title: Cluster analysis and association study of structured multilocus genotype data.
abstract: We propose an algorithm for testing association using structured multilocus genotype data. The algorithm implements the clustering of the data by a hierarchical clustering technique and a k-means algorithm. After clustering, the program analyzes all the clusters together using the Mantel-Haenszel (MH) test, by which common associations in the clusters are examined. To use the MH test, the number of subpopulations has to be determined. A method of cross-validation (CV) and the k-means algorithm are applied for estimating the number of subpopulations. The algorithm described was implemented in the computer program POPSTRUCT. In the simulation study, we found that when the two groups with different marker allele frequencies were combined, an inflation of the type I errors was observed. The inflation was more marked when the differences in the marker allele frequencies were larger, the difference in the minor allele frequencies at the disease locus was larger, and the genotype relative risk associated with the disease locus was higher. Our simulation study indicated that the MH test was efficient for decreasing type I errors and increasing the power compared with any test performed on each cluster. Then, we compared the results of STRUCTURE, a model-based method, and POPSTRUCT, a distance-based method. When two subgroups with different allele frequencies were mixed together at a high fixed ratio, POPSTRUCT was superior to STRUCTURE in classifying the combined population into the accurate clusters, each of which reflects one of the original groups.
authors.affiliation: Division of Statistical Genetics, Institute of Rheumatology, Tokyo Women's Medical University, 10-22 Kawada-cho, Shinjuku-ku, Tokyo, 162-0054, Japan. nakataka@ior.twmu.ac.jp.
authors.forename: Takahiro
authors.initials: T
authors.lastname: Nakamura
authors.affiliation: Division of Statistical Genetics, Institute of Rheumatology, Tokyo Women's Medical University, 10-22 Kawada-cho, Shinjuku-ku, Tokyo, 162-0054, Japan.
authors.forename: Akira
authors.initials: A
authors.lastname: Shoji
authors.affiliation: The Institute of Statistical Mathematics, Tokyo, Japan.
authors.forename: Hironori
authors.initials: H
authors.lastname: Fujisawa
authors.affiliation: Division of Statistical Genetics, Institute of Rheumatology, Tokyo Women's Medical University, 10-22 Kawada-cho, Shinjuku-ku, Tokyo, 162-0054, Japan.
authors.forename: Naoyuki
authors.initials: N
authors.lastname: Kamatani
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D016000:Cluster Analysis; D005787:Gene Frequency; D005838:Genotype; D006801:Humans; D012984:Software