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Functionally, structurally, and evolutionarily distinct set of genes linked to phenome wide variation in maize

View ORCID ProfileZhikai Liang, Yumou Qiu, View ORCID ProfileJames C. Schnable
doi: https://doi.org/10.1101/534503
Zhikai Liang
1Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
2Plant Science Innovation Center, University of Nebraska-Lincoln, Lincoln, NE, USA
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Yumou Qiu
3Department of Statistics, Iowa State University, Ames, IA, USA
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James C. Schnable
1Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
2Plant Science Innovation Center, University of Nebraska-Lincoln, Lincoln, NE, USA
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  • For correspondence: schnable@unl.edu
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ABSTRACT

In many eukaryotic species the organismal functions of only a small fraction of annotated genes are supported by individual genetic characterization. The organismal functions of a somewhat larger, but still strict minority, of gene models are supported by quantitative genetic analyses (e.g. GWAS). However the organismal functions of the vast majority of gene models are not supported by any direct evidence. Genes characterized by direct investigation exhibit a set of molecular, structural, population genetic, and evolutionary features which distinguish these genes from other gene models. Weaker versions of the same signatures are present among genes identified through conventional quantitative genetics approaches. A new multi-trait multi-SNP association test, the Genome-Phenome Wide Association Study (GPWAS) combines data from large sets of traits and dense resequencing data to identify that set of genes significantly associated with phenotypic variation per se. Genes identified using GPWAS and data for 260 phenotypic traits scored across a maize (Zea mays) exhibit many of the same molecular, structural, population genetic, and evolutionary signals indicative of genes with functions characterized by direct genetic investigation. The strength of these signals is significantly higher for genes identified using GPWAS than genes identified through conventional GWAS. These results were consistent with a large subset of annotated gene models in maize play little or no role in determining organismal phenotypes. GPWAS and future similar analytical approaches that leverage data from multiple correlated and uncorrelated traits across the same population may provide a method to prioritize those genes most involved in regulation phenotypic variation across diverse species.

Footnotes

  • Multiple changes including significant additional analyses (simulation-based comparisons of FDR/power trade offs; demonstration that 35 cycles is enough to saturate the GPWAS algorithm for this particular dataset; reframing the introduction and abstract to focus more on biology and less on algorithms; former Figure 2 is now supplemental; new Figure 2 to emphasize data previously only presented in tables).

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted August 20, 2019.
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Functionally, structurally, and evolutionarily distinct set of genes linked to phenome wide variation in maize
Zhikai Liang, Yumou Qiu, James C. Schnable
bioRxiv 534503; doi: https://doi.org/10.1101/534503
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Functionally, structurally, and evolutionarily distinct set of genes linked to phenome wide variation in maize
Zhikai Liang, Yumou Qiu, James C. Schnable
bioRxiv 534503; doi: https://doi.org/10.1101/534503

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