Genome-wide association study meta-analysis of neurofilament light (NfL) levels in blood reveals novel loci related to neurodegeneration.

Humans Genome-Wide Association Study Neurofilament Proteins / genetics Neurodegenerative Diseases / genetics Genetic Predisposition to Disease Genetic Loci Biomarkers / blood Polymorphism, Single Nucleotide Male Female Alzheimer Disease / genetics

Journal

Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179

Informations de publication

Date de publication:
09 Sep 2024
Historique:
received: 29 11 2023
accepted: 29 08 2024
medline: 10 9 2024
pubmed: 10 9 2024
entrez: 9 9 2024
Statut: epublish

Résumé

Neurofilament light chain (NfL) levels in circulation have been established as a sensitive biomarker of neuro-axonal damage across a range of neurodegenerative disorders. Elucidation of the genetic architecture of blood NfL levels could provide new insights into molecular mechanisms underlying neurodegenerative disorders. In this meta-analysis of genome-wide association studies (GWAS) of blood NfL levels from eleven cohorts of European ancestry, we identify two genome-wide significant loci at 16p12 (UMOD) and 17q24 (SLC39A11). We observe association of three loci at 1q43 (FMN2), 12q14, and 12q21 with blood NfL levels in the meta-analysis of African-American ancestry. In the trans-ethnic meta-analysis, we identify three additional genome-wide significant loci at 1p32 (FGGY), 6q14 (TBX18), and 4q21. In the post-GWAS analyses, we observe the association of higher NfL polygenic risk score with increased plasma levels of total-tau, Aβ-40, Aβ-42, and higher incidence of Alzheimer's disease in the Rotterdam Study. Furthermore, Mendelian randomization analysis results suggest that a lower kidney function could cause higher blood NfL levels. This study uncovers multiple genetic loci of blood NfL levels, highlighting the genes related to molecular mechanism of neurodegeneration.

Identifiants

DOI: 10.1038/s42003-024-06804-3 PMID: 39251807
pubmed: 39251807
doi: 10.1038/s42003-024-06804-3
pii: 10.1038/s42003-024-06804-3
doi:

Substances chimiques

Neurofilament Proteins 0
neurofilament protein L 0
Biomarkers 0

Types de publication

Journal Article Meta-Analysis

Langues

eng

Sous-ensembles de citation

IM

Pagination

1103

Informations de copyright

© 2024. The Author(s).

