Eleven susceptibility loci for late-onset Alzheimer’s disease (LOAD) were identified by previous studies; however, a large portion of the genetic risk for this disease remains unexplained. We conducted a large, two-stage meta-analysis of genome-wide association studies (GWAS) in individuals of European ancestry. In stage 1, we used genotyped and imputed data (7,055,881 SNPs) to perform meta-analysis on 4 previously published GWAS data sets consisting of 17,008 Alzheimer’s disease cases and 37,154 controls. In stage 2,11,632 SNPs were genotyped and tested for association in an independent set of 8,572 Alzheimer’s disease cases and 11,312 controls. In addition to the APOE locus (encoding apolipoprotein E), 19 loci reached genome-wide significance (P < 5 × 10−8) in the combined stage 1 and stage 2 analysis, of which 11 are newly associated with Alzheimer’s disease.

Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. We thank Drs. D. Stephen Snyder and Marilyn Miller from NIA who are ex-officio ADGC members. EADI. This work has been developed and supported by the LABEX (laboratory of excellence program investment for the future) DISTALZ grant (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease) including funding from MEL (Metropole européenne de Lille), ERDF (European Regional Development Fund) and Conseil Régional Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. The generation and management of GWAS genotype data for the Rotterdam Study (RS-I, RS-II, RS-III) was executed by the Human Genotyping Facility of the Genetic Laboratory of the

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

Cerebrospinal fluid (CSF) tau, tau phosphorylated at threonine 181 (ptau) and Aβ42 are established biomarkers for Alzheimer’s Disease (AD), and have been used as quantitative traits for genetic analyses. We performed the largest genome-wide association study for cerebrospinal fluid (CSF) tau/ptau levels published to date (n=1,269), identifying three novel genome-wide significant loci for CSF tau and ptau: rs9877502 (P=4.89×10−9 for tau) located at 3q28 between GEMC1 and OSTN, rs514716 (P=1.07×10−8 and P=3.22×10−9 for tau and ptau respectively), located at 9p24.2 within GLIS3 and rs6922617 (P = 3.58×10−8 for CSF ptau) at 6p21.1 within the TREM gene cluster, a region recently reported to harbor rare variants that increase AD risk. In independent datasets rs9877502 showed a strong association with risk for AD, tangle pathology and global cognitive decline (P=2.67×10−4, 0.039, 4.86×10−5 respectively) illustrating how this endophenotype-based approach can be used to identify new AD risk loci.

A genome-wide survival analysis of 14,406 Alzheimer’s disease (AD) cases and 25,849 controls identified eight previously reported AD risk loci and fourteen novel loci associated with age at onset. LD score regression of 220 cell types implicated regulation of myeloid gene expression in AD risk. In particular, the minor allele of rs1057233 (G), within the previously reported CELF1 AD risk locus, showed association with delayed AD onset and lower expression of SPI1 in monocytes and macrophages. SPI1 encodes PU.1, a transcription factor critical for myeloid cell development and function. AD heritability is enriched within the PU.1 cistrome, implicating a myeloid PU.1 target gene network in AD. Finally, experimentally altered PU.1 levels affect the expression of mouse orthologs of many AD risk genes and the phagocytic activity of mouse microglial cells. Our results suggest that lower SPI1 expression reduces AD risk by regulating myeloid gene expression and cell function.

