Limits...
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.

Show MeSH

Related in: MedlinePlus

Determination of the RHS cutoff and the probability that the disease-causing gene is contained in RHSs. (A) The false negative rate (Rfalse negative) and the false positive rate (Rfalse positive) were calculated using equations 3 and 7 using the genotyping data for 5 α1-antitrypsin deficiency patients. The false negative rate shown is for a child from a first-cousin marriage (m + n = 6). (B) The probability that RHSs contain the disease gene (PGeneIsInRHS) calculated for a child from a first-cousin marriage. The coefficient of consanguinity (F) used was 1/16, which was calculated according to Wright {Wright, S. Systems of Mating. V. General Considerations Genetics 1921: 6:167-178}. F can be more precisely calculated as the total length of RHSs divided by the total length of the autosomes for the actual calculation (equation 9). PGeneIsInRHS varies depending on the frequency of the gene in the population.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2957688&req=5

Figure 2: Determination of the RHS cutoff and the probability that the disease-causing gene is contained in RHSs. (A) The false negative rate (Rfalse negative) and the false positive rate (Rfalse positive) were calculated using equations 3 and 7 using the genotyping data for 5 α1-antitrypsin deficiency patients. The false negative rate shown is for a child from a first-cousin marriage (m + n = 6). (B) The probability that RHSs contain the disease gene (PGeneIsInRHS) calculated for a child from a first-cousin marriage. The coefficient of consanguinity (F) used was 1/16, which was calculated according to Wright {Wright, S. Systems of Mating. V. General Considerations Genetics 1921: 6:167-178}. F can be more precisely calculated as the total length of RHSs divided by the total length of the autosomes for the actual calculation (equation 9). PGeneIsInRHS varies depending on the frequency of the gene in the population.

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).


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

- BMC Bioinformatics (2010)

Determination of the RHS cutoff and the probability that the disease-causing gene is contained in RHSs. (A) The false negative rate (Rfalse negative) and the false positive rate (Rfalse positive) were calculated using equations 3 and 7 using the genotyping data for 5 α1-antitrypsin deficiency patients. The false negative rate shown is for a child from a first-cousin marriage (m + n = 6). (B) The probability that RHSs contain the disease gene (PGeneIsInRHS) calculated for a child from a first-cousin marriage. The coefficient of consanguinity (F) used was 1/16, which was calculated according to Wright {Wright, S. Systems of Mating. V. General Considerations Genetics 1921: 6:167-178}. F can be more precisely calculated as the total length of RHSs divided by the total length of the autosomes for the actual calculation (equation 9). PGeneIsInRHS varies depending on the frequency of the gene in the population.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Determination of the RHS cutoff and the probability that the disease-causing gene is contained in RHSs. (A) The false negative rate (Rfalse negative) and the false positive rate (Rfalse positive) were calculated using equations 3 and 7 using the genotyping data for 5 α1-antitrypsin deficiency patients. The false negative rate shown is for a child from a first-cousin marriage (m + n = 6). (B) The probability that RHSs contain the disease gene (PGeneIsInRHS) calculated for a child from a first-cousin marriage. The coefficient of consanguinity (F) used was 1/16, which was calculated according to Wright {Wright, S. Systems of Mating. V. General Considerations Genetics 1921: 6:167-178}. F can be more precisely calculated as the total length of RHSs divided by the total length of the autosomes for the actual calculation (equation 9). PGeneIsInRHS varies depending on the frequency of the gene in the population.
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).

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
Related in: MedlinePlus