Source: Lai, C-Q., Leips, J., Zou, W., Roberts, J.F., Wollenberg, K.R., Parnell, L.D., Zeng, Z-B., Ordovas, J.M., and Mackay, T.F.C. Speed-mapping quantitative trait loci using microarrays. 2007. Nature Methods, 4(10): 839-41
The authors used microarrays to genotype a large number of individuals for a QTL study into longevity. Instead of individually genotyping and measuring the phenotype, the authors instead selected a subset of the population based on their phenotype (longevity). Then they pooled this subset’s DNA and ran it across a microarray that had oligos from both parents. They compared each marker hybridization with a young group that should be equally mixed for the alleles at each marker. A simple t-test was computed for each marker (with FDR correcting) to determine whether that marker had a skewed allele ratio between samples. Multiple QTLs were found, more so than using previous genotyping methods.
Showing posts with label genotyping. Show all posts
Showing posts with label genotyping. Show all posts
Monday, February 2, 2009
Tuesday, October 7, 2008
The Metabolome as Phenotype
Source: Fiehn, O., Kopka, J., Dormann, P., Altmann, T., Trethewey, R.N., and Willmitzer, L. Metabolite profiling for plant functional genomics. 2000. Nature Biotechnology, 18: 1157 – 61
The authors grew up three strains of Arabidopsis: one wild-type, one with a DGD mutation, and one with a stomatal density mutation. They used GC/MS to profile the strains with 300+ metabolites in an unbiased approach that picked up a number of unknown molecules. Nearly 60 compounds were found to be significantly different between wt and the DGD mutation. The authors used an unbiased approach and found that a number of unidentified compounds were also noticeably different. Each strain has a unique metabolic profile, as determined through principal component analysis.
Significant ( p < 0.01) metabolite differences, between dgd and wt, sdd1-1 and wt. Known or high-fold different metabolites shown.
Other notes:
-used internal standards for correcting and as normalization
-also normalized to 1 mg plant leaf fresh weight
-testing reproducibility in samples that were measured multiple times found deviations for same day analysis at 8 +/- 6% over 149 polar compounds
-tested biological variability and found it in clear excess of preparation variability, averaging 40%
-single gene change showed 153 of 326 significant differences in metabolites
“This analysis demonstrates the power of the metabolite profiling method to identify and quantify previously overlooked alterations, following a more comprehensive interpretation of the consequences of genetic modifications.”
The authors grew up three strains of Arabidopsis: one wild-type, one with a DGD mutation, and one with a stomatal density mutation. They used GC/MS to profile the strains with 300+ metabolites in an unbiased approach that picked up a number of unknown molecules. Nearly 60 compounds were found to be significantly different between wt and the DGD mutation. The authors used an unbiased approach and found that a number of unidentified compounds were also noticeably different. Each strain has a unique metabolic profile, as determined through principal component analysis.
Significant ( p < 0.01) metabolite differences, between dgd and wt, sdd1-1 and wt. Known or high-fold different metabolites shown.
Other notes:
-used internal standards for correcting and as normalization
-also normalized to 1 mg plant leaf fresh weight
-testing reproducibility in samples that were measured multiple times found deviations for same day analysis at 8 +/- 6% over 149 polar compounds
-tested biological variability and found it in clear excess of preparation variability, averaging 40%
-single gene change showed 153 of 326 significant differences in metabolites
“This analysis demonstrates the power of the metabolite profiling method to identify and quantify previously overlooked alterations, following a more comprehensive interpretation of the consequences of genetic modifications.”
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