QTL ANALYSIS OF VITAMIN E TRAIT IN CAPSICUM F2 POPULATION

Transcription

1 QTL ANALYSIS OF VITAMIN E TRAIT IN CAPSICUM F2 POPULATION Yi Wu JULY 28, 2017 WAGENINGEN UR Plant Breeding Department

2 Contents Acknowledgement... 2 Abstract... 3 Introduction... 4 ɑ-tocopherol and its biosynthesis... 4 The ɑ-tocopherol function and deficiency... 5 Breeding plants for improved tocopherol content... 6 The Capsicum genus... 6 Preliminary data... 7 Research aim... 9 Material and method Plant materials and analysis of metabolites Marker analysis and map construction QTL analysis Results Genetic Map construction Maximum likelihood mapping algorithm Regression mapping algorithm QTL analysis of reconstructed map (chromosome-wide) Statistical analysis of ɑ-tocopherol content... Error! Bookmark not defined. QTL analysis of initial map (genome-wide) QTL analysis of other traits (chromosome-wide) Phylloquinone β-ɣ-tocopherol δ- tocopherol: QTL analysis on other traits (initial map) Phylloquinone β-ɣ-tocopherol β-ɣ-tocotrienol δ-tocopherol Discussion Recommendations Reference Appendix Appendix 1 Kruskal-Wallis test showing the association between markers and ɑ-tocopherol level Appendix 2. Genotype of F2 population

3 Acknowledgement I would like to express my sincere appreciation to my superior Dr. Chris Maliepaard and Dr. Arnaud Bovy for their constant guidance and immense knowledge. Whenever I ran into trouble, the door to their office was always open. Without their patient teaching, I cannot finish the experiments. Of course, I would like to thank all my friends in PBR group for their moral support and companies. 2

4 Abstract Capsicum, also known as pepper, is an important vegetable in human health not only because of its fresh taste but also as a source of multiple health-related metabolites such as vitamin E. Vitamin E is a group of antioxidants which can be synthesized by plants followed MEP biosynthetic pathway. Among the vitamin E components, ɑ-tocopherol is the one with the highest biological activity. In recent decades, quantitative trait loci (QTL) for ɑ-tocopherol were determined in many plants including Arabidopsis and tomato. However, the study of the regulatory mechanism of vitamin E in pepper is limited. A previous study indicated genetic variation in vitamin E content in Capsicum F2 ripened fruits and a significant association was found between variation on Capsicum chromosome 10 and ɑ-tocopherol level. To evaluate the genetic basis of ɑ-tocopherol, a genome-wide QTL analysis for ɑ-tocopherol content was carried out in an F2 population of Capsicum using ripe fruits of in total 104 individuals and based on a linkage map with 502 markers. A major QTL region was identified on chromosome 10 in the region between cm to cm responding to 5.94 ~ Mb on the physical map. Six candidate genes were identified in this region which are Gloden-2-Like (GLK), the I subunit of magnesium-chelatase (CHLI) and four homologs of fatty acyl-coa oxidase (ACX) genes. It is interesting to note that the QTL for phylloquinone (vitamin K) was highly co-localized with the QTL for ɑ-tocopherol. This research opens the doors to study the genetic determinants of vitamin E variation in Capsicum and provides valuable information to improve the content of this nutrient in the fruits 3

5 Introduction ɑ-tocopherol and its biosynthesis Tocopherols, first discovered in 1922 (Lansing 2011), belong to the Vitamin E group of metabolites from lipophilic antioxidants and have numerous functions in plants as well as animals. The vitamin E group is widely distributed in nature and tocopherols are the most abundant vitamin E components in nature and in the human diet. They are amphipathic molecules with hydrophobic tail and polar head groups and synthesised by chloroplast in photosynthetic organisms like plants and a group of cyanobacteria (Dellapenna, Dellapenna, and Pogson 2006). There are four different forms of tocopherols found in nature sharing a standard chroman-6-or-ring system and are designed ɑ-,β-,ɣ-, δ- (figure 1) (Dellapenna, Dellapenna, and Pogson 2006). Figure 1 The vitamin E components structure and activities. The difference in molecules is labeled R1 and R2 in red. The table indicated the different ring methyls in ɑ-,β-,ɣ- tocopherols and tocotrienols. Besides, the ɑ- tocopherol transfer protein (ɑ-ttp) binding activity and vitamin E activity were shown in the table. The rat resorption-gestation assay indicated the ɑ -TTP and vitamin E activity. The ɑ-tocopherol has the highest ɑ-tpp binding activity and vitamin E activity (Dellapenna, Dellapenna, and Pogson 2006). The different formations (ɑ-. β-, ɣ- and δ-) only vary in the position and number of methyl substituents (Lansing 2011). The synthesis of head group is from homogenitisic acid and isopentenyl diphosphate for hydrophobic tail in chloroplast envelope (Dellapenna, Dellapenna, and Pogson 2006). The ɑ- tocopherol is converted from ɣ-tocopherol by methyltransferase reaction in the final step of tocopherol biosynthesis pathway. After synthesis, tocopherols are stored in plastoglobuli of the chloroplast stroma (Munné Bosch 2007). Tocopherols are found in seeds, fruits, flowers and mostly leaves (Munné Bosch 2007). The vitamin E content varies from plant tissues and have different vitamin E activity. An early study showed that ɑ- and ɣ- tocopherol are the two principal formations of tocopherols and widely distributed in the plant kingdom (Dellapenna, Dellapenna, and Pogson 2006; Munné Bosch 2007). The ɑ-tocopherol is mainly found in primary photosynthetic tissues like leaves whereas β-tocopherol is primarily found in seeds. Although the total vitamin E level in photosynthetic tissues is relatively low, the high percentage of the ɑ-tocopherol level is distributed in these tissues. For the case of seeds, the total vitamin E content is high whereas the percentage of ɑ-tocopherol is relatively low. However, seeds show considerable variation in vitamin E content and exhibit a lower level of ɑ-tocopherol than primary photosynthetic tissues; in seeds, ɣ-tocopherol which has a more moderate relative vitamin E activity, is the major component (Munné Bosch 2007). ɑ-tocopherol is considered as having the highest vitamin E activity of all components (figure 1) (Dellapenna, Dellapenna, and Pogson 2006). The tocopherols and tocotrienols are absorbed equally by the intestine, but the vitamin E activities differ significantly. 4

6 The ɑ-tocopherol function and deficiency ɑ-tocopherol as an antioxidant plays a pivotal role in the human diet and in plant cells. ɑ-tocopherol as an antioxidant plays a pivotal role in the human diet and in plant cells. When vitamin E first discovered, it was regarded as a dietary factor for rat proliferation. With deep studies, it was determined as an antioxidant 40 years later. Over the last decades, vitamin E and other antioxidants have been widely studied for their effect on human health (Galli et al. 2017). However, most of the studies focused on the fundamental chemistry aspects of vitamin E (DellaPenna 2005) and tried to use the chemistry system to explain the function of antioxidants. A set of studies in the 1990s investigated that the different formations of vitamin E act as a signal of high oxidative stress and the gene regulation of vitamin E biosynthesis is independent of the antioxidant function ( DellaPenna 2005; Dellapenna, Dellapenna, and Pogson 2006). With high technology developing, more and more studies consider the genetic aspects of vitamin E biosynthesis regulation and this increases the understanding of the influence of vitamin E. Recent review showed that vitamin E can protect cell membranes against free radical damage resulting from aerobic metabolism, essential to help maintaining cellular integrity and homeostasis (Galli et al. 2017). In addition to its radical scavenging capacity, there is accumulating evidence that vitamin E also exerts its biological effects through additional mechanisms of action, such as by regulation membrane-associated signalling pathways and the modulation of gene expression. Severe vitamin E deficiency has a high risk of neurological disorders such as muscle weakness and damage to the retina of the eyes (Dror and Allen 2011). Insufficiency of vitamin E intake is associated with an increased risk of cancers and a decrease in the immune function. Although the insufficient intake of vitamin E can bring negative effects to human health, it is hard to determine both of the positive and negative effects of too large scale intake of it (Galli et al. 2017). Among all vitamin E components, the vitamin E activities of tocopherols are associated with the donation between phenolic hydrogen and free radicals with specific requirements of the molecule. The ɑ-tocopherol is the most common form and has the highest vitamin E activity than other tocopherol forms (a > b > g > d ) (Ã 2005; Galli et al. 2017; Munné Bosch 2007). Alpha-tocopherol is considered as a cytoprotective factor to prevent the degenerative process in human organisms like liver when exposed to environmental pollutants and dietary factors (Munné Bosch 2007). The primary source of naturally derived dietary ɑ-tocopherol is seed oils because of plenty of vegetable oils in the human diet ( DellaPenna 2005). Although the antioxidant activity is the major function of tocopherols, some studies in the past decades also showed other, non-antioxidant functions. An early study with ɑ-ttp knockout mice suggested that the particular group of genes like cholesterol homeostasis, cellular trafficking were affected by the relative absence of vitamin E. Various signaling components like protein kinase C and cyclooxygenase can be activated or inhibited by tocopherols (Lim 2007). Also, tocopherols can also affect the membrane fluidity (Dror and Allen 2011). Nonetheless, the intake of vitamin E is universally low with a similar amount all over the world (Dror and Allen 2011; Galli et al. 2017). In Us, the Recommended Dietary Allowance (RDA) for vitamin E is based on the red blood cell membrane integrity and suggested 12mg per day ɑ-tocopherol in adults (>14 years) (Galli et al. 2017). This value can satisfy the serum ɑ-tocopherol threshold level in 12 μmol/l defined by Estimated Average Requirement (EAR). However, only limited people of U.S has achieved the dietary intake level for now, but there are approximately 90% of the population has vitamin E intake below RDA (Marchese, Michelle E. 2014). Vitamin E low consumption has also happened in other developed countries like United Kingdom, Germany, Netherlands. Early reports showed that more than 2/3 of South Korean people have the problem of the suboptimal vitamin E status (12 30 μ mol α-tocopherol/l). The situation is even worse in developing countries. In rural Nepal, 1/3 of pregnant women are suffering the severe vitamin E deficiency (< 10 μmol α- tocopherol/l) (Galli et al. 2017). 5

