Welcome to the AD/PD™ 2021 Interactive Program
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- Live Session | 
- On Demand Session | 
- On Demand with Live Q&A
The viewing of sessions, cannot be accessed from this conference calendar. All sessions are accessible via the Main Lobby.
FOLLOWING THE LIVE DISCUSSION, THE RECORDING WILL BE AVAILABLE IN THE ON-DEMAND SECTION OF THE AUDITORIUM.
INTEGRATION OF ALZHEIMER'S DISEASE GENETICS, MYELOID CELL GENOMICS AND GENE REGULATORY NETWORKS REVEALS NOVEL DISEASE RISK MECHANISMS
Abstract
Aims
We aim to identify myeloid regulatory elements that are enriched in Alzheimer’s disease (AD) risk alleles, identify their target genes and nominate candidate causal variants in disease risk loci. We also want to study the likely downstream effects of AD risk genes and functionally validate these findings in microglia.
Methods
Integration of myeloid epigenomic, chromatin interactions and quantitative trait loci (QTL) datasets as well as a Mendelian Randomization framework were used to link AD enhancers to their target genes. Fine-mapping analyses were used to nominate candidate causal variants and generate the mechanism of action hypotheses. Gene regulatory networks were used to study the regulons of AD risk genes. Knockdown and overexpression studies were utilized to validate these findings.
Results
We found that myeloid active enhancers are enriched in AD risk alleles. We linked these enhancers to their likely target genes, nominating AD risk genes in twenty loci. Fine-mapping of these enhancers nominates candidate functional variants in these loci. In the MS4A locus we identified a candidate functional variant and validated it in microglia and the brain. We highlight the coalescence of candidate causal genes in the myeloid endolysosomal system. We constructed myeloid single-cell gene regulatory networks and found that the predicted targets of SPI1, an AD risk gene, were enriched in the endolysosomal compartment. We validate these findings in Spi1 knockdown and overexpression experiments in microglia.
Conclusions
This study explores the links between AD risk variants, myeloid enhancer activity, gene expression and subsequent network-level dysregulations that likely contribute to AD risk modification.
ALZHEIMER'S GENETIC RISK FACTOR FERMT2 (KINDLIN-2) CONTROLS AXONAL GROWTH AND SYNAPTIC PLASTICITY IN AN APP-DEPENDENT MANNER
Abstract
Aims
Although APP metabolism is being intensively investigated, a large fraction of its modulators are yet to be characterized. In this context, we combined two genome-wide high-content screenings to assess the functional impact of miRNAs and genes on APP metabolism and the signaling pathways involved.
Methods
We combined genome-wide high-content screenings to identify miRNAs (n = 2,555) and target genes (n = 18,107) involved in the APP metabolism. CRISPR/Cas9 technology has been used to generate cell lines carrying rs7143400 variant located in the FERMT2 3’UTR and its impact on miRNA binding. Involvement of FERMT2 in axonal growth and synaptic connectivity was assessed using primary neurons cultured in microfluidic devices. The functional impact of expression FERMT2 on CA1 basal synaptic transmission and LTP has been recorded in ex vivo mouse hippocampal slices.
Results
Our systematic approaches led us to characterize 180 genes targeted by 41 miRNAs as modulators of APP metabolism. Among these genes, the genetic risk factor of sporadic AD FERMT2, codes for a direct partner of APP. FERMT2 under-expression impacts axonal growth, synaptic connectivity and long-term potentiation in an APP-dependent manner. Lastly, the rs7143400-T allele, which is associated with an increased AD risk and localized within the 3’UTR of FERMT2, induced a down-regulation of FERMT2 expression through binding of miR-4504. This miRNA is mainly expressed in neurons and significantly overexpressed in AD brains compared to controls.
Conclusions
Altogether, our data provide strong evidence for a detrimental effect of FERMT2 under-expression in neurons and insight on how this may influence AD pathogenesis.
GENE ISOFORM SWITCHING IS A HALLMARK OF ALZHEIMER'S AND OTHER AGING-ASSOCIATED DISEASES
Abstract
Aims
In this work, we aimed at the identification of gene isoform switches in the brain of healthy and Alzheimer’s disease (AD) adult subjects using data from three large studies: Mayo Clinic; Mount Sinai Brain Bank (MSBB) and Religious Orders Study and Memory and Aging Project ROSMAP. We also evaluated isoform switches in two other aging-associated conditions: Progressive supranuclear palsy (PSP) and pathologic aging (PA).
Methods
We used DSeq2 and ISAR to identify gene expression alterations in RNAseq data generated from samples of different brain regions of healthy, AD, PSP and PA subjects. Next, we used scRNAseq to assign altered genes to unique cell types of the adult human brain.
