Dissecting the genetic architecture of agronomic traits in multiple segregating populations in rapeseed (Brassica napus L.)

Abstract

Detection of QTL in multiple segregating populations is of high interest as it includes more alleles than mapping in a single biparental population. In addition, such populations are routinely generated in applied plant breeding programs and can thus be used to identify QTL which are of direct relevance for a marker-assisted improvement of elite germplasm. Multiple-line cross QTL mapping and joint linkage association mapping were used for QTL detection. We empirically compared these two different biometrical approaches with regard to QTL detection for important agronomic traits in nine segregating populations of elite rapeseed lines. The plants were intensively phenotyped in multi-location field trials and genotyped with 253 SNP markers. Both approaches detected several additive QTL for diverse traits, including flowering time, plant height, protein content, oil content, glucosinolate content, and grain yield. In addition, we identified one epistatic QTL for flowering time. Consequently, both approaches appear suited for QTL detection in multiple segregating populations.

Mapping QTL for agronomic traits in breeding populations

“…The most promising approach to detect epistatic QTL appears to be a full two-dimensional scan for all possible pairwise interactions. Such scans are nowadays computationally feasible and have successfully been used to detect epistatic interactions in family mapping (e.g., Buckler et al 2009;Liu et al 2011;Steinhoff et al 2012;Würschum et al 2012c), and in population mapping (e.g., Li et al 2010;Massman et al 2011;Reif et al 2011;Würschum et al 2011a, b;Yu et al 2011).…”

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

“…Subsequently, a consensus linkage map from three DH mapping populations was constructed with 992 different markers (Lombard and Delourme 2001), and another integrated map was constructed with 240 SSRs and used to map 305 QTL controlling important agronomic characters in B. napus (Piquemal et al 2005). More recent efforts have also generated integrated maps for B. napus (Schwarzacher et al 2008;Würschum et al 2012;Delourme et al 2013). Using map integration, QTL hotspots for many agronomic traits have previously been identified in B. napus, including plant height (Mei et al 2009), flowering time (Long et al 2007), seed yield ), phosphorus efficiency Shi et al 2013), glucosinolate concentration (Feng et al 2012;Uzunova et al 1995), seed oil content (Delourme et al 2006;Qiu et al 2006), heterosis-related traits (Basunanda et al 2010;Shi et al 2011) and disease resistance (Pilet et al 2001;Manzanares-Dauleux et al 2000;Werner et al 2008;Kaur et al 2009).…”

“…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).…”

Genetic dissection of seed oil and protein content and identification of networks associated with oil content in Brassica napus

“…Additionally, a genome‑wide association study has revealed SOC variations in B. napus 14. Compared with SOC, relatively few studies have addressed SPC-QTLs in B. napus 15161718. In addition, a significant negative relationship between SOC and SPC has been reported, especially in soybean1920 and Brassica212223.…”