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families. NILs can be used to validate QTL, test
association mapping hits and characterize QTL.
Characterization of two NLB QTL on chromo-
some 1 revealed that they influenced the patho-
genesis process in distinct ways. The QTL in bin
6 reduced the pathogen's success during the ini-
tial stages of infection, whereas the QTL in bin
2 reduced the extent of the pathogen's invasion
of the leaf vasculature (Chung et al. 2010).
Another important question about the agro-
nomic use of disease resistance concerns the
trade-offs that may exist with other traits. There
are several examples from other plant systems
in which the presence of genes associated with
both qualitative and quantitative disease resis-
tance incur a yield cost (Heidel et al. 2004; Orgil
et al. 2007; Tian et al. 2003; Todesco et al. 2010).
The only study addressing this question in maize
thus far has been on the yield costs associated
with the large-effect dQTL
Rcg1
for anthracnose
stalk rot resistance (Frey et al. 2011). In this case,
NILs have been used to show that there are no fit-
ness costs associated with
Rcg1
in non-diseased
conditions and there is a yield benefit associ-
ated with
Rcg1
under inoculated conditions (Frey
et al. 2011).
In the past, the low resolution of mapping
results meant that recombination could read-
ily separate the marker being used for selec-
tion from the desired allele. The use of “per-
fect” markers (those targeting the polymorphism
that causes the phenotype of interest) avoids this
problem (Lande and Thompson 1990). As we
know more about the genetic architecture of
disease resistance in maize, we come closer to
having perfect markers. Historically, there has
been a trade-off between conventional breed-
ing approaches and MAS in terms of cost and
time, with MAS being faster but more costly and
conventional breeding schemes being slower but
cheaper (Morris et al. 2003). This led breeding
efforts with constrained budgets, including many
public breeding programs, to focus on conven-
tional breeding schemes (Morris et al. 2003).
However, this is changing as marker technolo-
gies improve and genotyping costs decline. In
one recent study, MAS in maize was shown to
be more cost-effective than phenotypic selection
when selecting for resistance to multiple foliar
pathogens (Asea et al. 2011) and for maize streak
virus (Abalo et al. 2009). The advantages of
MAS will become more compelling as genotyp-
ing costs continue to decline and more useful
trait-marker associations become available for
selection. Challenges associated with the effec-
tive deployment of MAS have been discussed in
several recent reviews (Holland 2004; Hospital
2009; Johnson 2004).
When a trait is controlled by multiple QTL,
or when multiple traits are being considered in
a breeding program, a form of MAS known
as genomic selection (GS) can be employed
(Goddard and Hayes 2007; Meuwissen et al.
2001). In GS, specific loci associated with a trait
are not identified and selected for; instead, the
effect of every marker is fitted as an effect in a
linear model and genome-wide marker informa-
tion is used to make selections. The availability
of low-cost, high-throughput genotyping meth-
ods has made GS a feasible and attractive form
of MAS. Using simulations, GS was predicted
to result in up to a 43% greater genetic gain over
Translating Knowledge to Action:
Breeding for Disease Resistance
As new resources and technologies permit
the identification of dQTL with unprece-
dented precision, a key challenge will be
translating the knowledge gained from map-
ping studies into breeding outcomes. This can
be achieved through marker-assisted selection
(MAS), including genomic selection (GS) and/
or cisgenesis (direct transfer within species).
Marker-assisted backcrossing and “forward
crossing” are well suited for the manipulation of
genes with large effects (Holland 2004), while
genomic selection is proving particularly use-
ful for improving on traits with low heritability
and/or under polygenic control (Heffner et al.
2009).
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