Gene hunting of the Genetic Analysis Workshop 16 rheumatoid arthritis data using rough set theory

Abstract

We propose to use the rough set theory to identify genes affecting rheumatoid arthritis risk from the data collected by the North American Rheumatoid Arthritis Consortium. For each gene, we employ generalized dynamic reducts in the rough set theory to select a subset of single-nucleotide polymorphisms (SNPs) to represent the genetic information from this gene. We then group the study subjects into different clusters based on their genotype similarity at the selected markers. Statistical association between disease status and cluster membership is then studied to identify genes associated with rheumatoid arthritis. Based on our proposed approach, we are able to identify a number of statistically significant genes associated with rheumatoid arthritis. Aside from genes on chromosome 6, our identified genes include known disease-associated genes such as PTPN22 and TRAF1. In addition, our list contains other biologically plausible genes, such as ADAM15 and AGPAT2. Our findings suggest that ADAM15 and AGPAT2 may contribute to a genetic predisposition through abnormal angiogenesis and adipose tissue.

Lysophospholipid acyltransferases

“…Another SNP (rs3130283) in the AGPAT1 locus was associated with Alzheimer's disease [47], although it is not clear whether this SNP is linked to early onset of Alzheimer's disease or is sex specific. Like AGPAT1, GWAS links AGPAT2 in the cause of rheumatoid arthritis (RA) [48]. This study has not been replicated, but how adipose tissue will affect the knee joints remains unclear.…”

Genome-wide gene-based association study

“…In spite of the use of various methods and procedures, some consistent results were obtained. The genes included AGPAT1 (62.04) [7], HLA-C (65.47) [8], and PHF19 (10.21) [9], where the numbers in parentheses were -log 10 ( P FDR ) in our study. In addition, we also identified some novel RA-associated genes/SNPs that have not been reported before, for example, NOTCH4 , TAP2 , and TNXB .…”

Multi-objective Phylogenetic Algorithm: Solving Multi-objective Decomposable Deceptive Problems

“…Thus (5), as described in [14] has been widely used in the literature [1,2,8,13] specially because it satisfies the triangle inequality, nonnegativity, indiscernibility and symmetric properties. In (5), H(X, Y ) is the entropy of the pair (X, Y ).…”

“…The moΦGA starts with a population of size P with random individuals and applies Θ tournament selections defined by (2).…”