Literature DB >> 26654793

Role of ABCA7 loss-of-function variant in Alzheimer's disease: a replication study in European-Americans.

Jorge L Del-Aguila1,2, Maria Victoria Fernández1,2, Jessica Jimenez1,2, Kathleen Black1,2, Shengmei Ma1,2, Yuetiva Deming1,2, David Carrell1, Ben Saef1,2, Bill Howells1,2, John Budde1,2, Carlos Cruchaga3,4.   

Abstract

INTRODUCTION: A recent study found a significant increase of ABCA7 loss-of-function variants in Alzheimer's disease (AD) cases compared to controls. Some variants were located on noncoding regions, but it was demonstrated that they affect splicing. Here, we try to replicate the association between AD risk and ABCA7 loss-of-function variants at both the single-variant and gene level in a large and well-characterized European American dataset.
METHODS: We genotyped the GWAS common variant and four rare variants previously reported for ABCA7 in 3476 European-Americans.
RESULTS: We were not able to replicate the association at the single-variant level, likely due to a lower effect size on the European American population which led to limited statistical power. However, we did replicate the association at the gene level; we found a significant enrichment of ABCA7 loss-of-function variants in AD cases compared to controls (P = 0.0388; odds ratio =1.54). We also confirmed that the association of the loss-of-function variants is independent of the previously reported genome-wide association study signal.
CONCLUSIONS: Although the effect size for the association of ABCA7 loss-of-function variants with AD risk is lower in our study (odds ratio = 1.54) compared to the original report (odds ratio = 2.2), the replication of the findings of the original report provides a stronger foundation for future functional applications. The data indicate that different independent signals that modify risk for complex traits may exist on the same locus. Additionally, our results suggest that replication of rare-variant studies should be performed at the gene level rather than focusing on a single variant.

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Year:  2015        PMID: 26654793      PMCID: PMC4675010          DOI: 10.1186/s13195-015-0154-x

Source DB:  PubMed          Journal:  Alzheimers Res Ther            Impact factor:   6.982


Introduction

A recent study found that loss-of-function variants in ABCA7 (ATP-binding cassette transporter A7) confer greater risk for Alzheimer’s disease (AD) [1]. Steinberg et al. [1] analyzed sequence, genome-wide association study (GWAS), and linkage data from 3419 individuals with AD and 151,805 controls from Iceland. Gene-based analyses, including nonsense, missense, frameshift splice-site variants and canonical splice-site variants (‘loss-of-function’), identified ABCA7 as the most significant gene (odds ratio (OR) = 2.12, P = 2.2 × 10–13) for AD. This association was mainly driven by a single splice-site variant, rs200538373 (OR = 4.47, P = 3.4 × 10–7), although other coding variants and splice variants were also found. This association was replicated at the gene level by genotyping the loss-of-function variants in more than 6500 AD cases and controls from four independent datasets (OR = 1.73, P = 0.0056). Interestingly, the OR for the variant (rs200538373) that led the association on the discovery series was in the opposite direction in the replication dataset (OR = 0.93). Additionally, none of these loss-of-function variants were in linkage disequilibrium (LD) with the ABCA7 common variant identified by GWAS [2], suggesting that there are multiple and independent mechanisms throughout the ABCA7 region that increase risk for AD. Based on these results, we tried to replicate the association of the ABCA7 loss-of-function variants in a large cohort of European–Americans.

Methods

A total of 1776 AD cases and 1700 controls were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Charles F. and Joanne Knight Alzheimer's Disease Research Center (Knight-ADRC) and the National Institute on Aging Genetics Initiative for Late-Onset Alzheimer’s Disease (NIALOAD) [3, 4]. The Institutional Review Board at the Washington University School of Medicine in Saint Louis approved the study. Research was carried out in accordance with the approved protocol. Written informed consent was obtained from participants and their family members by the Clinical Core of the Knight-ADRC. The approval number for the Knight-ADRC Genetics Core family studies is 93-0006. In the original study, Steinberg et al. [1] genotyped the ABCA7 variants on 6681 European non-Icelandic individuals and only four variants were polymorphic (Supplementary Table 5 from Steinberg et al. [1]). Therefore, we decided to genotype the variants that were found to be polymorphic on the non-Icelandic population together with the GWAS common variant. The ABCA7 GWAS common variant (rs4147929) as well as the loss-of-function variants (p.Glu709Alafs*86, p.Leu1403Argfs*7, rs200538373, and rs113809142) reported by Steinberg et al. [1] were genotyped in our dataset using competitive allele-specific polymerase chain reaction KASPar genotyping technologies as described previously [3, 4]. Allelic frequencies, Hardy-Weinberg equilibrium and single-variant association tests were performed with PLINK-1.9 [5]. For gene level analysis, burden analyses were performed using a combined multivariate and collapsing (CMC) test [6]. Age, gender, and principal component factors (PCs) were included in all association tests as covariates.

