The Genetic Basis of Disease Resistance in Maize - Translational Genomics for Crop Breeding: Biotic Stress - page 38

marker-assisted recurrent selection, depending
on levels of heritability and number of QTL
(Bernando and Yu 2007). For polygenic traits
with low heritability in maize, both GS and
MARS outperformed phenotypic selection in
terms of genetic gains (Bernardo and Yu 2007).
GS has the potential to improve disease resis-
tance traits in plants. Its use has been pro-
posed for achieving durable stem rust in wheat
(Rutkoski et al. 2011), and it has the potential to
increase genetic gain for traits with low heritabil-
ity (Heffner et al. 2009). Thus, GS could improve
gains for resistance to maize diseases, including
those such as Aspergillus ear rot and aflatoxin
accumulation, for which it is notoriously diffi-
cult to achieve genetic gains and for which dQTL
have small effects and are highly influenced by
the environment (Brooks et al. 2005; Paul et al.
2003; Warburton et al. 2009).
GS is gaining favor in maize breeding as
high-throughput genotyping and extensive phe-
notypic datasets are generated. Because GS does
not require the identification and careful char-
acterization of loci and genes associated with
variation in traits of interests, it may seem that
QTL identification and characterization (previ-
ously seen as the basis of MAS) is now unnec-
essary. However, basic science, plant pathology,
and QTL mapping can still inform breeding pro-
grams (including GS programs) in several ways.
These include understanding the mechanisms of
resistance, improving phenotyping methods, and
identifying sources of diverse alleles. An under-
standing of the mechanisms of resistance asso-
ciated with specific dQTL can be used to predict
complementary combinations of loci and alleles.
For instance, knowing that one dQTL is asso-
ciated with variation in susceptibility to pene-
tration, while another is associated resistance to
vascular invasion (Chung et al. 2010), can allow
a breeder to target both stages of pathogenesis
by selecting for both QTL. Analysis of phenotyp-
ing methods and development of new phenotypic
assays can also enhance breeding efficiency. For
example, a method developed by Mideros et al.
(2009) to estimate A. flavus biomass by qPCR

enables the separation of components of resis-
tance such as fungal infection in multiple tissues
and aflatoxin accumulation (Mideros 2012). Pre-
sumably, by breeding for components of resis-
tance, difficult traits can be improved.
By identifying and characterizing new,
diverse alleles at mapped loci and characteriz-
ing the effects they can have on disease, public
sector research into the genetic architecture of
disease resistance can prove useful. The NAM
analysis allowed outstanding alleles to be iden-
tified across a relatively broad set of maize
germplasm. These insights might contribute to
the strategic selection of parents in a breeding
program, as the incorporation of known sources
of resistance is essential to the success of GS
for disease resistance (Rutkoski et al. 2011). It
is important to keep in mind that major genes
can mask the effects of minor genes in GS sce-
narios, such that quantitative resistance is not
selected (Rutkoski et al. 2011). When this occurs,
breeders could select for resistance-associated
loci based on prior knowledge. Basic research
has and will continue to define major and minor
gene loci, and these data can be incorporated
into GS algorithms. Public mapping efforts to
identify causative genes and polymorphisms can
provide a basis for markers to include in genomic
selection models. In addition, while GS popula-
tions may not be evaluated specifically for some
diseases and traits, it is desirable to include resis-
tance for these diseases.
Disease resistance has long been and remains
an attractive target trait for genetically modified
crops (Godfray et al. 2010). Despite significant
resources devoted to this area, few commercially
viable plants with transgenically conferred dis-
ease resistance traits are available; the exception
are a few virus resistance traits (e.g., Gonsalves
1998). This is due to a combination of factors.
Biological considerations include small allele
effects, narrow spectra, and potentially short
durability of certain transgenically conferred dis-
ease resistance traits, as well as their yield costs
(see above; Hammond-Kosack and Parker 2003).
Nonbiological considerations include the cost of