黄铜:对基因关联研究中的二进制特征的变方法,使用结构化样本
Joelle Mbatchou1,2, Mark Abney3, Mary Sara McPeek2,3
1Regeneron Genetics Center, Tarrytown, New York, United States of America.
PLoS genetics
|November 7, 2023
概括
我们开发了BRASS,这是一种用于遗传关联研究中的二进制特征的新代方法. 在结构化样本中,BRASS控制了1型错误,超过了现有的方法,并确定了犬病的新关联.
科学领域:
- 遗传学 是一个遗传学.
- 统计遗传学 统计遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 在基因关联研究中,当测试统计分布未知时,对基因关联研究的显著性评估至关重要.
- 像MVNpermute这样的现有方法不适合结构化样本中的二进制特征,导致膨胀的1型错误.
- 需要准确的统计方法来绘制结构和相关性的人群中复杂的特征关联映射.
研究的目的:
- 介绍BRASS (协会研究的二进制特征重新采样),这是一个新的二进制特征在遗传关联分析中的换方法.
- 评估BRASS在复杂情景中控制1型错误方面的表现,包括人口结构和家族关系.
- 在现实数据集中证明BRASS在识别复杂疾病的显著遗传关联方面的实用性.
主要方法:
- 黄铜模型样本结构,共变量,确定,并允许同时测试多个标记器的二进制特征.
- 该方法容纳了各种测试统计数据,并与其他六种基于排列和重新采样的方法进行了比较.
- 模拟包括人口结构,家族关系,确定和表型模型的错误规范.
主要成果:
- 与其他六种方法相比,BRASS在各种具有挑战性的场景中展示了对1型错误的优越控制.
- 该方法成功地确定了已知的遗传关联,用于肘发育不良和在家养狗的异常性.
- 在BRASS检测出了35号染色体上的肘部发育不良的新型显著关联,这在以前的分析中被遗漏了.
结论:
- 铜是一种强大而有效的代方法,用于结构化样本中二进制特征的遗传关联映射.
- 该方法提供了改进的1型错误控制,提高了遗传关联发现的可靠性.
- 黄铜具有实际的实用性,正如其在识别犬种群中新型疾病相关变异中的应用所证明的那样.
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