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STOPGAP: a database for systematic target opportunity assessment by genetic association predictions
Author(s) -
Judong Shen,
Kijoung Song,
Andrew J. Slater,
Enrico Ferrero,
Matthew R. Nelson
Publication year - 2017
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btx274
Subject(s) - genome wide association study , genetic association , python (programming language) , computational biology , computer science , biology , gene , genetics , programming language , genotype , single nucleotide polymorphism
We developed the STOPGAP (Systematic Target OPportunity assessment by Genetic Association Predictions) database, an extensive catalog of human genetic associations mapped to effector gene candidates. STOPGAP draws on a variety of publicly available GWAS associations, linkage disequilibrium (LD) measures, functional genomic and variant annotation sources. Algorithms were developed to merge the association data, partition associations into non-overlapping LD clusters, map variants to genes and produce a variant-to-gene score used to rank the relative confidence among potential effector genes. This database can be used for a multitude of investigations into the genes and genetic mechanisms underlying inter-individual variation in human traits, as well as supporting drug discovery applications.

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