Onset of febrile seizures varies with calendar season. However, it has not previously been assessed, how season of birth interacts with age and peak risk of febrile seizures, and whether season of birth correlates with the cumulative risk of febrile seizures at 5 years of age (i.e., when children are no longer of risk of febrile seizures). METHODS: We identified all singleton children born in Denmark between 1977 and 2011 who were alive at 3 months of age (N = 2,103,232). We used the Danish Civil Registration System to identify age and sex of the children and the Danish National Patient Register to identify children hospitalized with febrile seizures from 3 months to 5 years of age. Follow-up ended on December 31, 2016, when all children had reached 5 years of age. RESULTS: The relative risk of admission with a first febrile seizure varied with calendar month; in February (a winter month in Denmark), the risk was more than doubled (hazard ratio: 2.10 [95% confidence interval [CI]: 2.03-2.18]) compared with August (a summer month in Denmark). The age-specific incidence of a first febrile seizure by birth month identified the highest peak incidence of a first febrile seizure among children born in November (reaching a peak incidence of 350 first admissions with a febrile seizure per 100,000 person months at age 16 months) as compared to children born in July (reaching a peak incidence of 200 first admissions with a febrile seizure per 100,000 person months at age 16 months). However, the cumulative incidence of any admission with febrile seizures before 5 years was not correlated with season of birth (3.69% [95% CI: 3.64-3.74%] for winter births, 3.57% [95% CI: 3.52-3.62%] for spring births, 3.55% [95% CI: 3.50-3.59%] for summer births, and 3.64% [95% CI: 3.59-3.69%] for fall births). DISCUSSION/CONCLUSION: The study found a significant seasonal variation in onset of the first febrile seizure and in the age-specific peak incidence of febrile seizures. However, there was no correlation between season of birth and cumulative incidence of febrile seizures at 5 years of age suggesting that children who are predisposed to febrile seizures will eventually go on to experience a febrile seizure regardless of season of birth.

Maternal health around pregnancy and autism risk: a diagnosis-wide, population-based study.

BACKGROUND: Many studies have reported an increased risk of autism spectrum disorder (ASD) associated with some maternal diagnoses in pregnancy. However, such associations have not been studied systematically, accounting for comorbidity between maternal disorders. Therefore our aim was to comprehensively test the associations between maternal diagnoses around pregnancy and ASD risk in offspring. METHODS: This exploratory case-cohort study included children born in Israel from 1997 to 2008, and followed up until 2015. We used information on all ICD-9 codes received by their mothers during pregnancy and the preceding year. ASD risk associated with each of those conditions was calculated using Cox proportional hazards regression, adjusted for the confounders (birth year, maternal age, socioeconomic status and number of ICD-9 diagnoses during the exposure period). RESULTS: The analytic sample consisted of 80 187 individuals (1132 cases, 79 055 controls), with 822 unique ICD-9 codes recorded in their mothers. After extensive quality control, 22 maternal diagnoses were nominally significantly associated with offspring ASD, with 16 of those surviving subsequent filtering steps (permutation testing, multiple testing correction, multiple regression). Among those, we recorded an increased risk of ASD associated with metabolic [e.g. hypertension; HR = 2.74 (1.92-3.90), p = 2.43 × 10-8], genitourinary [e.g. non-inflammatory disorders of cervix; HR = 1.88 (1.38-2.57), p = 7.06 × 10-5] and psychiatric [depressive disorder; HR = 2.11 (1.32-3.35), p = 1.70 × 10-3] diagnoses. Meanwhile, mothers of children with ASD were less likely to attend prenatal care appointment [HR = 0.62 (0.54-0.71), p = 1.80 × 10-11]. CONCLUSIONS: Sixteen maternal diagnoses were associated with ASD in the offspring, after rigorous filtering of potential false-positive associations. Replication in other cohorts and further research to understand the mechanisms underlying the observed associations with ASD are warranted.

Birth characteristics and risk of febrile seizures.

OBJECTIVE: Febrile seizure is a common childhood disorder that affects 2-5% of all children, and is associated with later development of epilepsy and psychiatric disorders. This study determines how the incidence of febrile seizures correlates with birth characteristics, age, sex and brain development. METHODS: This is a cohort study of all children born Denmark between 1977 and 2011 who were alive at 3 months of age (N = 2,103,232). The Danish National Patient Register was used to identify children with febrile seizures up to 5 years of age. Follow-up ended on 31 December 2016 when all cohort members had potentially reached 5 years of age. RESULTS: In total, 75,593 (3.59%, 95% CI: 3.57-3.62%) were diagnosed with febrile seizures. Incidence peaked at 16.7 months of age (median: 16.7 months, interquartile range: 12.5-24.0). The 5-year cumulative incidence of febrile seizures increased with decreasing birth weight (<1500 g; 5.42% (95% CI: 4.98-5.88% vs. 3,000-4,000 g; 3.53% (95% CI: 3.50-3.56%)) and with decreasing gestational age at birth (31-32 weeks; 5.90% (95% CI: 5.40-6.44%) vs. 39-40 weeks; 3.56% (95% CI: 3.53-3.60)). Lower gestational age at birth was associated with higher age at onset of a first febrile seizure; an association that essentially disappeared when correcting for age from conception. CONCLUSIONS: The risk of febrile seizures increased with decreasing birth weight and gestational age at birth. The association between low gestational age at birth and age at first febrile seizure suggests that onset of febrile seizures is associated with the stage of brain development.