Results and discussion

In our dataset, the common variant rs4147929[A] was significantly associated with AD risk (Table 1; OR = 1.162, P = 0.022), suggesting that we have enough power to replicate known associations for common variants. All the genotyped loss-of-function variants were polymorphic in our dataset with similar frequencies to those reported in the European non-Icelandic datasets. As initially reported, none of the genotyped variants were in LD with the common variant rs4147929, allowing for independent analysis. In our dataset, all rare variants were more frequent in AD cases than in controls with an OR ranging from 1.2 to 1.7; however, none of these values were statistically significant on their own (Table 1; P > 0.1).
Table 1

Association of ABCA7 variants with Alzheimer’s disease in studied groups based on Fisher’s association test, including age, gender, and PC as covariates. Gene-based analysis was conducted with CMC collapsing method

rsID/positiona MAFOR P
CasesControls(95 % CI)
rs41479290.1790.1581 1.162 0.022
(GWAS SNP) (1.02–1.31)
19:998507a 0.0060.00371.7350.121
p.Glu709AlafsX86(0.543.04)
19:1006907a 0.0030.00271.2850.569
p.Leu1403ArgfsX7(0.763.21)
rs1138091420.0020.00121.6950.451
c.4416+2T>G(0.345.91)
rs2005383730.0090.00761.2310.476
c.5570+5G>C(0.682.61)
Loss-of-function0.0160.0107 1.549 0.038
(All low frequency variants) (1.02–2.34)

aNCBI Build 36

MAF

Values shown in bold are significant at the P < 0.05 level

CI Confidence interval, CMC Combined multivariate and collapsing, GWAS Genome-wide association study, MAF Minor allele frequency, OR Odds ratio, PC Principal component factors, SNP Single Nucleotide Polymorphism

