Fishing for pleiotropic QTLs in a polygenic sea.
Fishing for pleiotropic QTLs in a polygenic sea.
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在多基因海洋中捕捞多效性 QTL。
DOI：
10.1111/j.1529-8817.2005.00181.x
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Lange,K
中科院分区：
文献类型：
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作者：
Bauman,LE;Almasy,L;Blangero,J;Duggirala,R;Sinsheimer,JS;Lange,K
因子分析在人类遗传学中的应用有可能发现共同环境、共同多基因或单个主基因对多个性状的协调控制。经典因子分析通过用少量不相关因子的线性变换近似随机向量来解释随机向量的分量之间的协变。在目前的文件中，我们将展示如何因子分析与经典的方差分解的生物统计遗传学。为了探索相关数量变量之间的关系，并避免复杂的正定性约束，我们采用了Cholesky和因子分析分解。我们推导出一个ECM算法和一个竞争的拟牛顿算法估计参数的最大似然，并提出策略选择初始参数值。我们还展示了这些参数化下的参数渐近标准误差如何传播到相关方差分量的渐近标准误差。我们的遗传分析程序孟德尔，现在合并了程序费舍尔，已经表现良好的各种数据集。我们说明了我们的方法，算法和模型上的两个数据集：一个双变量定量遗传的例子，使用总指嵴计数数据和多变量连锁的例子，使用胰岛素抵抗数据。
The application of factor analysis to human genetics has the potential to discover the coordinated control of multiple traits by common environment, common polygenes, or a single major gene. Classical factor analysis explains the covariation among the components of a random vector by approximating the vector by a linear transformation of a small number of uncorrelated factors. In the current paper we show how factor analysis dovetails with the classical variance decompositions of biometrical genetics. To explore the relationships between related quantitative variables, and avoid complicated positive definiteness constraints, we employ Cholesky and factor analytic decompositions. We derive an ECM algorithm and a competing quasi‐Newton algorithm for estimating parameters by maximum likelihood and propose tactics for selecting initial parameter values. We also show how parameter asymptotic standard errors under these parameterizations propagate to asymptotic standard errors of the underlying variance components. Our genetic analysis program Mendel, which now incorporates the program Fisher, has performed well on a variety of data sets. We illustrate our methods, algorithms, and models on two data sets: a bivariate quantitative genetic example using total finger ridge count data and a multivariate linkage example using insulin resistance data.