Références

Gaetani, L. et al. Neurofilament light chain as a biomarker in neurological disorders. J. Neurol. Neurosurg. Psychiatry 90, 870–881 (2019).
pubmed: 30967444 doi: 10.1136/jnnp-2018-320106
Zetterberg, H. Neurofilament light: A dynamic cross-disease fluid biomarker for neurodegeneration. Neuron 91, 1–3 (2016).
pubmed: 27387643 doi: 10.1016/j.neuron.2016.06.030
Disanto, G. et al. Serum Neurofilament light: A biomarker of neuronal damage in multiple sclerosis. Ann. Neurol. 81, 857–870 (2017).
pubmed: 28512753 pmcid: 5519945 doi: 10.1002/ana.24954
Khalil, M. et al. Serum neurofilament light levels in normal aging and their association with morphologic brain changes. Nat. Commun. 11, 812 (2020).
pubmed: 32041951 pmcid: 7010701 doi: 10.1038/s41467-020-14612-6
Khalil, M. et al. Neurofilaments as biomarkers in neurological disorders - towards clinical application. Nat. Rev. Neurol. 20, 269–287 (2024).
pubmed: 38609644 doi: 10.1038/s41582-024-00955-x
Alagaratnam, J. et al. Correlation between cerebrospinal fluid and plasma neurofilament light protein in treated HIV infection: results from the COBRA study. J. Neurovirol 28, 54–63 (2022).
pubmed: 34874540 doi: 10.1007/s13365-021-01026-3
Khalil, M. et al. Neurofilaments as biomarkers in neurological disorders. Nat. Rev. Neurol. 14, 577–589 (2018).
pubmed: 30171200 doi: 10.1038/s41582-018-0058-z
Bacioglu, M. et al. Neurofilament light chain in blood and CSF as marker of disease progression in mouse models and in neurodegenerative diseases. Neuron 91, 56–66 (2016).
pubmed: 27292537 doi: 10.1016/j.neuron.2016.05.018
Niu, L. D. et al. Genome-wide association study of cerebrospinal fluid neurofilament light levels in non-demented elders. Ann. Transl. Med. 7, 657 (2019).
pubmed: 31930058 pmcid: 6944624 doi: 10.21037/atm.2019.10.66
Li, J. Q. et al. Genome-wide association study identifies two loci influencing plasma neurofilament light levels. BMC Med. Genomics 11, 47 (2018).
pubmed: 29747637 pmcid: 5946407 doi: 10.1186/s12920-018-0364-8
Hong, S. et al. TMEM106B and CPOX are genetic determinants of cerebrospinal fluid Alzheimer’s disease biomarker levels. Alzheimers Dement 17, 1628–1640 (2021).
pubmed: 33991015 doi: 10.1002/alz.12330
Wuttke, M. et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat. Genet. 51, 957–972 (2019).
pubmed: 31152163 pmcid: 6698888 doi: 10.1038/s41588-019-0407-x
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).
pubmed: 22426310 pmcid: 3593158 doi: 10.1038/ng.2213
Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019).
pubmed: 30820047 pmcid: 6463297 doi: 10.1038/s41588-019-0358-2
Nalls, M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 18, 1091–1102 (2019).
pubmed: 31701892 pmcid: 8422160 doi: 10.1016/S1474-4422(19)30320-5
Yu, Y. et al. Characterization of the GufA subfamily member SLC39A11/Zip11 as a zinc transporter. J. Nutr. Biochem 24, 1697–1708 (2013).
pubmed: 23643525 doi: 10.1016/j.jnutbio.2013.02.010
Landers, J. E. et al. Reduced expression of the Kinesin-Associated Protein 3 (KIFAP3) gene increases survival in sporadic amyotrophic lateral sclerosis. Proc. Natl Acad. Sci. USA 106, 9004–9009 (2009).
pubmed: 19451621 pmcid: 2683883 doi: 10.1073/pnas.0812937106
Xie, T. et al. Genome-wide association study combining pathway analysis for typical sporadic amyotrophic lateral sclerosis in Chinese Han populations. Neurobiol. Aging 35, 1778.e1779–1778.e1723 (2014).
doi: 10.1016/j.neurobiolaging.2014.01.014
Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
pubmed: 10592173 pmcid: 102409 doi: 10.1093/nar/28.1.27
Uhlen, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
pubmed: 25613900 doi: 10.1126/science.1260419
Ádori, C. et al. Critical role of somatostatin receptor 2 in the vulnerability of the central noradrenergic system: New aspects on Alzheimer’s disease. Acta Neuropathol. 129, 541–563 (2015).
pubmed: 25676386 doi: 10.1007/s00401-015-1394-3
Stumm, R. K. et al. Somatostatin receptor 2 is activated in cortical neurons and contributes to neurodegeneration after focal ischemia. J. Neurosci. 24, 11404–11415 (2004).
pubmed: 15601946 pmcid: 6730368 doi: 10.1523/JNEUROSCI.3834-04.2004
