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A quantitatively-modeled homozygosity mapping algorithm, qHomozygosityMapping, utilizing whole genome single nucleotide polymorphism genotyping data.

- BMC Bioinformatics (2010)

Bottom Line: The genotyping error correction restored an average of 94.2% of the total length of all regions with run of homozygous SNPs, and 99.9% of the total length of them that were longer than 2 cM.At the end of the analysis, we would know the probability that regions identified contain a disease-causing gene, and we would be able to determine how much effort should be devoted to scrutinizing the regions.Our procedure will accelerate the identification of disease-causing genes using high-density SNP array data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Respiratory Medicine, Saitama Medical University, 38 Morohongo, Moroyama, Saitama 350-0495, Japan.

ABSTRACT
Homozygosity mapping is a powerful procedure that is capable of detecting recessive disease-causing genes in a few patients from families with a history of inbreeding. We report here a homozygosity mapping algorithm for high-density single nucleotide polymorphism arrays that is able to (i) correct genotyping errors, (ii) search for autozygous segments genome-wide through regions with runs of homozygous SNPs, (iii) check the validity of the inbreeding history, and (iv) calculate the probability of the disease-causing gene being located in the regions identified. The genotyping error correction restored an average of 94.2% of the total length of all regions with run of homozygous SNPs, and 99.9% of the total length of them that were longer than 2 cM. At the end of the analysis, we would know the probability that regions identified contain a disease-causing gene, and we would be able to determine how much effort should be devoted to scrutinizing the regions. We confirmed the power of this algorithm using 6 patients with Siiyama-type α1-antitrypsin deficiency, a rare autosomal recessive disease in Japan. Our procedure will accelerate the identification of disease-causing genes using high-density SNP array data.

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Genotyping error corrections. (A) RHSs for NA18987. (B) RHSs detected after introducing genotyping errors to 2,105 SNPs. (C) RHSs after the genotyping error correction algorithm was applied.
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Figure 3: Genotyping error corrections. (A) RHSs for NA18987. (B) RHSs detected after introducing genotyping errors to 2,105 SNPs. (C) RHSs after the genotyping error correction algorithm was applied.

Mentions: The power of the genotyping error correction algorithm was investigated using genotyping data for subject NA18987 (female) from HapMap JPT. The subject was independently genotyped in HapMap draft 3 and by Affymetrix, and data were made public from both sources. A comparison of these 2 datasets revealed that the genotyping results for 701,753 SNPs matched between these 2 sources, and they were therefore considered highly accurate. Using the matched data, RHSs were obtained with an RHS cutoff value of 0.6 cM (Figure 3A). The presence of a long RHS (36.2 cM at maximum) suggested that she had a family history of inbreeding, as described later. Considering the fact that the manufacturer (Affymetrix) claimed that the genotyping error rate for the SNP Array 6.0 is less than 0.003, we randomly introduced errors into selected 2,105 SNPs (701,753 SNPs × 0.003) and obtained RHSs. These error hampered the detection of RHSs, especially the long ones (Figure 3B). Following application of the genotyping error correction algorithm (Figure 1C), RHSs were restored (Figure 3C). The same trial repeated 100 times revealed that the genotyping error correction restored an average of 94.2% of the total length of all RHSs, and 99.9% of the total length of RHSs that were longer than 2 cM. This indicated that 99.9% of the total length of ASs resulting from first- or second cousin marriages would be correctly detected as RHSs after the correction. The total length of the regions that were erroneously detected as RHSs amounted to only 0.2% of the total length of the autosomes. These results indicated that the performance of the genotyping error correction algorithm was excellent.


A quantitatively-modeled homozygosity mapping algorithm, qHomozygosityMapping, utilizing whole genome single nucleotide polymorphism genotyping data.

- BMC Bioinformatics (2010)

Genotyping error corrections. (A) RHSs for NA18987. (B) RHSs detected after introducing genotyping errors to 2,105 SNPs. (C) RHSs after the genotyping error correction algorithm was applied.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2957688&req=5

Figure 3: Genotyping error corrections. (A) RHSs for NA18987. (B) RHSs detected after introducing genotyping errors to 2,105 SNPs. (C) RHSs after the genotyping error correction algorithm was applied.
Mentions: The power of the genotyping error correction algorithm was investigated using genotyping data for subject NA18987 (female) from HapMap JPT. The subject was independently genotyped in HapMap draft 3 and by Affymetrix, and data were made public from both sources. A comparison of these 2 datasets revealed that the genotyping results for 701,753 SNPs matched between these 2 sources, and they were therefore considered highly accurate. Using the matched data, RHSs were obtained with an RHS cutoff value of 0.6 cM (Figure 3A). The presence of a long RHS (36.2 cM at maximum) suggested that she had a family history of inbreeding, as described later. Considering the fact that the manufacturer (Affymetrix) claimed that the genotyping error rate for the SNP Array 6.0 is less than 0.003, we randomly introduced errors into selected 2,105 SNPs (701,753 SNPs × 0.003) and obtained RHSs. These error hampered the detection of RHSs, especially the long ones (Figure 3B). Following application of the genotyping error correction algorithm (Figure 1C), RHSs were restored (Figure 3C). The same trial repeated 100 times revealed that the genotyping error correction restored an average of 94.2% of the total length of all RHSs, and 99.9% of the total length of RHSs that were longer than 2 cM. This indicated that 99.9% of the total length of ASs resulting from first- or second cousin marriages would be correctly detected as RHSs after the correction. The total length of the regions that were erroneously detected as RHSs amounted to only 0.2% of the total length of the autosomes. These results indicated that the performance of the genotyping error correction algorithm was excellent.

Bottom Line: The genotyping error correction restored an average of 94.2% of the total length of all regions with run of homozygous SNPs, and 99.9% of the total length of them that were longer than 2 cM.At the end of the analysis, we would know the probability that regions identified contain a disease-causing gene, and we would be able to determine how much effort should be devoted to scrutinizing the regions.Our procedure will accelerate the identification of disease-causing genes using high-density SNP array data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Respiratory Medicine, Saitama Medical University, 38 Morohongo, Moroyama, Saitama 350-0495, Japan.

ABSTRACT
Homozygosity mapping is a powerful procedure that is capable of detecting recessive disease-causing genes in a few patients from families with a history of inbreeding. We report here a homozygosity mapping algorithm for high-density single nucleotide polymorphism arrays that is able to (i) correct genotyping errors, (ii) search for autozygous segments genome-wide through regions with runs of homozygous SNPs, (iii) check the validity of the inbreeding history, and (iv) calculate the probability of the disease-causing gene being located in the regions identified. The genotyping error correction restored an average of 94.2% of the total length of all regions with run of homozygous SNPs, and 99.9% of the total length of them that were longer than 2 cM. At the end of the analysis, we would know the probability that regions identified contain a disease-causing gene, and we would be able to determine how much effort should be devoted to scrutinizing the regions. We confirmed the power of this algorithm using 6 patients with Siiyama-type α1-antitrypsin deficiency, a rare autosomal recessive disease in Japan. Our procedure will accelerate the identification of disease-causing genes using high-density SNP array data.

Show MeSH