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Mendeley readers
| Title |
Retrospective analysis of main and interaction effects in genetic association studies of human complex traits
|
|---|---|
| Published in |
BMC Genomic Data, October 2007
|
| DOI | 10.1186/1471-2156-8-70 |
| Pubmed ID | |
| Authors |
Qihua Tan, Lene Christiansen, Charlotte Brasch-Andersen, Jing Hua Zhao, Shuxia Li, Torben A Kruse, Kaare Christensen |
| Abstract |
The etiology of multifactorial human diseases involves complex interactions between numerous environmental factors and alleles of many genes. Efficient statistical tools are demanded in identifying the genetic and environmental variants that affect the risk of disease development. This paper introduces a retrospective polytomous logistic regression model to measure both the main and interaction effects in genetic association studies of human discrete and continuous complex traits. In this model, combinations of genotypes at two interacting loci or of environmental exposure and genotypes at one locus are treated as nominal outcomes of which the proportions are modeled as a function of the disease trait assigning both main and interaction effects and with no assumption of normality in the trait distribution. Performance of our method in detecting interaction effect is compared with that of the case-only model. Results from our simulation study indicate that our retrospective model exhibits high power in capturing even relatively small effect with reasonable sample sizes. Application of our method to data from an association study on the catalase -262C/T promoter polymorphism and aging phenotypes detected significant main and interaction effects for age-group and allele T on individual's cognitive functioning and produced consistent results in estimating the interaction effect as compared with the popular case-only model. The retrospective polytomous logistic regression model can be used as a convenient tool for assessing both main and interaction effects in genetic association studies of human multifactorial diseases involving genetic and non-genetic factors as well as categorical or continuous traits. |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Cameroon | 1 | 6% |
| Unknown | 15 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 19% |
| Student > Doctoral Student | 2 | 13% |
| Student > Ph. D. Student | 2 | 13% |
| Professor | 2 | 13% |
| Student > Postgraduate | 2 | 13% |
| Other | 1 | 6% |
| Unknown | 4 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 4 | 25% |
| Social Sciences | 2 | 13% |
| Psychology | 2 | 13% |
| Biochemistry, Genetics and Molecular Biology | 1 | 6% |
| Mathematics | 1 | 6% |
| Other | 2 | 13% |
| Unknown | 4 | 25% |