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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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Case-control analysis. (A) The overlaps of RHSs for Patients 1-5. (B) The probability that the disease-causing gene is contained in the overlap (PGeneIsInRhsOverlap). The probability was calculated by multiplying PGeneIsInRHS for Patients 1-5. F for each patient was calculated as the total length of RHSs divided by the total length of the autosomes. (C) -log10(P) value obtained by a case-control analysis. The region pointed by an arrow attained the maximal value 16.47.
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Figure 5: Case-control analysis. (A) The overlaps of RHSs for Patients 1-5. (B) The probability that the disease-causing gene is contained in the overlap (PGeneIsInRhsOverlap). The probability was calculated by multiplying PGeneIsInRHS for Patients 1-5. F for each patient was calculated as the total length of RHSs divided by the total length of the autosomes. (C) -log10(P) value obtained by a case-control analysis. The region pointed by an arrow attained the maximal value 16.47.

Mentions: We then obtained the overlaps of the RHSs for Patients 1-5 whose parents were first cousins [Step (d)] (Figure 5A). The probability that these regions contained the disease-causing gene (PGeneIsInOverlap) was calculated by equation 10 and is shown in Figure 5B. The prevalence of Siiyama-type α1-antitrypsin deficiency is less than 1 in a million in Japan, and the frequency of the gene is suspected to be less than 0.001 in the general population, indicating that the overlaps likely contained the disease-causing gene.


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

- BMC Bioinformatics (2010)

Case-control analysis. (A) The overlaps of RHSs for Patients 1-5. (B) The probability that the disease-causing gene is contained in the overlap (PGeneIsInRhsOverlap). The probability was calculated by multiplying PGeneIsInRHS for Patients 1-5. F for each patient was calculated as the total length of RHSs divided by the total length of the autosomes. (C) -log10(P) value obtained by a case-control analysis. The region pointed by an arrow attained the maximal value 16.47.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Case-control analysis. (A) The overlaps of RHSs for Patients 1-5. (B) The probability that the disease-causing gene is contained in the overlap (PGeneIsInRhsOverlap). The probability was calculated by multiplying PGeneIsInRHS for Patients 1-5. F for each patient was calculated as the total length of RHSs divided by the total length of the autosomes. (C) -log10(P) value obtained by a case-control analysis. The region pointed by an arrow attained the maximal value 16.47.
Mentions: We then obtained the overlaps of the RHSs for Patients 1-5 whose parents were first cousins [Step (d)] (Figure 5A). The probability that these regions contained the disease-causing gene (PGeneIsInOverlap) was calculated by equation 10 and is shown in Figure 5B. The prevalence of Siiyama-type α1-antitrypsin deficiency is less than 1 in a million in Japan, and the frequency of the gene is suspected to be less than 0.001 in the general population, indicating that the overlaps likely contained the disease-causing gene.

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