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Estimating genome-wide IBD sharing from SNP data via an efficient hidden Markov model of LD with application to gene mapping

机译:通过有效的LD隐马尔可夫模型从SNP数据估计全基因组IBD共享,并将其应用于基因作图

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摘要

Motivation: Association analysis is the method of choice for studying complex multifactorial diseases. The premise of this method is that affected persons contain some common genomic regions with similar SNP alleles and such areas will be found in this analysis. An important disadvantage of GWA studies is that it does not distinguish between genomic areas that are inherited from a common ancestor [identical by descent (IBD)] and areas that are identical merely by state [identical by state (IBS)]. Clearly, areas that can be marked with higher probability as IBD and have the same correlation with the disease status of identical areas that are more probably only IBS, are better candidates to be causative, and yet this distinction is not encoded in standard association analysis.
机译:动机:关联分析是研究复杂的多因素疾病的一种选择方法。此方法的前提是受影响的人包含一些具有相似SNP等位基因的共同基因组区域,并且在此分析中将发现此类区域。 GWA研究的一个重要缺点是,它不能区分从共同祖先继承的基因组区域(按血统(IBD)确定)和仅按州划分的区域(按州确定(IBS)确定)。显然,可以被标记为IBD的可能性更高的区域,并且与可能只是IBS的相同区域的疾病状况具有相同的相关性,是更容易引起疾病的候选者,但是在标准关联分析中并未对此区分进行编码。

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  • 来源
    《Bioinformatics》 |2010年第12期|p.175-182|共8页
  • 作者

    Dan Geiger;

  • 作者单位
  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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