IntroductionLate-onset Alzheimer's disease (LOAD, onset age > 60 years) is the most prevalent dementia in the elderly 1 , and risk is partially driven by genetics 2 . Many of the loci responsible for this genetic risk were identified by genome-wide association studies (GWAS) [3][4][5][6][7][8] . To identify additional LOAD risk loci, the we performed the largest GWAS to date (89,769 individuals), analyzing both common and rare variants. We confirm 20 previous LOAD risk loci and identify four new genome-wide loci (IQCK, ACE, ADAM10, and ADAMTS1). Pathway analysis of these data implicates the immune system and lipid metabolism, and for the first time tau binding proteins and APP metabolism. These findings show that genetic variants affecting APP and Aβ processing are not only associated with early-onset autosomal dominant AD but also with LOAD. Analysis of AD risk genes and pathways show enrichment for rare variants (P = 1.32 x 10 -7 ) indicating that additional rare variants remain to be identified. Main TextOur previous work identified 19 genome-wide significant common variant signals in addition to APOE 9 , that influence risk for LOAD. These signals, combined with 'subthreshold' common variant associations, account for ~31% of the genetic variance of LOAD 2 , leaving the majority of genetic risk uncharacterized 10 . To search for additional signals, we conducted a GWAS metaanalysis of non-Hispanic Whites (NHW) using a larger sample (17 new, 46 total datasets) from our group, the International Genomics of Alzheimer's Project (IGAP) (composed of four AD consortia: ADGC, CHARGE, EADI, and GERAD). This sample increases our previous discovery sample (Stage 1) by 29% for cases and 13% for controls (N=21,982 cases; 41,944 controls) ( Supplementary Table 1 and 2, and Supplementary Note). To sample both common and rare variants (minor allele frequency MAF ≥ 0.01, and MAF < 0.01, respectively), we imputed the discovery datasets using a 1000 Genomes reference panel consisting of . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a 11 36,648,992 single-nucleotide variants, 1,380,736 insertions/deletions, and 13,805 structural variants. After quality control, 9,456,058 common variants and 2,024,574 rare variants were selected for analysis (a 63% increase from our previous common variant analysis in 2013).Genotype dosages were analyzed within each dataset, and then combined with meta-analysis ( Supplementary Figures 1 and 2 and Supplementary Table 3). The Stage 1 discovery metaanalysis was first followed by Stage 2 using the I-select chip we previously developed in Lambert et al (including 11,632 variants, N=18,845) and finally stage 3A (N=6,998). The final sample was 33,692 clinical AD cases and 56,077 controls.Meta-analysis of Stages 1 and 2 produced 21 associations with P ≤ 5x10 -8 (Table 1 and Figure 1). Of these, 18 were previously reported as genome-wide significant and three of them are signals not initially described in Lambert et al: the rare R47H TREM2 coding va...

Oncogenic transformation of postmitotic neurons triggers cell death, but the identity of genes critical for degeneration remain unclear. The antitumor antibiotic mithramycin prolongs survival of mouse models of Huntington’s disease in vivo and inhibits oxidative stress-induced death in cortical neurons in vitro. We had correlated protection by mithramycin with its ability to bind to GC-rich DNA and globally displace Sp1 family transcription factors. To understand how antitumor drugs prevent neurodegeneration, here we use structure-activity relationships of mithramycin analogs to discover that selective DNA-binding inhibition of the drug is necessary for its neuroprotective effect. We identify several genes (Myc, c-Src, Hif1α, and p21waf1/cip1) involved in neoplastic transformation, whose altered expression correlates with protective doses of mithramycin or its analogs. Most interestingly, inhibition of one these genes, Myc, is neuroprotective, whereas forced expression of Myc induces Rattus norvegicus neuronal cell death. These results support a model in which cancer cell transformation shares key genetic components with neurodegeneration.

