Dudoit S, Speed T P
Department of Statistics, University of California, Berkeley, 367 Evans Hall, #3860, Berkeley, CA 94720-3860, USA.
Biostatistics. 2000 Mar;1(1):1-26. doi: 10.1093/biostatistics/1.1.1.
We propose a general likelihood-based approach to the linkage analysis of qualitative and quantitative traits using identity by descent (IBD) data from sib-pairs. We consider the likelihood of IBD data conditional on phenotypes and test the null hypothesis of no linkage between a marker locus and a gene influencing the trait using a score test in the recombination fraction theta between the two loci. This method unifies the linkage analysis of qualitative and quantitative traits into a single inferential framework, yielding a simple and intuitive test statistic. Conditioning on phenotypes avoids unrealistic random sampling assumptions and allows sib-pairs from differing ascertainment mechanisms to be incorporated into a single likelihood analysis. In particular, it allows the selection of sib-pairs based on their trait values and the analysis of only those pairs having the most informative phenotypes. The score test is based on the full likelihood, i.e. the likelihood based on all phenotype data rather than just differences of sib-pair phenotypes. Considering only phenotype differences, as in Haseman and Elston (1972) and Kruglyak and Lander (1995), may result in important losses in power. The linkage score test is derived under general genetic models for the trait, which may include multiple unlinked genes. Population genetic assumptions, such as random mating or linkage equilibrium at the trait loci, are not required. This score test is thus particularly promising for the analysis of complex human traits. The score statistic readily extends to accommodate incomplete IBD data at the test locus, by using the hidden Markov model implemented in the programs MAPMAKER/SIBS and GENEHUNTER (Kruglyak and Lander, 1995; Kruglyak et al., 1996). Preliminary simulation studies indicate that the linkage score test generally matches or outperforms the Haseman-Elston test, the largest gains in power being for selected samples of sib-pairs with extreme phenotypes.
我们提出了一种基于似然性的通用方法，用于利用同胞对的同源等位基因（IBD）数据对定性和定量性状进行连锁分析。我们考虑给定表型条件下IBD数据的似然性，并使用两个位点之间重组率θ的得分检验来检验标记位点与影响该性状的基因之间无连锁的零假设。该方法将定性和定量性状的连锁分析统一到一个单一的推断框架中，产生了一个简单直观的检验统计量。以表型为条件避免了不切实际的随机抽样假设，并允许将来自不同确定机制的同胞对纳入单一的似然性分析中。特别是，它允许根据同胞对的性状值进行选择，并仅分析那些具有最具信息性表型的对。得分检验基于全似然性，即基于所有表型数据的似然性，而不仅仅是同胞对表型的差异。仅考虑表型差异，如在Haseman和Elston（1972年）以及Kruglyak和Lander（1995年）的研究中那样，可能会导致功效的重大损失。连锁得分检验是在性状的一般遗传模型下推导出来的，该模型可能包括多个不连锁的基因。不需要群体遗传假设，如性状位点的随机交配或连锁平衡。因此，这种得分检验对于复杂人类性状的分析特别有前景。通过使用MAPMAKER/SIBS和GENEHUNTER程序中实现的隐马尔可夫模型（Kruglyak和Lander，1995年；Kruglyak等人，1996年），得分统计量很容易扩展以适应测试位点处不完整的IBD数据。初步模拟研究表明，连锁得分检验通常与Haseman-Elston检验相当或优于该检验，功效最大的提升是针对具有极端表型的同胞对的选定样本。