7 Like the human, tocopherols are thought to play an essential role as lipid-soluble antioxidants in plants. Mutants technology can show it. A previous study using tocopherol-deficient mutants of Arabidopsis revealed that tocopherols and carotenoids are functionally interacting in avoiding lipid peroxidation and high-light stress (Dellapenna, Dellapenna, and Pogson 2006; Munné Bosch 2007). In plant cells, chloroplasts are often the target for reactive oxygen species (ROS) including singlet oxygen (Galli et al. 2017). Therefore, the high level of ROS induces the synthesis of ɑ-tocopherol to scavenge oxygen for protecting photosynthetic apparatus from oxidation and maintaining the cellular integrity. Besides, the detoxification of ROS by tocopherol can dissipate excess energy in chloroplasts when exposed to adverse environmental conditions. A recent study showed that the a-tocopherol can reduce the effect of photo-oxidative stress on Arabidopsis by tocopherol-deficient mutants. Besides, it can also inhibit the permeability of membranes to ions in thylakoids (Lushchak and Semchuk 2012). Breeding plants for improved tocopherol content In the past decades, considerable attention has been paid to use new breeding methods like mutation breeding and transgenic approaches to improve food and feed. Even if these new breeding approaches have the potential to modify the ɑ-tocopherol content level, the genome information and phenotype variation should be known. Only with such genotype and phenotype knowledge researchers could enhance ɑ-tocopherol content. Numerous studies on tocopherol variation have been done in many plant species like maize, soybean, potato, Arabidopsis and tomato (Marwede and Gu 2005; Munné Bosch 2007; Yang et al. 2011). In recent years, theoretical and experimental studies investigated the association between genetic information and phenotype variation in Arabidopsis and tomato because of their fully sequenced genome and excellent molecular genetic resources. These studies have shown that QTLs for ɑ-tocopherol were localized on chromosomes 6 and 9 in tomato (Rousseaux et al. 2005) and chromosomes 1 and 2 in Arabidopsis (Gilliland et al. 2006). It is interesting to note that some QTL intervals are associated with the enzyme encoding genes involved in the tocopherol pathway. The biosynthetic genes, VTE3 and VTE5, were linked to the QTL on chromosome 9 in tomato (Fernie et al. 2011). As for Arabidopsis, structural genes QVE1, QVE2 and QVE6 were underlying the QTLs (Gilliland et al. 2006). The Capsicum genus Capsicum, also known as pepper, is a genus of the Solanaceae family and originated in Bolivia. The genus includes 30 species (Paran and Van Der Knaap 2007). Based on its morphology, there are five major cultivated species which are Capsicum annuum L., Capsicum chinense Jacq., Capsicum frutescens L., Capsicum baccatum L. and Capsicum pubescens Ruiz and Pav. Most cultivated species are autogamous, and pollination is by pollinators like honey bees or by wind. Of these five species, Capsicum annuum. is the largest group of varieties and it is grown worldwide especially in the United States and Mexico (Paran and Van Der Knaap 2007). The fruit of Capsicum varies by mature stages. The colour of ripe Capsicum fruits ranges from yellow to red, while immature fruits are green. The shape of Capsicum fruits has also diverged, from long narrow to spherical. 6

8 Figure 2 The Capsicum genus which contains two groups which have 24 and 26 chromosomes, respectively (Wahyuni et al. 2013). Although we know that Capsicum is diploid, the number of chromosomes is not the same over all species (2n=24,26) (Barboza and Bianchetti 2005). From a previous study about historical and botanical perspectives of Capsicum (Barboza and Bianchetti 2005), it was shown that more semidomesticated species are from the group with 2n=24 and wild species are mostly from the group with 2n=26 (Wahyuni et al. 2013). Due to species diversity, the use of pepper fruit is colorful. Not only fresh but also dry pepper fruits as spice are widely used in the human diet around the world. The sweet cultivated species, known as sweet pepper or bell pepper, are widely planted around the world. The pungent cultivated species which is known as chili peppers are more popular in Mexico, Indonesia, and China where local people like the more spicy taste (Barboza and Bianchetti 2005). Since pepper fruits are abundant in healthrelated metabolites such as ascorbic acid (Vitamin C), tocopherols (vitamin E) and flavonoids (Wahyuni et al. 2013), pepper is also widely used in the pharmaceutical industry for as dietary supplement. Preliminary data A previous study of a metabolic analysis of a collection of 31 diverse pepper accessions revealed a pepper accession, C.annuum AC2212, with significantly increased vitamin E levels compared to all other pepper accessions in the collection, which can achieve the RDA with 100 gram serving. To elucidate the genetics underlying the high vitamin E trait, the high vitamin E accession AC2212 was crossed with accession, C. annuum Long Sweet, which is abundant in flavonoids but with low vitamin E level (Figure 3) (Wahyuni et al. 2013). 7

9 Figure 3 the vitamin E variation in the ripe fruits of diverse Capsicum species. The X-axis is mg. The vitamin E content of AC2212 accession is above the Recommended Daily Intake (RDI) 15mg/day and much higher than Long Sweet accession (3 mg) (Wahyuni et al. 2013). Based on this cross, a segregating F2 population was developed and genotyped as part of an Israelian collaboration. We investigated the vitamin E phenotype variation in the F2 offspring and constructed a genetic linkage map. Several genetic regions on chromosome 10 (figure 4) were indicated to associate with high vitamin E level, and they are the basis of this research. Figure 4 The simple interval mapping of chromosome 10. At approximately 20 cm and 70 cm on the map, sharply drops of LOD score were demonstrated. The markers which were close on the genetic map would not have a large difference in LOD value. Due to the theoretical reason, these huge difference were suggested as errors and needed to be retrieved by reconstructed genetic map. However, at the top of the genetic linkage map of chromosome 10, there is evidence of some errors (figure 4). The positions of marker 4308 and 4342 were pretty close, but the LOD score of marker 4308 is 0.68 while the LOD score of marker 4342 is The LOD score suggested the linkage between markers and used for genetic map construction. It is not likely that the two marker with the close genetic position but a large difference in LOD score. In this case, we expected that some errors might exist possibly due to the low-quality genetic map. 8

10 Research aim The main goal of this research is to identify QTL intervals for high ɑ-tocopherol phenotype in a Capsicum F2 population. A previous study suggested that QTLs are mostly localized on chromosome 10, but there were probably errors in genetic positions of the markers. Reconstruction of the genetic map of chromosome 10 is necessary to improve the QTL analysis. In addition to QTL identification, a further aim is to determine candidate genes. Moreover, genome-wide QTL analysis of other traits was also performed in order to declare the co-localization of different QTLs. Therefore, the following research aims were defined: 1. Genetic map construction of chromosome Identification of QTL intervals for ɑ-tocopherol level 3. Identification of QTL intervals for other traits related with vitamin E biosynthesis 4. Identification of candidate genes in the QTL for a-tocopherol level obtained from chromosome-wide analysis 9

