Geneticists continue to plumb the genome for clues to AD risk. New variants presented at this year’s Alzheimer’s Association International Conference, held in London July 16-20, reinforce the central role of microglia in AD and link new genes to traits such as Aβ and tau pathology or metabolic dysfunction early in the process.
Large genome-wide association studies (GWAS) have identified about 27 susceptibility loci for late-onset AD (LOAD), implicating diverse biological functions, such as immune responses, cholesterol transport, endocytosis, ubiquitination, and protein folding pathways (see Apr 2011 news; Jul 2013 conference news; International Genomics of Alzheimer's Disease Consortium (IGAP), 2015; June 2017 news). Those variants account for 30–40 percent of the estimated 58–76 percent heritability of LOAD, claimed Julie Williams from Cardiff University in Wales. Geneticists have since adopted other methods to identify the rest. In London, Williams described how she, with eight other principal investigators and more than 450 scientists, used exome genotyping to find rare variants associated with LOAD. They went undetected in GWAS, which generally identify only common variants.
Finding rare variants presents challenges. “In an ideal world, one would sequence the complete genomes of maybe hundreds of thousands of individuals,” Williams told Alzforum. Since that would require more time and money than genetics has at the moment, Rebecca Sims, also at Cardiff, Sven van der Lee of the Erasmus Medical Center in Rotterdam, the Netherlands, Adam Naj of the University of Pennsylvania in Philadelphia, and Céline Bellenguez of the INSERM in Lille, France, decided to focus on the exome. Using a microarray enriched in rare coding variants, they genotyped samples from the approximately 85,000 people covered in the International Genomics of Alzheimer's Project (IGAP). “The approach is notable for its very large sample size,” wrote Johnathan Cooper-Knock from the University of Sheffield in England (see full comment below). The results were recently published in Nature Genetics (Sims et al., 2017).
The researchers used the Illumina HumanExome BeadChip, which includes nearly 250,000 variants of which roughly 75 percent have minimum allele frequencies of less than 0.5 percent. Co-author Rita Guerreiro from University College London told Alzforum that this was the first time this type of chip had been used in AD studies. Sims and colleagues identified 43 candidate AD variants, excluding known risk loci, after genotyping about 16,000 LOAD cases and 18,000 cognitively normal, elderly controls from IGAP. The researchers validated these initial hits in two additional cohorts, also from IGAP. One had 14,000 cases and 22,000 controls, the other 6,000 cases and 8,000 controls. Sims and colleagues tracked the 43 candidates found in the first analysis and used imputation to look for the variants in samples that had been previously genotyped for common variants. Because DNA is inherited in blocks, imputation allows geneticists to predict the presence of alleles based on co-inheritance of other variants nearby. In this case, they imputed based on reference genotypes from the Haplotype Reference Consortium, which includes nearly 65,000 haplotypes covering close to 40 million single nucleotide polymorphisms (SNPs).
The results revealed three new non-synonymous coding substitutions associated with LOAD in the phospholipase C γ2 gene (PLCG2), the ABI family 3 gene (ABI3), and TREM2, a known susceptibility gene for AD. All genes are highly expressed in microglia. While the PLCG2 variant, an arginine for proline at position 522, was protective, the ABI3 Ser209Phe and TREM Arg62His variants were associated with increased AD risk.
“This is exciting news—these researchers provide more evidence for a causative role of microglial dysfunction in AD,” commented Christian Haass and Gernot Kleinberger of the German Center for Neurodegenerative Diseases in Munich. They noted TREM2 variants are risk factors for other neurodegenerative diseases as well. “Microglia are thus clearly not simple bystanders or only secondary troublemakers,” they wrote (see full comment below).
All three genes have a similar expression profile in the human cortex, said Williams: high in microglia, low in neurons, oligodendrocytes, astrocytes, and endothelial cells. The findings align well with a raft of recent studies implicating microglia in AD. Indeed, a GWAS published last month found that among the 112 genes lying within AD-associated loci, 60 were expressed in human microglia and contained binding sites for the master regulator of microglial function and identity, PU.1 (Jun 2017 news).
