In the following, description of QTL and digenic epistatic effects detection models:
2.7.1 QTLs detection
According to Bauer et al. (2009), the forward selection strategy is very effective to detect QTLs influencing the interested traits. We used a multiple QTL model iteratively extended and reduced by forward selection and backward elimination, respectively, using the PROC MIXED procedure in SAS software (SAS version 9.2, SAS, 2008). In each round of the forward selection process, the selection of the most significant and informative marker was added as a fixed factor (QTL) into the model according to the F value with the probability of false discovery rate (FDR ≤ 0.05) and then all remaining markers were scanned with the respective model containing the previously found QTLs. The process of the following iterations of this model was continued until no more additional QTL could be detected. The detection of QTL for studied traits was carried out using the following mixed hierarchical model in the MIXED procedure as starting point of forward selection process:
X
ijklmn=μ+M
i+L
j(M
i)+T
k+L
j*T
k+M
i*T
k+Y
l+T
k*Y
l+B
m(T
k*Y
l)+ε
n(ijklm),
where the total of phenotypic value was sum of general mean μ, fixed effect Mi of the i-th marker genotype, random effect
L
j(M
i)
of the j-th DH line nested in the i-th marker genotype, fixed effect Tk of the k-th treatment, fixed interaction effectL
j*T
k of the j-th DH line and the- 40 -
k-th treatment, fixed interaction effect
M
i*T
k of the i-th marker genotype and the k-th treatment, fixed effect Yl of the l-th year, fixed interaction effectT
k*Y
l of the k-th treatment and l-th year, random effectB
m(T
k*Y
l)
of m-th block nested in treatment and years, residueε
n(ijklm) ofX
ijklmn. P values from F-tests were adjusted genome-wide across all single marker tests using the false discovery rate (FDR). The significant marker main effects as well as marker × treatment interaction with PFDR ≤ 0.05 were accepted as putative QTLs for the next iteration, however, the final model was:X
ijklmn=μ+∑QTL+M
i+L
j(M
i)+T
k+L
j*T
k+M
i*T
k+Y
l+T
k*Y
l+B
m(T
k*Y
l)+ε
n(ijklm),
where ∑QTL represents the detected QTLs from the forward/backward selection process.2.7.2 Digenic epistatic effects
The digenic epistatic interactions between all DArT and SSR marker pairs were tested with SAS procedure MIXED (SAS ver. 9.2, SAS Institute, 2008) using the following mixed hierarchical model:
X
ijklmno=μ+∑QTL+M1
i+M2
j+M1
i*M2
j+L
k(M1
i*M2
j)+T
l+L
j*T
k+Y
m+T
l*Y
m+B
n
(T
l*Y
m)+ε
o (ijklmn),
Here M1i and M2j are the fixed effects of the i-th marker and j-th marker (M2). M1i*M2j is the
fixed interaction effect of the i-th M1 genotype with j-th M2 genotype, Lk(M1i*M2j) is the
random effect of the k-th BC2DH line nested in the i-th M1 and j-th M2 marker genotype interaction.
2.7.3 Calculation of relative performance of the exotic parent ( RP[Hsp])
To evaluate the performance of the homozygous exotic genotype (ISR 42-8) under drought conditions, the relative performance RP [Hsp] was computed by
RP[Hsp]=(([ Hsp]-[ Hv])/[ Hv])*100,
where [Hsp] represents LS-means of the homozygous exotic genotype and [Hv] LS-means of the elite genotype.
According to the relative performance of the exotic genotype (ISR 42-8), if it improves or debases the trait under drought conditions as well as matching with the breeding goals of drought tolerance at a given marker locus, the marker main effects as well as their interaction with the treatments were characterized as favorable or unfavorable QTL.
- 41 -
2.7.4 Calculation of the coefficient of determination (R2)
In order to explain the strength of the marker main effect (
R
2M) and the marker-treatment interaction (R
2M*T), the coefficient of determination was calculated to each as follow:R
2M=SS
M/SS
L, R
2
M*T
=SS
M*T/SS
L*TWhere, SSM, SSM*T andSSL*T represent the sum of squares of the marker main effect, the marker-treatment interaction and doubled haploid lines-treatment interaction, respectively.
- 42 -
3 Results
Since the developing of the advanced backcross quantitative trait locus (AB-QTL) mapping approach by Tanksley and Nelson (1996a) which allows a targeted transfer of favorable exotic alleles into elite breeding material, several studies have applied this strategy on different crops. In this study, the main aim was to identify the effects of exotic QTL alleles on drought tolerance related traits which were introgressed from exotic accessions into BC2DH lines of the population S42 which derived from crossing between a German elite cultivar of H.
vulgare ssp. vulgare „Scarlett‟ with an exotic accession of H. vulgare ssp. spontaneum
„ISR42-8‟. The population has been evaluated in a plastic tunnel for 15 traits under control and drought stress conditions in three successive summer seasons (2007, 2008 and 2009). A total of 15 quantitative traits were investigated for drought tolerance. The investigated traits, abbreviations, units, tested seasons and breeding goals are described in Table (1). The population was genotyped with 106 SSRs, 255 DArT and 10 gene-specific DNA markers in order to perform QTL analysis. In this chapter, the evaluation of the performance of the doubled haploid lines as well as their parents and the main effect and the interactions of the QTLs were described.