Identification of therapeutic targets from genetic association studies using hierarchical component analysis
Posted on 2020-06-18 - 03:54
Abstract Background Mapping disease-associated genetic variants to complex disease pathophysiology is a major challenge in translating findings from genome-wide association studies into novel therapeutic opportunities. The difficulty lies in our limited understanding of how phenotypic traits arise from non-coding genetic variants in highly organized biological systems with heterogeneous gene expression across cells and tissues. Results We present a novel strategy, called GWAS component analysis, for transferring disease associations from single-nucleotide polymorphisms to co-expression modules by stacking models trained using reference genome and tissue-specific gene expression data. Application of this method to genome-wide association studies of blood cell counts confirmed that it could detect gene sets enriched in expected cell types. In addition, coupling of our method with Bayesian networks enables GWAS components to be used to discover drug targets. Conclusions We tested genome-wide associations of four disease phenotypes, including age-related macular degeneration, Crohn’s disease, ulcerative colitis and rheumatoid arthritis, and demonstrated the proposed method could select more functional genes than S-PrediXcan, the previous single-step model for predicting gene-level associations from SNP-level associations.
CITE THIS COLLECTION
DataCite
3 Biotech
3D Printing in Medicine
3D Research
3D-Printed Materials and Systems
4OR
AAPG Bulletin
AAPS Open
AAPS PharmSciTech
Abhandlungen aus dem Mathematischen Seminar der Universität Hamburg
ABI Technik (German)
Academic Medicine
Academic Pediatrics
Academic Psychiatry
Academic Questions
Academy of Management Discoveries
Academy of Management Journal
Academy of Management Learning and Education
Academy of Management Perspectives
Academy of Management Proceedings
Academy of Management Review
Lee, Hao-Chih; Ichikawa, Osamu; Glicksberg, Benjamin S.; Divaraniya, Aparna A.; Becker, Christine E.; Agarwal, Pankaj; et al. (2020). Identification of therapeutic targets from genetic association studies using hierarchical component analysis. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.5026739.v1
or
Select your citation style and then place your mouse over the citation text to select it.
SHARE
Usage metrics
Read the peer-reviewed publication
AUTHORS (7)
HL
Hao-Chih Lee
OI
Osamu Ichikawa
BG
Benjamin S. Glicksberg
AD
Aparna A. Divaraniya
CB
Christine E. Becker
PA
Pankaj Agarwal
JD
Joel T. Dudley