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Untersuchte Arbeit:
Seite: 17, Zeilen: 14 ff.
Quelle: Mohamed 2009
Seite(n): 19, Zeilen: 3 ff.
Yu et al. (2006) developed new methodology, a mixed linear model (MLM) that combines both population structure information (Q-matrix) and level of pairwise relatedness coefficients—“kinship” (K-matrix) in the analysis, where the mixed linear model (MLM) approach found to be effective in removing the confounding effects of the population in association.

Overall approach of population-based association mapping in plants varies based on the methodology chosen, assuming structured population samples, the performance of association mapping includes the following steps as described by Abdurakhmonov and Abdukarimov (2008).

(1) Selection of a group of individuals from a natural population or germplasm collection with wide coverage of genetic diversity. (2) Recording or measuring the phenotypic characteristics (yield, quality, tolerance, or resistance) of selected population groups. (3) Genotyping a mapping population individuals with available molecular markers. (4) Assessment of the population structure (the level of genetic differentiation among groups within sampled population individuals) and kinship (coefficient of relatedness between pairs of each individual within a sample). And (5) based on information gained through population structure, correlation of phenotypic and genotypic/haplotypic data with the application of an appropriate statistical approach that reveals, consequently a specific gene(s) controlling a [QTL of interest can be cloned using the marker tags and annotated for an exact biological function. Association mapping offers three main advantages: increased mapping resolution, reduced research time, and greater allele numbers (Reich et al. 2001).]

4) recently, Yu et al. (2006) developed new methodology, a mixed linear model (MLM)

that combines both population structure information (Q-matrix) and level of pairwise relatedness coefficients—“kinship” (K-matrix) in the analysis, where the mixed linear model (MLM) approach found to be effective in removing the confounding effects of the population in association.

Although the overall approach of population-based association mapping in plants varies based on the methodology chosen (as above), assuming structured population samples, the performance of association mapping includes the following steps as described by Abdurakhmonov and Abdukarimov 2008. (1) Selection of a group of individuals from a natural population or germplasm collection with wide coverage of genetic diversity. (2) Recording or measuring the phenotypic characteristics (yield, quality, tolerance, or resistance) of selected population groups. (3) Genotyping a mapping population individuals with available molecular markers. (4) Assessment of the population structure (the level of genetic differentiation among groups within sampled population individuals) and kinship (coefficient of relatedness between pairs of each individual within a sample). And (5) based on information gained through population structure, correlation of phenotypic and genotypic/haplotypic data with the application of an appropriate statistical approach that reveals, consequently a specific gene(s) controlling a QTL of interest can be cloned using the marker tags and annotated for an exact biological function. [...] Compared to linkage mapping in traditional bioparental populations, association mapping offers three main advantages: increased mapping resolution, reduced research time, and greater allele numbers (Reich et al. 2001).

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