Dataset Information

Association mapping of loci controlling genetic and environmental interaction of soybean flowering time under various photo-thermal conditions.

ABSTRACT:

Background

Soybean (Glycine max (L.) Merr.) is a short day plant. Its flowering and maturity time are controlled by genetic and environmental factors, as well the interaction between the two factors. Previous studies have shown that both genetic and environmental factors, mainly photoperiod and temperature, control flowering time of soybean. Additionally, these studies have reported gene?×?gene and gene?×?environment interactions on flowering time. However, the effects of quantitative trait loci (QTL) in response to photoperiod and temperature have not been well evaluated. The objectives of the current study were to identify the effects of loci associated with flowering time under different photo-thermal conditions and to understand the effects of interaction between loci and environment on soybean flowering.

Methods

Different photoperiod and temperature combinations were obtained by adjusting sowing dates (spring sowing and summer sowing) or day-length (12 h, 16 h). Association mapping was performed on 91 soybean cultivars from different maturity groups (MG000-VIII) using 172 SSR markers and 5107 SNPs from the Illumina SoySNP6K iSelectBeadChip. The effects of the interaction between QTL and environments on flowering time were also analysed using the QTXNetwork.

Results

Large-effect loci were detected on Gm 11, Gm 16 and Gm 20 as in previous reports. Most loci associated with flowering time are sensitive to photo-thermal conditions. Number of loci associated with flowering time was more under the long day (LD) than under the short day (SD) condition. The variation of flowering time among the soybean cultivars mostly resulted from the epistasis?×?environment and additive?×?environment interactions. Among the three candidate loci, i.e. Gm04_4497001 (near GmCOL3a), Gm16_30766209 (near GmFT2a and GmFT2b) and Gm19_47514601 (E3 or GmPhyA3), the Gm04_4497001 may be the key locus interacting with other loci for controlling soybean flowering time.

Conclusion

The effects of loci associated with the flowering time of soybean were dependent upon the photo-thermal conditions. This study facilitates the understanding of the genetic mechanism of soybean flowering and molecular breeding for the improvement of soybean adaptability to specific and/or broad regions.

SUBMITTER: Mao T

PROVIDER: S-EPMC5446728 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Publications

Association mapping of loci controlling genetic and environmental interaction of soybean flowering time under various photo-thermal conditions.

Mao Tingting T Li Jinyu J Wen Zixiang Z Wu Tingting T Wu Cunxiang C Sun Shi S Jiang Bingjun B Hou Wensheng W Li Wenbin W Song Qijian Q Wang Dechun D Han Tianfu T

BMC genomics 20170526 1

<h4>Background</h4>Soybean (Glycine max (L.) Merr.) is a short day plant. Its flowering and maturity time are controlled by genetic and environmental factors, as well the interaction between the two factors. Previous studies have shown that both genetic and environmental factors, mainly photoperiod and temperature, control flowering time of soybean. Additionally, these studies have reported gene × gene and gene × environment interactions on flowering time. However, the effects of quantitative tr ...[more]

PMID: 28549456

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Project description:A new mixed-model method was developed for mapping quantitative trait loci (QTL) by incorporating multiple polygenic covariance structures. First, we used genome-wide markers to calculate six different kinship matrices. We then partitioned the total genetic variance into six variance components, one corresponding to each kinship matrix, including the additive, dominance, additive × additive, dominance × dominance, additive × dominance, and dominance × additive variances. The six different kinship matrices along with the six estimated polygenic variances were used to control the genetic background of a QTL mapping model. Simulation studies showed that incorporating epistatic polygenic covariance structure can improve QTL mapping resolution. The method was applied to yield component traits of rice. We analyzed four traits (yield, tiller number, grain number, and grain weight) using 278 immortal F2 crosses (crosses between recombinant inbred lines) and 1619 markers. We found that the relative importance of each type of genetic variance varies across different traits. The total genetic variance of yield is contributed by additive × additive (18%), dominance × dominance (14%), additive × dominance (48%), and dominance × additive (15%) variances. Tiller number is contributed by additive (17%), additive × additive (22%), and dominance × additive (43%) variances. Grain number is mainly contributed by additive (42%), additive × additive (19%), and additive × dominance (31%) variances. Grain weight is almost exclusively contributed by the additive (73%) variance plus a small contribution from the additive × additive (10%) variance. Using the estimated genetic variance components to capture the polygenic covariance structure, we detected 39 effects for yield, 39 effects for tiller number, 24 for grain number, and 15 for grain weight. The new method can be directly applied to polygenic-effect-adjusted genome-wide association studies (GWAS) in human and other species.

Project description:Pythium root rot is one of the significant diseases of soybean (Glycine max (L.) Merr.) in the United States. The causal agent of the disease is a soil-borne oomycete pathogen Pythium irregulare, the most prevalent and aggressive species of Pythium in North Central United States. However, few studies have been conducted in soybean for the identification of quantitative trait loci (QTL) for tolerance to P. irregulare In this study, two recombinant inbred line (RIL) populations (designated as POP1 and POP2) were challenged with P. irregulare (isolate CMISO2-5-14) in a greenhouse assay. POP1 and POP2 were derived from 'E09014' × 'E05226-T' and 'E05226-T' × 'E09088', and contained 113 and 79 lines, respectively. Parental tests indicated that 'E05226-T' and 'E09014' were more tolerant than 'E09088', while 'E09088' was highly susceptible to the pathogen. The disease indices, root weight of inoculation (RWI) and ratio of root weight (RRW) of both populations showed near normal distributions, with transgressive segregation, suggesting the involvement of multiple QTL from both parents contributed to the tolerance. All the lines were genotyped using Illumina Infinium BARCSoySNP6K iSelect BeadChip and yielded 1373 and 1384 polymorphic markers for POP1 and POP2, respectively. Notably, despite high density, polymorphic markers coverage was incomplete in some genomic regions. As such, 28 and 37 linkage groups were obtained in POP1 and POP2, respectively corresponding to the 20 soybean chromosomes. Using RRW, one QTL was identified in POP1 on Chromosome 20 that explained 12.7-13.3% of phenotypic variation. The desirable allele of this QTL was from 'E05226-T'. Another QTL was found in POP2 on Chromosome 11. It explained 15.4% of the phenotypic variation and the desirable allele was from 'E09088'. However, no QTL were identified using RWI in either population. These results supported that RRW was more suitable to be used to evaluate P. irregulare tolerance in soybean.

Dataset Information

Association mapping of loci controlling genetic and environmental interaction of soybean flowering time under various photo-thermal conditions.

Background

Methods

Results

Conclusion

Publications

Association mapping of loci controlling genetic and environmental interaction of soybean flowering time under various photo-thermal conditions.

Similar Datasets

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