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

Ziliang Luo,Chunyu Zhang,Lei Shi,Peifa Liu,Xiang Liu,Bruce D L Fitt,Rod J Snowdon,Yan Long,Yongju Huang,Jinling Meng,Jinxia Xiang,Meng Wang,Annaliese S Mason,Jun Zou

doi:10.1007/s00122-017-2911-7

Abstract

Key messageA comprehensive linkage atlas for seed yield in rapeseed.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.

Highlights

Crop production environments are subject to many abiotic and biotic factors affecting plant growth
Through trait association analysis and quantitative trait loci (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
We investigated a large resource of phenotypic data generated in large-scale field trials: a total of 19 independent experiments in a doubled-haploid (DH) B. napus mapping population (BnaTNDH), with accompanying single nucleotide polymorphism (SNP) genotype data

Summary

Introduction

Crop production environments are subject to many abiotic and biotic factors affecting plant growth. Larger population sizes and greater marker densities make experiments more difficult and costly, and often the trade-off is that fewer replicates are possible, hindering detection of fieldscale environmental variation To address this issue, a number of statistical or methodological approaches have been suggested to improve QTL detection efficiency. We hypothesize that by considering these component traits and their interactions we can more effectively dissect individual QTL and more accurately predict the trait performance To test this hypothesis, we investigated a large resource of phenotypic data generated in large-scale field trials: a total of 19 independent experiments in a doubled-haploid (DH) B. napus mapping population (BnaTNDH), with accompanying single nucleotide polymorphism (SNP) genotype data. We tested the impact of yield-associated SNP markers on the accuracy of genomic prediction of seed yield

Materials and methods

Results

Discussion