University of Hertfordshire

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By the same authors

Documents

  • Luo Ziliang
  • Wang Meng
  • Long Yan
  • Yongju Huang
  • Lei Shi
  • Chunyu Zhang
  • Xiang Liu
  • Bruce Fitt
  • Jinxia Xiang
  • Annaliese Mason
  • Rod Snowdon
  • Peifa Liu
  • Jinling Meng
  • Jun Zou
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Original languageEnglish
Number of pages17
Pages (from-to)1569-1585
JournalTAG: Theoretical and Applied Genetics
Journal publication date30 Aug 2017
Volume130
Issue8
Early online date28 Apr 2017
DOIs
Publication statusPublished - 30 Aug 2017

Abstract

Most agronomic traits of interest for crop improvement
(including seed yield) are highly complex quantitative
traits controlled by numerous genetic loci, which brings challenges
for comprehensively capturing associated markers/
genes. We propose that multiple trait interactions underlie
complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual
quantitative trait loci (QTL) effects more effectively and
improve yield predictions. Using a segregating rapeseed (Brassica
napus) population, we analyzed a large set of trait data
generated in 19 independent experiments to investigate correlations
between seed yield and other complex traits, and further
identified QTL in this population with a SNP-based genetic bin
map. A total of 1904 consensus QTL accounting for 22 traits,
including 80 QTL directly affecting seed yield, were anchored
to the B. napus reference sequence. Through trait association
analysis and QTL meta-analysis, we identified a total of 525
indivisible QTL that either directly or indirectly contributed
to seed yield, of which 295 QTL were detected across multiple
environments. A majority (81.5%) of the 525 QTL were
pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting
genetic effects, as well as 31 QTL with minor complementary
effects. Implementation of the 525 QTL in genomic
prediction models improved seed yield prediction accuracy.
Dissecting the genetic and phenotypic interrelationships underlying
complex quantitative traits using this method will provide
valuable insights for genomics-based crop improvement.

Notes

© The Author(s) 2017 This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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