Multilocus lod scores in large pedigrees: Combination of exact and approximate calculations
Multilocus lod scores in large pedigrees: Combination of exact and approximate calculations
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DOI：
10.1159/000109731
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发表时间：
2008-01-01
期刊：
影响因子：
1.8
通讯作者：
Thompson, Elizabeth
中科院分区：
文献类型：
--
作者：
Tong, Liping;Thompson, Elizabeth
为了检测疾病位点的位置，在给定谱系成员的性状和标记数据的情况下，计算多个染色体位置的 Lod 分数。当谱系规模和标记数量都很大时，精确的 lod 分数计算通常是不可能的。在这种情况下，马尔可夫链蒙特卡罗 (MCMC) 方法提供了近似值。然而，为了提供准确的结果，混合性能始终是这些 MCMC 方法中的关键问题。在本文中，我们提出了两种方法来改进 MCMC 采样，从而在更短的计算时间内获得更准确的 lod 分数估计。第一个改进将块吉布斯减数分裂 (M) 采样器推广到多个减数分裂 (MM) 采样器，其中多个减数分裂在所有基因座上联合更新。第二个通过以一些“关键”个体的单倍型为条件，将大谱系的计算分成几个部分。我们对通常有更多数据可用的后代部分进行精确计算，并将这些信息与祖先部分中隐藏变量的采样相结合。我们的方法预计对于具有大量缺失数据的大型谱系数据最有用。版权所有 (c) 2007 S. Karger AG，巴塞尔。
To detect the positions of disease loci, lod scores are calculated at multiple chromosomal positions given trait and marker data on members of pedigrees. Exact lod score calculations are often impossible when the size of the pedigree and the number of markers are both large. In this case, a Markov Chain Monte Carlo (MCMC) approach provides an approximation. However, to provide accurate results, mixing performance is always a key issue in these MCMC methods. In this paper, we propose two methods to improve MCMC sampling and hence obtain more accurate lod score estimates in shorter computation time. The first improvement generalizes the block-Gibbs meiosis (M) sampler to multiple meiosis (MM) sampler in which multiple meioses are updated jointly, across all loci. The second one divides the computations on a large pedigree into several parts by conditioning on the haplotypes of some 'key' individuals. We perform exact calculations for the descendant parts where more data are often available, and combine this information with sampling of the hidden variables in the ancestral parts. Our approaches are expected to be most useful for data on a large pedigree with a lot of missing data. Copyright (c) 2007 S. Karger AG, Basel.