Epistatic QTL Mapping

A large number of epistatic interactions were detected with the three different datasets for both heterotic and seed quality traits (Table 35), meaning that epistatic interactions play an important role not only in explaining phenotypic variation in the performance of the doubled haploid lines but also that epistasis contributes to the expression of heterosis in rapeseed, explaining as large and even larger portions of the phenotypic variation than the main effect QTL (Table 36). The epistatic interactions identified with the midparent heterosis data are these, which exclusively contribute to the expression of heterosis. For early fresh biomass and grain yield the phenotypic variance explained by the main effects in the midparent heterosis data was 14.8% and 18.1%, while with 39.3% and 36.6% (Table 36) the phenotypic variance explained by the digenic epistatic interactions was about twice as large. The difference between the phenotypic variance explained by the main effect QTL and epistatic interactions in the midparent heterosis data was even more pronounced for seeds per silique and plant height where 4.3% and 7.8%, respectively, were explained by the main effects, while the epistatic interactions accounted for 51.8% and 39.9% of the phenotypic variance, respectively.

In general the magnitude of the epistatic effects was lower than that of the main effects, but the epistatic QTL mapping identified much larger number of loci contributing to trait expression, than the single-QTL model. For example the main effect QTL mapping with midparent heterosis data for the traits showing the highest levels of heterosis early fresh biomass and grain yield resulted in the detection of 3 and 4 QTL, respectively, while 11 digenic epistatic interactions were identified for early fresh biomass and 9 for grain yield (Table 36). According to Li et al. (2001) the epistatic interactions could be classified in three groups depending on the main effects of the loci involved. Epistatic interaction between two loci with significant main effects represents type I, between a significant main effect QTL and a locus without main effect is type II, and between two background loci with no significant main effects is described as type III. Our results confirmed those of Li et al. (2001), Luo et al.

(2001) and Yu et al. (1997) in rice, showing that epistasis does not necessarily occur between main effect QTL. We observed just a single epistatic interaction of type I, 43 out of 312 (13.8%) were of type II and the remaining 268 (85.9%) were of type III. A larger part 61% of the type II interactions were detected in the doubled haploid population, which could be explained with the larger number of main effect QTL identified in this dataset. The remaining

39% were approximately equally distributed between midparent heterosis and testcross hybrid data with 17% and 22%, respectively.

Table 36 Summarized information for the number and congruency of main effect and epistatic QTL identified with the doubled haploid (DH), and testcross (TC) populations, and the midparent heterosis data (MPH)

Dominance^e Epistasis^f

Trait^a Set^b mQTL^c Overlap^d p f o I II III epQTL Vp(m)^g Vp(e) Vp(t)

