McKnight B, Tierney C, McGorray S P, Day N E
Department of Biostatistics, University of Washington, Seattle 98195, USA.
Biometrics. 1998 Jun;54(2):426-43.
Using genetic marker data from affected sibling pairs, we study likelihood-based linkage analysis under quasi-recessive, quasi-dominant, and general single-locus models. We use an epidemiologic parameterization under a model where the marker locus is closely linked to the putative disease susceptibility gene. This model and parameterization allow inferences about the relative risk associated with the susceptible genotype. We base inferences on approximate likelihoods that focus on the affected siblings in the sibship and, using these likelihoods, we derive closed-form maximum likelihood estimators for model parameters and closed-form likelihood ratio statistics for tests that the relative risk associated with the susceptible genotype is one. Under the general single-locus model, our likelihood ratio test is the same as the iteratively computed triangle test proposed by Holmans (1993, American Journal of Human Genetics 52, 362-374) for the case where marker identity-by-descent is known; our derivation gives a closed form for the test statistic. We present quartiles of the distribution of parameter estimates and critical values for the exact null distribution of our likelihood ratio test statistics; we also give large-sample approximations to their null distributions. We show that the powers of our likelihood ratio tests exceed the powers of more commonly used nonparametric affected-sibling-pair tests when the data meet the inheritance model assumptions used to derive the test; we also show that our tests' powers are robust to violation of model assumptions. We conclude that our model-based inferences provide a practical alternative to more common affected-sibling-pair tests when investigators have some knowledge about the mode of inheritance of a disease and that our methods may sometimes be useful for comparing the genetic relative risk with environmental relative risks.
利用来自患病同胞对的遗传标记数据，我们研究了准隐性、准显性和一般单基因座模型下基于似然性的连锁分析。我们在一个标记基因座与假定的疾病易感基因紧密连锁的模型下采用了一种流行病学参数化方法。这种模型和参数化方法允许推断与易感基因型相关的相对风险。我们基于关注同胞对中患病同胞的近似似然性进行推断，并利用这些似然性推导出模型参数的闭式最大似然估计量以及用于检验与易感基因型相关的相对风险是否为1的闭式似然比统计量。在一般单基因座模型下，我们的似然比检验与Holmans（1993年，《美国人类遗传学杂志》52卷，362 - 374页）针对已知标记同源性的情况提出的迭代计算三角检验相同；我们的推导给出了检验统计量的闭式形式。我们给出了参数估计分布的四分位数以及似然比检验统计量精确零分布的临界值；我们还给出了它们零分布的大样本近似值。我们表明，当数据符合用于推导检验的遗传模型假设时，我们似然比检验的功效超过了更常用的非参数患病同胞对检验的功效；我们还表明，我们检验的功效对于模型假设的违反具有稳健性。我们得出结论，当研究人员对疾病的遗传模式有一定了解时，我们基于模型的推断为更常见的患病同胞对检验提供了一种实用的替代方法，并且我们的方法有时可能有助于比较遗传相对风险与环境相对风险。
