Biology Reference
In-Depth Information
empirical than for other crops. However, despite
this tricky context, many breeders succeeded in
obtaining new local cultivars (Machado 2001)
that contributed to the steady increase in yield as
evidenced by world statistics (Figure 13.2). On
one hand, in the high polyploid and heterozygote
sugarcane, a lot of crosses between elite lines fre-
quently lead to large agronomic segregation of
many traits, providing opportunities for genetic
progress. On the other hand, conventional breed-
ing programs have to be rather large (and expen-
sive) to be efficient because of (1) the marked
disjunction of the target traits of the breeders
(yield components, disease resistance, etc.) in
the currently 'unfixed' breeding germplasm, and
(2) the absence of any information on the genetic
basis of agronomic traits likely to be useful
to rationalize experimental costs. Another rea-
son for the relatively high cost of conventional
breeding programs is the frequent genotype x
environment interaction observed in sugarcane
(Jackson and Hogarth 1992; Kang and Miller
1984), which requires investments in large exper-
imental networks. Moreover the ratooning abil-
ity of selection candidates needs to be moni-
tored over several crop cycles before the best
sustainable elites suitable for semi-perennial cul-
tivation can be identified. All these particulari-
ties explain why sugarcane breeding programs
still rely on massive screening of millions of
progenies. Programs call for tremendous exper-
imental resources. Between 7 and 10 years are
needed to select breeding parents and between
12 and 15 years to identify a commercial cul-
tivar after initial crossing (Cheavegatti-Gianotto
et al. 2011).
Further improvements in sugarcane yield are
expected to come from a better understanding of
the genetic bases of yield components that could
facilitate the development of marker-assisted
breeding approaches. However, the polygenic
nature of the factors controlling the expression
of yield traits, enhanced by the high ploıdy level
of sugarcane, might hinder the use of molecular-
assisted breeding approaches in sugarcane breed-
ing programs.
Marker-Assisted Selection Related to
Yield Component Traits
Marker-assisted selection (MAS) is based on
the exploitation of linkage disequilibrium (LD)
between markers and quantitative trait loci
(QTLs). LD is the non-random association of
alleles at distinct loci. If markers are tightly
linked to many and/or prominent QTLs under-
lying the variation of any trait of interest, direct
selection is possible using markers instead of
phenotypic data, which can be time consuming
to acquire. In this case, the advantages of MAS
over phenotypic selection should enable a gain
both in time and in cost, which could be even
greater because MAS could be efficiently applied
at a relatively early stage in the breeding program
(depending on the unit cost of genotyping). The
advantages and efficiency of MAS are based on
the fact that QTL effects need to be accurately
estimated and stable across genetic backgrounds
and environments and over time. The prohibitive
cost of sugarcane breeding programs provided
motivation for testing the use of MAS breeding
approaches for yield traits in sugarcane in order
to identify elite genotypes as early as possible.
In sugarcane, detection of QTLs for MAS
experiments relies on two strategies involving
the study of highly heterozygous clones: QTL
mapping of bi-parental crosses or association
mapping using a panel of clones.
QTL Studies
QTLs genetic studies have been carried out on
sugar yield, on cane yield, and on their agro-
nomic components (tillering, stalk length and
diameter, and brix). A total of 14 QTL stud-
ies based on ten different bi-parental progenies
are reported in the literature (Sills et al. 1995;
Ming et al. 2001; Hoarau et al. 2002; Ming
et al. 2002a; Ming et al. 2002b; Jordan et al.
2004; Da Silva and Bressiani 2005; Reffay et al.
2005; Aitken et al. 2006; Aitken et al. 2008;
Piperidis et al. 2008; Alwala et al. 2009; Pinto
et al. 2010; Pastina et al. 2012). Table 13.1
Search WWH ::




Custom Search