advanced swiftly in the past few years, a large number of mapped QTLs cannot be
utilized in the breeding program because of false-positive QTLs and low accuracy.
However, the accuracy can be enhanced by adapting different QTL mapping
methods and effective statistical analysis such as single marker analysis (SMA),
simple interval mapping (SIM), composite interval mapping (CIM), multiple interval
mapping (MIM), and Bayesian interval mapping (BIM). Also, a number of QTL
mapping software have been developed such as Mapmaker/QTL, QTL Cartographer, MapQTL, PLABQTL, PGRI, MapManager, QTLMAPPER, QGene,
QTLSTA, Ici Mapping, and QTL network. Further utilization of QTL information
for marker-assisted breeding has become challenging due to complex inheritance of
unstable QTLs (Deshmukh et al. 2014). Statistical tools such as “Meta-QTL analysis” have been advanced that compile QTL data from different reports together on
the same map for identification of precise QTL region (Deshmukh et al. 2012;
Sosnowski et al. 2012). Hwang et al. (2015) identified various QTLs related to
canopy wilting, during “Meta-QTL” study on five different populations (RILs),
among identified QTLs, one QTL on chromosome 8 in the 93,705 KS4895 Â Jackson
population co-segregated with a QTL for wilting published previously in a
Kefeng1 Â Nannong 1138-2 population. The advances in sequencing technologies,
statistical approaches, and software resulted in exponential intensification in soybean
studies to understand plants response to extreme climatic conditions importantly drought stress.
Identification of genes underlying root system architecture and canopy
characteristics is critical to develop soybean that is suited to water-limited
environments. Prince et al. (2015a) identified four significant QTLs associated
with different root architectural traits on Gm06 and Gm 07 in an interspecific RILs
population of G. max (V71–370) Â G. soja (PI407162). In an another study,
Manavalan et al. (2015) identified a major QTL on Gm08 that governed root traits
(tap root length and lateral root number) and shoot length and identified six transcription factors (MYBHD, TPR, C2H2 Zn, bZIP, GRAS, and Ring finger) and two
key cell wall expansion-related genes which encode xyloglucan endotransglycosylases as candidate genes in the confidence interval of the QTL. These
are key candidate genes for validation and to develop a better root ideotype in
soybean.
4.5
Genome-Wide Association Studies for Drought Tolerance
Related Traits
QTL mapping using biparental populations has limitations because of restricted
allelic diversity and genomic resolution. The allelic diversity can be increased to
some extent by using populations derived from multi-parental crosses (Deshmukh
et al. 2014). Recently, Multi-parent Advanced Generation Inter-Cross populations
(MAGIC) has been used to identify QTL for blast and bacterial blight resistance,
salinity and submergence tolerance, and grain quality traits in rice (Bandillo et al.
2013). Such multi-parental populations have mapping resolution limitations since it
4 Breeding and Molecular Approaches for Evolving Drought-Tolerant Soybeans
99
utilized in the breeding program because of false-positive QTLs and low accuracy.
However, the accuracy can be enhanced by adapting different QTL mapping
methods and effective statistical analysis such as single marker analysis (SMA),
simple interval mapping (SIM), composite interval mapping (CIM), multiple interval
mapping (MIM), and Bayesian interval mapping (BIM). Also, a number of QTL
mapping software have been developed such as Mapmaker/QTL, QTL Cartographer, MapQTL, PLABQTL, PGRI, MapManager, QTLMAPPER, QGene,
QTLSTA, Ici Mapping, and QTL network. Further utilization of QTL information
for marker-assisted breeding has become challenging due to complex inheritance of
unstable QTLs (Deshmukh et al. 2014). Statistical tools such as “Meta-QTL analysis” have been advanced that compile QTL data from different reports together on
the same map for identification of precise QTL region (Deshmukh et al. 2012;
Sosnowski et al. 2012). Hwang et al. (2015) identified various QTLs related to
canopy wilting, during “Meta-QTL” study on five different populations (RILs),
among identified QTLs, one QTL on chromosome 8 in the 93,705 KS4895 Â Jackson
population co-segregated with a QTL for wilting published previously in a
Kefeng1 Â Nannong 1138-2 population. The advances in sequencing technologies,
statistical approaches, and software resulted in exponential intensification in soybean
studies to understand plants response to extreme climatic conditions importantly drought stress.
Identification of genes underlying root system architecture and canopy
characteristics is critical to develop soybean that is suited to water-limited
environments. Prince et al. (2015a) identified four significant QTLs associated
with different root architectural traits on Gm06 and Gm 07 in an interspecific RILs
population of G. max (V71–370) Â G. soja (PI407162). In an another study,
Manavalan et al. (2015) identified a major QTL on Gm08 that governed root traits
(tap root length and lateral root number) and shoot length and identified six transcription factors (MYBHD, TPR, C2H2 Zn, bZIP, GRAS, and Ring finger) and two
key cell wall expansion-related genes which encode xyloglucan endotransglycosylases as candidate genes in the confidence interval of the QTL. These
are key candidate genes for validation and to develop a better root ideotype in
soybean.
4.5
Genome-Wide Association Studies for Drought Tolerance
Related Traits
QTL mapping using biparental populations has limitations because of restricted
allelic diversity and genomic resolution. The allelic diversity can be increased to
some extent by using populations derived from multi-parental crosses (Deshmukh
et al. 2014). Recently, Multi-parent Advanced Generation Inter-Cross populations
(MAGIC) has been used to identify QTL for blast and bacterial blight resistance,
salinity and submergence tolerance, and grain quality traits in rice (Bandillo et al.
2013). Such multi-parental populations have mapping resolution limitations since it
4 Breeding and Molecular Approaches for Evolving Drought-Tolerant Soybeans
99