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KomplettPlagiat
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SleepyHollow02
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Untersuchte Arbeit:
Seite: 19, Zeilen: 1 ff. (komplett)
Quelle: Mohamed 2009
Seite(n): 21 f., Zeilen: 21: 3 ff.; 22: 11 ff.
The most difficulties and problems with association mapping is that population structure can lead to spurious association between a candidate marker and a phenotype. One common solution has been to abandon case-control studies in favour of family-based test of association, but this comes at a considerable cost in the need collect DNA from close relative of affected individuals (Pritchared et al. 2000).

Breseghello and Sorrells (2006) studied AM in wheat for identification of genetic markers associated with kernel morphology and milling quality. They used in their study a population of 149 cultivars of soft winter wheat (Triticum aestivum L.), were genotyping with 93 SSR markers, Association between markers and traits was tested using a linear mixed-effect model, where the marker being tested was considered as a fixed effects factor and subpopulation was considered as a random-effects factor. Significant markers were detected in the three chromosomes tested; kernel width was associated with the locus Xwmc111-2D in both Ohio (OH) and New York (NY) and with Xgwm30-2D in NY only. A tow-marker model including both loci was significantly (P = 0.0002) more informative for KW in NY than either marker separately. The locus Xgwm539-2D was associated with kernel length in NY, although in this location it did not achieve the corrected threshold. Six loci in the LD block near the centromere of 5A were associated with kernel area, length, and weight, but not with kernel width.

Yu et al. (2006) observed six gene expression phenotypes as phenotypic traits in mapping expression quantitative trait loci (eQTL). For the sample containing complex familial relationships and population structure, and they studied three quantitative traits measured on 277 diverse maize inbred lines, representing the diversity present in public breeding programs around the world. The population differentiation (Fst) among the major subgroups in our sample ranged from 0.047 (SSR) to 0.073 (SNP)., Although 80% of the pair-wise kinship estimates were close to 0, the remaining estimates were distributed from 0.05 to 1.0, as expected from complex familial relationship and population structure. Furthermore they found 37.6% of SNPs were associated with flowering time at P < 0.05 by the simple model, compared with 14.1% by the Q model, 6.1% by the K model and only 6.0% by the Q+K model. For flowering time and ear height, the Q+k model had the highest power. For ear diameter, the k model yielded a slightly higher power than the Q+k model did. The most benefit of the Q+K model is able to systematically account for multiple levels of relatedness among individuals.

The most difficulties and problems with association mapping is that population structure

can lead to spurious association between a candidate marker and a phenotype. One common solution has been to abandon case-control studies in favour of family-based test of association, but this comes at a considerable cost in the need collect DNA from close relative of affected individuals (Pritchared et al. 2000).

[...]

Yu et al. (2006) observed six gene expression phenotypes as phenotypic traits in mapping expression quantitative trait loci (eQTL). For the sample containing complex familial relationships and population structure, and they studied three quantitative traits measured on 277 diverse maize inbred lines, representing the diversity present in public breeding programs around the world. The population differentiation (Fst) among the major subgroups in our sample ranged from 0.047 (SSR) to 0.073 (SNP)., Although 80% of the pair-wise kinship estimates were close to 0, the remaining estimates were distributed from 0.05 to 1.0, as expected from complex familial relationship and population structure. Furthermore they found 37.6% of SNPs were associated with flowering time at P < 0.05 by the simple model, compared with 14.1% by the Q model, 6.1% by the K model and only 6.0% by the Q+K model. For flowering time and ear height, the Q+k model had the highest power. For ear diameter, the k model yielded a slightly higher power than the Q+k model did. The most benefit of the Q+K model is able to systematically account for multiple levels of relatedness among individuals.

[p.22:]

Breseghello and Sorrells (2006) studied Association mapping (AM) in wheat for identification of genetic markers associated with kernel morphology and milling quality. They used in their study a population of 149 cultivars of soft winter wheat (Triticum aestivum L.), were genotyping with 93 SSR markers, Association between markers and traits was tested using a linear mixed-effect model, where the marker being tested was considered as a fixed effects factor and subpopulation was considered as a randomeffects factor. Significant markers were detected in the three chromosomes tested; kernel width was associated with the locus Xwmc111-2D in both Ohio (OH) and New York (NY) and with Xgwm30-2D in NY only. A tow-marker model including both loci was significantly (P = 0.0002) more informative for KW in NY than either marker separately. The locus Xgwm539-2D was associated with kernel length in NY, although in this location it did not achieve the corrected threshold. Six loci in the LD block near the centromere of 5A were associated with kernel area, length, and weight, but not with kernel width.

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