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

Abstract

Summary 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 (SVM), and Gaussian Process Regression (GPR), which have been successfully applied to integrating genetic, regulatory, and clinical information to predict clinical outcomes. These methods are likely to be successfully deployed in the nephrology field in the near future.

Defining Glomerular Disease in Mechanistic Terms: Implementing an Integrative Biology Approach in Nephrology

“…These data can be obtained from prospectively procured or archival tissue samples and analyzed by quantitative PCR for transcripts of interest, microarrays that can detect thousands of transcripts and transcript-wide sequencing (e.g., RNA-seq). Expression profiles can be analyzed at the specific transcript level, or prior knowledge can be used to aggregate transcripts known to interact in functional groups for association with disease groups or clinical disease progression (12). Of note, the mRNA levels may not necessarily correlate with actual protein levels given the additional regulation of protein production, modification, and degradation.…”