Letter | Published:

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

Nature volume 555, pages 520523 (22 March 2018) | Download Citation


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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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.

Author information


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

    • Karl A. G. Kremling
    • , Kelly L. Swarts
    •  & Edward S. Buckler
  2. Institute for Genomic Diversity, 175 Biotechnology Building, Cornell University, Ithaca, New York 14853, 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, New York 14853, USA

    • Nicholas K. Lepak
    • , Peter J. Bradbury
    •  & Edward S. Buckler
  5. Research Group for Ancient Genomics and Evolution, Department of Molecular Biology, Max Planck Institute for Developmental Biology, Spemannstr. 35, 72076 Tübingen, 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, California 95616, USA

    • Anne Lorant


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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.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

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

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

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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    Life Sciences Reporting Summary

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    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.

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