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. Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer's disease risk genes. Nat Genet. 2021 Mar;53(3):392-402. Epub 2021 Feb 15 PubMed. Correction.

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Comments

  1. It is great to see new putative genetic risk loci.

    Two issues: Are these new genetic risk loci certain? And how much of the genetic risk has been found?

    On the first point: The power of “simple GWAS” is that the statistical test is easy to understand. Its weakness is that the multiple test correction is so stringent that many true positives are lost. Any process that deviates from the simple GWAS by incorporating other factors (such as co-expression or biological information) undoubtedly helps find new loci, but are they certainly correct? I would not say “certain” but would say “likely.” Cheating Bonnferoni is good. … But comes at a price.

    One the second point of “missing heritability” and how much has been found: The problem is not how much has been found (the numerator), but rather how much there is to find (the denominator). Estimates of heritability are extremely problematic in diseases with age-dependent penetrance, especially if one believes (as I do) that we would all get it if we lived long enough.

    View all comments by John Hardy

  2. Undertaking genetic studies to identify causal factors for Alzheimer’s disease (AD) requires sufficient sample size and detailed analysis. In this study led by Bellenguez and Lambert et al., genome-wide association analysis (GWAS) based on imputed array data with a total of 788,989 participants, have identified 75 AD risk loci, 42 of which are novel AD risk loci.

    It is quite promising that new AD risk loci can be derived from this GWAS analysis. One possible reason for the new finding may be due to the utilization of a larger sample size in this study compared to previous studies (Schwartzentruber et al., 2021; Kunkle et al., 2019; Marioni et al., 2018). Moreover, instead of a commonly used imputation panel such as the 1000 Genomes Project data or the Haplotype Reference Consortium (HRC) reference panel, the authors used the Trans-Omics for Precision Medicine (TOPMed) reference panel, for the imputation of the array data used in this study. This is a newly launched panel comprising deep whole-genome sequencing (WGS) data (~30X) from more than 90,000 individuals and covering more than 300 million genomic variants (Kowalski et al., 2019; Taliun et al., 2021). Notably, it has been demonstrated that the selection of the reference panel for genotype imputation is critical for the discovery of disease-associated risk variants (McCarthy et al., 2016). 

    Thus, usage of the TOPMed reference panel may have enhanced imputation accuracy, resulting in the identification of the new risk signals. In addition, with the inclusion of more variants in the reference genome panel, this study has enabled a better recovery of rare variants or haplotype structures that are associated with AD (such as NCK2 rs143080277, identified in this study).

    With the identification of 42 new gene loci in this study, the authors then conducted fine-mapping analysis to identify key AD risk genes in each novel locus (e.g., EGFR in locus 18). This opens a new horizon for identifying candidate risk genes for subsequent mechanistic studies.

    However, the current imputed array approach also has some limitations: The haplotype structures could not be recovered by the limited variants assayed in the array, and the disease causal variants may not be captured by the array due to the limited detection capacity of the array genotyping. Thus, WGS, which can capture most of the genomic variations in the studied participants, would be a better option for both GWAS as well as fine mapping analysis of a specific locus (Prokopenko et al., 2020; Zhou et al., 2019). 

    Corroborating other studies, this work highlights the involvement of the immune pathway in AD pathogenesis. Thus, the identification of the novel loci may lead to the discovery of new molecular pathways in the immune system. Furthermore, as our previous AD GWAS study in the Chinese population also identified risk candidates that associate with immune functions (Zhou et al., 2018), is important to investigate whether and how ethnic backgrounds influence immune or other AD-associated pathways at the genetic level.

    References:

    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. Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer's disease risk genes. Nat Genet. 2021 Mar;53(3):392-402. Epub 2021 Feb 15 PubMed. Correction.

    Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, Boland A, Vronskaya M, van der Lee SJ, Amlie-Wolf A, Bellenguez C, Frizatti A, Chouraki V, Martin ER, Sleegers K, Badarinarayan N, Jakobsdottir J, Hamilton-Nelson KL, Moreno-Grau S, Olaso R, Raybould R, Chen Y, Kuzma AB, Hiltunen M, Morgan T, Ahmad S, Vardarajan BN, Epelbaum J, Hoffmann P, Boada M, Beecham GW, Garnier JG, Harold D, Fitzpatrick AL, Valladares O, Moutet ML, Gerrish A, Smith AV, Qu L, Bacq D, Denning N, Jian X, Zhao Y, Del Zompo M, Fox NC, Choi SH, Mateo I, Hughes JT, Adams HH, Malamon J, Sanchez-Garcia F, Patel Y, Brody JA, Dombroski BA, Naranjo MC, Daniilidou M, Eiriksdottir G, Mukherjee S, Wallon D, Uphill J, Aspelund T, Cantwell LB, Garzia F, Galimberti D, Hofer E, Butkiewicz M, Fin B, Scarpini E, Sarnowski C, Bush WS, Meslage S, Kornhuber J, White CC, Song Y, Barber RC, Engelborghs S, Sordon S, Voijnovic D, Adams PM, Vandenberghe R, Mayhaus M, Cupples LA, Albert MS, De Deyn PP, Gu W, Himali JJ, Beekly 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    Kowalski MH, Qian H, Hou Z, Rosen JD, Tapia AL, Shan Y, Jain D, Argos M, Arnett DK, Avery C, Barnes KC, Becker LC, Bien SA, Bis JC, Blangero J, Boerwinkle E, Bowden DW, Buyske S, Cai J, Cho MH, Choi SH, Choquet H, Cupples LA, Cushman M, Daya M, de Vries PS, Ellinor PT, Faraday N, Fornage M, Gabriel S, Ganesh SK, Graff M, Gupta N, He J, Heckbert SR, Hidalgo B, Hodonsky CJ, Irvin MR, Johnson AD, Jorgenson E, Kaplan R, Kardia SL, Kelly TN, Kooperberg C, Lasky-Su JA, Loos RJ, Lubitz SA, Mathias RA, McHugh CP, Montgomery C, Moon JY, Morrison AC, Palmer ND, Pankratz N, Papanicolaou GJ, Peralta JM, Peyser PA, Rich SS, Rotter JI, Silverman EK, Smith JA, Smith NL, Taylor KD, Thornton TA, Tiwari HK, Tracy RP, Wang T, Weiss ST, Weng LC, Wiggins KL, Wilson JG, Yanek LR, Zöllner S, North KE, Auer PL, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Hematology & Hemostasis Working Group, Raffield LM, Reiner AP, Li Y. Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations. PLoS Genet. 2019 Dec;15(12):e1008500. Epub 2019 Dec 23 PubMed.

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    Zhou X, Chen Y, Mok KY, Zhao Q, Chen K, Chen Y, Hardy J, Li Y, Fu AK, Guo Q, Ip NY, Alzheimer’s Disease Neuroimaging Initiative. Identification of genetic risk factors in the Chinese population implicates a role of immune system in Alzheimer's disease pathogenesis. Proc Natl Acad Sci U S A. 2018 Feb 20;115(8):1697-1706. Epub 2018 Feb 5 PubMed.

    View all comments by Nancy Ip

  3. This work is clearly a tour de force, but also a good example of what can be achieved through broad collaboration.

    It is worth noting that identifying genes or genetic regions just for the sake of it is useless. The important aspect is that the genes identified in these studies will be instrumental in understanding the pathways implicated in disease. This paper offers a good example because genes enriched in the endolysosomal pathway specially emerged. Other pathways involving microglia (TREM2 and others) or Ab metabolism are also very important and this study confirms their roles in AD.

    The authors were able to find so many new gene candidates because of the new AD cohorts they used and because of the UK Biobank data, which is increasing the number of samples significantly. However, it must be kept in mind that the UK Biobank phenotypes are not as clean as those in AD-specific studies. The biobank covers dementia in general, as the authors reflect in the title and the manuscript.

    This may explain why some hits are difficult to intercept, such as TMEM106B and GRN, which are FTD genes, and IDUA, which is a PD signal. These associations could indicate some contamination with FTD or PD samples in the UK Biobank, or that some common pathways exist between AD and FTD, or AD and PD. Previously, several studies, including some from us, suggested that TMEM106B is involved in AD (Li et al., 2020; Yang et al., 2020). 

    There are still a lot of genes to find. If we compare with other diseases, such as PD or psychiatry disorders, we can expect to find many new AD genes with additional GWAS, but also with sequencing studies. More GWAS hits will allow us to create better prediction models, lead to more pathways, and also identify new drug targets. Right now, there are candidate drugs or even trials targeting TREM2, CD33, MS4A4a and SPI1.

    But there is still a lot of work to do. For some loci we do not yet know the functional gene, so combining this data with novel approaches such as co-localization or Mendelian randomization will be necessary.  

    References:

    Li Z, Farias FH, Dube U, Del-Aguila JL, Mihindukulasuriya KA, Fernandez MV, Ibanez L, Budde JP, Wang F, Lake AM, Deming Y, Perez J, Yang C, Bahena JA, Qin W, Bradley JL, Davenport R, Bergmann K, Morris JC, Perrin RJ, Benitez BA, Dougherty JD, Harari O, Cruchaga C. The TMEM106B FTLD-protective variant, rs1990621, is also associated with increased neuronal proportion. Acta Neuropathol. 2020 Jan;139(1):45-61. Epub 2019 Aug 27 PubMed.

