Mining associations between genetic markers, phenotypes, and covariates

We used Haplotype Pattern Mining, HPM [Toivonen et al., Am J Hum Genet 67:1 33-45, 2000], for gene localization in Genetic Analysis Workshop (GAW) 12 i solate data. In HPM, association is analyzed by searching all trait-associa ted haplotype patterns. Data mining algorithms are utilized to make the sea rch efficient. The strength of the haplotype-trait associations is measured by a linear model, into which a pre-selected set of covariates is incorpor ated. Marker-wise patterns of association are used for predicting the disea se gene location. Genome-wide scans of susceptibility genes for affection s tatus as well as for the quantitative traits (Q1-Q5) were performed. First analyses were made with small sample sizes, 63-94 trios per trait, which is compared with a pilot study of a larger complex disease-mapping project. S ubsequently, the analysis was repeated with approximately 600 cases and 600 controls per trait to give higher power to the analyses. With small sample sizes, only the susceptibility genes having the strongest effects on the t raits could be localized. The larger sample size gave very good results: al l susceptibility genes, except one, could be correctly localized. First exp eriments on candidate genes suggested that HPM is applicable even to fine m apping of mutations in DNA sequence. (C) 2001 Wiley-Liss, Inc.