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31 datasets found
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Supplementary Tables: Chromatin interactions and expression quantitative...
The University of AucklandSpatial eGene overlap matrix for complex traitsCreated 1 August 2019 • Updated 29 March 2025 -
Machine learning identifies regulatory changes in the lung as key to the...
The University of AucklandAll datasets and supplementary information for this article.Created 1 November 2020 • Updated 29 March 2025 -
Supplementary data 3. Gene regulatory interactions of the SNPs associated...
The University of AucklandWe identified cis and trans regulatory interactions of the SNPs associated with 18 AiDs through chromatin-contact data and eQTL associations. The analysis was performed on each disease separately and the results are presented here. The coordinates of the SNPs and the genes are provided according to hg38 genome assembly.Created 1 April 2021 • Updated 29 March 2025 -
Untangling the genetic link between type 1 and type 2 diabetes using...
The University of AucklandAll datasets and supplementary information for this article.Created 1 November 2020 • Updated 29 March 2025 -
The Genetic Contributions to Pre-Eclampsia
The University of AucklandSupplementary data for Genevieve Boom's honours thesis 2021.Created 8 August 2022 • Updated 29 March 2025 -
Supplementary data 1. Significant eQTL-gene contacts identified within LGRN,...
The University of AucklandThe liver gene regulatory network (LGRN) we constructed consisted of 327,145 eQTLs and 8097 genes with 468,560 distinct spatially constrained eQTL-target gene interactions. The majority of the eQTLs (92% with single target genes, and 95% with multiple target genes) acted via cis regulatory interactions. Within the LGRN, 77% of the eQTL target genes are...Created 8 August 2022 • Updated 29 March 2025 -
Supplementary data 2. The gene regulatory interactions of the NAFLD-SNPs...
The University of AucklandWe identified SNPs that are strongly in linkage disequilibrium (r2 >= 0.8) with the NAFLD-associated SNPs reported by GWAS. The gene regulatory interactions of the NAFLD-SNPs (GWAS + LD) were extracted from the LGRN (spatially constrained; derived from a cohort of NAFLD unaffected individuals) and an eQTL-gene network derived from liver samples from a...Created 8 August 2022 • Updated 29 March 2025 -
Supplementary data 3. The gene regulatory interactions involving...
The University of AucklandThe spatially constrained eQTLs associated with the differential regulation of the NAFLD-candidate genes were identified from the LGRN. There were 122 spatially constrained eQTLs regulating 13 NAFLD-candidate genes. And the eQTLs were significantly enriched for the GWAS traits linked with NAFLD such as liver enzyme levels.Created 8 August 2022 • Updated 29 March 2025 -
Supplementary data 4. The interaction partners of the NAFLD-candidate genes...
The University of AucklandThe proteins directly interacting with the proteins encoded by NAFLD-candidate genes were identified from the liver-specific protein-protein interaction network (PPIN). The interacting partners were significantly enriched within biological pathways related to cancers and NAFLD pathogenesis. The eQTLs regulating the interacting partners were enriched for...Created 8 August 2022 • Updated 29 March 2025 -
Supplementary data 5. The non-NAFLD eQTLs associated with the NAFLD-eQTL...
