Transethnic Genetic-Correlation Estimates from Summary Statistics
Transethnic Genetic-Correlation Estimates from Summary Statistics
复制标题
DOI：
10.1016/j.ajhg.2016.05.001
复制
发表时间：
2016-07-07
影响因子：
9.8
通讯作者：
Zaitlen, Noah
中科院分区：
文献类型：
--
作者：
Brown, Brielin C.;Ye, Chun Jimmie;Zaitlen, Noah
在多个种群中进行的遗传关联研究数量的增加为研究复杂表型的遗传结构如何在种群之间变化提供了前所未有的机会，这对医学和种群遗传学都是一个重要的问题。在这里，我们开发了一种估算跨种族遗传相关性的方法：群体中常见snp的因果变异效应大小的相关性。该方法利用了SNP关联的整个谱，并且仅使用了全基因组关联研究的摘要级数据。这避免了与基因型水平信息相关的计算成本和隐私问题，同时保持可扩展到数十万个个体和数百万个snp。我们将我们的方法应用于基因表达、类风湿性关节炎和2型糖尿病的数据，并压倒性地发现遗传相关性显著小于1。我们的方法在一个名为Popcorn的Python包中实现。
The increasing number of genetic association studies conducted in multiple populations provides an unprecedented opportunity to study how the genetic architecture of complex phenotypes varies between populations, a problem important for both medical and population genetics. Here, we have developed a method for estimating the transethnic genetic correlation: the correlation of causal-variant effect sizes at SNPs common in populations. This methods takes advantage of the entire spectrum of SNP associations and uses only summary-level data from genome-wide association studies. This avoids the computational costs and privacy concerns associated with genotype-level information while remaining scalable to hundreds of thousands of individuals and millions of SNPs. We applied our method to data on gene expression, rheumatoid arthritis, and type 2 diabetes and overwhelmingly found that the genetic correlation was significantly less than 1. Our method is implemented in a Python package called Popcorn.