William Schierding

William's public data

dataset
Machine learning identifies regulatory changes in the lung as key to the conversion of genetic risk to Type 1 diabetes
Denis Nyaga
2020-07-07T00:10:51Z
collection
Collection Liggins Seminars Presents PSANZ
Eleanor Kennedy
2020-04-05T23:31:11Z
project
Project PSANZ
William Schierding
2020-04-03T01:29:39Z
Thumbnail
Liggins @ PSANZ - Schedule
William Schierding
2020-04-03T01:29:04Z
dataset
Supplementary Table 2. List of all Hi-C datasets used in this study and their details
Sreemol Gokuladhas
2020-03-12T09:47:43Z
dataset
Supplementary Table 1. Details on GWAS data used in the study
Sreemol Gokuladhas
2020-03-12T09:45:33Z
dataset
Supplementary Table 6. Gene ontology analysis of the eGenes shared between GS and MG, and GS and MS.
Sreemol Gokuladhas
2019-09-10T21:27:06Z
dataset
Supplementary Table 11. List of all shared druggable eGenes and druggable biological pathways between GS, MG, MS and ALS.
Sreemol Gokuladhas
2019-09-10T06:58:25Z
dataset
Supplementary Table 10. List of druggable eGenes associated with GS, MG, MS and ALS and their drug targets.
Sreemol Gokuladhas
2019-09-10T06:12:08Z
dataset
Supplementary Table 9 List of Druggable eGenes from GS, MG, MS and ALS from immortalized cell line and psoas muscle Hi-C dataset
Sreemol Gokuladhas
2019-09-10T03:21:23Z
dataset
Supplementary Table 8. Overlapping pathways identified between MG, MS and ALS from immortalized cell line Hi-C dataset and psoas muscle Hi-C dataset
Sreemol Gokuladhas
2019-09-10T03:13:32Z
dataset
Supplementary Table 7. List of overlapping pathways identified between GS and NMD (MG, MS and ALS) and the eGenes contributing to those pathways.
Sreemol Gokuladhas
2019-09-10T03:05:58Z
dataset
Supplementary Table 5. List of overlapping eGenes identified between GS, MG, MS and ALS
Sreemol Gokuladhas
2019-09-10T02:45:53Z
dataset
Supplementary Table 4. Summary statistics of the significant eQTLs from CoDeS3D run performed with GS, MG, MS and ALS SNPs on Psoas muscle Hi-C dataset.
Sreemol Gokuladhas
2019-09-10T02:35:33Z
dataset
Supplementary Table 3. Summary statistics of the significant eQTLs from CoDeS3D run performed with GS, MG, MS and ALS SNPs on Immortalized cell lines (GM12878, IMR90, HMEC, NHEK, K562, HUVEC, HeLa and KBM7)
Sreemol Gokuladhas
2019-09-10T02:23:39Z
dataset
Supplementary_tables_metabolites.xlsx
Tayaza Fadason
2019-05-22T03:48:35Z
dataset
Pleiotropy matrices for eGenes and eQTLs
Tayaza Fadason
2018-11-07T14:32:28Z
dataset
eQTLs in the poly-unsaturated fat metabolism cluster
Tayaza Fadason
2018-11-07T13:51:54Z
dataset
eGenes in mendelian disorders
Tayaza Fadason
2018-11-07T13:50:15Z
dataset
Phenotype-eGene associations in some multimorbid clusters.
Tayaza Fadason
2018-11-07T13:47:06Z
dataset
eQTL associations on Chromosome 22
Tayaza Fadason
2018-11-07T13:32:37Z
dataset
eGenes identified in GWAS traits
Tayaza Fadason
2018-11-04T22:11:38Z
dataset
Supplementary spreadsheet 3: Effects of spatial obesity and type 2 diabetes eQTLs in subcutaneous adipose, visceral adipose, skeletal muscle, liver and pancreas
Tayaza Fadason
2017-09-14T02:00:43Z
dataset
Supplementary spreadsheet 2: Type 2 diabetes spatial eQTL-gene interactions
Tayaza Fadason
2017-09-14T01:56:44Z
dataset
Supplementary spreadsheet 1: Obesity spatial eQTL-gene interactions
Tayaza Fadason
2017-09-14T01:49:08Z