Results
We show that isoform switches are a hallmark of AD, PSP and PA, allowing the identification of gene expression alterations overlooked in classical differential gene expression analyses. Importantly, several gene expression alterations identified by isoform switch analyses are associated with key pathological processes in the brain of AD, PSP and PA subjects. Finally, we also demonstrate a positive correlation between isoform switches and changes in the expression of splicing-associated genes in neuronal cells, suggesting that alternative splicing is altered in the diseased brain.
Conclusions
Our data indicate that isoform switches are an important source of gene expression alteration in the aging brain and might be associated with several biological processes affected in neurodegenerative conditions. Our results also suggest that altered alternative splicing could be a common mechanism in AD and other aging-related diseases.
CHARACTERIZING TRANSCRIPTIONAL REGULATORY MECHANISMS OF ALZHEIMER’S DISEASE NONCODING RISK VARIANTS IDENTIFIED BY AGGREGATING RESULTS ACROSS MULTIPLE META-ANALYZED GENOME-WIDE ASSOCIATION STUDIES
Abstract
Aims
Recent meta-analyses of genome-wide association studies (GWASs) have found many non-coding genetic variants associated with Alzheimer’s Disease (AD), but functional interpretation of these signals remains challenging. By integrating diverse functional genomics (FG) data-types and aggregating results from several GWASs, we aim to identify functional variants, affected genes, and cell-type specific enhancer-based regulatory mechanisms underlying these genetic associations.
Methods
We applied SparkINFERNO (Kuksa et al, 2020) to analyze genome-wide three of the largest AD GWASs (Lambert et al, 2013; Kunkle et al; Jansen et al, 2019). First, we integrated epigenetic, transcription, and chromatin interaction datasets from >1,000 tissues/cell types. For validation, we used brain-specific datasets, including 18 eQTL, 7 single-cell/bulk RNA-seq, and network analyses from multiple labs/consortiums. All FG datasets were systematically harmonized to prioritize noncoding genetic variants and target genes for functional follow-up.
Results
We identified 278 potentially causal variants targeting 361 genes via GWAS-eQTL colocalization analyses across GWASs. 10% of colocalized variants, and 7% of colocalized variant-gene pairs were found in all GWASs. 71% variant-gene pairs were supported by >=2 brain-specific eQTL datasets, and 77 pairs were further validated by enhancer-promoter interaction data (Nott et al, 2019). Using RNA-seq, we determined brain regions (temporal cortex, cerebellum) and cell types in which these target genes were differentially expressed in AD. We found microglial (FGF22), oligodendrocyte (BIN1), and neuronal genes (DPYSL2, MADD) across analyzed GWASs.
Conclusions
The principled integration of different FG data-types and GWAS datasets revealed variant-gene pairs with layered evidence (epigenetics+chromatin+RNA+eQTL) and identified cell-type specific regulatory mechanisms underlying genetic susceptibility to AD.
GENETIC CORRELATION AND CAUSALITY OF CANCERS AND PARKINSON’S DISEASE
Abstract
Aims
To examine whether certain types of cancers have causal relationships and shared genetic architecture with Parkinson's disease.
Methods
Mendelian randomization (MR) uses common single nucleotide polymorphisms (SNPs) as instrumental variables (IVs), as a tool to randomize subjects to different groups. In two-sample MR, SNPs identified through genome-wide association studies (GWAS) in one sample, are used as IVs for causality in a second GWAS sample. We applied a two-sample MR to study the causal relationship between 16 different types of cancers (exposures) and PD (outcome). As an outcome, we used data from the most recent PD GWAS. Linkage disequilibrium score regression(LDSC) was applied for four cancer studies with available full summary statistics to examine genetic correlations with PD.
Results
Overall, 16 cancer studies were selected for analysis. All selected IVs had high F-statistics(>10), supporting the strength of these chosen instruments. We did not reveal causal association between the tested cancers and PD. LDSC analysis revealed nominal genetic correlations with PD for prostate cancer (p=0.05), breast cancer (p=0.03) and melanoma (p=0.049). Only the genetic correlation between melanoma and PD was reproduced in both analyses excluding and including the UKBB cohort (p=0.035) in the PD summary statistics.
Conclusions
Our results do not support a causal relationship between the tested cancers and PD. Thus, it is possible that the low prevalence of most cancers observed in PD is due to survival biases. The genetic correlation between PD and melanoma may explain the increased frequency of melanoma in PD patients and the increased frequency of PD in melanoma patients.
