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Assessing the utility of whole-genome amplified serum DNA for array-based high throughput genotyping.

Bucasas KL, Pandya GA, Pradhan S, Fleischmann RD, Peterson SN, Belmont JW - BMC Genet. (2009)

Bottom Line: Heterozygote dropouts explained the majority (>85% in technical replicates, 50% in paired genomic/serum samples) of discordant results.Genotyping performance on WGA serum DNA samples was improved by implementation of Corrected Robust Linear Model with Maximum Likelihood Classification (CRLMM) algorithm but at the loss of many samples which failed to pass its quality threshold.We conclude that while it is possible to extract genomic DNA and subsequently perform whole-genome amplification from archived serum samples, WGA serum DNA did not perform well and appeared unsuitable for high-resolution genotyping on these arrays.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Immunology, Baylor College of Medicine, Houston, TX 77030, USA. lacuesta@bcm.edu

ABSTRACT

Background: Whole genome amplification (WGA) offers new possibilities for genome-wide association studies where limited DNA samples have been collected. This study provides a realistic and high-precision assessment of WGA DNA genotyping performance from 20-year old archived serum samples using the Affymetrix Genome-Wide Human SNP Array 6.0 (SNP6.0) platform.

Results: Whole-genome amplified (WGA) DNA samples from 45 archived serum replicates and 5 fresh sera paired with non-amplified genomic DNA were genotyped in duplicate. All genotyped samples passed the imposed QC thresholds for quantity and quality. In general, WGA serum DNA samples produced low call rates (45.00 +/- 2.69%), although reproducibility for successfully called markers was favorable (concordance = 95.61 +/- 4.39%). Heterozygote dropouts explained the majority (>85% in technical replicates, 50% in paired genomic/serum samples) of discordant results. Genotyping performance on WGA serum DNA samples was improved by implementation of Corrected Robust Linear Model with Maximum Likelihood Classification (CRLMM) algorithm but at the loss of many samples which failed to pass its quality threshold. Poor genotype clustering was evident in the samples that failed the CRLMM confidence threshold.

Conclusions: We conclude that while it is possible to extract genomic DNA and subsequently perform whole-genome amplification from archived serum samples, WGA serum DNA did not perform well and appeared unsuitable for high-resolution genotyping on these arrays.

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Chip quality assessment of good performing sample vs bad performing sample on SNP 6.0 genotyping platform. Scattergram plots of probe intensity values for allele B (Theta B) against allele A (Theta B) were generated from arrays of a (a) non-amplified genomic DNA sample, (b) good performing WGA DNA from serum and (c) bad performing WGA DNA from serum. Each point in the plot represents an assayed marker on the SNP 6.0 platform. Dark areas in the plot represent space with high-density points whereas light areas represent space with low-density points.
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Figure 5: Chip quality assessment of good performing sample vs bad performing sample on SNP 6.0 genotyping platform. Scattergram plots of probe intensity values for allele B (Theta B) against allele A (Theta B) were generated from arrays of a (a) non-amplified genomic DNA sample, (b) good performing WGA DNA from serum and (c) bad performing WGA DNA from serum. Each point in the plot represents an assayed marker on the SNP 6.0 platform. Dark areas in the plot represent space with high-density points whereas light areas represent space with low-density points.

Mentions: To investigate the basis for sample rejection by CRLMM, we visually assessed the chip quality of WGA samples. Signal intensities from probe A (Theta A) and probe B (Theta B) of SNPs assayed on SNP6.0 platform were plotted to capture the extent of differences between the intensity values AA, AB and BB genotypes. In figure 5, we show that sample performance correlated well with the separation of genotype clusters on the chip of a given sample. Genotyped samples with high confidence values and high call rates, such as genomic DNA (Figure 5a) or a representative successful WGA serum DNA (Figure 5b), showed three distinct clusters, corresponding to the three genotypes. On the other hand, very poor separation of clusters is the hallmark of the poor-performing and rejected WGA DNA samples (Figure 5c).


