A quantitatively-modeled homozygosity mapping algorithm, qHomozygosityMapping, utilizing whole genome single nucleotide polymorphism genotyping data.
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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.
Affiliation: Department of Respiratory Medicine, Saitama Medical University, 38 Morohongo, Moroyama, Saitama 350-0495, Japan.
ABSTRACT
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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. Related in: MedlinePlus |
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Mentions: The expected false negative and false positive rates for the SNP Array 6.0 from the Haldane's model were calculated by using equation 3 and 7 [Step (a)] (Figure 2A). We gave the priority to reducing the false positive rate than to reducing the false negative rate, because we empirically determined that it simplified the analysis. We chose 0.6 cM as the RHS cutoff value, at which the false negative rate was 0.0006 and the false positive rate was 0.0029. The probability that the RHSs contained the disease-causing gene (PGeneIsInRHS) at this condition was calculated using equation 8 (Figure 2B). |
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Affiliation: Department of Respiratory Medicine, Saitama Medical University, 38 Morohongo, Moroyama, Saitama 350-0495, Japan.