Automated Author Profile

Hawkes, Gareth

University of Exeter

Current S-Index

3.0

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.5

Average Dataset Index per dataset

Total Datasets

6

Total datasets for this author

Average FAIR Score

82.1%

Average FAIR Score per dataset

Total Citations

0

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Hawkes et al 2025 UKB-WGS-Olink Summary Statistics

Summary statistics for Hawkes et. al 202X "Whole genome sequencing analysis identifies rare, large-effect non-coding variants and regulatory regions associated with circulating protein levels". Single variant summary statistics (minor allele count >=5) for EUR, AFR and SAS ancestries, and aggregates for EUR-only .

Authors

  • Hawkes, Gareth
0 Citations0 Mentions77% FAIR0.5 Dataset Index
10.5281/zenodo.142036282025

Hawkes et al 2025 UKB-WGS-Olink Summary Statistics

Summary statistics for Hawkes et. al 202X "Whole genome sequencing analysis identifies rare, large-effect non-coding variants and regulatory regions associated with circulating protein levels". Single variant summary statistics (minor allele count >=5) for EUR, AFR and SAS ancestries, and aggregates for EUR-only .

Authors

  • Hawkes, Gareth
0 Citations0 Mentions77% FAIR0.5 Dataset Index
10.5281/zenodo.142036292025

Additional file 2 of Insights into the genetics of menopausal vasomotor symptoms: genome-wide analyses of routinely-collected primary care health records

Additional file 2: Supplementary Table 1. Read v2 and CTV3 codes used to identify women with vasomotor symptoms. Supplementary Table 2. Numbers of women included in the genome-wide analyses. Supplementary Table 3. Loss-of-function variants in TACR3 identified in analysis of UK Biobank exome sequencing data. Supplementary Table 4. Results of gene burden and single variant analyses of TACR3 in exome sequencing data from UK Biobank. Supplementary Table 5. Comparison of the effects on VMS and age at menarche of rs34867104 (GWAS signal) and rs144292455 (rare loss of function variant) in TACR3. Supplementary Table 6. Conditional analyses of variants in TACR3 associated with VMS and age at menarche. Supplementary Table 7. Genetic signals identified by GWAS of HRT phenotypes. Supplementary Table 8. Results of Mendelian randomisation analyses of association of age at menopause with HRT use. Supplementary Table 9. Heterogeneity in effect of genetic variants on HRT use before and after 2002. Supplementary Table 10. PsychENCODE brain eQTL data for TACR3.

Authors

  • Ruth, Katherine S. ;
  • Beaumont, Robin N. ;
  • Locke, Jonathan M. ;
  • Tyrrell, Jessica ;
  • Crandall, Carolyn J. ;
  • Hawkes, Gareth ;
  • Frayling, Timothy M. ;
  • Prague, Julia K. ;
  • Patel, Kashyap A. ;
  • Wood, Andrew R. ;
  • Weedon, Michael N. ;
  • Murray, Anna
0 Citations0 Mentions85% FAIR0.5 Dataset Index
10.6084/m9.figshare.242343312024

Additional file 2 of Insights into the genetics of menopausal vasomotor symptoms: genome-wide analyses of routinely-collected primary care health records

Additional file 2: Supplementary Table 1. Read v2 and CTV3 codes used to identify women with vasomotor symptoms. Supplementary Table 2. Numbers of women included in the genome-wide analyses. Supplementary Table 3. Loss-of-function variants in TACR3 identified in analysis of UK Biobank exome sequencing data. Supplementary Table 4. Results of gene burden and single variant analyses of TACR3 in exome sequencing data from UK Biobank. Supplementary Table 5. Comparison of the effects on VMS and age at menarche of rs34867104 (GWAS signal) and rs144292455 (rare loss of function variant) in TACR3. Supplementary Table 6. Conditional analyses of variants in TACR3 associated with VMS and age at menarche. Supplementary Table 7. Genetic signals identified by GWAS of HRT phenotypes. Supplementary Table 8. Results of Mendelian randomisation analyses of association of age at menopause with HRT use. Supplementary Table 9. Heterogeneity in effect of genetic variants on HRT use before and after 2002. Supplementary Table 10. PsychENCODE brain eQTL data for TACR3.

Authors

  • Ruth, Katherine S. ;
  • Beaumont, Robin N. ;
  • Locke, Jonathan M. ;
  • Tyrrell, Jessica ;
  • Crandall, Carolyn J. ;
  • Hawkes, Gareth ;
  • Frayling, Timothy M. ;
  • Prague, Julia K. ;
  • Patel, Kashyap A. ;
  • Wood, Andrew R. ;
  • Weedon, Michael N. ;
  • Murray, Anna
0 Citations0 Mentions85% FAIR0.5 Dataset Index
10.6084/m9.figshare.24234331.v12024

Additional file 2 of Clustering of predicted loss-of-function variants in genes linked with monogenic disease can explain incomplete penetrance

Additional file 2. Table S1. Contains Table S1 showing the clusters assigned to genes, along with descriptive data on the genes

Authors

  • Beaumont, Robin N. ;
  • Hawkes, Gareth ;
  • Gunning, Adam C. ;
  • Wright, Caroline F.
0 Citations0 Mentions85% FAIR0.5 Dataset Index
10.6084/m9.figshare.267129592024

Additional file 2 of Clustering of predicted loss-of-function variants in genes linked with monogenic disease can explain incomplete penetrance

Additional file 2. Table S1. Contains Table S1 showing the clusters assigned to genes, along with descriptive data on the genes

Authors

  • Beaumont, Robin N. ;
  • Hawkes, Gareth ;
  • Gunning, Adam C. ;
  • Wright, Caroline F.
0 Citations0 Mentions85% FAIR0.5 Dataset Index
10.6084/m9.figshare.26712959.v12024