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MPDA: microarray pooled DNA analyzer.

Yang HC, Huang MC, Li LH, Lin CH, Yu AL, Diccianni MB, Wu JY, Chen YT, Fann CS - BMC Bioinformatics (2008)

Bottom Line: This new technique uses pooled DNA, thereby reducing costs associated with the typing of DNA from numerous individuals.Thus, the technique provides an overall cost-effective solution for large-scale genomic/genetic research.The third analysis indicates that MPDA is cost-effective and reliable for association mapping.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. hsinchou@stat.sinica.edu.tw

ABSTRACT

Background: Microarray-based pooled DNA experiments that combine the merits of DNA pooling and gene chip technology constitute a pivotal advance in biotechnology. This new technique uses pooled DNA, thereby reducing costs associated with the typing of DNA from numerous individuals. Moreover, use of an oligonucleotide gene chip reduces costs related to processing various DNA segments (e.g., primers, reagents). Thus, the technique provides an overall cost-effective solution for large-scale genomic/genetic research. However, few publicly shared tools are available to systematically analyze the rapidly accumulating volume of whole-genome pooled DNA data.

Results: We propose a generalized concept of pooled DNA and present a user-friendly tool named Microarray Pooled DNA Analyzer (MPDA) that we developed to analyze hybridization intensity data from microarray-based pooled DNA experiments. MPDA enables whole-genome DNA preferential amplification/hybridization analysis, allele frequency estimation, association mapping, allelic imbalance detection, and permits integration with shared data resources online. Graphic and numerical outputs from MPDA support global and detailed inspection of large amounts of genomic data. Four whole-genome data analyses are used to illustrate the major functionalities of MPDA. The first analysis shows that MPDA can characterize genomic patterns of preferential amplification/hybridization and provide calibration information for pooled DNA data analysis. The second analysis demonstrates that MPDA can accurately estimate allele frequencies. The third analysis indicates that MPDA is cost-effective and reliable for association mapping. The final analysis shows that MPDA can identify regions of chromosomal aberration in cancer without paired-normal tissue.

Conclusion: MPDA, the software that integrates pooled DNA association analysis and allelic imbalance analysis, provides a convenient analysis system for extensive whole-genome pooled DNA data analysis. The software, user manual and illustrated examples are freely available online at the MPDA website listed in the Availability and requirements section.

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Related in: MedlinePlus

Genome-wide single-locus association tests based on individual genotyping data and pooled allelotyping data. In each subfigure, the horizontal axis denotes the physical position of SNPs (scale in megabases, Mb), and the vertical axis denotes the Bonferroni-type adjusted p-value (i.e., the raw p-value multiplied by the number of association tests) in a -log10 scale. Individual genotyping data points are in blue, and pooled allelotyping data points are in red. Each black dashed line denotes the critical line, -log10 (p) = 2, for the Bonferroni-type multiple tests correction.
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Figure 4: Genome-wide single-locus association tests based on individual genotyping data and pooled allelotyping data. In each subfigure, the horizontal axis denotes the physical position of SNPs (scale in megabases, Mb), and the vertical axis denotes the Bonferroni-type adjusted p-value (i.e., the raw p-value multiplied by the number of association tests) in a -log10 scale. Individual genotyping data points are in blue, and pooled allelotyping data points are in red. Each black dashed line denotes the critical line, -log10 (p) = 2, for the Bonferroni-type multiple tests correction.

Mentions: Genome-wide single-locus association tests were carried out based on modified chi-square statistics of common CPAs, where binomial sampling error and CPA calibration error were calculated by MPDA. An experimental standard error of 0.02 was assigned; the standard error was derived from our previous experiments in which different pool sizes, multiple DNA formation, and chip replications were taken into consideration [21]. The pooled DNA single-locus p-values for SNP markers were then calculated to provide a marginal effect of a single locus. We also conducted single-locus allele-based association tests based on individual genotyping data to evaluate the performance of the population-level pooled-DNA association tests. Figure 4 presents the Bonferroni-type adjusted p-values in a -log10 scale across the human genome versus physical position by chromosome, where the raw (unadjusted) p-values were multiplied by the number of SNPs and then transformed to a -log10 scale. Because the adjusted p-values may be greater than 1, it was possible to get negative values after taking a -log10 transformation. Under a test size of 0.01 (i.e., the black reference line in Figure 4), results of whole-genome association mapping based on the data from pooled allelotyping and individual genotyping experiments were highly consistent except for a few loci. Note that, it is always necessary to carefully examine genotyping quality and attributes of the identified SNPs in order to reduce the possibility of false positives. In this example, the significant SNPs on chromosomes 1, 8, 10 and 13, which were identified by pooled allelotyping but not validated by individual genotyping, have either very low FDS or minor AF. This suggests that the statistical significance may be induced by measurement error. In this example, all calculations of single-locus association tests were finished within seven minutes.


