Additional file 1 of Multivariate GWAS of Alzheimer’s disease CSF biomarker profiles implies GRIN2D in synaptic functioning Article Swipe
Alexander Neumann
,
Olena Ohlei
,
Fahri Küçükali
,
Isabelle Bos
,
Jigyasha Timsina
,
Stephanie J. B. Vos
,
Dmitry Prokopenko
,
Betty M. Tijms
,
Ulf Andréasson
,
Kaj Blennow
,
Rik Vandenberghe
,
Philip Scheltens
,
Charlotte E. Teunissen
,
Sebastiaan Engelborghs
,
Giovanni B. Frisoni
,
Oliver Blin
,
Jill Richardson
,
Régis Bordet
,
Alberto Lleó
,
Daniel Alcolea
,
Julius Popp
,
Thomas W. Marsh
,
Priyanka Gorijala
,
Christopher Clark
,
Gwendoline Peyratout
,
Pablo Martínez‐Lage
,
Mikel Tainta
,
Richard Dobson
,
Cristina Legido‐Quigley
,
Christine Van Broeckhoven
,
Rudolph E. Tanzi
,
Mara ten Kate
,
Christina M. Lill
,
Frederik Barkhof
,
Carlos Cruchaga
,
Simon Lovestone
,
Johannes Streffer
,
Henrik Zetterberg
,
Pieter Jelle Visser
,
Kristel Sleegers
,
Lars Bertram
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.6084/m9.figshare.24248104
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.6084/m9.figshare.24248104
Additional file 1: Table S1. Participant Characteristics CSF Biomarker Sample. Table S2. Participant Characteristics GWAS Discovery Sample. Table S3. Participant Characteristics GWAS Replication Sample. Table S4. Local heritability and variance explained. Table S5. PCA loadings (individual cohorts). Table S6. Sex differences in CSF biomarker PCs and associations with Alzheimer’s disease. Table S7. Comparison of PCA-based GWAS results vs. single biomarker GWAS results. Table S8. Comparison of meta-analysis results using fixed effect vs. random effects models. Table S9a. PC5 top hits in meta-analysis split by discovery datasets. Table S9b. PC5 non-AD synaptic functioning replication and meta-analysis for top SNPs (p < 10E-3). Table S9c. SNP-based association results in Knight-ADRC dataset in GRIN2D region (bold font highlights nominally significant signals). Table S10. TWAS Fine-mapping analyses. Table S11. Gene-set enrichment analyses based on PC-based GWAS (top 20 gene sets [by P-value] are shown; bold font = significant after FDR [0.05]).
Related Topics
Concepts
Multivariate statistics
Biomarker
Disease
Genome-wide association study
Multivariate analysis
Oncology
Medicine
Internal medicine
Neuroscience
Biology
Computer science
Genetics
Machine learning
Gene
Single-nucleotide polymorphism
Genotype
Metadata
- Type
- dataset
- Language
- en
- Landing Page
- https://doi.org/10.6084/m9.figshare.24248104
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394305205
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394305205Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.6084/m9.figshare.24248104Digital Object Identifier
- Title
-
Additional file 1 of Multivariate GWAS of Alzheimer’s disease CSF biomarker profiles implies GRIN2D in synaptic functioningWork title
- Type
-
datasetOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Alexander Neumann, Olena Ohlei, Fahri Küçükali, Isabelle Bos, Jigyasha Timsina, Stephanie J. B. Vos, Dmitry Prokopenko, Betty M. Tijms, Ulf Andréasson, Kaj Blennow, Rik Vandenberghe, Philip Scheltens, Charlotte E. Teunissen, Sebastiaan Engelborghs, Giovanni B. Frisoni, Oliver Blin, Jill Richardson, Régis Bordet, Alberto Lleó, Daniel Alcolea, Julius Popp, Thomas W. Marsh, Priyanka Gorijala, Christopher Clark, Gwendoline Peyratout, Pablo Martínez‐Lage, Mikel Tainta, Richard Dobson, Cristina Legido‐Quigley, Christine Van Broeckhoven, Rudolph E. Tanzi, Mara ten Kate, Christina M. Lill, Frederik Barkhof, Carlos Cruchaga, Simon Lovestone, Johannes Streffer, Henrik Zetterberg, Pieter Jelle Visser, Kristel Sleegers, Lars BertramList of authors in order
- Landing page
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https://doi.org/10.6084/m9.figshare.24248104Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.6084/m9.figshare.24248104Direct OA link when available
- Concepts
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Multivariate statistics, Biomarker, Disease, Genome-wide association study, Multivariate analysis, Oncology, Medicine, Internal medicine, Neuroscience, Biology, Computer science, Genetics, Machine learning, Gene, Single-nucleotide polymorphism, GenotypeTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.6084/m9.figshare.24248104 |
| publication_date | 2023-01-01 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.= | 142 |
| abstract_inverted_index.(p | 98 |
| abstract_inverted_index.1: | 2 |
| abstract_inverted_index.20 | 133 |
