Rare Variants Net New Alzheimer’s Gene, Links Between AD and CAA

Series - Alzheimer's Association International Conference (AAIC) - 2022:

As the mountain of whole-genome sequencing data grows, so does the likelihood of finding rare variants within it. Such mutations can have stronger associations with disease risk, or resilience, than do common variants, opening a window into the underlying biology.

  • Rare AD variants cluster within TREM2, SORL1, and EXOC3L4, a potential new risk gene.
  • Carriers of rare ABCA7 variants face higher odds of AD pathology, cerebral amyloid angiopathy.
  • Neurons from SORL1 variant carriers show damage in cell culture.

This is true for both known and new risk genes. For example, the rare Christchurch and Jacksonville APOE3 variants, and the newly discovered APOE4 mutation R251G, seem to shield their carriers from cognitive decline (Jun 2022 news; Oct 2021 news; Nov 2019 news).

Trouble is, rare mutations may occur only in one person or a single family. “Among 20,000 people with whole-genome sequencing in the Alzheimer’s Disease Sequencing Project, 54 percent of the single nucleotide polymorphisms are seen only in one individual,” wrote Yann Le Guen, Stanford University, California (full comment below).

How, then, can scientists tell if such variants influence disease? When small sample size makes it hard to know if a given association with disease is meaningful, researchers perform functional or multi-omic analyses, Rita Guerreiro of the Van Andel Institute in Grand Rapids, Michigan, explained. At the Alzheimer’s Association International Conference held from July 31 to August 4 online and in San Diego, California, Guerreiro and Le Guen co-chaired a session on such efforts. Some geneticists looked for groups of rare AD risk variants within genes to identify those linked to disease. Others studied neurons cultured from cells of rare variant carriers, or analyzed anomalies within brain tissue from carriers.

Variant Clumps
One new way to show that rare variants are AD risk factors is to find genes containing many such mutations. “Clusters of rare variants identify novel genes associated with disease, or indicate which domain of a known disease-linked protein may be malfunctioning,” said Guerreiro.

Along those lines, Bowen Jin of Case Western Reserve University, Cleveland, Ohio, searched for groups of rare variants within proteins to pinpoint functional hotspots (Jin et al., 2022). She started with whole-exome sequencing data from about 5,500 AD cases and 5,000 controls in ADSP, identifying more than 1.6 million rare variants within almost 21,000 genes. Jin mapped the variants onto the structures of more than 6,000 proteins in the Protein Data Bank and nearly 17,500 putative protein structures from AlphaFold, an artificial intelligence system that predicts structure from an amino acid sequence.

Of the thousands of proteins scanned, only a handful of the corresponding genes had clusters of rare variants and rose above the threshold of statistical significance, indicating association with AD. Jin then searched for variant hotspots within these genes in the ADSP whole-genome sequencing dataset of 3,700 AD cases and 4,000 controls, an independent cohort from ADSP WES.

Three genes remained statistically significant. Two are known AD risk genes: the microglial membrane receptor TREM2 and the endosomal trafficking protein SORL1. The third, EXOC3L4, is a poorly understood protein predicted to be a component of the exocyst complex, which is involved in vesicle trafficking and exocytosis. In Jin’s analysis, 33 rare variants from cases and controls were found in TREM2, 56 in SORL1, and 68 in EXOC3L4.

Many TREM2 and SORL1 variants carried by people with AD clustered in each protein’s extracellular domain, both of which bind Aβ (see image below; Feb 2015 news). This suggests dysregulation of Aβ handling by both proteins in AD.

Nests of Variants. Rare variants from AD cases (red) pack into the extracellular Aβ-binding domains of TREM2 (top) and SORL1 (middle). The cluster in EXOC3L4 (bottom) dots its Sec6 domain, which may be involved in endocytosis. Rare variants from controls are in blue. [Courtesy of Bowen Jin, Case Western Reserve University.]

Fifteen EXOC3L4 variants in AD cases fell into the C-terminal Sec6 domain (see image). Rare variants within this domain had previously been  linked to cortical glucose metabolism in AD (Miller et al., 2018). Jin thinks EXOC3L4 is a new AD risk gene that warrants further study.

“It is pleasing to see the AD genetics field rapidly expanding beyond traditional GWAS […] to the identification of biologically meaningful rare variants that functionally impact AD pathobiology,” wrote Rudolph Tanzi and Dmitry Prokopenko of Massachusetts General Hospital, Boston (full comment below).