11 Material and method Plant materials and analysis of metabolites An F2 Capsicum population was used in this research. It was developed from a cross between theoretically pure lines Long Sweet (C.annuum) and AC2212 (C.chinense). The accession Long Sweet is a red land variety from Zambia with low pungency. The origin of AC2212 is unknown, and the ripe fruit is dark brown with very high pungency. Seeds of the two accessions were obtained from the Centre for Genetic Resources (CGN), the Netherlands. After the first cross between two parental accessions, all F1 plants were grown in the green house. In a further step, two F1 plants showing identical phenotypes were self-pollinated to give the F2 population. A total amount of 230 F2 individuals were randomly selected for molecular marker analysis while 132 of these were used for phenotypic characterization. Analysis of metabolites was performed for these 132 F2 individuals. In the first step, the metabolite extraction was done following the protocol from de Vos (Vos et al. 2007). Generally, the 500 mg of fresh pepper fruits were extracted with 4.5 ml methanol: chloroform. After incubation, the 2.5 ml Tris-chloride were added and then centrifuged for 10 min at room temperature. In the further step, the chloroform layer was transformed into a new tube and wait for the HPLC analysis at -20 degree. Furthermore, the extracts were used for reversed phased liquid chromatography (HPLC) for the metabolites level determination. Briefly, the samples were added 1.0 ml of ethyl acetate with 0.1% BHT and was analysed by using a YMC-Pack reverse-phase C30 column at 40 degree. The chromatography was performed by using No.2475 fluorescence detector (FD) and No.996 photo diode array detector (PDA) to scan from 296 to 405 nm. Marker analysis and map construction Genotyping-by Sequencing (GBS) data was generated from 230 F2 lines of the cross between Long Sweet and AC2212. Each unique 100 nt length read was regarded as a GBS tag and the error tags were identified by the coverage less than 5 reads. In total, 2,818,262 tags were determined for the population. Polymorphic Tags were designed as the unique sequence of each parental lines. After that, reliable polymeric tags were defined by using the criteria of Mendelian segregation in the population. Meanwhile, alignment was performed between these reliable polymeric tags and reference genome of capsicum to identify the predicted location of each tag. In the end, 331,081 reliable tags were filtered with the genotyping pattern, and 5711 genotyping patterns (bins) were obtained which are corresponding cm on the linkage map. Pepper cdna and genomic clones from previously published pepper maps were used for alignment of the linkage groups to chromosomes. A complete map of cm was constructed by I.Paran which was used for QTL analysis directly. Since the high a-tocopherol content is highly associated with chromosome 10, a new linkage map only for chromosome 10. Also, a number of 132 of the total 230 individuals was used for the new linkage map constructed duo to only 132 individuals were carried out on the metabolic analysis (Appendix 2). The reconstruction of the linkage map of chromosome 10 was performed using JoinMap 4.1 (Van Ooijen 2006). Two mapping algorithms are available for F2 in JoinMap, regression mapping, and a maximum likelihood map algorithm. In this research, both these methods were used for mapping. In regression mapping, a minimum Log of odds (LOD) of 5.0 was used as a thresholds for grouping markers to a linkage group. Within each linkage group, the marker order was determined using recombination fractions below 0.4 and LOD values above 1. Segregation distortion was inferred using the chi-square goodness-of-fit tests provided in the program. The chi-square threshold at 5.00 was used for removal of loci. Map distances were calculated based on recombination fractions using Haldane's mapping function (Kosambi 2016). In the maximum likelihood map algorithm, the successive sampling threshold of the first pair of markers was set at 10%. In the following step, other markers were added into the framework map with 5%, 3%, 2%, 1%. Per marker was optimized three rounds for best order. Each round of order 10

12 optimization consists of 1000 simulations. Finally, the marker order was obtained by multipoint estimation recombination frequencies with five cycles of Monte Carlo Expectation Maximization (EM). QTL analysis MapQTL 6.0 was used to identify potential QTLs for vitamin E level. The initial QTL analysis was performed with the complete set of markers in the F2 population. Firstly, the Kruskal-Wallis analysis was used for detecting whether an association between markers and traits existed. In a further step, interval mapping (IM) analysis was carried out to identify markers significantly associated with vitamin E level. These were then used as an initial set of cofactors by a minimum empirical LOD of 3.0. The set of the original cofactors was used to apply an automatic cofactor selection. Only the markers retained at p < 0.02 were used as cofactors in the restricted multiple QTL method (rmqm) analysis (Jansen R C 1994).The final set of cofactors was determined by rmqm with significant markers at LOD >3.0. A mapping step size of 1 cm was used for all analyses. A permutation test with 1000 permutations was employed to give significance thresholds for genome-wide QTL analysis including all linkage group. Based on this permutation test A LOD threshold of three was used to declare the presence of a QTL. Two-LOD support intervals were used for the location of a QTL. A set of 514 markers on chromosome 10 in F2 population was used for QTL analysis. The Kruskal- Wallis analysis of markers was employed to indicate associations of marker loci with the phenotypic traits. Then, the LOD threshold obtained from the genome-wide permutation test was for detection of the presence of QTLs in interval mapping. After that, roughly a cofactor selection was carried out by using the results from simple interval mapping, and automatic cofactor tool together with multiple QTL model mapping was employed for the final cofactor determination. Two-LOD support intervals were used as approximations of 95% confidence regions for QTL positions. Results Genetic Map construction A total of 514 markers on chromosome 10 was used to construct the genetic map and matched with one linkage group. Maximum likelihood mapping algorithm Firstly, the Maximum likelihood mapping algorithm in JoinMap grouped 514 markers into one group since all markers are only located on chromosome 10 and 203 identical markers were excluded from the following analysis, with a minimum LOD of 2.0 and maximum recombination frequency of The total length of the Maximum likelihood linkage map is cm, with an average distance of 0.35 cm between adjacent markers, while the largest interval between any two markers was 6.83 cm. 11

13 Figure 5 The map chart of maximum likelihood map. The total length of maximum likelihood map is CM. From figure 6 it is clear that were several huge gaps at the top of the chromosome. Between marker 4302 and 4303, the distance was 6.9 CM which is much larger than the average distance in 0.35 cm. A large distance is also observed between markers 4305 and 4306 as well as between markers 4308 and For a deep insight of the maximum likelihood map, a relationship between genetic position and the physical position is shown on Figure 7. There are several gaps between the markers in the region between 2.56 and 5.21 Mb and especially differences are observed between 2.56 and 3.12Mb. Over this region, the markers are nearby in the physical map but have a large genetic distance, which is based on the recombinant fractions. Theoretically, nearby loci should carry the similar genetic information, but there was a large difference in genetic position between loci. Besides, the centromere cannot easily be distinguished on the map chart. 12

14 Figure 6 The relationship between physical positions and genetic positions. The Y-axis is genetic position while the X-axis is the physical position. A huge gap has appeared at the approximately 60~120CM between 2.5~5 Mb. The Locus Genotype Frequencies of the markers suggest segregation distortion of the region between marker 4280 till This segregation test is using the chi-square test to determine the classification of genotypes of each marker against the Mendelian expected ratio (1:2:1 in F2 individuals). However, the significance level of the chi-square statistic of the markers between 4280 and 4349 was high (Table 1). The segregation ratio of some markers can reach 1:4:1, which the frequency of heterozygous individuals much greater than the frequency of homozygous individuals. Table 1 The Locus Genot. Freq. table sheet. The "a" and b" means two homozygous genotypes for two parental alleles whereas the "h " indicates the heterozygous genotype. Locus Position a h b X2 Df Signif. Classification ** [a:h:b] ** [a:h:b] *** [a:h:b] **** [a:h:b] **** [a:h:b] **** [a:h:b] ***** [a:h:b] ****** [a:h:b] ****** [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] 13

15 ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ******* [a:h:b] ****** [a:h:b] ****** [a:h:b] ***** [a:h:b] **** [a:h:b] **** [a:h:b] **** [a:h:b] *** [a:h:b] ** [a:h:b] ** [a:h:b] ** [a:h:b] ** [a:h:b] * [a:h:b] ** [a:h:b] * [a:h:b] * [a:h:b] * [a:h:b] * [a:h:b] * [a:h:b] * [a:h:b] * [a:h:b] ** [a:h:b] ** [a:h:b] ** [a:h:b] * [a:h:b] ** [a:h:b] * [a:h:b] * [a:h:b] * [a:h:b] ** [a:h:b] 14

