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Dysregulation of expression correlates with rare-allele burden and fitness loss in maize

Nature volume 555, pages 520–523 (2018)Cite this article

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Abstract

Here we report a multi-tissue gene expression resource that represents the genotypic and phenotypic diversity of modern inbred maize, and includes transcriptomes in an average of 255 lines in seven tissues. We mapped expression quantitative trait loci and characterized the contribution of rare genetic variants to extremes in gene expression. Some of the new mutations that arise in the maize genome can be deleterious; although selection acts to keep deleterious variants rare, their complete removal is impeded by genetic linkage to favourable loci and by finite population size1,2,3,4. Modern maize breeders have systematically reduced the effects of this constant mutational pressure through artificial selection and self-fertilization, which have exposed rare recessive variants in elite inbred lines5. However, the ongoing effect of these rare alleles on modern inbred maize is unknown. By analysing this gene expression resource and exploiting the extreme diversity and rapid linkage disequilibrium decay of maize6, we characterize the effect of rare alleles and evolutionary history on the regulation of expression. Rare alleles are associated with the dysregulation of expression, and we correlate this dysregulation to seed-weight fitness. We find enrichment of ancestral rare variants among expression quantitative trait loci mapped in modern inbred lines, which suggests that historic bottlenecks have shaped regulation. Our results suggest that one path for further genetic improvement in agricultural species lies in purging the rare deleterious variants that have been associated with crop fitness.

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Figure 1: The abundance of local rare alleles correlates with extremes in expression.
Figure 2: Ancestral rare alleles are significantly enriched for highly explanatory cis eQTL in modern germplasm.
Figure 3: Dysregulation of expression can predict fitness.

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Acknowledgements

We thank J. Pardo, J. Wallace, R. Punna, K. Shirasawa and S. Miller for assistance with tissue collection; J. Budka and G. Inzinna for field and greenhouse assistance; R. Bukowski for running the maize HapMap genotyping pipeline; L. Johnson and Z. Miller for database curation; G. Gibson, M. Wolfe, J.-L. Jannink, M. Hufford and J. Ross-Ibarra for discussions; P. Schweitzer, J. Mosher, A. Tate, J. Mattison, M. Magallanes-Lundback, I. Holländer and D. Daujotyte for guidance on RNA extraction, library preparation automation and sequencing; and S. Miller for copy-editing. This work was supported by the US Department of Agriculture–Agricultural Research Service and the National Science Foundation grants IOS-0922493 and IOS-1238014 to E.S.B. The National Science Foundation Graduate Research Fellowship Program grant DGE-1650441 and the Section of Plant Breeding and Genetics at Cornell University provided support to K.A.G.K. The Taiwanese Ministry of Science and Technology Overseas Project for Post Graduate Research grant 104-2917-I-564-015 supported S.-Y.C.

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Authors and Affiliations

  1. Section of Plant Breeding and Genetics, 175 Biotechnology Building, Cornell University, Ithaca, 14853, New York, USA

    Karl A. G. Kremling, Kelly L. Swarts & Edward S. Buckler

  2. Institute for Genomic Diversity, 175 Biotechnology Building, Cornell University, Ithaca, 14853, New York, USA

    Shu-Yun Chen, Mei-Hsiu Su, M. Cinta Romay, Fei Lu & Edward S. Buckler

  3. Institute of Plant and Microbial Biology, Academia Sinica 128, Sec 2nd, Academia road, Taipei, 11529, Taiwan

    Shu-Yun Chen

  4. USDA-ARS, R. W. Holley Center, Cornell University, Ithaca, 14853, New York, USA

    Nicholas K. Lepak, Peter J. Bradbury & Edward S. Buckler

  5. Department of Molecular Biology, Research Group for Ancient Genomics and Evolution, Max Planck Institute for Developmental Biology, Spemannstr. 35, Tübingen, 72076, Germany

    Kelly L. Swarts

  6. The State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China

    Fei Lu

  7. Department of Plant Sciences, University of California Davis, Davis, 95616, California, USA

    Anne Lorant

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  1. Karl A. G. Kremling
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Contributions

K.A.G.K. and E.S.B. designed the experiments and wrote the manuscript. K.A.G.K performed the analyses and made the RNA-seq libraries. K.A.G.K., S.-Y.C., and M.-H.S. extracted RNA. N.K.L. managed germplasm and plants with K.A.G.K., M.C.R., K.L.S. and A.L. produced and imputed HapMap genotypic data. P.J.B. implemented matrixEQTL in Java/TASSEL. F.L. implemented SNP calling from RNA-seq data.

