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Genetics of Alzheimer’s disease: where we are, and where we are going
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ScienceDirect Genetics of Alzheimer’s disease: where we are, and where we are going Ce´line Bellenguez, Benjamin Grenier-Boley and Jean-Charles Lambert Alzheimer’s disease (AD) has a very strong genetic component, whose characterization has become an essential part of efforts to understand the pathophysiological processes of the disease. Thanks to the systematic use of high-throughput approaches over the last 10 years, more than 40 genes/loci have been linked to the AD risk. Although some of these signals are likely to be false positives, this genetic knowledge has shed new light on the pathogenesis of AD and, in particular, the major role of microglia. However, our knowledge of the genetics of AD is far from complete, and larger and more diverse genetic studies are required. Lastly, post-GWAS analyses will be needed to make sense of this genetic information without focusing too much on what we think we know about the disease. Address Univ. Lille, Inserm, Institut Pasteur de Lille, CHU Lille, U1167 - Labex DISTALZ - RID-AGE - Risk Factors and Molecular Determinants of Aging-Related Diseases, F-59000 Lille, France Corresponding author: Lambert, Jean-Charles ([email protected])
Current Opinion in Neurobiology 2020, 61:40–48 This review comes from a themed issue on Neurobiology of disease Edited by Michel Goedert and Christian Haass
https://doi.org/10.1016/j.conb.2019.11.024 0959-4388/ã 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction Since the 1930s, it has been known that rare forms of Alzheimer’s disease (AD) are fully genetically determined because they present an autosomal dominant mode of inheritance. As with all such genetic diseases, it was not until the 1980s that systematic linkage approaches for characterizing the causative genes were developed. In this context, three genes (APP, PSEN1 and PSEN2, coding respectively for amyloid precursor protein, presenilin-1 and presenilin-2) were found to be responsible for early onset, dominantly inherited forms of AD [1]. These rare mutations causing autosomal dominant forms of the disease gave rise to the amyloid cascade hypothesis, which radically changed our understanding of AD. Current Opinion in Neurobiology 2020, 61:40–48
The vast majority of cases of AD are late-onset ‘sporadic’ forms, with no obvious familial aggregation. However, AD appears to be one of the human multifactorial diseases with the highest level of heritability (70%) [2] — a similar level to that observed for schizophrenia (80%), and well above those found for diabetes (40%) [3] and Parkinson’s disease (30%) [4]. In 1993, it was reported that the e4 allele of the apolipoprotein E (APOE) gene was associated with the risk of developing AD [5]. Since then, this association has been detected in almost all ethnic groups — with the notable exception of some African populations [6,7]. Because of the discovery of APOE and the strength of the genetic component in AD, our knowledge of the genetics of this disease was expected to increase rapidly, but it was not until the late 2000s that other genetic risk factors were characterized thanks to the advent of high-throughput genomic approaches. In particular, genome-wide association studies (GWASs) enabled researchers to study tens of thousands of individuals and (after imputation) millions of genetic variations. In 2009, the publication of the results of the first two large-scale GWASs of AD constituted a milestone in this field, with the discovery of three new genetic risk factors: CLU, CR1 and PICALM [8,9].
High-throughput genomic approaches are shaping our understanding of the genetics of Alzheimer’s disease Since the publication of these seminal studies, the AD GWAS field has moved in several complementary directions and has led to the discovery of additional genetic risk factors of AD. Firstly, the development of large international GWAS consortia (e.g. the International Genomics of Alzheimer’s Project (IGAP)) has greatly increased the scale of the discovery phase (e.g. n = 17 008 AD cases and 37 154 controls analyzed in the first IGAP study) and thus its statistical power [10]. To note, the definition of around 40 000 proxy-AD cases in the UK biobank (based on selfreports of a family history of Alzheimer’s dementia) has increased the statistical power of the discovery phase of recent studies [11,12,13]. However, the relevance of this ‘virtual diagnosis’ can be criticized, and nevertheless constitutes a major limitation. Secondly, ever broader reference panels are being used for imputation, with 1092 individuals initially in the 1000 Genomes Project in 2012, 32 470 individuals in the Haplotype Reference Consortium and (in the near future) 62 784 individuals www.sciencedirect.com
Genetics of Alzheimer’s disease Bellenguez, Grenier-Boley and Grenier-Boley 41
in the Trans-Omics for Precision Medicine panel [14]. Better reference panel has expanded the number of variants analyzed and increased the imputation quality, especially for rare variants. Thirdly, the use of relevant endophenotypes can allow to detect novel signals [15]. Lastly, the development of new genotyping tools has made it possible to probe non-synonymous, rare variants [16]. In addition, a marked fall in the cost of next-generation sequencing has enabled the latter’s application to multifactorial diseases for which the detection of signals of interest, that is, rare variants, requires a large number of samples. These sequencing approaches have shown that rare variants in TREM2, SORL1 and ABCA7 for example are major genetic risk factors for AD [17–21]. Recently, the Alzheimer Disease Sequencing Project, the largest sequencing effort in AD to date, published its first whole-exome sequencing results for 5740 cases of lateonset AD and 5096 cognitively normal controls [22]. Rare (and common) variants in several additional genes were found to be potentially associated with the AD risk [23–42]. Hence, over the last ten years, more than 40 AD-associated genes/loci have been identified by GWASs and sequencing projects (Tables 1 and 2) [43].
Sequencing projects and GWASs do not represent the absolute truth: biases and limitations Even though high-throughput genomic approaches are very powerful means of detecting even weak associations, it is important to bear several important aspects in mind. In all cases, the quality of the research results depends on the quality of the populations studied, that is, the reliability of the diagnosis of AD, and the ability to limit classical epidemiologic biases. In view of the large number of tests, high-throughput approaches must strike a balance between the risk of observing apparently significant results by chance and the risk of rejecting biologically valid hypotheses on purely statistical grounds. Unfortunately, the solution to this problem has not yet been found. Correction for multiple testing is common to define the significance threshold, and is mainly based on the number of theoretically independent tests performed, for example, p < 5 10 8 for single variant analyses or p < 2 10 6 for gene-based analyses. These are arbitrary significance thresholds for determining whether or not a signal is valid, and they do not tell the researcher whether the observed signal is real. Nevertheless, a genuine association signal with the risk of developing AD should increase as the size of the study population grows - as long as the populations are similar with regard to the ethnic origins, age, diagnostic criteria, and so on. By way of an example, the signal for BIN1 was 1.59 10 11 (OR:1.15 [1.11–1.20]) in the initial study [44] and 2.1 10 44 (OR:1.20 [1.17–1.23]) in the latest study [45] — a clear indication that the signal is solid. In contrast, the CD33 gene was initially reported to be associated with the AD www.sciencedirect.com
risk in two independent GWASs published in 2008 [46] and in 2011 [47,48] but not in some of the larger GWASs published from 2013 to 2019 [10,45]; this raises questions about CD33’s relevance. However, when the signal fluctuates around the genome-wide significance threshold of 5 10 8 and gives rise to discrepancies between studies, one cannot rule out for example interactions with hidden heterogeneity in the studies such as as-yet unknown interaction between genetic or environmental factors. To note, fluctuation is particularly problematic in studies of rare variants; random fluctuations in rare variant frequencies in a given sample (controls and/or cases) can mask or artificially increase a variant’s association with the AD risk. Regardless of the variant frequency, the signal should never be detected in just one study (but homogeneous between different equivalent populations); its observation in several independent GWASs indicates that the result was not driven by unknown biases in the initial study population. This latter point can nevertheless be problematic; the IGAP’s summary statistics are publicly available since 2013 and most of the subsequent GWASs in AD are not necessarily independent of the IGAP results.
What meaning can we give to GWAS and sequencing data? One of the main objectives of basic genetic research on AD is to discover and characterize the underlying pathophysiological pathways and thus gain a better biological understanding of the disease. This work is based on the postulate whereby non-random groupings of genetic determinants constitute clues as to the nature of the disease pathway involved. In silico gene set enrichment approaches have been used to characterize potential pathways of interest. These analyses have confirmed the involvement of the expected pathways (such as APP and Tau processing) but have also highlighted the regulation of endocytosis, cholesterol transport, protein ubiquitination, and innate immunity [13,45,49]. However, it is important to bear in mind that gene set enrichment approaches have limitations and notably may not be detailed enough to accurately track the pathogenesis of AD. In view of this concern, researchers have developed biological approaches (referred to as ‘post-GWAS analyses’) for assessing the potential roles of genetic factors in APP/Ab metabolism, Tau toxicity and other potential pathophysiological processes [50–53]. For instance, many different genes appear to be involved in APP metabolism, Ab peptide clearance, and Ab toxicity [43]. In addition, there is high-quality evidence to suggest that BIN1 is involved in Tau pathology [54]. The combination of post-GWAS analyses with bibliographical data may reveal additional levels of complexity and thus generate various non-exclusive hypotheses, as proposed Current Opinion in Neurobiology 2020, 61:40–48
Locus for which the association with AD risk is validated by the most recent GWAS and/or sequencing projects Locus
Index variants
Chr
Position a
EA/OA b
EAF c
OR (95% CI)
Initial references
References for index variant/OR
CR1 BIN1 INPP5D HLA TREM2
rs4844610 rs6733839 rs10933431 rs9271058 rs75932628 (p.R47H) Rare variants rs9473117 rs12539172 rs10808026 rs73223431 rs9331896 rs7933202 rs3851179 rs11218343 Rare variants rs12881735 rs72824905
1 2 2 6 6 6 6 7 7 8 8 11 11 11 11 14 16
207 802 552 127 892 810 233 981 912 32 575 406 41 129 252 / 47 431 284 100 091 795 143 099 133 27 219 987 27 467 686 59 936 926 85 868 640 121 435 587 / 92 932 828 81 942 028
A/C T/C G/C A/T T/C / C/A T/C A/C T/C C/T C/A T/C C/T / C/T G/C
0.196 0.390 0.236 0.289 0.002 / 0.255 0.303 0.193 0.344 0.398 0.359 0.354 0.037 / 0.225 0.008
1.17 (1.13–1.21) 1.2 (1.17–1.23) 0.91 (0.88–0.94) 1.1 (1.07–1.13) 2.08 (1.73–2.49) up to 6 1.09 (1.06–1.12) 0.92 (0.90–0.95) 0.9 (0.88–0.93) 1.1 (1.07–1.13) 0.88 (0.85–0.90) 0.89 (0.87–0.92) 0.88 (0.86–0.90) 0.8 (0.75–0.85) up to 12 0.92 (0.89–0.94) 0.68 (0.60–0.77)
[9] [44] [10] [10] [17,18]
APOE
rs3752246 Rare variants rs429358/rs7412
19 19 19
1 056 492 / 45 411 941/45 412 079
G/C / e2e2 + e2e3/e3e3 e2e4/e3e3 e3e4/ e3e3 e4e4/e3e3
0.161 / 0.135 0.026 0.213 0.018
[48] [19] [5]
CASS4
rs6024870
20
54 997 568
A/G
0.075
1.15 (1.11–1.18) up to 4 0.56 (0.49–0.64) 2.64 (2.13–3.27) 3.63 (3.37–3.90) 14.49 (11.91– 17.64) 0.88 (0.85–0.92)
[45] [45] [45] [45] [45] [21,72] [45] [45] [45] [45] [45] [45] [45] [45] [21,75] [45] [16] see also Ref. [76] [45] [19,21,77,78] [79]
