【摘要】 目的 比较限制性最大似然估计(REML)和马尔可夫链蒙特卡罗(MCMC)方法在数量性状遗传方差分量模型中参数估计的偏差和精度。方法 计算机模拟50个数量性状核心家系数据集,运用SAS软件中的PROC MIXED程序和WinBUGS软件进行参数估计,比较不同样本含量时两法参数估计结果。结果 在本试验参数指定条件下,对固定效应参数,两法估计的相对偏差低于2％;对随机效应,估计偏差较高。小样本时,MCMC法估计结果比REML法更接近真值。结论 在模拟数量性状方差分量模型时,大样本家系资料可根据实际情况选用两法;小样本家系资料,MCMC法可得到比REML法更稳健的估计,推荐使用。更多还原

【Abstract】 Purpose To compare restricted estimation maximum likelihood (REML) to markov chain monto carlo (MCMC) for genetic variance component model of quantitative trait in nuclear family. Methods To simulate 50 data sets of nuclear family with quantitative traits, to estimate parameters by the PROC MIXED in SAS and WinBUGS, and to compare two methods on precision and bias of parameters estimation. Results Conditional on our given parameters values relative bias was lower than 2 per cent for fixed effect parameters,and bias for fixed effect parameters was larger than that for random effect parameters whichever method was used. Estimation of MCMC more approach to true vales than estimation of REML to small sample size. Conclusions Both methods can be chosen in simulating genetic variance component model to large sample family data. MCMC method is recommended to small sample family data due to its steadiness.更多还原