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Although autism spectrum disorders (ASDs) have a substantial genetic basis, most of the known genetic risk has been traced to rare variants, principally copy number variants (CNVs). To identify common risk variation, the Autism Genome Project (AGP) Consortium genotyped 1558 rigorously defined ASD families for 1 million single-nucleotide polymorphisms (SNPs) and analyzed these SNP genotypes for association with ASD. In one of four primary association analyses, the association signal for marker rs4141463, located within MACROD2, crossed the genome-wide association significance threshold of P < 5 × 10−8. When a smaller replication sample was analyzed, the risk allele at rs4141463 was again over-transmitted; yet, consistent with the winner's curse, its effect size in the replication sample was much smaller; and, for the combined samples, the association signal barely fell below the P < 5 × 10−8 threshold. Exploratory analyses of phenotypic subtypes yielded no significant associations after correction for multiple testing. They did, however, yield strong signals within several genes, KIAA0564, PLD5, POU6F2, ST8SIA2 and TAF1C.

Genes play a strong role in Alzheimer's disease (AD) with late-onset AD showing heritability of 58-79% and early-onset AD over 90%. Genetic association provides a robust platform to build our understanding of the etiology of this complex disease. Over 40 loci are now implicated for AD, suggesting that AD is a disease of multiple components as supported by pathway analyses (immunity, endocytosis, cholesterol transport, ubiquitination, amyloid-β and tau processing). Over 50% of late-onset AD (LOAD) heritability has been captured and allows the calculation of the accumulation of AD genetic risk through polygenic risk scores (PRS). PRS predicts disease with up to 90% accuracy and is an exciting tool in our research armoury that could allow selection of those with high PRS for clinical trials and precision medicine, as well as the cellular modelling of the combined risk. Here we propose the multiplex model as a new perspective from which to understand AD. The multiplex model reflex's the combination of some, or all, of these model components (genetic and environmental), in a tissue specific manner, to trigger or sustain a disease cascade, which ultimately results in the cell/synaptic loss observed in AD.

BackgroundGenetic influences on gene expression in the human fetal brain plausibly impact upon a variety of postnatal brain-related traits, including susceptibility to neuropsychiatric disorders. However, to date, there have been no studies that have mapped genome-wide expression quantitative trait loci (eQTL) specifically in the human prenatal brain.ResultsWe performed deep RNA sequencing and genome-wide genotyping on a unique collection of 120 human brains from the second trimester of gestation to provide the first eQTL dataset derived exclusively from the human fetal brain. We identify high confidence cis-acting eQTL at the individual transcript as well as whole gene level, including many mapping to a common inversion polymorphism on chromosome 17q21. Fetal brain eQTL are enriched among risk variants for postnatal conditions including attention deficit hyperactivity disorder, schizophrenia, and bipolar disorder. We further identify changes in gene expression within the prenatal brain that potentially mediate risk for neuropsychiatric traits, including increased expression of C4A in association with genetic risk for schizophrenia, increased expression of LRRC57 in association with genetic risk for bipolar disorder, and altered expression of multiple genes within the chromosome 17q21 inversion in association with variants influencing the personality trait of neuroticism.ConclusionsWe have mapped eQTL operating in the human fetal brain, providing evidence that these confer risk to certain neuropsychiatric disorders, and identifying gene expression changes that potentially mediate susceptibility to these conditions.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1567-1) contains supplementary material, which is available to authorized users.

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BackgroundVariation in the gene encoding zinc finger binding protein 804A (ZNF804A) is associated with schizophrenia and bipolar disorder. Evidence suggests that ZNF804A is a regulator of gene transcription and is present in nuclear and extranuclear compartments. However, a detailed examination of ZNF804A distribution and its neuronal functions has yet to be performed.MethodsThe localization of ZNF804A protein was examined in neurons derived from human neural progenitor cells, human induced pluripotent stem cells, or in primary rat cortical neurons. In addition, small interfering RNA-mediated knockdown of ZNF804A was conducted to determine its role in neurite formation, maintenance of dendritic spine morphology, and responses to activity-dependent stimulations.ResultsEndogenous ZNF804A protein localized to somatodendritic compartments and colocalized with the putative synaptic markers in young neurons derived from human neural progenitor cells and human induced pluripotent stem cells. In mature rat neurons, Zfp804A, the homolog of ZNF804A, was present in a subset of dendritic spines and colocalized with synaptic proteins in specific nanodomains, as determined by super-resolution microscopy. Interestingly, knockdown of ZNF804A attenuated neurite outgrowth in young neurons, an effect potentially mediated by reduced neuroligin-4 expression. Furthermore, knockdown of ZNF804A in mature neurons resulted in the loss of dendritic spine density and impaired responses to activity-dependent stimulation.ConclusionsThese data reveal a novel subcellular distribution for ZNF804A within somatodendritic compartments and a nanoscopic organization at excitatory synapses. Moreover, our results suggest that ZNF804A plays an active role in neurite formation, maintenance of dendritic spines, and activity-dependent structural plasticity.

Next-generation RNA sequencing (RNA-seq) maps and analyzes transcriptomes and generates data on sequence variation in expressed genes. There are few reported studies on analysis strategies to maximize the yield of quality RNA-seq SNP data. We evaluated the performance of different SNP-calling methods following alignment to both genome and transcriptome by applying them to RNA-seq data from a HapMap lymphoblastoid cell line sample and comparing results with sequence variation data from 1000 Genomes. We determined that the best method to achieve high specificity and sensitivity, and greatest number of SNP calls, is to remove duplicate sequence reads after alignment to the genome and to call SNPs using SAMtools. The accuracy of SNP calls is dependent on sequence coverage available. In terms of specificity, 89% of RNA-seq SNPs calls were true variants where coverage is >10X. In terms of sensitivity, at >10X coverage 92% of all expected SNPs in expressed exons could be detected. Overall, the results indicate that RNA-seq SNP data are a very useful by-product of sequence-based transcriptome analysis. If RNA-seq is applied to disease tissue samples and assuming that genes carrying mutations relevant to disease biology are being expressed, a very high proportion of these mutations can be detected.

The mechanisms underlying weight gain resulting from antipsychotic drugs are not fully understood, although antagonism of the 5-HT2C receptor is likely to contribute. Animal studies indicate that the drugs most likely to cause weight gain, clozapine and olanzapine, have direct effects on the NPY-containing neurons of the hypothalamus; these neurons mediate the effects of the circulating anorexigenic hormone leptin on the control of food intake. The substantial differences between individuals in the extent of antipsychotic-induced weight gain suggest that genetic factors may be important. We have been studying pharmacogenetic correlates and find that a common 5-HT2C receptor promoter region polymorphisms demonstrates strong associations with weight gain in two first episode psychotic samples. In both series, we have found further association of antipsychotic drug-induced weight gain with a common and functional polymorphism of the gene for leptin. Along with initial BMI, these two pharmacogenetic factors account for almost 30% of the variance in drug-induced weight gain. Interestingly, the 5-HT2C polymorphism appears to determine levels of circulating leptin, providing a potential mechanism underlying the genetic association of the 5-HT2C receptor with weight gain. We have undertaken functional studies of haplotypes of the 5-HT2C promoter region and find the allele associated with protection from weight gain results in reduced promoter activity. These findings demonstrate the value of pharmacogenetics in determining liability to a major side effect of antipsychotic treatment, and indicate both the molecular and physiological mechanisms underlying this side effect.