Bastone Laurel A, Spielman Richard S, Wang Xingmei, Ten Have Thomas R, Putt Mary E
Global Biometrics Science, Bristol-Myers Squibb, Pennington, N.J., USA.
Hum Hered. 2010;70(2):75-91. doi: 10.1159/000312819. Epub 2010 Jun 17.
In a quantitative trait locus (QTL) study, it is usually not feasible to select families with offspring that simultaneously display variability in more than one phenotype. When multiple phenotypes are of interest, the sample will, with high probability, contain 'non-segregating' families, i.e. families with both parents homozygous at the QTL. These families potentially reduce the power of regression-based methods to detect linkage. Moreover, follow-up studies in individual families will be inefficient, and potentially even misleading, if non-segregating families are selected for the study. Our work extends Haseman-Elston regression using a latent class model to account for the mixture of segregating and non-segregating families. We provide theoretical motivation for the method using an additive genetic model with two distinct functions of the phenotypic outcome, squared difference (SqD) and mean-corrected product (MCP). A permutation procedure is developed to test for linkage; simulation shows that the test is valid for both phenotypic functions. For rare alleles, the method provides increased power compared to a 'marginal' approach that ignores the two types of families; for more common alleles, the marginal approach has better power. These results appear to reflect the ability of the algorithm to accurately assign families to the two classes and the relative weights of segregating and non-segregating families to the test of linkage. An application of Bayes rule is used to estimate the family-specific probability of segregating. High predictive value positive values for segregating families, particularly for MCP, suggest that the method has considerable value for identifying segregating families. The method is illustrated for gene expression phenotypes measured on 27 candidate genes previously demonstrated to show linkage in a sample of 14 families.
在数量性状基因座（QTL）研究中，选择其后代同时在多种表型上表现出变异性的家系通常是不可行的。当关注多个表型时，样本很可能包含“非分离”家系，即双亲在QTL处均为纯合子的家系。这些家系可能会降低基于回归的方法检测连锁的效能。此外，如果选择非分离家系进行研究，对各个家系的后续研究将效率低下，甚至可能产生误导。我们的工作使用潜在类别模型扩展了哈斯曼-埃尔斯顿回归，以考虑分离家系和非分离家系的混合情况。我们使用具有两种不同表型结果函数（平方差（SqD）和均值校正乘积（MCP））的加性遗传模型为该方法提供理论依据。开发了一种置换程序来检验连锁；模拟表明该检验对两种表型函数均有效。对于稀有等位基因，与忽略这两种家系类型的“边际”方法相比，该方法具有更高的效能；对于较常见的等位基因，边际方法具有更好的效能。这些结果似乎反映了该算法将家系准确分配到两类的能力以及分离家系和非分离家系对连锁检验的相对权重。应用贝叶斯法则来估计家系特异性的分离概率。分离家系的高预测值阳性值，特别是对于MCP，表明该方法在识别分离家系方面具有相当大的价值。在先前证明在14个家系样本中显示连锁的27个候选基因上测量的基因表达表型对该方法进行了说明。