The University of AucklandThe non-NAFLD eQTLs regulating the NAFLD-eQTL target genes were identified from the LGRN. The trait enrichment analysis found that those non-NAFLD eQTLs were enriched for the GWAS traits that were previously associated with NAFLD (e.g. obesity) as well as the traits that were not previously linked to NAFLD (e.g. breast size).Created 8 August 2022 • Updated 29 March 2025 -
Whole blood spatial gene regulatory network
The University of AucklandThis dataset consists of spatially constrained eQTLs. Briefly, all common single nucleotide polymorphisms (SNPs; MAF >0.05) present in whole blood were obtained from GTEx v8 and used to interrogate prepared Hi-C libraries. This allowed for the identification of SNP-gene pairs that are physically interacting. The identified SNP-gene pairs were then...Created 8 August 2022 • Updated 29 March 2025 -
Adult brain cortex spatial gene regulatory network
The University of AucklandThis dataset consists of spatially constrained eQTLs. Briefly, all common single nucleotide polymorphisms (SNPs; MAF >0.05) present in adult brain cortex tissue were obtained from GTEx v8 and used to interrogate prepared Hi-C libraries. This allowed for the identification of SNP-gene pairs that are physically interacting. The identified SNP-gene pairs...Created 8 August 2022 • Updated 29 March 2025 -
Left ventricle spatial gene regulatory network
The University of AucklandThis dataset consists of spatially constrained eQTLs. Briefly, all common single nucleotide polymorphisms (SNPs; MAF >0.05) present in left ventricle tissue were obtained from GTEx v8 and used to interrogate prepared Hi-C libraries. This allowed for the identification of SNP-gene pairs that are physically interacting. The identified SNP-gene pairs were...Created 8 August 2022 • Updated 29 March 2025 -
Chapter 2 (Farrow et al. 2022; Brain) Supplementary tables
The University of AucklandThis repository contains the supplementary tables for chapter 2: Establishing gene regulatory networks from Parkinson's disease risk loci. See below for supplementary table descriptions. Supplementary table 2.1: Nalls et al. 2019 GWAS dataset. Data downloaded directly from the publication Supplementary files. Odds ratios obtained from the IPDGC PD locus...Created 8 August 2022 • Updated 29 March 2025 -
Chapter 3 Supplementary tables
The University of AucklandThis repository contains the supplementary tables for Chapter 3: Network-based identification of pleiotropic genes for Parkinson's disease and Type II Diabetes Supplementary Table 1: GWAS SNP sets used for analyses - T2D and PD Supplementary Table 2: PD GWAS: Sig. eQTLS CoDeS3D output - CNS tissues only Supplementary Table 3: PD GWAS: Sig. eQTLS CoDeS3D...Created 8 August 2022 • Updated 29 March 2025 -
Chapter 4 (Schierding & Farrow et al 2020; Movement Disorders) Supplementary tables
The University of AucklandThis repository contains the supplementary tables for chapter 4: Common variants co-regulate expression of GBA and modifier genes to delay Parkinson’s disease onset. See below for supplementary table descriptions. Supplementary Table 4.1. Hi-C datasets used in this study. List of all Hi-C datasets used in this study and their reference to original article...Created 8 August 2022 • Updated 29 March 2025 -
Chapter 5 Supplementary tables
The University of AucklandThis repository contains the supplementary tables for chapter 5: Mapping enhancer activity for loci marked by Parkinson’s disease variants using a Massively Parallel Reporter AssaySupplementary table 5.1: MPRA variants that have already been tested in previous MPRA studiesSupplementary table 5.2: Additional GWAS SNPs included in the assay, an extension...Created 8 August 2022 • Updated 29 March 2025 -
Supplementary Tables - Polyglutamine Disorders
The University of AucklandSupplementary Tables supporting the spatial constraint analysis (CoDeS3D) of Polyglutamine Disorder-related genetic markers regulating 9 diseases.Diseases: Spinocerebellar Ataxia 1 (SCA 1), Spinocerebellar Ataxia 2 (SCA 2), Spinocerebellar Ataxia 3 (SCA 3), Spinocerebellar Ataxia 6 (SCA 6), Spinocerebellar Ataxia 7 (SCA 7), Spinocerebellar Ataxia 17 (SCA...Created 8 August 2022 • Updated 29 March 2025 -
Supplementary Tables - DRPLA Disease
The University of AucklandSupplementary Tables supporting the spatial constraint analysis (CoDeS3D) of DRPLA-related genetic markers regulating or located within the ATN1 locus.Created 8 August 2022 • Updated 29 March 2025 -
Supplementary Tables - Huntington's Disease
The University of AucklandSupplementary Tables supporting the spatial constraint analysis (CoDeS3D) of HD-related genetic markers and haplotypes of HTT.Created 8 August 2022 • Updated 29 March 2025
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