Literature DB >> 24464286

Epigenome-wide association studies without the need for cell-type composition.

James Zou1, Christoph Lippert2, David Heckerman2, Martin Aryee3, Jennifer Listgarten2.   

Abstract

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/.

Mesh:

Year:  2014        PMID: 24464286     DOI: 10.1038/nmeth.2815

Source DB:  PubMed          Journal:  Nat Methods        ISSN: 1548-7091            Impact factor:   28.547


  21 in total

1.  Genomic control for association studies.

Authors:  B Devlin; K Roeder
Journal:  Biometrics       Date:  1999-12       Impact factor: 2.571

2.  Improved linear mixed models for genome-wide association studies.

Authors:  Jennifer Listgarten; Christoph Lippert; Carl M Kadie; Robert I Davidson; Eleazar Eskin; David Heckerman
Journal:  Nat Methods       Date:  2012-05-30       Impact factor: 28.547

3.  FaST linear mixed models for genome-wide association studies.

Authors:  Christoph Lippert; Jennifer Listgarten; Ying Liu; Carl M Kadie; Robert I Davidson; David Heckerman
Journal:  Nat Methods       Date:  2011-09-04       Impact factor: 28.547

4.  Factors underlying variable DNA methylation in a human community cohort.

Authors:  Lucia L Lam; Eldon Emberly; Hunter B Fraser; Sarah M Neumann; Edith Chen; Gregory E Miller; Michael S Kobor
Journal:  Proc Natl Acad Sci U S A       Date:  2012-10-08       Impact factor: 11.205

5.  μ-Opioid receptor gene A118G polymorphism predicts survival in patients with breast cancer.

Authors:  Andrey V Bortsov; Robert C Millikan; Inna Belfer; Richard L Boortz-Marx; Harendra Arora; Samuel A McLean
Journal:  Anesthesiology       Date:  2012-04       Impact factor: 7.892

6.  RUNX3 acts as a tumor suppressor in breast cancer by targeting estrogen receptor α.

Authors:  B Huang; Z Qu; C W Ong; Y-H N Tsang; G Xiao; D Shapiro; M Salto-Tellez; K Ito; Y Ito; L-F Chen
Journal:  Oncogene       Date:  2011-06-27       Impact factor: 9.867

Review 7.  Epigenome-wide association studies for common human diseases.

Authors:  Vardhman K Rakyan; Thomas A Down; David J Balding; Stephan Beck
Journal:  Nat Rev Genet       Date:  2011-07-12       Impact factor: 53.242

8.  Expression of interleukin 11 and its receptor and their prognostic value in human breast cancer.

Authors:  Satheesha Hanavadi; Tracey A Martin; Gareth Watkins; Robert E Mansel; Wen G Jiang
Journal:  Ann Surg Oncol       Date:  2006-04-14       Impact factor: 5.344

9.  The benefits of selecting phenotype-specific variants for applications of mixed models in genomics.

Authors:  Christoph Lippert; Gerald Quon; Eun Yong Kang; Carl M Kadie; Jennifer Listgarten; David Heckerman
Journal:  Sci Rep       Date:  2013       Impact factor: 4.379

10.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists.

Authors:  Da Wei Huang; Brad T Sherman; Richard A Lempicki
Journal:  Nucleic Acids Res       Date:  2008-11-25       Impact factor: 16.971

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  99 in total

Review 1.  Epigenetic regulation of ageing: linking environmental inputs to genomic stability.

Authors:  Bérénice A Benayoun; Elizabeth A Pollina; Anne Brunet
Journal:  Nat Rev Mol Cell Biol       Date:  2015-09-16       Impact factor: 94.444

2.  Dissecting differential signals in high-throughput data from complex tissues.

Authors:  Ziyi Li; Zhijin Wu; Peng Jin; Hao Wu
Journal:  Bioinformatics       Date:  2019-10-15       Impact factor: 6.937

3.  Correcting for cell-type heterogeneity in epigenome-wide association studies: revisiting previous analyses.

Authors:  Shijie C Zheng; Stephan Beck; Andrew E Jaffe; Devin C Koestler; Kasper D Hansen; Andres E Houseman; Rafael A Irizarry; Andrew E Teschendorff
Journal:  Nat Methods       Date:  2017-02-28       Impact factor: 28.547

4.  Clinical epigenomics for cardiovascular disease: Diagnostics and therapies.

Authors:  Matthew A Fischer; Thomas M Vondriska
Journal:  J Mol Cell Cardiol       Date:  2021-02-06       Impact factor: 5.000

Review 5.  Influence of environmental exposure on human epigenetic regulation.

Authors:  Carmen J Marsit
Journal:  J Exp Biol       Date:  2015-01-01       Impact factor: 3.312

6.  Epigenome-wide association of liver methylation patterns and complex metabolic traits in mice.

Authors:  Luz D Orozco; Marco Morselli; Liudmilla Rubbi; Weilong Guo; James Go; Huwenbo Shi; David Lopez; Nicholas A Furlotte; Brian J Bennett; Charles R Farber; Anatole Ghazalpour; Michael Q Zhang; Renata Bahous; Rima Rozen; Aldons J Lusis; Matteo Pellegrini
Journal:  Cell Metab       Date:  2015-06-02       Impact factor: 27.287

Review 7.  Epigenetic mechanisms underlying the pathogenesis of neurogenetic diseases.

Authors:  Irfan A Qureshi; Mark F Mehler
Journal:  Neurotherapeutics       Date:  2014-10       Impact factor: 7.620

8.  A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets.

Authors:  Johannes Brägelmann; Justo Lorenzo Bermejo
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

9.  Cell type specific DNA methylation in cord blood: A 450K-reference data set and cell count-based validation of estimated cell type composition.

Authors:  Kristina Gervin; Christian Magnus Page; Hans Christian D Aass; Michelle A Jansen; Heidi Elisabeth Fjeldstad; Bettina Kulle Andreassen; Liesbeth Duijts; Joyce B van Meurs; Menno C van Zelm; Vincent W Jaddoe; Hedvig Nordeng; Gunn Peggy Knudsen; Per Magnus; Wenche Nystad; Anne Cathrine Staff; Janine F Felix; Robert Lyle
Journal:  Epigenetics       Date:  2016-09       Impact factor: 4.528

10.  Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.

Authors:  Vitor Onuchic; Ryan J Hartmaier; David N Boone; Michael L Samuels; Ronak Y Patel; Wendy M White; Vesna D Garovic; Steffi Oesterreich; Matt E Roth; Adrian V Lee; Aleksandar Milosavljevic
Journal:  Cell Rep       Date:  2016-11-15       Impact factor: 9.423

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