16 ** [a:h:b] ** [a:h:b] df degrees of freedom. Significant at *P < 0.1, **P < 0.05, ***P < 0.01, **** P < 0.005, ***** P < 0.001, ****** P < In order to remove this peak, several markers were removed and maximum likelihood mapping was performed again. However, the unexpected pattern remained there and did not make much sense. Regression mapping algorithm In the next step, regression mapping algorithm was performed. It is a time-consuming way but we didn t get expected result from maximum likelihood algorithm, it would be worth to try regression mapping algorithm to demonstrate the difference between two function. Three rounds of regression mapping performed and each round of order optimization consist of 1000 simulations. In the first round of regression mapping, 112 markers of 203 were used for creating genetic map. The total length of this regression map is cm with an average distance of 1.1 cm. The correlation between physical positions and genetic positions remained a peak at the region between 2.31~5.0 Mb, but other peaks disappeared (figure 8). Figure 7 The relationship between physical position and genetic position. The x-axis is the physical position in bp units while the y-axis is genetic position in cm units. The Mean Chisquare Contribs. sheet showed the chi-square contribution of each marker. The nearest neighbour fit (N.N Fit) was proposed the possibility that the loci were placed flanking the region they belong (table 2). Table 2 Compared with regression map round 1 and round 2, the 11 markers with high contribution and were likely to be the problematic markers. Locus Position Mean X2 Ctbs. NN Fit (cm)

17 As is shown in table 2, the markers 4460, 4660, 4604, 4536, 4346, 4308, 4309, 4748, 4668, 4326 and 4585 were with the high value which indicated these markers did not fit very well with their neighbour markers. Also, the Genotype Probabilities showed the genotype with low probabilities. This probability was calculated by the genotype of the neighbour markers and then compared with the real genotype. These markers with low probabilities may indicate the possible data errors. By comparing the Genotype Probabilities, the individuals 93, 9, 82, 79, 59, 53, 50, 28, 26, 129, 122 were removed because these individuals have more than four markers with low probabilities together with the individuals belongs to outliers ( 27,105, 126, 128, 90 and 60). Besides, the markers with low probabilities and high contribution from N.N.Fit were removed which are including 4460, 4660, 4604, 4536, 4346, 4308, 4309, 4748, 4668, 4326, 4585, 4470, 4458, 4552, 4588, 4444, 4603, 4433, 4431, 4406, 4404, 4495, 4405, 4420, 4454 and 4577 were removed. Based on the result of regression mapping algorithm, 11 individuals and 26 markers were removed. The final map constructed by regression mapping is cm and the average distance between markers is 0.25 cm per marker. The relation between genetic positions and physical positions is shown in figure 9. Although at the beginning of the chromosome there was a peak, the other regions became much more smooth and according to expectation genetic positions physical positions Figure 8 The relationship between genetic and physical positions. The x-axis is the physical position and unit by bp, while the y-axis is the genetic position united by cm. When regression map and maximum likelihood map were compared, there is a large region which has high recombination in the ML map while low recombination in RM (figure 10). Due to the theory of regression map, it is likely to underestimate the distance between markers compared with the maximum likelihood mapping algorithm. However, this also could be a question mark of genetic map construction. 16

18 Figure 9 The relationship between regression map and maximum likelihood map. The red cycle showed the loci with high recombination fraction in maximum likelihood map but low in regression map. When we compare the genetic distance obtained from maximum likelihood map and regression map, there is a set of markers were quickly increasing in maximum likelihood map but almost not change in regression map. The Haldane s mapping function was carried out to select the most accurate one. First, three markers were randomly selected between 70 and 80 CM in maximum likelihood map which are 4398, 4420 and The distances between each two loci were compared with the genetic distance calculated by Haldane s mapping function (table 3). Table 3 Haldane s mapping function. D1 is the distance between 4398 and 4420, while d2 is the distance between 4420 and Marker d1 d2 ML cm cm RM 5.57 cm cm Direct pairwise 16 cm 7 cm Compared with maximum likelihood algorithm, regression map would be shorter. This is because it calculates the marker distance for each pair and the best position is searched. After that, the goodness-of fit measure is calculated in regression map, while a pair of markers is randomly selected and other markers random put into ML map. Associated with the result in table 1 and the relationship between physical position verse genetic positions, RM is better than ML. QTL analysis of reconstructed map (chromosome-wide) First of all, the Kruskal-Wallis (nonparametric) test showed a total number of 477 associated loci separated by two regions (Appendix 1) using P<0.05 as criteria. One region is at the beginning of the chromosome 10between 2.11 cm and 3.67 cm and four markers were in this region. Another region is extending approximately 74 cm (45.534cM~ cM) including 443 markers. Among these 443 markers underlying the QTL regions, 426 markers were higher statistically supported by P< A genome-wide permutation test provided a LOD significance threshold of 4.3 which was used to declare the presence of a QTL. Simple interval mapping identified four regions, and all of them were in the region between cm ~ cm and in agreement with the Kruskal-Wallis test (figure 17

19 11). However, two of the putative QTLs were likely to be the ghost QTLs which means the QTLs elsewhere on the chromosome have the interfering effect, and non-existing QTL would appear. The ghost QTL could be detected between the two real QTLs. In this case, the multiple QTL mapping methods is needed for more accurate QTL detection. Figure 10 Simple interval mapping of ɑ-tocopherol on chromosome 10. Four peaks of LOD score were above the significant LOD threshold (4.3). The Multiple QTL mapping method (MQM) uses markers as cofactors in the multiple-qtl model. The four markers close to the detected putative QTLs were selected as cofactors: 4334, 4417, 4673 and There is a cofactor tool in JoinMap which is automatic cofactor selection. After the automatic cofactor selection, only marker 4334 and marker 4694 were within the significant difference in Ln- Likelihood (p < 0.02). Moreover, the first QTL region was ~ CM between marker 4317 and 4382 can explain 25.6% of variation by using marker 4334 as a cofactor in MQM analysis. In addition to the QTL variance explanation, the cofactor marker 4334 itself can also explain 25.6% of phenotypic variance. Another putative spanned a region of 90.7 ~ cm between marker 4682 and 4734 and explained 21.8% of the variation using marker 4694 as a cofactor. However, the peak LOD score of the second QTL explained by cofactor 4694 was below the LOD threshold (4.3). In this case, the QTL spanned a region of 16 cm from 48.3 till 64.2 cm is the major QTL, and the other one could be a suggestive QTL for further study. Hence, an approximation of a 95% confidence interval was obtained by the two-lod support interval. Since the peak of LOD score was 6.67, a LOD interval of 4.67 or above was obtained close to marker 4334 to declare 2-LOD support interval (Figure 12). The interval was ranging from 50.7 cm to 56.5 cm and comprised 32 markers. This QTL was corresponding to a region of 5.94 ~ Mb on the physical map of chromosome

20 Figure 11 Two-LOD support interval. The peak of LOD score was localized on the marker The significant QTL was identified close to the peak with a region of LOD score above 4.67, which was from marker 4321 to marker QTL analysis of initial map (genome-wide) The initial map was containing the whole genome including 12 linkage group. First, Kruskal-Wallis test was performed, and it suggested that the potential QTL for high ɑ-tocopherol trait was underlying in 9 linkage groups (1, 2, 3, 6, 7, 8, 10, 11 and 12) by criteria value P< 0.05 (Appendix 1). Two regions were associated with ɑ-tocopherol in linkage group 1. One region was from the beginning of linkage group 1 and 34 markers at the proximal region, extending 9 cm. Another region was relative smaller (0.2 cm) underlying three markers. The associated region of linkage group 2 was in the middle and contained 31 markers significant linked with the high ɑ-tocopherol trait (P<0.05). In the case of linkage group 3, 205 markers underlying the proximal region of the group showed a significant association (P<0.05). Two nearby regions were in linkage group 6. One region was spared from 11.4 cm to 26.7 cm and 12 markers underlying. Also, a vast region extending 30 cm in the middle of the group was associated with the high ɑ-tocopherol level. Besides, 42 markers in the proximal region of linkage group 7 were associated with a high ɑ-tocopherol phenotype. Besides, seven markers in linkage group 8 and three markers in linkage group 9 showed significantly associated with the high ɑ-tocopherol trait. Be the case with linkage group 10, and the associated region was spread across 485 markers of total 514 markers from 14.2 cm till the end of the group. Moreover, 19 markers at the beginning of linkage group 11, three markers in the middle and three markers at the end of linkage group 12 showed the significant association with the high ɑ-tocopherol trait (P<0.05). After K-W test, interval mapping was performed to identify the location of significant QTL on putative regions. The interval mapping was conducted for all linkage groups. The permutation test showed the significant LOD threshold is 4.3, which is higher than the LOD threshold in approximately 3.0 from literature (Collard et al. 2005). However, although the Kruskal-Wallis test showed 10 of total linkage groups had an association with ɑ-tocopherol level, only the peak of LOD threshold in linkage group 10 was above the significant empirical threshold of 4.3. However, in this case, a threshold of 3.0 for the cofactor selection in the other linkage groups. If the LOD of 3.0 was used for the presence of QTL detection, there were three linkage groups linked with the ɑ-tocopherol trait (figure 13). 19