SORL1 Cells
Brooke DeRosa at the University of Miami works on characterizing a rare SORL1 mutation in cell culture (DeRosa et al., 2022). DeRosa derived induced pluripotent stem cells (iPSCs) from two sisters with early onset AD. Each carried a frameshift variant of SORL1 that creates a truncated protein lacking 30 percent of its C-terminus (see Aug 2017 news). This is at the other end of the protein from the extracellular domain with the rare variant cluster reported by Jin.

DeRosa differentiated the iPSCs into neurons, then assessed synaptic trafficking by labeling the cells with the early endosome marker EEA1, a marker for APP, and the synapse marker Synapsin. At AAIC, she showed on a poster that, compared to control neurons, those carrying the SORL1 variant had 33 percent more early endosomes stuffed with APP and 35 percent fewer synapses. This suggests that the SORL1 variant contributes to dysregulated vesicle trafficking and synapse death. Taken together with the dementia diagnoses of its carriers, DeRosa believes that this rare mutation is pathogenic.

Trouble in the Tissue
Beyond cells, brain tissue from rare variant carriers can provide insight into how the mutation affects AD pathology. To this end, Elisabeth Hendrickx Van de Craen of University Hospital Antwerp, Belgium, studied hippocampal and cortical tissue from carriers of rare variants within ABCA7. Mutations in this lipid transporter increase the risk of developing AD (Oct 2020 news; Jul 2020 conference news).

Hendrickx Van de Craen searched for ABCA7 missense mutations and premature stop codons within whole-exome sequences of 491 early onset AD cases, 885 late-onset cases, and 976 controls. Of 102 ABCA7 mutation carriers, 14 had donated brain tissue samples.

Did these rare variant carriers have altered AD pathology? Labeling the tissue with the Aβ antibody 4G8, Hendrickx Van de Craen was struck by the hefty burden of cerebral amyloid angiopathy she saw, which exceeded the mild-to-moderate CAA typically seen in AD.

Curious about a potential genetic link between AD and CAA, Hendrickx Van de Craen searched for rare ABCA7 variants within whole-exome sequences from 90 Belgian people with CAA, then searched for functional mutations in ABCA7. Ten were carriers—more than double the prevalence in the AD cohort. Brain tissue from four carriers showed diffuse 4G8-positive amyloid plaques and AT8-positive neurofibrillary tangles.

Had the CAA ABCA7 carrier cases had cognitive problems late in life? Indeed, seven of the 10 were clinically diagnosed with AD. “ABCA7 rare variant carriers seem to have a spectrum of AD and CAA,” Hendrickx Van de Craen told the audience at AAIC. “Although these two diseases are distinct clinical entities, they may have a common genetic background.”—Chelsea Weidman Burke

Comments

  1. While not all associations have been discovered for common variants, notably in populations from ancestries under-represented in scientific studies, most of the remaining common associations are likely to have a small effect size. This is because the statistical power to identify a significant association at a given threshold depends on the sample size, the frequency of the considered variant, and its effect size.

    Thus, for a fixed sample size and a given statistical significance threshold, the borderline-significant rare variants have a larger effect size than the borderline-significant common variants. This explains partly why the genetic-association field is moving its interest from common to rare variants. 

    An important concept in the field of genetics is the “heritability” of a phenotype, i.e., the proportion of phenotypic variance explained by the genome. For AD, the heritability is estimated to be between 60 and 80 percent with only 10 to 20 percent of it explained by variants identified in AD GWAS (Sierksma et al., 2020). Thus, an important part of this heritability remains to be uncovered. This missing heritability is likely accounted for by rare variants as well as structural variants (SVs) which remain to be genotyped/characterized. 

    The importance of rare variants is emphasized by the fact that in a database the size of ADSP WGS, 54 percent of the SNPs are singletons, meaning that these genetic variants are seen in only one individual among 20,000 with whole-genome sequencing (WGS). This observation also holds in larger whole-genome project such as TOPMed (and gnomAD). 

    Among rare variants, some may have a high impact on the disease, larger than common variants (the APOE-e4 allele being an exception among common variants). Because of this, one can assume that targeting the pathway/ molecular mechanism of some of these rare variants may be therapeutically interesting. For example, the recently discovered protective missense variants on APOE including APOE-Christchurch (R136S), APOE-Jacksonville (V236E), and APOE-R251G are candidate therapeutic targets to reproduce their effects on ApoE with small molecules modifying ApoE conformation. 