[10]
[45]
CD2AP ZCWPW1/NYAP1 EPHA1 PTK2B CLU MS4A PICALM SORL1 SLC24A4/RIN3 PLCG2 ABCA7
[47,48] [10] [47,48] [10] [8,9] [47,48] [8] [73] [20,74] [10] [16]
Chr: chromosome; EA: effect allele; OA: other allele; EAF: effect allele frequency; OR: odds-ratio; CI: confidence interval. GRCh37 assembly. b For APOE, effect genotype/reference genotype is provided. c Effect allele frequency in GnomAD r2.1.1 exomes and genomes, restricted to Finnish and non-Finnish European samples [77]. For APOE, the effect genotype frequency reported in Ref. [6] for the controls of the Caucasian ethnic group is provided. a
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Table 1
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Table 2 Locus for which the association with AD risk need to be further investigated before being fully accepted Index variants
Chr
Position a
EA/OA b
EAF c
OR (95% CI)
Initial references
References for index variant/OR
ADAMTS4 HESX1/IL17RD/APPL1 CLNK OARD1 CNTNAP2 ECHDC3 FRMD4A
rs4575098 rs184384746 rs6448453 rs114812713 rs114360492 rs7920721 rs7081208, rs2446581, rs17314229 rs3740688 rs17125924 rs593742 rs117618017 rs139709573 rs7185636 rs59735493 rs62039712 rs113260531 rs2732703 rs616338 rs2632516 rs138190086 rs76726049 rs76320948 rs3865444 rs2830500
1 3 4 6 7 10 10
161 155 392 57 226 150 11 026 028 41 034 000 145 950 029 11 720 308 13 991 865, 14 008 445, 14 016 159 47 380 340 53 391 680 59 045 774 63 569 902 102 186 966 19 808 163 31 133 100 79 355 857 5 138 980 44 353 222 47 297 297 56 409 089 61 538 148 56 189 459 46 241 841 51 727 962 28 156 856
A/G T/C A/G C/G T/C G/A AAC/GGC
0.237 0.002 0.242 0.019 2.12 10 4 0.391 0.025/0.637
/ / / 1.32 (1.22–1.42) / 1.08 (1.06–1.11) 1.68 (1.43–1.96)
[13] [13] [13] [45] [13] [81] [82]
[13] [13] [13] [45] [13] [45] [82]
G/T G/A G/A T/C A/G C/T A/G A/G A/G G/T T/C C/G A/G C/T T/C A/C A/C
0.455 0.095 0.314 0.129 2.20 10 0.156 0.303 0.110 0.132 0.191 0.010 0.468 0.017 0.013 0.042 0.328 0.309
0.92 (0.89–0.94) 1.14 (1.09–1.18) 0.93 (0.91–0.95) / 7.45 (3.49–15.90) 0.92 (0.89–0.95) / 1.16 (1.10–1.23) / 0.73 (0.65–0.81) 1.43 (1.28–1.60) 0.94 (0.91–0.96) 1.3 (1.19–1.42) / / / 0.93 (0.91–0.96)
[10] [10] [12] [13] [83] [45] [12] [45] [11] [84] [16] [85] [12] [13] [13] [46–48] [45]
[45] [45] [45] [13] [83] [45] [13] [45] [13] [84] [16] [45] [45] [13] [13] [13] [45]
CELF1/SPI1 FERMT2 ADAM10 APH1B TM2D3 IQCK KAT8 WWOX/MAF SCIMP/RABEP1 MAPT ABI3 TSPOAP1 ACE ALPK2 AC074212.3 CD33 ADAMTS1
11 14 15 15 15 16 16 16 17 17 17 17 17 18 19 19 21
4
Chr: chromosome; EA: effect allele; OA: other allele; EAF: effect allele frequency; OR: odds-ratio; CI: confidence interval. GRCh37 assembly. b For FRMD4A, effect haplotype/reference haplotype is provided. c Effect allele frequency in GnomAD r2.1.1 exomes and genomes, restricted to Finnish and non-Finnish European samples [77]. For FRMDA, the effect and reference haplotype frequencies reported in Ref. [10] for the French EADI controls are provided. For MAPT, the effect allele frequency in HRC r1.1 [86], without 1000 Genomes samples, is reported. a
Genetics of Alzheimer’s disease Bellenguez, Grenier-Boley and Grenier-Boley 43
Current Opinion in Neurobiology 2020, 61:40–48
Locus
44 Neurobiology of disease
in the cell phase or genetically driven synaptic failure hypotheses [43,55]. Another means of studying the genetic factors in AD is to better characterize functional variants. In some cases, a variant’s impact on biological function is obvious (e.g. loss-of-function frameshift mutations in SORL1 and ABCA7). These genetic observations obviously will facilitate the development of relevant models. For instance, the genetic data have unambiguously revealed the importance of a specific cell type — the microglia — in the development of AD [16]. Reports of non-synonymous variants in TREM2, associated with AD risk led to obviously develop relevant hypothesis-driven studies in microglia and TREM2 is currently the most studied genetic risk factor in the context of the microgliadependent pathophysiological process in AD. TREM2 has been proposed to be protective in such a context since the AD-associated mutations (R47H, R62H) likely impair its physiological functions at two levels: (i) phagocytosis and clearance of Ab peptides and (ii) compaction of amyloid plaques and barrier formation [56,57]. It is important to keep in mind that, most of the GWAS signals are located in noncoding (and thus potentially regulatory) regions of the genome. However, it is possible to combine for example expression quantitative trait loci, active enhancers and/or gene expression (and so on) in different cell types and pathological tissues to determine functional variants and pathways in which the genes are potentially involved. A recent publication even suggested that the functional variants responsible for GWAS signals in many genes mainly drive gene expression in myeloid cells (and thus potentially in microglia) [58]. It is necessary to bear in mind that this was a hypothesis-driven study based on a human myeloid cell data repository and for obvious reasons, the absence of equivalent large datasets for human neurons and other brain cell types precludes similar analyses in these cell types [59]. However, it has been also suggested that numerous AD risk variants may be located in microglial-specific enhancers, strongly supporting again the importance of microglia in AD [60].
What is the future for genetic analyses of Alzheimer’s disease? As mentioned above, our knowledge of the genetic component of AD has been greatly enhanced over the last 10 years. This knowledge has already changed the way the research community thinks about the AD process — notably by highlighting the potentially major role of microglia. However, the knowledge is far from complete; as is the case for other aging-related multifactorial diseases, major efforts to exhaustively characterize genetic factors in AD are still needed. This will probably require the implementation of several complementary strategies. Current Opinion in Neurobiology 2020, 61:40–48
Firstly, larger GWASs of Caucasian and other ethnic groups are now underway or being planned. The European Alzheimer DNA Biobank project has just been launched; with approximately 38 000 AD cases and 60 000 controls in the discovery phase, it will be the world’s largest GWAS. In the USA, large studies of African-American and Caribbean populations are being developed by the Alzheimer’s Disease Genetics Consortium [61,62]. Furthermore, reports from large GWASs in Asian populations are expected. Lastly, initiatives (albeit at a lower level) are also being taken in South America and Africa. These studies will leverage the most recent imputation panels, and are likely to highlight new genetic risk factors of AD (whether specific or not for various ethnic groups) and prompt the development of in-depth approaches for mapping functional variants as already performed in other multifactorial diseases [63]. Secondly, large sequencing projects are underway in various ethnic groups [22,26,36]. Since the imputation quality is low for very rare variants, these sequencing approaches complement the GWASs results [22,23–42]. It is important to bear in mind that this type of study is very sensitive to the available statistical power; most of the studies described to date were probably underpowered, resulting in the publication of false positives. However, this problem should rapidly disappear as lower sequencing costs enable the assessment of ever larger populations. Another direct consequence of the drop in cost will be a shift from whole-exome sequencing to whole-genome sequencing; given that the exome accounts for only 3% of the genome, this move will broaden the analysis of rare variants. New sequencing technologies (e.g. those based on long reads) should enable analyses of structural variants in a more efficient way that currently possible. Thirdly, the diagnostic criteria for AD and related syndromes will become refined — notably through the increasing use of biomarkers [64,65]. Although it is difficult to predict how this trend will impact studies of AD genetics, it is already clear that there will be a gap between today’s case-control studies and those implemented in the future. Genetic analyses will probably be broadened to other, better diagnosed neurodegenerative diseases, and will potentially be able to define disease-specific genes and pathways or, on the contrary, common genes and pathways. This trend in diagnosis is also likely to prompt the redefinition of most of today’s polygenic risk scores results and thus make them more relevant [66,67]. Following on from the characterization of missing genetic risk factors for AD, post-genomic studies will be required to give meaning to this data. As mentioned above, this is the most important challenge in studies of the pathophysiological processes in AD. One of the main temptations is www.sciencedirect.com
Genetics of Alzheimer’s disease Bellenguez, Grenier-Boley and Grenier-Boley 45
to position newly identified genes in processes that are already thought to be involved in AD: Although this may well be the case, this prompts a move from a hypothesis-free approach (e.g. GWAS) to a hypothesisdriven approach. However, a main challenge will be to develop read-outs, more or less independently of our current knowledge, by developing new models. In fact, this type of holistic approach is already developed by making available “omics” databases in the brain, potentially including single-cell sequencing and atlases for all cell types [68,69,70]. These efforts are clearly needed for the most comprehensive, least biased vision of AD. In any case, the combination of these strategies will either independently confirm known mechanisms or highlight new, unexpected processes.
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Conclusion
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Gureje O, Ogunniyi A, Baiyewu O, Price B, Unverzagt FW, Evans RM, Smith-Gamble V, Lane KA, Gao S, Hall KS et al.: APOE e4 is not associated with Alzheimer’s disease in elderly Nigerians. Ann Neurol 2006, 59:182-185.
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Harold D, Abraham R, Hollingworth P, Sims R, Gerrish A, Hamshere ML, Pahwa JS, Moskvina V, Dowzell K, Williams A et al.: Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet 2009, 41:1088-1093.
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Lambert J-C, Heath S, Even G, Campion D, Sleegers K, Hiltunen M, Combarros O, Zelenika D, Bullido MJ, Tavernier B et al.: Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat Genet 2009, 41:1094-1099.
Thanks to high-throughput genomic approaches, our knowledge of the genetics of AD has progressed significantly over the last 10 years. However, much work remains to be done to characterize the missing genetic factors and to separate the wheat from the chaff when considering published loci. Again, it is important to bear in mind that some published loci are probably false positives. Furthermore, the real objective of this research is to give biological meaning to genetic information, rather than to draw up a list of loci. This challenge is still in its early stages, and will require more and more effort. In this respect, purely reductionist approaches will not be able to capture the complexity induced by the growing number of genes/loci; only approaches that take account of this complexity are likely to be able to determine which pathophysiological processes are involved. Characterizing the missing AD genetic factors and developing post-GWAS approaches to understanding the factors’ pathophysiological roles are probably among most promising and exciting areas of research in AD. Ultimately, this should lead to the development of personalized treatments that match the individual patient’s genetic profile.
Conflict of interest statement Nothing declared.
Acknowledgements This work was funded by Inserm, Institut Pasteur de Lille, the Lille Me´tropole Communaute´ Urbaine, the French government’s LABEX DISTALZ program (development of innovative strategies for a transdisciplinary approach to Alzheimer’s disease).
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
Cacace R, Sleegers K, Van Broeckhoven C: Molecular genetics of early-onset Alzheimer’s disease revisited. Alzheimer’s Dement 2016, 12:733-748.