EFB DH 5 2 2 10 12 31.80 34.10 65.90

EFB MPH 3 2 1 3 11 11 14.80 39.30 54.10

EFB TC 3 2 2 8 10 14.60 38.10 52.70

GY DH 6 3 4 2 6 32.70 8.47 41.17

GY MPH 4 3 2 2 9 9 18.10 36.60 54.70

GY TC 1 0 1 6 7 2.30 33.40 35.70

TKW DH 8 4 3 5 8 27.80 20.90 48.70

TKW MPH 3 3 1 3 2 2 26.50 11.90 38.40

TKW TC 4 4 1 5 6 28.70 28.40 57.10

S/Sil DH 3 3 1 7 8 25.50 19.30 44.80

S/Sil MPH 2 2 2 14 14 4.30 51.80 56.10

S/Sil TC 2 1 2 2 18.30 12.00 30.30

Sil/dm² DH 7 0 1 8 9 32.70 33.50 66.20

Sil/dm² MPH 0 0 2 2 0.00 10.50 10.50

Sil/dm² TC 1 0 1 1 6.30 8.00 14.30

PH DH 7 1 1 1 27.70 3.30 31.00

PH MPH 2 1 1 1 1 4 5 7.80 39.30 47.10

PH TC 2 0 1 2 3 16.50 17.90 34.40

BF DH 9 7 1 3 4 50.40 12.50 62.90

BF MPH 4 4 4 1 1 1 7 8 15.00 27.70 42.70

BF TC 6 5 2 2 4 22.70 20.20 42.90

EF DH 9 2 1 5 6 34.50 19.20 53.70

EF MPH 3 2 3 11 11 17.60 40.30 57.90

EF TC 3 0 9 9 21.00 49.40 70.40

DF DH 6 2 3 4 7 22.00 16.40 38.40

DF MPH 2 2 1 1 1 10 11 10.50 44.70 55.20

DF TC 0 0 14 14 0.00 54.70 54.70

Oil DH 11 5 1 7 8 29.60 10.50 40.10

Oil MPH 3 2 1 1 1 2 3 16.00 14.20 30.20

Oil TC 3 2 3 9 12 37.80 29.40 67.20

Pro DH 9 1 1 6 7 31.20 23.30 54.50

Pro MPH 1 0 1 1 9 10 4.30 39.90 44.20

Pro TC 3 1 1 2 3 16.10 15.70 31.80

Mladen Radoev PhD Thesis Discussion

Table 36/Continued from page 108

Dominance^e Epistasis^f

Trait^a Set^b mQTL^c Overlap^d p f o I II III epQTL Vp(m)^g Vp(e) Vp(t)

GLS DH 5 2 8 8 26.00 3.30 29.30

GLS MPH 3 2 1 2 2 6 8 31.00 26.20 57.20

GLS TC 3 1 8 8 24.30 6.70 31.00

C22:1 DH 2 1 2 5 7 66.30 11.60 77.90

C22:1 MPH 2 1 1 1 13 13 26.60 39.32 65.92

C22:1 TC 1 1 2 11 13 52.90 27.48 80.38

Sin DH 10 3 1 1 7 9 33.50 10.20 43.70

Sin MPH 1 1 3 1 4 5 2.70 41.60 44.30

Sin TC 3 2 9 9 14.20 23.70 37.90

aEFB: early fresh biomass, GY: grain yield, TKW: thousand kernel weight, S/Sil: seeds per silique, Sil/dm²: siliques per square decimeter, PH: plant height, BF: beginning of flowering, EF: end of flowering, DF: duration of flowering, Oil: oil content, Pro: protein content, GLS: glucosinolate content, C22:1: erucic acid content, and Sin: sinapine content

bDH, MPH, and TC – doubled haploid line, midparent heterosis, and testcross hybrid data, respectively

cmQTL and epQTL – number of main effect and epistatic QTL, respectively

d Overlap – Number of coinciding QTL detected in more than one dataset

eDominance – number of dominance effects displaying partial- (p), full- (f), and overdominance (o). The discrepancy between the sum of QTL with different dominance effects and the QTL detected in MPH is due to dominance effects calculated indirectly from the other datasets at loci not significant in MPH

fEpistasis I, II, III – number of first, second, and third type epistatic interactions, respectively

gPhenotypic variance explained by the main effect [Vp(m)], epistatic effects [Vp(e)], and the sum of them [Vp(t)]

Variation in the number of main effect QTL involved in epistasis was observed not only between the different datasets used for QTL mapping but between different traits as well. The largest number of type II epistatic interactions was identified in the doubled haploid population for grain yield, thousand kernel weight and duration of flowering, 4 out of 6, 3 out of 8, and 3 out of 7 interactions, respectively. In the current study oil content was not among the traits with the highest number of loci with significant main effect involved in epistasis.

These results were in discrepancy with the results reported by Zhao et al. (2005), who identified 11 digenic interactions for oil content in a doubled haploid population developed from a cross between an European and a Chinese cultivar. Seven of these epistatic interactions were of type I, and 4 of type II. No type III interactions were detected.

Considering the large number of type III interactions observed in the rapeseed population under study, our results more closely resembled the observations of Li et al. (2001) and Luo et al. (2001) in rice, who detected prevailingly type III epistatic interactions as well. Some of the loci involved in epistasis interacted with more than one locus, for example a locus for thousand kernel weight located in the doubled haploid population on N6, or loci for seeds per silique mapped on N2 and N4 with the midparent heterosis data etc. The participation of loci

in multiple digenic epistatic interactions could be a reflection of the existence of higher order epistatic interactions, meaning that the number of epistatic interactions may still be underestimated in the present study, as we restricted our analysis to digenic interactions.

Currently the contribution of higher order interactions can not be estimated since there is no available software handling such a complex issue. Moreover a population of 250 doubled haploid lines is not big enough to resolve higher order epistatic interactions as pointed out by Mei et al. (2005) and Zhao et al. (2005).