TY - JOUR
T1 - TIGA
T2 - target illumination GWAS analytics
AU - Yang, Jeremy J
AU - Grissa, Dhouha
AU - Lambert, Christophe G
AU - Bologa, Cristian G
AU - Mathias, Stephen L
AU - Waller, Anna
AU - Wild, David J
AU - Jensen, Lars Juhl
AU - Oprea, Tudor I.
N1 - © The Author(s) (2021). Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].
PY - 2021
Y1 - 2021
N2 - MOTIVATION: Genome wide association studies (GWAS) can reveal important genotype-phenotype associations, however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study.METHODS: Here, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite Relative Citation Ratio, and meanRank scores, to aggregate multivariate evidence.RESULTS: This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists.AVAILABILITY: Web application, datasets, and source code via: https://unmtid-shinyapps.net/tiga/.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
AB - MOTIVATION: Genome wide association studies (GWAS) can reveal important genotype-phenotype associations, however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study.METHODS: Here, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite Relative Citation Ratio, and meanRank scores, to aggregate multivariate evidence.RESULTS: This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists.AVAILABILITY: Web application, datasets, and source code via: https://unmtid-shinyapps.net/tiga/.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
U2 - 10.1093/bioinformatics/btab427
DO - 10.1093/bioinformatics/btab427
M3 - Journal article
C2 - 34086846
VL - 37
SP - 3865
EP - 3873
JO - Computer Applications in the Biosciences
JF - Computer Applications in the Biosciences
SN - 1471-2105
IS - 21
ER -