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. 2021 May 27;22(1):17.
doi: 10.1186/s12863-021-00970-7.

Chromosomal characteristics of salt stress heritable gene expression in the rice genome

Affiliations

Chromosomal characteristics of salt stress heritable gene expression in the rice genome

Matthew T McGowan et al. BMC Genom Data. .

Abstract

Background: Gene expression is potentially an important heritable quantitative trait that mediates between genetic variation and higher-level complex phenotypes through time and condition-dependent regulatory interactions. Therefore, we sought to explore both the genomic and condition-specific characteristics of gene expression heritability within the context of chromosomal structure.

Results: Heritability was estimated for biological gene expression using a diverse, 84-line, Oryza sativa (rice) population under optimal and salt-stressed conditions. Overall, 5936 genes were found to have heritable expression regardless of condition and 1377 genes were found to have heritable expression only during salt stress. These genes with salt-specific heritable expression are enriched for functional terms associated with response to stimulus and transcription factor activity. Additionally, we discovered that highly and lowly expressed genes, and genes with heritable expression are distributed differently along the chromosomes in patterns that follow previously identified high-throughput chromosomal conformation capture (Hi-C) A/B chromatin compartments. Furthermore, multiple genomic hot-spots enriched for genes with salt-specific heritability were identified on chromosomes 1, 4, 6, and 8. These hotspots were found to contain genes functionally enriched for transcriptional regulation and overlaps with a previously identified major QTL for salt-tolerance in rice.

Conclusions: Investigating the heritability of traits, and in-particular gene expression traits, is important towards developing a basic understanding of how regulatory networks behave across a population. This work provides insights into spatial patterns of heritable gene expression at the chromosomal level.

Keywords: Agronomy; Genetics; Heritability; RNAseq; Transcriptomics.

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Conflict of interest statement

The authors declare they have no competing interests.

Figures

Fig. 1
Fig. 1
Bimodal Gene Expression Patterns: Plot A shows the proportion of samples with missing values calculated for each gene. The overall distribution of the missing rate is bimodal with the majority of genes either having few (< 5%) or many (> 95%) missing values. Genes were classified as ‘constitutive’ (< 5% missing), mixed (5–95% missing), or repressed (> 95% missing). Constitutive genes are those to the left of the red dashed line. The mean value of non-zero TPMs for expressed genes also had a bimodal distribution based on the missing rate. Plot B shows the density plots of constitutive and non-constitutive genes
Fig. 2
Fig. 2
Comparison of Heritability Calculation Methods for the Control condition: Pairwise correlation between repeatability (Pearson’s), single-step GREML (with replicates), and two-step GREML (using the genotypic mean) for the control condition. The lower triangle shows correlation scatterplots of the pairwise comparisons, the diagonal provides the density distribution plots for each individual method and the upper right triangle provides the corresponding pairwise correlation values
Fig. 3
Fig. 3
Classification of gene expression heritability. Plot A shows the heritability distribution of randomly shuffled gene expression values. This distribution serves as the null-distribution used for determining non-significant heritability estimates for genes. The dashed red line indicates the quantile for a fixed type-1 error (□=0.01). Plot B shows the comparison of salt and control heritability estimates. A quantile threshold was used to classify each gene as having significant heritability in salt treatment, control or general (i.e. both)
Fig. 4
Fig. 4
Gene density distributions across chromosomes. Plots A-D represent chromosomes 1, 4, 6, and 8 respectively. The black lines at the bottom of each plot represent the relative chromosome length, with the position and relative size of pericentromeric regions indicated by overlapping red boxes. Overall gene frequency represented by the red line appears roughly uniform across each chromosome. Genes with constitutive expression (expressed in > 95% of samples), represented by the lime-colored line, are enriched on the distal ends of chromosome arms and depleted near pericentromeric regions. Genes with repressed expression (< 5% of samples), represented by the cyan colored line, are enriched near pericentromeric regions. Genes with mixed expression (5–95% of samples), represented by the pink line, largely follow the same distribution as repressed genes
Fig. 5
Fig. 5
Salt-specific Heritable Gene Enrichment. Plots A-D represent chromosomes 1, 4, 6, and 8 respectively. The black lines at the bottom of each plot represent the relative chromosome length, with the position and relative size of pericentromeric regions indicated by overlapping red boxes. Using a sliding window size of 1.5 Mb at 100 Kb intervals, chromosomes were tested for enrichment of genes with salt-specific heritability using all genes with heritable expression (salt-specific, optimal-specific, and general) as the null distribution. P-values were adjusted for multiple-testing using a permutation based approach. Using a critical value of 0.001, indicated by the dashed red line, significant windows enriched for salt-specific heritability were identified on chromosomes 1, 4, 6, and 8

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References

    1. Hardy J, Singleton A. Genomewide association studies and human disease. N Engl J Med. 2009;360(17):1759–1768. doi: 10.1056/NEJMra0808700. - DOI - PMC - PubMed
    1. West MAL, Kim K, Kliebenstein DJ, Van Leeuwen H, Michelmore RW, Doerge RW, et al. Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis. Genetics. 2007;175(3):1441–1450. doi: 10.1534/genetics.106.064972. - DOI - PMC - PubMed
    1. Liu H, Luo X, Niu L, Xiao Y, Chen L, Liu J, Wang X, Jin M, Li W, Zhang Q, Yan J. Distant eQTLs and non-coding sequences play critical roles in regulating gene expression and quantitative trait variation in maize. Mol Plant. 2017;10(3):414–426. doi: 10.1016/j.molp.2016.06.016. - DOI - PubMed
    1. Ingvarsson PK, Street NR. Association genetics of complex traits in plants. New Phytol. 2011;189(4):909–922. doi: 10.1111/j.1469-8137.2010.03593.x. - DOI - PubMed
    1. Hammond JP, Mayes S, Bowen HC, Graham NS, Hayden RM, Love CG, Spracklen WP, Wang J, Welham SJ, White PJ, King GJ, Broadley MR. Regulatory hotspots are associated with plant gene expression under varying soil phosphorus supply in brassica rapa. Plant Physiol. 2011;156(3):1230–1241. doi: 10.1104/pp.111.175612. - DOI - PMC - PubMed

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