Estimating quantitative genetic parameters using sibships reconstructed from marker data.
Estimating quantitative genetic parameters using sibships reconstructed from marker data.
复制标题
使用从标记数据重建的同胞关系估计定量遗传参数。
作者：
Stuart C. Thomas;W. G. Hill
以前用于估计定量遗传参数的技术，例如确切关系未知但从标记基因型推断的群体中的遗传力，仅使用来自个体的成对水平的数据。在这个级别上，家庭根据每个家庭出现的配对数量进行加权，因此是根据大小而不是信息内容进行加权，并且来自多个关系的信息会丢失。因此，参数估计并不是最有效的方法。在这里，马尔可夫链蒙特卡罗技术已用于将人口划分为完整的同胞关系，包括（如果已知）家庭规模分布的先验知识。然后在动物模型下以有限的最大可能性使用这些谱系来估计定量遗传参数。将参数估计的特性与现有技术的特性进行比较的模拟表明，同胞关系重建的使用优于早期的方法，具有较低的均方误差并且显示出不显着的向下偏差。此外，同胞关系重建允许估计群体等位基因频率，从而解释样本内的关系，因此不需要假设等位基因频率的先验知识。当部分或全部母体基因型已知时，这些技术的扩展允许重建半同胞关系。
Previous techniques for estimating quantitative genetic parameters, such as heritability in populations where exact relationships are unknown but are instead inferred from marker genotypes, have used data from individuals on a pairwise level only. At this level, families are weighted according to the number of pairs within which each family appears, hence by size rather than information content, and information from multiple relationships is lost. Estimates of parameters are therefore not the most efficient achievable. Here, Markov chain Monte Carlo techniques have been used to partition the population into complete sibships, including, if known, prior knowledge of the distribution of family sizes. These pedigrees have then been used with restricted maximum likelihood under an animal model to estimate quantitative genetic parameters. Simulations to compare the properties of parameter estimates with those of existing techniques indicate that the use of sibship reconstruction is superior to earlier methods, having lower mean square errors and showing nonsignificant downward bias. In addition, sibship reconstruction allows the estimation of population allele frequencies that account for the relationships within the sample, so prior knowledge of allele frequencies need not be assumed. Extensions to these techniques allow reconstruction of half sibships when some or all of the maternal genotypes are known.
影响因子：
3.3
作者：
M. Kuhner;Jon A Yamato;J. Felsenstein
通讯作者：
M. Kuhner;Jon A Yamato;J. Felsenstein
影响因子：
3.3
作者：
M. Lynch;K. Ritland
通讯作者：
M. Lynch;K. Ritland
影响因子：
10.7
作者：
Yang, ZH;Rannala, B
通讯作者：
Rannala, B