Liu, D. et al. Involvement of mitochondrial K+ release and cellular efflux in ischemic and apoptotic neuronal death. J. Neurochem 86, 966–979 (2003).
pubmed: 12887694 doi: 10.1046/j.1471-4159.2003.01913.x
Korley, F. K. et al. Serum NfL (neurofilament light chain) levels and incident stroke in adults with diabetes mellitus. Stroke 50, 1669–1675 (2019).
pubmed: 31138085 pmcid: 6591022 doi: 10.1161/STROKEAHA.119.024941
Akamine, S. et al. Renal function is associated with blood neurofilament light chain level in older adults. Sci. Rep. 10, 20350 (2020).
pubmed: 33230211 pmcid: 7683708 doi: 10.1038/s41598-020-76990-7
Barro, C., Chitnis, T. & Weiner, H. L. Blood neurofilament light: a critical review of its application to neurologic disease. Ann. Clin. Transl. Neurol. 7, 2508–2523 (2020).
pubmed: 33146954 pmcid: 7732243 doi: 10.1002/acn3.51234
Garzone, D. et al. Neurofilament light chain and retinal layers’ determinants and association: A population-based study. Ann. Clin. Transl. Neurol. 9, 564–569 (2022).
pubmed: 35243826 pmcid: 8994982 doi: 10.1002/acn3.51522
van der Plas, E. et al. Associations between neurofilament light-chain protein, brain structure, and chronic kidney disease. Pediatr. Res 91, 1735–1740 (2022).
pubmed: 34274959 doi: 10.1038/s41390-021-01649-6
Scheppach, J. B. et al. Albuminuria and estimated GFR as risk factors for dementia in midlife and older age: Findings from the ARIC study. Am. J. Kidney Dis. 76, 775–783 (2020).
pubmed: 32428540 pmcid: 7669634 doi: 10.1053/j.ajkd.2020.03.015
Scheppach, J. B. et al. Association of kidney function measures with signs of neurodegeneration and small vessel disease on brain magnetic resonance imaging: The atherosclerosis risk in communities (ARIC) study. Am. J. Kidney Dis. 81, 261–269.e261 (2023).
pubmed: 36179945 doi: 10.1053/j.ajkd.2022.07.013
Adam, J. et al. Endoplasmic reticulum stress in UMOD-related kidney disease: A human pathologic study. Am. J. Kidney Dis. 59, 117–121 (2012).
pubmed: 21978600 doi: 10.1053/j.ajkd.2011.08.014
Williams, S. E. et al. Uromodulin mutations causing familial juvenile hyperuricaemic nephropathy lead to protein maturation defects and retention in the endoplasmic reticulum. Hum. Mol. Genet 18, 2963–2974 (2009).
pubmed: 19465746 pmcid: 2714724 doi: 10.1093/hmg/ddp235
Zhang, K. & Kaufman, R. J. From endoplasmic-reticulum stress to the inflammatory response. Nature 454, 455–462 (2008).
pubmed: 18650916 pmcid: 2727659 doi: 10.1038/nature07203
Piperi, C., Adamopoulos, C., Dalagiorgou, G., Diamanti-Kandarakis, E. & Papavassiliou, A. G. Crosstalk between advanced glycation and endoplasmic reticulum stress: emerging therapeutic targeting for metabolic diseases. J. Clin. Endocrinol. Metab. 97, 2231–2242 (2012).
pubmed: 22508704 doi: 10.1210/jc.2011-3408
Li, H. et al. Crystal and solution structures of human protein-disulfide isomerase-like protein of the testis (PDILT) provide insight into its chaperone activity. J. Biol. Chem. 293, 1192–1202 (2018).
pubmed: 29203529 doi: 10.1074/jbc.M117.797290
Preische, O. et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat. Med. 25, 277–283 (2019).
pubmed: 30664784 pmcid: 6367005 doi: 10.1038/s41591-018-0304-3
Gudjonsson, A. et al. A genome-wide association study of serum proteins reveals shared loci with common diseases. Nat. Commun. 13, 480 (2022).
pubmed: 35078996 pmcid: 8789779 doi: 10.1038/s41467-021-27850-z
Manini, A. et al. TMEM106B acts as a modifier of cognitive and motor functions in amyotrophic lateral sclerosis. Int. J. Mol. Sci. 23, https://doi.org/10.3390/ijms23169276 (2022).
Bellenguez, C. et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat. Genet. 54, 412–436 (2022).
pubmed: 35379992 pmcid: 9005347 doi: 10.1038/s41588-022-01024-z
Hu, Y. et al. rs1990622 variant associates with Alzheimer’s disease and regulates TMEM106B expression in human brain tissues. BMC Med. 19, 11 (2021).
pubmed: 33461566 pmcid: 7814705 doi: 10.1186/s12916-020-01883-5
Brady, O. A., Zheng, Y., Murphy, K., Huang, M. & Hu, F. The frontotemporal lobar degeneration risk factor, TMEM106B, regulates lysosomal morphology and function. Hum. Mol. Genet 22, 685–695 (2013).
pubmed: 23136129 doi: 10.1093/hmg/dds475
