Integrating Genomic Analysis with the Genetic Basis of Gene Expression: Preliminary Evidence of the Identification of Causal Genes for Cardiovascular and Metabolic Traits Related

Whole-transcriptome expression profiling provides novel phenotypes for analysis of complex traits. Gene expression measurements reflect quantitative variation in transcript-specific messenger RNA levels and represent phenotypes lying close to the action of genes. Understanding the genetic basis of gene expression will provide insight into the processes that connect genotype to clinically significant traits representing a central tenetofsystembiology.Synchronousinvivoexpression profilesoflymphocytes,muscle,and subcutaneousfat wereobtained fromhealthy Mexican men. Mostgeneswereexpressed atdetectable levels inmultipletissues,and RNA levels werecorrelatedbetween tissuetypes. A subsetof transcripts with highreliabilityofexpression across tissues (estimatedbyintraclasscorrelationcoefficients)was enrichedforcis-regulatedgenes, suggestingthat proximal sequence variants may influence expression similarly in different cellular environments. This integrative global gene expression profiling approach is proving extremely useful for identifying genes and pathways that contribute to complex clinical traits. Clearly, the coincidence of clinical trait quantitative trait loci and expression quantitative trait loci can help in the prioritization of positional candidate genes. Such data will be crucial for the formal integration of positional and transcriptomic information characterized as genetical genomics. Adv. Nutr. 3: 596S–604S, 2012.