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10. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, Beecham GW, GrenierBoley B et al.: Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet 2013, 45:1452-1458. 11. Liu JZ, Erlich Y, Pickrell JK: Case-control association mapping by proxy using family history of disease. Nat Genet 2017, 49:325-331 The first study reporting the use of proxy AD cases using family history from the UK biobank. 12. Marioni RE, Harris SE, Zhang Q, McRae AF, Hagenaars SP, Hill WD, Davies G, Ritchie CW, Gale CR, Starr JM et al.: GWAS on family history of Alzheimer’s disease. Transl Psychiatry 2018, 8:99. 13. Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S, Sealock J, Karlsson IK, Ha¨gg S, Athanasiu L et al.: Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet 2019, 51:404-413 One of the two most recent AD GWASs compelling a large panel of different GWAS summary statistics including proxy-AD cases generating from the UK biobank. 14. Das S, Abecasis GR, Browning BL: Genotype imputation from large reference panels. Annu Rev Genomics Hum Genet 2018, 19:73-96. 15. Huang KL, Marcora E, Pimenova AA, Di Narzo AF, Kapoor M, Jin SC, Harari O, Bertelsen S, Fairfax BP, Czajkowski J et al.: A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer’s disease. Nat Neurosci 2017, 20:1052-1061 One of the first study in the AD genetic field using AD GWAS and related endophenotypes, that is, age at onset, to characterize new AD genetic risk factors. 16. Sims R, Van Der Lee SJ, Naj AC, Bellenguez C, Badarinarayan N, Jakobsdottir J, Kunkle BW, Boland A, Raybould R, Bis JC et al.: Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease. Nat Genet 2017, 49:1373-1384 Current Opinion in Neurobiology 2020, 61:40–48
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A major GWAS report clearly demonstrating that microglia is a major actor of the AD pathology. The three genes characterized in this study are mainly (or almost only) expressed in this type of cells and participate to the same Protein-protein interaction network than TREM2. 17. Guerreiro R, Wojtas A, Bras J, Carrasquillo M, Rogaeva E, Majounie E, Cruchaga C, Sassi C, Kauwe JSK, Younkin S et al.: TREM2 variants in Alzheimer’s disease. N Engl J Med 2013, 368:117-127. 18. Jonsson T, Stefansson H, Steinberg S, Jonsdottir I, Jonsson PV, Snaedal J, Bjornsson S, Huttenlocher J, Levey a I, Lah JJ et al.: Variant of TREM2 associated with the risk of Alzheimer’s disease. N Engl J Med 2013, 368:107-116. 19. Steinberg S, Stefansson H, Jonsson T, Johannsdottir H, Ingason A, Helgason H, Sulem P, Magnusson OT, Gudjonsson SA, Unnsteinsdottir U et al.: Loss-of-function variants in ABCA7 confer risk of Alzheimer’s disease. Nat Genet 2015, 47:445-447. 20. Pottier C, Hannequin D, Coutant S, Rovelet-Lecrux a, Wallon D, Rousseau S, Legallic S, Paquet C, Bombois S, Pariente J et al.: High frequency of potentially pathogenic SORL1 mutations in autosomal dominant early-onset Alzheimer disease. Mol Psychiatry 2012, 17:875-879. 21. Bellenguez C, Charbonnier C, Grenier-Boley B, Quenez O, Le Guennec K, Nicolas G, Chauhan G, Wallon D, Rousseau S, Richard AC et al.: Contribution to Alzheimer’s disease risk of rare variants in TREM2, SORL1, and ABCA7 in 1779 cases and 1273 controls. Neurobiol Aging 2017, 59:220. 22. Bis JC, Jian X, Kunkle BW, Chen Y, Hamilton-Nelson KL, Bush WS, Salerno WJ, Lancour D, Ma Y, Renton AE et al.: Whole exome sequencing study identifies novel rare and common Alzheimer’s-associated variants involved in immune response and transcriptional regulation. Mol Psychiatry 2019. in press The first publication of the ADSP project (to date, the largest AD WES database available worldwide) potentially characterizing three new genes (to be validated). Importantly, ADSP makes publicly available the complete raw whole exome sequencing data. This has to be highlighted and the authors have to be congratulated for this. 23. Asanomi Y, Shigemizu D, Miyashita A, Mitsumori R, Mori T, Hara N, Ito K, Niida S, Ikeuchi T, Ozaki K: A rare functional variant of SHARPIN attenuates the inflammatory response and associates with increased risk of late-onset Alzheimer’s disease. Mol Med 2019, 25:20. 24. Zhang X, Zhu C, Beecham G, Vardarajan BN, Ma Y, Lancour D, Farrell JJ, Chung J, Bellair M, Dinh H et al.: A rare missense variant of CASP7 is associated with familial late-onset Alzheimer’s disease. Alzheimer’s Dement 2019, 15:441-452. 25. Hartl D, May P, Gu W, Mayhaus M, Pichler S, Spaniol C, Glaab E, Bobbili DR, Antony P, Koegelsberger S, et al.: A rare loss-offunction variant of ADAM17 is associated with late-onset familial Alzheimer disease. Mol Psychiatry, in press. 26. Vardarajan BN, Barral S, Jaworski J, Beecham GW, Blue E, Tosto G, Reyes-Dumeyer D, Medrano M, Lantigua R, Naj A et al.: Whole genome sequencing of Caribbean Hispanic families with late-onset Alzheimer’s disease. Ann Clin Transl Neurol 2018, 5:406-417. 27. Wang B, Bao S, Zhang Z, Zhou X, Wang J, Fan Y, Zhang Y, Li Y, Chen L, Jia Y et al.: A rare variant in MLKL confers susceptibility to ApoE e4-negative Alzheimer’s disease in Hong Kong Chinese population. Neurobiol Aging 2018, 68:160. 28. Ridge PG, Karch CM, Hsu S, Arano I, Teerlink CC, Ebbert MTW, Gonzalez Murcia JD, Farnham JM, Damato AR et al.: Linkage, whole genome sequence, and biological data implicate variants in RAB10 in Alzheimer’s disease resilience. Genome Med 2017, 10:4. 29. Vardarajan BN, Tosto G, Lefort R, Yu L, Bennett DA, De Jager PL, Barral S, Reyes-Dumeyer D, Nagy PL, Lee JH et al.: Ultra-rare mutations in SRCAP segregate in Caribbean Hispanic families with Alzheimer disease. Neurol Genet 2017, 3:e178. 30. N’songo A, Carrasquillo MM, Wang X, Burgess JD, Nguyen T, Asmann YW, Serie DJ, Younkin SG, Allen M, Pedraza O et al.: African American exome sequencing identifies potential risk variants at Alzheimer disease loci. Neurol Genet 2017, 3:e141. Current Opinion in Neurobiology 2020, 61:40–48
31. Bras J, Djaldetti R, Alves AM, Mead S, Darwent L, Lleo A, Molinuevo JL, Blesa R, Singleton A, Hardy J et al.: Exome sequencing in a consanguineous family clinically diagnosed with early-onset Alzheimer’s disease identifies a homozygous CTSF mutation. Neurobiol Aging 2016, 46:236. 32. Cruchaga C, Karch CM, Jin SC, Benitez BA, Cai Y, Guerreiro R, Harari O, Norton J, Budde J, Bertelsen S et al.: Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer’s disease. Nature 2014, 505:550-554. 33. Wetzel-Smith MK, Hunkapiller J, Bhangale TR, Srinivasan K, Maloney JA, Atwal JK, Sa SM, Yaylaoglu MB, Foreman O, Ortmann W et al.: A rare mutation in UNC5C predisposes to late-onset Alzheimer’s disease and increases neuronal cell death. Nat Med 2014, 20:1152-1157. 34. Jonsson T, Atwal JK, Steinberg S, Snaedal J, Jonsson PV, Bjornsson S, Stefansson H, Sulem P, Gudbjartsson D, Maloney J et al.: A mutation in APP protects against Alzheimer's disease and age-related cognitive decline. Nature 2012, 488:96-99. 35. Ma Y, Jun GR, Zhang X, Chung J, Naj AC, Chen Y, Bellenguez C, Hamilton-Nelson K, Martin ER, Kunkle BW et al.: Analysis of whole-exome sequencing data for Alzheimer disease stratified by APOE genotype. JAMA Neurol 2019. in press. 36. Tosto G, Vardarajan B, Sariya S, Brickman AM, Andrews H, Manly JJ, Schupf N, Reyes-Dumeyer D, Lantigua R, Bennett DA et al.: Association of variants in PINX1 and TREM2 with lateonset Alzheimer disease. JAMA Neurol 2019. in press. 37. Fleck D, Phu L, Verschueren E, Hinkle T, Reichelt M, Bhangale T, Haley B, Wang Y, Graham R, Kirkpatrick DS et al.: PTCD1 is required for mitochondrial oxidative-phosphorylation: possible genetic association with Alzheimer’s disease. J Neurosci 2019, 39:4636-4656. 38. Patel T, Brookes KJ, Turton J, Chaudhury S, Guetta-Baranes T, Guerreiro R, Bras J, Hernandez D, Singleton A, Francis PT et al.: Whole-exome sequencing of the BDR cohort: evidence to support the role of the PILRA gene in Alzheimer’s disease. Neuropathol Appl Neurobiol 2018, 2:e1911350. 39. Beecham GW, Vardarajan B, Blue E, Bush W, Jaworski J, Barral S, Destefano A, Hamilton-Nelson K, Kunkle B, Martin ER et al.: Rare genetic variation implicated in non-Hispanic white families with Alzheimer disease. Neurol Genet 2018, 4:e286. 40. Paracchini L, Beltrame L, Boeri L, Fusco F, Caffarra P, Marchini S, Albani D, Forloni G: Exome sequencing in an Italian family with Alzheimer’s disease points to a role for seizure-related gene 6 (SEZ6) rare variant R615H. Alzheimer’s Res Ther 2018, 10:106. 41. Logue MW, Lancour D, Farrell J, Simkina I, Fallin MD, Lunetta KL, Farrer LA: Targeted sequencing of Alzheimer disease genes in African Americans implicates novel risk variants. Front Neurosci 2018, 12:592. 42. Raghavan NS, Brickman AM, Andrews H, Manly JJ, Schupf N, Lantigua R, Wolock CJ, Kamalakaran S, Petrovski S, Tosto G et al.: Whole-exome sequencing in 20,197 persons for rare variants in Alzheimer’s disease. Ann Clin Transl Neurol 2018, 5:832-842. 43. Dourlen P, Kilinc D, Malmanche N, Chapuis J, Lambert JC: The new genetic landscape of Alzheimer’s disease: from amyloid cascade to genetically driven synaptic failure hypothesis? Acta Neuropathol 2019, 138:221-236 One of the first examples of how genetic data can be integrated into and thus support a new hypothesis. 44. Seshadri S, Fitzpatrick AL, Ikram MA, DeStefano AL, Gudnason V, Boada M, Bis JC, Smith AV, Carassquillo MM, Lambert JC et al.: Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA 2010, 303:1832-1840. 45. Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, Boland A, Vronskaya M, van der Lee SJ, Amlie-Wolf A et al.: Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Ab, tau, immunity and lipid processing. Nat Genet 2019, 51:414-430 One of the two most recent AD GWASs compelling a large panel of different GWAS and using a recent imputation panel. This study only includes clinically diagnosed AD cases conversely to Jansen et al. www.sciencedirect.com