The Eating Disorders Genetics Initiative (EDGI): study protocol.

BACKGROUND: The Eating Disorders Genetics Initiative (EDGI) is an international investigation exploring the role of genes and environment in anorexia nervosa, bulimia nervosa, and binge-eating disorder. METHODS: A total of 14,500 individuals with eating disorders and 1500 controls will be included from the United States (US), Australia (AU), New Zealand (NZ), and Denmark (DK). In the US, AU, and NZ, participants will complete comprehensive online phenotyping and will submit a saliva sample for genotyping. In DK, individuals with eating disorders will be identified by the National Patient Register, and genotyping will occur using bloodspots archived from birth. A genome-wide association study will be conducted within EDGI and via meta-analysis with other data from the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED). DISCUSSION: EDGI represents the largest genetic study of eating disorders ever to be conducted and is designed to rapidly advance the study of the genetics of the three major eating disorders (anorexia nervosa, bulimia nervosa, and binge-eating disorder). We will explicate the genetic architecture of eating disorders relative to each other and to other psychiatric and metabolic disorders and traits. Our goal is for EDGI to deliver "actionable" findings that can be transformed into clinically meaningful insights. TRIAL REGISTRATION: EDGI is a registered clinical trial: clinicaltrials.gov NCT04378101 .

Brain proteome changes in female Brd1+/- mice unmask dendritic spine pathology and show enrichment for schizophrenia risk.

Genetic and molecular studies have implicated the Bromodomain containing 1 (BRD1) gene in the pathogenesis of schizophrenia and bipolar disorder. Accordingly, mice heterozygous for a targeted deletion of Brd1 (Brd1+/- mice) show behavioral phenotypes with broad translational relevance to psychiatric disorders. BRD1 encodes a scaffold protein that affects the expression of many genes through modulation of histone acetylation. BRD1 target genes have been identified in cell lines; however the impact of reduced Brd1 levels on the brain proteome is largely unknown. In this study, we applied label-based quantitative mass spectrometry to profile the frontal cortex, hippocampus and striatum proteome and synaptosomal proteome of female Brd1+/- mice. We successfully quantified between 1537 and 2196 proteins and show widespread changes in protein abundancies and compartmentalization. By integrative analysis of human genetic data, we find that the differentially abundant proteins in frontal cortex and hippocampus are enriched for schizophrenia risk further linking the actions of BRD1 to psychiatric disorders. Affected proteins were further enriched for proteins involved in processes known to influence neuronal and dendritic spine morphology e.g. regulation of cytoskeleton dynamics and mitochondrial function. Directly prompted in these findings, we investigated dendritic spine morphology of pyramidal neurons in anterior cingulate cortex and found them significantly altered, including reduced size of small dendritic spines and decreased number of the mature mushroom type. Collectively, our study describes known as well as new mechanisms related to BRD1 dysfunction and its role in psychiatric disorders, and provides evidence for the molecular and cellular dysfunctions underlying altered neurosignalling and cognition in Brd1+/- mice.

LandScape: a simple method to aggregate p-values and other stochastic variables without a priori grouping.

In many areas of science it is custom to perform many, potentially millions, of tests simultaneously. To gain statistical power it is common to group tests based on a priori criteria such as predefined regions or by sliding windows. However, it is not straightforward to choose grouping criteria and the results might depend on the chosen criteria. Methods that summarize, or aggregate, test statistics or p-values, without relying on a priori criteria, are therefore desirable. We present a simple method to aggregate a sequence of stochastic variables, such as test statistics or p-values, into fewer variables without assuming a priori defined groups. We provide different ways to evaluate the significance of the aggregated variables based on theoretical considerations and resampling techniques, and show that under certain assumptions the FWER is controlled in the strong sense. Validity of the method was demonstrated using simulations and real data analyses. Our method may be a useful supplement to standard procedures relying on evaluation of test statistics individually. Moreover, by being agnostic and not relying on predefined selected regions, it might be a practical alternative to conventionally used methods of aggregation of p-values over regions. The method is implemented in Python and freely available online (through GitHub, see the Supplementary information).