21 Figure 12 Linkage groups with the region of LOD score above threshold in 3.0. Linkage group 1, 2 and 3 indicated small regions above the LOD threshold whereas there was a three obviously peaks. The cofactors were selected close to the QTLs in each linkage groups, including marker 8 in linkage group 1, marker 780 in linkage group 2, marker 1109 and marker 1219 in linkage group 3, marker 4342, marker 4568 and marker 4713 in linkage group 10. When the initial cofactor set was carried out the automatic cofactor selection, only the marker 8 in linkage group 1, marker 780 in linkage group 2, marker 1219 in linkage group 3, marker 4342 and marker 4713 in linkage group 10 were significant at the minimum difference in Ln-Likelihood (P<0.02). These markers organized the final cofactor set and were used for rmqm and MQM analysis in the following steps. The restricted Multiple QTL Method is used all indicated cofactors except the cofactors on the target linkage group the QTL is fitted. However, the LOD peak in linkage group 2 was below the LOD threshold in 3.0 and the QTL in linkage group 2 only explained 4% of the variation. As a result, cofactor 780 was removed from the cofactor set, and the putative QTL was not indicated in linkage group 2. For the case of linkage group 1, the QTL was spanned 7.5 CM cross 30 markers and explained 6.3% of variation by using marker eight as a cofactor in MQM analysis. One QTL region was suggested in linkage group 3 from marker 1209 and marker 1226, extending 5CM and explained 7.3% of variation by cofactor 1219 in MQM analysis. There were two QTL regions were identified by cofactor 4342 and 4713, separated by 20 CM. The first QTL was close to the marker 4342, and the peak LOD value (5.93) of the group was much higher than the LOD threshold in 3.0. Over this region, marker 4342 explained 17.6% variation and selected as co-factor and Two-LOD support interval was obtained and it was spared from 20.9~21.1 cm between marker 4342 and In the case of second genomic region revealed from interval mapping, it was spanned a region of 35.0 cm and explained 26.7% of variation by marker 4713 as a cofactor. If we use the critical LOD threshold from permutation test of 4.3, significant loci about high ɑ- tocopherol trait only in linkage group 10. The LOD peaks at linkage group10 (marker 4342 together with marker 4713) can explain up to 45.4% phenotypic variance. The first QTL was near marker 4342, and the significant interval was obtained between marker 4342 and 4343, extending 0.4 CM at the starting position in linkage group10 (figure 14 a). Besides, marker 4713 had highest LOD score (8.9), and the presence of this allele contributed to higher ɑ-tocopherol level in the F2 population. 20

22 The QTL effect can be determined by the presence of marker 4713, which resulted in the increase 36% of variance explanation of ɑ-tocopherol level (figure 14 b). Figure 13 Two LOD support interval on linkage group 10 linked to ɑ-tocopherol level. a. Two-LOD interval of the first peak close to marker The region was spanned between marker b. Two-LOD support interval of the second QTL close to marker This QTLs was spanned cross 180 markers extending 35.0 CM. QTL analysis of other traits (chromosome-wide) The ɑ-tocopherol biosynthesis in plants is following a complex pathway and numerous enzymes involved in it. Some of the enzymes are from other compounds biosynthesis. For example, the ɣ- tocopherol is synthesised first and then converted by γ-tocopherol methyltransferase into ɑ- tocopherol (Dellapenna, Dellapenna, and Pogson 2006). It is necessary to get an insight whether any correlation between QTLs of ɑ-tocopherol and other metabolites. The chromosome-wide QTL analysis would provide sufficient information about the QTL associated with metabolites correlated with ɑ- tocopherol. First of all, the Kruskal-Wallis test suggested the putative regions associated with phylloquinone, β- ɣ-tocopherol, β-ɣ-tocotrienol, δ-tocopherol existed on chromosome 10 by using P< 0.05 as a threshold. Among these five metabolites, the existed QTLs for phylloquinone, β-ɣ-tocopherol and δ- tocopherol shared a similar region between and cm cross a large number of markers. Also, there were 19 markers associated with β-ɣ-tocopherol trait at the beginning of the chromosome (2.63 ~23.91 cm). Two regions were associated with high β-ɣ-tocotrienol trait on chromosome 10. One region was across 18 markers at the beginning of the chromosome, extending 9.25 cm. Another region was spread from 45.53cM till cm containing 421 markers. Phylloquinone Kruskal-Wallis test showed 476 markers (32.18~ cm ) were significantly associated with high phylloquinone. The permutation test obtained significant threshold of 4.4 (genome-wide, P<0.05) which was used for significant QTL identification in simple interval map(figure 15). 21

23 Figure 14 The LOD value of linkage group was above a threshold of 4.4 (genome-wide, P<0.05). The first peak was around marker 4334 and the second one was at marker In the further analysis, the marker 4334 and 4689 were selected in the initial cofactor set. This set was tested by the automatic cofactor selection tool and only the cofactors with a significant minimum difference in the ln-likelihood level were put in the final set (P<0.02). Both of them were at the significant level. In the next analysis, the MQM was carried out to identify the significant loci except for the ghost QTL. The inclusion of cofactors can help to extract the effect of ghost QTL as well as minimize the background effect. In MQM, marker 4334 and 4689 explained 10.6% and 6.8% of phenotypic variation as cofactors. Finally, the LOD value would combine with MQM result, and there were two regions associated with phylloquinone. The first region was from marker 4324 to 4333 in interval map referred to 50.1~53.19 CM (figure 16 a) and explained 16.0% of variation by using marker 4334 as a cofactor in MQM analysis. Another region of 91.76~94.29 CM (figure 16 b) can explain 11.4% of variation by using cofactor marker 4689 in MQM. Figure 16 The confidence interval for phylloquinone. a. The first QTL on chromosome 10 spanned across seven markers. B. The second QTL was including seven markers extending 2.4 CM. 22

24 β-ɣ-tocopherol Term to β-ɣ-tocopherol, the Kruskal-Wallis test revealed two regions associated with the trait. One region was at the beginning of chromosome 10 cross 19 markers, extending 22 cm. Moreover, a large region from to cm was significantly associated with β-ɣ-tocopherol trait on chromosome 10 (P<0.05). In the next step, the permutation test obtained the significant LOD threshold resulted in a LOD interval of 4.3 (genome wide, P<0.05). Two regions with the LOD value above LOD threshold of 4.6 were present as putative QTLs for β-ɣ-tocopherol in interval map. To extract the background effect, marker 4336 and 4689 were selected as cofactors. However, only the LOD score of marker 4689 was higher than the genome-wide LOD significance level of 4.3. The peak of the QTL was 3.66, and the 95% confidence interval was determined by two-lod support interval, from ~ cm across marker 4615 ~ 4715 (figure 17). Figure 17 The QTL for β-ɣ-tocopherol on chromosome 10 between to CM. δ- tocopherol: K-W test suggested a significant associated putative region between ~ cm. In the next step, the permutation test with 1000 replicates was carried out and the genome-wide LOD threshold for declaring the presence of QTLs was 4.2. Meanwhile, the simple interval mapping calculated the likelihood of each locus for the presence of a segregating QTL. However, although the Kruskal-Wallis test indicated various associated regions over ten linkage groups, only the LOD peak of linkage group 10 (LOD=4.26) was above the LOD threshold of 4.2 (figure 18). 23

