From plant genomics to breeding practice
Introduction
Species relevant to agriculture have a long breeding history. During this period, two bottlenecks have restricted the genetic base of breeding populations: species domestication, which for a large part of crops has been monophyletic 1., 2., 3., and the post-Mendelian adoption of breeding procedures separating environmental from genetic effects (i.e. only genetically superior varieties have entered intentional crossing programs).
Despite the narrow base of current breeding materials, substantial genetic progress has been achieved for crop plants [4]. In almost all cases the rate of genetic gain appears linear over time (i.e. lasting for hundreds of generations [5••]). This type of response calls for the existence of very many genes affecting each relevant trait, a situation known as the Fisher’s infinitesimal model [6]. Recent major contributions to the study of quantitative variation in natural and experimental populations 5.••, 7. indicate, however, that quantitative trait loci (QTL) can support large phenotypic effects. When superior alleles at relevant QTL are fixed, intentional selection should have exhausted a large part of the genetic variance present in populations, thus reducing the rate of genetic advance. It is true that the number of QTLs supporting a specific trait may be under-estimated [5••], an evidence in favour of the infinitesimal model, but it is also known that nucleotide diversity is not random but structured in haplotypes 8.•, 9., 10. having significant allelic differences in terms of phenotype [7]. Barton and Keightley [5••] have discussed a genetic model that explains long-term selection responses, while accepting the existence of relatively few and major QTL for a trait. The model includes the possibility that during intentional selection new QTL alleles appear and reinforce trait variances. Besides recombination, the model assigns a central role to mutation as supportive of genetic advances [11], a situation explaining the rate of progress reported for gene pools with a narrow genetic base. The model calls for a better understanding of the origin, nature, number, allele multiplicity and phenotypic value of QTL genes supporting quantitative variation. This review covers recent work to address the molecular nature of quantitative variation. In addition, the article summarizes valuable information on linkage disequilibrium (LD)-based QTL mapping and on the difficulty of understanding and using gene regulatory variation.
Section snippets
From quantitative trait loci to quantitative trait nucleotides
The genomics revolution of the past 10 years has improved our understanding of the genetic make up of living organisms. Together with the achievements represented by complete genomic sequences (Arabidopsis [12] and rice 13., 14.), high-throughput and parallel approaches are available for the analysis of transcripts, proteins, insertional mutants and chemically induced mutants. All this information allows us to understand the function of genes in terms of their relationship to the phenotype.
A new paradigm
With the recent advances in DNA sequencing and single nucleotide polymorphism (SNP) genotyping, new approaches to QTL mapping and quantitative trait nucleotide (QTN) identification are available. The emerging concept is to exploit the possibility of looking at variation directly in genes and not at anonymous markers (candidate gene association studies), as well as to saturate the genome with markers (whole genome scan) 31., 32.. Both approaches rely on the detection of LD (i.e. non-random
The dark side
When considering the molecular basis of phenotypic variation, besides a simple understanding of the point mutations affecting gene transcription units, additional layers of complexity should be considered. For example, the role of epistasis in QTL variation is still poorly understood when based on current QTL mapping designs and population sizes. Nevertheless, in silico experiments suggest that epistasis has a key role in the long-term evolution of adaptive traits and in the dynamics of
Conclusions
The positional cloning approach to the identification of QTL genes has proven successful. Two components seem to be needed: genomic technologies and biological resources. Although in the near future genomic resources will no longer be limiting, biological resources are already insufficient. Thus, an effort is required to standardize methods and provide models for the analysis of crop trait variability. In this sense, and due to the fact that fine QTL mapping via LD can be carried out in
References and recommended reading
Papers of particular interest, published within the annual period of review, have been highlighted as:
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of special interest
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of outstanding interest
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