Association of ABCA7 variants with Alzheimer’s disease in studied groups based on Fisher’s association test, including age, gender, and PC as covariates. Gene-based analysis was conducted with CMC collapsing method aNCBI Build 36 MAF Values shown in bold are significant at the P < 0.05 level CI Confidence interval, CMC Combined multivariate and collapsing, GWAS Genome-wide association study, MAF Minor allele frequency, OR Odds ratio, PC Principal component factors, SNP Single Nucleotide Polymorphism Although we had statistical power to replicate the association of rs113809142 with AD risk based on the effect size reported in the Icelandic population (OR = 4.42, power = 0.887), we did not have enough power to replicate the single-variant analyses based on the minor allele frequency (MAF) and effect sizes reported on the European non-Icelandic dataset. There is some debate concerning what is the best approach to replicate the association of rare variants with complex traits [7-11]. It is clear that the MAF for these low-frequency variants varies widely, not only among populations, but also within populations [10-12]. If the cases and controls are not very well matched for local genetic background, the study can produce false-positive or false-negative results [7, 11, 13]. Additionally, it is possible that a specific variant is not found, or found in an extremely low frequency in a specific population; as with the case of the TREM2 R47H variant in Asian [14, 15] or African–American populations. Although the association of the R47H variant with AD risk has been widely replicated in European–Americans, no significant association is found in Asian or African–American populations, because the MAF for this variant (R47H) is extremely low. However, other variants in the same gene could increase risk for diseases in these populations. Steinberg et al. [1] failed to replicate the association of the rs113809142 in the European non-Icelandic population, but they were able to replicate the association at the gene level. These results support the notion that different (local) populations have varying genetic make-ups, and therefore single-variant analyses may not be the best approach for replicating these studies. This hypothesis is also supported by recent studies from Jin et al. [3, 15] in which deep resequencing of TREM2 was performed on European–Americans and African–Americans; different variants were found in each population, and the variants in common presented very different MAFs and ORs. However, in both cases, the gene-based analyses supported the association of TREM2 with AD risk. For this reason, we decided to perform a gene-based analysis for all the reported polymorphic ABCA7 loss-of-function variants. Since all variants presented the same direction of effect, we performed a CMC test. In our dataset, we found a significant enrichment of ABCA7 loss-of-function variants in AD cases compared to controls (P = 0.0388; OR = 1.54). Therefore, despite none of the individual loss-of-function variants reported a significant association with ABCA7 in this study, we were able to replicate two independent signals of the correlation of the ABCA7 gene with AD: the common variant and the aggregation of the loss-of-function variants. The point estimate for the OR in this study for the gene-based analysis is slightly lower than the reported OR for the Icelandic population (OR = 2.12) or the European non-Icelandic replication datasets (OR = 1.73), although the 95 % confidence interval in this study (1.02–2.34) includes both the Icelandic OR and the European non-Icelandic OR published by Steinberg et al. [1]. The current findings and those of the European non-Icelandic population support a possible "winner's curse" for the Icelandic discovery. Our sample size was smaller than both of the discovery series. Additionally, a proper gene-based replication would entail resequencing the candidate region to identify novel functional variants, and not just genotyping the reported variants; therefore, the real OR for the ABCA7 loss-of-function variants remains to be determined. Despite these limitations, we were able to replicate the original report. Our data also indicate that the gene-based association of these loss-of-function variants is independent of the GWAS variant, and that the aggregate effect of these variants is larger than that of the common variant alone. Our study validates the role of noncoding loss-of-function ABCA7 variants in AD risk. Other population-specific independent variants with similar loss-of-function effects may contribute to AD risk or other complex traits. Supporting this hypothesis, a recent study has reported on an additional intronic low-frequency variant of ABCA7 (rs78117248; OR = 2.07, P = 0.0016) that increases risk for AD, also independently of the common variant. Together, these results suggest that different and independent variants modify risk for complex diseases by different mechanisms existing on the same locus [16, 17]. Other genes will also harbor rare variants increasing risk for AD, independently of the GWAS hits [17].

Conclusions

In summary, our study replicates the association of ABCA7 loss-of-function variants with AD risk, and highlights the necessity of performing gene-based, rather than single-variant analyses to replicate the association in this type of studies. Our study also confirms that there is high variability in the MAF of low-frequency variants within a population, so matching cases and controls for genetic background is a key step to avoiding false negatives or positives.
  17 in total

1.  Loss-of-function variants in ABCA7 confer risk of Alzheimer's disease.

Authors:  Stacy Steinberg; Hreinn Stefansson; Thorlakur Jonsson; Hrefna Johannsdottir; Andres Ingason; Hannes Helgason; Patrick Sulem; Olafur Th Magnusson; Sigurjon A Gudjonsson; Unnur Unnsteinsdottir; Augustine Kong; Seppo Helisalmi; Hilkka Soininen; James J Lah; Dag Aarsland; Tormod Fladby; Ingun D Ulstein; Srdjan Djurovic; Sigrid B Sando; Linda R White; Gun-Peggy Knudsen; Lars T Westlye; Geir Selbæk; Ina Giegling; Harald Hampel; Mikko Hiltunen; Allan I Levey; Ole A Andreassen; Dan Rujescu; Palmi V Jonsson; Sigurbjorn Bjornsson; Jon Snaedal; Kari Stefansson
Journal:  Nat Genet       Date:  2015-03-25       Impact factor: 38.330

2.  PLD3 in non-familial Alzheimer's disease.

Authors:  Stefanie Heilmann; Dmitriy Drichel; Jordi Clarimon; Victoria Fernández; André Lacour; Holger Wagner; Mathias Thelen; Isabel Hernández; Juan Fortea; Montserrat Alegret; Rafael Blesa; Ana Mauleón; Maitée Rosende Roca; Johannes Kornhuber; Oliver Peters; Reinhard Heun; Lutz Frölich; Michael Hüll; Michael T Heneka; Eckart Rüther; Steffi Riedel-Heller; Martin Scherer; Jens Wiltfang; Frank Jessen; Tim Becker; Lluís Tárraga; Mercè Boada; Wolfgang Maier; Alberto Lleó; Agustin Ruiz; Markus M Nöthen; Alfredo Ramirez
Journal:  Nature       Date:  2015-04-02       Impact factor: 49.962

