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首页> 外文期刊>Journal of biomedical informatics. >Estimation of epistasis among finite polygenic loci for complex traits with a mixed model using Gibbs sampling.
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Estimation of epistasis among finite polygenic loci for complex traits with a mixed model using Gibbs sampling.

机译：使用吉布斯采样的混合模型估算复杂性状的有限多基因基因座中的上位性。

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摘要

Epistasis among loci is important factor behind the expression of many complex traits, but many analyses have ruled out its possibility. A method to estimate epistasis was introduced with a mixed model using Gibbs sampling (MMGS). The posterior mean estimate for every possible genotype combined from multiple loci was calculated as the mean of the conditional expected values of the parameters in post warming-up rounds from Gibbs sampling. A simulation study was performed to compare MMGS with restricted partition method (RPM). Mean square prediction error (MSPE) using MMGS was smaller than that using RPM (P<0.05), which might be due to information loss introduced by grouping of genotypes in RPM. This was also supported by the result that MSPE increased as the number of merged groups decreased. The simulation study implied that MMGS was more plausible in estimating epistatic effects than the RPM.
机译：基因座之间的上位性是表达许多复杂性状的重要因素，但许多分析排除了其可能性。使用吉布斯采样（MMGS）的混合模型介绍了一种估计上位性的方法。计算来自多个基因座的每个可能基因型的后验均值，作为吉布斯采样后预热后参数条件期望值的均值。进行了仿真研究，以比较MMGS与受限分配方法（RPM）。使用MMGS的均方预测误差（MSPE）小于使用RPM的均方预测误差（P <0.05），这可能是由于RPM中基因型分组引起的信息丢失。 MSPE随着合并组数的减少而增加的结果也证明了这一点。仿真研究表明，MMGS在估计上位效应方面比RPM更合理。

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