25 Figure 18 The significant LOD threshold of 4.2 was used for the detection of QTLs presence. The peak of LOD was 4-26 slightly above the LOD threshold (4.2). In the following analysis, the marker 4688 with the highest LOD value was selected as a cofactor in MQM and explained 17.2% of the variation. The QTL region was determined by Two-LOD support interval method and a region of 15 CM from to CM cross 47 markers was with 95% confidence probability coverage (figure 18). This region was localized on chromosome 10 between and Mb. QTL analysis on other traits (initial map) After the analysis including reconstructed map, it is necessary to use the original map for a comparison. This was achieved by using the genome-wide map by I.Paran and four metabolites, phylloquinone, β-ɣ-tocopherol, β-ɣ-tocotrienol, and δ-tocopherol, have association on linkage group 10. Phylloquinone First, the Kruskal-Wallis test for phylloquinone was carried out on the F2 population with the whole genome and it suggested that there were significant putative intervals associated with the phylloquinone on all linkage groups except linkage group 12 (Table 4, P<0.05). Among all the main intervals, the most proposed QTL intervals were located on linkage group 3, linkage group 4 and linkage group 10 due to plenty of markers significantly associated with the trait. Table 4 The Kruskal-Wallis analysis of phylloquinone. There were the largest amount of markers in linkage group 10, following by linkage group 4 with 82 markers and linkage group 3 with 69 markers. linkage marker amount group

26 The permutation test (1000 replicates) was undertaken and determined the threshold for LOD in 4.4 (genome-wide, P<0.05). This value was used for detecting the presence of QTLs over all linkage groups. Meanwhile, the simple interval mapping was carried out and the likelihood of the presence of a segregating QTL was calculated and shown by LOD score. However, only the peak LOD value of the intervals in linkage groupx (11.64) was higher than the significant LOD threshold (4.4) from permutation test (figure 19). For the other regions determined by Kruskal-Wallis test, no high LOD peak was above the genome-wide LOD significance level in interval mapping. Figure 15 Only the LOD peak of linkage group 10 was above significance LOD threshold value of 4.4. In the further analysis, the background markers can result in the ghost QTL and included them as cofactors would help to obtain the significant loci. The marker 4336 and 4696 which was close to the putative QTL were selected as cofactors. The automatic cofactor selection tool showed both of them were above the significant Ln-likelihood level. Marker 4696 as a cofactor can explain the 14.7% of the variation. Together with marker 4336 as a cofactor, the two loci can explain 30% of phenotypic variation. After that, the two QTLs were identified. The peak LOD value of the first QTL was 11.64, and it was used for the two-lod support interval identification with 95% confidence interval probability coverage. A region of 16.4~20.3 CM including 18 markers from 4324 to 4341 and responding 6.73 ~10.47 Mb in physical positions (Figure 20 a). Another peak LOD value was of marker 4696 which is above the genome-wide significant LOD threshold in 4.3. The 95% significant QTL interval was determined from 86.6 CM to CM and spanned 10 Mb ( ~ Mb) on physical positions (Figure 20 b). 25

27 Figure 16 Two-LOD support intervals in linkage group 10. a. The 95% confidence interval was from 16.4 to 20.3 cm. b. The second QTL in linkage group 10 was between 86.6 and 89.7 CM and explained 14.7% of variation by using marker 4696 as a cofactor. β-ɣ-tocopherol The first analysis was by Kruskal-Wallis for the β-ɣ-tocopherol. It suggested 38 markers were significantly associated with the trait in linkage group 1, 9 markers in linkage group 2, 86 markers were significant in linkage group 3, 4 markers in linkage group 4, 20 markers in linkage group 5, 119 markers in linkage group 6, 20 markers in linkage group 7, 2 markers in linkage group 8, 488 markers in linkage group 10, 27 markers in linkage group 11 and 5 markers on linkage group 12. The high potential QTL were located on linkage group 1, linkage group 3, linkage group 6 and linkage group 10 because of the number of markers (Table 5). Table 5 The Kruskal-Wallis analysis of β-ɣ-tocopherol. There was the largest amount of markers in linkage group 10, following by linkage group 6 with 119 markers and linkage group 3 with 86 markers. linkage marker amount group The permutation test determines the genome-wide significant LOD threshold in 4.6 which can declare the QTL presence among all linkage groups. Meanwhile, the simple interval mapping was carried out for the identification of significant QTL with the help of LOD threshold. There are significant QTL intervals located on linkage group1 and linkage group10 which was agreed with Kruskal-Wallis test (Figure 21). 26

28 Figure 21 Two linkage groups existed putative QTLs for β-ɣ-tocopherol. The LOD peak of linkage group 1 was slightly higher than the LOD threshold in 4.6, while an obvious peak was observed in linkage group 10. After removing the background markers, the marker 8 was as a cofactor in linkage group1 explained 10.2% of the variation, and 4713 in linkage group10 explained 13.3% of phenotypic variance in Multiple QTL Method (MQM). As a consequence, three QTLs for β-ɣ-tocopherol were suggested in linkage group 1 and 10. One region was associated with β-ɣ-tocopherol in linkage group 1. The peak LOD value linkage group1 was obtained from marker 8 which is 4.63 above the LOD threshold in 4.6 (figure 22a). Then, the significant QTL was determined by two- LOD support interval and was at the beginning of linkage group 1 (0~4.3 CM corresponding 0~5.27Mb in physical positions). Also, a QTL localized in linkage group 10 was from 92.2 to 96.1 CM corresponding ~ Mb (figure 22 b). Figure 17 Two-LOD support intervals in linkage group 1 and 10. a. The 95% confidence interval was from the starting of the linkage group and spanned 4.3 CM. b. The QTL in linkage group 10 was cross 16 markers extending 3.9 CM. β-ɣ-tocotrienol In the Kruskal-Wallis test of F2, the significant intervals associated with β-ɣ-tocotrienol existed in almost all linkage groups except linkage group 2 and linkage group 9. Most of the significantly associated loci located on linkage group1, linkage group2, linkage group10. Among these linkage groups, there were 470 markers associated with the trait over total 514 markers in linkage group10 27

29 (table 6). All these information was used for simple interval mapping, cofactors identification, and multiple QTL method mapping analysis. Table 6 The Kruskal-Wallis analysis of β-ɣ-tocopherol. There were the largest amount of markers in linkage group 10, following by linkage group 2 with 138 markers and linkage group 1 with 121 markers. linkage marker amount group For the further step, the permutation test (10000 replicates) was performed to determine significant LOD threshold in 4.2 (genome-wide, P<0.05) which can declare the presence of putative QTLs above the threshold. Despite significant association in numerous linkage groups, only the peak LOD value of linkage group 1 (LOD=6.94) and linkage group 10 (LOD=4.39) were above the LOD significance value of 4.2 determined from permutation test (figure 21). Figure 21 Two linkage groups existed putative QTLs for β-ɣ-tocotrienol. The LOD peak of linkage group 10 was slightly higher than the LOD threshold in 4.2, while an obviously peak was observed in linkage group 1. Meanwhile, the marker eight close to the putative QTL in linkage group 1 and marker 4690 close to the putative QTL in linkage group 10 were selected as a cofactor and explained 18.4% and 10.5% of variation separately in MQM analysis. Two QTL intervals were identified in linkage group 1 and ten by two-lod support interval in simple interval map. The 95%confidence interval in linkage group 1 was close to the cofactor, marker 8. A region of 0.4~6.5 CM in linkage group 1 responding 1.74~6.78 Mb of physical position (figure 22a). For the QTL on linkage group 10, it was spanned 23.5 cm including 122 markers between 75.4 and 98.9 cm (figure 22b). This region was localized at the end of the chromosome from ~ Mb. 28

30 Figure 18 Two-LOD support intervals in linkage group 1 and 10. a. The 95% confidence interval was from the starting of the linkage group and spanned 6.1 CM. b. The QTL plays a major role in linkage group 10 was cross 122 markers from 75.4 CM to 98.9 cm. δ-tocopherol The first screening was by Kruskal-Wallis, and it suggested that linkage group 1,3,4,6,7,8,9, 10 and 11 associated with the δ-tocopherol level in the F2 population. Owe to many associated loci, the linkage group 10 would be the most potent group underlying QTLs, followed by linkage group 3 with 124 associated loci (table 7). Table 7 The Kruskal-Wallis analysis of δ-tocopherol. There were the largest amount of markers in linkage group 10, following by linkage group 3 with 124 markers (significant P<0.05). linkage marker amount group In the next step, the permutation test with 1000 replicates was carried out, and the genome-wide LOD threshold for declaring the presence of QTLs was 4.1. Meanwhile, the simple interval mapping calculated the likelihood of each locus for the presence of a segregating QTL. However, although the Kruskal-Wallis test indicated many associated regions over ten linkage groups, only the LOD peak of linkage group 10 (LOD=6.14) was above the LOD threshold of 4.4 (figure 23). 29

