Biology Reference
In-Depth Information
information now permits genome-wide asso-
ciation studies (GWAS), in which the entire
genome is scanned for marker-trait associations
in an unbiased way (Belo et al. 2008; Cook
et al. 2012). One difficulty with GWAS is that
the multiple test corrections associated with the
very large number of tests conducted lead to
very high significance thresholds, such that even
for traits with high heritabilities, few significant
'hits' may be identified. In a GWAS of kernel
starch, protein, and oil traits, with broad-sense
heritabilities ranging from 83% to 91%, no
significant associations were identified after
multiple test corrections (Cook et al. 2012).
Significant or otherwise intriguing GWAS
“hits” need to be validated independently,
using mutants, transgenics, and/or fine-mapping
studies.
Another new breeding tool utilized to under-
stand the genetic architecture of disease resis-
tance in maize is the nested association map-
ping (NAM) population (McMullen et al. 2009;
Yu et al. 2008). The NAM population consists
of 25 linked recombinant inbred populations of
∼
position traits that have been analyzed in this
population (Buckler et al. 2009; Cook et al. 2012;
Tian et al. 2011).
The results of NAM GWAS provide a pre-
liminary look at the genes that may underlie the
trait of quantitative disease resistance. GWAS
revealed more than 200 associations with spe-
cific SNPs for SLB and NLB resistance traits in
the NAM population. It is likely that in many
cases, the SNPs identified are at or very near
to the actual causal genes (Cook et al. 2012;
Tian et al. 2011). For both NLB and SLB, many
of the associated SNPs were within or adjacent
to genes that have been previously implicated
in disease resistance or the defense response.
For NLB, genes implicated by GWAS included
many defense candidates including those encod-
ing serine-threonine protein kinases, receptor-
like kinases, antifreeze proteins, a germin pro-
tein, and an ABC transporter, among others.
Results were similar for SLB, with candidate
genes including those encoding serine-threonine
kinases, an ABC transporter, a GST, and an
LRR receptor kinase, among others. The iden-
tification of receptor-like kinases as candidate
genes for quantitative resistance loci for both
diseases is consistent with the hypothesis that
modest levels of resistance are associated with
host recognition of conserved pathogen features.
Recognition of “pathogen-associated molecular
patterns” (PAMPs) has been linked to disease
resistance in several cases and is associated with
partial restriction of pathogen infection (Bent
and Mackey 2007). As previously noted, genes
implicated by GWAS need to be confirmed with
complementary evidence.
There are several lines of evidence suggest-
ing that some loci may condition resistance to
more than one disease (reviewed by Poland
et al., 2009; Kou and Wang 2010; Krattinger
et al., 2009). Loci conditioning multiple disease
resistance (MDR) would make breeding for dis-
ease resistance more efficient. In synthesizing
the results of 50 mapping studies, Wisser et al.
(2006) found that dQTL were non-randomly
distributed in the maize genome. At several
200 lines each. Each of these populations is
derived from a cross between B73 and one of a
set of 25 diverse lines. Analyzed as a single pop-
ulation, the NAM population has unprecedented
mapping power due to its large size (
5,000
lines) and the effective combination of linkage
and linkage-disequilibrium approaches (Yu et al.
2008). This, in theory, allows resolution to the
single-gene level (Cook et al. 2012; Poland et al.
2011; Tian et al. 2011). The NAM population has
been evaluated for SLB, NLB, and GLS (Benson
et al. 2011; Kump et al. 2011; Poland et al. 2011).
The genetic architectures controlling variation in
resistance to SLB and NLB were found to be
broadly similar: 32 and 29 dQTL were identified
for the two diseases respectively. These dQTL
were of relatively small effect and no epistatic
interactions were identified. In these respects,
the genetic architectures controlling variation in
SLB and NLB resistance were similar to those
controlling other quantitative traits, including
flowering time and various leaf and kernel com-
∼
Search WWH ::
Custom Search