    Identifying risk-increasing rare variants is important to understand which genetic pathway is “the most dysregulated” in a given AD brain, and to use a precision-medicine approach to provide the appropriate drug cocktail to each rare variant carrier. This assumes that late-onset AD cases would be caused by such mutations as TREM2, SORL1, or ABCA7. This is also relevant for rare mutation carriers who have autosomal-dominant AD, with mutations on PSEN1, PSEN2, or APP. 

    Most of the genes in which rare variants were recently discovered are known AD genes, but there are also a few new ones that are identified by these rare variants' studies, for example ATB8B4 in Holstege et al., 2022, or EXOC3L4 in Jin et al., 2022. Generally, though, most of these “novel” genes may also have a previous suggestive link in the AD literature (for example for EXOC3L4, Miller et al., 2018). 

    The goal is thus less to identify new genes, but rather to understand how these genes play a role in AD pathogenesis, and to firm up the line of evidence for genes that still need additional replication/validation. 

    Regarding Bowen Jin’s proposed statistical method to evaluate rare missense variants while accounting for their spatial distribution within the protein, this method uses the protein structures information available from PDB (protein data bank) and AlphaFold2 (in silico prediction developed by DeepMind). To my knowledge, this method is novel in the AD field and particularly helps to aggregate singletons that spatially cluster together, as opposed to simply performing a gene-level Burden or SKATO test, or an exon-level test. With an a priori on the spatial organization, one is able to group relevant variants together, and the significant results indicate which domain of the protein may be the most relevant to AD.

    For example, in the case of TREM2, its extracellular domain was emphasized, while for SORL1 the 10CCb subunit was underlined, and for the novel gene EXOC3L4 her study showed that in AD cases rare variants clustered around its SEC6 domain while being depleted for variants seen in cognitively unimpaired controls. This informs future drug developments on which domain of the protein should be targeted to have an impact on the disease.

    References:

    Sierksma A, Escott-Price V, De Strooper B. Translating genetic risk of Alzheimer's disease into mechanistic insight and drug targets. Science. 2020 Oct 2;370(6512):61-66. PubMed.

    Holstege H,Hulsman M,Charbonnier C,Grenier-Boley B,Quenez O,Grozeva D, van Rooij JGJ,Sims R,Ahmad S,Amin N,Norsworthy P,Dols-Icardo O,Hummerich H,Kawalia A,Amouyel P,Beecham GW,Berr C,Bis JC,Boland A,Bossù P,Bouwman FH,Bras J,Campion D,Cochran JN,Daniele A, Dartigues JF,Debette S, Deleuze JF,Denning N,Destefano AL,Farrer LA,Fernandez V, Fox NC,Galimberti D,Génin E,Gille H,Le Guen Y,Guerreiro R,Haines JL,Holmes C,Ikram MA,Ikram MK,Jansen IE,Kraaij R,Lathrop M,Lemstra AW,Lleó AL,Luckcuck L,Marshall R,Martin ER,Masullo C,Mayeux R,Mecocci P,Meggy A,Mol MO,Morgan K,Myers RM,Nacmias B,Naj AC,Napolioni V,Pastor P,Pericak-Vance MA,Raybould R,Redon R,Reinders MJT, Richard AC,Riedel-Heller SG,Rivadeneira F,Rousseau S,Ryan NS,Saad S,Sanchez-Juan P,Schellenberg GD,Scheltens P,Schott JM,Seripa D,Sie D,Sistermans E,Sorbi S, van Spaendonk RML,Spalletta G,Tesi N,Tijms BM,Van Der Lee SJ,Uitterlinden AG,Visser PJ,Wagner M,Wallon D, Wang WM,Cruchaga C,Van Duijn CM,Williams J,Nicolas G,Bellenguez C, Lambert JC. Exome sequencing identifies rare damaging variants in the ATB8B4 and ABCA1 genes as novel risk factors for Alzheimer’s disease. Supplement: Basic Science and Pathogenesis Alzheimer's & Dementia

    Jin B, Capra JA, Benchek P, Wheeler N, Naj AC, Hamilton-Nelson KL, Farrell JJ, Leung YY, Kunkle B, Vadarajan B, Schellenberg GD, Mayeux R, Wang LS, Farrer LA, Pericak-Vance MA, Martin ER, Haines JL, Crawford DC, Bush WS. An association test of the spatial distribution of rare missense variants within protein structures identifies Alzheimer's disease-related patterns. Genome Res. 2022 Apr;32(4):778-790. Epub 2022 Feb 24 PubMed.