Analysis of t(9;17)(q33.2;q25.3) chromosomal breakpoint regions and genetic association reveals novel candidate genes for bipolar disorder.

OBJECTIVES: Breakpoints of chromosomal abnormalities facilitate identification of novel candidate genes for psychiatric disorders. Genome-wide significant evidence supports the linkage between chromosome 17q25.3 and bipolar disorder (BD). Co-segregation of translocation t(9;17)(q33.2;q25.3) with psychiatric disorders has been reported. We aimed to narrow down these chromosomal breakpoint regions and to investigate the associations between single nucleotide polymorphisms within these regions and BD as well as schizophrenia (SZ) in large genome-wide association study samples. METHODS: We cross-linked Danish psychiatric and cytogenetic case registers to identify an individual with both t(9;17)(q33.2;q25.3) and BD. Fluorescent in situ hybridization was employed to map the chromosomal breakpoint regions of this proband. We accessed the Psychiatric Genomics Consortium BD (n = 16,731) and SZ (n = 21,856) data. Genetic associations between these disorders and single nucleotide polymorphisms within these breakpoint regions were analysed by BioQ, FORGE, and RegulomeDB programmes. RESULTS: Four protein-coding genes [coding for (endonuclease V (ENDOV), neuronal pentraxin I (NPTX1), ring finger protein 213 (RNF213), and regulatory-associated protein of mammalian target of rapamycin (mTOR) (RPTOR)] were found to be located within the 17q25.3 breakpoint region. NPTX1 was significantly associated with BD (p = 0.004), while ENDOV was significantly associated with SZ (p = 0.0075) after Bonferroni correction. CONCLUSIONS: Prior linkage evidence and our findings suggest NPTX1 as a novel candidate gene for BD.

Phenotypic and ancestry-related assortative mating in autism.

BACKGROUND: Positive assortative mating (AM) in several neuropsychiatric traits, including autism, has been noted. However, it is unknown whether the pattern of AM is different in phenotypically defined autism subgroups [e.g., autism with and without intellectually disability (ID)]. It is also unclear what proportion of the phenotypic AM can be explained by the genetic similarity between parents of children with an autism diagnosis, and the consequences of AM on the genetic structure of the population. METHODS: To address these questions, we analyzed two family-based autism collections: the Simons Foundation Powering Autism Research for Knowledge (SPARK) (1575 families) and the Simons Simplex Collection (SSC) (2283 families). RESULTS: We found a similar degree of phenotypic and ancestry-related AM in parents of children with an autism diagnosis regardless of the presence of ID. We did not find evidence of AM for autism based on autism polygenic scores (PGS) (at a threshold of |r|> 0.1). The adjustment of ancestry-related AM or autism PGS accounted for only 0.3-4% of the fractional change in the estimate of the phenotypic AM. The ancestry-related AM introduced higher long-range linkage disequilibrium (LD) between single nucleotide polymorphisms (SNPs) on different chromosomes that are highly ancestry-informative compared to SNPs that are less ancestry-informative (D2 on the order of 1 × 10-5). LIMITATIONS: We only analyzed participants of European ancestry, limiting the generalizability of our results to individuals of non-European ancestry. SPARK and SSC were both multicenter studies. Therefore, there could be ancestry-related AM in SPARK and SSC due to geographic stratification. The study participants from each site were unknown, so we were unable to evaluate for geographic stratification. CONCLUSIONS: This study showed similar patterns of AM in autism with and without ID, and demonstrated that the common genetic influences of autism are likely relevant to both autism groups. The adjustment of ancestry-related AM and autism PGS accounted for < 5% of the fractional change in the estimate of the phenotypic AM. Future studies are needed to evaluate if the small increase of long-range LD induced by ancestry-related AM has impact on the downstream analysis.

Polygenic scores for autism are associated with neurite density in adults and children from the general population.

Genetic variants linked to autism are thought to change cognition and behaviour by altering the structure and function of the brain. Although a substantial body of literature has identified structural brain differences in autism, it is unknown whether autism-associated common genetic variants are linked to changes in cortical macro- and micro-structure. We investigated this using neuroimaging and genetic data from adults (UK Biobank, N = 31,748) and children (ABCD, N = 4,928). Using polygenic scores and genetic correlations we observe a robust negative association between common variants for autism and a magnetic resonance imaging derived phenotype for neurite density (intracellular volume fraction) in the general population. This result is consistent across both children and adults, in both the cortex and in white matter tracts, and confirmed using polygenic scores and genetic correlations. There were no sex differences in this association. Mendelian randomisation analyses provide no evidence for a causal relationship between autism and intracellular volume fraction, although this should be revisited using better powered instruments. Overall, this study provides evidence for shared common variant genetics between autism and cortical neurite density.