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A two-stage approach to detect gene-gene and gene-environment interaction : application to GAW 17 dataset
| Content Provider | Semantic Scholar |
|---|---|
| Author | Shu-Rong, Fang Wei-Jun, Ma Shuang-Lin, Zhang Qiu-Ying, Sha |
| Copyright Year | 2012 |
| Abstract | Identifying gene gene or gene-environment interactions in genome-wide association studies ( GWAS) ,especially when rare variants are included,is a major challenge in statistical genetics. A gene-based two-stage approach to identify gene-gene and gene-environment interactions in GWAS which can include rare variants is proposed. In the first stage,each variant for equal variance under each genotype is tested,which is equivalent to test interaction between this variant and other variants or environmental factors. Then,variants are ranked according to the p-values. In the second stage,interactions are tested based on a linear model among top ranked variants. When rare variants are included,the rare variants in a gene are collapsed to a combined rare variant ( CRV) which is considers as a variant. The twostage approach is applied to the GAW 17 dataset to identify the interaction between KDR gene and smoking status for the quantitative trait Q1. It is demonstrated that the two-stage approach may be more powerful than the one-stage approach that does not use the first stage. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.researchgate.net/profile/Shurong_Fang/publication/233752142_A_two-stage_approach_to_detect_gene-gene_and_gene-environment_interaction_and_application_to_GAW17_data_set/links/09e4150b2441abdcf7000000.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
