首页> 美国卫生研究院文献>American Journal of Human Genetics >Inferring a major gene for quantitative traits by using segregation analysis with tests on transmission probabilities: how often do we miss?
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Inferring a major gene for quantitative traits by using segregation analysis with tests on transmission probabilities: how often do we miss?

机译:通过使用隔离分析和传播概率检验推断出数量性状的主要基因:我们多久错过一次?

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摘要

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.
机译:为了防止在分离分析中对主要基因的错误推断,要求不拒绝孟德尔传递假设(Mendelian tau's)和拒绝不传递假设(equal tau's)已成为一种普遍的做法。但是,尚不清楚通过使用这些标准,如果存在一个基因,实际会推断出一个主要基因的频率。进行了模拟研究以调查此问题。在有父母和三个孩子的家庭中,模拟了孟德尔基因在各种模型下的分离。使用POINTER分析数据。生成和分析模型下的假设是相同的。通过设计,对于所有考虑的模型,拒绝无重大影响假设(q = 0)的功效> 60%;仅当拒绝q = 0时才对传输概率进行测试,所有测试均使用alpha = 0.05。在隐性遗传条件下,孟德尔推理的比率大多在22%-50%的范围内,而在显性遗传条件下,孟德尔的推断率在60%-99%的范围内。值得注意的是,在隐性模型下,大约20%-70%的案例无法解决传播(从孟德尔tau,相等tau和普通无约束tau之间),而在显性模型下,则无法解决3%-15%。因此,尽管对传播概率的测试可以降低主要基因的错误推断率,但当确实存在一个主要基因时,尤其是在隐性遗传下,也可能无法推断一个主要基因。

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