Luo Z W, Suhai S
Laboratory of Population and Quantitative Genetics, Institute of Genetics, Fudan University, Shanghai 200433, People's Republic of
Genetics. 1999 Jan;151(1):359-71. doi: 10.1093/genetics/151.1.359.
Positional cloning of gene(s) underlying a complex trait requires a high-resolution linkage map between the trait locus and genetic marker loci. Recent research has shown that this may be achieved through appropriately modeling and screening linkage disequilibrium between the candidate marker locus and the major trait locus. A quantitative genetics model was developed in the present study to estimate the coefficient of linkage disequilibrium between a polymorphic genetic marker locus and a locus underlying a quantitative trait as well as the relevant genetic parameters using the sample from randomly mating populations. Asymptotic covariances of the maximum-likelihood estimates of the parameters were formulated. Convergence of the EM-based statistical algorithm for calculating the maximum-likelihood estimates was confirmed and its utility to analyze practical data was exploited by use of extensive Monte-Carlo simulations. Appropriateness of calculating the asymptotic covariance matrix in the present model was investigated for three different approaches. Numerical analyses based on simulation data indicated that accurate estimation of the genetic parameters may be achieved if a sample size of 500 is used and if segregation at the trait locus explains not less than a quarter of phenotypic variation of the trait, but the study reveals difficulties in predicting the asymptotic variances of these maximum-likelihood estimates. A comparison was made between the statistical powers of the maximum-likelihood analysis and the previously proposed regression analysis for detecting the disequilibrium.
对复杂性状相关基因进行定位克隆需要在性状基因座与遗传标记基因座之间构建高分辨率连锁图谱。近期研究表明,这可以通过对候选标记基因座与主要性状基因座之间的连锁不平衡进行适当建模和筛选来实现。本研究建立了一个数量遗传学模型,用于利用随机交配群体的样本估计多态遗传标记基因座与数量性状基因座之间的连锁不平衡系数以及相关遗传参数。推导了这些参数最大似然估计值的渐近协方差。证实了基于期望最大化(EM)的统计算法在计算最大似然估计值时的收敛性,并通过广泛的蒙特卡罗模拟探讨了其在分析实际数据中的效用。针对三种不同方法研究了本模型中渐近协方差矩阵计算的恰当性。基于模拟数据的数值分析表明,如果样本量为500且性状基因座的分离解释了该性状不少于四分之一的表型变异,则可以实现对遗传参数的准确估计,但该研究揭示了预测这些最大似然估计值渐近方差的困难。对最大似然分析和先前提出的用于检测不平衡的回归分析的统计功效进行了比较。