There is increasing interest in understanding the molecular basis of complex traits. Initially, the genetic dissection of quantitative traits involved measurements of gross phenotypes. Subsequently, specific physiological and developmental components of individual traits have been dissected. Most recently, the underlying mechanisms of inheritance have been studied through various approaches that are supported by modern technological and methodological advances, namely quantitative trait locus/loci (QTL) analysis (Mackay, 2001; Mauricio, 2001; Doerge, 2002) and mutant analysis (Rossant and Spence, 1998; Hughes et al., 2000) in genetics; genome sequencing (Jang et al., 1999; The Arabidopsis Genome Initiative, 2000; Mouse Genome Sequencing Consortium, 2002) and gene expression analysis (Duggan et al., 1999; Lipshutz et al., 1999) in genomics; and protein structure analysis (Service, 1999) and protein assay (Kodadek, 2001; MacBeath, 2002) in proteomics. Since each technology and approach focuses on specific pieces of the larger, poorly understood systems biology, the challenge is to integrate these different types of information to elucidate the genetic architecture of complex traits. In particular, the regulation of complex traits remains poorly understood, and there are still large gaps in our understanding of regulatory networks. Statistically, QTLanalysis has offered many interesting theoretical challenges and complex models that have resulted in useful software. The conclusions of QTL analysis often point to large regions of the genome, typically containing many genes, being associated with a measured quantitative (phenotypic) trait of interest. These QTL are largely regions of unknown function that often disappear in the next experiment or environment. If QTL are localized, and a small number of candidate genes established, it requires large populations of recombinants and extensive replicated experimentation. The focus now is to move beyond the association of molecular markers with quantitative phenotypes to understand the regulation of gene expression and its consequences on the variation of quantitative traits. To achieve this goal, more powerful statistical methods are needed to reveal the genes controlling the expression of complex traits. Proper experimental design and the application of appropriate statistical methodologies to gene expression levels will provide insights into regulatory networks controlling transcript levels and ultimately the regulation of complex trait phenotypes.
ASJC Scopus subject areas
- Materials Science(all)