. Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies. BioRxiv, October 28, 2020 bioRxiv.

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  1. The Polygenic Risk Score (PRS), capturing AD risk across the whole genome, does not work uniformly well in all datasets. For example, we have shown in our earlier work that SNPs with AD association at p<0.5, and recently at 0.1, improve AD risk prediction over and above the genome-wide significant/suggestive SNPs (Escott-Price et al., 2017Leonenko et al., 2021). Others have shown best p-value thresholds for SNP selection to be p<5x10-8, or p<1x10-5 (de Rojas et al., 2021; Zang et al., 2020). These differences could be explained by the specifics of the datasets, including age of the participants, clinical or pathological assessments of cases and/or controls, etc.

    There is a need in the field to understand these differences. Dissection of the AD risk by genes involved in different mechanisms of disease development and progression could help. One way forward is to look at the biological pathways suggested by GWAS studies (e.g., Kunkle et al., 2019). However, the definition of disease pathways is based mostly on the different functional categories defined by, for example, gene ontology. This can be noisy because there is little expert scrutiny, inclusion thresholds are low, and almost all AD genes are implicated in more than one pathway (Koopmans et al., 2019). Our analyses also show low predictability by PRS limited to these gene sets (Bellou et al., 2020). 

    At AAIC there were a number of presentations, including by Hyun-Sik Yang and Lianne Reus, suggesting that refining the PRS by genes expressed in relevant tissues, for example microglia or neuron-specific genes, may lead to improved prediction of a particular aspect of the disease. The work of Katia Lopes introducing the microglia genomic atlas, showing that many neurological disease susceptibility loci are mediated through gene expression or splicing in microglia, and suggesting causal variants, may help to further refine gene and SNP lists to make the PRS even more relevant to the disease and therefore improve prediction.

    References:

    . Polygenic risk score analysis of pathologically confirmed Alzheimer disease. Ann Neurol. 2017 Aug;82(2):311-314. Epub 2017 Aug 9 PubMed.

    . Identifying individuals with high risk of Alzheimer's disease using polygenic risk scores. Nat Commun. 2021 Jul 23;12(1):4506. PubMed.

    . Common variants in Alzheimer's disease and risk stratification by polygenic risk scores. Nat Commun. 2021 Jun 7;12(1):3417. PubMed.

    . Risk prediction of late-onset Alzheimer's disease implies an oligogenic architecture. Nat Commun. 2020 Sep 23;11(1):4799. PubMed.

    . Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat Genet. 2019 Mar;51(3):414-430. Epub 2019 Feb 28 PubMed.

    . Age-dependent effect of APOE and polygenic component on Alzheimer's disease. Neurobiol Aging. 2020 Sep;93:69-77. Epub 2020 Apr 30 PubMed.

    . SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse. Neuron. 2019 Jul 17;103(2):217-234.e4. Epub 2019 Jun 3 PubMed.

    View all comments by Valentina Escott-Price

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