Introduction: Large variability among Alzheimer's disease (AD) cases might impact genetic discoveries and complicate dissection of underlying biological pathways. Methods: Genome Research at Fundacio ACE (GR@ACE) is a genome-wide study of dementia and its clinical endophenotypes, defined based on AD's clinical certainty and vascular burden. We assessed the impact of known AD loci across endophenotypes to generate loci categories. We incorporated gene coexpression data and conducted pathway analysis per category. Finally, to evaluate the effect of heterogeneity in genetic studies, GR@ACE series were meta-analyzed with additional genome-wide association study data sets. Results: We classified known AD loci into three categories, which might reflect the disease clinical heterogeneity. Vascular processes were only detected as a causal mechanism in probable AD. The meta-analysis strategy revealed the ANKRD31-rs4704171 and NDUFAF6-rs10098778 and confirmed SCIMP-rs7225151 and CD33-rs3865444. Discussion: The regulation of vasculature is a prominent causal component of probable AD. GR@ACE meta-analysis revealed novel AD genetic signals, strongly driven by the presence of clinical heterogeneity in the AD series.

Alzheimer's Disease Neuroimaging Initiative: Data used in preparing this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found 6 at Abstract Background Genetics plays a major role in Alzheimer's Disease (AD). To date, 40 genes associated with AD have been identified, although most remain undiscovered. Clinical, neuropathological and genetic variability might impact genetic discoveries and complicate dissection of the biological pathways underlying AD.

Alzheimer’s disease (AD) is the most common form of dementia, currently affecting 35 million people worldwide. Apolipoprotein E (APOE) ε4 allele is the major risk factor for sporadic, late-onset AD (LOAD), which comprises over 95% of AD cases, increasing the risk of AD 4-12 fold. Despite this, the role of APOE in AD pathogenesis is still a mystery. Aiming for a better understanding of APOE-specific effects, the ADAPTED consortium analysed and integrated publicly available data of multiple OMICS technologies from both plasma and brain stratified by APOE haplotype ( APOE2, APOE3 and APOE4 ). Combining genome-wide association studies (GWAS) with differential mRNA and protein expression analyses and single-nuclei transcriptomics, we identified genes and pathways contributing to AD in both APOE dependent and independent fashion. Interestingly, we characterised a set of biomarkers showing plasma and brain consistent protein profiles and opposite trends in APOE2 and APOE4 AD cases that could constitute screening tools for a disease that lacks specific blood biomarkers. Beside the identification of APOE-specific signatures, our findings advocate that this novel approach, based on the concordance across OMIC layers and tissues, is an effective strategy for overcoming the limitations of often underpowered single-OMICS studies.

There is an urgent need to identify biomarkers for Alzheimer’s disease (AD), but the identification of reliable blood-based biomarkers has proven to be much more difficult than initially expected. The current availability of high-throughput multi-omics data opens new possibilities in this titanic task. Candidate Single Nucleotide Polymorphisms (SNPs) from large, genome-wide association studies (GWAS), meta-analyses exploring AD (case-control design), and quantitative measures for cortical structure and general cognitive performance were selected. The Genotype-Tissue Expression (GTEx) database was used for identifying expression quantitative trait loci (eQTls) among candidate SNPs. Genes significantly regulated by candidate SNPs were investigated for differential expression in AD cases versus controls in the brain and plasma, both at the mRNA and protein level. This approach allowed us to identify candidate susceptibility factors and biomarkers of AD, facing experimental validation with more evidence than with genetics alone.

Long runs of homozygosity (ROH) are contiguous stretches of homozygous genotypes, which are a footprint of inbreeding and recessive inheritance. The presence of recessive loci is suggested for Alzheimer’s disease (AD); however, their search has been poorly assessed to date. To investigate homozygosity in AD, here we performed a fine-scale ROH analysis using 10 independent cohorts of European ancestry (11,919 AD cases and 9181 controls.) We detected an increase of homozygosity in AD cases compared to controls [βAVROH (CI 95%) = 0.070 (0.037–0.104); P = 3.91 × 10−5; βFROH (CI95%) = 0.043 (0.009–0.076); P = 0.013]. ROHs increasing the risk of AD (OR > 1) were significantly overrepresented compared to ROHs increasing protection (p < 2.20 × 10−16). A significant ROH association with AD risk was detected upstream the HS3ST1 locus (chr4:11,189,482‒11,305,456), (β (CI 95%) = 1.09 (0.48 ‒ 1.48), p value = 9.03 × 10−4), previously related to AD. Next, to search for recessive candidate variants in ROHs, we constructed a homozygosity map of inbred AD cases extracted from an outbred population and explored ROH regions in whole-exome sequencing data (N = 1449). We detected a candidate marker, rs117458494, mapped in the SPON1 locus, which has been previously associated with amyloid metabolism. Here, we provide a research framework to look for recessive variants in AD using outbred populations. Our results showed that AD cases have enriched homozygosity, suggesting that recessive effects may explain a proportion of AD heritability.

