Dataset Information

Maturity group classification and maturity locus genotyping of early-maturing soybean varieties from high-latitude cold regions.

ABSTRACT:

Background

With the migration of human beings, advances of agricultural sciences, evolution of planting patterns and global warming, soybeans have expanded to both tropical and high-latitude cold regions (HCRs). Unlike other regions, HCRs have much more significant and diverse photoperiods and temperature conditions over seasons or across latitudes, and HCR soybeans released there show rich diversity in maturity traits. However, HCR soybeans have not been as well classified into maturity groups (MGs) as other places. Therefore, it is necessary to identify MGs in HCRs and to genotype the maturity loci.

Methods

Local varieties were collected from the northern part of Northeast China and the far-eastern region of Russia. Maturity group reference (MGR) soybeans of MGs MG000, MG00, and MG0 were used as references during field experiments. Both local varieties and MGR soybeans were planted for two years (2010-2011) in Heihe (N 50°15', E 127°27', H 168.5 m), China. The days to VE (emergence), R1 (beginning bloom) and R7 (beginning maturity) were recorded and statistically analyzed. Furthermore, some varieties were further genotyped at four molecularly-identified maturity loci E1, E2, E3 and E4.

Results

The HCR varieties were classified into MG0 or even more early-maturing. In Heihe, some varieties matured much earlier than MG000, which is the most early-maturing known MG, and clustered into a separate group. We designated the group as MG0000, following the convention of MGs. HCR soybeans had relatively stable days to beginning bloom from emergence. The HCR varieties diversified into genotypes of E1, E2, E3 and E4. These loci had different effects on maturity.

Conclusion

HCRs diversify early-maturing MGs of soybean. MG0000, a new MG that matures much earlier than known MGs, was developed. HCR soybean breeding should focus more on shortening post-flowering reproductive growth. E1, E2, E3, and E4 function differentially.

SUBMITTER: Jia H

PROVIDER: S-EPMC3989213 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Maturity group classification and maturity locus genotyping of early-maturing soybean varieties from high-latitude cold regions.

Jia Hongchang H Jiang Bingjun B Wu Cunxiang C Lu Wencheng W Hou Wensheng W Sun Shi S Yan Hongrui H Han Tianfu T

PloS one 20140416 4

<h4>Background</h4>With the migration of human beings, advances of agricultural sciences, evolution of planting patterns and global warming, soybeans have expanded to both tropical and high-latitude cold regions (HCRs). Unlike other regions, HCRs have much more significant and diverse photoperiods and temperature conditions over seasons or across latitudes, and HCR soybeans released there show rich diversity in maturity traits. However, HCR soybeans have not been as well classified into maturity ...[more]

PMID: 24740097

Similar Datasets

Project description: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.

Project description:Soybean has a recognized narrow genetic base that often makes it difficult to visualize available genetic and phenotypic variability and identify superior genotypes during the selection process. However, the phenotypic expression of soybean plants is highly affected by photoperiod and the cultivation of a given variety is performed in the latitude range that presents ideal conditions for its development based on its relative maturity group (RMG) for the optimization of the phenotypic expression of its genotype. Based on the above, this study aimed to evaluate the efficiency of artificial neural networks (ANNs) as a tool for the correct discrimination and classification of tropical soybean genotypes according to their relative maturity group during the population selection process with the aim of optimizing the phenotypic performance of these selected genotypes. For this purpose, three biparental populations were synthesized, one with a wide genetic variability for the RMG character obtained from the hybridization between genitors of maturity groups RMG 5 (Sub-tropical 23° LS) × RMG 9.4 (Tropical 0° LS) and two populations with a narrow variability obtained between genitors RMG 7.3 (Tropical 20° LS) × RMG 9.4 and RMG 5.3 × RMG 6.7, respectively. Criteria for comparing the developed ANN architecture with Fisher’s linear and Anderson’s quadratic parametric discriminant methodologies were applied to the data for the discrimination and classification of the genotypes. ANN showed an apparent error rate of less than 8.16% as well as a low influence of environmental factors, correctly classifying the genotypes in the populations even in cases of reduced genetic variability such as in the RMG 5 × RMG 6 population. In contrast, the discriminant functions were inefficient in correctly classifying the genotypes in the populations with genealogical similarity (RMG 5 × RMG 6) and wide genetic variability, with an error rate of more than 50%. Based on the results of this study, ANN can be used for the discrimination of genotypes in the initial generations of selection in breeding programs for the development of high performance cultivars for wide and reduced photoperiod amplitudes, even with fewer selection environments, more efficiently, and with fewer time and resources applied. As a result of similarity between the parents, ANN can correctly classify genotypes from populations with a narrow genetic base, in addition to pure lines and genotypes with a high degree of inbreeding.

Dataset Information

Maturity group classification and maturity locus genotyping of early-maturing soybean varieties from high-latitude cold regions.

Background

Methods

Results

Conclusion

Publications

Maturity group classification and maturity locus genotyping of early-maturing soybean varieties from high-latitude cold regions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets