Dataset Information

Genes Caught In Flagranti: Integrating Renal Transcriptional Profiles With Genotypes and Phenotypes.

ABSTRACT: In the past decade, population genetics has gained tremendous success in identifying genetic variations that are statistically relevant to renal diseases and kidney function. However, it is challenging to interpret the functional relevance of the genetic variations found by population genetics studies. In this review, we discuss studies that integrate multiple levels of data, especially transcriptome profiles and phenotype data, to assign functional roles of genetic variations involved in kidney function. Furthermore, we introduce state-of-the-art machine learning algorithms, Bayesian networks, support vector machines, and Gaussian process regression, which have been applied successfully to integrating genetic, regulatory, and clinical information to predict clinical outcomes. These methods are likely to be deployed successfully in the nephrology field in the near future.

SUBMITTER: Guan Y

PROVIDER: S-EPMC4518206 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Genes Caught In Flagranti: Integrating Renal Transcriptional Profiles With Genotypes and Phenotypes.

Guan Yuanfang Y Martini Sebastian S Mariani Laura H LH

Seminars in nephrology 20150501 3

In the past decade, population genetics has gained tremendous success in identifying genetic variations that are statistically relevant to renal diseases and kidney function. However, it is challenging to interpret the functional relevance of the genetic variations found by population genetics studies. In this review, we discuss studies that integrate multiple levels of data, especially transcriptome profiles and phenotype data, to assign functional roles of genetic variations involved in kidney ...[more]

PMID: 26215861

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Project description:Eucalyptus species constitutes the most widely planted hardwood trees in temperate and subtropical regions. In this study, we compared the transcript levels of genes involved in lignocellulose formation such as cellulose, hemicellulose and lignin biosynthesis in two selected 3-year old hybrid Eucalyptus (Eucalyptus urophylla × Eucalyptus grandis) genotypes (AM063 and AM380) that have different lignin content. AM063 and AM380 had 20.2 and 35.5% of Klason lignin content and 59.0 and 48.2%, ?-cellulose contents, respectively. We investigated the correlation between wood properties and transcript levels of wood formation-related genes using RNA-seq with total RNAs extracted from developing xylem tissues at a breast height. Transcript levels of cell wall construction genes such as cellulose synthase (CesA) and sucrose synthase (SUSY) were almost the same in both genotypes. However, AM063 exhibited higher transcript levels of UDP-glucose pyrophosphorylase and xyloglucan endotransglucoxylase than those in AM380. Most monolignol biosynthesis-related isozyme genes showed higher transcript levels in AM380. These results indicate monolignol biosynthesis-related genes may regulate wood composition in Eucalyptus. Flavonoids contents were also observed at much higher levels in AM380 as a result of the elevated transcript levels of common phenylpropanoid pathway genes, phenylalanine ammonium lyase, cinnamate-4-hydroxylase (C4H) and 4-coumarate-CoA ligase (4CL). Secondary plant cell wall formation is regulated by many transcription factors. We analyzed genes encoding NAC, WRKY, AP2/ERF, and KNOX transcription factors and found higher transcript levels of these genes in AM380. We also observed increased transcription of some MYB and LIM domain transcription factors in AM380 compared to AM063. All these results show that genes related to monolignol biosynthesis may regulate the wood composition and help maintain the ratio of cellulose and lignin contents in Eucalyptus plants.

Dataset Information

Genes Caught In Flagranti: Integrating Renal Transcriptional Profiles With Genotypes and Phenotypes.

Publications

Genes Caught In Flagranti: Integrating Renal Transcriptional Profiles With Genotypes and Phenotypes.

Similar Datasets

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