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Correspondence to Karl A. G. Kremling or Edward S. Buckler.

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Reviewer Information Nature thanks N. Springer and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Tissues that were expression profiled by 3′ RNA-seq.

See additional details regarding tissue collection in Methods. Illustrations inspired by ref. 41.

Extended Data Figure 2 Higher numbers of rare alleles are upstream of genes in extreme-expressing individuals, for the most highly expressed genes.

Quadratic regression of the expression rank of each line, for each of the top 5,000 most-expressed genes versus the average local (5-kb upstream) rare-allele count. a, Base of leaf three (n = 263 unique inbred samples). b, Tip of leaf three (n = 265 unique inbred samples). c, Adult leaves collected during the day (n = 204 unique inbred samples). d, Adult leaves collected at night (n = 260 unique inbred samples). e, Kernels at 350-growing-degree days (n = 229 unique inbred samples). f, Roots of germinating seedling (n = 273 unique inbred samples). g, Shoots of germinating seedling (n = 278 unique inbred samples).

Extended Data Figure 3 Higher numbers of rare alleles are upstream of genes in extreme-expressing individuals, for the medium-expressed genes.

Quadratic regression of the expression rank of each line, for each of the top 5,001–10,000 most-expressed genes versus the average local (5-kb upstream) rare-allele count. a, Base of leaf three (n = 263 unique inbred samples). b, Tip of leaf three (n = 265 unique inbred samples). c, Adult leaves collected during the day (n = 204 unique inbred samples). d, Adult leaves collected at night (n = 260 unique inbred samples). e, Kernels at 350-growing-degree days (n = 229 unique inbred samples).f, Roots of germinating seedling (n = 273 unique inbred samples). g, Shoots of germinating seedling (n = 278 unique inbred samples).

Extended Data Figure 4 Comparison of the number of rare cis alleles near genes with differing expression levels.

The 10,000 most-expressed genes in each tissue are divided into groups of 1,000 on the basis of expression level. Plots in each panel show genes ranked 1–1,000, 1,001–2,000, …, 9,001–10,000 from left to right. Each of the individuals represented in each tissue is ranked for expression for each of the 1,000 genes in each group. Individuals in the bottom five expression ranks (fuchsia) versus the middle two quartiles (yellow) versus the top five expression ranks (blue) (mean ± s.e.m.). Y axes refer to mean upstream (within 5 kb) rare-allele count. a, Roots of germinating seedling (n = 273 unique inbred samples). b, Shoots of germinating seedling (n = 278 unique inbred samples). c, Kernels at 350-growing-degree days (n = 229 unique inbred samples). d, Base of leaf three (n = 263 unique inbred samples). e, Tip of leaf three (n = 265 unique inbred samples). f, Adult leaves collected during the day (n = 204 unique inbred samples). g, Adult leaves collected at night (n = 260 unique inbred samples).

Extended Data Figure 5 eQTL R2 distribution comparisons between SNPs in 0.0–0.1 (tropical MAF) and 0.1–0.2 (RNA-set MAF) versus 0.1–0.2 (RNA-set and tropical MAF).

a, Adult leaves collected at night (n = 260 unique inbred samples). b, Adult leaves collected during the day (n = 204 unique inbred samples). c, Tip of leaf three (n = 265 unique inbred samples). d, Base of leaf three (n = 263 unique inbred samples). e, Kernels at 350-growing-degree days (n = 229 unique inbred samples). f, Shoots of germinating seedling (n = 278 unique inbred samples). g, Roots of germinating seedling (n = 273 unique inbred samples). All pairs of distributions within each tissue are significantly different. P < 2.2 × 10−16 two-sided Wilcoxon signed-rank test and Kolmogorov–Smirnov test.