Williams’ lab is exploring how the new PLCγ2 variant might protect. PLCγ2 is a transmembrane signaling enzyme that generates two second messengers. Myoinositol 1,4,5-trisphosphate (IP3) regulates cytoplasmic calcium levels, and diacyl glycerol initiates the NF-κB and mitogen-activated protein kinase (MAPK) signaling pathways. To dissect how the Pro522Arg substitution alters its functions, Georgina Menzies in Williams’ lab modeled the molecular structure of three parts of the protein. In her poster, she showed that the overall structure and flexibility of the protein appears to remain unchanged, despite the removal of a proline, which often introduces kinks into protein backbones. Nevertheless, Menzies showed how the positively-charged arginine resides in a loop on the edge of the enzyme’s active site. Because it can attract surrounding negatively charged amino acids, the arginine likely changes the structure of the loop. Menzies said the loop could partially cover the entrance for the substrate and affect catalysis. The researchers will track how the variant affects calcium dynamics in cultured cells. PLCγ2 might also be linked to TREM2. Gary Landreth of Indiana University School of Medicine in Indianapolis noted that the TREM2 binding partner DAP12 activates the phospholipase in osteoclasts (Mao et al., 2006).
ABI3 is an adaptor protein. It forms part of a complex that regulates actin polymerization. Although mostly studied in T cells, in the brain it is predominantly expressed in microglia. How the AD risk variant alters ABI3 function is unclear. Haass said he wanted to investigate this. “ABI3 activities may be related to cytoskeletal rearrangement and the formation of membrane protrusions,” he and Kleinberger wrote. “It is therefore tempting to speculate that its dysfunction may interfere with a central function of TREM2 in chemotaxis and phagocytosis (Mazaheri et al., 2017; Kleinberger et al., 2014).”
TREM2 is a microglial transmembrane receptor. Previous studies have shown that loss-of-function variants reduce microglial phagocytosis, impair lipid sensing, prevent binding of lipoproteins, and disrupt microglial chemotaxis (Apr 2017 conference news). Sims and colleagues revealed not only the new Arg62His variant, but also confirmed the previously reported Arg47His variant. The exome analysis hinted at the existence of additional risk variants in this gene.
Connecting the Dots
While TREM2, ABI3, and PLCG2 might at first glance seem unrelated, Peter Holmans, also at Cardiff University, found they are part of a single protein-protein interaction network. Holmans discovered the network by looking for interactions between proteins related to GWAS hits. He used data from a previous study that mapped GWAS risk variants to clusters of co-expressed genes found in the brains of healthy individuals (International Genomics of Alzheimer's Disease Consortium (IGAP), 2015). Four of the 117 clusters found were enriched with AD GWAS genes. A set of 151 genes captured this GWAS signal.
Microglial AD Network? An interaction network of 56 proteins is enriched by genes harboring common and rare variants associated with AD (in boldface). It includes the three new exome hits. [Courtesy of Sims et al., Nature Genetics.]
Holmans then used high-confidence human protein-protein interaction data to see if proteins encoded by the 151 genes formed a network. In step-wise fashion, he used one protein as a start and looked to see how each of the other 150 might link to it, then how each of the remaining 149 might link to that, and so on. He reiterated this exercise 151 times, using each protein as a hub. At the end of this analysis, one network stood out for its size. It contained 56 proteins—more than would occur by chance. Surprisingly, it included TREM2, PLCG2, and ABI3 (see image above). It also contained a list of microglia-related genes that have been genetically linked to AD. They are two master regulators of microglial function, SPI1, aka PU.1, and TYROBP, aka DAP12 (Zhang et al., 2013; May 2013 Alzforum webinar); SYK, a signaling protein downstream of TREM2/DAP12 that controls PLCγ2 activity (Xing et al., 2015; Paris et al., 2014); INPP5D, which forms a complex that regulates SYK; and CD33, which interacts with TREM2 and promotes microglial phagocytosis of Aβ processing (Aug 2013 news; Oct 2015 news). The network also includes CSF1R, a master regulator of microglial proliferation. Curiously, pathway databases, such as Gene Ontology or the Kyoto Encyclopedia of Genes and Genomes, don’t link ABI3 to these genes. Furthermore, because the co-expression and protein interaction data are derived from healthy controls, the clusters of correlated genes and the protein network cannot have arisen as consequences of neurodegeneration, noted Holmans. This, plus its enrichment with AD risk genes, indicates that the network is consistent with microglial responses in LOAD being directly involved in disease, rather than simply a downstream consequence of neurodegeneration, he said.
