Wightman DP, Jansen IE, Savage JE, Shadrin AA, Bahrami S, Holland D, Rongve A, Børte S, Winsvold BS, Drange OK, Martinsen AE, Skogholt AH, Willer C, Bråthen G, Bosnes I, Nielsen JB, Fritsche LG, Thomas LF, Pedersen LM, Gabrielsen ME, Johnsen MB, Meisingset TW, Zhou W, Proitsi P, Hodges A, Dobson R, Velayudhan L, 23andMe Research Team, Sealock JM, Davis LK, Pedersen NL, Reynolds CA, Karlsson IK, Magnusson S, Stefansson H, Thordardottir S, Jonsson PV, Snaedal J, Zettergren A, Skoog I, Kern S, Waern M, Zetterberg H, Blennow K, Stordal E, Hveem K, Zwart JA, Athanasiu L, Selnes P, Saltvedt I, Sando SB, Ulstein I, Djurovic S, Fladby T, Aarsland D, Selbæk G, Ripke S, Stefansson K, Andreassen OA, Posthuma D. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease. Nat Genet. 2021 Sep;53(9):1276-1282. Epub 2021 Sep 7 PubMed. Correction.

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  1. My concern about this GWAS is that it is not a GWAS for Alzheimer’s disease but rather a GWAS for dementia. We know the diagnostic accuracy even in the highly cited clinic-based GWAS is only about 80 percent, and so is undoubtedly less in these GWAS which use reported (parental) cases. AS FTD genes start to show up, perhaps we should note this concern.

    Another concern (not at all limited to this GWAS for dementia) is that everyone meta-analyses their data with previous datasets, so errors that include these diagnostic ones, but also other errors, get baked into the ever-increasing size and reach statistically significant but biologically misleading conclusions.

    View all comments by John Hardy

  2. Douglas Wightman and colleagues report seven new loci associated with AD risk based on a large meta-analysis of GWAS. Even if the number of samples claimed by the authors is impressive, several points deserve comment and precision, some of them already fairly mentioned by the authors.

    Why are the numbers of genes discovered in Wightman et al. and Bellenguez et al. so different, i.e., seven and 42, respectively? Below I briefly describe some of the characteristics and results of the main recently published GWAS in AD.

    First, it is important to keep in mind that, following our first IGAP publication in 2013, and the use of the U.K. biobank and proxy-AD cases in 2018, most of the GWAS meta-analyses shared the same main GWAS datasets, making these studies not independent of each other.

    In addition, the number of controls grew, but not the number of cases. However, at the level of statistics, it becomes useless to have only more and more controls. Finally, methodologies are dissimilar between the studies: (i) with or without replication stage; (ii) using different panels of imputation, to name the most differentiating elements.

    Taking into account these points, we can describe major differences between our GWAS:

    (i) We analyzed almost 30,000 fully new, clinically diagnosed AD cases (discovery/replication), whereas Wightman et al. included mainly new controls through 23&Me and FinnGen. 

    (ii) We used the novel TopMed imputation panel, allowing us to double the number of SNPs analyzed with high imputation quality.

    (iii) Wightman et al. included no replication stage in their study, unlike us (respectively, stage I = 90,338 cases, stage I+II = 85,934 + 25,392).

    (iv) Wightman et al. included a new 23&Me dataset. This approach had been powerful in Parkinson's, helping to report dozens of new loci. However, this needs to be evaluated in Alzheimer's. The diagnosis is declarative, and no demographic information is reported in the paper, making it difficult to understand the main characteristics of this population and how this may impact the results.

    It is important to note that the new loci described in the De Rojas, Schwartzentruber, and now the Wightman papers, are not in common (they share the main GWAS datasets), but a large part of them are detected in the Bellenguez’s paper. This likely indicates lack of statistical power and variability due to different designs.

    Inversely, for those loci that are found only in one of these three studies, this may indicate that they are potentially false positives. This is the case with three of the loci described by Wightman et al., i.e., AGRN, HAVCR2, NTN5; they clearly require further investigation in independent datasets.

    To conclude, it is more than likely that clinically diagnosed cases add power and, potentially, uncharacterized controls add noise. The difference in the number of novel cases defines the potential for novel discoveries in GWA studies.

    More generally, we must carry out a GWAS gathering all the data, at least in populations of European origins, in order to present a landscape of the genetics of AD as clearly as possible to our community. As stated by John Hardy, this is important in order to avoid misleading the post-genomic studies that will follow. This also implies that GWAS of larger sizes relating to other neurodegenerative diseases, but also based on pathological diagnosis, have to be performed. Again as mentioned by John, it is indeed interesting but also disturbing to see genes involved in other neurodegenerative diseases being genetic determinants of AD. Does this represent a pathophysiological reality, or is it a bias associated with diagnostic uncertainty in GWASs?

    It is important to answer these questions, because this can have a significant impact on future research strategies. In the Bellenguez paper, we also observed that some AD genes are linked to other neurodegenerative diseases, including Parkinson’s disease (the IDUA and CTSB loci), frontotemporal dementia (the GRN and TMEM106B loci) and amyotrophic lateral sclerosis (the TNIP1 locus). The presence of common causal variants in the same gene may indicate that these genetic factors have a shared pathological role downstream, whereas the presence of different causal variants may indicate specific mechanisms upstream. Importantly, these signals seemed to be independent of the U.K. biobank proxy-AD cases, and were still present and replicated in clinically diagnosed AD cases.

    References:

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    Schwartzentruber J, Cooper S, Liu JZ, Barrio-Hernandez I, Bello E, Kumasaka N, Young AM, Franklin RJ, Johnson T, Estrada K, Gaffney DJ, Beltrao P, Bassett A. Author Correction: Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer's disease risk genes. Nat Genet. 2021 Apr;53(4):585-586. PubMed.

    View all comments by Jean-Charles Lambert

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