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46. Bertram L, Lange C, Mullin K, Parkinson M, Hsiao M, Hogan MF, Schjeide BMM, Hooli B, DiVito J, Ionita I et al.: Genome-wide association analysis reveals putative Alzheimer’s disease susceptibility loci in addition to APOE. Am J Hum Genet 2008, 83:623-632. 47. Naj AC, Jun G, Beecham GW, Wang LS, Vardarajan BN, Buros J, Gallins PJ, Buxbaum JD, Jarvik GP, Crane PK et al.: Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat Genet 2011, 43:436-441. 48. Hollingworth P, Harold D, Sims R, Gerrish A, Lambert J-C, Carrasquillo MM, Abraham R, Hamshere ML, Pahwa JS, Moskvina V et al.: Common variants at ABCA7, MS4A6A/ MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat Genet 2011, 43:429-435. 49. Jones L, Lambert J-C, Wang L-S, Choi S-H, Harold D, Vedernikov A, Escott-Price V, Stone T, Richards A, Bellenguez C et al.: Convergent genetic and expression data implicate immunity in Alzheimer’s disease. Alzheimer’s Dement 2014, 11:658-671. 50. Shulman JM, Imboywa S, Giagtzoglou N, Powers MP, Hu Y, Devenport D, Chipendo P, Chibnik LB, Diamond A, Perrimon N et al.: Functional screening in Drosophila identifies Alzheimer’s disease susceptibility genes and implicates taumediated mechanisms. Hum Mol Genet 2014, 23:870-877. 51. Dourlen P, Fernandez-Gomez FJ, Dupont C, Grenier-Bboley B, Bellenguez C, Obriot H, Caillierez R, Sottejeau Y, Chapuis J, Bretteville A et al.: Functional screening of Alzheimer risk loci identifies PTK2B as an in vivo modulator and early marker of Tau pathology. Mol Psychiatry 2017, 22:874-883. 52. Chapuis J, Flaig A, Grenier-Boley B, Eysert F, Pottiez V, Deloison G, Vandeputte A, Ayral AM, Mendes T, Desai S et al.: Genome-wide, high-content siRNA screening identifies the Alzheimer’s genetic risk factor FERMT2 as a major modulator of APP metabolism. Acta Neuropathol 2017, 133:955-966. 53. Al-Ali H, Gao H, Dalby-Hansen C, Peters VA, Shi Y, Brambilla R: High content analysis of phagocytic activity and cell morphology with PuntoMorph. J Neurosci Methods 2017, 291:43-50. 54. Sartori M, Mendes T, Desai S, Lasorsa A, Herledan A, Malmanche N, Ma¨kinen P, Marttinen M, Malki I, Chapuis J et al.: IN1 recovers tauopathy-induced long-term memory deficits in mice and interacts with Tau through Thr 348 phosphorylation. Acta Neuropathol 2019, 138:631-652. 55. De Strooper B, Karran E: The cellular phase of Alzheimer’s disease. Cell 2016, 164:603-615 One of the first examples of how genetic data can be integrated into and thus support a new hypothesis. 56. Jay TR, Hirsch AM, Broihier ML, Miller CM, Neilson LE, Ransohoff RM et al.: Disease progression-dependent effects of trem2 deficiency in a mouse model of Alzheimer’s disease. J Neurosci 2017, 37:637-647. 57. Wang Y, Ulland TK, Ulrich JD, Song W, Tzaferis JA, Hole JT et al.: TREM2-mediated early microglial response limits diffusion and toxicity of amyloid plaques. J Exp Med 2016, 213:667-675. 58. Novikova G, Kapoor M, Julia TCW, Abud EM, Efthymiou AG, Cheng H, Fullard JF, Bendl J, Roussos P, Poon WW et al.: Integration of Alzheimer’s disease genetics and myeloid cell genomics identifies novel causal variants, regulatory elements, genes and pathways. bioRxiv 2019 http://dx.doi.org/ 10.1101/694281 A very recent example of how AD GWAS, related endophenotypes and independent databases, can be combined to further decipher the genetic component of AD. This work supports the implication of microglia in AD. 59. Ecker JR, Geschwind DH, Kriegstein AR, Ngai J, Osten P, Polioudakis D, Regev A, Sestan N, Wickersham IR, Zeng H: The BRAIN initiative cell census consortium: lessons learned toward generating a comprehensive brain cell atlas. Neuron 2017, 96:542-557 An essential development of single RNA-seq databases in the brain allowing to better assess in what types of brain cells, key players of the AD pathology are expressed (or not). This will allow to better target www.sciencedirect.com
potential pathophysiological processes and such a database has to be improved and includes more and more tissues/cells. 60. Nott A, Holtman IR, Coufal NG, Schlachetzki JC, Yu M, Hu R, Han CZ, Pena M, Xiao J, Wu Y, et al.: Brain cell type-specific enhancer-promoter interactome maps and disease risk association. Science, in press. 61. Reitz C, Jun G, Naj A, Rajbhandary R, Vardarajan BN, Wang LS, Valladares O, Lin CF, Larson EB, Graff-Radford NR et al.: Variants in the ATP-binding cassette transporter (ABCA7), apolipoprotein e epsilon4, and the risk of late-onset Alzheimer disease in African Americans. JAMA 2013, 309:1489-1492. 62. Reitz C, Mayeux R: Genetics of Alzheimer’s disease in Caribbean Hispanic and African American populations. Biol Psychiatry 2014, 75:534-541. 63. Mahajan A, Go MJ, Zhang W, Below JE, Gaulton KJ, Ferreira T, Horikoshi M, Johnson AD, Ng MCY, Prokopenko I et al.: Genomewide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 2014, 46:234-244. 64. Molinuevo JL, Ayton S, Batrla R, Bednar MM, Bittner T, Cummings J, Fagan AM, Hampel H, Mielke MM, Mikulskis A et al.: Current state of Alzheimer’s fluid biomarkers. Acta Neuropathol 2018, 136:821-853. 65. Jack CR, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, Holtzman DM, Jagust W, Jessen F, Karlawish J et al.: NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimer’s Dement 2018, 14:535-562 This paper proposes the AT(N) system to diagnose MCI and dementia stages of AD based on biomarkers. Even if not yet generalized, these diagnosis criteria may strongly impact the AD genetic field and the constitution of new cohorts (and subsequently creating strong heterogeneity between previous and new databases). 66. Escott-Price V, Sims R, Bannister C, Harold D, Vronskaya M, Majounie E, Badarinarayan N, Morgan K, Passmore P, Holmes C et al.: Common polygenic variation enhances risk prediction for Alzheimer’s disease. Brain 2015, 138:3673-3684. 67. Tan CH, Desikan RS: Interpreting Alzheimer disease polygenic scores. Ann Neurol 2018, 88:443-445. 68. De Jager PL, Ma Y, McCabe C, Xu J, Vardarajan BN, Felsky D, Klein HU, White CC, Peters MA, Lodgson B et al.: Data descriptor: a multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci Data 2018, 5:180142 To date, it is the most complete and comprehensive ‘omics’ database available from human post-mortem brain tissues. By making these data public, the authors allow the possibility of developing more complex, complete and informative system biology approaches as well as the possibility of validating/replicating results of other independent studies. 69. Wang M, Beckmann ND, Roussos P, Wang E, Zhou X, Wang Q, Ming C, Neff R, Ma W, Fullard JF et al.: The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease. Sci Data 2018, 5:180185 The constitution of large and particularly comprehensive ‘omics’ databases from human post-mortem brain tissues is to be welcomed. 70. Su¨gis E, Dauvillier J, Leontjeva A, Adler P, Hindie V, Moncion T, Collura V, Daudin R, Loe-Mie Y, Herault Y et al.: HENA, heterogeneous network-based data set for Alzheimer’s disease. Sci Data 2019, 6:151 The constitution of public databases linking and integrating independent data related to AD is particularly helpful to generate complex and original approaches. 72. Jin SC, Benitez BA, Karch CM, Cooper B, Skorupa T, Carrell D, Norton JB, Hsu S, Harari O, Cai Y et al.: Coding variants in TREM2 increase risk for Alzheimer’s disease. Hum Mol Genet 2014, 23:5838-5846. 73. Rogaeva E, Meng Y, Lee JH, Gu Y, Kawarai T, Zou F, Katayama T, Baldwin CT, Cheng R, Hasegawa H et al.: The neuronal sortilinrelated receptor SORL1 is genetically associated with Alzheimer disease. Nat Genet 2007, 39:168-177. 74. Nicolas G, Charbonnier C, Wallon D, Quenez O, Bellenguez C, Grenier-Boley B, Rousseau S, Richard A-C, Rovelet-Lecrux A, Le Guennec K et al.: SORL1 rare variants: a major risk factor for Current Opinion in Neurobiology 2020, 61:40–48
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familial early-onset Alzheimer’s disease. Mol Psychiatry 2015, 21:881-886. 75. Holstege H, Van Der Lee SJ, Hulsman M, Wong TH, Van Rooij JGJ, Weiss M, Louwersheimer E, Wolters FJ, Amin N, Uitterlinden AG et al.: Characterization of pathogenic SORL1 genetic variants for association with Alzheimer’s disease: a clinical interpretation strategy. Eur J Hum Genet 2017, 25:973-981. 76. van der Lee SJ, Conway OJ, Jansen I, Carrasquillo MM, Kleineidam L, van den Akker E, Herna´ndez I, van Eijk KR, Stringa N, Chen JA et al.: A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer’s disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity. Acta Neuropathol 2019, 138:237-250. 77. Cuyvers E, De Roeck A, Van den Bossche T, Van Cauwenberghe C, Bettens K, Vermeulen S, Mattheijssens M, Peeters K, Engelborghs S, Vandenbulcke M et al.: Mutations in ABCA7 in a Belgian cohort of Alzheimer’s disease patients: a targeted resequencing study. Lancet Neurol 2015, 14:814-822. 78. Allen M, Lincoln SJ, Corda M, Watzlawik JO, Carrasquillo MM, Reddy JS, Burgess JD, Nguyen T, Malphrus K, Petersen RC et al.: ABCA7 loss-of-function variants, expression, and neurologic disease risk. Neurol Genet 2016, 48:204-211. 79. Genin E, Hannequin D, Wallon D, Sleegers K, Hiltunen M, Combarros O, Bullido MJ, Engelborghs S, De Deyn P, Berr C et al.: APOE and Alzheimer disease: a major gene with semidominant inheritance. Mol Psychiatry 2011, 6:903-907.
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81. Desikan RS, Schork AJ, Wang Y, Thompson WK, Dehghan A, Ridker PM, Chasman DI, Mcevoy LK, Holland D, Chen CH et al.: Polygenic overlap between C-reactive protein, plasma lipids, and Alzheimer disease. Circulation 2015, 9:2061-2069. 82. Lambert JC, Grenier-Boley B, Harold D, Zelenika D, Chouraki V, Kamatani Y, Sleegers K, Ikram MA, Hiltunen M, Reitz C et al.: Genome-wide haplotype association study identifies the FRMD4A gene as a risk locus for Alzheimer’s disease. Mol Psychiatry 2013, 18:461-470. 83. Jakobsdottir J, van der Lee SJ, Bis JC, Chouraki V, Li-Kroeger D, Yamamoto S, Grove ML, Naj A, Vronskaya M, Salazar JL et al.: Rare functional variant in TM2D3 is associated with late-onset Alzheimer’s disease. PLoS Genet 2016, 12:e100632. 84. Jun G, Ibrahim-Verbaas CA, Vronskaya M, Lambert JC, Chung J, Naj AC, Kunkle BW, Wang LS, Bis JC, Bellenguez C et al.: A novel Alzheimer disease locus located near the gene encoding tau protein. Mol Psychiatry 2016, 21:108-117. 85. Jun GR, Chung J, Logue MW, Sherva R, Farrer LA, Mez J, Barber R, Beecham GW, Hamilton-Nelson KL, Kunkle BW et al.: Transethnic genome-wide scan identifies novel Alzheimer’s disease loci. Alzheimer’s Dement 2017, 13:727-738. 86. McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, Kang HM, Fuchsberger C, Danecek P, Sharp K et al.: A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 2016, 48:1279-1283.