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed a meta-analysis of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders identifying three groups of inter-related disorders. We detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning in the second trimester prenatally, and play prominent roles in a suite of neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction. Genetic correlations among eight neuropsychiatric disorders indicate three genetic factors.After standardized and uniform quality control, additive logistic regression analyses were performed on individual disorders (Methods). A total of 6,786,994 SNPs were common across all datasets and were retained for further study. Using the summary statistics of these SNPs, we first estimated pairwise genetic correlations among the eight disorders using linkage disequilibrium (LD) score regression analyses (Bulik-Sullivan et al., 2015a) (Methods; Fig. 1a; Supplementary Table 2). The results were broadly concordant with previous estimates (Brainstorm Consortium, 2018; Cross-Disorder Group of the Psychiatric Genomics Consoritum, 2013). The genetic correlation was highest between SCZ and BIP (rg = 0.70 ±0.02), followed by OCD and AN (rg = 0.50 ±0.12). Interestingly, based on genome-wide genetic correlations, MD was closely correlated with ASD (rg=0.45 ±0.04) and ADHD (rg=0.44 ±0.03), two childhood-onset disorders. Despite variation in magnitude, significant genetic correlations were apparent for most pairs of disorders, suggesting a complex, higher-order genetic structure underlying psychopathology ( Fig. 1b).We modeled the genome-wide joint architecture of the eight neuropsychiatric disorders using an exploratory factor analysis (EFA) (Gorsuch, 1988), followed by genomic structural equation modeling (SEM) (Grotzinger et al., 2018) (Methods). EFA identified three correlated factors, which together explained 51% of the genetic variation in the eight neuropsychiatric disorders ( Supplementary Table 3). The first factor consisted primarily of disorders characterized by compulsive behaviors, specifically AN, OCD, and, more weakly, TS. The second factor was characterized by mood and psychotic disorders (MD, BIP, and SCZ), and the third factor by three early-onset neurodeve...

Most genetic risk for human diseases lies within non-coding regions of the genome, which is predicted to regulate gene expression, often in a tissue and stage specific manner. This has motivated building of extensive eQTL resources to understand how human allelic variation affects gene expression and splicing throughout the body, focusing primarily on adult tissue.Given the importance of regulatory pathways during brain development, we characterize the genetic control of the developing human cerebral cortical transcriptome, including expression and splicing, in 201 mid-gestational human brains, to understand how common allelic variation affects gene regulation during development. We leverage expression and splice quantitative trait loci to identify genes and isoforms relevant to neuropsychiatric disorders and brain volume.These findings demonstrate genetic mechanisms by which early developmental events have a striking and widespread influence on adult anatomical and behavioral phenotypes, as well as the evolution of the human cerebral cortex. Highlights• Genome wide map of human fetal brain eQTLs and sQTLs provides a new view of genetic control of expression and splicing.• There is substantial contrast between genetic control of transcript regulation in mature versus developing brain.• We identify novel regulatory regions specific to fetal brain development.• Integration of eQTLs and GWAS reveals specific relationships between expression and disease risk for neuropsychiatric diseases and relevant human brain phenotypes. ResultsTo identify genetic variants regulating gene expression in the developing brain, we performed high-throughput RNA sequencing and high-density genotyping at 2.5 million sites in a set of 233 fetal brains (Figure 1). After quality control and normalization of gene expression quantifications and genotype imputation into the 1000 Genomes Project phase 3 multi-ethnic reference panel (Methods, Figure S1; (Genomes Project et al., 2015), we obtained a starting dataset of 15,925 expressed genes (12,943 protein coding and 767 long noncoding RNAs) and 6.6 million autosomal single nucleotide polymorphisms (SNPs) from each individual. PCA-based (principle component analysis) analysis of ancestry (Methods) indicate that the donors in our study come from admixed ancestries of Mexican, European, African American, and Chinese descent ( Figure S2). The resulting dataset is the first population level fetal brain expression dataset. Robust identification of fetal brain cis-eQTLsWe identified cis-eQTLs by testing all SNPs within a 1MB window from the transcription start site (TSS) of each gene using a permutation procedure implemented in FastQTL (Ongen, Buil, Brown, Dermitzakis, & Delaneau, 2016) while adjusting for known (RIN, sex, age, and genotype PCs) and inferred covariates (Methods, Figure S3), which have been shown to greatly increase sensitivity for cis-eQTL detection (H. M. Kang et al., 2008;Leek & Storey, 2007;Mostafavi et al., 2013). We identified 6,546 genes with a cis-eQTL at a 5% false discovery r...

During organogenesis, patterns and gradients of gene expression underlie organization and diversified cell specification to generate complex tissue architecture. While the cerebral cortex is organized into six excitatory neuronal layers, it is unclear whether glial cells are diversified to mimic neuronal laminae or show distinct layering.To determine the molecular architecture of the mammalian cortex, we developed a highcontent pipeline that can quantify single-cell gene expression in situ. The Large-area Spatial Transcriptomic (LaST) map confirmed expected cortical neuron layer organization and also revealed a novel neuronal identity signature. Screening 46 candidate genes for astrocyte diversity across the cortex, we identified grey matter superficial, mid and deep astrocyte identities in gradient layer patterns that were distinct from neurons. Astrocyte layers formed in early postnatal cortex and mostly persisted in adult mouse and human cortex. Mutations that shifted neuronal post-mitotic identity or organization were sufficient to alter glial layering, indicating an instructive role for neuronal cues. In normal mouse cortex, astrocyte layer patterns showed area diversity between functionally distinct cortical regions. These findings indicate that excitatory neurons and astrocytes cells are organized into distinct lineage-associated laminae, which give rise to higher order neuroglial complexity of cortical architecture.