    Miller JE, Shivakumar MK, Lee Y, Han S, Horgousluoglu E, Risacher SL, Saykin AJ, Nho K, Kim D, Alzheimer’s Disease Neuroimaging Initiative. Rare variants in the splicing regulatory elements of EXOC3L4 are associated with brain glucose metabolism in Alzheimer's disease. BMC Med Genomics. 2018 Sep 14;11(Suppl 3):76. PubMed.

    View all comments by Yann Le Guen

  2. We are pleased to see the AD genetics field rapidly expanding beyond traditional GWAS—which reveal common variants with highly significant genetic association with AD, but with usually no effects on gene function or AD pathobiology—to the identification of biologically meaningful rare variants that functionally impact AD pathobiology. GWAS using common variants (minor frequency > 0.01) are very useful for identifying novel AD loci containing one or more AD gene candidates (see Bertram and Tanzi, 2019). However, systematic whole-genome sequencing (WGS) and whole-exome sequencing (WES)-based GWAS are needed to identify rare functional variants that can be biologically tested for actual functional roles in AD neuropathogenesis (Bertram and Tanzi, 2019). 

    The growing use of WGS and WES data to identify rare functional variants associated with AD has been challenging, because GWAS with rare variants (minor frequency < 0.01 and usually < 0.001) most often do not generate the genome-wide significant p-values (p < 5 X 10e-8) that are usually necessary for publication in high-impact genetics journals. Fortunately, the AD field is seeing an increasing number of studies identifying and validating rare functional variants in both established and candidate AD genes, now being published in relatively high-impact journals, e.g., Science Translational Medicine, Alzheimer’s and Dementia, J. Experimental Medicine, and Molecular Psychiatry. 

    AD genetic studies focusing on rare functional variants generally fall into three categories:

    1. Studies that search for rare functional variants, e.g., missense mutations, insertion/deletions, splice variants, frame shifts, and early stop codons, in known and established AD genes, e.g. Bossaerts et al., 2022; Zhang et al., 2021).

    2. Studies that are agnostic to previously identified known and candidate AD genes, and systematically implement WGS/WES rare variants as input for GWAS to identify AD-associated rare functional variants in novel (or known) AD genes, e.g. Sims et al., 2017; Jin et al., 2022; Prokopenko et al., 2022. 

    3. Studies that search for rare functional variants in novel hypothesis-driven AD candidate genes, which include functional validation studies of the rare variants identified, e.g., Lomoio et al., 2020. 

    An example of a category 1 study is the elegant recent study by Bossaerts et al. (2022), in which the authors searched for rare functional variants in the well-established AD gene ABCA7. Using a Belgian AD and control cohort, Bossaerts et al. explored the pathogenetic effects of 10 missense mutations on protein localization in vitro using immunocytochemistry, and demonstrated that those functional variants can contribute to AD by inducing protein mislocalization, resulting in a lack of functional protein at the plasma membrane. 

    An example of a category 2 study is a very interesting recent study by Jin et al. (2022) that implemented a protein structure–based approach and applied it to ADSP WES data to identify AD-associated spatially-grouped clusters of rare variants. Using a kernel matrix, which is based on spatial proximity of exonic variants in the three-dimensional structure of the corresponding protein, they performed a systematic analysis of groups of variants within protein-coding genes, where the corresponding protein has an existing three-dimensional structure in public databases. They identified three genes in this screen: TREM2 and SORL1 (previously identified by GWAS) and EXOC3L4 (a novel AD candidate gene). While the authors report that the signal for TREM2 and SORL1 is stable even if they remove known AD-associated variants, it remains to be shown that the known AD-associated variants and novel rare variants are contained in different haplotypes.

    In other examples of category 2 studies, our laboratory has been pursuing alternative methods for increasing statistical power of rare variant testing, implementing different grouping strategies. We have developed such methods (Loehlein Fier et al., 2017) and applied them to family based and case-control WGS AD cohorts (Prokopenko et al., 2021), using a spatial-clustering approach, which groups variants together based on their proximity along the genome. Using this region-based approach, we have identified clusters of rare variants in loci implicating these novel AD candidate genes: FNBP1L, SEL1L, LINC00298, PRKCH, C15ORF41, C2CD3, KIF2A, APC, LHX9, NALCN, CTNNA2, SYTL3, and CLSTN2. In another WGS study using nonoverlapping consecutive sets of rare variants across the whole genome and a newly developed exact framework for family based association studies, we identified two novel Alzheimer’s disease-associated genes: DTNB and DLG2 (Prokopenko et al., 2022). It is important to mention that rare variants in DTNB were found to be associated with AD CSF biomarker levels in an independent study (Neumann et al., 2022). 

