Negative evidence for a role of APH1B T27I variant in Alzheimer's disease

Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits

While large-scale, genome-wide association studies (GWAS) have identified hundreds of loci associated with brain-related traits, identification of the variants, genes and molecular mechanisms underlying these traits remains challenging. Integration of GWAS with expression quantitative trait loci (eQTLs) and identification of shared genetic architecture have been widely adopted to nominate genes and candidate causal variants. However, this approach is limited by sample size, statistical power and linkage disequilibrium. We developed the multivariate multiple QTL approach and performed a large-scale, multi-ancestry eQTL meta-analysis to increase power and fine-mapping resolution. Analysis of 3,983 RNA-sequenced samples from 2,119 donors, including 474 non-European individuals, yielded an effective sample size of 3,154. Joint statistical fine-mapping of eQTL and GWAS identified 329 variant-trait pairs for 24 brain-related traits driven by 204 unique candidate causal variants for 189 unique genes. This integrative analysis identifies candidate causal variants and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer's disease.

Role of RhoA/ROCK signaling in Alzheimer's disease

Rho-associated coiled-coil kinase (ROCK), a serine/threonine kinase regulated by the small GTPase RhoA, is involved in regulating cell migration, proliferation, and survival. Numerous studies have shown that the RhoA/ROCK signaling pathway can promote Alzheimer's disease (AD) occurrence. ROCK activation increases β-secretase activity and promotes amyloid-beta (Aβ) production; moreover, Aβ further activates ROCK. This is suggestive of a possible positive feedback role for Aβ and ROCK. Moreover, ROCK activation promotes the formation of neurofibrillary tangles and abnormal synaptic contraction. Additionally, ROCK activation can promote the neuroinflammatory response by activating microglia and astrocytes to release inflammatory cytokines. Therefore, ROCK is a promising drug target in AD; further, there is a need to elucidate the specific mechanism of action.

An APP ectodomain mutation outside of the Aβ domain promotes Aβ production in vitro and deposition in vivo

Familial Alzheimer’s disease (FAD)–linked mutations in the APP gene occur either within the Aβ-coding region or immediately proximal and are located in exons 16 and 17, which encode Aβ peptides. We have identified an extremely rare, partially penetrant, single nucleotide variant (SNV), rs145081708, in APP that corresponds to a Ser198Pro substitution in exon 5. We now report that in stably transfected cells, expression of APP harboring the S198P mutation (APPS198P) leads to elevated production of Aβ peptides by an unconventional mechanism in which the folding and exit of APPS198P from the endoplasmic reticulum is accelerated. More importantly, coexpression of APP S198P and the FAD-linked PS1ΔE9 variant in the brains of male and female transgenic mice leads to elevated steady-state Aβ peptide levels and acceleration of Aβ deposition compared with age- and gender-matched mice expressing APP and PS1ΔE9. This is the first AD-linked mutation in APP present outside of exons 16 and 17 that enhances Aβ production and deposition.

Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer's disease risk genes

Genome-wide association studies have discovered numerous genomic loci associated with Alzheimer's disease (AD); yet the causal genes and variants are incompletely identified. We performed an updated genome-wide AD meta-analysis, which identified 37 risk loci, including new associations near CCDC6, TSPAN14, NCK2 and SPRED2. Using three SNP-level fine-mapping methods, we identified 21 SNPs with >50% probability each of being causally involved in AD risk and others strongly suggested by functional annotation. We followed this with colocalization analyses across 109 gene expression quantitative trait loci datasets and prioritization of genes by using protein interaction networks and tissue-specific expression. Combining this information into a quantitative score, we found that evidence converged on likely causal genes, including the above four genes, and those at previously discovered AD loci, including BIN1, APH1B, PTK2B, PILRA and CASS4.