31 Figure 19 The significant LOD threshold of 4.4 was used for the detection of QTLs presence. Only the peak value of linkage group 10 was above 4.4 and for the other regions determined from the Kruskal-Wallis test were not linked with the trait anymore. In the following analysis, the marker 4713 with the highest LOD value was selected as a cofactor in MQM. The QTL region was determined by Two-LOD support interval method and a region of 11.2 CM from 83.6 to 94.8 CM cross 47 markers was with 95% confidence probability coverage (figure 24). This region was localized on chromosome 10 between and Mb. Figure 20 The 95% confidence QTL interval was localized in linkage group 10. It was spanned from 83.6 to 94.8 CM cross 47 markers. 30

32 Discussion This research was carried out to find the position of a major QTL affecting ɑ-tocopherol biosynthesis in a Capsicum F2 population. A previous study indicated that the chromosome 10 was highly associated with the ɑ-tocopherol level. Regression mapping algorithm constructed the new genetic map in chromosome 10 by JoinMap 5.0. By using this new map, one QTL interval associated with high ɑ-tocopherol level was indicated on chromosome 10 between ~ cm. However, two chromosomal regions were containing significant QTLs on chromosome 10 at genome-wide by using the original map. In contrast, two QTL shared a region between marker 4342 and marker Due to the map error in the initial map of chromosome 10, the analysis of initial map would underestimate the QTL regions. In this research, the final QTL was determined as a 2-LOD support region between marker 4321 and marker 4352 responding to 5.94~11.28 Mb on the physical map of pepper chromosome 10. The individual with the QTL which was declared from chromosome-wide analysis showed a significantly higher a-tocopherol level compared with the individuals and it can prove that the QTL can enhance the a-tocopherol biosynthesis in this F2 population. Because ɑ-tocopherol is the downstream product of the vitamin E biosynthetic pathway, the biosynthesis of it can be influenced by other vitamin E components and the upstream regulated enzymes. Also, the vitamin E biosynthesis in the plant is also affected by other metabolites and can cooperate with other metabolites biosynthesis. For these reasons, the QTL identification of other metabolites is also necessary to figure out the correlation with ɑ-tocopherol QTLs. The analysis of other metabolites was performed on the initial map and reconstructed map. The two analysis showed two different results. By using the initial map, the QTL for phylloquinone is partially overlapping the QTL for ɑ-tocopherol (table 8). There were two QTLs for both ɑ-tocopherol and phylloquinone. However, the first QTL for ɑ-tocopherol was from 20.9 till 21.3 CM whereas the QTL for phylloquinone was from 16.4 to 20.3 cm. These two regions are pretty close but not overlapping. Although they are not overlapping, the significant loci were close, and that means the functional candidate genes are close. Besides, the QTLs for β-ɣ-tocopherol, β-ɣ-tocotrienol, and δ-tocopherol were almost entirely overlapping the QTL for ɑ-tocopherol. The QTL for β-ɣ-tocopherol shared a region of ~ Mb with the QTL for ɑ-tocopherol. The β-ɣ-tocopherol was methylation side chain of ɣ-tocopherol (Caretto et al. 2010). Similar to β-ɣ-tocotrienol, it is from ɣ-tocotrienol by methyltransferase. Both of tocotrienol and tocopherol were synthesis from geranylgeranyl diphosphate (GGDP) (Dellapenna 2005; Dellapenna, Dellapenna, and Pogson 2006). Proceeding the vitamin E biosynthetic pathway, the GGDP can be used for the synthesis of tocotrienols or reduced into phytyl-diphosphate (PDP) then synthesis tocopherols. For the case of δ-tocopherol, since the ɑ-tocopherol can be converted by δ-tocopherol directly, it shared a large region of 202.7~ Mb with the associated QTL with ɑ-tocopherol (Dellapenna, Dellapenna, and Pogson 2006). Table 8 Correlation between the QTL for ɑ-tocopherol and the QTL for phylloquinone, β-ɣ-tocopherol, β-ɣtocotrienol and δ-tocopherol on chromosome 10 in the initial map. Trait peak linkage group Starting End ɑ-tocopherol phylloquinone β-ɣ-tocopherol β-ɣ-tocotrienol

33 δ-tocopherol The reconstructed genetic map only indicated the QTLs for phylloquinone, β-ɣ-tocopherol, and δ- tocopherol were correlated with the chromosome 10. However, only the QTL for phylloquinone was overlapping the QTL for ɑ-tocopherol, and they share a region of 5.94 to Mb (table 9). Table 9 Correlation between the QTL for ɑ-tocopherol and the QTL for phylloquinone and δ-tocopherol on chromosome 10 in reconstructed map. Trait peak linkage group Starting Ending ɑ-tocopherol phylloquinone β-ɣ-tocopherol δ-tocopherol Both of the QTL analysis of genome wide and chromosome wide, the QTL for phylloquinone was overlapping the QTL for high a-tocopherol content. Especially for the chromosome analysis, one of the QTLs for phylloquinone (49.9~53.19 cm) was almost fully overlapping with the QTL for a- tocopherol (49.9~56.7 cm). It is interesting to note the interaction between a-tocopherol and phylloquinone biosynthesis. Normally, the synthesis study was more focus on the relationship between vitamin E and carotenoids synthesis due to their biosynthetic pathway linked. For previous study, the phylloquinone is also with the w-hydroxylated side chain and can be catabolised by betaoxidation (Caretto et al. 2010).The chorismate is the precursor for both ubiquinone and phylloquinone biosynthesis. Also, a precursor of tocopherol biosynthesis which is homogentisate can be catalysed into ubiquinone. However, the relationship between tocopherol synthetic pathway and phylloquinone synthesis is unclear now. This result would lead to a new popular study topic and increase the understanding of the lipophilic biosynthesis.on chromosome 10, the QTL region obtained from limited linkage group was between 5.94 and Mb. 57 genes were underlying this region based on the pepper data base ( Furthermore, two genes in this QTL region were selected based on annotation including Capana10g (GLK1) and Capana10g (CHLI). In addition, four candidate Capana10g000322, Capana10g000323, Capana10g000324, Capana10g (ACX) were also determined. One candidate gene in this region is Capana10g (GLK1), which is a transcription factor regulating chloroplast biogenesis. Chloroplast biogenesis is a complex process and is cooperation between nuclear genome and chloroplast genome. A significant amount of proteins are accumulated in chloroplast, but they are controlled by the nuclear genome. Furthermore, the transportation of the proteins is also by the nuclear genome. The GLK gene is an example that regulates the chloroplast development (Fitter et al. 2002; Moylan et al. 2015). The previous study showed that the DNɑbinding region of GLK is highly similar with the other GARP proteins like ARR1 in Arabidopsis thus it is likely that the GLK gene is from the GARP transcription factor family. Likely to GARP family, the GLK gene can regulate the chloroplast development and involved many chloroplast biogenesis processes. By mutant study, the double mutants showed that the reduction in granal thylakoids (Moylan et al. 2015). More important, several genes associated with the light harvesting process also exhibited the decrease level in the double mutants chloroplasts (Fitter et al. 2002). In addition, the tocopherols biosynthesis is started with the chlorophyll degradation and converted chlorophyll into PDP and then follow tocopherol biosynthetic pathway(seeds et al. 2014). The light harvesting 32

34 complex is a complex of the subunit proteins involved in the photosynthesis. Each of the complexes would bind many chlorophyll molecules and harvest energy from the incoming light and then transfer the energy to the photosynthetic reaction center. In addition to the photosynthesis, a recent study suggested the light-harvesting complex II (LHCII) can cooperate with the Non-Yellowing 1 (NYE1), also called stat-green 1(SGR1) and plays an essential role in chlorophyll breakdown during leaves senescence (Seeds et al. 2014). The I subunit of magnesium-chelatase (CHLI) is encoded by two candidate genes which are CHLI1 and CHLI2 (Huang and Li 2009). From the study in Arabidopsis, the CHLI genes play an essential role in chlorophyll biosynthesis. The first step of chlorophyll biosynthesis is the insertion of magnesium into protoporphyrin IX and achieved by magnesium-chelatase. The magnesiumchelatase catalysis needs energy by ATP hydrolysis which can be attributed the subunit, CHLI (Huang and Li 2009; Kobayashi et al. 2008). As we are known, the chlorophyll plays an important role in tocopherol biosynthesis. The chlorophyll can be degraded into phytyl diphosphate (PDP) by chlorophyllase (CLH) which is a precursor of the tocopherol synthesis. Furthermore, the PDP is converted by HPT and the tocopherol biosynthetic pathway (Seeds et al. 2014). Besides, four Acyl-coenzyme A oxidase (ACX3) genes were selected based on their annotation: Capana10g000322, Capana10g000323, Capana10g000324, Capana10g The acyl-coenzyme A oxidase is from the fatty acyl CoA oxidase (FAO) family which can be induced by the peroxisome proliferator and it is common to be present in animals. The ACX genes are considered as a core enzyme with suggested role in the β-oxidation (Froman et al. 2000). The first step is achieved by ACX, which can the acyl-coa into 2-trans-enoyl-CoA participating the following steps (Froman et al. 2000; Seeds et al. 2014). It is interesting to note that the QTLs for ɑ-tocopherol and phylloquinone are almost entirely overlapping and the ACX is also a candidate for phylloquinone regulatory mechanism since the ACX can function in the β-oxidation and produce the final product of phylloquinone. Still due to the limited data of pepper, a synteny study with tomato was considered and would be necessary to determine the orthologous candidate genes about vitamin E. Tomato and pepper are from the same family but different genus. Some study showed that the chromosome 10 of pepper contains all of the experimental markers of tomato with the same order but only one paracentric inversion differentiates them (Rinaldi 2016). The Capanna10g0322 is syntenic with Solyc10g and both of them are the ACX candidate based on annotation. In addition, Capana10g (GLK) is syntenic with Solyc10g and Solyc07g Both of the two orthologous candidates genes have the function as GLK. 33

