QTL analysis and localization

For each trait, one or more QTL were identified under control or stress conditions. For instance, in the population of Alesi and H30, two QTL, GP-1C and GP-STI-1, were co-localized on LG A9. Likewise, the two QTL, GP-2C and G%-1S, were co-co-localized on LG C1 (Figure 7). This co-localization is expected because these traits are related.

Furthermore, STI expresses performance under control and salt stress. This co-localization of QTL on LG A9 indicates that in this genomic region there may be one gene with a pleiotropic effect or two tightly linked genes independently responsible for the variation on these traits. In both cases, on LG A9 and LG C1, the additive effect of the QTL was negative, meaning that the alleles on each LG are in a couple phase.

One adaptive QTL GP-3C, which controls the variation under control conditions, was mapped on LG C4b. This means in this genomic region, gene(s) govern (s) the GP variation only under control conditions. Additionally, two constitutive QTL, G%-STI-1 and GP-STI-2 which regulate the variation of one trait under control and salt stress,

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A3 and A10, there are genomic regions that harbor gene(s) to control the GP variation under control and salt stress.

Similarly, in the Mansholts and Samourai population two QTL, GP-1S and GP-STI-1, were co-localized on LG A8 (Figure 8). Since, STI expresses the performance under control and salt stress, the QTL related to this trait were constitutive that regulate the variation of the corresponding trait under control and salt stress conditions. The overlapping of these QTL intervals suggests that one gene with pleiotropic effect lies behind the variation of this trait under control and salt stress conditions, or two closely linked genes that control the two traits independently. The additive effect of both QTL was negative. This means that these Mansholts alleles increase the corresponding traits and that they are in a couple phase. Additionally, four adaptive QTL were mapped on LGs; A9, C1 and C5, respectively.

No common QTL were mapped between the two populations of B. napus, which indicates that these QTL were population specific. The inconsistency in identifying QTL in different populations and in different environments can be attributed to a number of factors such as different sets of markers, genetic background of parental lines or population types (Collins et al. 2008). In the case of B. napus populations, the most likely explanation would be the difference in parental line sources: there was no common parental line between the two populations. This is consistent with the findings of Monforte et al. (1997) in tomato; they found that the QTL effect varies between populations, depending on the genetic background of the population. Moreover, the QTL effect changes in the presence/absence of salinity. This QTL explained 58% of fruit fresh weight under non-stress conditions. Under salt stress, this QTL explain 14%

of the variation for the same trait.

In B. oleracea, the Bo1TBDH population the intervals of three constitutive QTL; G%-3C, GP-2C and GP-1S are overlapped on LG C4. Moreover, near the middle of this LG two adaptive QTL are co-localized, namely GP-1C and G%-2C (Figure 9). In these genomic regions there might be one gene with a pleiotropic effect controlling the variation in these traits. Another possibility is that three genes underlie these trait variations; the alleles for increasing the GP and G% are descended from TO1000DH3, which are in a couple phase because the additive effect of all QTL was positive. In B.

oleracea, our results are in agreement with the results of Bettey et al. (2000), who detected four QTL under control condition on LGs: C1, C4, C5 and C6. They detected one QTL under stressful conditions, while we found two QTL; this discrepancy might be due to the different plant materials.

According to Collins et al. (2008), the QTL that were mapped in the three populations can be classified into two types; constitutive QTL, which exist and sustain their effect under both control and salt stress conditions, and adaptive QTL, which control the variation of one trait under either control or salt stress conditions. In all populations, both types were detected. These findings indicate that distinct genomic regions control

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Chapter III ــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــ germination both under non-stress and stress conditions, while other genomic regions affect germination either under control or under stress. Our results are in agreement with the findings of other authors for different crops; in tomato (Foolad et al. 1999), in Arabidopsis (Quesada et al. 2002; Clerkx et al. 2004; Galpaz and Reymond 2010) and in B. oleracea (Bettey et al. 2000). These authors speculate that some genomic regions regulate the germination event under control and stress conditions; these QTL are termed stress-nonspecific or constitutive QTL. They also found salt-specific (adaptive) QTL.

A transgressive distribution was found for all traits (Figures 4, 5 and 6). This transgressive segregation indicates that the alleles responsible for increasing or decreasing a particular trait are scattered in the parental lines of each mapping population. Similar results concerning the contribution of salt-sensitive parents in increasing salt tolerance were found in Arabidopsis (Quesada et al. 2002, Clerkx et al.

2004, Galpaz and Reymond 2010), and in tomato (Foolad et al. 1999).

Conclusion

We observed a large variation in all investigated traits in the tested populations. The effect of salinity on seed germination may be attributed to osmotic stress or ion-toxicity or a combination of both. Mostly, the distribution of the traits was normal, with a transgressive segregation, meaning that both parents could contribute positively to increasing a particular trait. We mapped several QTL underlying seed germination traits such as germination percentage, germination pace and the performance of genotypes under control and salt stress. These results might prove helpful in understanding the genetic and physiological mechanisms that control salt tolerance in the seed germination stage of Brassica species. The markers associated with the mapped QTL can be employed for selecting the best genotypes without further phenotyping evaluation.

Of great importance is the presence of stress-nonspecific QTL that control the seed germination under control and salt stress conditions. Also, the fine mapping of these QTL might help us to uncover the causal genes that reside within their intervals and to understand their contributions.

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Mapping QTL for salt tolerance at the young plant stage and leaf glucosinolates in a Brassica napus DH population

4.1 Introduction

Brassica napus originated from interspecific hybridizations between turnip rape (Brassica rapa AA, 2n = 20) and cabbage (Brassica oleracea; CC, 2n = 18) that occurred spontaneously (Morinaga 1934; U 1935) during medieval times (Iñiguez-Luy and Federico 2011). It is thought to be a relatively new species, about 500 years old, and no wild populations have been recorded (Gómez-Campo and Prakash 1999).

Brassica napus has been classified as a moderately salt tolerant plant (Mass and Hoffman 1977). Under salt stress, polyploid species of Brassica such as B. napus show superiority over diploid species (Mailk 1990; He and Carmer 1992; Ashraf et al.

2001). Several approaches have been pursued to enhance salinity tolerance in Brassica, such as conventional breeding, somaclonal variation and gene transfer (Purty et al. 2008). Until now, the QTL for salinity tolerance in Brassica species have never been reported, making it difficult to understand the genetic basis of salinity stress tolerance in Brassica species (Nayidu et al. 2013). A number of studies have reported successes in improving salt tolerance in B. napus by gene transfer (Huang et al. 2000; Prasad et al. 2000; Zhang et al. 2001; Srivastava et. al. 2004; Song et al.

2014). These reports demonstrate the considerable increase in salt tolerance that can be achieved by single gene overexpression, despite the fact that salt tolerance is a polygenic trait.

Brassica napus has a unique aliphatic glucosinolate profile (Mithen 2001). Our knowledge about the genetic control of leaf glucosinolates (GSL) variation in B. napus is rather limited compared to the genetic control of seed glucosinolates. The role of glucosinolates in biotic stresses such as insect attack and pathogens resistance has been energetically studied. In contrast, knowledge of the role of GSL under abiotic stressors such as light, drought, salinity and heat is still vague. Several environmental factors affect the concentration and composition of glucosinolates, such as light, drought, temperature and salinity (Qasim et al. 2003; Velasco et al. 2007; López-Berenguer et al. 2008; Mewis et al. 2012).