基于 COGA 数据的方差成分连锁分析中离散环境与连续环境的比较。
A comparison of discrete versus continuous environment in a variance components-based linkage analysis of the COGA data.
机构信息
Department of Epidemiology, Emory University, Atlanta, Georgia, USA.
出版信息
BMC Genet. 2005 Dec 30;6 Suppl 1(Suppl 1):S57. doi: 10.1186/1471-2156-6-S1-S57.
BACKGROUND
The information content of a continuous variable exceeds that of its categorical counterpart. The parameterization of a model may diminish the benefit of using a continuous variable. We explored the use of continuous versus discrete environment in variance components based analyses examining gene x environment interaction in the electrophysiological phenotypes from the Collaborative Study on the Genetics of Alcoholism.
RESULTS
The parameterization using the continuous environment produced a greater number of significant gene x environment interactions and lower AICs (Akaike's information criterion). In these cases, the genetic variance increased with increasing cigarette pack-years, the continuous environment of interest. This did not, however, result in enhanced LOD scores when linkage analyses incorporated the gene x continuous environment interaction.
CONCLUSION
Alternative parameterizations may better represent the functional relationship between the continuous environment and the genetic variance.
背景
连续变量的信息含量超过其分类变量的信息含量。模型的参数化可能会降低使用连续变量的优势。我们探讨了在基于方差分量的分析中使用连续与离散环境的方法，该分析检查了酒精中毒遗传学合作研究中电生理表型的基因 x 环境相互作用。
结果
使用连续环境进行参数化会产生更多的显著基因 x 环境相互作用和更低的 AIC（Akaike 信息准则）。在这些情况下，遗传方差随着感兴趣的连续环境——香烟包年数的增加而增加。然而，当连锁分析纳入基因 x 连续环境相互作用时，这并没有导致 LOD 分数的提高。
结论
替代参数化可能更好地表示连续环境与遗传方差之间的功能关系。
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