Epigenome-wide association studies without the need for cell-type composition.
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/.
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.
James
J
Zou
eScience Research Group, Microsoft Research, Los Angeles, California, USA.
Christoph
C
Lippert
eScience Research Group, Microsoft Research, Los Angeles, California, USA.
David
D
Heckerman
1] Harvard Medical School, Boston, Massachusetts, USA. [2] Massachusetts General Hospital, Boston, Massachusetts, USA.
Martin
0000000268481344
M
Aryee
eScience Research Group, Microsoft Research, Los Angeles, California, USA.
Jennifer
J
Listgarten
D002477:Cells; D057890:Epigenomics; D055106:Genome-Wide Association Study; D006801:Humans; D016014:Linear Models