    In summary, WGS and WES AD datasets are increasingly being implemented to render the field of AD genetics more amenable to biological studies assessing the roles of novel rare functional variants in the neuropathogenesis of AD. Moving beyond traditional common-variant-based GWAS to WGS/WES-based GWAS aimed at identifying rare functional variants in established and novel candidate AD genes, will hopefully accelerate the translation of AD genetic data into meaningful biological data and a better understanding of AD and how to treat this devastating disorder.

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    Bertram L, Tanzi RE. Alzheimer disease risk genes: 29 and counting. Nat Rev Neurol. 2019 Apr;15(4):191-192. PubMed.

    Bossaerts L, Hendrickx Van de Craen E, Cacace R, Asselbergh B, Van Broeckhoven C. Rare missense mutations in ABCA7 might increase Alzheimer's disease risk by plasma membrane exclusion. Acta Neuropathol Commun. 2022 Mar 31;10(1):43. PubMed.

    Zhang X, Zhang CM, Prokopenko D, Liang Y, Zhen SY, Weigle IQ, Han W, Aryal M, Tanzi RE, Sisodia SS. An APP ectodomain mutation outside of the Aβ domain promotes Aβ production in vitro and deposition in vivo. J Exp Med. 2021 Jun 7;218(6) PubMed.

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    Prokopenko D, Lee S, Hecker J, Mullin K, Morgan S, Katsumata Y, Alzheimer’s Disease Neuroimaging Initiative (ADNI), Weiner MW, Fardo DW, Laird N, Bertram L, Hide W, Lange C, Tanzi RE. Region-based analysis of rare genomic variants in whole-genome sequencing datasets reveal two novel Alzheimer's disease-associated genes: DTNB and DLG2. Mol Psychiatry. 2022 Apr;27(4):1963-1969. Epub 2022 Mar 4 PubMed.

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References

News Citations

  1. Two ApoE Mutations Decrease Risk for Alzheimer's Disease
  2. Protective APOE3 Variant Binds More Lipids, Self-Aggregates Less
  3. Can an ApoE Mutation Halt Alzheimer’s Disease?
  4. Voilà SorLA! Sorting Receptor’s Structure Solved
  5. The Search for the Missing AD Heritability Turns Up New Rare Variants
  6. Largest Alzheimer GWAS in African Americans Finds New Variants
  7. Doubling Down on Sequencing Serves up More Alzheimer’s Genes

Mutation Interactive Images Citations

  1. TREM2

Alzpedia Citations

  1. SORLA (SORL1)

Paper Citations

  1. Jin B, Capra JA, Benchek P, Wheeler N, Naj AC, Hamilton-Nelson KL, Farrell JJ, Leung YY, Kunkle B, Vadarajan B, Schellenberg GD, Mayeux R, Wang LS, Farrer LA, Pericak-Vance MA, Martin ER, Haines JL, Crawford DC, Bush WS. An association test of the spatial distribution of rare missense variants within protein structures identifies Alzheimer's disease-related patterns. Genome Res. 2022 Apr;32(4):778-790. Epub 2022 Feb 24 PubMed.
  2. Miller JE, Shivakumar MK, Lee Y, Han S, Horgousluoglu E, Risacher SL, Saykin AJ, Nho K, Kim D, Alzheimer’s Disease Neuroimaging Initiative. Rare variants in the splicing regulatory elements of EXOC3L4 are associated with brain glucose metabolism in Alzheimer's disease. BMC Med Genomics. 2018 Sep 14;11(Suppl 3):76. PubMed.
  3. DeRosa BA, Simon SA, Velez CA, Vance JM, Pericak-Vance MA, Dykxhoorn DM. Generation of two iPSC lines (UMi038-A & UMi039-A) from siblings bearing an Alzheimer's disease-associated variant in SORL1. Stem Cell Res. 2022 Jul;62:102823. Epub 2022 May 30 PubMed.

External Citations

  1. Protein Data Bank
  2. AlphaFold

Further Reading

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