Quantitative genetics in the age of omics

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The use of natural variation in the genetic dissection of quantitative traits has a long-standing tradition. Recent advances in high-throughput technologies for the quantification of biological molecules have shifted the focus in quantitative genetics from single traits to comprehensive large-scale analyses. So-called omic technologies now enable geneticists to take a look in the black box that translates genetic information into biological function. These processes include transcriptional and (post) translational regulation as well as metabolic signaling pathways. The progress made in analytical and statistical techniques now allows the construction of regulatory networks that integrate the different levels of the biological information flow from gene-to-function.

Introduction

For most organisms, including plants, variation between individuals of the same species is observed in nature, which can partly be explained by genetic differences [1]. Natural variation among different genotypes (accessions, varieties, etc.) can be classified as qualitative or quantitative. Qualitative traits are characterized by distinct phenotypic classes and are often a result of differences at single genes. Such traits can relatively easily be dissected genetically because of their clear segregation pattern in the progeny of crosses. Quantitative traits, on the contrary, often display a more continuous variation in phenotypes because of a multiplicity of genes involved and a relatively large effect of environmental factors on the expression of the trait. Recombination of genes results in a large number of phenotypic classes, which cannot unambiguously be associated with genotypic classes [2] because various genes can contribute positively or negatively to a quantifiable trait. The complexity of quantitative traits is further enhanced by the presence of epistatic interactions and interactions between genes and the environment [3••].

Quantitative natural variation controls adaptive strategies of organisms to cope with biotic and abiotic influences and its understanding can provide insight into ecological mechanisms and the evolutionary history of plants [4]. Moreover, it is the basis of variation for many agronomic traits [5]. Arabidopsis thaliana has proven to be a very efficient model plant because of a number of biological properties and available genetic resources that make genetic and molecular analyses very efficient [6]. These advantages also make A. thaliana very suitable for the genetic analysis of natural variation [7]. Because of the growing impact of large-scale molecular detection techniques (collectively nicknamed ‘omics’ technologies) in the dissection of complex traits, we aim to present a brief overview of the key technological advances and some of the recent findings in the field with emphasis on A. thaliana.

Section snippets

Genetic analysis of natural variation in quantitative traits

Despite the complexity in genetic regulation of quantitative traits much progress has been made over the past decades in dissecting these traits using molecular markers. The increasing ease by which molecular markers can be generated [8] in combination with the application of sophisticated mapping methods [9] has led to a strong interest in the use of natural variation for studying quantitative traits [10]. Specific advantages are associated with the study of multiple natural perturbations in

Genetical genomics: variation in genome sequence and expression

In A. thaliana as well as in other species, genome-wide analyses of genomic polymorphisms in a large collection of accessions have revealed extensive sequence variation [1, 14, 15, 16•]. Polymorphisms, when converted to molecular markers, are indispensable for (fine) mapping of quantitative traits in experimental populations. When surveyed in natural populations at high density, polymorphisms will enable high-resolution mapping through linkage disequilibrium [17]. The best marker, however, is

Genetic regulation of complementary omic traits

The impact of variation in gene expression on quantitative traits is now widely acknowledged and the use of high-throughput genomic analyses has become an important tool in genetic analyses of natural variation [31]. An important mechanism in controlling transcriptional activity is through epigenetic modulation of cis-regulatory elements by cytosine methylation. With the recent development of genome-wide detection techniques [32], comprehensive genetic analyses of variation in methylation are

Regulatory network construction

To functionally link the large data sets obtained in ‘omic’ experiments as an order of events that ultimately result in a specific phenotype, network construction provides a useful tool. Biological networks describe relationships between individual components of a biological process [48]. Such components can either be genes, proteins, metabolites, or a combination thereof (Figure 1). Depending on the data source, networks can be constructed in various ways but all aim at resolving the complex

Conclusions

The combination of genetic analyses and large-scale omic analyses in experimental mapping populations has shown to have great potential in unraveling meaningful biological regulatory networks. The dissection of the genetic architecture of quantitative traits will require multiparallel analyses of the different transducers of the biological information flow. The ultimate goal is to link genetic variation to phenotypic variation and the identification of the molecular pathway from

References and recommended reading

Papers of particular interest, published within the annual period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

We apologize to all colleagues whose relevant work we could not cite because of space limitations, and thank Linus van der Plas for critically reading the manuscript. We acknowledge support from the Netherlands Organization for Scientific Research, Program Genomics (050-10-029) and the Centre for Biosystems Genomics (CBSG, Netherlands Genomics Initiative).

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