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This article is part of the supplement: Ninth International Conference on Bioinformatics (InCoB2010): Bioinformatics

Open Access Proceedings

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

Huqun*12, Shun-ichiro Fukuyama1, Hiroyuki Morino3, Hiroshi Miyazawa1, Tomoaki Tanaka1, Tomoko Suzuki1, Masakazu Kohda4, Hideshi Kawakami3, Yasushi Okazaki4, Kuniaki Seyama5 and Koichi Hagiwara1*

Author Affiliations

1 Department of Respiratory Medicine, Saitama Medical University, 38 Morohongo, Moroyama, Saitama 350-0495, Japan

2 Department of Medical Oncology, The Affiliated Hospital of Inner Mongolia Medical College, Tong Dao Bei Jie, 010050 Hohhot, China

3 Department of Epidemiology, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima 734-8553, Japan

4 Division of Functional Genomics and Systems Medicine, Research Center for Genomic Medicine, Saitama Medical University, 1397-1 Yamane, Hidaka City, Saitama 350-1241, Japan

5 Department of Respiratory Medicine, Juntendo University, School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan

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BMC Bioinformatics 2010, 11(Suppl 7):S5  doi:10.1186/1471-2105-11-S7-S5

Published: 15 October 2010

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.