3.  Lack of genetic association between TREM2 and late-onset Alzheimer's disease in a Japanese population.

Authors:  Akinori Miyashita; Yanan Wen; Nobutaka Kitamura; Etsuro Matsubara; Takeshi Kawarabayashi; Mikio Shoji; Naoki Tomita; Katsutoshi Furukawa; Hiroyuki Arai; Takashi Asada; Yasuo Harigaya; Masaki Ikeda; Masakuni Amari; Haruo Hanyu; Susumu Higuchi; Masatoyo Nishizawa; Masaichi Suga; Yasuhiro Kawase; Hiroyasu Akatsu; Masaki Imagawa; Tsuyoshi Hamaguchi; Masahito Yamada; Takashi Morihara; Masatoshi Takeda; Takeo Takao; Kenji Nakata; Ken Sasaki; Ken Watanabe; Kenji Nakashima; Katsuya Urakami; Terumi Ooya; Mitsuo Takahashi; Takefumi Yuzuriha; Kayoko Serikawa; Seishi Yoshimoto; Ryuji Nakagawa; Yuko Saito; Hiroyuki Hatsuta; Shigeo Murayama; Akiyoshi Kakita; Hitoshi Takahashi; Haruyasu Yamaguchi; Kohei Akazawa; Ichiro Kanazawa; Yasuo Ihara; Takeshi Ikeuchi; Ryozo Kuwano
Journal:  J Alzheimers Dis       Date:  2014       Impact factor: 4.472

Review 4.  Lack of genetic association between TREM2 and Alzheimer's disease in East Asian population: a systematic review and meta-analysis.

Authors:  Man Huang; Dejun Wang; Zhijun Xu; Yongshan Xu; Xiaoping Xu; Yuefeng Ma; Zheng Xia
Journal:  Am J Alzheimers Dis Other Demen       Date:  2015-09       Impact factor: 2.035

5.  TREM2 is associated with the risk of Alzheimer's disease in Spanish population.

Authors:  Bruno A Benitez; Breanna Cooper; Pau Pastor; Sheng-Chih Jin; Elena Lorenzo; Sebastian Cervantes; Carlos Cruchaga
Journal:  Neurobiol Aging       Date:  2013-02-05       Impact factor: 4.673

6.  Differential confounding of rare and common variants in spatially structured populations.

Authors:  Iain Mathieson; Gil McVean
Journal:  Nat Genet       Date:  2012-02-05       Impact factor: 38.330

7.  Rare variants in APP, PSEN1 and PSEN2 increase risk for AD in late-onset Alzheimer's disease families.

Authors:  Carlos Cruchaga; Gabe Haller; Sumitra Chakraverty; Kevin Mayo; Francesco L M Vallania; Robi D Mitra; Kelley Faber; Jennifer Williamson; Tom Bird; Ramon Diaz-Arrastia; Tatiana M Foroud; Bradley F Boeve; Neill R Graff-Radford; Pamela St Jean; Michael Lawson; Margaret G Ehm; Richard Mayeux; Alison M Goate
Journal:  PLoS One       Date:  2012-02-01       Impact factor: 3.240

8.  Second-generation PLINK: rising to the challenge of larger and richer datasets.

Authors:  Christopher C Chang; Carson C Chow; Laurent Cam Tellier; Shashaank Vattikuti; Shaun M Purcell; James J Lee
Journal:  Gigascience       Date:  2015-02-25       Impact factor: 6.524

9.  Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease.

