This leads to the potential of association mapping for complex trait analyses [7]. Compared with linkage mapping, association mapping is a high-resolution method based on linkage disequilibrium (LD), and has recently been applied to plant populations [8], [9] and [10]. Here we propose that instead of just using

SNPs as variants in LD analysis for the detection of QTL, the molecular variants of four -omics datasets can also be used as generalized genotypes in association mapping for complex traits. This multi-omics approach would be crucial for the identification of what we term quantitative trait SNPs (QTS), quantitative trait transcripts (QTT) check details [5], quantitative trait proteins (QTP), and quantitative trait metabolites (QTM). The association mapping

based on the four -omics datasets can in compendium or in conjunction be called QTX mapping, a more general term we suggest for use in this type of research. In addition to the detection of QTX themselves, G × G interaction (epistasis) and G × E interaction can also be detected by QTX mapping. These interaction effects may explain a considerable proportion of the missing heritability associated with QTL based on individual molecular marker loci [11]. In general, the size of datasets involved in QTX mapping will be an order of magnitude larger than the size of datasets for typical QTL detection. This has presented a challenge that has hardly been matched by contemporary hardware, making QTX analysis difficult selleck chemical to perform efficiently until recently with the application of GPU (Graphic Processor Unit) parallel computation which has significantly increased the ability to solve computationally intensive biological problems [12] and [13]. GPU parallel computation addresses the ever-increasing demand for higher computational speed and has paved the way for the analysis of -omics data from large scale or multiple layer experiments. Tobacco (Nicotiana tabacum L.) is one of the most important model plants in genetic

analysis. The quality of tobacco leaves is determined by the composition and quantity of metabolites [14], which are quantitative traits controlled by multiple genes and environmental factors. Previous studies on genetic architecture and regulated ID-8 network of such complex traits were unable to comprehensively dissect the mechanism of catabolism, anabolism or accumulation of these metabolites [15], [16], [17], [18], [19], [20], [21] and [22]. Implementation of QTX mapping by using various types of -omics datasets in tobacco was predicted as a useful opportunity to illustrate the regulated networks involved in genetic control of these complex traits. Therefore, for this study, we conducted QTX mapping to reveal the genetic architecture of two complex traits in tobacco leaves.