• 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>BMC Genetics >Identifying genomic regions for fine-mapping using genome scan meta-analysis (GSMA) to identify the minimum regions of maximum significance (MRMS) across populations
【2h】

Identifying genomic regions for fine-mapping using genome scan meta-analysis (GSMA) to identify the minimum regions of maximum significance (MRMS) across populations

机译：使用基因组扫描荟萃分析（GSMA）识别用于精细映射的基因组区域以识别整个人群的最大显着性最小区域（MRMS）

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

In order to detect linkage of the simulated complex disease Kofendrerd Personality Disorder across studies from multiple populations, we performed a genome scan meta-analysis (GSMA). Using the 7-cM microsatellite map, nonparametric multipoint linkage analyses were performed separately on each of the four simulated populations independently to determine p-values. The genome of each population was divided into 20-cM bin regions, and each bin was rank-ordered based on the most significant linkage p-value for that population in that region. The bin ranks were then averaged across all four studies to determine the most significant 20-cM regions over all studies. Statistical significance of the averaged bin ranks was determined from a normal distribution of randomly assigned rank averages. To narrow the region of interest for fine-mapping, the meta-analysis was repeated two additional times, with each of the 20-cM bins offset by 7 cM and 13 cM, respectively, creating regions of overlap with the original method. The 6–7 cM shared regions, where the highest averaged 20-cM bins from each of the three offsets overlap, designated the minimum region of maximum significance (MRMS). Application of the GSMA-MRMS method revealed genome wide significance (p-values refer to the average rank assigned to the bin) at regions including or adjacent to all of the simulated disease loci: chromosome 1 (p < 0.0001 for 160–167 cM, including D1), chromosome 3 (p-value < 0.0000001 for 287–294 cM, including D2), chromosome 5 (p-value < 0.001 for 0–7 cM, including D3), and chromosome 9 (p-value < 0.05 for 7–14 cM, the region adjacent to D4). This GSMA analysis approach demonstrates the power of linkage meta-analysis to detect multiple genes simultaneously for a complex disorder. The MRMS method enhances this powerful tool to focus on more localized regions of linkage.
机译：为了从多个人群的研究中检测出模拟复杂疾病科芬德雷德人格障碍的关联性，我们进行了基因组扫描荟萃分析（GSMA）。使用7-cM微卫星图，分别对四个模拟种群中的每一个分别进行非参数多点链接分析，以确定p值。将每个种群的基因组划分为20个cM bin区域，并根据该区域中该种群的最重要连锁p值对每个bin进行排序。然后将所有四项研究的bin等级平均，以确定所有研究中最重要的20-cM区域。从随机分配的秩平均值的正态分布确定平均bin秩的统计显着性。为了缩小目标区域以进行精细映射，再次进行了两次荟萃分析，每个20-cM单元格分别偏移了7cM和13cM，从而创建了与原始方法重叠的区域。 6-7个cM共享区域（三个偏移量中每一个的最高平均20-cM单元重叠）指定了最大重要性最小区域（MRMS）。 GSMA-MRMS方法的应用揭示了在包括或邻近所有模拟疾病位点的区域（1号染色体，p-0.0001，对于160-167 cM，p <0.0001，包括D1），染色体3（287-294 cM，包括D2的p值<0.0000001，包括D2），染色体5（对于0-7 cM，包括D3的p值<0.001）和9号染色体（对于D7，p值<0.05 7-14 cM，与D4相邻的区域）。这种GSMA分析方法证明了连锁荟萃分析功能可同时检测复杂疾病的多个基因。 MRMS方法增强了此强大的工具，可专注于链接的更多局部区域。

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