Dataset Information

Differential urinary specific gravity as a molecular phenotype of the bladder cancer genetic association in the urea transporter gene, SLC14A1.

ABSTRACT: Genome-wide association studies (GWAS) identified associations between markers within the solute carrier family 14 (urea transporter), member 1 (SLC14A1) gene and risk of bladder cancer. SLC14A1 defines the Kidd blood groups in erythrocytes and is also involved in concentration of the urine in the kidney. We evaluated the association between a representative genetic variant (rs10775480) of SLC14A1 and urine concentration, as measured by urinary specific gravity (USG), in a subset of 275 population-based controls enrolled in the New England Bladder Cancer Study. Overnight urine samples were collected, and USG was measured using refractometry. Analysis of covariance was used to estimate adjusted least square means for USG in relation to rs10775480. We also examined the mRNA expression of both urea transporters, SLC14A1 and SLC14A2, in a panel of human tissues. USG was decreased with each copy of the rs10775480 risk T allele (p-trend = 0.011) with a significant difference observed for CC vs. TT genotypes (p-value(tukey) =?0.024). RNA-sequencing in the bladder tissue showed high expression of SLC14A1 and the absence of SLC14A2, while both transporters were expressed in the kidney. We suggest that the molecular phenotype of this GWAS finding is the genotype-specific biological activity of SLC14A1 in the bladder tissue. Our data suggest that SLC14A1 could be a unique urea transporter in the bladder that has the ability to influence urine concentration and that this mechanism might explain the increased bladder cancer susceptibility associated with rs10775480.

SUBMITTER: Koutros S

PROVIDER: S-EPMC3797230 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Differential urinary specific gravity as a molecular phenotype of the bladder cancer genetic association in the urea transporter gene, SLC14A1.

Koutros Stella S Baris Dalsu D Fischer Alexander A Tang Wei W Garcia-Closas Montserrat M Karagas Margaret R MR Schwenn Molly M Johnson Alison A Figueroa Jonine J Waddell Richard R Prokunina-Olsson Ludmila L Rothman Nathaniel N Silverman Debra T DT

International journal of cancer 20130716 12

Genome-wide association studies (GWAS) identified associations between markers within the solute carrier family 14 (urea transporter), member 1 (SLC14A1) gene and risk of bladder cancer. SLC14A1 defines the Kidd blood groups in erythrocytes and is also involved in concentration of the urine in the kidney. We evaluated the association between a representative genetic variant (rs10775480) of SLC14A1 and urine concentration, as measured by urinary specific gravity (USG), in a subset of 275 populati ...[more]

PMID: 23754249

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Project description:Polygenic diseases are usually caused by the dysfunction of multiple genes. Unravelling such disease genes is crucial to fully understand the genetic landscape of diseases on molecular level. With the advent of 'omic' data era, network-based methods have prominently boosted disease gene discovery. However, how to make better use of different types of data for the prediction of disease genes remains a challenge.In this study, we improved the performance of disease gene prediction by integrating the similarity of disease phenotype, biological function and network topology. First, for each phenotype, a phenotype-specific network was specially constructed by mapping phenotype similarity information of given phenotype onto the protein-protein interaction (PPI) network. Then, we developed a gene gravity-like algorithm, to score candidate genes based on not only topological similarity but also functional similarity. We tested the proposed network and algorithm by conducting leave-one-out and leave-10%-out cross validation and compared them with state-of-art algorithms. The results showed a preference to phenotype-specific network as well as gene gravity-like algorithm. At last, we tested the predicting capacity of proposed algorithms by test gene set derived from the DisGeNET database. Also, potential disease genes of three polygenic diseases, obesity, prostate cancer and lung cancer, were predicted by proposed methods. We found that the predicted disease genes are highly consistent with literature and database evidence.The good performance of phenotype-specific networks indicates that phenotype similarity information has positive effect on the prediction of disease genes. The proposed gene gravity-like algorithm outperforms the algorithm of Random Walk with Restart (RWR), implicating its predicting capacity by combing topological similarity with functional similarity. Our work will give an insight to the discovery of disease genes by fusing multiple similarities of genes and diseases.

Dataset Information

Differential urinary specific gravity as a molecular phenotype of the bladder cancer genetic association in the urea transporter gene, SLC14A1.

Publications

Differential urinary specific gravity as a molecular phenotype of the bladder cancer genetic association in the urea transporter gene, SLC14A1.

Similar Datasets

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