Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital Columbus, OH, USA.
Front Genet. 2013 Apr 19;4:59. doi: 10.3389/fgene.2013.00059. eCollection 2013.
The increased feasibility of whole-genome (or whole-exome) sequencing has led to renewed interest in using family data to find disease mutations. For clinical phenotypes that lend themselves to study in large families, this approach can be particularly effective, because it may be possible to obtain strong evidence of a causal mutation segregating in a single pedigree even under conditions of extreme locus and/or allelic heterogeneity at the population level. In this paper, we extend our capacity to carry out positional mapping in large pedigrees, using a combination of linkage analysis and within-pedigree linkage trait-variant disequilibrium analysis to fine map down to the level of individual sequence variants. To do this, we develop a novel hybrid approach to the linkage portion, combining the non-stochastic approach to integration over the trait model implemented in the software package Kelvin, with Markov chain Monte Carlo-based approximation of the marker likelihood using blocked Gibbs sampling as implemented in the McSample program in the JPSGCS package. We illustrate both the positional mapping template, as well as the efficacy of the hybrid algorithm, in application to a single large pedigree with phenotypes simulated under a two-locus trait model.
全基因组（或外显子组）测序的可行性增加，使得人们重新关注利用家族数据来寻找疾病突变。对于适合在大家庭中进行研究的临床表型，这种方法尤其有效，因为即使在群体水平上存在极端的基因座和/或等位基因异质性的情况下，也有可能在单个家系中获得因果突变分离的有力证据。在本文中，我们扩展了在大型家系中进行定位映射的能力，使用连锁分析和家系内连锁性状-变异不平衡分析的组合，将精细映射细化到个体序列变异的水平。为此，我们开发了一种新的混合方法来进行连锁部分，将软件包 Kelvin 中实现的基于非随机方法的性状模型整合与基于马尔可夫链蒙特卡罗的标记似然近似相结合，使用 JPSGCS 包中的 McSample 程序中的块 Gibbs 采样进行实现。我们将位置映射模板以及混合算法的功效都应用于一个具有两种基因座性状模型模拟表型的大型家系中进行说明。
