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SEQCHIP: a powerful method to integrate sequence and genotype data for the detection of rare variant associations

机译:SEQCHIP:整合序列和基因型数据以检测稀有变异关联的强大方法

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摘要

Motivation: Next-generation sequencing greatly increases the capacity to detect rare-variant complex-trait associations. However, it is still expensive to sequence a large number of samples and therefore often small datasets are used. Given cost constraints, a potentially more powerful two-step strategy is to sequence a subset of the sample to discover variants, and genotype the identified variants in the remaining sample. If only cases are sequenced, directly combining sequence and genotype data will lead to inflated type-I errors in rare-variant association analysis. Although several methods have been developed to correct for the bias, they are either underpowered or theoretically invalid. We proposed a new method SEQCHIP to integrate genotype and sequence data, which can be used with most existing rare-variant tests.
机译:动机:下一代测序大大提高了检测稀有变异复杂性状关联的能力。但是,对大量样本进行测序仍然很昂贵,因此经常使用小的数据集。给定成本约束,一种可能更强大的两步策略是对样本的子集进行排序以发现变体,并对其余样本中已识别的变体进行基因分型。如果仅对病例进行测序,则将序列和基因型数据直接组合会导致稀有变异关联分析中的I型错误膨胀。尽管已开发出几种方法来校正偏差,但它们要么功率不足,要么在理论上是无效的。我们提出了一种新的方法SEQCHIP来整合基因型和序列数据,可用于大多数现有的稀有变异测试。

著录项

  • 来源
    《Bioinformatics》 |2012年第13期|p.1745-1751|共7页
  • 作者

    Suzanne M. Leal;

  • 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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