Genotyping of single nucleotide polymorphism using model-based clustering

• Published in: Bioinformatics Vol. 20; no. 5; pp. 718 - 726
• Main Authors: Fujisawa, H., Eguchi, S., Ushijima, M., Miyata, S., Miki, Y., Muto, T., Matsuura, M.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 22-03-2004; Oxford Publishing Limited (England)
• Subjects: Algorithms; Base Sequence; Biological and medical sciences; Cluster Analysis; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; General aspects; Genotype; Likelihood Functions; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Genetic; Models, Statistical; Pattern Recognition, Automated; Polymorphism, Single Nucleotide - genetics; Sequence Alignment - methods; Sequence Analysis, DNA - methods; Sequence Homology, Nucleic Acid; Statistical method; High throughput screening; Typing; Biological marker; Genotype; Models; Single nucleotide polymorphism; Flexibility; Result
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btg475

Motivation: Single nucleotide polymorphisms have been investigated as biological markers and the representative high-throughput genotyping method is a combination of the Invader assay and a statistical clustering method. A typical statistical clustering method is the k-means method, but it often fails because of the lack of flexibility. An alternative fast and reliable method is therefore desirable. Results: This paper proposes a model-based clustering method using a normal mixture model and a well-conceived penalized likelihood. The proposed method can judge unclear genotypings to be re-examined and also work well even when the number of clusters is unknown. Some results are illustrated and then satisfactory genotypings are shown. Even when the conventional maximum likelihood method and the typical k-means clustering method failed, the proposed method succeeded.