Allelic Combinations of Soybean Maturity Loci E1, E2, E3 and E4 Result in Diversity of Maturity and Adaptation to Different Latitudes

Jiang, Bingjun; Nan, Haiyang; Gao, Yin; Tang, Lili; Yue, Yanlei; Lü, Sijia; Ma, Liming; Cao, Dong; Sun, Shi; Wang, Jialin; Wu, Cunxiang; Yuan, Xiaohui; Hou, Wensheng; Kong, Fanjiang; Han, Tianfu; Liu, Baohui

doi:10.1371/journal.pone.0106042

Cited by 98 publications

(123 citation statements)

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“…Soybean is an important crop globally, with high nutrition, protein and oil values [1], [2]. Despite this it is still a minor crop in Kazakhstan where farmers predominantly grow wheat.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Soybean Flowering and Seed Maturation Time in Different Latitude Regions of Kazakhstan

et al. 2016

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Soybean is still a minor crop in Kazakhstan despite an increase in planting area from 4,500 to 11,400 km2 between 2006 and 2014. However, the Government’s recently accepted crop diversification policy projects the expansion of soybean cultivation area to more than 40,000 km2 by 2020. The policy is targeting significant expansion of soybean production in South-eastern, Eastern, and Northern regions of Kazakhstan. Successful realization of this policy requires a comprehensive characterization of plant growth parameters to identify optimal genotypes with appropriate adaptive phenotypic traits. In this study 120 soybean accessions from different parts of the World, including 18 accessions from Kazakhstan, were field tested in South-eastern, Eastern, and Northern regions of the country. These studies revealed positive correlation of yield with flowering time in Northern Kazakhstan, with seed maturity time in Eastern Kazakhstan, and with both these growth stages in South-eastern Kazakhstan. It was determined that in South-eastern, Eastern and Northern regions of Kazakhstan the majority of productive genotypes were in maturity groups MGI, MG0, and MG00, respectively. The accessions were genotyped for four major maturity genes (E1, E2, E3, and E4) in order to assess the relationship between E loci and agronomic traits. The allele composition of the majority of accessions was e1-as/e2/E3/E4 (specific frequencies 57.5%, 91.6%, 65.0%, and 63.3%, respectively). Accessions with dominant alleles in either E3 or E4 genes showed higher yield in all three regions, although the specific genotype associated with greatest productivity was different for each site. Genotype-environment interaction studies based on yield performances suggest that South-east and East regions formed one mega-environment, which was well separated from North Kazakhstan where significantly earlier time to maturation is required. The results provide important insights into the relationship between genetic and phenotypic patterns in new soybean growing territories in Kazakhstan.

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“…Soybean is an important crop globally, with high nutrition, protein and oil values [1], [2]. Despite this it is still a minor crop in Kazakhstan where farmers predominantly grow wheat.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Soybean Flowering and Seed Maturation Time in Different Latitude Regions of Kazakhstan

et al. 2016

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“…Association and quantitative trait locus (QTL) mapping studies of photoperiod response have implicated anywhere from a handful to as many as 14 loci contributing to intraspecific variation (e.g. Coles et al, 2010;Anderson et al, 2011;Hung et al, 2012;Henry et al, 2014;Jiang et al, 2014). Some of these loci represent allelic series, and different subsets of the loci may be combined to produce equivalent day-neutral phenotypes (e.g.…”

Section: Variation In Magnitude Of Photoperiodic Responsementioning

confidence: 99%

“…Some of these loci represent allelic series, and different subsets of the loci may be combined to produce equivalent day-neutral phenotypes (e.g. Xu et al, 2013;Jiang et al, 2014).…”

Section: Variation In Magnitude Of Photoperiodic Responsementioning

confidence: 99%

“…Spontaneous and induced mutations in the barley (Hordeum vulgare) ELF3 homolog also have contributed to the production of barley breeds cultivated in the short growing seasons of northern regions (Faure et al, 2012;Zakhrabekova et al, 2012). In cultivated soybean (Glycine max), coding polymorphisms at four loci, including variants leading to premature stops in two paralogs homologous to phytochrome A and a homolog of GIGANTEA, combine to build the latitudinal cline such that photoperiod sensitivity decreases with increasing latitude (Zhang et al, 2008;Xu et al, 2013;Jiang et al, 2014). These same alleles do not explain a similar cline in wild soybean, however.…”