Van Es, M. A. et al. Analysis of FGGY as a risk factor for sporadic amyotrophic lateral sclerosis. Amyotroph. Lateral Scler. 10, 441–447 (2009).
pubmed: 19922138 doi: 10.3109/17482960802673042
Aberg, K. et al. Genomewide association study of movement-related adverse antipsychotic effects. Biol. Psychiatry 67, 279–282 (2010).
pubmed: 19875103 doi: 10.1016/j.biopsych.2009.08.036
Daoud, H., Valdmanis, P. N., Dion, P. A. & Rouleau, G. A. Analysis of DPP6 and FGGY as candidate genes for amyotrophic lateral sclerosis. Amyotroph. Lateral Scler. 11, 389–391 (2010).
pubmed: 20001489 doi: 10.3109/17482960903358857
Zhang, Y., Zagnitko, O., Rodionova, I., Osterman, A. & Godzik, A. The FGGY carbohydrate kinase family: Insights into the evolution of functional specificities. PLoS Comput Biol. 7, e1002318 (2011).
pubmed: 22215998 pmcid: 3245297 doi: 10.1371/journal.pcbi.1002318
Dunckley, T. et al. Whole-genome analysis of sporadic amyotrophic lateral sclerosis. N. Engl. J. Med. 357, 775–788 (2007).
pubmed: 17671248 doi: 10.1056/NEJMoa070174
Mullard, A. NfL makes regulatory debut as neurodegenerative disease biomarker. Nat. Rev. Drug Discov. 22, 431–434 (2023).
pubmed: 37198334 doi: 10.1038/d41573-023-00083-z
Garcia, M. L. et al. NF-M is an essential target for the myelin-directed “outside-in” signaling cascade that mediates radial axonal growth. J. cell Biol. 163, 1011–1020 (2003).
pubmed: 14662745 pmcid: 2173620 doi: 10.1083/jcb.200308159
Campos-Melo, D., Hawley, Z. C. E. & Strong, M. J. Dysregulation of human NEFM and NEFH mRNA stability by ALS-linked miRNAs. Mol. Brain 11, 43 (2018).
pubmed: 30029677 pmcid: 6054723 doi: 10.1186/s13041-018-0386-3
Lavedan, C., Buchholtz, S., Nussbaum, R. L., Albin, R. L. & Polymeropoulos, M. H. A mutation in the human neurofilament M gene in Parkinson’s disease that suggests a role for the cytoskeleton in neuronal degeneration. Neurosci. Lett. 322, 57–61 (2002).
pubmed: 11958843 doi: 10.1016/S0304-3940(01)02513-7
Peleg, S. et al. Altered histone acetylation is associated with age-dependent memory impairment in mice. Science 328, 753–756 (2010).
pubmed: 20448184 doi: 10.1126/science.1186088
Sherva, R. et al. Genome-wide association study of the rate of cognitive decline in Alzheimer’s disease. Alzheimers Dement 10, 45–52 (2014).
pubmed: 23535033 doi: 10.1016/j.jalz.2013.01.008
Cronin, S. et al. A genome-wide association study of sporadic ALS in a homogenous Irish population. Hum. Mol. Genet 17, 768–774 (2008).
pubmed: 18057069 doi: 10.1093/hmg/ddm361
Agís-Balboa, R. C. et al. Formin 2 links neuropsychiatric phenotypes at young age to an increased risk for dementia. Embo j. 36, 2815–2828 (2017).
pubmed: 28768717 pmcid: 5623844 doi: 10.15252/embj.201796821
Sun, L. et al. Genome-wide DNA methylation profiles of autism spectrum disorder. Psychiatr. Genet 32, 131–145 (2022).
pubmed: 35353793 doi: 10.1097/YPG.0000000000000314
Wexler, E. M. et al. Genome-wide analysis of a Wnt1-regulated transcriptional network implicates neurodegenerative pathways. Sci. Signal 4, ra65 (2011).
pubmed: 21971039 doi: 10.1126/scisignal.2002282
Qian, L. et al. β2-adrenergic receptor activation prevents rodent dopaminergic neurotoxicity by inhibiting microglia via a novel signaling pathway. J. Immunol. 186, 4443–4454 (2011).
pubmed: 21335487 doi: 10.4049/jimmunol.1002449
Peterson, L., Ismond, K. P., Chapman, E. & Flood, P. Potential benefits of therapeutic use of β2-adrenergic receptor agonists in neuroprotection and Parkinsonμs disease. J. Immunol. Res. 2014, 103780 (2014).
pubmed: 24741572 pmcid: 3987873 doi: 10.1155/2014/103780
Oksanen, M. et al. Astrocyte alterations in neurodegenerative pathologies and their modeling in human induced pluripotent stem cell platforms. Cell Mol. Life Sci. 76, 2739–2760 (2019).
pubmed: 31016348 pmcid: 6588647 doi: 10.1007/s00018-019-03111-7
Park, H. M. et al. The CRL3(gigaxonin) ubiquitin ligase-USP15 pathway governs the destruction of neurofilament proteins. Proc. Natl Acad. Sci. USA 120, e2306395120 (2023).
pubmed: 37903270 pmcid: 10636361 doi: 10.1073/pnas.2306395120
