Mapping epistasis and environment×QTX interaction based on four -omics genotypes for the detected QTX loci controlling complex traits in tobacco

Zhou, Liyuan; Li, Ruiyuan; Fan, Longjiang; Yuewei, Shi; Wang, Zhihong; Xie, Shengdong; Ying, Gui Shuang; Ren, Xueliang; Zhu, Jun

doi:10.1016/j.cj.2013.09.001

Cited by 6 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The software was used to study the genetic variations of lint yield and its component traits in cotton [35], and the chromium content and total sugar level in tobacco leaf [36]. …”

Section: Introductionmentioning

confidence: 99%

“…QTXNetwork is a GPU parallel computing software to reveal genetic and environmental interaction underlying the genetic architecture of complex traits [ 35 ], the algorithm of the software was based on a mixed linear model. The software was used to study the genetic variations of lint yield and its component traits in cotton [ 35 ], and the chromium content and total sugar level in tobacco leaf [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Mao

Zhang

et al. 2017

BMC Genomics

View full text Add to dashboard Cite

BackgroundSoybean (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.MethodsDifferent 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.ResultsLarge-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.ConclusionThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3778-3) contains supplementary material, which is available to authorized users.

show abstract

“…The software was used to study the genetic variations of lint yield and its component traits in cotton [35], and the chromium content and total sugar level in tobacco leaf [36]. …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Mao

Zhang

et al. 2017

BMC Genomics

View full text Add to dashboard Cite

show abstract

“…The high statistical power and unbiased estimation indicates that QTT association is also a useful approach to map individual transcripts and pair-wise interaction, which are significantly associated with the quantitative traits. In addition, the approach could also be extended to mapping quantitative trait protein (QTP) and quantitative trait metabolite (QTM)50. Combining the results of transcript association with the QTSs mapped by association analyses, we could further understand the function of the candidate genes.…”

Section: Discussionmentioning

confidence: 99%

Mixed Linear Model Approaches of Association Mapping for Complex Traits Based on Omics Variants

Zhang

Zhu

Tong

et al. 2015

Sci Rep

Self Cite

View full text Add to dashboard Cite

Precise prediction for genetic architecture of complex traits is impeded by the limited understanding on genetic effects of complex traits, especially on gene-by-gene (GxG) and gene-by-environment (GxE) interaction. In the past decades, an explosion of high throughput technologies enables omics studies at multiple levels (such as genomics, transcriptomics, proteomics, and metabolomics). The analyses of large omics data, especially two-loci interaction analysis, are very time intensive. Integrating the diverse omics data and environmental effects in the analyses also remain challenges. We proposed mixed linear model approaches using GPU (Graphic Processing Unit) computation to simultaneously dissect various genetic effects. Analyses can be performed for estimating genetic main effects, GxG epistasis effects, and GxE environment interaction effects on large-scale omics data for complex traits, and for estimating heritability of specific genetic effects. Both mouse data analyses and Monte Carlo simulations demonstrated that genetic effects and environment interaction effects could be unbiasedly estimated with high statistical power by using the proposed approaches.

show abstract

“…The association mapping based on the four-omics datasets was called QTX mapping, including quantitative trait SNPs (QTS), QTT, quantitative trait proteins (QTP), and quantitative trait metabolites (QTM) (Zhou et al, 2013 ). In this study, QTT association mapping was conducted to detect QTT differences on phenotype.…”

Section: Methodsmentioning

confidence: 99%

Root Transcriptomic Analysis Revealing the Importance of Energy Metabolism to the Development of Deep Roots in Rice (Oryza sativa L.)

et al. 2017

View full text Add to dashboard Cite

Drought is the most serious abiotic stress limiting rice production, and deep root is the key contributor to drought avoidance. However, the genetic mechanism regulating the development of deep roots is largely unknown. In this study, the transcriptomes of 74 root samples from 37 rice varieties, representing the extreme genotypes of shallow or deep rooting, were surveyed by RNA-seq. The 13,242 differentially expressed genes (DEGs) between deep rooting and shallow rooting varieties (H vs. L) were enriched in the pathway of genetic information processing and metabolism, while the 1,052 DEGs between the deep roots and shallow roots from each of the plants (D vs. S) were significantly enriched in metabolic pathways especially energy metabolism. Ten quantitative trait transcripts (QTTs) were identified and some were involved in energy metabolism. Forty-nine candidate DEGs were confirmed by qRT-PCR and microarray. Through weighted gene co-expression network analysis (WGCNA), we found 18 hub genes. Surprisingly, all these hub genes expressed higher in deep roots than in shallow roots, furthermore half of them functioned in energy metabolism. We also estimated that the ATP production in the deep roots was faster than shallow roots. Our results provided a lot of reliable candidate genes to improve deep rooting, and firstly highlight the importance of energy metabolism to the development of deep roots.

show abstract

Mapping epistasis and environment×QTX interaction based on four -omics genotypes for the detected QTX loci controlling complex traits in tobacco

Cited by 6 publications

References 26 publications

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

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

Mixed Linear Model Approaches of Association Mapping for Complex Traits Based on Omics Variants

Root Transcriptomic Analysis Revealing the Importance of Energy Metabolism to the Development of Deep Roots in Rice (Oryza sativa L.)

Contact Info

Product

Resources

About