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结构化探索性数据分析:对三种SEDA统计量在评估遗传模式中的应用评估

Structured exploratory data analysis: an evaluation of the use of three SEDA statistics in assessing mode of inheritance.

作者信息

MacCluer J W, Kammerer C M

出版信息

Prog Clin Biol Res. 1984;147:297-315.

PMID:6739490
Abstract

Several SEDA statistics are described and three of them (the Midparent-Child Correlation Coefficient, the Major Gene Index, and the Offspring Between Parents Function) are evaluated with respect to their ability to distinguish monogenic, polygenic and sporadic effects on quantitative traits. The MPCC, MGI, and OBP, used in combination, are sensitive but not specific in identifying monogenic inheritance. In our tests, traits determined by a simple type of polygenic inheritance were almost invariably classified as monogenic. Sporadic traits, on the other hand, were correctly identified in 84 percent of cases. The concurrent use of these SEDA statistics and two sibship variance tests yielded some improvement whenever the two procedures agreed (which happened about 50 percent of the time). These results suggest that under some circumstances, SEDA can be a valuable adjunct to other methods of genetic analysis. SEDA methodology also appears promising as an aid in understanding the contribution of various nongenetic factors to quantitative traits.

摘要

文中描述了几种SEDA统计方法,并对其中三种方法(中亲-子代相关系数、主基因指数和亲子间子代函数)区分单基因、多基因和散发性对数量性状影响的能力进行了评估。综合使用的MPCC、MGI和OBP在识别单基因遗传方面敏感但不特异。在我们的测试中,由简单类型的多基因遗传决定的性状几乎总是被归类为单基因性状。另一方面,散发性性状在84%的病例中被正确识别。每当这两种方法一致时(约占50%的时间),同时使用这些SEDA统计方法和两种同胞方差检验会有一些改进。这些结果表明,在某些情况下,SEDA可以作为其他遗传分析方法的有价值辅助手段。SEDA方法在帮助理解各种非遗传因素对数量性状的贡献方面似乎也很有前景。

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