Williams thinks the new TREM2, AIB3, and PCLG2 variants found in the exome search account for a small portion of AD’s missing heritability. The authors speculate that the remainder may reside anywhere in the genome. There could be common variants of small effect size, rare variants found in other exons, even rarer variants that may only be identified by analysis of larger cohorts, and variants within introns and intergenic sites. Researchers at AAIC asked about additional variants in other populations. Williams agreed this was important, as IGAP includes mostly Caucasians of European descent. “Looking at other populations will help us understand AD mechanisms and help refine risk predictions,” she said.
Exactly how much of AD’s heritability remains to be found is subject to debate. A recent study from John Hardy and colleagues at University College London, concluded that a polygenic score based on known AD variants predicts 84 percent of the risk for AD, which comes close to the concordance seen in studies of twins, said Hardy (Escott-Price et al., 2017). These authors concluded that, though rare variants are still likely to be found, studies would be well advised to focus on targeted sequencing of known AD pathways and on cell and animal experiments to further delineate those pathways.
Other Methods to Find AD Variants
Geneticists are inventing new ways to hunt AD variants that went undetected in GWAS and might shed light on AD pathogenesis. Many labs are searching for polymorphisms tied to specific quantitative traits. As Yuetiva Deming from Carlos Cruchaga’s group at Washington University, St. Louis, pointed out, GWAS identify risk variants but say nothing about how that risk manifests. Finding variants that associate with specific endophenotypes could be extremely informative, she said. Leigh Christopher, who works with Michael Greicius at Stanford University, California, agreed. “We are not only interested in causative genes, but in those that modify the course of the disease or the rate of decline, she told Alzforum. “We can’t necessarily find those genes in case-control GWAS,” she said.
Deming and colleagues ran a GWAS to find variants linked to CSF Aβ, tau, or phosphorylated tau, using data from more than 3,000 people in nine different cohorts. About half of them were women, the average age was 72. Deming and colleagues found four variants for CSF tau or p-tau and two for Aβ. The former lie near the genes GMNC, GLIS3, PCDH8, and NFATC1, while the latter lie near the GLIS1 and SERPINB1 genes (see Deming et al., 2017).
Deming used independent data sets to test whether these variants also associated with AD, age of disease onset, or progression. GLIS1 popped out in the AD risk and progression analysis, while SERPINB1 associated with age at onset. Deming said that the GLIS1 locus might alter expression of the gene SLCA17, while the SERPINB1 variant, which lies in an intron, seems to alter its own expression. SERPINB1 encodes an elastase found in immune cells; Deming thinks the variant might control levels of the enzyme in macrophages, perhaps explaining its association with amyloid accumulation.
Also trying to tease apart genetic links to tau and Aβ, Jaeyoon Chung from Lindsay Farrer’s lab at Boston University took a so-called bivariant approach, asking if genetic polymorphisms associated with any two of plaques, tangles, or cerebral amyloid angiopathy. “We thought we might find variants with pleiotropic effects,” he said.
Chung built on a recent univariant analysis that used data from the Alzheimer's Disease Genetics Consortium to find SNPs that associated with AD neuropathology among 3,135 people, 463 of whom were healthy controls (Beecham et al., 2014). Chung found a variant upstream of the ECRG4 gene that associated with plaques and tangles, and another in the HDAC9 gene that associated with tangles and CAA.
How the ECRG4 gene might be involved in AD pathology is unclear, but Chung noted that RNAseq analysis of brain samples from the Mayo Clinic hints that the risk allele reduces expression of the gene. It has also been reported to be suppressed in brain injury, he said.
An AD link to HDAC9 seemed more plausible, since the gene associated with other neurological conditions, as well, including stroke and schizophrenia. Prior data suggest it protects against neuronal apoptosis, said Chung. HDAC9 inhibits expression of MEF2C, another AD risk gene (Jul 2013 conference news). Chung presented expression quantitative trait loci analysis that suggests the HDAC9 risk allele reduces expression of the gene in the brain. Analyzing postmortem brain samples, he found lower HDAC9 expression in the prefrontal and visual cortices of AD patients than controls, and lower expression to correlate with Braak stages.
