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This page shows the basic information of the selected gene.
This page shows the basic information of the selected drug.
Gene | |
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Name | |
Type | |
Alias |
Quantitative Trait Locus (Beta > 0)
SNP ID | Beta | S.E. | P | SOURCE |
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Loading xQTL data for this gene... |
Quantitative Trait Locus (Beta < 0)
SNP ID | Beta | S.E. | P | SOURCE |
---|---|---|---|---|
Loading xQTL data for this gene... |
Drug | |
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Type | |
Groups | |
ATC | |
SMILES |
PLOT CONTROL
LEGEND
QTL Dataset # 1 | |
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Name | pQTL_ROSMAP_DLPFC |
Type | pQTL |
Year | 2021 |
Cohort | ROSMAP |
Brain Region | Dorsolateral Prefrontal Cortex |
Sample | NCI, MCI, and AD |
Reference | Genetic control of the human brain proteome. Am J Hum Genet. 2021;108(3):400-410. |
PubMed | 33571421 |
Source | https://www.synapse.org/#!Synapse:syn24847777 |
QTL Dataset # 2 | |
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Name | pQTL_ROSMAP_NCI_DLPFC |
Type | pQTL |
Year | 2021 |
Cohort | ROSMAP |
Brain Region | Dorsolateral Prefrontal Cortex |
Sample | NCI only |
Reference | Genetic control of the human brain proteome. Am J Hum Genet. 2021;108(3):400-410. |
PubMed | 33571421 |
Source | https://www.synapse.org/#!Synapse:syn24847777 |
QTL Dataset # 3 | |
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Name | pQTL_BannerBBDP_DLPFC |
Type | pQTL |
Year | 2021 |
Cohort | Banner BBDP |
Brain Region | Dorsolateral Prefrontal Cortex |
Sample | NCI, MCI, and AD |
Reference | Genetic control of the human brain proteome. Am J Hum Genet. 2021;108(3):400-410. |
PubMed | 33571421 |
Source | https://www.synapse.org/#!Synapse:syn24847777 |
QTL Dataset # 4 | |
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Name | eQTL_Cortex_MetaAnalysis |
Type | eQTL |
Year | 2020 |
Cohort | Multiple |
Brain Region | Dorsolateral Prefrontal Cortex |
Sample | |
Reference | Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions. Sci Data. 2020;7(1):340. |
PubMed | 33046718 |
Source | https://www.synapse.org/#!Synapse:syn17015233 |
QTL Dataset # 5 | |
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Name | eQTL_DLPFC_ROSMAP |
Type | eQTL |
Year | 2020 |
Cohort | ROSMAP |
Brain Region | Dorsolateral Prefrontal Cortex |
Sample | |
Reference | Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions. Sci Data. 2020;7(1):340. |
PubMed | 33046718 |
Source | https://www.synapse.org/#!Synapse:syn17015233 |
QTL Dataset # 6 | |
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Name | eQTL_CER_Mayo |
Type | eQTL |
Year | 2020 |
Cohort | Mayo RNAseq |
Brain Region | Cerebellum |
Sample | |
Reference | Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions. Sci Data. 2020;7(1):340. |
PubMed | 33046718 |
Source | https://www.synapse.org/#!Synapse:syn17015233 |
QTL Dataset # 7 | |
---|---|
Name | eQTL_TCX_Mayo |
Type | eQTL |
Year | 2020 |
Cohort | Mayo RNAseq |
Brain Region | Temporal Cortex |
Sample | |
Reference | Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions. Sci Data. 2020;7(1):340. |
PubMed | 33046718 |
Source | https://www.synapse.org/#!Synapse:syn17015233 |
QTL Dataset # 8 | |
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Name | eQTL_MetaBrain_BasalGanglia-EUR |
Type | eQTL |
Year | 2021 |
Cohort | Multiple |
Brain Region | Basal Ganglia |
Sample | EUR |
Reference | Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases. bioRxiv 2021.03.01.433439 |
PubMed | |
Source | https://www.biorxiv.org/content/10.1101/2021.03.01.433439v2.supplementary-material |
QTL Dataset # 9 | |
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Name | eQTL_MetaBrain_Cerebellum-EUR |
Type | eQTL |
Year | 2021 |
Cohort | Multiple |
Brain Region | Cerebellum |
Sample | EUR |
Reference | Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases. bioRxiv 2021.03.01.433439 |
PubMed | |
Source | https://www.biorxiv.org/content/10.1101/2021.03.01.433439v2.supplementary-material |
QTL Dataset # 10 | |
