Generating a robust statistical causal structure over 13 cardiovascular disease risk factors using genomics data

Journal of Biomedical Informatics

Volumn 60, Issue , 2016, Pages 114-119

  Yazdani, Azam ^a   Yazdani, Akram ^a   Samiei, Ahmad ^b   Boerwinkle, Eric ^a  

^a UNIVERSITY OF TEXAS SCHOOL OF PUBLIC HEALTH   (United States)

^b Department of Software Systematic ^*  (Germany)

Author keywords

Cardiovascular disease risk factors; Causal network; Conditional independency; Data integration; Granularity DAG; Partial correlation

Indexed keywords

CARDIOLOGY; DATA INTEGRATION; DIRECTED GRAPHS; DISEASES; GENES; GRAPH THEORY; RISK ASSESSMENT;

CARDIO-VASCULAR DISEASE RISK FACTORS; CAUSAL NETWORK; CONDITIONAL INDEPENDENCY; GRANULARITY DAG; PARTIAL CORRELATION;

PRINCIPAL COMPONENT ANALYSIS;

ACCESS TO INFORMATION; ARTICLE; BODY MASS; CARDIOVASCULAR DISEASE; CARDIOVASCULAR RISK; DIRECTED ACYCLIC GRAPH; GENETIC DATABASE; GENOTYPE; MEDICAL INFORMATION SYSTEM; OBSERVATIONAL STUDY; PHENOTYPE; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROBABILITY; RISK ASSESSMENT; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL MODEL; ALGORITHM; GENETICS; GENOMICS; HUMAN; MEDICAL INFORMATICS; RISK FACTOR;

ALGORITHMS; CARDIOVASCULAR DISEASES; GENOMICS; GENOTYPE; HUMANS; MEDICAL INFORMATICS; MODELS, STATISTICAL; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; PRINCIPAL COMPONENT ANALYSIS; RISK FACTORS;

EID: 84962866775     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2016.01.012     Document Type: Article

References (42)