Emrin Horgusluoglu in Andrew Saykin’s lab at Indiana University School of Medicine, Indianapolis, took a different tack. She ran GWAS of ADNI samples, then applied a gene set enrichment analysis called GSA-SNP to identify functional pathways associated with tau accumulation (Nam et al. 2010). From 1,200 volunteers with either CSF tau/p-tau or tau PET data, she identified 39 pathways. Fifteen were related to neurogenesis. Horgusluoglu then used gene-based association analysis to pull out specific genes in the pathways that correlated with tau levels. Four genes fit the bill: APOE, PVRL2, APOC4, and MAP3K10.
Tweaking Tau. Gene set enrichment analysis points to pathways that influence accumulation of tau or p-tau in the CSF. [Image courtesy of Ermin Horgusluoglu and AAIC 2017.]
In her presentation, Stanford’s Christopher reported a variant that associated with glucose hypometabolism in the posterior cingulate cortex, an early marker of AD. She found a single SNP, rs2273647, among 606 participants in the ADNI study. The SNP lies in the SMEK1 gene, which encodes regulatory subunit 3a of protein phosphatase 4 (PP4R3a). The minor T allele seems protective, associating with less hypometabolism than seen in FDG PET scans. People with two T alleles metabolized glucose normally.
Looking further, the researchers found that the polymorphism protected people with mild cognitive impairment from progressing to Alzheimer’s dementia. Carriers of the C/T or T/T genotype were at lower risk of progressing. The T allele also slowed cognitive decline in those with AD. Carriers performed better over time than noncarriers on the MMSE and in the Boston naming task.
How does this variant protect? It may alter expression of the phosphatase subunit, Christopher said. She reported that healthy controls make less of one mRNA isoform than AD patients do. Among healthy controls, T allele carriers also make less of this isoform than noncarriers. However, among people with AD, carriers and noncarriers appear to express the gene equally well. “I think this suggests that something turns on the gene in the disease state,” suggested Christopher.
Given that glucose hypometabolism is not unique to AD, others at the meeting asked if the variant might protect against other neurodegenerative diseases as well. Christopher considers this plausible. Some scientists found it curious that the effect of a second T allele was additive in the FDG PET analysis but not the cognitive testing, and Christopher agreed. “There is definitely an additive effect in imaging and a dominant effect for cognitive traits, but we are not sure why,” she said.
Chloe Sarnowski from Boston University focused on different imaging endophenotypes. Working with Sudha Seshadri and colleagues at BU and at the University of California, Davis, Sarnowski used whole-genome sequence analysis to look for variants and genes that associate with total cerebral volume, hippocampal volume, or white-matter hyperintensities (WMH). She mined data from Trans-Omics for Precision Medicine (TOPMed), an NHLBI program, to identify genetic variants and other factors that associated with vascular disorders.
Variants and Volume. WGS analysis uncovers variants in chromosomes 1 and 16 that associate with total cerebral volume (top) and hippocampal volume (bottom), respectively.
Sarnowski found previously reported variants at chromosome 12q24 and 17q25 that associated with hippocampal volume and WMHs, respectively. She also detected two new variants. From 2,180 TOPMed samples, she found a 1p21 variant that associated with total cerebral volume, and from 2,170 samples a 16q23 variant that associated with hippocampal volume (see image above). Sarnowski found about 10 other genes that almost reached statistical significance, including the GBA3 gene associated with Parkinson’s and the UNC5D gene that has been linked to AD. Those genes appeared to be enriched in immunity, inflammation, and related AD and PD pathways.—Marina Chicurel and Tom Fagan
- Large Genetic Analysis Pays Off With New AD Risk Genes
- Pooled GWAS Reveals New Alzheimer’s Genes and Pathways
- Microglial Master Regulator Tunes AD Risk Gene Expression, Age of Onset
- New Evidence Confirms TREM2 Binds Aβ, Drives Protective Response
- Protective Microglial Gene Variant Promotes Phagocytosis
- Alzheimer’s Risk Genes Interact in Immune Cells
- International Genomics of Alzheimer's Disease Consortium (IGAP). Convergent genetic and expression data implicate immunity in Alzheimer's disease. Alzheimers Dement. 2015 Jun;11(6):658-71. Epub 2014 Dec 20 PubMed.