Section: Variation In Magnitude Of Photoperiodic Responsementioning

confidence: 99%

“…Many more possible mutations eliminate gene function than selectively reduce, modulate, or add new function. Consequently, when loss of function does confer a favorable phenotype, the adaptive mutation rate is higher, and convergent evolution through allelic series like those seen for FRI in Arabidopsis (Shindo et al, 2005), pseudo-response regulators in grasses (Yan et al, 2004;Xue et al, 2008;Koo et al, 2013), or soybean phytochromes (Xu et al, 2013;Jiang et al, 2014) are more likely. Table I.…”

Section: Genetic Underpinningsmentioning

confidence: 99%

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Changing Responses to Changing Seasons: Natural Variation in the Plasticity of Flowering Time

Blackman

2016

Plant Physiol.

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For plants that live in seasonally changing environments, timing is everything. Matching developmental transitions with the best times of year for growth and reproduction is necessary to maintain high fitness. Consequently, plants employ many mechanisms to sense and integrate multiple predictive seasonal cues to regulate their major developmental shifts. As the annual timing with which the growing season starts and ends changes across the landscape, natural selection has led to the evolution of the mechanisms that regulate the developmental plasticity of flowering among populations or varieties of species and crop plants that inhabit broad geographic ranges. There has been significant recent progress in describing the diversity of this variation in flowering time plasticity and in identifying the specific genetic changes responsible. Such work is an essential step toward understanding the processes that have shaped current and past adaptation, managing genetic diversity and improving crops in the face of climate change, and forecasting how populations may respond plastically and evolutionarily to future environmental challenges. In this Update, I review the findings of recent studies of natural variation in the plasticity of flowering to photoperiod, vernalization, and ambient temperature, and the implications and open questions raised by this work are considered.A fundamental adaptation of plants inhabiting seasonal environments is their ability to match the annual timing of major life history transitions to the local growing season. Most species achieve this synchrony through developmental plasticity. In other words, individuals sense how environmental cues like daylength and temperature change from winter to spring to summer to fall. The information gleaned from these cycles is then integrated molecularly so that germination, flowering, and other key transitions occur during periods favorable for growth, reproduction, and seed set. However, as the climate changes over the 21st century and the relative timing of annual cycles in temperature and precipitation shifts, once adaptive responses will no longer effectively predict the best calendar dates to initiate these essential developmental events (Nicotra et al., 2010;Wilczek et al., 2010). Because natural populations and cultivated landraces of many taxa have evolved to thrive in geographically diverse habitats and climates as their ranges have expanded, they harbor natural variants that may prove instructive in breeding crops and conserving native plant diversity in the face of future environmental challenges. Thus, a critical objective in plant biology is

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The accuracy of different strategies for building training sets for genomic predictions in segregating soybean populations

Mendonça

Fritsche‐Neto

2020

Crop Science

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The design of the training set is a key factor in the success of the genomic selection approach. The nature of line inclusion in soybean [Sorghum bicolor (L.) Moench.] breeding programs is highly dynamic, so generating a training set that endures across the years and regions is challenging. Therefore, we aimed to define the best strategies for building training sets to apply genomic selection in segregating soybean populations for traits with different genetic architectures. We used two datasets for grain yield (GY) and maturity group (MG) from two different soybean breeding regions in Brazil. Five training set schemes were tested. In addition, we included a training set formed by an optimization algorithm based on the predicted error variance. The predictions achieved good values for both traits, reaching 0.5 in some scenarios. The best scenario changed according to the trait. Although the best performance was achieved with the use of full‐sibs in the MG dataset, for GY, full‐sibs and a set of advanced lines were equivalent. For both traits, no improvement in predictive ability resulted from training set optimization. Furthermore, the use of advanced lines from the same breeding program is recommended as a training set for GY, so the training set is continually renewed and closely related to the breeding populations, and no additional phenotyping is needed. On the other hand, to improve prediction accuracies for MG, it is necessary to use training sets with less genetic variability but with more segregation resolution.

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Allelic Combinations of Soybean Maturity Loci E1, E2, E3 and E4 Result in Diversity of Maturity and Adaptation to Different Latitudes

Cited by 98 publications

References 17 publications

Assessment of Soybean Flowering and Seed Maturation Time in Different Latitude Regions of Kazakhstan

Assessment of Soybean Flowering and Seed Maturation Time in Different Latitude Regions of Kazakhstan

Changing Responses to Changing Seasons: Natural Variation in the Plasticity of Flowering Time

The accuracy of different strategies for building training sets for genomic predictions in segregating soybean populations

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