Dataset Information

Natural variations of FT family genes in soybean varieties covering a wide range of maturity groups.

ABSTRACT:

Background

Flowering time and maturity are among the most important adaptive traits in soybean (Glycine max (L.) Merill). Flowering Locus T (FT) family genes function as key flowering integrators, with flowering-promoting members GmFT2a/GmFT5a and flowering-inhibiting members GmFT4/GmFT1a antagonistically regulating vegetative and reproductive growth. However, to date, the relations between natural variations of FT family genes and the diversity of flowering time and maturity in soybean are not clear. Therefore, we conducted this study to discover natural variations in FT family genes in association with flowering time and maturity.

Results

Ten FT family genes, GmFT1a, GmFT1b, GmFT2a, GmFT2b, GmFT3a, GmFT3b, GmFT4, GmFT5a, GmFT5b and GmFT6, were cloned and sequenced in the 127 varieties evenly covering all 14 known maturity groups (MG0000-MGX). They were diversified at the genome sequence polymorphism level. GmFT3b and GmFT5b might have experienced breeding selection in the process of soybean domestication and breeding. Haplotype analysis showed that a total of 17 haplotypes had correlative relationships with flowering time and maturity among the 10 FT genes, namely, 1a-H3, 1b-H1, 1b-H6, 1b-H7, 2a-H1, 2a-H3, 2a-H4, 2a-H9, 2b-H3, 2b-H4, 2b-H6, 2b-H7, 3b-H4, 5a-H1, 5a-H2, 5a-H4 and 5b-H1. Based on the association analysis, 38 polymorphic sites had a significant association with flowering time at the level of p < 0.01.

Conclusions

Some natural variations exist within the 10 FT family genes, which might be involved in soybean adaptation to different environments and have an influence on diverse flowering time and maturity. This study will facilitate the understanding of the roles of FTs in flowering and maturity.

SUBMITTER: Jiang B

PROVIDER: S-EPMC6425728 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.