- Sims R, van der Lee SJ, Naj AC, Bellenguez C, Badarinarayan N, Jakobsdottir J, Kunkle BW, Boland A, Raybould R, Bis JC, Martin ER, Grenier-Boley B, Heilmann-Heimbach S, Chouraki V, Kuzma AB, Sleegers K, Vronskaya M, Ruiz A, Graham RR, Olaso R, Hoffmann P, Grove ML, Vardarajan BN, Hiltunen M, Nöthen MM, White CC, Hamilton-Nelson KL, Epelbaum J, Maier W, Choi SH, Beecham GW, Dulary C, Herms S, Smith AV, Funk CC, Derbois C, Forstner AJ, Ahmad S, Li H, Bacq D, Harold D, Satizabal CL, Valladares O, Squassina A, Thomas R, Brody JA, Qu L, Sánchez-Juan P, Morgan T, Wolters FJ, Zhao Y, Garcia FS, Denning N, Fornage M, Malamon J, Naranjo MC, Majounie E, Mosley TH, Dombroski B, Wallon D, Lupton MK, Dupuis J, Whitehead P, Fratiglioni L, Medway C, Jian X, Mukherjee S, Keller L, Brown K, Lin H, Cantwell LB, Panza F, McGuinness B, Moreno-Grau S, Burgess JD, Solfrizzi V, Proitsi P, Adams HH, Allen M, Seripa D, Pastor P, Cupples LA, Price ND, Hannequin D, Frank-García A, Levy D, Chakrabarty P, Caffarra P, Giegling I, Beiser AS, Giedraitis V, Hampel H, Garcia ME, Wang X, Lannfelt L, Mecocci P, Eiriksdottir G, Crane PK, Pasquier F, Boccardi V, Henández I, Barber RC, Scherer M, Tarraga L, Adams PM, Leber M, Chen Y, Albert MS, Riedel-Heller S, Emilsson V, Beekly D, Braae A, Schmidt R, Blacker D, Masullo C, Schmidt H, Doody RS, Spalletta G, Jr WT, Fairchild TJ, Bossù P, Lopez OL, Frosch MP, Sacchinelli E, Ghetti B, Yang Q, Huebinger RM, Jessen F, Li S, Kamboh MI, Morris J, Sotolongo-Grau O, Katz MJ, Corcoran C, Dunstan M, Braddel A, Thomas C, Meggy A, Marshall R, Gerrish A, Chapman J, Aguilar M, Taylor S, Hill M, Fairén MD, Hodges A, Vellas B, Soininen H, Kloszewska I, Daniilidou M, Uphill J, Patel Y, Hughes JT, Lord J, Turton J, Hartmann AM, Cecchetti R, Fenoglio C, Serpente M, Arcaro M, Caltagirone C, Orfei MD, Ciaramella A, Pichler S, Mayhaus M, Gu W, Lleó A, Fortea J, Blesa R, Barber IS, Brookes K, Cupidi C, Maletta RG, Carrell D, Sorbi S, Moebus S, Urbano M, Pilotto A, Kornhuber J, Bosco P, Todd S, Craig D, Johnston J, Gill M, Lawlor B, Lynch A, Fox NC, Hardy J, ARUK Consortium, Albin RL, Apostolova LG, Arnold SE, Asthana S, Atwood CS, Baldwin CT, Barnes LL, Barral S, Beach TG, Becker JT, Bigio EH, Bird TD, Boeve BF, Bowen JD, Boxer A, Burke JR, Burns JM, Buxbaum JD, Cairns NJ, Cao C, Carlson CS, Carlsson CM, Carney RM, Carrasquillo MM, Carroll SL, Diaz CC, Chui HC, Clark DG, Cribbs DH, Crocco EA, DeCarli C, Dick M, Duara R, Evans DA, Faber KM, Fallon KB, Fardo DW, Farlow MR, Ferris S, Foroud TM, Galasko DR, Gearing M, Geschwind DH, Gilbert JR, Graff-Radford NR, Green RC, Growdon JH, Hamilton RL, Harrell LE, Honig LS, Huentelman MJ, Hulette CM, Hyman BT, Jarvik GP, Abner E, Jin LW, Jun G, Karydas A, Kaye JA, Kim R, Kowall NW, Kramer JH, LaFerla FM, Lah JJ, Leverenz JB, Levey AI, Li G, Lieberman AP, Lunetta KL, Lyketsos CG, Marson DC, Martiniuk F, Mash DC, Masliah E, McCormick WC, McCurry SM, McDavid AN, McKee AC, Mesulam M, Miller BL, Miller CA, Miller JW, Morris JC, Murrell JR, Myers AJ, O'Bryant S, Olichney JM, Pankratz VS, Parisi JE, Paulson HL, Perry W, Peskind E, Pierce A, Poon WW, Potter H, Quinn JF, Raj A, Raskind M, Reisberg B, Reitz C, Ringman JM, Roberson ED, Rogaeva E, Rosen HJ, Rosenberg RN, Sager MA, Saykin AJ, Schneider JA, Schneider LS, Seeley WW, Smith AG, Sonnen JA, Spina S, Stern RA, Swerdlow RH, Tanzi RE, Thornton-Wells TA, Trojanowski JQ, Troncoso JC, Van Deerlin VM, Van Eldik LJ, Vinters HV, Vonsattel JP, Weintraub S, Welsh-Bohmer KA, Wilhelmsen KC, Williamson J, Wingo TS, Woltjer RL, Wright CB, Yu CE, Yu L, Garzia F, Golamaully F, Septier G, Engelborghs S, Vandenberghe R, De Deyn PP, Fernadez CM, Benito YA, Thonberg H, Forsell C, Lilius L, Kinhult-Stählbom A, Kilander L, Brundin R, Concari L, Helisalmi S, Koivisto AM, Haapasalo A, Dermecourt V, Fievet N, Hanon O, Dufouil C, Brice A, Ritchie K, Dubois B, Himali JJ, Keene CD, Tschanz J, Fitzpatrick AL, Kukull WA, Norton M, Aspelund T, Larson EB, Munger R, Rotter JI, Lipton RB, Bullido MJ, Hofman A, Montine TJ, Coto E, Boerwinkle E, Petersen RC, Alvarez V, Rivadeneira F, Reiman EM, Gallo M, O'Donnell CJ, Reisch JS, Bruni AC, Royall DR, Dichgans M, Sano M, Galimberti D, St George-Hyslop P, Scarpini E, Tsuang DW, Mancuso M, Bonuccelli U, Winslow AR, Daniele A, Wu CK, GERAD/PERADES, CHARGE, ADGC, EADI, Peters O, Nacmias B, Riemenschneider M, Heun R, Brayne C, Rubinsztein DC, Bras J, Guerreiro R, Al-Chalabi A, Shaw CE, Collinge J, Mann D, Tsolaki M, Clarimón J, Sussams R, Lovestone S, O'Donovan MC, Owen MJ, Behrens TW, Mead S, Goate AM, Uitterlinden AG, Holmes C, Cruchaga C, Ingelsson M, Bennett DA, Powell J, Golde TE, Graff C, De Jager PL, Morgan K, Ertekin-Taner N, Combarros O, Psaty BM, Passmore P, Younkin SG, Berr C, Gudnason V, Rujescu D, Dickson DW, Dartigues JF, DeStefano AL, Ortega-Cubero S, Hakonarson H, Campion D, Boada M, Kauwe JK, Farrer LA, Van Broeckhoven C, Ikram MA, Jones L, Haines JL, Tzourio C, Launer LJ, Escott-Price V, Mayeux R, Deleuze JF, Amin N, Holmans PA, Pericak-Vance MA, Amouyel P, van Duijn CM, Ramirez A, Wang LS, Lambert JC, Seshadri S, Williams J, Schellenberg GD. Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease. Nat Genet. 2017 Sep;49(9):1373-1384. Epub 2017 Jul 17 PubMed.
- Mao D, Epple H, Uthgenannt B, Novack DV, Faccio R. PLCgamma2 regulates osteoclastogenesis via its interaction with ITAM proteins and GAB2. J Clin Invest. 2006 Nov;116(11):2869-79. Epub 2006 Oct 19 PubMed.
- Mazaheri F, Snaidero N, Kleinberger G, Madore C, Daria A, Werner G, Krasemann S, Capell A, Trümbach D, Wurst W, Brunner B, Bultmann S, Tahirovic S, Kerschensteiner M, Misgeld T, Butovsky O, Haass C. TREM2 deficiency impairs chemotaxis and microglial responses to neuronal injury. EMBO Rep. 2017 Jul;18(7):1186-1198. Epub 2017 May 8 PubMed.