Authors:  J C Lambert; C A Ibrahim-Verbaas; D Harold; A C Naj; R Sims; C Bellenguez; A L DeStafano; J C Bis; G W Beecham; B Grenier-Boley; G Russo; T A Thorton-Wells; N Jones; A V Smith; V Chouraki; C Thomas; M A Ikram; D Zelenika; B N Vardarajan; Y Kamatani; C F Lin; A Gerrish; H Schmidt; B Kunkle; M L Dunstan; A Ruiz; M T Bihoreau; S H Choi; C Reitz; F Pasquier; C Cruchaga; D Craig; N Amin; C Berr; O L Lopez; P L De Jager; V Deramecourt; J A Johnston; D Evans; S Lovestone; L Letenneur; F J Morón; D C Rubinsztein; G Eiriksdottir; K Sleegers; A M Goate; N Fiévet; M W Huentelman; M Gill; K Brown; M I Kamboh; L Keller; P Barberger-Gateau; B McGuiness; E B Larson; R Green; A J Myers; C Dufouil; S Todd; D Wallon; S Love; E Rogaeva; J Gallacher; P St George-Hyslop; J Clarimon; A Lleo; A Bayer; D W Tsuang; L Yu; M Tsolaki; P Bossù; G Spalletta; P Proitsi; J Collinge; S Sorbi; F Sanchez-Garcia; N C Fox; J Hardy; M C Deniz Naranjo; P Bosco; R Clarke; C Brayne; D Galimberti; M Mancuso; F Matthews; S Moebus; P Mecocci; M Del Zompo; W Maier; H Hampel; A Pilotto; M Bullido; F Panza; P Caffarra; B Nacmias; J R Gilbert; M Mayhaus; L Lannefelt; H Hakonarson; S Pichler; M M Carrasquillo; M Ingelsson; D Beekly; V Alvarez; F Zou; O Valladares; S G Younkin; E Coto; K L Hamilton-Nelson; W Gu; C Razquin; P Pastor; I Mateo; M J Owen; K M Faber; P V Jonsson; O Combarros; M C O'Donovan; L B Cantwell; H Soininen; D Blacker; S Mead; T H Mosley; D A Bennett; T B Harris; L Fratiglioni; C Holmes; R F de Bruijn; P Passmore; T J Montine; K Bettens; J I Rotter; A Brice; K Morgan; T M Foroud; W A Kukull; D Hannequin; J F Powell; M A Nalls; K Ritchie; K L Lunetta; J S Kauwe; E Boerwinkle; M Riemenschneider; M Boada; M Hiltuenen; E R Martin; R Schmidt; D Rujescu; L S Wang; J F Dartigues; R Mayeux; C Tzourio; A Hofman; M M Nöthen; C Graff; B M Psaty; L Jones; J L Haines; P A Holmans; M Lathrop; M A Pericak-Vance; L J Launer; L A Farrer; C M van Duijn; C Van Broeckhoven; V Moskvina; S Seshadri; J Williams; G D Schellenberg; P Amouyel
Journal:  Nat Genet       Date:  2013-10-27       Impact factor: 38.330

10.  Missense variant in TREML2 protects against Alzheimer's disease.

Authors:  Bruno A Benitez; Sheng Chih Jin; Rita Guerreiro; Rob Graham; Jenny Lord; Denise Harold; Rebecca Sims; Jean-Charles Lambert; J Raphael Gibbs; Jose Bras; Celeste Sassi; Oscar Harari; Sarah Bertelsen; Michelle K Lupton; John Powell; Celine Bellenguez; Kristelle Brown; Christopher Medway; Patrick C G Haddick; Marcel P van der Brug; Tushar Bhangale; Ward Ortmann; Tim Behrens; Richard Mayeux; Margaret A Pericak-Vance; Lindsay A Farrer; Gerard D Schellenberg; Jonathan L Haines; Jim Turton; Anne Braae; Imelda Barber; Anne M Fagan; David M Holtzman; John C Morris; Julie Williams; John S K Kauwe; Philippe Amouyel; Kevin Morgan; Andy Singleton; John Hardy; Alison M Goate; Carlos Cruchaga
Journal:  Neurobiol Aging       Date:  2013-12-21       Impact factor: 4.673

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  14 in total

1.  Polygenic risk score of sporadic late-onset Alzheimer's disease reveals a shared architecture with the familial and early-onset forms.

