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nucleotide changes. However, there are limits to
the precision available in GWAS, particularly in
inbreeding organisms (Morrell et al. 2012).
To overcome many of the limitations of bi-
parental QTL mapping and GWAS, a new gen-
eration of genetic-mapping populations has been
designed, such as the nested association mapping
(NAM) populations (Buckler et al. 2009), multi-
parent advanced generation intercross (MAGIC)
(Cavanagh et al. 2008) populations, and recom-
binant inbred intercross line (RIAIL) popu-
lations (Rockman and Kruglyak 2008). All
of these populations involve the crossing of
multiple parents and advancement of populations
through several generations to improve resolu-
tion in genetic mapping compared with tradi-
tional bi-parental mapping (Morrell et al. 2012).
The controlled nature of the crosses involved in
next-generation design can overcome some of
the difficulties of association mapping, including
population structure and the unknown frequency
of causative mutations. Next-generation design
can also allow for better estimation of allelic
effects because of the approximately even con-
tribution of all parents (Cavanagh et al. 2008).
Today, genomics-based strategies for crop
improvement, such as GWAS and GS, receive
considerable attention among plant breeders
(Morrell et al. 2012). Breeders can predict
plant phenotypes by the use of genome-wide
marker data rather than by direct phenotyping.
As a result, these two methods can dramatically
reduce the time and expenses involved in pheno-
typing breeding lines.
sible that some protein and epigenome QTLs
will also be gained. This information will pro-
vide an important addition to the tools cur-
rently employed in genomics-assisted selection
for crop improvement in the future (Fernie and
Schauer 2009).
Transgenic Approaches to Crop
Improvement
Transgenic crops, including corn, soybean, cot-
ton, and potato, are grown commercially in
the United States. In addition, field trials of
transgenics from at least 52 species are ongo-
ing (Dunwell 2000). The trends of these tri-
als are to progress from simple, single-gene
traits, such as herbicide and insect resistance,
toward more complex agronomic traits. These
complex agronomic traits include photosyn-
thetic enhancement, yield increase, modification
of seed compositions, alteration in senescence,
sugar and starch metabolism, and improve-
ment in responses to abiotic and biotic stresses
(Dunwell 2000). Along with this trend, trans-
genic approaches are also shifting from con-
stitutive promoter and single-gene to tissue-
or time-specific promoters and multiple genes
stacking (Walker et al. 2002). One potato line
being tested by Monsanto (APHIS Application
98-069-23N) contains seven transgenes, namely
three selectable markers (gus, npt II, and CBI);
a cry IIIA Bt gene to provide resistance to
Colorado potato beetle; virus coat protein and
replicase genes to give resistance to two viral
diseases; another CBI gene associated with resis-
tance to Verticillium ; improvement in bruising
resistance; and altered carbohydrate metabolism.
These examples represent the future trend of
transgenic crop developments (Dunwell 2000).
Combined with the development of transgenic
technologies such as tasiRNA (Vaucheret 2005),
virus-induced silencing (Lu et al. 2003), and ZF
nuclease/TAL effector nuclease/Maganuclease
technology (de Souza 2012), more agronomic
interest genes will be identified and their func-
tions elucidated. These progresses will facilitate
Other Omics-Based Crop
Improvement
With the advancement of technologies, espe-
cially NGS and metabolite profiling technolo-
gies, all kinds of “omics” research have been
conducted, namely transcriptomics, proteomics,
metabolomics, and epigenomics. Combined with
genomics information, some metabolic QTL
(mQTL) and expression QTL (eQTL) have been
identified (Fernie and Schauer 2009). It is pos-
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