title: Discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics.
abstract: Environment has long been known to play an important part in disease etiology. However, not many genome-wide association studies take environmental factors into consideration. There is also a need for new methods to identify the gene-environment interactions. In this study, we propose a 2-step approach incorporating an influence measure that capturespure gene-environment effect. We found that pure gene-age interaction has a stronger association than considering the genetic effect alone for systolic blood pressure, measured by counting the number of single-nucleotide polymorphisms (SNPs)reaching a certain significance level. We analyzed the subjects by dividing them into two age groups and found no overlap in the top identified SNPs between them. This suggested that age might have a nonlinear effect on genetic association. Furthermore, the scores of the top SNPs for the two age subgroups were about 3times those obtained when using all subjects for systolic blood pressure. In addition, the scores of the older age subgroup were much higher than those for the younger group. The results suggest that genetic effects are stronger in older age and that genetic association studies should take environmental effects into consideration, especially age.
authors.affiliation: Division of Biostatistics, School of Public Health and Primary Care, the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR.
authors.forename: Maggie Haitian
authors.initials: MH
authors.lastname: Wang
authors.affiliation: Department of Statistics, Columbia University, 1255 Amsterdam Avenue, New York, NY 10027-5927, USA.
authors.forename: Chien-Hsun
authors.initials: CH
authors.lastname: Huang
authors.affiliation: Department of Statistics, Columbia University, 1255 Amsterdam Avenue, New York, NY 10027-5927, USA.
authors.forename: Tian
authors.initials: T
authors.lastname: Zheng
authors.affiliation: Department of Statistics, Columbia University, 1255 Amsterdam Avenue, New York, NY 10027-5927, USA.
authors.forename: Shaw-Hwa
authors.initials: SH
authors.lastname: Lo
authors.affiliation: Department of ISOM, the Hong Kong University of Science and Technology, Clearwater Bay, Kowloon, Hong Kong SAR.
authors.forename: Inchi
authors.initials: I
authors.lastname: Hu