Multi-tissue neocortical transcriptome-wide association study implicates 8 genes across 6 genomic loci in Alzheimer’s disease

Abstract

Background Alzheimer’s disease (AD) is an incurable neurodegenerative disease currently affecting 1.75% of the US population, with projected growth to 3.46% by 2050. Identifying common genetic variants driving differences in transcript expression that confer AD risk is necessary to elucidate AD mechanism and develop therapeutic interventions. We modify the FUSION transcriptome-wide association study (TWAS) pipeline to ingest gene expression values from multiple neocortical regions. Methods A combined dataset of 2003 genotypes clustered to 1000 Genomes individuals from Utah with Northern and Western European ancestry (CEU) was used to construct a training set of 790 genotypes paired to 888 RNASeq profiles from temporal cortex (TCX = 248), prefrontal cortex (FP = 50), inferior frontal gyrus (IFG = 41), superior temporal gyrus (STG = 34), parahippocampal cortex (PHG = 34), and dorsolateral prefrontal cortex (DLPFC = 461). Following within-tissue normalization and covariate adjustment, predictive weights to impute expression components based on a gene’s surrounding cis-variants were trained. The FUSION pipeline was modified to support input of pre-scaled expression values and support cross validation with a repeated measure design arising from the presence of multiple transcriptome samples from the same individual across different tissues. Results Cis-variant architecture alone was informative to train weights and impute expression for 6780 (49.67%) autosomal genes, the majority of which significantly correlated with gene expression; FDR < 5%: N = 6775 (99.92%), Bonferroni: N = 6716 (99.06%). Validation of weights in 515 matched genotype to RNASeq profiles from the CommonMind Consortium (CMC) was (72.14%) in DLPFC profiles. Association of imputed expression components from all 2003 genotype profiles yielded 8 genes significantly associated with AD (FDR < 0.05): APOC1, EED, CD2AP, CEACAM19, CLPTM1, MTCH2, TREM2, and KNOP1. Conclusions We provide evidence of cis-genetic variation conferring AD risk through 8 genes across six distinct genomic loci. Moreover, we provide expression weights for 6780 genes as a valuable resource to the community, which can be abstracted across the neocortex and a wide range of neuronal phenotypes.

Fine‐mapping and replication of EWAS loci harboring putative epigenetic alterations associated with AD neuropathology in a large collection of human brain tissue samples

“…These findings support the notion that aberrant DNA methylation of these genes may affect AD pathology through changing their expression. [26][27][28] Nonetheless, we did not observe significant associations between DNA methylation and baseline expression of other genes (e.g., TMEM18, DLEU1, and PCNT) suggesting that altered DNA methylation may affect AD through pathways other than gene expression. Interestingly, isoform-specific associations with DNA methylation were detected for the ANK1 gene, suggesting DNA methylation might contribute to AD by influencing gene splicing.…”

Shared mechanisms across the major psychiatric and neurodegenerative diseases

“…We used 888 human brain transcriptomes profiled mainly from the dPFC of post-mortem brain samples of 783 donors of European ancestry. Alignment, quality control, and normalization of the data have been described in detail before 22 , 23 , 101 . Briefly, RNA-sequencing data was normalized for gene length and GC content.…”

Integrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder

“…Given the central dogma that DNA is transcribed into mRNA, which is translated into protein, we investigated the relationship between brain transcript level and PTSD for these 11 PTSD genes identified herein as potentially causal. To this end, we performed a TWAS of PTSD by integrating the MVP PTSD GWAS results 17 with the 888 transcriptomes mainly profiled from the frontal cortex of postmortem brain tissues donated by participants of the Accelerating Medicines Partnership Alzheimer’s Disease (AMP-AD) 21 , 23 . This will be referred to as the TWAS of PTSD.…”

“…The intention of our modifications of FUSION was to generate the most predictive models of cortical brain transcript expression that were not unduly influenced by either a single individual or a particular brain region. This approach was described and evaluated in detail by Gockley et al 23 and is summarized here. First, we used the flag -scale 1 to handle pre-scaled expression values, as expression was scaled across individual brain regions before filtering for matched genotype and combining across brain regions.…”