The Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital, Columbus, Ohio 43205, USA.
Genet Epidemiol. 2010 Dec;34(8):835-45. doi: 10.1002/gepi.20537.
In this paper, we extend the PPL framework to the analysis of case-control (CC) data and introduce three new linkage disequilibrium (LD) statistics. These statistics measure the evidence for or against LD, rather than testing the null hypothesis of no LD, and they therefore avoid the need for multiple testing corrections. They are suitable not only for CC designs but also can be used in application to family data, ranging from trios to complex pedigrees, all under the same statistical framework, allowing for the seamless analysis of disparate data structures. They also provide other core advantages of the PPL framework, including the use of sequential updating to accumulate LD evidence across potentially heterogeneous sets or subsets of data; parameterization in terms of a very general trait likelihood, which simultaneously considers dominant, recessive, and additive models; and a straightforward mechanism for modeling two-locus epistasis. Finally, by implementing the new statistics within the PPL framework, we have a ready mechanism for incorporating linkage information, obtained from distinct data, into LD analyses in the form of a prior distribution. Here we examine the performance of the proposed LD statistics using simulated data, as well as assessing the effects of key modeling violations on this performance.
在本文中，我们将 PPL 框架扩展到病例对照（CC）数据的分析中，并引入了三个新的连锁不平衡（LD）统计量。这些统计量用于衡量 LD 的证据，而不是检验 LD 不存在的零假设，因此避免了多重检验校正的需要。它们不仅适用于 CC 设计，还可以应用于家族数据，从三胞胎到复杂的家系，都在相同的统计框架下，允许对不同的数据结构进行无缝分析。它们还提供了 PPL 框架的其他核心优势，包括使用顺序更新在潜在异质的数据集或数据子集之间累积 LD 证据；参数化采用非常通用的性状似然，同时考虑显性、隐性和加性模型；以及一种简单的机制用于建模两基因座上位性。最后，通过在 PPL 框架内实现新的统计量，我们有一个现成的机制，将来自不同数据的连锁信息以先验分布的形式纳入 LD 分析中。在这里，我们使用模拟数据来检验所提出的 LD 统计量的性能，并评估关键建模违反对该性能的影响。
