Fengxian Wang, United States of America
Washington University in St. Louis PsychiatryAuthor Of 5 Presentations
IDENTIFICATION OF GENETIC MODIFIERS FOR ALZHEIMER DISEASE – THE FAMILIAL ALZHEIMER SEQUENCING (FASE) PROJECT
Abstract
Aims
The Familial Alzheimer Sequencing (FASe) project aims to identify rare and high penetrant variants that have strong effect in the etiology of Alzheimer Disease (AD) by using sequencing data from families densely affected by late onset AD (fLOAD).
Methods
We have generated whole genome sequence (WGS) data for 952 samples (758 cases, 194 controls) from the Knight-ADRC at Washington University (WASHU), the NIALOAD and NCRAD repositories. These samples are being added to our current dataset of whole exome (WES) and WGS from 1,235 non-hispanic white participants (824 cases, 411 controls) across 285 fLOAD families. These samples have no or minimum overlap with the families sequenced by the ADSP consortia which will also be incorporated to our dataset; a total of 440 families and 3,187 samples (average of 5 cases and 2 controls per family) will be analyzed. We are processing all the data using the same bioinformatics pipeline. Briefly, sequence reads are aligned against reference build GRCh38 using BWA; variant calling is restricted to exonic regions following GATK v4.1.2 best practices. Data analysis includes single variant association, segregation, gene-based and pathway analysis.
Results
We have detected a genetic cross-over between AD, Frontotemporal Dementia and Parkinson disease, and we also identified rare variants in novel candidate genes for AD (PLD3, UNC5C, CPAMD8) highlighting the power of our dataset and the feasibility of our approach.
Conclusions
We hope to identify novel variants and pathways implicated on AD, which will be followed-up in the case-control ADSP.
INTEGRATIVE MULTI-TISSUE MULTI-OMICS FOR BIOMARKER AND THERAPEUTIC TARGET DISCOVERY IN ALZHEIMER DISEASE (AD) AND PARKINSON DISEASE (PD)
Abstract
Aims
To identify causal pathway of AD and PD risk and drug targets by generating and analyzing multi-tissue proteomic and genetic data from a large cohort.
Methods
We generated a genomic atlas of protein levels in multiple neurologically relevant tissues (380 brain, 835 cerebrospinal fluid (CSF) and 529 plasma), by profiling thousands of proteins in a large and well-characterized cohort. We used Mendelian Randomization (MR) and colocalization methods to identify proteins in the causal pathway of two neurological diseases and drug targets for repurposing.
Results
Combining both MR and colocalization results, we found that one CSF, 13 plasma and six brain proteins were likely to be in the causal pathways for AD risk. Among these proteins, plasma CD33 was a risk factor towards AD and had been used as a drug target for other diseases, such as prostate cancer. As for PD risk, 13 CSF, 12 plasma and 23 brain proteins were likely to be the cause. Among these proteins, plasma IDUA was prioritized as it was encoded by a risk locus for PD and as a drug target for chondroitin sulfate, reported to treat osteoarthritis. IDUA is required for the lysosomal degradation of glycosaminoglycans, dermatan sulfate and heparan sulfate.
Conclusions
Our results prioritized several proteins likely to be in the causal pathways leading to AD and PD risk. These nominated proteins can facilitate mapping the disease GWAS results into biological mechanisms, and further leading to precision medicine in neurological and/or psychiatric traits.
PREDICTION OF ALZHEIMER DISEASE USING PLASMA RNA SEQUENCES
Abstract
Aims
The aim of this study was to generate predictive models for AD using plasma cell-free RNA species at different stages of the disease.
Methods
We generated cfRNA-Sequence data from AD cases at CDR=1 (N=44) and controls (N=45) and applied standard quality control. Gene expression was quantified with Salmon and corrected by library complexity and log transformed prior to analysis. Genes known to be involved in AD and other neurodegenerative diseases (N=25) were used to create a predictive model using step-wise discriminant analysis in the CDR=1. APOE genotype was included in the model afterwards. The predictive power was tested in early (N=27) and pre-symptomatic (N=21) stages of the disease.
Results
Out of the 25 genes, eight were included in the predictive model after step-wise discriminant analysis. After inclusion of APOE genotype, the area under the ROC curve was 0.96, 0.99 and 0.82 for CDR=1, CDR=0.5 and pre-symptomatic stages respectively (Figure1).