Assessing the utility of whole-genome amplified serum DNA for array-based high throughput genotyping.

Bucasas KL, Pandya GA, Pradhan S, Fleischmann RD, Peterson SN, Belmont JW - BMC Genet. (2009)

Chip quality assessment of good performing sample vs bad performing sample on SNP 6.0 genotyping platform. Scattergram plots of probe intensity values for allele B (Theta B) against allele A (Theta B) were generated from arrays of a (a) non-amplified genomic DNA sample, (b) good performing WGA DNA from serum and (c) bad performing WGA DNA from serum. Each point in the plot represents an assayed marker on the SNP 6.0 platform. Dark areas in the plot represent space with high-density points whereas light areas represent space with low-density points.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2803178&req=5

Figure 5: Chip quality assessment of good performing sample vs bad performing sample on SNP 6.0 genotyping platform. Scattergram plots of probe intensity values for allele B (Theta B) against allele A (Theta B) were generated from arrays of a (a) non-amplified genomic DNA sample, (b) good performing WGA DNA from serum and (c) bad performing WGA DNA from serum. Each point in the plot represents an assayed marker on the SNP 6.0 platform. Dark areas in the plot represent space with high-density points whereas light areas represent space with low-density points.
Mentions: To investigate the basis for sample rejection by CRLMM, we visually assessed the chip quality of WGA samples. Signal intensities from probe A (Theta A) and probe B (Theta B) of SNPs assayed on SNP6.0 platform were plotted to capture the extent of differences between the intensity values AA, AB and BB genotypes. In figure 5, we show that sample performance correlated well with the separation of genotype clusters on the chip of a given sample. Genotyped samples with high confidence values and high call rates, such as genomic DNA (Figure 5a) or a representative successful WGA serum DNA (Figure 5b), showed three distinct clusters, corresponding to the three genotypes. On the other hand, very poor separation of clusters is the hallmark of the poor-performing and rejected WGA DNA samples (Figure 5c).

Bottom Line: Heterozygote dropouts explained the majority (>85% in technical replicates, 50% in paired genomic/serum samples) of discordant results.Genotyping performance on WGA serum DNA samples was improved by implementation of Corrected Robust Linear Model with Maximum Likelihood Classification (CRLMM) algorithm but at the loss of many samples which failed to pass its quality threshold.We conclude that while it is possible to extract genomic DNA and subsequently perform whole-genome amplification from archived serum samples, WGA serum DNA did not perform well and appeared unsuitable for high-resolution genotyping on these arrays.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Immunology, Baylor College of Medicine, Houston, TX 77030, USA. lacuesta@bcm.edu

ABSTRACT

Background: Whole genome amplification (WGA) offers new possibilities for genome-wide association studies where limited DNA samples have been collected. This study provides a realistic and high-precision assessment of WGA DNA genotyping performance from 20-year old archived serum samples using the Affymetrix Genome-Wide Human SNP Array 6.0 (SNP6.0) platform.

Results: Whole-genome amplified (WGA) DNA samples from 45 archived serum replicates and 5 fresh sera paired with non-amplified genomic DNA were genotyped in duplicate. All genotyped samples passed the imposed QC thresholds for quantity and quality. In general, WGA serum DNA samples produced low call rates (45.00 +/- 2.69%), although reproducibility for successfully called markers was favorable (concordance = 95.61 +/- 4.39%). Heterozygote dropouts explained the majority (>85% in technical replicates, 50% in paired genomic/serum samples) of discordant results. Genotyping performance on WGA serum DNA samples was improved by implementation of Corrected Robust Linear Model with Maximum Likelihood Classification (CRLMM) algorithm but at the loss of many samples which failed to pass its quality threshold. Poor genotype clustering was evident in the samples that failed the CRLMM confidence threshold.

Conclusions: We conclude that while it is possible to extract genomic DNA and subsequently perform whole-genome amplification from archived serum samples, WGA serum DNA did not perform well and appeared unsuitable for high-resolution genotyping on these arrays.

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