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: journals.permissions@oup.com.

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 -