Gene x gene and gene x environment interactions for complex disorders.
Gene x gene and gene x environment interactions for complex disorders.
复制标题
基因X基因和基因X环境相互作用，用于复杂疾病。
DOI：
10.1186/1753-6561-1-s1-s72
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Suarez BK
中科院分区：
文献类型：
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作者：
Culverhouse R;Hinrichs AL;Jin CH;Suarez BK
限制分割法（RPM）提供了一种检测定量或二元表型变异相关的定性因素（如基因型、环境暴露）的方法，即使这种贡献主要是在单变量分析中显示很少或没有信号的相互作用。RPM提供了预测协变量与表型之间关系的模型（可能是非线性的），以及该模型的统计和临床意义的测量。对生成模型不透明，我们使用RPM筛选了一个数据集，包括1500个不相关病例和2000个不相关对照，这些数据集来自遗传分析研讨会15问题3数据的复制1，用于研究导致类风湿关节炎（RA）风险的遗传和环境因素。使用性别、吸烟、亲本DRB1 HLA微卫星等位基因和来自整个基因组的9187个单核苷酸多态性基因型进行单变量和成对分析。通过这种方法，我们正确地确定了三个直接影响RA风险的遗传位点，以及一个影响内表型IgM水平的数量性状位点。我们没有错误地识别任何不在生成模型中的因素。我们发现的所有因素都可以通过单变量RPM分析检测到。我们未能确定两个基因位点改变RA的风险。在打破盲法后，我们检查了前50个数据重复中的真实建模因素，发现即使我们将前50个重复的所有数据合并到一个数据集中，我们也不会确定其他重要因素。
The restricted partition method (RPM) provides a way to detect qualitative factors (e.g. genotypes, environmental exposures) associated with variation in quantitative or binary phenotypes, even if the contribution is predominantly an interaction displaying little or no signal in univariate analyses. The RPM provides a model (possibly non-linear) of the relationship between the predictor covariates and the phenotype as well as measures of statistical and clinical significance for the model. Blind to the generating model, we used the RPM to screen a data set consisting 1500 unrelated cases and 2000 unrelated controls from Replicate 1 of the Genetic Analysis Workshop 15 Problem 3 data for genetic and environmental factors contributing to rheumatoid arthritis (RA) risk. Both univariate and pair-wise analyses were performed using sex, smoking, parental DRB1 HLA microsatellite alleles, and 9187 single-nucleotide polymorphisms genotypes from across the genome. With this approach we correctly identified three genetic loci contributing directly to RA risk, and one quantitative trait locus for the endophenotype IgM level. We did not mistakenly identify any factors not in the generating model. All the factors we found were detectable with univariate RPM analyses. We failed to identify two genetic loci modifying the risk of RA. After breaking the blind, we examined the true modeling factors in the first 50 data replicates and found that we would not have identified the additional factors as important even had we combined all the data from the first 50 replicates in a single data set.