Background Genome‐wide studies have identified diverse susceptibility genes for Alzheimer’s disease, with the APOE locus being the major risk factor known so far. However, pathogenic mechanisms and the influence of other known AD loci or patient characteristics such as gender have been poorly explored. The ADAPTED IMI consortium, focused on exploring the APOE biology, has conducted the first genome‐wide meta‐analysis of AD stratified according to the three main APOE haplotypes. Additionally, we also explored the effect of gender on AD by performing sex‐stratified meta‐analysis. Methods APOE stratified meta‐analysis was performed in three stages comprising 39,186 human samples from sixteen datasets including ADNI, AddNeuroMed, ADGC, Mayo, Neocodex‐Murcia, NIA, ROSMAP, TGEN study for Stage I, GR@ACE and GERAD for Stage II and ARIC, CHS, FHS and RS (CHARGE consortium) for Stage III. Sex‐stratified meta‐analysis was performed on Stage I and Stage II datasets. After quality control and imputation, association of genotype dosages with the AD case‐control status for each dataset was investigated through regression models adjusted by age, sex and the first four PC vectors, plus APOE status in the sex stratified analysis. Meta‐analysis was performed using the inverse variance weighing method. Results were summarized on gene‐level and explored for enrichment in known pathways and processes. Results In the combined analysis, we identified genome‐wide significant signals for APOE, BIN1, CLU, PICALM and a large intergenic region in 4p15 in the APOE ε4 stratum, and for ABCA7, BIN1 and PICALM in the APOE ε3 stratum, whereas no genome‐wide significant results were found in the APOE ε2 stratum. Sex‐stratified analysis identified genome‐wide significant signals for BIN1 and APOE as well as suggestive signals for PICALM, MYLK, SOX5 and SCEL in the female stratum. By contrast in males, only suggestive signals for BIN1, APOE, ZCCHC2, the ABI3BP/IMPG2 locus, ESRRB and the 19q13.4 leukocyte receptor cluster were identified. Accordingly, APOE‐ and sex‐specific pathways were highlighted. Conclusions This systematic stratified analysis led to the identification of loci associated with AD risk, which are specific for certain APOE haplotypes and specific combinations of APOE haplotypes and gender. This novel insight contributes to unravel the complex architecture of AD pathology.

Background While being the most prominent genetic risk factor for AD, the effects of APOE variants on biology and the development of AD is still poorly understood. The IMI ADAPTED consortium strives to illuminate the mechanisms underlying the effect of APOE with several approaches. Complementing the reanalysis of the publicly available human data, hiPSC lines with different APOE genotypes (ε3/ε3, ε4/ε4, ε3/ε4, ε2/ε2), differentiated into distinct brain cell types provide a tool to study cell‐type level effects of the APOE genotype. Further, OMICS data of human APOE modified mouse model (ε2/ε2, ε3/ε3, ε4/ε4) allowed cross species comparison and model validation. Method For multiple brain cell types differentiated from the APOE modified isogenic hiPSC (neurons, astrocytes, microglia and macrophages) and mice transcriptomics and proteomics data were generated. Differential gene expression (DGE) and protein expression (DPE) was calculated, followed by gene set enrichment. Clustering approaches were used to identify shared and differing gene signatures across genotypes. We further applied upstream regulator and network analysis on the individual cell‐type results and integrated these results across cell types. The results were compared with DGE and DPE results from the humanized APOE mouse model. Finally, the identified genes and mechanisms were combined with the results of the integrated analysis of data from postmortem human brain samples of AD cases and controls. Results The observed transcriptional changes confirmed and extended the phenotypic observations and refined the insight of genes identified in the integrated analysis of APOE genotype stratified human OMICS data. Several genes/proteins and pathways were consistently identified on transcriptome and proteome level. Further, shared patterns of expressions of genes across genotypes and potential mechanisms involved in this were detected. Conclusions In depth transcriptomics and proteomics analysis of APOE modified hiPSCs‐derived cells enabled to study cell type and genotype specific effects and contributed with this to the understanding of mechanisms affected by different APOE genotypes. The findings in hiPSC we be compared with human blood and CSF OMICS analysis to be potentially used as biomarker for AD and AD progression.

Background Alzheimer’s Disease (AD) is the most common cause of dementia in the elderly and affects over 35 million people worldwide, imposing increasing social and economic burden as the population ages. While it is widely known that the most prominent genetic risk factor for AD is the presence of the Apolipoprotein E (APOE) ε4 allele, the effects of APOE in the development of AD is still poorly understood. As part of the IMI ADAPTED consortium, we aim to clarify the role of APOE as a risk factor in the development of AD. Here we present an in‐depth analysis of the effect of the APOE genotype on the transcriptome of brain cells derived from human‐induced pluripotent stem cells (hiPSCs). Method Isogenic hiPSC lines were modified to carry different APOE genotypes: ε3/ε3, ε4/ε4, ε3/ε4, ε2/ε2, as well as an APOE knock‐out (KO) cell line. Lines carrying each of these genotypes were differentiated into distinct cell types. Differential gene expression (DGE) and protein expression (DPE) was calculated, followed by gene set enrichment. Clustering approaches were used to identify shared and differing gene signatures across genotypes. We further applied upstream regulator and network analysis on the individual cell‐type results and integrated these results across cell types. The results were compared with DGE and DEP results from an APOE mouse model. Finally, the identified genes and mechanisms were combined with the results of data from postmortem human brain samples of AD cases and controls. Results The observed transcriptional changes confirmed phenotypic observations made for the hiPSCs and refined the insight of genes identified human brain OMICS data. Several genes and pathways were identified, which showed consistent gene expression on transcriptome and proteome level. Further, shared patterns of expressions of genes across genotypes and potential mechanisms involved in this were detected. Conclusions In depth transcriptomics and proteomics analysis of APOE modified hiPSCs enabled to study cell type specific effects and contributed with this to the understanding of mechanisms affected by different APOE genotypes.