1] eScience Research Group, Microsoft Research, Los Angeles, California, USA. [2] The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. [3] School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA.
eScience Research Group, Microsoft Research, Los Angeles, California, USA.
Nat Methods. 2014 Mar;11(3):309-11. doi: 10.1038/nmeth.2815. Epub 2014 Jan 26.
In epigenome-wide association studies, cell-type composition often differs between cases and controls, yielding associations that simply tag cell type rather than reveal fundamental biology. Current solutions require actual or estimated cell-type composition--information not easily obtainable for many samples of interest. We propose a method, FaST-LMM-EWASher, that automatically corrects for cell-type composition without the need for explicit knowledge of it, and then validate our method by comparison with the state-of-the-art approach. Corresponding software is available from http://www.microsoft.com/science/.
在全基因组关联研究中，病例和对照之间的细胞类型组成通常不同，导致关联仅仅标记细胞类型，而不能揭示基本生物学。当前的解决方案需要实际或估计的细胞类型组成-对于许多感兴趣的样本来说，这是不容易获得的信息。我们提出了一种方法，FaST-LMM-EWASher，它可以在不需要明确了解细胞类型组成的情况下自动进行校正，然后通过与最先进的方法进行比较来验证我们的方法。相应的软件可从 http://www.microsoft.com/science/ 获得。