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ScienceDirect Genetics of Alzheimer’s disease: where we are, and where we are going Ce´line Bellenguez, Benjamin Grenier-Boley and Jean-Charles Lambert Alzheimer’s disease (AD) has a very strong genetic component, whose characterization has become an essential part of efforts to understand the pathophysiological processes of the disease. Thanks to the systematic use of high-throughput approaches over the last 10 years, more than 40 genes/loci have been linked to the AD risk. Although some of these signals are likely to be false positives, this genetic knowledge has shed new light on the pathogenesis of AD and, in particular, the major role of microglia. However, our knowledge of the genetics of AD is far from complete, and larger and more diverse genetic studies are required. Lastly, post-GWAS analyses will be needed to make sense of this genetic information without focusing too much on what we think we know about the disease. Address Univ. Lille, Inserm, Institut Pasteur de Lille, CHU Lille, U1167 - Labex DISTALZ - RID-AGE - Risk Factors and Molecular Determinants of Aging-Related Diseases, F-59000 Lille, France Corresponding author: Lambert, Jean-Charles ([email protected])
Current Opinion in Neurobiology 2020, 61:40–48 This review comes from a themed issue on Neurobiology of disease Edited by Michel Goedert and Christian Haass
https://doi.org/10.1016/j.conb.2019.11.024 0959-4388/ã 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction Since the 1930s, it has been known that rare forms of Alzheimer’s disease (AD) are fully genetically determined because they present an autosomal dominant mode of inheritance. As with all such genetic diseases, it was not until the 1980s that systematic linkage approaches for characterizing the causative genes were developed. In this context, three genes (APP, PSEN1 and PSEN2, coding respectively for amyloid precursor protein, presenilin-1 and presenilin-2) were found to be responsible for early onset, dominantly inherited forms of AD [1]. These rare mutations causing autosomal dominant forms of the disease gave rise to the amyloid cascade hypothesis, which radically changed our understanding of AD. Current Opinion in Neurobiology 2020, 61:40–48
The vast majority of cases of AD are late-onset ‘sporadic’ forms, with no obvious familial aggregation. However, AD appears to be one of the human multifactorial diseases with the highest level of heritability (70%) [2] — a similar level to that observed for schizophrenia (80%), and well above those found for diabetes (40%) [3] and Parkinson’s disease (30%) [4]. In 1993, it was reported that the e4 allele of the apolipoprotein E (APOE) gene was associated with the risk of developing AD [5]. Since then, this association has been detected in almost all ethnic groups — with the notable exception of some African populations [6,7]. Because of the discovery of APOE and the strength of the genetic component in AD, our knowledge of the genetics of this disease was expected to increase rapidly, but it was not until the late 2000s that other genetic risk factors were characterized thanks to the advent of high-throughput genomic approaches. In particular, genome-wide association studies (GWASs) enabled researchers to study tens of thousands of individuals and (after imputation) millions of genetic variations. In 2009, the publication of the results of the first two large-scale GWASs of AD constituted a milestone in this field, with the discovery of three new genetic risk factors: CLU, CR1 and PICALM [8,9].
High-throughput genomic approaches are shaping our understanding of the genetics of Alzheimer’s disease Since the publication of these seminal studies, the AD GWAS field has moved in several complementary directions and has led to the discovery of additional genetic risk factors of AD. Firstly, the development of large international GWAS consortia (e.g. the International Genomics of Alzheimer’s Project (IGAP)) has greatly increased the scale of the discovery phase (e.g. n = 17 008 AD cases and 37 154 controls analyzed in the first IGAP study) and thus its statistical power [10]. To note, the definition of around 40 000 proxy-AD cases in the UK biobank (based on selfreports of a family history of Alzheimer’s dementia) has increased the statistical power of the discovery phase of recent studies [11,12,13]. However, the relevance of this ‘virtual diagnosis’ can be criticized, and nevertheless constitutes a major limitation. Secondly, ever broader reference panels are being used for imputation, with 1092 individuals initially in the 1000 Genomes Project in 2012, 32 470 individuals in the Haplotype Reference Consortium and (in the near future) 62 784 individuals www.sciencedirect.com
Genetics of Alzheimer’s disease Bellenguez, Grenier-Boley and Grenier-Boley 41
in the Trans-Omics for Precision Medicine panel [14]. Better reference panel has expanded the number of variants analyzed and increased the imputation quality, especially for rare variants. Thirdly, the use of relevant endophenotypes can allow to detect novel signals [15]. Lastly, the development of new genotyping tools has made it possible to probe non-synonymous, rare variants [16]. In addition, a marked fall in the cost of next-generation sequencing has enabled the latter’s application to multifactorial diseases for which the detection of signals of interest, that is, rare variants, requires a large number of samples. These sequencing approaches have shown that rare variants in TREM2, SORL1 and ABCA7 for example are major genetic risk factors for AD [17–21]. Recently, the Alzheimer Disease Sequencing Project, the largest sequencing effort in AD to date, published its first whole-exome sequencing results for 5740 cases of lateonset AD and 5096 cognitively normal controls [22]. Rare (and common) variants in several additional genes were found to be potentially associated with the AD risk [23–42]. Hence, over the last ten years, more than 40 AD-associated genes/loci have been identified by GWASs and sequencing projects (Tables 1 and 2) [43].
Sequencing projects and GWASs do not represent the absolute truth: biases and limitations Even though high-throughput genomic approaches are very powerful means of detecting even weak associations, it is important to bear several important aspects in mind. In all cases, the quality of the research results depends on the quality of the populations studied, that is, the reliability of the diagnosis of AD, and the ability to limit classical epidemiologic biases. In view of the large number of tests, high-throughput approaches must strike a balance between the risk of observing apparently significant results by chance and the risk of rejecting biologically valid hypotheses on purely statistical grounds. Unfortunately, the solution to this problem has not yet been found. Correction for multiple testing is common to define the significance threshold, and is mainly based on the number of theoretically independent tests performed, for example, p < 5 10 8 for single variant analyses or p < 2 10 6 for gene-based analyses. These are arbitrary significance thresholds for determining whether or not a signal is valid, and they do not tell the researcher whether the observed signal is real. Nevertheless, a genuine association signal with the risk of developing AD should increase as the size of the study population grows - as long as the populations are similar with regard to the ethnic origins, age, diagnostic criteria, and so on. By way of an example, the signal for BIN1 was 1.59 10 11 (OR:1.15 [1.11–1.20]) in the initial study [44] and 2.1 10 44 (OR:1.20 [1.17–1.23]) in the latest study [45] — a clear indication that the signal is solid. In contrast, the CD33 gene was initially reported to be associated with the AD www.sciencedirect.com
risk in two independent GWASs published in 2008 [46] and in 2011 [47,48] but not in some of the larger GWASs published from 2013 to 2019 [10,45]; this raises questions about CD33’s relevance. However, when the signal fluctuates around the genome-wide significance threshold of 5 10 8 and gives rise to discrepancies between studies, one cannot rule out for example interactions with hidden heterogeneity in the studies such as as-yet unknown interaction between genetic or environmental factors. To note, fluctuation is particularly problematic in studies of rare variants; random fluctuations in rare variant frequencies in a given sample (controls and/or cases) can mask or artificially increase a variant’s association with the AD risk. Regardless of the variant frequency, the signal should never be detected in just one study (but homogeneous between different equivalent populations); its observation in several independent GWASs indicates that the result was not driven by unknown biases in the initial study population. This latter point can nevertheless be problematic; the IGAP’s summary statistics are publicly available since 2013 and most of the subsequent GWASs in AD are not necessarily independent of the IGAP results.
What meaning can we give to GWAS and sequencing data? One of the main objectives of basic genetic research on AD is to discover and characterize the underlying pathophysiological pathways and thus gain a better biological understanding of the disease. This work is based on the postulate whereby non-random groupings of genetic determinants constitute clues as to the nature of the disease pathway involved. In silico gene set enrichment approaches have been used to characterize potential pathways of interest. These analyses have confirmed the involvement of the expected pathways (such as APP and Tau processing) but have also highlighted the regulation of endocytosis, cholesterol transport, protein ubiquitination, and innate immunity [13,45,49]. However, it is important to bear in mind that gene set enrichment approaches have limitations and notably may not be detailed enough to accurately track the pathogenesis of AD. In view of this concern, researchers have developed biological approaches (referred to as ‘post-GWAS analyses’) for assessing the potential roles of genetic factors in APP/Ab metabolism, Tau toxicity and other potential pathophysiological processes [50–53]. For instance, many different genes appear to be involved in APP metabolism, Ab peptide clearance, and Ab toxicity [43]. In addition, there is high-quality evidence to suggest that BIN1 is involved in Tau pathology [54]. The combination of post-GWAS analyses with bibliographical data may reveal additional levels of complexity and thus generate various non-exclusive hypotheses, as proposed Current Opinion in Neurobiology 2020, 61:40–48
Locus for which the association with AD risk is validated by the most recent GWAS and/or sequencing projects Locus
Index variants
Chr
Position a
EA/OA b
EAF c
OR (95% CI)
Initial references
References for index variant/OR
CR1 BIN1 INPP5D HLA TREM2
rs4844610 rs6733839 rs10933431 rs9271058 rs75932628 (p.R47H) Rare variants rs9473117 rs12539172 rs10808026 rs73223431 rs9331896 rs7933202 rs3851179 rs11218343 Rare variants rs12881735 rs72824905
1 2 2 6 6 6 6 7 7 8 8 11 11 11 11 14 16
207 802 552 127 892 810 233 981 912 32 575 406 41 129 252 / 47 431 284 100 091 795 143 099 133 27 219 987 27 467 686 59 936 926 85 868 640 121 435 587 / 92 932 828 81 942 028
A/C T/C G/C A/T T/C / C/A T/C A/C T/C C/T C/A T/C C/T / C/T G/C
0.196 0.390 0.236 0.289 0.002 / 0.255 0.303 0.193 0.344 0.398 0.359 0.354 0.037 / 0.225 0.008
1.17 (1.13–1.21) 1.2 (1.17–1.23) 0.91 (0.88–0.94) 1.1 (1.07–1.13) 2.08 (1.73–2.49) up to 6 1.09 (1.06–1.12) 0.92 (0.90–0.95) 0.9 (0.88–0.93) 1.1 (1.07–1.13) 0.88 (0.85–0.90) 0.89 (0.87–0.92) 0.88 (0.86–0.90) 0.8 (0.75–0.85) up to 12 0.92 (0.89–0.94) 0.68 (0.60–0.77)
[9] [44] [10] [10] [17,18]
APOE
rs3752246 Rare variants rs429358/rs7412
19 19 19
1 056 492 / 45 411 941/45 412 079