Authors:  Carlos Cruchaga; Jorge L Del-Aguila; Benjamin Saef; Kathleen Black; Maria Victoria Fernandez; John Budde; Laura Ibanez; Yuetiva Deming; Manav Kapoor; Giuseppe Tosto; Richard P Mayeux; David M Holtzman; Anne M Fagan; John C Morris; Randall J Bateman; Alison M Goate; Oscar Harari
Journal:  Alzheimers Dement       Date:  2017-09-21       Impact factor: 21.566

2.  Alzheimer's Disease Genetics and ABCA7 Splicing.

Authors:  Jared B Vasquez; James F Simpson; Ryan Harpole; Steven Estus
Journal:  J Alzheimers Dis       Date:  2017       Impact factor: 4.472

3.  Perspective: The Potential Role of Circulating Lysophosphatidylcholine in Neuroprotection against Alzheimer Disease.

Authors:  Richard D Semba
Journal:  Adv Nutr       Date:  2020-07-01       Impact factor: 8.701

4.  Assessment of the Genetic Architecture of Alzheimer's Disease Risk in Rate of Memory Decline.

Authors:  Jorge L Del-Aguila; Maria Victoria Fernández; Suzanne Schindler; Laura Ibanez; Yuetiva Deming; Shengmei Ma; Ben Saef; Kathleen Black; John Budde; Joanne Norton; Rachel Chasse; Oscar Harari; Alison Goate; Chengjie Xiong; John C Morris; Carlos Cruchaga
Journal:  J Alzheimers Dis       Date:  2018       Impact factor: 4.472

5.  ABCA7 loss-of-function variants, expression, and neurologic disease risk.

Authors:  Mariet Allen; Sarah J Lincoln; Morgane Corda; Jens O Watzlawik; Minerva M Carrasquillo; Joseph S Reddy; Jeremy D Burgess; Thuy Nguyen; Kimberly Malphrus; Ronald C Petersen; Neill R Graff-Radford; Dennis W Dickson; Nilüfer Ertekin-Taner
Journal:  Neurol Genet       Date:  2017-01-05

6.  Deleterious ABCA7 mutations and transcript rescue mechanisms in early onset Alzheimer's disease.

Authors:  Arne De Roeck; Tobi Van den Bossche; Julie van der Zee; Jan Verheijen; Wouter De Coster; Jasper Van Dongen; Lubina Dillen; Yalda Baradaran-Heravi; Bavo Heeman; Raquel Sanchez-Valle; Albert Lladó; Benedetta Nacmias; Sandro Sorbi; Ellen Gelpi; Oriol Grau-Rivera; Estrella Gómez-Tortosa; Pau Pastor; Sara Ortega-Cubero; Maria A Pastor; Caroline Graff; Håkan Thonberg; Luisa Benussi; Roberta Ghidoni; Giuliano Binetti; Alexandre de Mendonça; Madalena Martins; Barbara Borroni; Alessandro Padovani; Maria Rosário Almeida; Isabel Santana; Janine Diehl-Schmid; Panagiotis Alexopoulos; Jordi Clarimon; Alberto Lleó; Juan Fortea; Magda Tsolaki; Maria Koutroumani; Radoslav Matěj; Zdenek Rohan; Peter De Deyn; Sebastiaan Engelborghs; Patrick Cras; Christine Van Broeckhoven; Kristel Sleegers
Journal:  Acta Neuropathol       Date:  2017-04-27       Impact factor: 17.088

Review 7.  Late onset Alzheimer's disease genetics implicates microglial pathways in disease risk.

Authors:  Anastasia G Efthymiou; Alison M Goate
Journal:  Mol Neurodegener       Date:  2017-05-26       Impact factor: 14.195

Review 8.  ABCA7 and Pathogenic Pathways of Alzheimer's Disease.

Authors:  Tomonori Aikawa; Marie-Louise Holm; Takahisa Kanekiyo
Journal:  Brain Sci       Date:  2018-02-05

9.  Genetic variants associated with Alzheimer's disease confer different cerebral cortex cell-type population structure.

Authors:  Zeran Li; Jorge L Del-Aguila; Umber Dube; John Budde; Rita Martinez; Kathleen Black; Qingli Xiao; Nigel J Cairns; Joseph D Dougherty; Jin-Moo Lee; John C Morris; Randall J Bateman; Celeste M Karch; Carlos Cruchaga; Oscar Harari
Journal:  Genome Med       Date:  2018-06-08       Impact factor: 11.117

Review 10.  The role of ABCA7 in Alzheimer's disease: evidence from genomics, transcriptomics and methylomics.

Authors:  Arne De Roeck; Christine Van Broeckhoven; Kristel Sleegers
Journal:  Acta Neuropathol       Date:  2019-03-22       Impact factor: 17.088

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