    Yang HS, White CC, Klein HU, Yu L, Gaiteri C, Ma Y, Felsky D, Mostafavi S, Petyuk VA, Sperling RA, Ertekin-Taner N, Schneider JA, Bennett DA, De Jager PL. Genetics of Gene Expression in the Aging Human Brain Reveal TDP-43 Proteinopathy Pathophysiology. Neuron. 2020 Aug 5;107(3):496-508.e6. Epub 2020 Jun 10 PubMed.

    View all comments by Carlos Cruchaga

  4. GWAS finds regions in the genome, not genes per se. The difficult part is actually finding the underlying causal variant. However, we are obtaining a full picture of locations in the genome that may harbor important genetic variants that would facilitate the ability to identify potential targets for drug development.

    Data from the UK Biobank lacks diagnostic precision. Afterall, having only a family history of Alzheimer’s disease can make one a “case” in this cohort.  It is also clear that the misdiagnosis of Alzheimer’s disease, clinically, is somewhere between 10 percent to 20 percent. Therefore, it is possible that some of the loci reported are not related to Alzheimer’s disease, but some other form of dementia. 

    View all comments by Richard Mayeux

  5. The new genome-wide association study by Bellenguez et al. identified 75 risk loci associated with AD, of which 42 were novel. This effort was able to discover so many new loci primarily by increasing the sample size of the study, with the number of clinical cases increasing to 57,169 from 35,274 (Kunkle et al., 2019; Mar 2019 news). In addition, they included 46,828 “proxy cases” from the UK Biobank, where individuals reported that either one or both of their biological parents had dementia. Furthermore, by using the TOPMed imputation panel they were able to increase the number of the variants tested (21 million) and the imputation quality of rare variants.

    In addition to conducting a GWAS to identify risk loci, the authors also performed a series of analyses integrating molecular quantitative trait loci (QTL) datasets to prioritize candidate causal genes at each locus. This analysis proposed five new candidate genes (DGKQ, RASA1, ICA1, DOC2A, and LIME1) that modulate APP metabolism and nine new candidate genes that are highly expressed in microglia (OTULIN, RASGEF1C, TSPAN14, BLNK, ATP8B4, MAF, GRN, SIGLE11C and LILRB2). Taking into account genes that were previously linked to microglia function from earlier GWAS, 25 percent of the loci described by Bellenguez and colleagues are credibly linked to AD-related microglia dysfunction. Interestingly, the authors also observed a statistical interaction between APP/Aβ pathways and expression of genes in microglia, suggesting that there is a functional relationship between these two pathways.

    It has recently been suggested that there may only be ~100 common causal variants associated with risk of AD—though there is some debate around this (Zhang et al., 2020, and related comments). This would suggest that GWAS are potentially reaching the limit of discovering new loci associated with AD. However, our knowledge of the genetic architecture underlying AD is still far from complete.

    To date, most GWAS of AD have been conducted in populations of European ancestry. As such it is imperative to expand genetic studies of AD into other underrepresented ethnic groups, which will likely highlight new risk loci and improve the mapping of functional variants. Furthermore, ongoing whole-exome and whole-genome sequencing studies will likely further identify rare genetic variation that are not well captured by GWAS.

    Finally, GWAS of other AD endophenotypes (CSF Aβ/tau biomarkers, neuropathology, progression, resilience), which are more proximal to the direct effect of a gene, will identify novel loci and further aid in the prioritization of likely causal genes.

    References:

    Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, Boland A, Vronskaya M, van der Lee SJ, Amlie-Wolf A, Bellenguez C, Frizatti A, Chouraki V, Martin ER, Sleegers K, Badarinarayan N, Jakobsdottir J, Hamilton-Nelson KL, Moreno-Grau S, Olaso R, Raybould R, Chen Y, Kuzma AB, Hiltunen M, Morgan T, Ahmad S, Vardarajan BN, Epelbaum J, Hoffmann P, Boada M, Beecham GW, Garnier JG, Harold D, Fitzpatrick AL, Valladares O, Moutet ML, Gerrish A, Smith AV, Qu L, Bacq D, Denning N, Jian X, Zhao Y, Del Zompo M, Fox NC, Choi SH, Mateo I, Hughes JT, Adams HH, Malamon J, Sanchez-Garcia F, Patel Y, Brody JA, Dombroski BA, Naranjo MC, Daniilidou M, Eiriksdottir G, Mukherjee S, Wallon D, Uphill J, Aspelund T, Cantwell LB, Garzia F, Galimberti D, Hofer E, Butkiewicz M, Fin B, Scarpini E, Sarnowski C, Bush WS, Meslage S, Kornhuber J, White CC, Song Y, Barber RC, Engelborghs S, Sordon S, Voijnovic D, Adams PM, Vandenberghe R, Mayhaus M, Cupples LA, Albert MS, De Deyn PP, Gu W, Himali JJ, Beekly 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Mutations

  1. APOE R176C (ApoE2)