Development of exposome correlation globes to map out environment-wide associations

Pacific Symposium on Biocomputing

Volumn , Issue , 2015, Pages 231-242

  Patel, Chirag J ^a   Manrai, Arjun K ^a,b  

^a Harvard Medical School Center for Biomedical Informatics   (United States)

^b HARVARD MIT DIVISION OF HEALTH SCIENCES AND TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

POLLUTION;

COMPLEX RELATIONSHIPS; DENSE CORRELATIONS; ENVIRONMENTAL EXPOSURE; EXPOSURE FACTORS; INDIVIDUAL EXPOSURES; INTER-DEPENDENCIES; OVERALL CHARACTERISTICS; POLLUTANT EXPOSURES;

SOFTWARE PACKAGES;

ADVERSE EFFECTS; BIOLOGY; DISEASE; ENVIRONMENTAL EXPOSURE; ENVIRONMENTAL HEALTH; HUMAN; NUTRITION; RISK FACTOR; UNSUPERVISED MACHINE LEARNING;

COMPUTATIONAL BIOLOGY; DISEASE; ENVIRONMENTAL EXPOSURE; ENVIRONMENTAL HEALTH; HUMANS; NUTRITION SURVEYS; RISK FACTORS; UNSUPERVISED MACHINE LEARNING;

EID: 84971326365     PISSN: 23356928     EISSN: 23356936     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (24)

1. Wild, C.P., Complementing the genome with an "exposome": the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomarkers Prev, 2005. 14(8): p. 1847-50.
2. Rappaport, S.M. and M.T. Smith, Environment and Disease Risks. Science, 2010. 330(6003): p. 460-461.
3. Patel, C.J. and J.P. Ioannidis, Studying the elusive environment in large scale. J Am Med Assoc, 2014. 311(21): p. 2173-4.
4. Patel, C.J., J. Bhattacharya, and A.J. Butte, An Environment-Wide Association Study (EWAS) on type 2 diabetes mellitus. PLoS ONE, 2010. 5(5): p. e10746.
5. Patel, C.J., et al., Systematic evaluation of environmental and behavioural factors associated with all-cause mortality in the United States National Health and Nutrition Examination Survey. Int J Epidemiol, 2013. 42(6): p. 1795-810.
6. Hall, M.A., et al., Environment-wide association study (EWAS) for type 2 diabetes in the Marshfield Personalized Medicine Research Project Biobank. Pac Symp Biocomput, 2014: p. 200-11.
7. Ioannidis, J.P.A., et al., Researching Genetic Versus Nongenetic Determinants of Disease: A Comparison and Proposed Unification. Sci Transl Med, 2009. 1(7): p. 8.
8. Smith, G.D., et al., Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology. PLoS Med, 2007. 4(12): p. e352.
9. Patel, C.J. and J.P. Ioannidis, Placing epidemiological results in the context of multiplicity and typical correlations of exposures. J Epidemiol Community Health, 2014.
10. Carlin, D., et al., Unraveling the Health Effects of Environmental Mixtures: An NIEHS Priority. Environ Health Perspect, 2012. 121(1): p. a6-a8.
11. Horvath, S., Weighted Network Analysis: Applications in Genomics and Systems Biology2011, New York: Springer.
12. Eisen, M.B., et al., Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A, 1998. 95(25): p. 14863-8.
13. Butte, A.J. and I.S. Kohane, Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput, 2000: p. 418-29.
14. Benjamini, Y. and Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B, 1995. 57(1): p. 289-300.
15. Borenstein, M., et al., Introduction to Meta-Analysis2009, Chichester, UK: John Wiley and Sons.
16. Krzywinski, M., et al., Circos: an information aesthetic for comparative genomics. Genome Res, 2009. 19(9): p. 1639-45.
17. Porta, M., Persistent organic pollutants and the burden of diabetes. Lancet, 2006. 368(9535): p. 558-9.
18. Pearson, T.A. and T.A. Manolio, How to interpret a genome-wide association study. J Am Med Assoc, 2008. 299(11): p. 1335-44.
19. Patel, C.J., R. Chen, and A.J. Butte, Data-driven integration of epidemiological and toxicological data to select candidate interacting genes and environmental factors in association with disease. Bioinformatics, 2012. 28(12): p. i121-6.
20. Patel, C.J., et al., Systematic identification of interaction effects between genome-and environment-wide associations in type 2 diabetes mellitus. Hum Genet, 2013. 132(5): p. 495-508.
21. Park, S.K., et al., Environmental risk score as a new tool to examine multi-pollutants in epidemiologic research: an example from the NHANES study using serum lipid levels. PLoS One, 2014. 9(6): p. e98632.
22. Yang, J., et al., Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet, 2012. 44(4): p. 369-75, S1-3.
23. Magwene, P.M. and J. Kim, Estimating genomic coexpression networks using first-order conditional independence. Genome Biol, 2004. 5(12): p. R100.
24. Friedman, J., T. Hastie, and R. Tibshirani, Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 2008. 9(3): p. 432-41.