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使用总结统计数据和链接不平衡矩阵准确有效地估计局部遗传性

Hui Li¹, Rahul Mazumder², Xihong Lin^3,4

¹Harvard T.H. Chan School of Public Health, Department of Biostatistics, Boston, MA, USA.

Nature communications

|December 1, 2023

概括

我们使用LD和关联总结统计 (HEELS) 开发了高效的遗传性估计,这是从全基因组关联研究 (GWAS) 总结统计数据中估计遗传性的新方法. HEELS显著提高了统计效率,匹配了限制最大概率 (REML) 估计器的性能.

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:

从全基因组关联研究 (GWAS) 总结统计数据中估计遗传性对于理解遗传对特征的贡献至关重要.
使用总结统计数据的现有方法在统计学上效率低于限制最大概率 (REML) 估计器,这些估计器需要个体级遗传数据.

研究的目的:

引入一种新的方法,使用LD和协会总结统计 (HEELS) 进行高效的遗传性估计,用于本地遗传性估计.
显著提高利用总结统计数据的遗传性估计器的统计效率.
为了实现与REML估计器可比的统计效率.

主要方法:

开发了HEELS,一种使用GWAS总结统计和链接不平衡 (LD) 信息进行遗传性估计的新统计方法.
通过将其建模为低等级矩阵和带状矩阵的总和,提出了一个LD矩阵近似.
通过对英国生物库数据的模拟和经验分析,验证了HEELS和LD近似策略.

主要成果:

从总结统计数据来看,HEELS在遗传性估计方面的统计效率显著提高.
拟议的LD矩阵近似降低了存储,内存和计算成本.
与REML相比,HEELS实现了超过92%的相对统计效率.

结论:

HEELS提供了一个高效的替代方案,用于使用GWAS总结统计数据进行遗传性估计.
LD矩阵近似策略提高了计算效率,减少了资源需求.
在不需要个人级别数据的情况下,HEELS为遗传架构研究提供了强大的工具.

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