Novikova G, Kapoor M, TCW J, Abud EM, Efthymiou AG, Cheng H, Fullard JF, Bendl J, Roussos P, Poon WW, Hao K, Marcora E, Goate AM. Integration of Alzheimer’s disease genetics and myeloid cell genomics identifies novel causal variants, regulatory elements, genes and pathways. 2019 Jul 6. bioRxiv. BioRxiv.
Recommends
Please login to recommend the paper.
Comments
University of Southampton
These findings provide strong mechanistic association of a dysfunctional myeloid compartment with AD, adding to the existing evidence arising from GWAS studies. The data provided is very exciting, and already pinpoints specific mechanisms altered in myeloid cells. We now need to understand how this translates into a chronic neurodegenerative process, and whether the myeloid dysfunction is sufficient to drive disease, and how is this related to the main pathological hallmarks observed present in AD, Aβ and tau.
The case for the role of neuroinflammation, and more specifically microglia, in Alzheimer’s grows at an unprecedented pace, but still lacks clinical validation. The field is in need of validation of this hypothesis in experimental medicine studies, and hopeful that for once an approach will provide benefit for this devastating disease.
View all comments by Diego Gómez-NicolaCardiff University
Novikova et al. report results from integrating Alzheimer’s disease GWAS with multiple functional genomic data sets generated from myeloid cells, including microglia. The path from identification of common variant risk loci to actionable biology has proven challenging. For complex disorders, most of the associated alleles reside in noncoding regions of the genome and are inherited along with non-functional alleles. Nominating causal variants, genes, and tissue types is therefore necessary in order to conduct appropriate, disease-relevant, investigations of mechanism.
Consistent with previous work, they show that AD risk variants are enriched at DNA regulatory elements active in monocytes, macrophages and microglia (Gagliano et al., 2016; Gjoneska et al., 2015; Tansey et al., 2018). They then link AD risk variation to target genes through studies of gene expression and chromatin interactions. For many risk loci they now propose causal genes and provide additional support for previously nominated targets. Those genes supported by multiple lines of evidence, chromatin conformation, and gene expression should be considered as high-priority targets for future biological investigation. Many of these have not been previously been considered in the context of Alzheimer’s disease but do appear to be related to the endolysosomal system.
This study is an important step toward extracting meaningful biology from genomic studies of Alzheimer’s. The prioritization of variants and genes will accelerate the generation of disease-relevant models, ultimately leading to a better understanding of pathogenesis. Although the authors have used state-of-the-art methods and data sets, it is important to note that many of the findings rely on studies of peripheral immune cells. As similar data becomes available for microglia it will be vital to evaluate the specific contributions of each cell linage to the genetic risk mechanisms of Alzheimer’s disease.
References:
Gagliano SA, Pouget JG, Hardy J, Knight J, Barnes MR, Ryten M, Weale ME. Genomics implicates adaptive and innate immunity in Alzheimer's and Parkinson's diseases. Ann Clin Transl Neurol. 2016 Dec;3(12):924-933. Epub 2016 Nov 4 PubMed.
Gjoneska E, Pfenning AR, Mathys H, Quon G, Kundaje A, Tsai LH, Kellis M. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease. Nature. 2015 Feb 19;518(7539):365-9. PubMed.
Tansey KE, Cameron D, Hill MJ. Genetic risk for Alzheimer's disease is concentrated in specific macrophage and microglial transcriptional networks. Genome Med. 2018 Feb 26;10(1):14. PubMed.
View all comments by Matthew HillWashington University School of Medicine
Clearly GWAS studies have been very successful in identifying genetic regions associated with AD risk. But GWAS, in general, not only for AD, have two major problems: One is that the GWAS do not identify genes but regions, and second that the effect size of those regions are normally small. This makes it very difficult to functionally follow up GWAS studies. Additional studies that identify the functional gene or variant are needed.
This study is a very good example of how, using additional genomic approaches, it is possible to identify not only the functional gene, but the specific variant that drives the GWAS association. These results are instrumental to really understand the biology of AD and identify novel targets.
Recent studies have highlighted the importance of microglia in AD. For this reason Novikova et al. decided to use myeloid-specific epigenomic and transcriptomic data. As their data shows very nicely, they were able to resolve a good number of loci. This also highlights the role of microglia in disease. At the same time, we know other cell types are involved in AD, and similar studies with neuron-specific or oligodendrocyte-specific analyses should able to resolve more loci. In any case, this study includes a very large amount of work, with functional validation. Very elegant work.
View all comments by Carlos CruchagaVan Andel Institute
This is an interesting follow-up from the authors’ previous study showing the role of PU.1 in microglial AD risk gene networks. Now the authors extend their analyses to functional variants in monocyte and macrophage enhancers that alter the expression of AD genes, and they nominate candidate functional variants in several GWAS loci.
Interestingly, many of these new nominated genes are functionally associated with the endolysosomal system, indicating a significant role for myeloid endolysosomal pathways in Alzheimer's disease.
The specific enrichment of AD alleles in myeloid enhancers is striking, making these a very relevant cell type to study. It will also be interesting to extend these studies to other cell types relevant to disease. This will become feasible and easier with the generation of single cell sequencing data for AD. It will be important to see from AD-specific data if the same biological pathways associated with disease are relevant in all cell types and to start mapping the relevance of pathways for AD across different types of cell.
View all comments by Rita GuerreiroMake a Comment
To make a comment you must login or register.