The Genetic Basis of Disease Resistance in Maize - Translational Genomics for Crop Breeding: Biotic Stress - page 34

silencing of the Hm1 homolog in barley rendered
the plant susceptible to C. carbonum race 1. Hm1
appears to have evolved early in the grass lin-
eage, possibly under selection for resistance to
HC-toxin (Sindhu et al. 2008).
Quantitative, or partial, disease resistance is
generally controlled by multiple loci, each with
relatively small effects. In general, this form of
resistance is more durable in the field than qual-
itative resistance and is therefore agronomically
important (McDonald and Linde 2002). The
underlying mechanisms associated with quan-
titative disease resistance in plants are not well
understood. To date, the identity of five quanti-
tative genes or gene clusters associated with dis-
ease resistance in plants have been determined
(Broglie et al. 2006; Fu et al. 2009; Fukuoka et al.
2009; Krattinger et al. 2009; Manosalva et al.
2009). These genes appear to be unrelated and
confer resistance by a variety of mechanisms,
although these mechanisms are not entirely clear
at this point. They include an NBS-LRR gene
(Broglie et al. 2006), a START kinase (Fu et al.
2009), an ABC transporter (Krattinger et al.
2009), a proline-rich protein of unknown func-
tion (Fukuoka et al. 2009), and a family of
germin-like proteins (Manosalva et al. 2009).
This diversity of gene classes is consistent with
the emerging consensus that variation in quanti-
tative disease resistance in plants is likely based
on variation in genes involved in a number of dif-
ferent mechanisms and pathways (Kliebenstein
and Rowe 2009; Poland et al. 2009).
Generally, disease resistance quantitative trait
loci (dQTL) are thought to be race nonspe-
cific (Vanderplank 1968), but there are multiple
examples of race-specific QTL (e.g., Kolmer and
Leonard 1986; Leonards-Schippers et al. 1994;
Marcel et al. 2008; Qi et al. 1999; Talukder
et al. 2004). Therefore, to ensure the effective
deployment of dQTL, it is important to assess
the effectiveness of the resistance with respect to
the pathogen populations against which the resis-
tance is intended to perform. Preliminary assess-
ments can be made by testing source germplasm
and/or derived lines with pathogen isolates con-

sidered to represent the target population. Candi-
date germplasm should be tested over a number
of different environments to ensure as much as
possible that the resistance is broadly effective.
Numerous dQTL studies in maize have been
carried out. Genotypic variation has been asso-
ciated with variation in resistance to all classes
of disease, including viral, bacterial, and fun-
gal leaf blights, ear rots, and stalk rots (e.g. Ali
et al. 2005; Brown et al. 2001; McMullen et al.
1994; Ming et al. 1997; Paul et al. 2003; Pernet
et al. 1999; Robertson-Hoyt et al. 2006; Xia et al.
1999). A synthesis of 50 studies reporting the
locations of 437 QTL associated with resistance
to 19 maize diseases identified QTL on both
arms of all 10 maize chromosomes (Wisser et al.
2006). The composite map showed 89% of the
maize genome to be associated with dQTL inter-
vals, reflecting the low resolution of the mapping
procedures employed, as well as indicating that
there are large numbers of dQTL in the maize
genome.
In recent years, new resources and datasets
have been generated to gain a more precise idea
of the genetic architecture underlying quantita-
tive disease resistance in maize. Maize is well
suited to association mapping (Yan et al. 2011)
due to the high genetic diversity among lines
(Liu et al. 2003). The generally low levels of
linkage disequilibrium found in maize (Reming-
ton et al. 2001) mean that, given an appropriate
population and accurate genotypic and pheno-
typic data, association mapping has the potential
to resolve QTL to their causal genes and poten-
tially nucleotides. A number of maize associa-
tion mapping populations have been developed
in the public sector, including a 300-line panel
(Flint-Garcia et al. 2005) mentioned below that
has been evaluated for NLB, SLB, and GLS
(Wisser et al. 2011).
Association mapping in maize was formerly
limited to the analysis of candidate genes (i.e.,
genes already suspected of being important in
controlling variation for the trait of interest)
(Harjes et al. 2008; Krill et al. 2010; Wilson
et al. 2004). The increasing quantity of genotypic