A quantitatively-modeled homozygosity mapping algorithm, qHomozygosityMapping, utilizing whole genome single nucleotide polymorphism genotyping data
A quantitatively-modeled homozygosity mapping algorithm, qHomozygosityMapping, utilizing whole genome single nucleotide polymorphism genotyping data
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DOI：
10.1186/1471-2105-11-s7-s5
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发表时间：
2010-10-15
影响因子：
3
通讯作者：
Hagiwara, Koichi
中科院分区：
文献类型：
--
作者：
Huqun;Fukuyama, Shun-ichiro;Hagiwara, Koichi
纯合性作图是一种强大的方法，能够检测来自有近亲繁殖家族史的少数患者的隐性致病基因。我们在此报告了一种用于高密度单核苷酸多态性阵列的纯合性作图算法，该算法能够（i）纠正基因分型错误，（ii）通过纯合SNP运行的区域在全基因组范围内搜索自合片段，（iii）检查近交历史的有效性，以及（iv）计算致病基因位于所识别区域的概率。基因分型误差校正平均恢复了所有纯合SNP区域总长度的94.2%，以及长度超过2 cM的区域总长度的99.9%。在分析结束时，我们将知道所识别的区域包含致病基因的概率，并且我们将能够确定应该投入多少精力来仔细检查这些区域。我们通过 6 名患有 Siiyama 型 α1-抗胰蛋白酶缺乏症（日本罕见的常染色体隐性遗传病）的患者证实了该算法的功效。我们的程序将利用高密度 SNP 阵列数据加速致病基因的识别。
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 alpha 1-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.