Devlin Bernie, Jones Bobby L, Bacanu Silviu-Alin, Roeder Kathryn
Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O'Hara Street, Pittsburgh, PA 15213, USA.
Genet Epidemiol. 2002 Jan;22(1):52-65. doi: 10.1002/gepi.1043.
To determine the genetic etiology of complex diseases, a common study design is to recruit affected sib/relative pairs (ASP/ARP) and evaluate their genome-wide distribution of identical by descent (IBD) sharing using a set of highly polymorphic markers. Other attributes or environmental exposures of the ASP/ARP, which are thought to affect liability to disease, are sometimes collected. Conceivably, these covariates could refine the linkage analysis. Most published methods for ASP/ARP linkage with covariates can be conceptualized as logistic models in which IBD status of the ASP is predicted by pair-specific covariates. We develop a different approach to the problem of ASP analysis in the presence of covariates, one that extends naturally to ARP under certain conditions. For ASP linkage analysis, we formulate a mixture model in which a disease mutation is segregating in only a fraction alpha of the sibships, with 1 - alpha sibships being unlinked. Covariate information is used to predict membership within groups; in this report, the two groups correspond to the linked and unlinked sibships. For an ASP with covariate(s) Z = z and multilocus genotype X = x, the mixture model is alpha(z)g(x; lambda) + [1 - alpha(z)]g(0)(x), in which g(0)(x) follows the distribution of genotypes under the null IBD distribution and g(x; lambda) allows for increased IBD sharing. Two mixture models are developed. The pre-clustering model uses covariate information to form probabilistic clusters and then tests for excess IBD sharing independent of the covariates. The Cov-IBD model determines probabilistic group membership by joint consideration of covariate and IBD values. Simulations show that incorporating covariates into linkage analysis can enhance power substantially. A feature of our conceptualization of ASP linkage analysis, with covariates, is that it is apparent how data analysis might evaluate covariates prior to the linkage analysis, thus avoiding the loss of power described by Leal and Ott [2000] when data are stratified.
为了确定复杂疾病的遗传病因，一种常见的研究设计是招募患病同胞/亲属对（ASP/ARP），并使用一组高度多态性标记评估其全基因组同源等位基因（IBD）共享的分布情况。有时还会收集ASP/ARP的其他特征或环境暴露因素，这些因素被认为会影响疾病易感性。可以想象，这些协变量可能会改进连锁分析。大多数已发表的用于有协变量的ASP/ARP连锁分析的方法都可以概念化为逻辑模型，其中ASP的IBD状态由成对特定的协变量预测。我们针对存在协变量时的ASP分析问题开发了一种不同的方法，该方法在某些条件下可以自然地扩展到ARP。对于ASP连锁分析，我们构建了一个混合模型，其中疾病突变仅在一部分（α）同胞对中分离，而1 - α的同胞对不连锁。协变量信息用于预测组内成员身份；在本报告中，这两组对应于连锁和不连锁的同胞对。对于具有协变量Z = z和多位点基因型X = x的ASP，混合模型为α(z)g(x; λ) + [1 - α(z)]g(0)(x)，其中g(0)(x)遵循零IBD分布下的基因型分布，g(x; λ)允许增加IBD共享。我们开发了两种混合模型。预聚类模型使用协变量信息形成概率簇，然后独立于协变量测试是否存在过多的IBD共享。Cov - IBD模型通过联合考虑协变量和IBD值来确定概率组成员身份。模拟表明，将协变量纳入连锁分析可以显著提高功效。我们对有协变量的ASP连锁分析进行概念化的一个特点是，很明显数据分析如何在连锁分析之前评估协变量，从而避免了Leal和Ott [2000]所描述的在数据分层时出现的功效损失。