- Kleinberger G, Yamanishi Y, Suárez-Calvet M, Czirr E, Lohmann E, Cuyvers E, Struyfs H, Pettkus N, Wenninger-Weinzierl A, Mazaheri F, Tahirovic S, Lleó A, Alcolea D, Fortea J, Willem M, Lammich S, Molinuevo JL, Sánchez-Valle R, Antonell A, Ramirez A, Heneka MT, Sleegers K, van der Zee J, Martin JJ, Engelborghs S, Demirtas-Tatlidede A, Zetterberg H, Van Broeckhoven C, Gurvit H, Wyss-Coray T, Hardy J, Colonna M, Haass C. TREM2 mutations implicated in neurodegeneration impair cell surface transport and phagocytosis. Sci Transl Med. 2014 Jul 2;6(243):243ra86. PubMed.
- Zhang B, Gaiteri C, Bodea LG, Wang Z, McElwee J, Podtelezhnikov AA, Zhang C, Xie T, Tran L, Dobrin R, Fluder E, Clurman B, Melquist S, Narayanan M, Suver C, Shah H, Mahajan M, Gillis T, Mysore J, MacDonald ME, Lamb JR, Bennett DA, Molony C, Stone DJ, Gudnason V, Myers AJ, Schadt EE, Neumann H, Zhu J, Emilsson V. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell. 2013 Apr 25;153(3):707-20. PubMed.
- Xing J, Titus AR, Humphrey MB. The TREM2-DAP12 signaling pathway in Nasu-Hakola disease: a molecular genetics perspective. Res Rep Biochem. 2015;5:89-100. Epub 2015 Mar 17 PubMed.
- Paris D, Ait-Ghezala G, Bachmeier C, Laco G, Beaulieu-Abdelahad D, Lin Y, Jin C, Crawford F, Mullan M. The spleen tyrosine kinase (Syk) regulates Alzheimer amyloid-β production and Tau hyperphosphorylation. J Biol Chem. 2014 Dec 5;289(49):33927-44. Epub 2014 Oct 20 PubMed.
- Escott-Price V, Myers AJ, Huentelman M, Hardy J. Polygenic risk score analysis of pathologically confirmed Alzheimer disease. Ann Neurol. 2017 Aug;82(2):311-314. Epub 2017 Aug 9 PubMed.
- Deming Y, Li Z, Kapoor M, Harari O, Del-Aguila JL, Black K, Carrell D, Cai Y, Fernandez MV, Budde J, Ma S, Saef B, Howells B, Huang KL, Bertelsen S, Fagan AM, Holtzman DM, Morris JC, Kim S, Saykin AJ, De Jager PL, Albert M, Moghekar A, O'Brien R, Riemenschneider M, Petersen RC, Blennow K, Zetterberg H, Minthon L, Van Deerlin VM, Lee VM, Shaw LM, Trojanowski JQ, Schellenberg G, Haines JL, Mayeux R, Pericak-Vance MA, Farrer LA, Peskind ER, Li G, Di Narzo AF, Alzheimer’s Disease Neuroimaging Initiative (ADNI), Alzheimer Disease Genetic Consortium (ADGC), Kauwe JS, Goate AM, Cruchaga C. Genome-wide association study identifies four novel loci associated with Alzheimer's endophenotypes and disease modifiers. Acta Neuropathol. 2017 May;133(5):839-856. Epub 2017 Feb 28 PubMed.
- Beecham GW, Hamilton K, Naj AC, Martin ER, Huentelman M, Myers AJ, Corneveaux JJ, Hardy J, Vonsattel JP, Younkin SG, Bennett DA, De Jager PL, Larson EB, Crane PK, Kamboh MI, Kofler JK, Mash DC, Duque L, Gilbert JR, Gwirtsman H, Buxbaum JD, Kramer P, Dickson DW, Farrer LA, Frosch MP, Ghetti B, Haines JL, Hyman BT, Kukull WA, Mayeux RP, Pericak-Vance MA, Schneider JA, Trojanowski JQ, Reiman EM, Alzheimer's Disease Genetics Consortium (ADGC), Schellenberg GD, Montine TJ. Genome-wide association meta-analysis of neuropathologic features of Alzheimer's disease and related dementias. PLoS Genet. 2014 Sep;10(9):e1004606. Epub 2014 Sep 4 PubMed.
- Nam D, Kim J, Kim SY, Kim S. GSA-SNP: a general approach for gene set analysis of polymorphisms. Nucleic Acids Res. 2010 Jul;38(Web Server issue):W749-54. Epub 2010 May 25 PubMed.
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