Unraveling the Complex Trait of Crop Yield With Quantitative Trait Loci Mapping in Brassica napus

Abstract

Yield is the most important and complex trait for the genetic improvement of crops. Although much research into the genetic basis of yield and yield-associated traits has been reported, in each such experiment the genetic architecture and determinants of yield have remained ambiguous. One of the most intractable problems is the interaction between genes and the environment. We identified 85 quantitative trait loci (QTL) for seed yield along with 785 QTL for eight yield-associated traits, from 10 natural environments and two related populations of rapeseed. A trait-by-trait meta-analysis revealed 401 consensus QTL, of which 82.5% were clustered and integrated into 111 pleiotropic unique QTL by metaanalysis, 47 of which were relevant for seed yield. The complexity of the genetic architecture of yield was demonstrated, illustrating the pleiotropy, synthesis, variability, and plasticity of yield QTL. The idea of estimating indicator QTL for yield QTL and identifying potential candidate genes for yield provides an advance in methodology for complex traits.

In silico integration of quantitative trait loci for seed yield and yield-related traits in Brassica napus

“…For B. napus, many studies report QTL mapping of seed yield and related yield traits, including traits such as seed weight, pod number, seed number per pod, anthesis, and others (Basunanda et al 2010;Chen et al 2007;Fan et al 2010;Long et al 2011;Shi et al 2009;Zhang et al 2012). Due to this available body of research, it is possible to summarize and compare QTL maps for seed yield and the yield-related traits in B. napus, advancing QTL mapping research.…”

“…QTLs for seed yield and yield-related traits in B. napus were collected from 15 articles published over the last 20 years (Butruille et al 1999;Quijada et al 2006;Udall et al 2006;Zhao et al 2005;Chen et al 2007;Shi et al 2009;Fan et al 2010;Wang and Guan 2010;Radoev et al 2008;Basunanda et al 2010;Shi et al 2011;Zhang et al 2011;Würschum et al 2012;Zhang et al 2012;Yang et al 2012b). The collected information included traits, species, QTL-linked markers, name of the main QTL, linkage group, logarithm of odds (LOD) value, confidence interval, proportion of the phenotypic variation explained by the QTL, additive effect, dominance effect, peak position, reference, first author, and published time (as shown in Supplementary Table 1).…”

Meta-analysis of QTLs Involved in Pre-harvest Sprouting Tolerance and Dormancy in Bread Wheat

“…Meta-QTL analysis has also been successfully conducted in several other crops like maize Truntzler et al, 2010;Hao et al, 2010;Li et al, 2011;Wang et al, 2006;Coque et al, 2008), cotton (Rong et al, 2007), rice (Ballini et al, 2008;Norton et al, 2008;Khowaja et al, 2009), rapeseed (Shi et al, 2009), potato (Danan et al, 2011), cocoa (Lanaud et al, 2009), soybean (Guo et al, 2006;Sun et al, 2012) and apricot (Marandel et al, 2009). …”

QTLs for oil yield components in an elite oil palm (Elaeis guineensis) cross

“…In barley (Hordeum vulgare L.), a dwarf gene (uzu1) with pleiotropic effects has been recognized that influences spike morphology, including grain density, spike length and awn length (Chen et al 2012). Using meta-analysis in Brassica napus, Shi et al (2009) reported that 82.5 % consensus QTLs were clustered and integrated into 111 pleiotropic unique QTLs, 47 of which were associated with seed yield.…”