35 Recommendations The mapping of the ɑ-tocopherol QTL provided valuable information for the QTL location. It is interesting to note the candidate genes can involve in the vitamin E biosynthesis. Besides, the QTL for phylloquinone was overlapping with QTL for ɑ-tocopherol. However, the study of regulatory mechanism between the phylloquinone and ɑ-tocopherol was limited. In the further study, the QTL regions would be desirable to narrow and specific candidate genes identified. Further QTL studies should concentrate on the fine-mapping the QTL identified on chromosome 10 and search for the new QTL in the other linkage groups. The candidate genes identification in the new QTLs and functional analysis would provide more suggestions to understand the regulatory mechanism of ɑ-tocopherol biosynthesis. A further QTL studies would also for phylloquinone since the QTL for it is firmly linked with the QTL for ɑ-tocopherol. The QTL can be introduced into background lines to confirm the function 34

36 Reference Dean Dellapenna A Decade of Progress in Understanding Vitamin E Synthesis in Plants Babujee, Lavanya et al The Proteome Map of Spinach Leaf Peroxisomes Indicates Partial Compartmentalization of Phylloquinone ( Vitamin K1 ) Biosynthesis in Plant Peroxisomes. 61(5): Barboza, Gloria E., and Luciano Bem Bianchetti Three New Species of Capsicum (Solanaceae) and a Key to the Wild Species from Brazil. Systematic Botany 30(4): Caretto, Sofia et al Tocopherol Production in Plant Cell Cultures. : Collard, B C Y, M Z Z Jahufer, J B Brouwer, and E C K Pang An Introduction to Markers, Quantitative Trait Loci ( QTL ) Mapping and Marker-Assisted Selection for Crop Improvement : The Basic Concepts. : Dellapenna, Dean, Dean Dellapenna, and Barry J Pogson Vitamin Synthesis in Plants : Tocopherols and Carotenoids Vitamin Synthesis in Plants : Tocopherols and Carotenoids. (July). Dror, Daphna K, and Lindsay H Allen Vitamin E Deficiency in Developing Countries. 32(2): Fernie, Alisdair R et al Genetic Dissection of Vitamin E Biosynthesis in Tomato. 62(11): Fitter, David W et al GLK Gene Pairs Regulate Chloroplast Development in Diverse Plant Species. 31. Froman, Byron E et al ACX3, a Novel Medium-Chain Acyl-Coenzyme A Oxidase from Arabidopsis. 123(June): Galli, Francesco et al Vitamin E : Emerging Aspects and New Directions. Free Radical Biology and Medicine 102(June 2016): Gilliland, Laura U et al Genetic Basis for Natural Variation in Seed Vitamin E Levels in Arabidopsis Thaliana. 103(49). Gross J The Biosynthesis of Phylloquinone (Vitamin K1) in Higher Plants. lmu. Huang, Yi-shiuan, and Hsou-min Li Arabidopsis CHLI2 Can Substitute for CHLI1. 150(June): Jansen R C, Stam P High Resolution of Quantitative Traits Into Multiple Loci via Interval Mapping. 1455: Kobayashi, Koichi, Nobuyoshi Mochizuki, Naho Yoshimura, and Ken Motohashi Functional Analysis of Arabidopsis Thaliana Isoforms of the Mɣ-Chelatase CHLI Subunit. : Kosambi, D D The Estimation of Map Distances from Recombination Values. Lansing, East Department of Biochemistry and Molecular Biology, Michigan State I. A BRIEF HISTORY OF VITAMIN E RESEARCH. Liebert, Mary Ann et al Expression of the Hydrogen Peroxide-G Enerating Enzyme. 19(2): Lim, Yunsook Alphɑ-A further QTL study Transfer Protein ( α -TTP ): Insights from Alphɑ-tocopherol Transfer Protein Knockout Mice *. 1: Luis, A, and Río Editor Peroxisomes and Their Key Role in Cellular Signaling and Metabolism. Lushchak, Volodymyr I, and Nadia M Semchuk Tocopherol Biosynthesis : Chemistry, Regulation and Effects of Environmental Factors. : Marchese, Michelle E., et al The Vitamin E Isoforms α-tocopherol and γ-tocopherol Have Opposite Associations with Spirometric Parameters: The CARDIA Study. : 15(1): 31. Marwede, V, and M K Gu Mapping of QTL Controlling Tocopherol Content in Winter Oilseed Rape. 26: Moylan, Elizabeth C, Mark T Waters, Elizabeth C Moylan, and Jane A Langdale GLK Transcription Factors Regulate Chloroplast Development in a Cell-Autonomous Manner GLK Transcription Factors Regulate Chloroplast Development in a Cell-Autonomous Manner. (November 2008). 35

37 Munné Bosch α Tocopherol: A Multifaceted Molecule in Plants. Vitamins & Hormones 76(7): Mustacich D J, Leonard S W, Patel N K, et al α-tocopherol β-oxidation LOCALIZED TO RAT LIVER MITOCHONDRIA. : Ooijen, J W Van JoinMap 4.1. (July). Paran, Ilan, and Esther Van Der Knaap Genetic and Molecular Regulation of Fruit and Plant Domestication Traits in Tomato and Pepper. Journal of Experimental Botany 58(14): Rinaldi, Riccardo New Insights on Eggplant / Tomato / Pepper Synteny and Identification of Eggplant and Pepper Orthologous QTL Article in Frontiers in Plant Science July 2016 New Insights on Eggplant / Tomato / Pepper Synteny and Identification of Eggplant and Pepper Orthologous QTL. (July). Rousseaux, M Cecilia et al QTL Analysis of Fruit Antioxidants in Tomato Using Lycopersicon Pennellii Introgression Lines. : Seeds, Arabidopsis et al Chlorophyll Degradation : The Tocopherol Biosynthesis-Related Phytol Hydrolase in. 166(September): Vos, Ric C H De et al Untargeted Large-Scale Plant Metabolomics Using Liquid Chromatography Coupled to Mass Spectrometry. 2(4): Wahyuni, Yuni et al Secondary Metabolites of Capsicum Species and Their Importance in the Human Diet. Journal of Natural Products 76(4): Yang, Wenyu et al Vitamin E Biosynthesis : Functional Characterization of the Monocot Homogentisate Geranylinkage grouperanyl Transferase. :

38 Appendix Appendix 1 Kruskal-Wallis test showing the association between markers and ɑ-tocopherol level Significant at *P < 0.1, **P < 0.05, ***P < 0.01, **** P < 0.005, ***** P < 0.001, ****** P < Positon Locus Test statistics Df Significant ** ** ** ** ** ** ** ** ** ******* ****** ****** ****** ******* ****** ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* 37

39 ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ****** ****** ****** ****** ****** ******* ******* ******* 38

40 ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* 39

41 ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* 40

42 ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* 41

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46 ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ******* ****** ****** ****** ****** 45

47 ****** ****** ****** ****** ****** ****** ****** ****** ****** ****** ****** ****** ****** ****** ****** ****** ****** ****** ***** ***** **** **** **** **** **** **** **** **** **** **** **** **** *** ** ** ** ** ** ** ** ** ** ** ** ** ** 46

48 ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** 47

49 Appendix 2. Genotype of F2 population. 48