Curtis D, Sham P C
Department of Psychological Medicine, Institute of Psychiatry, London, United Kingdom.
Am J Hum Genet. 1995 Sep;57(3):703-16.
Misspecification of transmission model parameters can produce artifactually negative lod scores at small recombination fractions and in multipoint analysis. To avoid this problem, we have tried to devise a test that aims to detect a genetic effect at a particular locus, rather than attempting to estimate the map position of a locus with specified effect. Maximizing likelihoods over transmission model parameters, as well as linkage parameters, can produce seriously biased parameter estimates and so yield tests that lack power for the detection of linkage. However, constraining the transmission model parameters to produce the correct population prevalence largely avoids this problem. For computational convenience, we recommend that the likelihoods under linkage and non-linkage are independently maximized over a limited set of transmission models, ranging from Mendelian dominant to null effect and from null effect to Mendelian recessive. In order to test for a genetic effect at a given map position, the likelihood under linkage is maximized over admixture, the proportion of families linked. Application to simulated data for a wide range of transmission models in both affected sib pairs and pedigrees demonstrates that the new method is well behaved under the null hypothesis and provides a powerful test for linkage when it is present. This test requires no specification of transmission model parameters, apart from an approximate estimate of the population prevalence. It can be applied equally to sib pairs and pedigrees, and, since it does not diminish the lod score at test positions very close to a marker, it is suitable for application to multipoint data.
在小重组率和多点分析中，传递模型参数的错误设定可能会产生人为的负对数优势比分。为避免此问题，我们尝试设计一种检验方法，旨在检测特定位点的遗传效应，而非试图估计具有特定效应的位点的图谱位置。对传递模型参数以及连锁参数最大化似然性，会产生严重偏差的参数估计，从而导致检验缺乏检测连锁的能力。然而，通过约束传递模型参数以产生正确的群体患病率，在很大程度上可避免此问题。为便于计算，我们建议在从孟德尔显性到无效应以及从无效应到孟德尔隐性的有限传递模型集合上，独立地最大化连锁和非连锁情况下的似然性。为了检验给定图谱位置的遗传效应，在连锁情况下的似然性在混合比例（即连锁家庭的比例）上最大化。将其应用于受影响同胞对和家系中广泛的传递模型的模拟数据表明，新方法在无效假设下表现良好，并且在存在连锁时提供了强大的连锁检验。此检验除了对群体患病率进行近似估计外，无需指定传递模型参数。它可同等地应用于同胞对和家系，并且由于它不会在非常接近标记的检验位置降低对数优势比分，所以适用于多点数据。
