GS using different models. A GS study conducted in soybean has used a panel of
288 accessions and 79 SCAR markers to predict 100 seed weight (Shu et al. 2012).
In this report, high correlation (r2 ¼ 0.9) has been observed among the genomic
estimated breeding value (GEBV) and the phenotypic value. Predicting the precision
of GS will need more investigations involving high-throughput genotyping of larger
populations evaluated with multi-environment. These multi-environmental trials not
only include the effect of G Â E but also increase the number of breeding cycles per
year. The challenge for GS is to get accurate GEBV with respect to the G Â E effect.
Improved factorial regression models have been proposed for GS that consider stress
covariates derived from daily weather data, which revealed increased accuracy by
11.1% for predicting GEBV in unobserved environments where weather data is
available (Heslot et al. 2014). This study suggests possible utilization of phenotypic
data and historical data of weather conditions accumulated over decades in different
soybean breeding programs. Similar information can be used for drought tolerance
improvement in soybean (Deshmukh et al. 2014). Most of the GS studies have used
RIL populations to train the prediction model. Therefore, GS and QTL mapping can
be performed simultaneously. A set of diverse cultivars can be used for GS and
GWAS altogether, so GWAS, GS, and QTL mapping can be combined together for
marker-assisted breeding for drought tolerance related traits (Deshmukh et al. 2014).
QTL or GWAS loci possess hundreds of genes which make the identification of
candidate genes difficult (Sonah et al. 2012). This is similar in transcriptome
profiling where thousands of genes have been found to be differentially expressed
even with genetically similar isogenic lines (Table 4.5). Therefore, combining QTL
mapping or GWAS with transcriptome profiling can complement each other
(Deshmukh et al. 2014). Recently, several sequences based data sets have been
generated by resequencing efforts (Lam et al. 2010; Li et al. 2013b, 2014; Chung
et al. 2014; Qiu et al. 2014; Zhou et al. 2015c; Valliyodan et al. 2016). The
availability of well-annotated soybean genome sequence and resequencing based
data sets also facilitates development of large number of SNP and Indel markers
which are being utilized in QTL mapping and molecular breeding for drought
tolerance in soybean.
4.9
Genetic Engineering Approaches for Developing Drought
Tolerance in Soybean
The complexities of mechanisms controlling drought adaptive traits and the limited
availability of germplasm for tolerance to drought stress have restricted genetic
advances in soybean for increase in yield and improvement of other traits associated
with drought stress tolerance. Understanding the mechanisms by which plants
perceive and transduce the stress signals to initiate adaptive responses and their
engineering using molecular biology and genomic approaches is essential for
improving drought stress tolerance in soybean crops. Attempts have been made to
enhance drought stress tolerance through biotechnological approaches and droughttolerant varieties of soybean have been produced. Zhang et al. (2019) reported that
4 Breeding and Molecular Approaches for Evolving Drought-Tolerant Soybeans
107
288 accessions and 79 SCAR markers to predict 100 seed weight (Shu et al. 2012).
In this report, high correlation (r2 ¼ 0.9) has been observed among the genomic
estimated breeding value (GEBV) and the phenotypic value. Predicting the precision
of GS will need more investigations involving high-throughput genotyping of larger
populations evaluated with multi-environment. These multi-environmental trials not
only include the effect of G Â E but also increase the number of breeding cycles per
year. The challenge for GS is to get accurate GEBV with respect to the G Â E effect.
Improved factorial regression models have been proposed for GS that consider stress
covariates derived from daily weather data, which revealed increased accuracy by
11.1% for predicting GEBV in unobserved environments where weather data is
available (Heslot et al. 2014). This study suggests possible utilization of phenotypic
data and historical data of weather conditions accumulated over decades in different
soybean breeding programs. Similar information can be used for drought tolerance
improvement in soybean (Deshmukh et al. 2014). Most of the GS studies have used
RIL populations to train the prediction model. Therefore, GS and QTL mapping can
be performed simultaneously. A set of diverse cultivars can be used for GS and
GWAS altogether, so GWAS, GS, and QTL mapping can be combined together for
marker-assisted breeding for drought tolerance related traits (Deshmukh et al. 2014).
QTL or GWAS loci possess hundreds of genes which make the identification of
candidate genes difficult (Sonah et al. 2012). This is similar in transcriptome
profiling where thousands of genes have been found to be differentially expressed
even with genetically similar isogenic lines (Table 4.5). Therefore, combining QTL
mapping or GWAS with transcriptome profiling can complement each other
(Deshmukh et al. 2014). Recently, several sequences based data sets have been
generated by resequencing efforts (Lam et al. 2010; Li et al. 2013b, 2014; Chung
et al. 2014; Qiu et al. 2014; Zhou et al. 2015c; Valliyodan et al. 2016). The
availability of well-annotated soybean genome sequence and resequencing based
data sets also facilitates development of large number of SNP and Indel markers
which are being utilized in QTL mapping and molecular breeding for drought
tolerance in soybean.
4.9
Genetic Engineering Approaches for Developing Drought
Tolerance in Soybean
The complexities of mechanisms controlling drought adaptive traits and the limited
availability of germplasm for tolerance to drought stress have restricted genetic
advances in soybean for increase in yield and improvement of other traits associated
with drought stress tolerance. Understanding the mechanisms by which plants
perceive and transduce the stress signals to initiate adaptive responses and their
engineering using molecular biology and genomic approaches is essential for
improving drought stress tolerance in soybean crops. Attempts have been made to
enhance drought stress tolerance through biotechnological approaches and droughttolerant varieties of soybean have been produced. Zhang et al. (2019) reported that
4 Breeding and Molecular Approaches for Evolving Drought-Tolerant Soybeans
107