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SleepyHollow02
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Untersuchte Arbeit:
Seite: 32, Zeilen: 1 ff.
Quelle: Mohamed 2009
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2.10.4 Marker-Trait association analysis

A major problem with association mapping is the presence of a population structure, which can lead to false positives and failure to detect genuine associations (i.e., false negatives), particularly in highly selfing species (Iwata et al. 2007). The association analysis was performed with a mixed linear model (MLM) including Q and K matrix using SAS Software version 9.2. This analysis was achieved to identify DArT markers which are associated with the Fusarium graminearum stress tolerance traits and heading date trait considering the population based on population structure and the relatedness relationships.

Two statistical models used as follows:

First one with year’s effect

Yijkmnf = μ + Yi + Mj + Yi*Mj + ΣPCAk + Am(Mj)Kn + Yi*Am(Mj)Kn + εf(ijkmn)

where μ is the general mean, Yi is the fixed effect of the i-th Year, Mj is the fixed effect of j-th marker, Yi*Mj is the fixed interaction of i-th year with j-th marker, PCAk is the fixed effect of k-th subgroup of the population structure (PC values), Am(Mj)Kn is the random effect of m-th accession nested in the j-th marker associated with n-th kinship coefficient, Yi*Am(Mj)Kn is the random interaction effect of j-th year with m-th accession nested in the j-th marker associated with nth kinship coefficient, εf(ijkmn) is the error.

Second one with the fungus isolates effect

Yijkmnf = μ + Ti + Mj + Ti*Mj+ ΣPCAk + Am(Mi)Kn + Ti*Am(Mj)Kn + εf(ijkmn)

where μ is the general mean, Ti is the fixed effect of the i-th disease isolates, Mj is the fixed effect of j-th marker, Ti*Mj is the fixed interaction effect of j-th disease isolates with l-th marker, PCAk is the fixed effect of k-th subgroup of the population structure (PC values), Mj is the fixed effect of l-th marker, Am(Ml)Kn is the random effect of mth accession nested in the l-th marker associated with n-th kinship coefficient , Ti*Am(Mj)Kn is the random interaction effect of i-th disease isolates with m-th accession nested in the j-th marker associated with n-th kinship coefficient, εf(ijkmn) is the error.

2.6.3 Marker-Trait association analysis

A major problem with association mapping is the presence of a population structure, which can lead to false positives and failure to detect genuine associations (i.e. false negatives), particularly in highly selfing species (Iwata et al. 2007). Therefore we include the relatedness relationships and population structure of the tested accessions using the Q and K matrixes in our analysis. The association analysis was performed with a mixed linear model (MLM) using “ASReml” Software version 2 according to Stich et al. (2008). The statistical model for the association analysis identifying DArT markers which are associated with the tested drought tolerance traits considering population structure and the relatedness relationships is as follows:

Yijklmnf = μ + Yi + Tj + Yi*Tj + Qk + ML + Yi*ML + Tj*ML + Yi*Tj*ML + Am(ML)Kn + Yi*Am(ML)Kn + Tj*Am(ML)Kn + εf(ijkmn)

where μ is the general mean, Yi is the fixed effect of the ith Year, Tj is the fixed effect of the jth Drought treatment, Yi*Tj is the fixed interaction effect of ith year with jth drought treatment, Qk is the fixed effect of kth subgroup of the population structure (Q matrix), ML is the fixed effect of Lth marker, Yi*ML is the fixed interaction of ith year with Lth marker, Tj*ML is the fixed interaction effect of jth drought treatment with Lth marker, Yi*Tj*ML is the fixed interaction effect of ith year with jth drought treatment and Lth marker, Am(ML)Kn is the random effect of mth accession nested in the Lth marker associated with nth kinship coefficient , Yi*Am(ML)Kn is the random interaction effect of ith year with mth accession nested in the Lth marker associated with nth kinship coefficient, Tj*Am(ML)Kn is the random interaction effect of jth drought treatment with mth accession nested in the Lth marker associated with nth kinship coefficient, εf(ijkmn) is the error.

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