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METHODS FOR ASSOCIATING QUANTITATIVE TRAITS WITH ALLELES IN SIBLING PAIRS

机译:双性对中等位基因与数量性状的关联方法

摘要

Identifying the genetic components of complex diseases is one of the most important goals of the human genome project. These diseases and their underlying risk factors are often better described by quantitative phenotypes than by an arbitrary distinction between affected and unaffected individuals. Association studies are able to identify genetic loci contributing to these quantitative trait loci directly at the cost of requiring large population sizes. Studies of sib pair populations have been suggested to increase power when populations are stratified, and tests on pooled DNA may reduce the experimental burden, but these approaches have been analyzed primarily in the context of affected/unaffected disease phenotypes. Disclosed herein are efficient methods for QTL mapping using DNA pooled from sib pairs. A preferred test using a single set of pools is to select unrelated sibs with extreme phenotypic values, requiring a population size approximately 1.5Xlarger than for individual genotyping. A preferred strategy overall, with a population size requirement only 1.24Xlarger than for individual genotyping, is a combined test of DNA pooled according to sib-difference phenotypic values. The optimal pooling fraction is 27 % and is insensitive to all model parameters including allele frequency, inheritance mode, and the magnitude of the QTL effect.
机译:鉴定复杂疾病的遗传成分是人类基因组计划的最重要目标之一。这些疾病及其潜在的危险因素通常通过定量表型来描述,而不是通过对患病个体和未患病个体之间的任意区分来更好地描述。关联研究能够直接鉴定导致这些数量性状基因座的遗传基因座,而这需要大量的人口。有人建议对同胞对种群进行研究,以便对种群进行分层,从而提高其能力,并且对合并的DNA进行测试可以减轻实验负担,但是这些方法主要是在受影响/未受影响的疾病表型的背景下进行分析的。本文公开了使用从同胞对中合并的DNA进行QTL作图的有效方法。使用单个集合池的首选测试是选择具有极端表型值的不相关同胞,而这些同胞需要的种群大小比个别基因分型大约1.5倍。总体上首选的策略是根据同胞差异表型值对合并的DNA进行组合测试,总体规模要求仅比个体基因分型大1.24倍。最佳合并分数为27%,并且对所有模型参数(包括等位基因频率,遗传模式和QTL效应的大小)不敏感。

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