Unknown

Dataset Information

0

Genome-wide pathogenesis interpretation using a heat diffusion-based systems genetics method and implications for gene function annotation.


ABSTRACT: BACKGROUND:Genetics is best dedicated to interpreting pathogenesis and revealing gene functions. The past decade has witnessed unprecedented progress in genetics, particularly in genome-wide identification of disorder variants through Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS). However, it is still a great challenge to use GWAS/PheWAS-derived data to elucidate pathogenesis. METHODS:In this study, we used HotNet2, a heat diffusion-based systems genetics algorithm, to calculate the networks for disease genes obtained from GWAS and PheWAS, with an attempt to get deeper insights into disease pathogenesis at a molecular level. RESULTS:Through HotNet2 calculation, significant networks for 202 (for GWAS) and 167 (for PheWAS) types of diseases were identified and evaluated, respectively. The GWAS-derived disease networks exhibit a stronger biomedical relevance than PheWAS counterparts. Therefore, the GWAS-derived networks were used for pathogenesis interpretation by integrating the accumulated biomedical information. As a result, the pathogenesis for 64 diseases was elucidated in terms of mutation-caused abnormal transcriptional regulation, and 47 diseases were preliminarily interpreted in terms of mutation-caused varied protein-protein interactions. In addition, 3,802 genes (including 46 function-unknown genes) were assigned with new functions by disease network information, some of which were validated through mice gene knockout experiments. CONCLUSIONS:Systems genetics algorithm HotNet2 can efficiently establish genotype-phenotype links at the level of biological networks. Compared with original GWAS/PheWAS results, HotNet2-calculated disease-gene associations have stronger biomedical significance, hence provide better interpretations for the pathogenesis of genome-wide variants, and offer new insights into gene functions as well. These results are also helpful in drug development.

SUBMITTER: Quan Y 

PROVIDER: S-EPMC7549611 | BioStudies | 2020-01-01

REPOSITORIES: biostudies

Similar Datasets

2018-01-01 | S-EPMC5789733 | BioStudies
2012-01-01 | S-EPMC3476448 | BioStudies
2015-01-01 | S-EPMC4666492 | BioStudies
2014-01-01 | S-EPMC3904236 | BioStudies
2013-01-01 | S-EPMC3969265 | BioStudies
2016-01-01 | S-EPMC5035188 | BioStudies
2018-01-01 | S-EPMC6191429 | BioStudies
2016-01-01 | S-EPMC4718547 | BioStudies
2020-01-01 | S-EPMC7178436 | BioStudies
2017-01-01 | S-EPMC5846687 | BioStudies