Degradomic Identification of Membrane Type 1-Matrix Metalloproteinase as an ADAMTS9 and ADAMTS20 Substrate

Nandadasa, Sumeda; Martin, Daniel R.; Deshpande, Gauravi; Robert, Karyn L; Stack, M. Sharon; Itoh, Yoshifumi

doi:10.1016/j.mcpro.2023.100566

Cited by 2 publications

(1 citation statement)

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“…The pathway analyses showed enrichment in several metabolic pathways, especially a secondary metabolic pathway, glucuronidation [ 33 , 34 ], which was enriched in four databases. The top genes included ADAMTS20 , a metalloprotease that targets glycosylated proteins [ 35 , 36 ], and MUC21 , a gene that encodes a large membrane-bound glycoprotein [ 37 , 38 ]. The right module (purple) included conventional indicators of liver damage regulated by 175 pleiotropic genes.…”

Section: Resultsmentioning

confidence: 99%

Identifying pleiotropic genes via the composite test amidst the complexity of polygenic traits

Lai,

Huang

2024

Briefings in Bioinformatics

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Identifying the causal relationship between genotype and phenotype is essential to expanding our understanding of the gene regulatory network spanning the molecular level to perceptible traits. A pleiotropic gene can act as a central hub in the network, influencing multiple outcomes. Identifying such a gene involves testing under a composite null hypothesis where the gene is associated with, at most, one trait. Traditional methods such as meta-analyses of top-hit $P$-values and sequential testing of multiple traits have been proposed, but these methods fail to consider the background of genome-wide signals. Since Huang’s composite test produces uniformly distributed $P$-values for genome-wide variants under the composite null, we propose a gene-level pleiotropy test that entails combining the aforementioned method with the aggregated Cauchy association test. A polygenic trait involves multiple genes with different functions to co-regulate mechanisms. We show that polygenicity should be considered when identifying pleiotropic genes; otherwise, the associations polygenic traits initiate will give rise to false positives. In this study, we constructed gene–trait functional modules using the results of the proposed pleiotropy tests. Our analysis suite was implemented as an R package PGCtest. We demonstrated the proposed method with an application study of the Taiwan Biobank database and identified functional modules comprising specific genes and their co-regulated traits.

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