G/C / e2e2 + e2e3/e3e3 e2e4/e3e3 e3e4/ e3e3 e4e4/e3e3
0.161 / 0.135 0.026 0.213 0.018
[48] [19] [5]
CASS4
rs6024870
20
54 997 568
A/G
0.075
1.15 (1.11–1.18) up to 4 0.56 (0.49–0.64) 2.64 (2.13–3.27) 3.63 (3.37–3.90) 14.49 (11.91– 17.64) 0.88 (0.85–0.92)
[45] [45] [45] [45] [45] [21,72] [45] [45] [45] [45] [45] [45] [45] [45] [21,75] [45] [16] see also Ref. [76] [45] [19,21,77,78] [79]
[10]
[45]
CD2AP ZCWPW1/NYAP1 EPHA1 PTK2B CLU MS4A PICALM SORL1 SLC24A4/RIN3 PLCG2 ABCA7
[47,48] [10] [47,48] [10] [8,9] [47,48] [8] [73] [20,74] [10] [16]
Chr: chromosome; EA: effect allele; OA: other allele; EAF: effect allele frequency; OR: odds-ratio; CI: confidence interval. GRCh37 assembly. b For APOE, effect genotype/reference genotype is provided. c Effect allele frequency in GnomAD r2.1.1 exomes and genomes, restricted to Finnish and non-Finnish European samples [77]. For APOE, the effect genotype frequency reported in Ref. [6] for the controls of the Caucasian ethnic group is provided. a
42 Neurobiology of disease
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Table 1
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Table 2 Locus for which the association with AD risk need to be further investigated before being fully accepted Index variants
Chr
Position a
EA/OA b
EAF c
OR (95% CI)
Initial references
References for index variant/OR
ADAMTS4 HESX1/IL17RD/APPL1 CLNK OARD1 CNTNAP2 ECHDC3 FRMD4A
rs4575098 rs184384746 rs6448453 rs114812713 rs114360492 rs7920721 rs7081208, rs2446581, rs17314229 rs3740688 rs17125924 rs593742 rs117618017 rs139709573 rs7185636 rs59735493 rs62039712 rs113260531 rs2732703 rs616338 rs2632516 rs138190086 rs76726049 rs76320948 rs3865444 rs2830500
1 3 4 6 7 10 10
161 155 392 57 226 150 11 026 028 41 034 000 145 950 029 11 720 308 13 991 865, 14 008 445, 14 016 159 47 380 340 53 391 680 59 045 774 63 569 902 102 186 966 19 808 163 31 133 100 79 355 857 5 138 980 44 353 222 47 297 297 56 409 089 61 538 148 56 189 459 46 241 841 51 727 962 28 156 856
A/G T/C A/G C/G T/C G/A AAC/GGC
0.237 0.002 0.242 0.019 2.12 10 4 0.391 0.025/0.637
/ / / 1.32 (1.22–1.42) / 1.08 (1.06–1.11) 1.68 (1.43–1.96)
[13] [13] [13] [45] [13] [81] [82]
[13] [13] [13] [45] [13] [45] [82]
G/T G/A G/A T/C A/G C/T A/G A/G A/G G/T T/C C/G A/G C/T T/C A/C A/C
0.455 0.095 0.314 0.129 2.20 10 0.156 0.303 0.110 0.132 0.191 0.010 0.468 0.017 0.013 0.042 0.328 0.309
0.92 (0.89–0.94) 1.14 (1.09–1.18) 0.93 (0.91–0.95) / 7.45 (3.49–15.90) 0.92 (0.89–0.95) / 1.16 (1.10–1.23) / 0.73 (0.65–0.81) 1.43 (1.28–1.60) 0.94 (0.91–0.96) 1.3 (1.19–1.42) / / / 0.93 (0.91–0.96)
[10] [10] [12] [13] [83] [45] [12] [45] [11] [84] [16] [85] [12] [13] [13] [46–48] [45]
[45] [45] [45] [13] [83] [45] [13] [45] [13] [84] [16] [45] [45] [13] [13] [13] [45]
CELF1/SPI1 FERMT2 ADAM10 APH1B TM2D3 IQCK KAT8 WWOX/MAF SCIMP/RABEP1 MAPT ABI3 TSPOAP1 ACE ALPK2 AC074212.3 CD33 ADAMTS1
11 14 15 15 15 16 16 16 17 17 17 17 17 18 19 19 21
4
Chr: chromosome; EA: effect allele; OA: other allele; EAF: effect allele frequency; OR: odds-ratio; CI: confidence interval. GRCh37 assembly. b For FRMD4A, effect haplotype/reference haplotype is provided. c Effect allele frequency in GnomAD r2.1.1 exomes and genomes, restricted to Finnish and non-Finnish European samples [77]. For FRMDA, the effect and reference haplotype frequencies reported in Ref. [10] for the French EADI controls are provided. For MAPT, the effect allele frequency in HRC r1.1 [86], without 1000 Genomes samples, is reported. a
Genetics of Alzheimer’s disease Bellenguez, Grenier-Boley and Grenier-Boley 43
Current Opinion in Neurobiology 2020, 61:40–48
Locus
44 Neurobiology of disease
in the cell phase or genetically driven synaptic failure hypotheses [43,55]. Another means of studying the genetic factors in AD is to better characterize functional variants. In some cases, a variant’s impact on biological function is obvious (e.g. loss-of-function frameshift mutations in SORL1 and ABCA7). These genetic observations obviously will facilitate the development of relevant models. For instance, the genetic data have unambiguously revealed the importance of a specific cell type — the microglia — in the development of AD [16]. Reports of non-synonymous variants in TREM2, associated with AD risk led to obviously develop relevant hypothesis-driven studies in microglia and TREM2 is currently the most studied genetic risk factor in the context of the microgliadependent pathophysiological process in AD. TREM2 has been proposed to be protective in such a context since the AD-associated mutations (R47H, R62H) likely impair its physiological functions at two levels: (i) phagocytosis and clearance of Ab peptides and (ii) compaction of amyloid plaques and barrier formation [56,57]. It is important to keep in mind that, most of the GWAS signals are located in noncoding (and thus potentially regulatory) regions of the genome. However, it is possible to combine for example expression quantitative trait loci, active enhancers and/or gene expression (and so on) in different cell types and pathological tissues to determine functional variants and pathways in which the genes are potentially involved. A recent publication even suggested that the functional variants responsible for GWAS signals in many genes mainly drive gene expression in myeloid cells (and thus potentially in microglia) [58]. It is necessary to bear in mind that this was a hypothesis-driven study based on a human myeloid cell data repository and for obvious reasons, the absence of equivalent large datasets for human neurons and other brain cell types precludes similar analyses in these cell types [59]. However, it has been also suggested that numerous AD risk variants may be located in microglial-specific enhancers, strongly supporting again the importance of microglia in AD [60].
What is the future for genetic analyses of Alzheimer’s disease? As mentioned above, our knowledge of the genetic component of AD has been greatly enhanced over the last 10 years. This knowledge has already changed the way the research community thinks about the AD process — notably by highlighting the potentially major role of microglia. However, the knowledge is far from complete; as is the case for other aging-related multifactorial diseases, major efforts to exhaustively characterize genetic factors in AD are still needed. This will probably require the implementation of several complementary strategies. Current Opinion in Neurobiology 2020, 61:40–48
Firstly, larger GWASs of Caucasian and other ethnic groups are now underway or being planned. The European Alzheimer DNA Biobank project has just been launched; with approximately 38 000 AD cases and 60 000 controls in the discovery phase, it will be the world’s largest GWAS. In the USA, large studies of African-American and Caribbean populations are being developed by the Alzheimer’s Disease Genetics Consortium [61,62]. Furthermore, reports from large GWASs in Asian populations are expected. Lastly, initiatives (albeit at a lower level) are also being taken in South America and Africa. These studies will leverage the most recent imputation panels, and are likely to highlight new genetic risk factors of AD (whether specific or not for various ethnic groups) and prompt the development of in-depth approaches for mapping functional variants as already performed in other multifactorial diseases [63]. Secondly, large sequencing projects are underway in various ethnic groups [22,26,36]. Since the imputation quality is low for very rare variants, these sequencing approaches complement the GWASs results [22,23–42]. It is important to bear in mind that this type of study is very sensitive to the available statistical power; most of the studies described to date were probably underpowered, resulting in the publication of false positives. However, this problem should rapidly disappear as lower sequencing costs enable the assessment of ever larger populations. Another direct consequence of the drop in cost will be a shift from whole-exome sequencing to whole-genome sequencing; given that the exome accounts for only 3% of the genome, this move will broaden the analysis of rare variants. New sequencing technologies (e.g. those based on long reads) should enable analyses of structural variants in a more efficient way that currently possible. Thirdly, the diagnostic criteria for AD and related syndromes will become refined — notably through the increasing use of biomarkers [64,65]. Although it is difficult to predict how this trend will impact studies of AD genetics, it is already clear that there will be a gap between today’s case-control studies and those implemented in the future. Genetic analyses will probably be broadened to other, better diagnosed neurodegenerative diseases, and will potentially be able to define disease-specific genes and pathways or, on the contrary, common genes and pathways. This trend in diagnosis is also likely to prompt the redefinition of most of today’s polygenic risk scores results and thus make them more relevant [66,67]. Following on from the characterization of missing genetic risk factors for AD, post-genomic studies will be required to give meaning to this data. As mentioned above, this is the most important challenge in studies of the pathophysiological processes in AD. One of the main temptations is www.sciencedirect.com
Genetics of Alzheimer’s disease Bellenguez, Grenier-Boley and Grenier-Boley 45
to position newly identified genes in processes that are already thought to be involved in AD: Although this may well be the case, this prompts a move from a hypothesis-free approach (e.g. GWAS) to a hypothesisdriven approach. However, a main challenge will be to develop read-outs, more or less independently of our current knowledge, by developing new models. In fact, this type of holistic approach is already developed by making available “omics” databases in the brain, potentially including single-cell sequencing and atlases for all cell types [68,69,70]. These efforts are clearly needed for the most comprehensive, least biased vision of AD. In any case, the combination of these strategies will either independently confirm known mechanisms or highlight new, unexpected processes.
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Gatz M, Reynolds CA, Fratiglioni L, Johansson B, Mortimer JA, Berg S, Fiske A, Pedersen NL: Role of genes and environments for explaining Alzheimer disease. Arch Gen Psychiatry 2006, 63:168-174.
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Elbein SC: Obesity: common symptom of diverse genebased metabolic dysregulations the genetics of human noninsulin-dependent (type 2) diabetes mellitus 1. J Nutr 1997, 127:668-677.
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Warner TT, Schapira AH: Genetic and environmental factors in the cause of Parkinson’s disease. Ann Neurol 2003, 53:316-323.
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Strittmatter WJ, Saunders AM, Schmechel D, Pericak-Vance M, Enghild J, Salvesen GS, Roses AD: Apolipoprotein E: highavidity binding to b-amyloid and increased frequency of type 4 allele in late-onset familial Alzheimer disease. Proc Natl Acad Sci U S A 1993, 90:8098-8102.
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Conclusion
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Gureje O, Ogunniyi A, Baiyewu O, Price B, Unverzagt FW, Evans RM, Smith-Gamble V, Lane KA, Gao S, Hall KS et al.: APOE e4 is not associated with Alzheimer’s disease in elderly Nigerians. Ann Neurol 2006, 59:182-185.
8.
Harold D, Abraham R, Hollingworth P, Sims R, Gerrish A, Hamshere ML, Pahwa JS, Moskvina V, Dowzell K, Williams A et al.: Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet 2009, 41:1088-1093.
9.
Lambert J-C, Heath S, Even G, Campion D, Sleegers K, Hiltunen M, Combarros O, Zelenika D, Bullido MJ, Tavernier B et al.: Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat Genet 2009, 41:1094-1099.