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Name | eQTL_MetaBrain_Cortex-EUR |
Type | eQTL |
Year | 2021 |
Cohort | Multiple |
Brain Region | Cortex |
Sample | EUR |
Reference | Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases. bioRxiv 2021.03.01.433439 |
PubMed | |
Source | https://www.biorxiv.org/content/10.1101/2021.03.01.433439v2.supplementary-material |
QTL Dataset # 11 | |
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Name | eQTL_MetaBrain_Hippocampus-EUR |
Type | eQTL |
Year | 2021 |
Cohort | Multiple |
Brain Region | Hippocampus |
Sample | EUR |
Reference | Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases. bioRxiv 2021.03.01.433439 |
PubMed | |
Source | https://www.biorxiv.org/content/10.1101/2021.03.01.433439v2.supplementary-material |
QTL Dataset # 12 | |
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Name | eQTL_MetaBrain_SpinalCord-EUR |
Type | eQTL |
Year | 2021 |
Cohort | Multiple |
Brain Region | Spinal Cord |
Sample | EUR |
Reference | Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases. bioRxiv 2021.03.01.433439 |
PubMed | |
Source | https://www.biorxiv.org/content/10.1101/2021.03.01.433439v2.supplementary-material |
QTL Dataset # 13 | |
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Name | eQTL_MetaBrain_Cortex-AFR |
Type | eQTL |
Year | 2021 |
Cohort | Multiple |
Brain Region | Cortex |
Sample | AFR |
Reference | Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases. bioRxiv 2021.03.01.433439 |
PubMed | |
Source | https://www.biorxiv.org/content/10.1101/2021.03.01.433439v2.supplementary-material |
QTL Dataset # 14 | |
---|---|
Name | eQTL_MetaBrain_Cortex-EAS |
Type | eQTL |
Year | 2021 |
Cohort | Multiple |
Brain Region | Cortex |
Sample | EAS |
Reference | Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases. bioRxiv 2021.03.01.433439 |
PubMed | |
Source | https://www.biorxiv.org/content/10.1101/2021.03.01.433439v2.supplementary-material |
QTL Dataset # 15 | |
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Name | eQTL_MetaBrain_Cortex-EUR-AFR-NoENA-NoPCA |
Type | eQTL |
Year | 2021 |
Cohort | Multiple |
Brain Region | Cortex |
Sample | EUR-AFR-NoENA |
Reference | Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases. bioRxiv 2021.03.01.433439 |
PubMed | |
Source | https://www.biorxiv.org/content/10.1101/2021.03.01.433439v2.supplementary-material |
QTL Dataset # 16 | |
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Name | eQTL_Microglia_MFG |
Type | eQTL |
Year | 2022 |
Cohort | NBB |
Brain Region | Medial Frontal Gyrus (MFG) |
Sample | Neurodegenerative, neurological, or neuropsychiatric disorders, and unaffected controls |
Reference | Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nat Genet. 2022;54(1):4-17. |
PubMed | 34992268 |
Source | https://doi.org/10.5281/zenodo.4118605 |
QTL Dataset # 17 | |
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Name | eQTL_Microglia_STG |
Type | eQTL |
Year | 2022 |
Cohort | NBB |
Brain Region | Superior Temporal Gyrus (STG) |
Sample | Neurodegenerative, neurological, or neuropsychiatric disorders, and unaffected controls |
Reference | Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nat Genet. 2022;54(1):4-17. |
PubMed | 34992268 |
Source | https://doi.org/10.5281/zenodo.4118605 |
QTL Dataset # 18 | |
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Name | eQTL_Microglia_THA |
Type | eQTL |
Year | 2022 |
Cohort | NBB |
Brain Region | Thalamus (THA) |
Sample | Neurodegenerative, neurological, or neuropsychiatric disorders, and unaffected controls |
Reference | Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nat Genet. 2022;54(1):4-17. |
PubMed | 34992268 |
Source | https://doi.org/10.5281/zenodo.4118605 |
QTL Dataset # 19 | |
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Name | eQTL_Microglia_SVZ |
Type | eQTL |
Year | 2022 |
Cohort | NBB |
Brain Region | Subventricular Zone (SVZ) |
Sample | Neurodegenerative, neurological, or neuropsychiatric disorders, and unaffected controls |
Reference | Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nat Genet. 2022;54(1):4-17. |
PubMed | 34992268 |
Source | https://doi.org/10.5281/zenodo.4118605 |