Dubiel, W., Dubiel, D., Wolf, D. A. & Naumann, M. Cullin 3-based ubiquitin ligases as master regulators of Mammalian cell differentiation. Trends Biochem Sci. 43, 95–107 (2018).
pubmed: 29249570 doi: 10.1016/j.tibs.2017.11.010
Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
pubmed: 26432245 doi: 10.1038/nature15393
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
pubmed: 27548312 pmcid: 5388176 doi: 10.1038/ng.3643
Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).
pubmed: 24762786 pmcid: 4083217 doi: 10.1038/nprot.2014.071
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
pubmed: 20616382 pmcid: 2922887 doi: 10.1093/bioinformatics/btq340
Mägi, R. et al. Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution. Hum. Mol. Genet 26, 3639–3650 (2017).
pubmed: 28911207 pmcid: 5755684 doi: 10.1093/hmg/ddx280
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
pubmed: 29184056 pmcid: 5705698 doi: 10.1038/s41467-017-01261-5
Watanabe, K., Umićević Mirkov, M., de Leeuw, C. A., van den Heuvel, M. P. & Posthuma, D. Genetic mapping of cell type specificity for complex traits. Nat. Commun. 10, 3222 (2019).
pubmed: 31324783 pmcid: 6642112 doi: 10.1038/s41467-019-11181-1
Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).
pubmed: 20634204 pmcid: 2935401 doi: 10.1093/bioinformatics/btq419
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet 47, 291–295 (2015).
pubmed: 25642630 pmcid: 4495769 doi: 10.1038/ng.3211
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 11, e1004219 (2015).
pubmed: 25885710 pmcid: 4401657 doi: 10.1371/journal.pcbi.1004219
Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
doi: 10.1038/ng.2653
CAG repeat not polyglutamine length determines timing of Huntington’s disease onset. Cell 178, 887–900.e814 (2019).
van Rheenen, W. et al. Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology. Nat. Genet 53, 1636–1648 (2021).
pubmed: 34873335 pmcid: 8648564 doi: 10.1038/s41588-021-00973-1
Damotte, V. et al. Plasma amyloid β levels are driven by genetic variants near APOE, BACE1, APP, PSEN2: A genome-wide association study in over 12,000 non-demented participants. Alzheimers Dement 17, 1663–1674 (2021).
pubmed: 34002480 doi: 10.1002/alz.12333
Sarnowski, C. et al. Meta-analysis of genome-wide association studies identifies ancestry-specific associations underlying circulating total tau levels. Commun. Biol. 5, 336 (2022).
pubmed: 35396452 pmcid: 8993877 doi: 10.1038/s42003-022-03287-y
van der Meer, D. et al. Brain scans from 21,297 individuals reveal the genetic architecture of hippocampal subfield volumes. Mol. Psychiatry 25, 3053–3065 (2020).
pubmed: 30279459 doi: 10.1038/s41380-018-0262-7
Zhao, B. et al. Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat. Genet 51, 1637–1644 (2019).
pubmed: 31676860 pmcid: 6858580 doi: 10.1038/s41588-019-0516-6
Sargurupremraj, M. et al. Cerebral small vessel disease genomics and its implications across the lifespan. Nat. Commun. 11, 6285 (2020).
pubmed: 33293549 pmcid: 7722866 doi: 10.1038/s41467-020-19111-2
MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–d901 (2017).
pubmed: 27899670 doi: 10.1093/nar/gkw1133
Choi, S. W. & O’Reilly, P. F. PRSice-2: Polygenic risk score software for biobank-scale data. Gigascience 8, https://doi.org/10.1093/gigascience/giz082 (2019).
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet 10, e1004383 (2014).
pubmed: 24830394 pmcid: 4022491 doi: 10.1371/journal.pgen.1004383
Liu, B., Gloudemans, M. J., Rao, A. S., Ingelsson, E. & Montgomery, S. B. Abundant associations with gene expression complicate GWAS follow-up. Nat. Genet. 51, 768–769 (2019).
pubmed: 31043754 pmcid: 6904208 doi: 10.1038/s41588-019-0404-0
Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 7, https://doi.org/10.7554/eLife.34408 (2018).
Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 40, 304–314 (2016).
pubmed: 27061298 pmcid: 4849733 doi: 10.1002/gepi.21965
Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression. Int J. Epidemiol. 44, 512–525 (2015).
pubmed: 26050253 pmcid: 4469799 doi: 10.1093/ije/dyv080
Bowden, J. et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int. J. Epidemiol. 47, 1264–1278 (2018).
pubmed: 29961852 pmcid: 6124632 doi: 10.1093/ije/dyy101