| abstract_inverted_index.by | 83 |
| abstract_inverted_index.in | 41, 80, 106, 109 |
| abstract_inverted_index.of | 53, 65 |
| abstract_inverted_index.on | 129 |
| abstract_inverted_index.CSF | 7, 42 |
| abstract_inverted_index.FDR | 145 |
| abstract_inverted_index.PC5 | 77, 88 |
| abstract_inverted_index.PCA | 33 |
| abstract_inverted_index.PCs | 44 |
| abstract_inverted_index.S1. | 4 |
| abstract_inverted_index.S2. | 11 |
| abstract_inverted_index.S3. | 18 |
| abstract_inverted_index.S4. | 25 |
| abstract_inverted_index.S5. | 32 |
| abstract_inverted_index.S6. | 38 |
| abstract_inverted_index.S7. | 51 |
| abstract_inverted_index.S8. | 63 |
| abstract_inverted_index.Sex | 39 |
| abstract_inverted_index.[by | 136 |
| abstract_inverted_index.and | 28, 45, 93 |
| abstract_inverted_index.are | 138 |
| abstract_inverted_index.for | 95 |
| abstract_inverted_index.top | 78, 96 |
| abstract_inverted_index.vs. | 57, 71 |
| abstract_inverted_index.< | 99 |
| abstract_inverted_index.(top | 132 |
| abstract_inverted_index.GWAS | 14, 21, 55, 60, 131 |
| abstract_inverted_index.S10. | 119 |
| abstract_inverted_index.S11. | 124 |
| abstract_inverted_index.S9a. | 76 |
| abstract_inverted_index.S9b. | 87 |
| abstract_inverted_index.S9c. | 102 |
| abstract_inverted_index.SNPs | 97 |
| abstract_inverted_index.TWAS | 120 |
| abstract_inverted_index.bold | 140 |
| abstract_inverted_index.file | 1 |
| abstract_inverted_index.font | 113, 141 |
| abstract_inverted_index.gene | 134 |
| abstract_inverted_index.hits | 79 |
| abstract_inverted_index.sets | 135 |
| abstract_inverted_index.with | 47 |
| abstract_inverted_index.(bold | 112 |
| abstract_inverted_index.Local | 26 |
| abstract_inverted_index.Table | 3, 10, 17, 24, 31, 37, 50, 62, 75, 86, 101, 118, 123 |
| abstract_inverted_index.after | 144 |
| abstract_inverted_index.based | 128 |
| abstract_inverted_index.fixed | 69 |
| abstract_inverted_index.split | 82 |
| abstract_inverted_index.using | 68 |
| abstract_inverted_index.GRIN2D | 110 |
| abstract_inverted_index.effect | 70 |
| abstract_inverted_index.non-AD | 89 |
| abstract_inverted_index.random | 72 |
| abstract_inverted_index.region | 111 |
| abstract_inverted_index.shown; | 139 |
| abstract_inverted_index.single | 58 |
| abstract_inverted_index.10E-3). | 100 |
| abstract_inverted_index.Sample. | 9, 16, 23 |
| abstract_inverted_index.dataset | 108 |
| abstract_inverted_index.effects | 73 |
| abstract_inverted_index.models. | 74 |
| abstract_inverted_index.results | 56, 67, 105 |
| abstract_inverted_index.Gene-set | 125 |
| abstract_inverted_index.P-value] | 137 |
| abstract_inverted_index.PC-based | 130 |
| abstract_inverted_index.[0.05]). | 146 |
| abstract_inverted_index.analyses | 127 |
| abstract_inverted_index.disease. | 49 |
| abstract_inverted_index.loadings | 34 |
| abstract_inverted_index.results. | 61 |
| abstract_inverted_index.synaptic | 90 |
| abstract_inverted_index.variance | 29 |
| abstract_inverted_index.Biomarker | 8 |
| abstract_inverted_index.Discovery | 15 |
| abstract_inverted_index.PCA-based | 54 |
| abstract_inverted_index.SNP-based | 103 |
| abstract_inverted_index.analyses. | 122 |
| abstract_inverted_index.biomarker | 43, 59 |
| abstract_inverted_index.cohorts). | 36 |
| abstract_inverted_index.datasets. | 85 |
| abstract_inverted_index.discovery | 84 |
| abstract_inverted_index.nominally | 115 |
| abstract_inverted_index.signals). | 117 |
| abstract_inverted_index.Additional | 0 |
| abstract_inverted_index.Comparison | 52, 64 |
| abstract_inverted_index.enrichment | 126 |
| abstract_inverted_index.explained. | 30 |
| abstract_inverted_index.highlights | 114 |
| abstract_inverted_index.(individual | 35 |
| abstract_inverted_index.Knight-ADRC | 107 |
| abstract_inverted_index.Participant | 5, 12, 19 |
| abstract_inverted_index.Replication | 22 |
| abstract_inverted_index.association | 104 |
| abstract_inverted_index.differences | 40 |
| abstract_inverted_index.functioning | 91 |
| abstract_inverted_index.replication | 92 |
| abstract_inverted_index.significant | 116, 143 |
| abstract_inverted_index.Fine-mapping | 121 |
| abstract_inverted_index.associations | 46 |
| abstract_inverted_index.heritability | 27 |
| abstract_inverted_index.Alzheimer’s | 48 |
| abstract_inverted_index.meta-analysis | 66, 81, 94 |
| abstract_inverted_index.Characteristics | 6, 13, 20 |
| cited_by_percentile_year | |
| countries_distinct_count | 12 |
| institutions_distinct_count | 41 |
| citation_normalized_percentile |