SYSTEMATIC ANALYSIS OF GENETIC INTERACTIONS IN PARKINSON’S DISEASE REVEALS INTERACTIONS WITH KNOWN RISK GENES
Abstract
Aims
Traditional approaches to genome-wide association studies (GWAS) on Parkinson’s disease (PD) are based largely on single-locus tests, despite the genetic complexity of the disease. Genetic interactions refer to combinations of two or more genes whose contribution to a phenotype cannot be fully explained by their independent effects. Detecting genetic interactions systematically with statistical significance remains a major challenge due to the daunting number of variant combinations possible in the human genome.
Methods
We developed a method called BridGE for identifying genetic interactions between pathways from human population genetic data (Wang et al. 2017, Fang, Wang et al. 2019), which leverages the expected structure of genetic interactions revealed by large-scale interaction screens in model organisms. Here, we describe improvements to the BridGE method along with its application to two PD cohorts.
Results
We identified 20 between-pathway interactions (FDR<0.05) and 12 within-pathway interactions (FDR<0.1) associated with PD risk, with a large fraction (10 of 32) of the interactions replicating on an independent cohort (Fig.1). All replicating interactions are connected to the Parkinson’s Disease Gene Set and the Parkin Pathway.
Conclusions
Many of the discovered pathways show clear relevance to PD (Fig.1). The majority of replicated interactions involved the known Parkinson’s disease risk genes, suggesting that many of the established risk variants are modified by variants in multiple, previously unappreciated distinct pathways. We expect further exploration of discovered interactions is likely to be fruitful for understanding the underlying genetic basis of PD.
DNA METHYLATION IN AUTOSOMAL DOMINANTLY INHERITED AND SPORADIC ALZHEIMER DISEASE BRAINS
Abstract
Aims
Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder with many biological processes, and molecular changes. The etiology of AD is complex and not specific to a single genetic factor. Epigenetic changes could help explain the missing heritability not capture in GWAS chips and determine functional variants in genome-wide significant loci.
Methods
Our discovery cohort includes 477 post-mortem brains, 452 AD and 25 controls from Knight-ADRC and Dominantly Inherited Alzheimer Network cohorts. Our study included late-onset (LOAD), but also subjects with Mendelian mutations in APP, PSEN1 and PSEN2 genes (Autosomal Dominant AD; ADAD) and controls. We performed a genome-wide methylation study using DNA from parietal cortex. We used Infinium MethylationEPIC Beadchip arrays (Illumina) to measure DNA methylation. All statistical analyses were adjusted for sex, age at death and neuron proportion.
Results
Completion of this project will provide an enhanced characterization of the epigenetic factors associated with AD etiology. This data will also be integrated with RNA-seq data from the same samples to bring clarification in the interconnections between molecular layers and disrupted pathways involved in AD pathology. This study will enhance the understanding of the molecular dynamics underlying the pathophysiology of AD, and may lead to novel clues for its early detection, prevention and treatment.
Conclusions
Epigenetics of AD brains have been previously studied, but this is the first study to analyze both LOAD and ADAD. These results will be presented in the conference.
EXPLORING THE DNA METHYLATION LANDSCAPES IN POST-MORTEM BRAIN TISSUE OF FRONTOTEMPORAL LOBAR DEGENERATION PATIENTS
Abstract
Aims
Frontotemporal lobar degeneration (FTLD) is the term used to describe the neuropathology of frontotemporal dementia (FTD). The current neuropathological classification of FTLD recognises five major subgroups, three of which are characterised by specific proteinaceous inclusions: transactive response DNA-binding protein (TDP-43) in FTLD-TDP, tau in FTLD-TAU, and fused in sarcoma (FUS) in FTLD-FUS. The main goal of this study was to investigate DNA methylation changes associated with FTLD-TDP subtypes.
Methods
We dissected grey matter from frontal cortex tissue of pathologically confirmed FTLD-TDP cases (n=16, including subtype A carrying the C9orf72 expansion and subtype C) and controls (n=8). We profiled DNA methylation patterns using EPIC arrays, and performed differential methylation analysis.
Results
We found no genome-wide significant DNA methylation changes between FTLD-TDP cases and controls. Interestingly, however, we found a CpG in ARHGAP35 (p = 1.99x10-5), a gene previously associated with the risk of FTD, among our top 10 loci. The DNA methylation difference at this CpG seems more pronounced in FTLD-TDP subtype A (delta-beta = -5%) than in subtype C (delta-beta = -1%) when compared to controls. Regarding DNA methylation patterns at C9orf72, we observed a similar pattern in FTLD-TDP subtype C and controls. In FTLD-TDP subtype A (C9orf72 expansion carriers), however, we found hypermethylation at 6/14 C9orf72 promoter or 5’UTR CpGs (delta beta > 5%) when compared to controls or FTLD-TDP subtype C.
Conclusions
Similar to what has been described for Alzheimer’s disease, our data suggest that there may be a convergence of genetic and epigenetic variation at certain loci contributing to FTLD-TDP.