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Detection of genome-wide polymorphisms in the AT-rich Plasmodium falciparum genome using a high-density microarray.

Jiang H, Yi M, Mu J, Zhang L, Ivens A, Klimczak LJ, Huyen Y, Stephens RM, Su XZ - BMC Genomics (2008)

Bottom Line: Genomic copy number variation (CNV) among the parasites was also cataloged and compared.A large number of mSFP were discovered from the P. falciparum genome using a high-density microarray, most of which were in clusters of highly polymorphic genes at chromosome ends.Our method for accurate mSFP detection and the mSFP identified will greatly facilitate large-scale studies of genome variation in the P. falciparum parasite and provide useful resources for mapping important parasite traits.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA. hojiang@niaid.nih.gov

ABSTRACT

Background: Genetic mapping is a powerful method to identify mutations that cause drug resistance and other phenotypic changes in the human malaria parasite Plasmodium falciparum. For efficient mapping of a target gene, it is often necessary to genotype a large number of polymorphic markers. Currently, a community effort is underway to collect single nucleotide polymorphisms (SNP) from the parasite genome. Here we evaluate polymorphism detection accuracy of a high-density 'tiling' microarray with 2.56 million probes by comparing single feature polymorphisms (SFP) calls from the microarray with known SNP among parasite isolates.

Results: We found that probe GC content, SNP position in a probe, probe coverage, and signal ratio cutoff values were important factors for accurate detection of SFP in the parasite genome. We established a set of SFP calling parameters that could predict mSFP (SFP called by multiple overlapping probes) with high accuracy (> or = 94%) and identified 121,087 mSFP genome-wide from five parasite isolates including 40,354 unique mSFP (excluding those from multi-gene families) and approximately 18,000 new mSFP, producing a genetic map with an average of one unique mSFP per 570 bp. Genomic copy number variation (CNV) among the parasites was also cataloged and compared.

Conclusion: A large number of mSFP were discovered from the P. falciparum genome using a high-density microarray, most of which were in clusters of highly polymorphic genes at chromosome ends. Our method for accurate mSFP detection and the mSFP identified will greatly facilitate large-scale studies of genome variation in the P. falciparum parasite and provide useful resources for mapping important parasite traits.

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Related in: MedlinePlus

Relationship of receiver operating characteristic (ROC) curve and Z-score values and estimates of SFP call rates. The black line is the ROC curve, and the red line is the Z-score curve. The vertical dash line indicates false positive rate (1-specificity) of 5%, and horizon lines point to a Z-score value of 1.5 and sensitivity level (call rate) of approximately 81%, respectively. The curves were generated using data from all replicates of hybridization. SFP calls were compared with known NIAID SNP described previously (see text).
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Figure 3: Relationship of receiver operating characteristic (ROC) curve and Z-score values and estimates of SFP call rates. The black line is the ROC curve, and the red line is the Z-score curve. The vertical dash line indicates false positive rate (1-specificity) of 5%, and horizon lines point to a Z-score value of 1.5 and sensitivity level (call rate) of approximately 81%, respectively. The curves were generated using data from all replicates of hybridization. SFP calls were compared with known NIAID SNP described previously (see text).

Mentions: To further test the reliability of our method in calling SFP, we also used a ROC curve to evaluate SFP calling accuracy and applied local pooled error (LPE) analysis to obtain Z-scores for calling SFP [30]. LPE generates corrected Z-scores that reduce Fp, which might result when sample variance happens to be low, by using a 'pooled' variance for all the probes that show similar intensities. The ROC curve is a graphic plot of sensitivity vs. (1-specificty) or fraction of true positive vs. the fraction of Fp [31]. As shown in Figure 3, if we allowed a Fp rate of approximately 2% (1-specificity), and at a Z-score of ~1.5, we could obtain a sensitivity of call rate ~81% genome-wide for data from 7G8, Dd2, and HB3.