Auteurs

Shahzad Ahmad (S)

Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000, CA, Rotterdam, the Netherlands.
Oxford-GSK Institute of Computational and Molecular Medicine (IMCM), Centre for Human Genetics, Nuffield Department of Medicine (NDM), University of Oxford, Oxford, OX3 7BN, UK.
ORCID: 0000-0002-8658-3790

Mohammad Aslam Imtiaz (MA)

Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1/99, 53127, Bonn, Germany.
ORCID: 0000-0003-2762-3227

Aniket Mishra (A)

University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France.
ORCID: 0000-0002-8141-1543

Ruiqi Wang (R)

Boston University, Boston, MA, 02215, USA.

Marisol Herrera-Rivero (M)

Department of Genetic Epidemiology, Institute of Human Genetics, University of Münster, Münster, Germany.
Department of Psychiatry, University of Münster, Münster, Germany.
ORCID: 0000-0001-7064-9487

Joshua C Bis (JC)

Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 1730 Minor Ave #1360, Seattle, WA, 98101, USA.
ORCID: 0000-0002-3409-1110

Myriam Fornage (M)

Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, 1825 Pressler Street Houston, Houston, 77030, TX, USA.
ORCID: 0000-0003-0677-8158

Gennady Roshchupkin (G)

Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000, CA, Rotterdam, the Netherlands.
ORCID: 0000-0002-3403-2313

Edith Hofer (E)

Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria.
Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerplatz 2, Fifth Floor, Graz, 8036, Austria.