Thanks to high-throughput genomic approaches, our knowledge of the genetics of AD has progressed significantly over the last 10 years. However, much work remains to be done to characterize the missing genetic factors and to separate the wheat from the chaff when considering published loci. Again, it is important to bear in mind that some published loci are probably false positives. Furthermore, the real objective of this research is to give biological meaning to genetic information, rather than to draw up a list of loci. This challenge is still in its early stages, and will require more and more effort. In this respect, purely reductionist approaches will not be able to capture the complexity induced by the growing number of genes/loci; only approaches that take account of this complexity are likely to be able to determine which pathophysiological processes are involved. Characterizing the missing AD genetic factors and developing post-GWAS approaches to understanding the factors’ pathophysiological roles are probably among most promising and exciting areas of research in AD. Ultimately, this should lead to the development of personalized treatments that match the individual patient’s genetic profile.
Conflict of interest statement Nothing declared.
Acknowledgements This work was funded by Inserm, Institut Pasteur de Lille, the Lille Me´tropole Communaute´ Urbaine, the French government’s LABEX DISTALZ program (development of innovative strategies for a transdisciplinary approach to Alzheimer’s disease).
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
Cacace R, Sleegers K, Van Broeckhoven C: Molecular genetics of early-onset Alzheimer’s disease revisited. Alzheimer’s Dement 2016, 12:733-748.
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10. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, Beecham GW, GrenierBoley B et al.: Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet 2013, 45:1452-1458. 11. Liu JZ, Erlich Y, Pickrell JK: Case-control association mapping by proxy using family history of disease. Nat Genet 2017, 49:325-331 The first study reporting the use of proxy AD cases using family history from the UK biobank. 12. Marioni RE, Harris SE, Zhang Q, McRae AF, Hagenaars SP, Hill WD, Davies G, Ritchie CW, Gale CR, Starr JM et al.: GWAS on family history of Alzheimer’s disease. Transl Psychiatry 2018, 8:99. 13. Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S, Sealock J, Karlsson IK, Ha¨gg S, Athanasiu L et al.: Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet 2019, 51:404-413 One of the two most recent AD GWASs compelling a large panel of different GWAS summary statistics including proxy-AD cases generating from the UK biobank. 14. Das S, Abecasis GR, Browning BL: Genotype imputation from large reference panels. Annu Rev Genomics Hum Genet 2018, 19:73-96. 15. Huang KL, Marcora E, Pimenova AA, Di Narzo AF, Kapoor M, Jin SC, Harari O, Bertelsen S, Fairfax BP, Czajkowski J et al.: A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer’s disease. Nat Neurosci 2017, 20:1052-1061 One of the first study in the AD genetic field using AD GWAS and related endophenotypes, that is, age at onset, to characterize new AD genetic risk factors. 16. Sims R, Van Der Lee SJ, Naj AC, Bellenguez C, Badarinarayan N, Jakobsdottir J, Kunkle BW, Boland A, Raybould R, Bis JC et al.: Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease. Nat Genet 2017, 49:1373-1384 Current Opinion in Neurobiology 2020, 61:40–48
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A major GWAS report clearly demonstrating that microglia is a major actor of the AD pathology. The three genes characterized in this study are mainly (or almost only) expressed in this type of cells and participate to the same Protein-protein interaction network than TREM2. 17. Guerreiro R, Wojtas A, Bras J, Carrasquillo M, Rogaeva E, Majounie E, Cruchaga C, Sassi C, Kauwe JSK, Younkin S et al.: TREM2 variants in Alzheimer’s disease. N Engl J Med 2013, 368:117-127. 18. Jonsson T, Stefansson H, Steinberg S, Jonsdottir I, Jonsson PV, Snaedal J, Bjornsson S, Huttenlocher J, Levey a I, Lah JJ et al.: Variant of TREM2 associated with the risk of Alzheimer’s disease. N Engl J Med 2013, 368:107-116. 19. Steinberg S, Stefansson H, Jonsson T, Johannsdottir H, Ingason A, Helgason H, Sulem P, Magnusson OT, Gudjonsson SA, Unnsteinsdottir U et al.: Loss-of-function variants in ABCA7 confer risk of Alzheimer’s disease. Nat Genet 2015, 47:445-447. 20. Pottier C, Hannequin D, Coutant S, Rovelet-Lecrux a, Wallon D, Rousseau S, Legallic S, Paquet C, Bombois S, Pariente J et al.: High frequency of potentially pathogenic SORL1 mutations in autosomal dominant early-onset Alzheimer disease. Mol Psychiatry 2012, 17:875-879. 21. Bellenguez C, Charbonnier C, Grenier-Boley B, Quenez O, Le Guennec K, Nicolas G, Chauhan G, Wallon D, Rousseau S, Richard AC et al.: Contribution to Alzheimer’s disease risk of rare variants in TREM2, SORL1, and ABCA7 in 1779 cases and 1273 controls. Neurobiol Aging 2017, 59:220. 22. Bis JC, Jian X, Kunkle BW, Chen Y, Hamilton-Nelson KL, Bush WS, Salerno WJ, Lancour D, Ma Y, Renton AE et al.: Whole exome sequencing study identifies novel rare and common Alzheimer’s-associated variants involved in immune response and transcriptional regulation. Mol Psychiatry 2019. in press The first publication of the ADSP project (to date, the largest AD WES database available worldwide) potentially characterizing three new genes (to be validated). Importantly, ADSP makes publicly available the complete raw whole exome sequencing data. This has to be highlighted and the authors have to be congratulated for this. 23. Asanomi Y, Shigemizu D, Miyashita A, Mitsumori R, Mori T, Hara N, Ito K, Niida S, Ikeuchi T, Ozaki K: A rare functional variant of SHARPIN attenuates the inflammatory response and associates with increased risk of late-onset Alzheimer’s disease. Mol Med 2019, 25:20. 24. Zhang X, Zhu C, Beecham G, Vardarajan BN, Ma Y, Lancour D, Farrell JJ, Chung J, Bellair M, Dinh H et al.: A rare missense variant of CASP7 is associated with familial late-onset Alzheimer’s disease. Alzheimer’s Dement 2019, 15:441-452. 25. Hartl D, May P, Gu W, Mayhaus M, Pichler S, Spaniol C, Glaab E, Bobbili DR, Antony P, Koegelsberger S, et al.: A rare loss-offunction variant of ADAM17 is associated with late-onset familial Alzheimer disease. Mol Psychiatry, in press. 26. Vardarajan BN, Barral S, Jaworski J, Beecham GW, Blue E, Tosto G, Reyes-Dumeyer D, Medrano M, Lantigua R, Naj A et al.: Whole genome sequencing of Caribbean Hispanic families with late-onset Alzheimer’s disease. Ann Clin Transl Neurol 2018, 5:406-417. 27. Wang B, Bao S, Zhang Z, Zhou X, Wang J, Fan Y, Zhang Y, Li Y, Chen L, Jia Y et al.: A rare variant in MLKL confers susceptibility to ApoE e4-negative Alzheimer’s disease in Hong Kong Chinese population. Neurobiol Aging 2018, 68:160. 28. Ridge PG, Karch CM, Hsu S, Arano I, Teerlink CC, Ebbert MTW, Gonzalez Murcia JD, Farnham JM, Damato AR et al.: Linkage, whole genome sequence, and biological data implicate variants in RAB10 in Alzheimer’s disease resilience. Genome Med 2017, 10:4. 29. Vardarajan BN, Tosto G, Lefort R, Yu L, Bennett DA, De Jager PL, Barral S, Reyes-Dumeyer D, Nagy PL, Lee JH et al.: Ultra-rare mutations in SRCAP segregate in Caribbean Hispanic families with Alzheimer disease. Neurol Genet 2017, 3:e178. 30. N’songo A, Carrasquillo MM, Wang X, Burgess JD, Nguyen T, Asmann YW, Serie DJ, Younkin SG, Allen M, Pedraza O et al.: African American exome sequencing identifies potential risk variants at Alzheimer disease loci. Neurol Genet 2017, 3:e141. Current Opinion in Neurobiology 2020, 61:40–48
31. Bras J, Djaldetti R, Alves AM, Mead S, Darwent L, Lleo A, Molinuevo JL, Blesa R, Singleton A, Hardy J et al.: Exome sequencing in a consanguineous family clinically diagnosed with early-onset Alzheimer’s disease identifies a homozygous CTSF mutation. Neurobiol Aging 2016, 46:236. 32. Cruchaga C, Karch CM, Jin SC, Benitez BA, Cai Y, Guerreiro R, Harari O, Norton J, Budde J, Bertelsen S et al.: Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer’s disease. Nature 2014, 505:550-554. 33. Wetzel-Smith MK, Hunkapiller J, Bhangale TR, Srinivasan K, Maloney JA, Atwal JK, Sa SM, Yaylaoglu MB, Foreman O, Ortmann W et al.: A rare mutation in UNC5C predisposes to late-onset Alzheimer’s disease and increases neuronal cell death. Nat Med 2014, 20:1152-1157. 34. Jonsson T, Atwal JK, Steinberg S, Snaedal J, Jonsson PV, Bjornsson S, Stefansson H, Sulem P, Gudbjartsson D, Maloney J et al.: A mutation in APP protects against Alzheimer's disease and age-related cognitive decline. Nature 2012, 488:96-99. 35. Ma Y, Jun GR, Zhang X, Chung J, Naj AC, Chen Y, Bellenguez C, Hamilton-Nelson K, Martin ER, Kunkle BW et al.: Analysis of whole-exome sequencing data for Alzheimer disease stratified by APOE genotype. JAMA Neurol 2019. in press. 36. Tosto G, Vardarajan B, Sariya S, Brickman AM, Andrews H, Manly JJ, Schupf N, Reyes-Dumeyer D, Lantigua R, Bennett DA et al.: Association of variants in PINX1 and TREM2 with lateonset Alzheimer disease. JAMA Neurol 2019. in press. 37. Fleck D, Phu L, Verschueren E, Hinkle T, Reichelt M, Bhangale T, Haley B, Wang Y, Graham R, Kirkpatrick DS et al.: PTCD1 is required for mitochondrial oxidative-phosphorylation: possible genetic association with Alzheimer’s disease. J Neurosci 2019, 39:4636-4656. 38. Patel T, Brookes KJ, Turton J, Chaudhury S, Guetta-Baranes T, Guerreiro R, Bras J, Hernandez D, Singleton A, Francis PT et al.: Whole-exome sequencing of the BDR cohort: evidence to support the role of the PILRA gene in Alzheimer’s disease. Neuropathol Appl Neurobiol 2018, 2:e1911350. 39. Beecham GW, Vardarajan B, Blue E, Bush W, Jaworski J, Barral S, Destefano A, Hamilton-Nelson K, Kunkle B, Martin ER et al.: Rare genetic variation implicated in non-Hispanic white families with Alzheimer disease. Neurol Genet 2018, 4:e286. 40. Paracchini L, Beltrame L, Boeri L, Fusco F, Caffarra P, Marchini S, Albani D, Forloni G: Exome sequencing in an Italian family with Alzheimer’s disease points to a role for seizure-related gene 6 (SEZ6) rare variant R615H. Alzheimer’s Res Ther 2018, 10:106. 41. Logue MW, Lancour D, Farrell J, Simkina I, Fallin MD, Lunetta KL, Farrer LA: Targeted sequencing of Alzheimer disease genes in African Americans implicates novel risk variants. Front Neurosci 2018, 12:592. 42. Raghavan NS, Brickman AM, Andrews H, Manly JJ, Schupf N, Lantigua R, Wolock CJ, Kamalakaran S, Petrovski S, Tosto G et al.: Whole-exome sequencing in 20,197 persons for rare variants in Alzheimer’s disease. Ann Clin Transl Neurol 2018, 5:832-842. 43. Dourlen P, Kilinc D, Malmanche N, Chapuis J, Lambert JC: The new genetic landscape of Alzheimer’s disease: from amyloid cascade to genetically driven synaptic failure hypothesis? Acta Neuropathol 2019, 138:221-236 One of the first examples of how genetic data can be integrated into and thus support a new hypothesis. 44. Seshadri S, Fitzpatrick AL, Ikram MA, DeStefano AL, Gudnason V, Boada M, Bis JC, Smith AV, Carassquillo MM, Lambert JC et al.: Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA 2010, 303:1832-1840. 45. Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, Boland A, Vronskaya M, van der Lee SJ, Amlie-Wolf A et al.: Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Ab, tau, immunity and lipid processing. Nat Genet 2019, 51:414-430 One of the two most recent AD GWASs compelling a large panel of different GWAS and using a recent imputation panel. This study only includes clinically diagnosed AD cases conversely to Jansen et al. www.sciencedirect.com
