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  1. Abstract Background There is growing evidence indicating that a number of functional connectivity networks are disrupted at each stage of the full clinical Alzheimer’s disease spectrum. Such differences are also detectable in cognitive normal (CN) carrying mutations of AD risk genes, suggesting a substantial relationship between genetics and AD-altered functional brain networks. However, direct genetic effect on functional connectivity networks has not been measured. Methods Leveraging existing AD functional connectivity studies collected in NeuroSynth, we performed a meta-analysis to identify two sets of brain regions: ones with altered functional connectivity in resting state network and ones without. Then with the brain-wide gene expression data in the Allen Human Brain Atlas, we applied a new biclustering method to identify a set of genes with differential co-expression patterns between these two set of brain regions. Results Differential co-expression analysis using biclustering method led to a subset of 38 genes which showed distinctive co-expression patterns between AD-related and non AD-related brain regions in default mode network. More specifically, we observed 4 sub-clusters with noticeable co-expression difference, where the difference in correlations is above 0.5 on average. Conclusions This work applies a new biclustering method to search for a subset of genes with altered co-expression patterns in AD-related default mode network regions. Compared with traditional differential expression analysis, differential co-expression analysis yielded many more significant hits with extra insights into the wiring mechanism between genes. Particularly, the differential co-expression pattern was observed between two sets of genes, suggesting potential upstream genetic regulators in AD development. 
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  2. Abstract Background Large-scale genome-wide association studies have successfully identified many genetic variants significantly associated with Alzheimer’s disease (AD), such as rs429358, rs11038106, rs723804, rs13591776, and more. The next key step is to understand the function of these SNPs and the downstream biology through which they exert the effect on the development of AD. However, this remains a challenging task due to the tissue-specific nature of transcriptomic and proteomic data and the limited availability of brain tissue.In this paper, instead of using coupled transcriptomic data, we performed an integrative analysis of existing GWAS findings and expression quantitative trait loci (eQTL) results from AD-related brain regions to estimate the transcriptomic alterations in AD brain. Results We used summary-based mendelian randomization method along with heterogeneity in dependent instruments method and were able to identify 32 genes with potential altered levels in temporal cortex region. Among these, 10 of them were further validated using real gene expression data collected from temporal cortex region, and 19 SNPs from NECTIN and TOMM40 genes were found associated with multiple temporal cortex imaging phenotype. Conclusion Significant pathways from enriched gene networks included neutrophil degranulation, Cell surface interactions at the vascular wall, and Regulation of TP53 activity which are still relatively under explored in Alzheimer’s Disease while also encouraging a necessity to bind further trans-eQTL effects into this integrative analysis. 
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