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Transcriptome-wide characterization of candidate genes for improving the water use efficiency of energy crops grown on semiarid land.

Fan Y, Wang Q, Kang L, Liu W, Xu Q, Xing S, Tao C, Song Z, Zhu C, Lin C, Yan J, Li J, Sang T - J. Exp. Bot. (2015)

Bottom Line: The field measurements showed that WUE of M. lutarioriparius in the semiarid location was significantly higher than that in the wet location.It was also found that the relatively high expression variation of the WUE-related genes was affected to a larger extent by environment than by genetic variation.The study demonstrates that transcriptome-wide correlation between physiological phenotypes and expression levels offers an effective means for identifying candidate genes involved in the adaptation to environmental changes.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Plant Resources and Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China University of Chinese Academy of Sciences, Beijing 100049, China.

No MeSH data available.


Expression reaction norms for WUE-related genes with genetic variation responding to growth environments. Detecting significant effects of those factors on the gene expression level represent, respectively, genetic variation for gene expression (G), phenotypic plasticity (E), and genetic variation for phenotypic plasticity (GEI, genotype-by-environment interaction). The graphs (A)–(P) represent the genes: MluLR17106 (ycf4), MluLR5294 (OAT4), MluLR2876 (UBE3), MluLR7126 (SSII-2), MluLR16886 (WRKY4), MluLR13061 (LSD1), MluLR16034 (Cyclophilin-type PPIase), MluLR4945 (LCAT1-like), MluLR12611 (mbd106), MluLR15146 (FKBP-type PPIase), MluLR17624 (RLC), MluLR14116, MluLR18370 (ASRGL1-like), MluLR3563, MluLR9412, and MluLR18082, respectively. The detailed functional annotation of these genes are given in Table 2. The average expression levels (FPKM) are computed only on genotypes with more than or equal to three individuals. Different genotypes are in a different colour and the error bars indicate the standard deviations.
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Figure 6: Expression reaction norms for WUE-related genes with genetic variation responding to growth environments. Detecting significant effects of those factors on the gene expression level represent, respectively, genetic variation for gene expression (G), phenotypic plasticity (E), and genetic variation for phenotypic plasticity (GEI, genotype-by-environment interaction). The graphs (A)–(P) represent the genes: MluLR17106 (ycf4), MluLR5294 (OAT4), MluLR2876 (UBE3), MluLR7126 (SSII-2), MluLR16886 (WRKY4), MluLR13061 (LSD1), MluLR16034 (Cyclophilin-type PPIase), MluLR4945 (LCAT1-like), MluLR12611 (mbd106), MluLR15146 (FKBP-type PPIase), MluLR17624 (RLC), MluLR14116, MluLR18370 (ASRGL1-like), MluLR3563, MluLR9412, and MluLR18082, respectively. The detailed functional annotation of these genes are given in Table 2. The average expression levels (FPKM) are computed only on genotypes with more than or equal to three individuals. Different genotypes are in a different colour and the error bars indicate the standard deviations.

Mentions: The contribution of genotype, environment, and genotype×environment interaction on gene expression variation was measured using an ANOVA model for each of 19 genes with SNPs. Detecting significant effects of those factors on gene expression levels represent, respectively, genetic variation for gene expression (G), phenotypic plasticity (E), and genetic variation for phenotypic plasticity (GEI, genotype-by-environment interaction). Of the 19 genes, 16 with a genotype found at least three times in a field site were analysed for GEI through ANOVA (Fig. 6).


Transcriptome-wide characterization of candidate genes for improving the water use efficiency of energy crops grown on semiarid land.

Fan Y, Wang Q, Kang L, Liu W, Xu Q, Xing S, Tao C, Song Z, Zhu C, Lin C, Yan J, Li J, Sang T - J. Exp. Bot. (2015)

Expression reaction norms for WUE-related genes with genetic variation responding to growth environments. Detecting significant effects of those factors on the gene expression level represent, respectively, genetic variation for gene expression (G), phenotypic plasticity (E), and genetic variation for phenotypic plasticity (GEI, genotype-by-environment interaction). The graphs (A)–(P) represent the genes: MluLR17106 (ycf4), MluLR5294 (OAT4), MluLR2876 (UBE3), MluLR7126 (SSII-2), MluLR16886 (WRKY4), MluLR13061 (LSD1), MluLR16034 (Cyclophilin-type PPIase), MluLR4945 (LCAT1-like), MluLR12611 (mbd106), MluLR15146 (FKBP-type PPIase), MluLR17624 (RLC), MluLR14116, MluLR18370 (ASRGL1-like), MluLR3563, MluLR9412, and MluLR18082, respectively. The detailed functional annotation of these genes are given in Table 2. The average expression levels (FPKM) are computed only on genotypes with more than or equal to three individuals. Different genotypes are in a different colour and the error bars indicate the standard deviations.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4588889&req=5

Figure 6: Expression reaction norms for WUE-related genes with genetic variation responding to growth environments. Detecting significant effects of those factors on the gene expression level represent, respectively, genetic variation for gene expression (G), phenotypic plasticity (E), and genetic variation for phenotypic plasticity (GEI, genotype-by-environment interaction). The graphs (A)–(P) represent the genes: MluLR17106 (ycf4), MluLR5294 (OAT4), MluLR2876 (UBE3), MluLR7126 (SSII-2), MluLR16886 (WRKY4), MluLR13061 (LSD1), MluLR16034 (Cyclophilin-type PPIase), MluLR4945 (LCAT1-like), MluLR12611 (mbd106), MluLR15146 (FKBP-type PPIase), MluLR17624 (RLC), MluLR14116, MluLR18370 (ASRGL1-like), MluLR3563, MluLR9412, and MluLR18082, respectively. The detailed functional annotation of these genes are given in Table 2. The average expression levels (FPKM) are computed only on genotypes with more than or equal to three individuals. Different genotypes are in a different colour and the error bars indicate the standard deviations.
Mentions: The contribution of genotype, environment, and genotype×environment interaction on gene expression variation was measured using an ANOVA model for each of 19 genes with SNPs. Detecting significant effects of those factors on gene expression levels represent, respectively, genetic variation for gene expression (G), phenotypic plasticity (E), and genetic variation for phenotypic plasticity (GEI, genotype-by-environment interaction). Of the 19 genes, 16 with a genotype found at least three times in a field site were analysed for GEI through ANOVA (Fig. 6).

Bottom Line: The field measurements showed that WUE of M. lutarioriparius in the semiarid location was significantly higher than that in the wet location.It was also found that the relatively high expression variation of the WUE-related genes was affected to a larger extent by environment than by genetic variation.The study demonstrates that transcriptome-wide correlation between physiological phenotypes and expression levels offers an effective means for identifying candidate genes involved in the adaptation to environmental changes.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Plant Resources and Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China University of Chinese Academy of Sciences, Beijing 100049, China.

No MeSH data available.