Genetics of Alzheimer’s disease Bellenguez, Grenier-Boley and Grenier-Boley 47
46. Bertram L, Lange C, Mullin K, Parkinson M, Hsiao M, Hogan MF, Schjeide BMM, Hooli B, DiVito J, Ionita I et al.: Genome-wide association analysis reveals putative Alzheimer’s disease susceptibility loci in addition to APOE. Am J Hum Genet 2008, 83:623-632. 47. Naj AC, Jun G, Beecham GW, Wang LS, Vardarajan BN, Buros J, Gallins PJ, Buxbaum JD, Jarvik GP, Crane PK et al.: Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat Genet 2011, 43:436-441. 48. Hollingworth P, Harold D, Sims R, Gerrish A, Lambert J-C, Carrasquillo MM, Abraham R, Hamshere ML, Pahwa JS, Moskvina V et al.: Common variants at ABCA7, MS4A6A/ MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat Genet 2011, 43:429-435. 49. Jones L, Lambert J-C, Wang L-S, Choi S-H, Harold D, Vedernikov A, Escott-Price V, Stone T, Richards A, Bellenguez C et al.: Convergent genetic and expression data implicate immunity in Alzheimer’s disease. Alzheimer’s Dement 2014, 11:658-671. 50. Shulman JM, Imboywa S, Giagtzoglou N, Powers MP, Hu Y, Devenport D, Chipendo P, Chibnik LB, Diamond A, Perrimon N et al.: Functional screening in Drosophila identifies Alzheimer’s disease susceptibility genes and implicates taumediated mechanisms. Hum Mol Genet 2014, 23:870-877. 51. Dourlen P, Fernandez-Gomez FJ, Dupont C, Grenier-Bboley B, Bellenguez C, Obriot H, Caillierez R, Sottejeau Y, Chapuis J, Bretteville A et al.: Functional screening of Alzheimer risk loci identifies PTK2B as an in vivo modulator and early marker of Tau pathology. Mol Psychiatry 2017, 22:874-883. 52. Chapuis J, Flaig A, Grenier-Boley B, Eysert F, Pottiez V, Deloison G, Vandeputte A, Ayral AM, Mendes T, Desai S et al.: Genome-wide, high-content siRNA screening identifies the Alzheimer’s genetic risk factor FERMT2 as a major modulator of APP metabolism. Acta Neuropathol 2017, 133:955-966. 53. Al-Ali H, Gao H, Dalby-Hansen C, Peters VA, Shi Y, Brambilla R: High content analysis of phagocytic activity and cell morphology with PuntoMorph. J Neurosci Methods 2017, 291:43-50. 54. Sartori M, Mendes T, Desai S, Lasorsa A, Herledan A, Malmanche N, Ma¨kinen P, Marttinen M, Malki I, Chapuis J et al.: IN1 recovers tauopathy-induced long-term memory deficits in mice and interacts with Tau through Thr 348 phosphorylation. Acta Neuropathol 2019, 138:631-652. 55. De Strooper B, Karran E: The cellular phase of Alzheimer’s disease. Cell 2016, 164:603-615 One of the first examples of how genetic data can be integrated into and thus support a new hypothesis. 56. Jay TR, Hirsch AM, Broihier ML, Miller CM, Neilson LE, Ransohoff RM et al.: Disease progression-dependent effects of trem2 deficiency in a mouse model of Alzheimer’s disease. J Neurosci 2017, 37:637-647. 57. Wang Y, Ulland TK, Ulrich JD, Song W, Tzaferis JA, Hole JT et al.: TREM2-mediated early microglial response limits diffusion and toxicity of amyloid plaques. J Exp Med 2016, 213:667-675. 58. Novikova G, Kapoor M, Julia TCW, Abud EM, Efthymiou AG, Cheng H, Fullard JF, Bendl J, Roussos P, Poon WW et al.: Integration of Alzheimer’s disease genetics and myeloid cell genomics identifies novel causal variants, regulatory elements, genes and pathways. bioRxiv 2019 http://dx.doi.org/ 10.1101/694281 A very recent example of how AD GWAS, related endophenotypes and independent databases, can be combined to further decipher the genetic component of AD. This work supports the implication of microglia in AD. 59. Ecker JR, Geschwind DH, Kriegstein AR, Ngai J, Osten P, Polioudakis D, Regev A, Sestan N, Wickersham IR, Zeng H: The BRAIN initiative cell census consortium: lessons learned toward generating a comprehensive brain cell atlas. Neuron 2017, 96:542-557 An essential development of single RNA-seq databases in the brain allowing to better assess in what types of brain cells, key players of the AD pathology are expressed (or not). This will allow to better target www.sciencedirect.com
potential pathophysiological processes and such a database has to be improved and includes more and more tissues/cells. 60. Nott A, Holtman IR, Coufal NG, Schlachetzki JC, Yu M, Hu R, Han CZ, Pena M, Xiao J, Wu Y, et al.: Brain cell type-specific enhancer-promoter interactome maps and disease risk association. Science, in press. 61. Reitz C, Jun G, Naj A, Rajbhandary R, Vardarajan BN, Wang LS, Valladares O, Lin CF, Larson EB, Graff-Radford NR et al.: Variants in the ATP-binding cassette transporter (ABCA7), apolipoprotein e epsilon4, and the risk of late-onset Alzheimer disease in African Americans. JAMA 2013, 309:1489-1492. 62. Reitz C, Mayeux R: Genetics of Alzheimer’s disease in Caribbean Hispanic and African American populations. Biol Psychiatry 2014, 75:534-541. 63. Mahajan A, Go MJ, Zhang W, Below JE, Gaulton KJ, Ferreira T, Horikoshi M, Johnson AD, Ng MCY, Prokopenko I et al.: Genomewide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 2014, 46:234-244. 64. Molinuevo JL, Ayton S, Batrla R, Bednar MM, Bittner T, Cummings J, Fagan AM, Hampel H, Mielke MM, Mikulskis A et al.: Current state of Alzheimer’s fluid biomarkers. Acta Neuropathol 2018, 136:821-853. 65. Jack CR, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, Holtzman DM, Jagust W, Jessen F, Karlawish J et al.: NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimer’s Dement 2018, 14:535-562 This paper proposes the AT(N) system to diagnose MCI and dementia stages of AD based on biomarkers. Even if not yet generalized, these diagnosis criteria may strongly impact the AD genetic field and the constitution of new cohorts (and subsequently creating strong heterogeneity between previous and new databases). 66. Escott-Price V, Sims R, Bannister C, Harold D, Vronskaya M, Majounie E, Badarinarayan N, Morgan K, Passmore P, Holmes C et al.: Common polygenic variation enhances risk prediction for Alzheimer’s disease. Brain 2015, 138:3673-3684. 67. Tan CH, Desikan RS: Interpreting Alzheimer disease polygenic scores. Ann Neurol 2018, 88:443-445. 68. De Jager PL, Ma Y, McCabe C, Xu J, Vardarajan BN, Felsky D, Klein HU, White CC, Peters MA, Lodgson B et al.: Data descriptor: a multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci Data 2018, 5:180142 To date, it is the most complete and comprehensive ‘omics’ database available from human post-mortem brain tissues. By making these data public, the authors allow the possibility of developing more complex, complete and informative system biology approaches as well as the possibility of validating/replicating results of other independent studies. 69. Wang M, Beckmann ND, Roussos P, Wang E, Zhou X, Wang Q, Ming C, Neff R, Ma W, Fullard JF et al.: The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease. Sci Data 2018, 5:180185 The constitution of large and particularly comprehensive ‘omics’ databases from human post-mortem brain tissues is to be welcomed. 70. Su¨gis E, Dauvillier J, Leontjeva A, Adler P, Hindie V, Moncion T, Collura V, Daudin R, Loe-Mie Y, Herault Y et al.: HENA, heterogeneous network-based data set for Alzheimer’s disease. Sci Data 2019, 6:151 The constitution of public databases linking and integrating independent data related to AD is particularly helpful to generate complex and original approaches. 72. Jin SC, Benitez BA, Karch CM, Cooper B, Skorupa T, Carrell D, Norton JB, Hsu S, Harari O, Cai Y et al.: Coding variants in TREM2 increase risk for Alzheimer’s disease. Hum Mol Genet 2014, 23:5838-5846. 73. Rogaeva E, Meng Y, Lee JH, Gu Y, Kawarai T, Zou F, Katayama T, Baldwin CT, Cheng R, Hasegawa H et al.: The neuronal sortilinrelated receptor SORL1 is genetically associated with Alzheimer disease. Nat Genet 2007, 39:168-177. 74. Nicolas G, Charbonnier C, Wallon D, Quenez O, Bellenguez C, Grenier-Boley B, Rousseau S, Richard A-C, Rovelet-Lecrux A, Le Guennec K et al.: SORL1 rare variants: a major risk factor for Current Opinion in Neurobiology 2020, 61:40–48
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familial early-onset Alzheimer’s disease. Mol Psychiatry 2015, 21:881-886. 75. Holstege H, Van Der Lee SJ, Hulsman M, Wong TH, Van Rooij JGJ, Weiss M, Louwersheimer E, Wolters FJ, Amin N, Uitterlinden AG et al.: Characterization of pathogenic SORL1 genetic variants for association with Alzheimer’s disease: a clinical interpretation strategy. Eur J Hum Genet 2017, 25:973-981. 76. van der Lee SJ, Conway OJ, Jansen I, Carrasquillo MM, Kleineidam L, van den Akker E, Herna´ndez I, van Eijk KR, Stringa N, Chen JA et al.: A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer’s disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity. Acta Neuropathol 2019, 138:237-250. 77. Cuyvers E, De Roeck A, Van den Bossche T, Van Cauwenberghe C, Bettens K, Vermeulen S, Mattheijssens M, Peeters K, Engelborghs S, Vandenbulcke M et al.: Mutations in ABCA7 in a Belgian cohort of Alzheimer’s disease patients: a targeted resequencing study. Lancet Neurol 2015, 14:814-822. 78. Allen M, Lincoln SJ, Corda M, Watzlawik JO, Carrasquillo MM, Reddy JS, Burgess JD, Nguyen T, Malphrus K, Petersen RC et al.: ABCA7 loss-of-function variants, expression, and neurologic disease risk. Neurol Genet 2016, 48:204-211. 79. Genin E, Hannequin D, Wallon D, Sleegers K, Hiltunen M, Combarros O, Bullido MJ, Engelborghs S, De Deyn P, Berr C et al.: APOE and Alzheimer disease: a major gene with semidominant inheritance. Mol Psychiatry 2011, 6:903-907.
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