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

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 Associati...

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Vydáno v:	Molecular genetics & genomic medicine Ročník 8; číslo 10; s. e1456 - n/a
Hlavní autoři:	Quan, Yuan, Zhang, Qing‐Ye, Lv, Bo‐Min, Xu, Rui‐Feng, Zhang, Hong‐Yu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States John Wiley & Sons, Inc 01.10.2020 John Wiley and Sons Inc Wiley
Témata:	Algorithms Annotations Binding sites Consortia Deoxyribonucleic acid Disease DNA Drug development drug discovery Electronic health records gene function annotation Gene regulation Genes Genetic algorithms Genetic disorders Genetics Genome-wide association studies Genomes Genomics Genotype & phenotype Genotypes Mathematical analysis Medical Subject Headings-MeSH Mutation Networks Original Pathogenesis Phenome‐Wide Association Studies Phenotypes Protein interaction Proteins Research methodology systems genetics Transcription pathogenesis systems genetics drug discovery gene function annotation Phenome-Wide Association Studies Genome-Wide Association Studies
ISSN:	2324-9269, 2324-9269
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Popis
Shrnutí:	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. It is still a great challenge to use GWAS/PheWAS‐derived data to elucidate pathogenesis. 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. 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.
Bibliografie:	Funding information This work was supported by the National Natural Science Foundation of China (grant 31670779) and the Fundamental Research Funds for the Central Universities (Grant 2662017PY115). ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2324-9269 2324-9269
DOI:	10.1002/mgg3.1456