Estimating the power of variance component linkage analysis in large pedigrees
Estimating the power of variance component linkage analysis in large pedigrees
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DOI：
10.1002/gepi.20160
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发表时间：
2006-09-01
影响因子：
2.1
通讯作者：
Abecasis, Goncalo R.
中科院分区：
文献类型：
--
作者：
Chen, Wei-Min;Abecasis, Goncalo R.
方差分量连锁分析通常用于一般谱系中的数量性状基因座（QTL）定位。大家系对这些研究特别有吸引力，因为它们比小家系提供更大的功率每个基因分型个体。我们提出了准确和计算效率高的方法来计算方差分量连锁分析的分析能力，可以容纳大谱系。我们的分析功率计算涉及的似然比测试的泰勒展开的非中心性参数的近似。我们开发了有效的算法来计算相同的下降（IBD）共享分布的二阶和三阶矩，并使快速计算的泰勒展开。我们的算法利用自然的对称性在谱系，可以在几秒钟内准确地分析许多大的谱系。我们通过在2-5代和每个同胞2-8个同胞的家系中进行模拟来验证我们的功效计算的准确性。我们应用这个建议的分析能力计算98个数量性状的队列研究的6,148撒丁岛，其中最大的谱系包括625个表型的个人。基于8个代表性性状的模拟表明，我们的分析估计的预期LOD得分和模拟LOD得分的平均值之间的差异小于0.05 - 0.5%）。尽管我们的分析计算是针对完全信息化的标记基因座，但在我们检查的设置中，功效类似于用单核苷酸多态性（SNP）作图组（具有> 1 SNP/cM）可以获得的功效。我们的算法的功率分析与多基因分析一起实现在一个免费的计算机程序，POLY。Genet.流行病学30：471-484，2006. (c)2006 Wiley-Liss，Inc.
Variance component linkage analysis is commonly used to map quantitative trait loci (QTLs) in general pedigrees. Large pedigrees are especially attractive for these studies because they provide greater power per genotyped individual than small pedigrees. We propose accurate and computationally efficient methods to calculate the analytical power of variance component linkage analysis that can accommodate large pedigrees. Our analytical power computation involves the approximation of the noncentrality parameter for the likelihood-ratio test by its Taylor expansions. We develop efficient algorithms to compute the second and third moments of the identical by descent (IBD) sharing distribution and enable rapid computation of the Taylor expansions. Our algorithms take advantage of natural symmetries in pedigrees and can accurately analyze many large pedigrees in a few seconds. We verify the accuracy of our power calculation via simulation in pedigrees with 2-5 generations and 2-8 siblings per sibship. We apply this proposed analytical power calculation to 98 quantitative traits in a cohort study of 6,148 Sardinians in which the largest pedigree includes 625 phenotyped individuals. Simulations based on eight representative traits show that the difference between our analytical estimation of the expected LOD score and the average of simulated LOD scores is less than 0.05 0.5%). Although our analytical calculations are for a fully informative marker locus, in the settings we examined power was similar to what could be attained with a single nucleotide polymorphism (SNP) mapping panel (with > 1 SNP/cM). Our algorithms for power analysis together with polygenic analysis are implemented in a freely available computer program, POLY. Genet. Epidemiol. 30:471-484, 2006. (c) 2006 Wiley-Liss, Inc.