Integrating genetic and gene expression data: application to cardiovascular and metabolic traits in mice

The millions of common DNA variations that occur in the human population, or among inbred strains of mice and rats, perturb the expression (transcript levels) of a large fraction of the genes expressed in a particular tissue. The hundreds or thousands of common cis-acting variations that occur in the population may in turn affect the expression of thousands of other genes by affecting transcription factors, signaling molecules, RNA processing, and other processes that act in trans. The levels of transcripts are conveniently quantitated using expression arrays, and the cis- and trans-acting loci can be mapped using quantitative trait locus (QTL) analysis, in the same manner as loci for physiologic or clinical traits. Thousands of such expression QTL (eQTL) have been mapped in various crosses in mice, as well as other experimental organisms, and less detailed maps have been produced in studies of cells from human pedigrees. Such an integrative genetics approach (sometimes referred to as “genetical genomics”) is proving useful for identifying genes and pathways that contribute to complex clinical traits. The coincidence of clinical trait QTL and eQTL can help in the prioritization of positional candidate genes. More importantly, mathematical modeling of correlations between levels of transcripts and clinical traits in genetic crosses can allow prediction of causal interactions and the identification of “key driver” genes. An important objective of such studies will be to model biological networks in physiologic processes. When combined with high-density single nucleotide polymorphism (SNP) mapping, it should be feasible to identify genes that contribute to transcript levels using association analysis in outbred populations. In this review we discuss the basic concepts and applications of this integrative genomic approach to cardiovascular and metabolic diseases

Genetic Networks of Liver Metabolism Revealed by Integration of Metabolic and Transcriptional Profiling

Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes

Plasma Soluble VEGFR-1 Is a Potential Dual Biomarker of Response and Toxicity for Bevacizumab with Chemoradiation in Locally Advanced Rectal Cancer

The article explores plasma and urinary concentrations of two members of the vascular endothelial growth factor family and their receptors as potential response and toxicity biomarkers of bevacizumab with neoadjuvant chemoradiation in patients with localized rectal cancer

A Safety and Survival Analysis of Neoadjuvant Bevacizumab with Standard Chemoradiation in a Phase I/II Study Compared with Standard Chemoradiation in Locally Advanced Rectal Cancer

This study examines the long-term outcome of clinical stage II/III rectal cancer patients treated in a prospective phase II study of bevacizumab with chemoradiation and surgery. Neoadjuvant bevacizumab with standard chemoradiation and surgery shows promising long-term efficacy and safety profiles in locally advanced rectal cancer patients

Combining QTL data for HDL cholesterol levels from two different species leads to smaller confidence intervals.

Quantitative trait locus (QTL) analysis detects regions of a genome that are linked to a complex trait. Once a QTL is detected, the region is narrowed by positional cloning in the hope of determining the underlying candidate gene-methods used include creating congenic strains, comparative genomics and gene expression analysis. Combined cross analysis may also be used for species such as the mouse, if the QTL is detected in multiple crosses. This process involves the recoding of QTL data on a per-chromosome basis, with the genotype recoded on the basis of high- and low-allele status. The data are then combined and analyzed; a successful analysis results in a narrowed and more significant QTL. Using parallel methods, we show that it is possible to narrow a QTL by combining data from two different species, the rat and the mouse. We combined standardized high-density lipoprotein phenotype values and genotype data for the rat and mouse using information from one rat cross and two mouse crosses. We successfully combined data within homologous regions from rat Chr 6 onto mouse Chr 12, and from rat Chr 10 onto mouse Chr 11. The combinations and analyses resulted in QTL with smaller confidence intervals and increased logarithm of the odds ratio scores. The numbers of candidate genes encompassed by the QTL on mouse Chr 11 and 12 were reduced from 1343 to 761 genes and from 613 to 304 genes, respectively. This is the first time that QTL data from different species were successfully combined; this method promises to be a useful tool for narrowing QTL intervals