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Yield QTLome distribution correlates with gene density in maize.

Ana Karine Martinez1, Jose Miguel Soriano2, Roberto Tuberosa1

  • 1Department of Agricultural Sciences, University of Bologna, Viale Fanin 44, 40127 Bologna, Italy.

Plant Science : an International Journal of Experimental Plant Biology
|November 15, 2015
PubMed
Summary

This study synthesized maize yield quantitative trait locus (QTL) data, revealing 84 key meta-QTLs. Findings highlight the genetic complexity of grain yield and suggest gene density influences QTL distribution on chromosomes.

Keywords:
HeterosisMeta-analysisQTLQTLomeYieldZea mays

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Area of Science:

  • Agricultural Science
  • Genetics
  • Genomics

Background:

  • Quantitative trait locus (QTL) studies have generated extensive data on the genetic control of maize yield.
  • A comprehensive understanding of the maize yield QTLome is crucial for genetic improvement.

Purpose of the Study:

  • To assemble a maize yield QTLome database.
  • To perform a meta-analysis of published QTL studies to identify major yield-contributing regions.
  • To investigate the distribution patterns of yield QTLs on maize chromosomes.

Main Methods:

  • Assembled a database of maize yield QTLs from 44 published studies.
  • Conducted a QTL meta-analysis on 32 independent mapping populations.
  • Projected 808 unique QTLs onto 10 maize chromosomes, condensing them into 84 meta-QTLs.

Main Results:

  • Identified 84 meta-QTLs for maize grain yield.
  • Confirmed the high genetic complexity of grain yield, with 74% of QTLs explaining <10% of phenotypic variance.
  • Observed pericentromeric enrichment of QTLs on the genetic map but depletion on the physical map, indicating gene density as a key factor in QTL distribution.

Conclusions:

  • Maize grain yield is controlled by numerous genes with small effects, reflecting its complex genetic architecture.
  • Gene density significantly influences the distribution of yield QTLs across maize chromosomes.
  • Additive, dominant, and overdominant yield QTLs show similar distribution patterns.