Pattern-based mining strategy to detect multi-locus association and gene x environment interaction.

Department of Computational Genetics, High Throughput Biology Inc, 513 West Mount Pleasant Avenue, Livingston, New Jersey 07039, USA. zli@htbiology.com.

ABSTRACT : As genome-wide association studies grow in popularity for the identification of genetic factors for common and rare diseases, analytical methods to comb through large numbers of genetic variants efficiently to identify disease association are increasingly in demand. We have developed a pattern-based data-mining approach to discover unlinked multilocus genetic effects for complex disease and to detect genotype x phenotype/genotype x environment interactions. On a densely mapped chromosome 18 data set for rheumatoid arthritis that was made available by Genetic Analysis Workshop 15, this method detected two potential two-locus associations as well as a putative two-locus gene x gender interaction.

PMID: 18466505 [PubMed - in process]

PMCID: PMC2367515