Extended Data Figure 6 eQTL R2 distribution comparisons between SNPs in 0.0–0.1 (tropical MAF) and 0.4–0.5 (RNA-set MAF) versus 0.4–0.5 (RNA-set and tropical MAF).

a, Adult leaves collected at night (n = 260 unique inbred samples). b, Adult leaves collected during the day (n = 204 unique inbred samples). c, Tip of leaf three (n = 265 unique inbred samples). d, Base of leaf three (n = 263 unique inbred samples). e, Kernels at 350-growing-degree days (n = 229 unique inbred samples). f, Shoots of germinating seedling (n = 278 unique inbred samples). g, Roots of germinating seedling (n = 273 unique inbred samples). All pairs of distributions within each tissue are significantly different. P < 2.2 × 10−16 two-sided Wilcoxon signed-rank test and Kolmogorov–Smirnov test.

Extended Data Figure 7 Expression value and dysregulation of 5,000 most-expressed genes are both predictive of fitness.

Orange boxes represent correlations between predicted and true seed weight when using expression values. Yellow boxes represent correlations between predicted and true seed weight when using absolute deviation in expression from the population mean. Range of correlations between predicted and true seed weight is displayed from ten repetitions of nested tenfold cross validation (ten inner and ten outer) using ridge regression. In the box plots, the middle horizontal lines represent the median, hinges represent the 25th and 75th percentiles (the interquartile range), the upper and lower whiskers extend to maximum and minimum points no more than 1.5× interquartile range beyond the hinges, and individual dots are outliers beyond the whiskers. Sample sizes: 2-cm root tips of germinating seedlings (unique n = 181) and whole shoots of germinating seedlings (unique n = 183); the 2-cm base (unique n = 181) and tip (unique n = 182) of leaf 3; leaves collected in the field during the day (unique n = 135) and night (unique n = 187); and 350-growing-degree-day kernels (unique n = 171), post sexual maturity (anthesis).

Extended Data Figure 8 Cumulative expression dysregulation of the 5,000 most-expressed genes in each tissue versus seed weight.

a, Adult leaves collected at night (n = 221 unique inbred samples). b, Adult leaves collected during the day (n = 171 unique inbred samples). c, Tip of leaf three (n = 226 unique inbred samples). d, Base of leaf three (n = 224 unique inbred samples). e, Kernels at 350-growing-degree days (n = 195 unique inbred samples). f, Shoots of germinating seedling (n = 235 unique inbred samples). g, Roots of germinating seedling (n = 226 unique inbred samples). Regression statistics in Extended Data Table 1. Sweet corn and popcorn lines were excluded from these regressions.

Extended Data Figure 9 Mean upstream rare-allele count from the 5,000 most highly expressed genes versus seed weight.

a, Adult leaves collected at night (n = 221 unique inbred samples). b, Adult leaves collected during the day (n = 171 unique inbred samples). c, Tip of leaf three (n = 226 unique inbred samples). d, Base of leaf three (n = 224 unique inbred samples). e, Kernels at 350-growing-degree days (n = 195 unique inbred samples). f, Shoots of germinating seedling (n = 235 unique inbred samples). g, Roots of germinating seedling (n = 226 unique inbred samples).

Extended Data Table 1 Regression statistics for cumulative expression dysregulation in each tissue against seed-weight fitness
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Supplementary information

Life Sciences Reporting Summary (PDF 72 kb)

Supplementary Table 1

This table contains collection details for all sampled genotypes. Sequencing batch, tissue of origin, RNAseq depth, and subpopulation membership are specified for each sample. (XLS 445 kb)

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Kremling, K., Chen, SY., Su, MH. et al. Dysregulation of expression correlates with rare-allele burden and fitness loss in maize. Nature 555, 520–523 (2018). https://doi.org/10.1038/nature25966

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