• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>other >Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels
【2h】

Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels

机译：使用来自分布式参考面板的多组估算基因型提高关联测试的能力

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The accuracy of genotype imputation depends upon two factors: the sample size of the reference panel and the genetic similarity between the reference panel and the target samples. When multiple reference panels are not consented to combine together, it is unclear how to combine the imputation results to optimize the power of genetic association studies. We compared the accuracy of 9,265 Norwegian genomes imputed from three reference panels – 1000 Genomes Phase 3 (1000G), Haplotype Reference Consortium (HRC), and a reference panel containing 2,201 Norwegian participants from the population-based Nord Trøndelag Health Study (HUNT) from low-pass genome sequencing. We observed that the population-matched reference panel allowed for imputation of more population-specific variants with lower frequency (minor allele frequency (MAF) between 0.05% and 0.5%). The overall imputation accuracy from the population-specific panel was substantially higher than 1000G and was comparable with HRC, despite HRC being 15-fold larger. These results recapitulate the value of the population-specific reference panels for genotype imputation. We also evaluated different strategies to utilize multiple sets of imputed genotypes to increase the power of association studies. We observed that testing association for all variants imputed from any panel results in higher power to detect association than the alternative strategy of including only one version of each genetic variant, selected for having the highest imputation quality metric. This was particularly true for lower-frequency variants (MAF < 1%), even after adjusting for the additional multiple testing burden.
机译：基因型估算的准确性取决于两个因素：参考样本的样本大小以及参考样本与目标样本之间的遗传相似性。当不同的参考小组不同意合并在一起时，不清楚如何合并估算结果以优化遗传关联研究的能力。我们比较了从三个参考专家组推算出的9,265个挪威基因组的准确性– 1000个基因组第3阶段（1000G），单倍型参考协会（HRC）和一个参考专家组，该参考专家组包含来自以人群为基础的北特伦德拉格健康研究（HUNT）的2,201名挪威参与者低通基因组测序。我们观察到，与人群匹配的参考指标组允许以较低的频率（0.05％至0.5％的次要等位基因频率（MAF））估算更多的特定人群变异。尽管HRC的大小要大15倍，但特定于人群的面板的总插补精度显着高于1000G，与HRC相当。这些结果概括了针对基因型插补的特定人群参考面板的价值。我们还评估了不同策略，以利用多套估算的基因型来增加关联研究的能力。我们观察到，从任何面板推算出的所有变体的关联性测试均比选择仅包含每个基因变体的一个版本（具有最高插补质量指标）的替代策略具有更高的检测关联性的能力。即使在调整了额外的多重测试负担之后，对于低频变量（MAF <1％）尤其如此。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号