Flowering Time Quantitative Trait Loci Analysis of Oilseed Brassica in Multiple Environments and Genomewide Alignment with Arabidopsis

Abstract

Most agronomical traits exhibit quantitative variation, which is controlled by multiple genes and are environmentally dependent. To study the genetic variation of flowering time in Brassica napus, a DH population and its derived reconstructed F 2 population were planted in 11 field environments. The flowering time varied greatly with environments; 60% of the phenotypic variation was attributed to genetic effects. Five to 18 QTL at a statistically significant level (SL-QTL) were detected in each environment and, on average, two new SL-QTL were discovered with each added environment. Another type of QTL, microreal QTL (MR-QTL), was detected repeatedly from at least 2 of the 11 environments; resulting in a total of 36 SL-QTL and 6 MR-QTL. Sixty-three interacting pairs of loci were found; 50% of them were involved in QTL. Hundreds of floral transition genes in Arabidopsis were aligned with the linkage map of B. napus by in silico mapping; 28% of them aligned with QTL regions and 9% were consistent with interacting loci. One locus, BnFLC10, in N10 and a QTL cluster in N16 were specific to spring-and winter-cropped environments respectively. The number of QTL, interacting loci, and aligned functional genes revealed a complex genetic network controlling flowering time in B. napus.

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

“…The threshold for goodness of fit was set to #5.0 with logarithm of the odds ratio (LOD) scores .1.0 and a recombination frequency ,0.4. The order of the markers on the linkage map agreed well with our published maps (Qiu et al 2006;Long et al 2007). The genotype of each RC-F 2 line was deduced from the corresponding genotype of their parents.…”

“…Nearly 80% of the 401 consensus QTL were from only one or two specific microenvironments, and no consensus QTL, except one for flowering time (qFT.C6-4), appeared in all microenvironments (Figure 2A). This QTL has not been detected in two spring-cropped microenvironments with the same population (Long et al 2007). This suggests that few QTL are likely to be expressed universally.…”

“…Details of the alignment are descried in Long et al (2007). Several syntenic blocks or insertion fragments (islands) were identified between the Arabidopsis genome and the TNDH linkage map (Table S2 B).…”

“…To avoid missing QTL with very small effects, a lower LOD score corresponding to P ¼ 0.50 (2.1-2.3 for the DH population and 2.9-3.1 for the RC-F 2 population) was adopted for the presence of suggestive QTL. The overlapping suggestive QTL and all significant QTL were admitted (Long et al 2007) and named as identified QTL.…”

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

“…B. napus (canola or rapeseed) is planted worldwide, and is an essential source of oil, fodder and biodiesel. Since the first molecular linkage map in B. napus was reported (Landry et al 1991), thousands of important QTLs for traits such as seed yield, yield components, biomass, flowering time, seed quality, disease resistance or tolerance, mineral nutrition and other yield-related traits have been identified in B. napus (Basunanda et al 2010;Cai et al 2012;Chen et al 2007;Fan et al 2010;Feng et al 2012;Long et al 2007;Rygulla et al 2008;Yang et al 2011;Shi et al 2013;Raman et al 2013). However, to date, it is still very difficult for Brassica breeders to effectively utilize this large body of QTL data.…”