Mark Logue (M)

National Center for PTSD, Behavioral Sciences Division at VA Boston Healthcare System, Boston, 150 South Huntington Avenue, Boston, MA, 02130, USA.
Department of Psychiatry and Biomedical Genetics, Boston University School of Medicine, Boston, 72 East Concord Street E200, Boston, MA, 02118, USA.
ORCID: 0000-0001-9347-7892

W T Longstreth (WT)

Departments of Neurology and Epidemiology, University of Washington, Seattle, 3980 15th Ave NE Seattle, Seattle, WA, 98195, USA.

Rui Xia (R)

Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, 1825 Pressler Street Houston, Houston, 77030, TX, USA.

Vincent Bouteloup (V)

University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France.

Thomas Mosley (T)

MIND Center, University of Mississippi Medical Center, Jackson, 2500 North State Street, Jackson, MS, 39216, USA.

Lenore J Launer (LJ)

Laboratory of Epidemiology and Population Science, NIA Intramural Research Program, 251 Bayview Blvd, Baltimore, MD, 21224, USA.
ORCID: 0000-0002-3238-7612

Michael Khalil (M)

Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria.
ORCID: 0000-0002-5350-3328

Jens Kuhle (J)

Research Center for Clinical Neuroimmunology and Neuroscience University Hospital, Spitalstrasse 2, CH-4031, Basel, Switzerland.

Robert A Rissman (RA)

Department of Physiology and Neuroscience, Alzheimer's Therapeutic Research Institute, Keck School of Medicine of the University of Southern California, California, USA.

Genevieve Chene (G)

University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France.

Carole Dufouil (C)

University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France.
ORCID: 0000-0003-2442-4476

Luc Djoussé (L)

Brigham and Women's Hospital, Harvard Medical School, Boston, 75 FRANCIS STREET, BOSTON MA 02115, MA, Boston, USA.

Michael J Lyons (MJ)

Department of Psychological & Brain Sciences, Boston University, Boston, 64 Cummington Mall # 149, Boston, MA, 02215, USA.

Kenneth J Mukamal (KJ)

Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, 330 Brookline Avenue Boston, MA, 02215, USA.
ORCID: 0000-0001-8450-6970

William S Kremen (WS)

Department of Psychiatry and Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, 92093, USA.
ORCID: 0000-0002-8629-5609

Carol E Franz (CE)

Department of Psychiatry and Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, 92093, USA.
ORCID: 0000-0002-8987-1755

Reinhold Schmidt (R)

Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036, Graz, Austria.

Stephanie Debette (S)

University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France.
CHU de Bordeaux, Department of Neurology, Institute for Neurodegenerative Diseases, F-33000, Bordeaux, France.
ORCID: 0000-0001-8675-7968

Monique M B Breteler (MMB)

Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1/99, 53127, Bonn, Germany.
Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.
ORCID: 0000-0002-0626-9305

Klaus Berger (K)

Institute of Epidemiology and Social Medicine, University of Münster, Münster, Institut für Epidemiologie und Sozialmedizin Albert-Schweitzer-Campus 1, Gebäude D3 48149, Münster, Germany.

Qiong Yang (Q)

Boston University, Boston, MA, 02215, USA.
ORCID: 0000-0002-3658-1375

Sudha Seshadri (S)

Boston University, Boston, MA, 02215, USA.
Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA.
ORCID: 0000-0001-6135-2622

N Ahmad Aziz (NA)

Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Venusberg-Campus 1/99, 53127, Bonn, Germany.
Department of Neurology, Faculty of Medicine, University of Bonn, 53127, Bonn, Germany.
ORCID: 0000-0001-6184-458X

Mohsen Ghanbari (M)

Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000, CA, Rotterdam, the Netherlands.
ORCID: 0000-0002-9476-7143

M Arfan Ikram (MA)

Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000, CA, Rotterdam, the Netherlands. m.a.ikram@erasmusmc.nl.
ORCID: 0000-0003-0372-8585

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Classifications MeSH

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questionsmedicales.fr › Phénomènes génétiques › Génotype › Prédisposition génétique à une maladie › Genome-wide association study meta-analysis of...
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questionsmedicales.fr › Troubles mentaux › Troubles neurocognitifs › Démence › Maladie d'Alzheimer › Genome-wide association study meta-analysis of...