INTRODUCTION Genetic data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) has been crucial in advancing the understanding of AD pathophysiology. Here we provide an update on sample collection, scientific progress and opportunities, conceptual issues, and future plans. METHODS Lymphoblastoid cell lines and DNA and RNA samples from blood have been collected and banked, and data and biosamples have been widely disseminated. To date, APOE genotyping, genome-wide association study (GWAS), and whole exome and whole genome sequencing (WES, WGS) data have been obtained and disseminated. RESULTS ADNI genetic data have been downloaded thousands of times and over 300 publications have resulted, including reports of large scale GWAS by consortia to which ADNI contributed. Many of the first applications of quantitative endophenotype association studies employed ADNI data, including some of the earliest GWAS and pathway-based studies of biospecimen and imaging biomarkers, as well as memory and other clinical/cognitive variables. Other contributions include some of the first WES and WGS data sets and reports in healthy controls, MCI, and AD. DISCUSSION Numerous genetic susceptibility and protective markers for AD and disease biomarkers have been identified and replicated using ADNI data, and have heavily implicated immune, mitochondrial, cell cycle/fate, and other biological processes. Early sequencing studies suggest that rare and structural variants are likely to account for significant additional phenotypic variation. Longitudinal analyses of transcriptomic, proteomic, metabolomic, and epigenomic changes will also further elucidate dynamic processes underlying preclinical and prodromal stages of disease. Integration of this unique collection of multi-omics data within a systems biology framework will help to separate truly informative markers of early disease mechanisms and potential novel therapeutic targets from the vast background of less relevant biological processes. Fortunately, a broad swath of the scientific community has accepted this grand challenge.

Background The human genome contains “dark” gene regions that cannot be adequately assembled or aligned using standard short-read sequencing technologies, preventing researchers from identifying mutations within these gene regions that may be relevant to human disease. Here, we identify regions with few mappable reads that we call dark by depth, and others that have ambiguous alignment, called camouflaged. We assess how well long-read or linked-read technologies resolve these regions. Results Based on standard whole-genome Illumina sequencing data, we identify 36,794 dark regions in 6054 gene bodies from pathways important to human health, development, and reproduction. Of these gene bodies, 8.7% are completely dark and 35.2% are ≥ 5% dark. We identify dark regions that are present in protein-coding exons across 748 genes. Linked-read or long-read sequencing technologies from 10x Genomics, PacBio, and Oxford Nanopore Technologies reduce dark protein-coding regions to approximately 50.5%, 35.6%, and 9.6%, respectively. We present an algorithm to resolve most camouflaged regions and apply it to the Alzheimer’s Disease Sequencing Project. We rescue a rare ten-nucleotide frameshift deletion in CR1, a top Alzheimer’s disease gene, found in disease cases but not in controls. Conclusions While we could not formally assess the association of the CR1 frameshift mutation with Alzheimer’s disease due to insufficient sample-size, we believe it merits investigating in a larger cohort. There remain thousands of potentially important genomic regions overlooked by short-read sequencing that are largely resolved by long-read technologies. Electronic supplementary material The online version of this article (10.1186/s13059-019-1707-2) contains supplementary material, which is available to authorized users.

The apolipoprotein E (APOE) genotype is the major genetic risk factor for Alzheimer's disease (AD). We have access to cerebrospinal fluid (CSF) and plasma APOE protein levels from 641 individuals and genome-wide genotyped data from 570 of these samples. The aim of this study was to test whether CSF or plasma APOE levels could be a useful endophenotype for AD and to identify genetic variants associated with APOE levels. We found that CSF (P = 8.15 × 10(-4)) but not plasma (P = 0.071) APOE protein levels are significantly associated with CSF Aβ(42) levels. We used Mendelian randomization and genetic variants as instrumental variables to confirm that the association of CSF APOE with CSF Aβ(42) levels and clinical dementia rating (CDR) is not because of a reverse causation or confounding effect. In addition the association of CSF APOE with Aβ(42) levels was independent of the APOE ε4 genotype, suggesting that APOE levels in CSF may be a useful endophenotype for AD. We performed a genome-wide association study to identify genetic variants associated with CSF APOE levels: the APOE ε4 genotype was the strongest single-genetic factor associated with CSF APOE protein levels (P = 6.9 × 10(-13)). In aggregate, the Illumina chip single nucleotide polymorphisms explain 72% of the variability in CSF APOE protein levels, whereas the APOE ε4 genotype alone explains 8% of the variability. No other genetic variant reached the genome-wide significance threshold, but nine additional variants exhibited a P-value <10(-6). Pathway mining analysis indicated that these nine additional loci are involved in lipid metabolism (P = 4.49 × 10(-9)).