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slowly being dealt with, as discussed later in this
chapter.
The most popular inbred development tech-
nique currently used by practical tomato breed-
ing programs is pedigree-based selection, with
the goal of producing desirable inbred lines and
subsequently F
1
hybrid cultivars utilizing the
inbred lines as parents. By nature, this process
favors traits that are simply inherited and can be
visually evaluated. Further, traits such as yield,
vine cover and size, disease resistance, and matu-
rity are generally considered higher breeding
priorities than modulating complex fruit quality
traits such as viscosity and carotenoid content.
With a suitably large number of diverse materi-
als entering the inbred development pipeline, one
can obtain desired fruit quality parameters using
phenotypic selection; but obtaining inbred lines
with the combination of these parameters along-
side improved disease resistance and/or accept-
able yield remains difficult, since selection in
early generations focuses primarily on horticul-
tural traits, not fruit quality. For example, in
a processing tomato breeding program, a plant
with an unacceptably small vine but desirable
viscosity is not useful and often would not be
selected. Thus, in a hypothetical breeding pro-
gram where marker-assisted selection (MAS) is
not applied for fruit quality traits, the number of
inbred lines with desirable horticultural charac-
teristics, disease resistance, and thick viscosity
at the end of an inbred development pipeline
is a function of the number of F
2
populations
used, the frequency and number of alleles con-
ferring the desirable fruit quality characteristics,
the presence of genetic linkages, the breeder's
skill, and random chance. In most cases, this sit-
uation results in a low probability of success, as
well as a highly inefficient germplasm develop-
ment program. In tomato, MAS is widely used
for single-gene traits, such as disease resistance
or determinacy, but traits with more complicated
inheritance, such as sugar profile, viscosity, and
carotenoid profile, are still largely subject to tra-
ditional phenotypic selection (Foolad 2007a).
However, if one desires “enhanced β-carotene
content” as a breeding objective, while also tak-
ing into account the grower requirements for
successful tomato varieties, utilizing solely tra-
ditional breeding techniques is exceedingly inef-
ficient (Barone et al. 2009). This area of diffi-
culty is precisely where “omics” tools applied
to large, relevant, diverse populations, using
large numbers of informative molecular mark-
ers and high-throughput phenotyping protocols,
can have the largest impact on practical tomato
breeding.
With the dawn of the age of genomics
and next-generation sequencing, the role of
genomics in applied tomato breeding has
come into focus. The overall goal of apply-
ing genomics strategies to aid breeding, that
is, association of phenotypic variation with
sequence variation on a genome-wide scale,
will not change. Successful application of next-
generation “omics” techniques in combination
with conventional breeding techniques may
result in the identification and commercial appli-
cation of novel traits, as seen with other crop
species (Tuberosa and Salvi 2006; Tuberosa et al.
2007; Dahmani-Mardas et al. 2010). Such traits
may be difficult to deal with using conven-
tional breeding techniques alone. For example,
various tomato fruit quality traits are expen-
sive to measure, and modulating these traits at
the phenotypic level often results in negative
consequences to fruit yield (see Schauer et al.
2006). The tomato genome has been sequenced
and the information is freely available (Tomato
Genome Consortium 2012), genomic and tran-
scriptomic resources and bioinformatic meth-
ods are publicly available, metabolomic meth-
ods have been established and reported in the
literature, and research groups are beginning
to analyze segregating populations for alter-
ations in key metabolic traits on an “omic”
scale, while concurrently collecting data from
relevant agronomic traits. Further, mutagenized
tomato populations have been developed for
use in TILLING approaches (Menda et al.
2004; Minoia et al. 2010; Okabe et al. 2011)
and
the
bioinformatic
workflows
to
handle
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