Wingo AP, Liu Y, Gerasimov ES, Gockley J, Logsdon BA, Duong DM, Dammer EB, Robins C, Beach TG, Reiman EM, Epstein MP, De Jager PL, Lah JJ, Bennett DA, Seyfried NT, Levey AI, Wingo TS. Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer's disease pathogenesis. Nat Genet. 2021 Feb;53(2):143-146. Epub 2021 Jan 28 PubMed.

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Comments

  1. The beauty of standard genome-wide association studies is the simplicity of the analysis pipeline … a high (p) value is required for Bonnferroni correction, but, once that is past, the result is credible and reproducible. However, the very high correction (declaring results only with p>1x10-7) means many signals are probably lost.

    To try to find these lost signals, Wingo and colleagues (and many others, including us) have tried to use biological information essentially to “cheat” the multiple testing correction, e.g., by looking only at PU.1/SPI1-regulated genes or only at amyloid responsive genes. This does “work,” but it also means that the results are a little less certain because of the more complex hypotheses being tested. The GWAs purists are generally not impressed by these shortcuts, but clearly they can be valuable. 

    This is a long way of saying caveat emptor for this paper. However, a few points are worth making. First, STX6 is a PSP (Höglinger et al., 2011) and a prion disease GWAS hit (Jones et al., 2020). Second, several of the genes reported had previously been shown by us, and I suspect others, to be amyloid-responsive in transgenic mice (Salih et al., 2019). So, I think there is some wheat in these data, but there may also be some chaff.

    References:

    Höglinger GU, Melhem NM, Dickson DW, Sleiman PM, Wang LS, Klei L, Rademakers R, de Silva R, Litvan I, Riley DE, van Swieten JC, Heutink P, Wszolek ZK, Uitti RJ, Vandrovcova J, Hurtig HI, Gross RG, Maetzler W, Goldwurm S, Tolosa E, Borroni B, Pastor P, PSP Genetics Study Group, Cantwell LB, Han MR, Dillman A, van der Brug MP, Gibbs JR, Cookson MR, Hernandez DG, Singleton AB, Farrer MJ, Yu CE, Golbe LI, Revesz T, Hardy J, Lees AJ, Devlin B, Hakonarson H, Müller U, Schellenberg GD. Identification of common variants influencing risk of the tauopathy progressive supranuclear palsy. Nat Genet. 2011 Jun 19;43(7):699-705. PubMed.

    Jones E, Hummerich H, Viré E, Uphill J, Dimitriadis A, Speedy H, Campbell T, Norsworthy P, Quinn L, Whitfield J, Linehan J, Jaunmuktane Z, Brandner S, Jat P, Nihat A, How Mok T, Ahmed P, Collins S, Stehmann C, Sarros S, Kovacs GG, Geschwind MD, Golubjatnikov A, Frontzek K, Budka H, Aguzzi A, Karamujić-Čomić H, van der Lee SJ, Ibrahim-Verbaas CA, van Duijn CM, Sikorska B, Golanska E, Liberski PP, Calero M, Calero O, Sanchez-Juan P, Salas A, Martinón-Torres F, Bouaziz-Amar E, Haïk S, Laplanche JL, Brandel JP, Amouyel P, Lambert JC, Parchi P, Bartoletti-Stella A, Capellari S, Poleggi A, Ladogana A, Pocchiari M, Aneli S, Matullo G, Knight R, Zafar S, Zerr I, Booth S, Coulthart MB, Jansen GH, Glisic K, Blevins J, Gambetti P, Safar J, Appleby B, Collinge J, Mead S. Identification of novel risk loci and causal insights for sporadic Creutzfeldt-Jakob disease: a genome-wide association study. Lancet Neurol. 2020 Oct;19(10):840-848. Epub 2020 Sep 16 PubMed.

    View all comments by John Hardy

  2. How AD genetic risk variants eventually lead to dementia is a difficult but important topic of research.

    Wingo and colleagues took a PWAS approach to study relationships between AD genetic risk variants and abundances of 1,475 proteins quantified in the dorsolateral prefrontal cortex from a total of 528 individuals with or without AD. They found that only 10 (<1 percent) genes were associated with cis-protein levels in both discovery and replication cohorts. It is surprising that none of these 10 genes were amongst the GWAS hits most strongly associated with AD previously, even though those AD-case control weights were used in the PWAS (Jansen et al., 2019). 

    Still, one of the hits, ACE, was reported to be associated with AD by another GWAS study (Kunkle et al., 2019). The cis-results for ACE and CTSH were also reported by another study that performed PWAS with cerebrospinal fluid proteins using Washington University’s ONTIME GWAS database, but none of the other hits have been linked to AD (Yang et al., 2020).

    Conversely, the strongest cis-hits in ONTIME, are not amongst those presented by Wingo and colleagues, e.g., SIGLEC9, ICAM1, LPR, IL1RL1, and CHI3L1. Possibly, regressing out clinical status from the proteomic data makes it difficult to find relationships between AD genetic risk variants and abundances in their proteomic counterparts. Alternatively, the proteomic technique used may not adequately capture protein alterations caused by genetic variants, and/or there may be differences for proteins quantified in tissue and CSF. For example, using a targeted technique, Spellman et al. showed clear relationships between the presence of an APOE e4 allele and the detectability of APOE e4 peptide concentrations in CSF (Spellman et al., 2015). 

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    View all comments by Betty Tijms

  3. Although genome-wide association studies (GWAS) have been pivotal at increasing our understanding of the genetic basis of AD, it has remained challenging to understand what the effect is of an implicated variant (i.e., which gene it affects). Novel approaches integrating GWAS studies with transcriptome (TWAS) or proteome (PWAS) data is a promising way forward to pinpoint affected genes (Gusev et al., 2016; Suhre et al., 2021). Wingo et al. apply such methods to the dorsolateral cortex of AD patients and controls and were able to shed new light on the functional effects of genetic variants associated with Alzheimer’s risk.

    For example, genetic variants in ACE have been linked to AD on numerous occasions, including a recent GWAS (Kunkle et al., 2019), but given the high gene density in this region it remained unclear which gene was most likely affected. The strength of this paper lies in the numerous methods and additional datasets they have employed to infer the likelihood that variants in the ACE locus are also in fact affecting ACE protein expression.

    The authors also highlight that eight of the 11 identified causal genes did not meet the stringent p-value cut-offs used in GWAS, but still had suggestive AD associations (p-values of 5.3x10-5 to 1.9x10-7). This supports our view that there is still much to learn with regard to the genetic underpinning of AD, as for example also shown by polygenic risk assessment (Escott-Price, 2015; Escott-Price et al., 2017). These types of studies enable the identification, while we await more powerful AD GWAS studies (Sierksma et al., 2020), of key molecular players and pathways that lie at the heart of AD pathogenesis.

    Although follow-up work delving deeper into the function of the identified proteins is required, it is already very interesting to see more genes converging onto the endolysosomal/phagocytic pathway, including CTSH, SNX32, STX4, STX6, and PLEKHA1 (Sierksma et al., 2020; Podleśny-Drabiniok et al., 2020). According to the authors, by leveraging previous single nuclei data from (Mathys et al., 2019), only CTSH showed enriched expression in microglia, but it remains tempting to speculate about the potential role of the other proteins in microglial phagocytosis.

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