1. Rodin S., Boerwinkle E. Mining genetic epidemiology data with Bayesian networks I: Bayesian networks and example application (plasma APOE levels). Bioinformatics 2005, 21(15):3273-3278.
2. Voight B.F., Peloso G.M., Orho-Melander M., et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomization study. Lancet 2012, 380(9841):572-580.
3. Thomas D.C., Conti D.V. Commentary: the concept of 'Mendelian randomization'. Int. J. Epidemiol. 2004, 33:21-25.
4. Clayton D., McKeigue P.M. Epidemiological methods for studying genes and environmental factors in complex diseases. Lancet 2001, 358:1356-1360.
5. Kulp D.C., Jagalur M. Causal inference of regulator-target paris by gene mapping of expression phenotypes. BMC Genom. 2006, 7:125.
6. Thompson J.R., Minelli C., Abrams K.R., Tobin M.D., Riley R.D. Metaanalysis of genetic studies using Mendelian randomization - a multivariate approach. Stat. Med. 2005, 24:2241-2254.
7. Mehrabian M., Allayee H., Stockton J., Lum P.Y., Drake T.A., Castellani L.W., Suh M., Armour C., Edwards S., Lamb J., Lusis A.J., Schadt E.E. Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits. Nat. Genet. 2005, 37:1224-1233.
8. Zhu J., Zhang B., Smith E.N., Drees B., Brem R.B., Kruglyak L., et al. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat. Genet. 2008, 40(7):854-861.
9. Aten J.E., Fuller T.F., Lusis A.J., Horvath S. Using genetic markers to orient the edges in quantitative trait networks: the NEO software. BMC Syst. Biol. 2008, 2:34.
10. Neto E.C., Keller M.P., Attie A.D., Yandell B.S. Causal graphical models in systems genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes. Ann. Appl. Stat. 2010, 4(1):320.
11. Burgess S., Thompson S. Avoiding bias from weak instruments in Mendelian randomization studies. Int. J. Epidemiol. 2011, 40(3):755-764.
12. Pearl J. Causality: Models, Reasoning, and Inference 2009, Cambridge University Press, New York.
13. A.P. Dawid, Fundamentals of statistical causality. Research Report 279, Department of Statistical Science, University College London, 2007.
14. Lauritzen S.L., Dawid A.P., Larsen B.N., Leimer H.G. Independence properties of directed Markov fields. Networks 1990, 20:491-505.
15. Verma T., Pearl J. Causal Networks: Semantics and Expressiveness. Uncertainty in Artificial Intelligence 4 1990, 69-76. North-Holland, Amsterdam. R.D. Shachter, T.S. Levitt, L.N. Kanal, J.F. Lemmer (Eds.).
16. Robinson R.W. Counting labeled acyclic digraphs. New Directions in the Theory of Graphs: Proc. of the Third Ann Arbor Conf. on Graph Theory (1971) 1973, 239-273. Academic Press, NY. F. Haray (Ed.).
17. Inouye M., Kettunen J., Soininen P., Silander K., Ripatti S., Kumpula L.S., et al. Metabonomic, transcriptomic, and genomic variation of a population cohort. Mol. Syst. Biol. 2010, 6:441.
18. T. Verma, J. Pearl. An algorithm for deciding if a set of observational independencies has a causal explanation, in: D. Dubois, M.P. Wellman, B. D'Ambrosio, P. Smets (Eds.), Proceedings of the Eight Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, 1992, pp. 323-330.
19. P. Sprites, G. Cooper, An experiment in causal discovery using a pneumonia database, in: D. Heckerman, J. Whittaker (Eds.), Processing of the Seventh International Workshop on Artificial Intelligence and Statistics, Morgan Kaufmann, 1999.
20. Meinshausen N., Buhlmann P. High dimensional graphs and variable selection with the lasso. Ann. Stat. 2006, 34:1436-1462.
21. Friedman Jerome, Hastie Trevor, Tibshirani Robert Sparse inverse covariance estimation with the graphical lasso. Biostatistics 2008, 9(3):432-441.
22. Hsieh Cho-Jui, et al. Sparse inverse covariance matrix estimation using quadratic approximation. Adv Neural Inform Process Syst 2011.
23. Shojaie Ali, Michailidis George Penalized likelihood methods for estimation of sparse high-dimensional directed acyclic graphs. Biometrika 2010, 97(3):519-538.
24. Ringnér M. What is principal component analysis?. Nat. Biotechnol. 2008, 26(3):303-304.
25. Alter O., Brown P., Botstein D. Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. 2000, 97:10101-10106.
26. Price Alkes L., et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 2006, 38(8):904-909.
27. Hastie T., Tibshirani R., Eisen M., Brown P., Ross D., Scherf U., Weinstein J., Alizadeh A., Staudt L., Botstein D. 'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol. 2000, 1:1-21.
28. Freudenberg Jan, et al. Partial correlation analysis indicates causal relationships between GC-content, exon density and recombination rate in the human genome. BMC Bioinformatics 2009, 10(Suppl. 1):S66.
29. Baba Kunihiro, Shibata Ritei, Sibuya Masaaki Partial correlation and conditional correlation as measures of conditional independence. Aust. NZ J. Stat. 2004, 46(4):657-664.
30. Kalisch M., Bühlmann P. Estimating high-dimensional directed acyclic graphs with the PC-algorithm. J. Mach. Learn. Res. 2007, 8:613-636.
31. Lauritzen S. Graphical Models 1996, Oxford University Press.
32. Schaffner S.F., Foo C., Gabriel S., Reich D., Daly M.J., Altshuler D. Calibrating a coalescent simulation of human genome sequence variation. Genome Res. 2005, 15:1576-1583.
33. Spirtes P., Glymour C., Scheines R. Causationn, Prediction, and Search 2000, Springer-Verlag.
34. de Campos L.M., Castellano J.G. Bayesian network learning algorithms using structural restrictions. Int. J. Approximate Reasoning 2007, 45(2):233-254.
35. The ARIC Investigators The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am. J. Epidemiol. 1989, 129(4):687-702.
36. http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000287.v3.p1.
37. http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000401.v9.p10.
38. http://www.ncbi.nlm.nih.gov/gap.
39. Yazdani A., Dunson D.B. A hybrid Bayesian approach for genome-wide association studies on related individuals. Bioinformatics 2015, btv496.
40. Bennett, Joel S. Platelet-fibrinogen interactions. Ann. N. Y. Acad. Sci. 2001, 936(1):340-354.
41. Shimizu S., Hoyer P., Hyvarinen A., Kerminen A. A linear non-Gaussian acyclic model for causal discovery. J. Mach. Learn. Res. 2006, 7:2003-2023.
42. Yazdani A., Boerwinkle E. Causal inference in the age of decision medicine. J Data Min. Genom. Proteomics 2014, 6:163.