br Understanding AD through Transcriptome Analysis Both clas

Understanding AD through Transcriptome Analysis Both classical gene discovery and pathway-based analysis of AD GWAS shed light on the range of biological processes that contribute to AD in addition to amyloid pathology. The straightforward interpretation and functional investigation of the association between a genetic variant and AD remains challenging, however, due to difficulty in identifying disease variants, pleiotropy of risk genes, and pathophysiological complexity. Hundreds of molecular factors putatively interact in multiple networks at different time points and at distinct biological levels including subcellular, cellular, tissue, and organic level. Increasingly, studies address this complexity by combining genetic data with gene sars-cov studies. This allows studying the effect of genetic risk factors at the level of the transcriptome in a tissue, topology, time and cell-type-dependent manner, to identify similarities between disease networks, and uncover molecular interactors that relate to hub nodes connecting pathways. Transcriptome profiling is commonly performed by either microarray hybridization or next-generation RNA sequencing. Although both methods enable large-scale investigation of gene expression, the underlying principles of expression profiling differ fundamentally (Box 1).
Transcriptome-wide Profiling of Postmortem AD Brain Correlation between variants within GWAS loci and transcriptome regulation are typically investigated by expression quantitative trait loci (eQTL) and splice QTL (sQTL) analysis. QTLs have been reported for variants in CR1, HLA-DRB1, ZCWPW1, SLC24A4, CLU and MS4A4A. However, the identified QTLs are not always within the GWAS haplotype, hence may not be relevant for the disease process, and replication of QTL findings remains inconclusive. One drawback of some genome-wide expression profiling studies is the relatively limited number of samples included in analyses. This is partly due to sparse availability of suitable source tissue, such as clinically characterized postmortem human brain. Due to the huge multiple testing burden of genome-wide and transcriptome-wide analyses, and the presence of potentially high-impact low-frequency variants, integrative analysis of DNA and RNA sequencing requires large well-characterized cohorts. Investigating e- and s-QTL effects in AD risk genes could further benefit from increased resolution introduced by multiple-tissue sequencing. In recent years, the extent of data generated by RNA sequencing is rapidly increasing. Access to large scale transcriptome profiling studies, such as through the Synapse platform, the Brain eQTL Almanac web server, and the Genotype-Tissue expression (GTEx) project greatly facilitates genome-transcriptome integrated analysis. Recently, GWAS methods have been extended to enable transcriptome-wide association studies (TWAS), through imputation of eQTL and sQTL data from these reference datasets onto large-scale GWAS data. A TWAS on AD revealed 61 sQTLs in known genes including CLU, PTK2B, and CR1; it also proposed novel candidates, including AP2A1 which is an interactor of PICALM[60]. With rapid increase in sequencing depth and cohort size in RNA sequencing, eQTL and sQTL analysis is anticipated to elucidate additional genetic regulation of expression of risk genes. Beyond hypothesis-driven expression studies focusing on known AD risk genes, microarray and RNA-sequencing based transcriptome studies of postmortem brain in AD and healthy controls have identified a myriad of differentially expressed genes and associated functional pathways. This yielded a core set of differentially expressed pathways including immune response, apoptosis, cell proliferation, energy metabolism, and synaptic transmission 43, 46. RNA-Seq of human AD postmortem brains additionally showed a large fraction of novel isoforms deregulated in parietal cortex and enrichment of pathways associated with neurite differentiation, immune response, and lipid metabolism [59]. These analyses thus corroborate findings of GWAS pathway analyses (Table 1).