A combined linkage and regional association mapping validation and fine mapping of two major pleiotropic QTLs for seed weight and silique length in rapeseed (Brassica napus L.)

Abstract

BackgroundSeed weight (SW) and silique length (SL) are important determinants of the yield potential in rapeseed (Brassica napus L.). However, the genetic basis of both traits is poorly understood. The main objectives of this study were to dissect the genetic basis of SW and SL in rapeseed through the preliminary mapping of quantitative trait locus (QTL) by linkage analysis and fine mapping of the target major QTL by regional association analysis.ResultsPreliminary linkage mapping identified thirteen and nine consensus QTLs for SW and SL, respectively. These QTLs explained 0.7-67.1% and 2.1-54.4% of the phenotypic variance for SW and SL, respectively. Of these QTLs, three pairs of SW and SL QTLs were co-localized and integrated into three unique QTLs. In addition, the significance level and genetic effect of the three co-localized QTLs for both SW and SL showed great variation before and after the conditional analysis. Moreover, the allelic effects of the three QTLs for SW were highly consistent with those for SL. Two of the three co-localized QTLs, uq.A09-1 (mean R2 = 20.1% and 19.0% for SW and SL, respectively) and uq.A09-3 (mean R2 = 13.5% and 13.2% for SW and SL, respectively), were detected in all four environments and showed the opposite additive-effect direction. These QTLs were validated and fine mapped (their confidence intervals were narrowed down from 5.3 cM to 1 cM for uq.A09-1 and 13.2 cM to 2.5 cM for uq.A09-3) by regional association analysis with a panel of 576 inbred lines, which has a relatively rapid linkage disequilibrium decay (0.3 Mb) in the target QTL region.ConclusionsA few QTLs with major effects and several QTLs with moderate effects might contribute to the natural variation of SW and SL in rapeseed. The meta-, conditional and allelic effect analyses suggested that pleiotropy, rather than tight linkage, was the genetic basis of the three pairs of co-localized of SW and SL QTLs. Regional association analysis was an effective and highly efficient strategy for the direct fine mapping of target major QTL identified by preliminary linkage mapping.

Natural variation in ARF18 gene simultaneously affects seed weight and silique length in polyploid rapeseed

“…This QTL was localized to chromosome A09 and explained ∼30% of the phenotypic variation observed in SW. Interestingly, other rapeseed lines also harbored this QTL, as detected by Yang et al (26) and Li et al (27). In the present study, we used map-based cloning and targeted-region association to clone and characterize the gene related to the major QTL for SW. We further elucidated the possible mechanism…”

“…Rapeseed is an important global agricultural crop that has recently gained attention in the field of plant genomics. Despite the identification of more than 80 QTLs for SW across the 19 chromosomes of the B. napus genome (24)(25)(26)(27), no genes associated with these QTLs have been identified.…”

Identification of BnaYUCCA6 as a candidate gene for branch angle in Brassica napus by QTL-seq

“…Limiting factors for the failure of gene prediction in the previous study51 include lacking of B. napus reference genome sequence and re-sequencing information of parental lines at that time. Since there are usually frequent rearrangements of genome fragments between the A genomes from B. napus and B. rapa 5354, as well as among genomes from different varieties among B. napus lines5556, genomic sequence information of parental lines is of great importance for candidate gene prediction within the targeted QTL region. Our study taking the advantage of re-sequencing the parental lines, made prediction of candidate genes easier by sequence comparison of genes within the 580 kb QTL genomic region.…”

Genome-Wide Association Mapping of Fertility Reduction upon Heat Stress Reveals Developmental Stage-Specific QTLs in Arabidopsis thaliana

“…Total yield is determined by several plant traits, such as total fruits per plant, seed weight, seeds per fruit, and fruit length. The last three are correlated and many coinciding QTLs have been reported (Alonso-Blanco et al, 1999;Dechaine et al, 2014;N. Li et al, 2014).…”