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通过对传递概率进行检验的分离分析来推断数量性状的主基因:我们有多少次会错过?

Inferring a major gene for quantitative traits by using segregation analysis with tests on transmission probabilities: how often do we miss?

作者信息

Borecki I B, Province M A, Rao D C

机构信息

Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63110.

出版信息

Am J Hum Genet. 1995 Jan;56(1):319-26.

Abstract

In an effort to safeguard against false inference of a major gene in segregation analysis, it has become common practice to require nonrejection of the Mendelian-transmission hypothesis (Mendelian tau's) and rejection of the no-transmission hypothesis (equal tau's). However, it is not known how often one would actually infer a major gene, when one exists, by using these criteria. A simulation study was undertaken to investigate this issue. Segregation of a Mendelian gene under a variety of models was simulated in families with both parents and three children. The data were analyzed by using POINTER; the assumptions under the generating and analysis models were identical. By design, the power to reject the no-major-effect hypothesis (q = 0) was > 60% for all models considered; tests on the transmission probabilities were carried out only when q = 0 was rejected, using alpha = 0.05 for all tests. The rates of Mendelian inference were mostly in the range of 22%-50% under recessive inheritance, versus 60%-99% under dominant inheritance. Notably, it was not possible to resolve the transmission (from among Mendelian tau's, equal tau's, and general unconstrained tau's) in approximately 20%-70% of the cases under recessive models, versus 3%-15% under dominant models. Therefore, while tests on transmission probabilities can serve to reduce rates of false inference of a major gene, it is also possible to fail to infer a major gene when one indeed exists, especially under recessive inheritance.

摘要

为了防止在分离分析中错误推断主基因,目前的常见做法是要求不拒绝孟德尔传递假说(孟德尔τ值)并拒绝无传递假说(相等τ值)。然而,尚不清楚使用这些标准时,实际能多频繁地推断出存在的主基因。为此进行了一项模拟研究来探讨这个问题。在有父母和三个孩子的家庭中模拟了各种模型下孟德尔基因的分离情况。使用POINTER对数据进行分析;生成模型和分析模型的假设相同。根据设计,对于所有考虑的模型,拒绝无主效应假说(q = 0)的检验效能均> 60%;仅当拒绝q = 0时才对传递概率进行检验,所有检验的α值均为0.05。在隐性遗传下,孟德尔推断率大多在22% - 50%范围内,而在显性遗传下为60% - 99%。值得注意的是,在隐性模型下,约20% - 70%的案例无法确定传递情况(在孟德尔τ值、相等τ值和一般无约束τ值之间),而在显性模型下这一比例为3% - 15%。因此,虽然对传递概率的检验有助于降低主基因错误推断率,但当主基因确实存在时,也有可能无法推断出来,尤其是在隐性遗传情况下。

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