Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example

• DOI: 10.1007/s00122-017-2911-7
• Publication Date: 2017-04-28T09:23:24Z
• Authors: Ziliang Luo; Meng Wang; Yan Long; Yongju Huang; Lei Shi; Chunyu Zhang; Xiang Liu; Bruce D. L. Fitt; Jinxia Xiang; Annaliese S. Mason; Rod J. Snowdon; Peifa Liu; Jinling Meng; Jun Zou
• Keywords: Genetic Markers; 0301 basic medicine; 2. Zero hunger; 0303 health sciences; 03 medical and health sciences; Phenotype; Genetic Linkage; Brassica napus; Quantitative Trait Loci; Seeds; Chromosome Mapping; Original Article; Polymorphism, Single Nucleotide

Abstract

Key message

A comprehensive linkage atlas for seed yield in rapeseed.

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.