A kernel of truth: statistical advances in polygenic variance component models for complex human pedigrees.
A kernel of truth: statistical advances in polygenic variance component models for complex human pedigrees.
复制标题
DOI：
10.1016/b978-0-12-407677-8.00001-4
复制
发表时间：
2013
影响因子：
--
通讯作者：
Goering, Harald H. H.
中科院分区：
文献类型：
--
作者：
Blangero, John;Diego, Vincent P.;Dyer, Thomas D.;Almeida, Marcio;Peralta, Juan;Kent, Jack W., Jr.;Williams, Jeff T.;Almasy, Laura;Goering, Harald H. H.
大家系数量性状的统计遗传分析是一项艰巨的计算任务，因为必须考虑到亲属间的非独立性。随着人们越来越认识到罕见序列变异在人类数量变异中可能很重要，由于在相关个体中观察到罕见变异多拷贝的机会更大，涉及大谱系的遗传力和关联研究设计的频率将增加。因此，重要的是要有统计遗传测试程序，利用所有可用的信息，以提取有关遗传关联的证据。标记/表型关联的最佳测试涉及似然比统计量的精确计算，这需要对潜在的大矩阵进行重复求逆。在全基因组序列关联背景下，这样的计算可能是禁止的。为此，我们已经开发了一种快速有效的特征简化的可能性，使家庭数据的分析与分析的可比样本无关的个人。我们的理论结果是基于谱表示的可能性产生简单的精确表达式的预期似然比检验统计量（ELRT）的任意大小和复杂性的谱系。对于遗传力，ELRT为：其中λ 2和λgi分别为遗传力和家系遗传关系核（GRK）的特征值。对于序列变异的关联分析，ELRT由下式给出，其中，和分别是总的、数量性状核苷酸和残余遗传力。使用这些结果，快速和准确的分析功率分析是可能的，消除了对计算机模拟的需要。本征简化的其他好处包括一个简单的方法来计算ELRT的确切分布下的零假设，这原来是不同于通常的渐近理论下的预期。此外，当结合使用经验GRKs估计了大量的遗传标记，我们的理论揭示了潜在的问题与非正半定内核。这些程序正在被添加到我们的通用统计遗传计算机包SOLAR中。
Statistical genetic analysis of quantitative traits in large pedigrees is a formidable computational task due to the necessity of taking the non-independence among relatives into account. With the growing awareness that rare sequence variants may be important in human quantitative variation, heritability and association study designs involving large pedigrees will increase in frequency due to the greater chance of observing multiple copies of rare variants amongst related individuals. Therefore, it is important to have statistical genetic test procedures that utilize all available information for extracting evidence regarding genetic association. Optimal testing for marker/phenotype association involves the exact calculation of the likelihood ratio statistic which requires the repeated inversion of potentially large matrices. In a whole genome sequence association context, such computation may be prohibitive. Toward this end, we have developed a rapid and efficient eigensimplification of the likelihood that makes analysis of family data commensurate with the analysis of a comparable sample of unrelated individuals. Our theoretical results which are based on a spectral representation of the likelihood yield simple exact expressions for the expected likelihood ratio test statistic (ELRT) for pedigrees of arbitrary size and complexity. For heritability, the ELRT is: where ĥ2 and λgi are respectively the heritability and eigenvalues of the pedigree-derived genetic relationship kernel (GRK). For association analysis of sequence variants, the ELRT is given by where , and are the total, quantitative trait nucleotide, and residual heritabilities, respectively. Using these results, fast and accurate analytical power analyses are possible, eliminating the need for computer simulation. Additional benefits of eigensimplification include a simple method for calculation of the exact distribution of the ELRT under the null hypothesis which turns out to differ from that expected under the usual asymptotic theory. Further, when combined with the use of empirical GRKs—estimated over a large number of genetic markers— our theory reveals potential problems associated with non positive semi-definite kernels. These procedures are being added to our general statistical genetic computer package, SOLAR.