Conclusions
Cell-free RNA is a promising minimally invasive biomarker for AD with an accuracy comparable to the one obtained using CSF biomarkers. This approach can provide a new screening tool for AD that can be used at population level and to evaluate disease-modifying therapies that target amyloid beta and tau.
MULTI-TISSUE PROTEOMIC SIGNATURES OF GENETICALLY-DEFINED ALZHEIMER DISEASE CASES: A WINDOW INTO PRECISION MEDICINE
Abstract
Aims
We and others recently identified several Alzheimer disease (AD) risk variants in TREM2. Here we aim to elucidate the downstream effect of genes and functional mechanisms leading to AD through multi-tissue proteomics study of AD, autosomal-dominant AD (ADAD) and TREM2 risk variant carriers.
Methods
Deep proteomics profiling was obtained (SOMAscan; 1305 proteins) from brain, cerebrospinal fluid (CSF), and plasma tissue. These neurologically relevant tissues were from Knight-ADRC and DIAN cohorts with comprehensive clinical information about AD pathology and cognition. After stringent QC, we analyzed 1079 proteins in brain (n=370), 713 proteins in CSF (n=699), and 931 proteins in plasma (n=486).
Results
We identified 27, 38 and 69 TREM2-specific proteins in brain, CSF, and plasma, respectively (at Bonferroni-corrected significance). Twenty-three plasma proteins showed nominal differential levels in brain and CSF and led to a prediction model, which discriminates TREM2 carriers from controls (AUC=0.94) and other AD cases (AUC=0.91) well. We identified 371 ADAD-specific proteins, among which 225 were nominally associated with AD neuropath traits. Furthermore, 54, 89, and 85 proteins showed nominal differential levels in sporadic AD vs controls in brain, CSF, and plasma. TREM2-specific proteins are involved in growth factors including VEGF, PDGF, EGF and immunological response. ADAD-specific proteins converge in immunological response pathways including cytokine-mediated signaling and DAP12-mediated pathway.
Conclusions
Our multi-tissue proteomics study for genetically defined AD cases identified multiple novel AD biomarker candidates. These findings not only help create novel prediction models but also point to specific pathways implicated in AD, supporting its potential utility as a clinically useful biomarker.
AN ENRICHMENT OF RARE VARIANTS AND THE LYSOSOMAL PATHWAYS ARE IMPORTANT CONTRIBUTORS TO EARLY ONSET ALZHEIMER DISEASE
Abstract
Aims
A limited number of studies have looked at the genetics of early onset (≤65 years old) Alzheimer disease (EOAD); hence, there is much unidentified heritability contributing to this form of the disease. This study aims to identify novel genes associated with EOAD risk and investigate its differences compared to late onset AD (LOAD).
Methods
We have combined five cohorts (Knight-ADRC, NIA-LOAD, Cache County, Mayo Clinic and ADSP) to generate the largest to date whole exome sequence (WES) non-Hispanic white EOAD dataset (1,385 cases and 3,867 controls). Sequence data was aligned against GRCh37 reference genome using BWA and GATKv3.5 was used to perform variant calling and QC. Statistical analyses included single variant, gene-based association of rare (MAF<1%) and pathogenic (CADD>20) variants, and pathway analyses.
Results
We found that EOAD is enriched in rare nonsynonymous variants compared to LOAD cases (OR=5.67, p=2.2×10-16). We identified novel associations (HOXA1 - OR=2.11, p=4.60×10-14, ADAM29 - OR=6.77, p=2.58×10-08, DHX16 OR=1.65, p=3.18×10-08,) and a higher effect of certain variants (TREM2 p.Arg47His, OR=7.28, p=2.02×10-09) in EOAD compared to previous LOAD studies (OR=2.08-4.07). We identified nine statistically significant (p<0.5×10-04) genes (SMG5, BCAM, KCNJ1, UBXN6, MIEN1, FRMPD1, ABCD2, ADAT2 and HADHB) in both the MAF<1% and CADD>20 gene-based analysis. These genes are involved in fatty-acid metabolic processes (pval=2.18×10-04) and in endosome to lysosome transport (p=0.003) and we highlight UBXN6, a known FrontoTemporal Dementia gene.
Conclusions
EOAD is enriched in rare nonsynonymous variants with a higher effect compared to LOAD. The lysosomal pathway arises as important contributor to EOAD with UBXN6 as main character.