Detection of genome-wide polymorphisms in the AT-rich Plasmodium falciparum genome using a high-density microarray.

Jiang H, Yi M, Mu J, Zhang L, Ivens A, Klimczak LJ, Huyen Y, Stephens RM, Su XZ - BMC Genomics (2008)

Relationship of receiver operating characteristic (ROC) curve and Z-score values and estimates of SFP call rates. The black line is the ROC curve, and the red line is the Z-score curve. The vertical dash line indicates false positive rate (1-specificity) of 5%, and horizon lines point to a Z-score value of 1.5 and sensitivity level (call rate) of approximately 81%, respectively. The curves were generated using data from all replicates of hybridization. SFP calls were compared with known NIAID SNP described previously (see text).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2543026&req=5

Figure 3: Relationship of receiver operating characteristic (ROC) curve and Z-score values and estimates of SFP call rates. The black line is the ROC curve, and the red line is the Z-score curve. The vertical dash line indicates false positive rate (1-specificity) of 5%, and horizon lines point to a Z-score value of 1.5 and sensitivity level (call rate) of approximately 81%, respectively. The curves were generated using data from all replicates of hybridization. SFP calls were compared with known NIAID SNP described previously (see text).
Mentions: To further test the reliability of our method in calling SFP, we also used a ROC curve to evaluate SFP calling accuracy and applied local pooled error (LPE) analysis to obtain Z-scores for calling SFP [30]. LPE generates corrected Z-scores that reduce Fp, which might result when sample variance happens to be low, by using a 'pooled' variance for all the probes that show similar intensities. The ROC curve is a graphic plot of sensitivity vs. (1-specificty) or fraction of true positive vs. the fraction of Fp [31]. As shown in Figure 3, if we allowed a Fp rate of approximately 2% (1-specificity), and at a Z-score of ~1.5, we could obtain a sensitivity of call rate ~81% genome-wide for data from 7G8, Dd2, and HB3.

Bottom Line: Genomic copy number variation (CNV) among the parasites was also cataloged and compared.A large number of mSFP were discovered from the P. falciparum genome using a high-density microarray, most of which were in clusters of highly polymorphic genes at chromosome ends.Our method for accurate mSFP detection and the mSFP identified will greatly facilitate large-scale studies of genome variation in the P. falciparum parasite and provide useful resources for mapping important parasite traits.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA. hojiang@niaid.nih.gov

ABSTRACT

Background: Genetic mapping is a powerful method to identify mutations that cause drug resistance and other phenotypic changes in the human malaria parasite Plasmodium falciparum. For efficient mapping of a target gene, it is often necessary to genotype a large number of polymorphic markers. Currently, a community effort is underway to collect single nucleotide polymorphisms (SNP) from the parasite genome. Here we evaluate polymorphism detection accuracy of a high-density 'tiling' microarray with 2.56 million probes by comparing single feature polymorphisms (SFP) calls from the microarray with known SNP among parasite isolates.

Results: We found that probe GC content, SNP position in a probe, probe coverage, and signal ratio cutoff values were important factors for accurate detection of SFP in the parasite genome. We established a set of SFP calling parameters that could predict mSFP (SFP called by multiple overlapping probes) with high accuracy (> or = 94%) and identified 121,087 mSFP genome-wide from five parasite isolates including 40,354 unique mSFP (excluding those from multi-gene families) and approximately 18,000 new mSFP, producing a genetic map with an average of one unique mSFP per 570 bp. Genomic copy number variation (CNV) among the parasites was also cataloged and compared.

Conclusion: A large number of mSFP were discovered from the P. falciparum genome using a high-density microarray, most of which were in clusters of highly polymorphic genes at chromosome ends. Our method for accurate mSFP detection and the mSFP identified will greatly facilitate large-scale studies of genome variation in the P. falciparum parasite and provide useful resources for mapping important parasite traits.

Show MeSH
Related in: MedlinePlus