MPDA: microarray pooled DNA analyzer.

Yang HC, Huang MC, Li LH, Lin CH, Yu AL, Diccianni MB, Wu JY, Chen YT, Fann CS - BMC Bioinformatics (2008)

Genome-wide single-locus association tests based on individual genotyping data and pooled allelotyping data. In each subfigure, the horizontal axis denotes the physical position of SNPs (scale in megabases, Mb), and the vertical axis denotes the Bonferroni-type adjusted p-value (i.e., the raw p-value multiplied by the number of association tests) in a -log10 scale. Individual genotyping data points are in blue, and pooled allelotyping data points are in red. Each black dashed line denotes the critical line, -log10 (p) = 2, for the Bonferroni-type multiple tests correction.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2387178&req=5

Figure 4: Genome-wide single-locus association tests based on individual genotyping data and pooled allelotyping data. In each subfigure, the horizontal axis denotes the physical position of SNPs (scale in megabases, Mb), and the vertical axis denotes the Bonferroni-type adjusted p-value (i.e., the raw p-value multiplied by the number of association tests) in a -log10 scale. Individual genotyping data points are in blue, and pooled allelotyping data points are in red. Each black dashed line denotes the critical line, -log10 (p) = 2, for the Bonferroni-type multiple tests correction.
Mentions: Genome-wide single-locus association tests were carried out based on modified chi-square statistics of common CPAs, where binomial sampling error and CPA calibration error were calculated by MPDA. An experimental standard error of 0.02 was assigned; the standard error was derived from our previous experiments in which different pool sizes, multiple DNA formation, and chip replications were taken into consideration [21]. The pooled DNA single-locus p-values for SNP markers were then calculated to provide a marginal effect of a single locus. We also conducted single-locus allele-based association tests based on individual genotyping data to evaluate the performance of the population-level pooled-DNA association tests. Figure 4 presents the Bonferroni-type adjusted p-values in a -log10 scale across the human genome versus physical position by chromosome, where the raw (unadjusted) p-values were multiplied by the number of SNPs and then transformed to a -log10 scale. Because the adjusted p-values may be greater than 1, it was possible to get negative values after taking a -log10 transformation. Under a test size of 0.01 (i.e., the black reference line in Figure 4), results of whole-genome association mapping based on the data from pooled allelotyping and individual genotyping experiments were highly consistent except for a few loci. Note that, it is always necessary to carefully examine genotyping quality and attributes of the identified SNPs in order to reduce the possibility of false positives. In this example, the significant SNPs on chromosomes 1, 8, 10 and 13, which were identified by pooled allelotyping but not validated by individual genotyping, have either very low FDS or minor AF. This suggests that the statistical significance may be induced by measurement error. In this example, all calculations of single-locus association tests were finished within seven minutes.

Bottom Line: This new technique uses pooled DNA, thereby reducing costs associated with the typing of DNA from numerous individuals.Thus, the technique provides an overall cost-effective solution for large-scale genomic/genetic research.The third analysis indicates that MPDA is cost-effective and reliable for association mapping.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. hsinchou@stat.sinica.edu.tw

ABSTRACT

Background: Microarray-based pooled DNA experiments that combine the merits of DNA pooling and gene chip technology constitute a pivotal advance in biotechnology. This new technique uses pooled DNA, thereby reducing costs associated with the typing of DNA from numerous individuals. Moreover, use of an oligonucleotide gene chip reduces costs related to processing various DNA segments (e.g., primers, reagents). Thus, the technique provides an overall cost-effective solution for large-scale genomic/genetic research. However, few publicly shared tools are available to systematically analyze the rapidly accumulating volume of whole-genome pooled DNA data.

Results: We propose a generalized concept of pooled DNA and present a user-friendly tool named Microarray Pooled DNA Analyzer (MPDA) that we developed to analyze hybridization intensity data from microarray-based pooled DNA experiments. MPDA enables whole-genome DNA preferential amplification/hybridization analysis, allele frequency estimation, association mapping, allelic imbalance detection, and permits integration with shared data resources online. Graphic and numerical outputs from MPDA support global and detailed inspection of large amounts of genomic data. Four whole-genome data analyses are used to illustrate the major functionalities of MPDA. The first analysis shows that MPDA can characterize genomic patterns of preferential amplification/hybridization and provide calibration information for pooled DNA data analysis. The second analysis demonstrates that MPDA can accurately estimate allele frequencies. The third analysis indicates that MPDA is cost-effective and reliable for association mapping. The final analysis shows that MPDA can identify regions of chromosomal aberration in cancer without paired-normal tissue.

Conclusion: MPDA, the software that integrates pooled DNA association analysis and allelic imbalance analysis, provides a convenient analysis system for extensive whole-genome pooled DNA data analysis. The software, user manual and illustrated examples are freely available online at the MPDA website listed in the Availability and requirements section.

Show MeSH
Related in: MedlinePlus