Structural equation modeling for hypertension and type 2 diabetes based on multiple SNPs and multiple phenotypes
Autoři:
Saebom Jeon aff001; Ji-yeon Shin aff002; Jaeyong Yee aff003; Taesung Park aff004; Mira Park aff005
Působiště autorů:
Department of Marketing Information Consulting, Mokwon University, Daejeon, KOREA
aff001; Department of Preventive Medicine, School of Medicine, Kyungpook National University, Daegu, KOREA
aff002; Department of Physiology and Biophysics, Eulji University, Daejeon, KOREA
aff003; Department of Statistics, Seoul National University, Seoul, KOREA
aff004; Department of Preventive Medicine, Eulji University, Daejeon, KOREA
aff005
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0217189
Souhrn
Genome-wide association studies (GWAS) have been successful in identifying genetic variants associated with complex diseases. However, association analyses between genotypes and phenotypes are not straightforward due to the complex relationships between genetic and environmental factors. Moreover, multiple correlated phenotypes further complicate such analyses. To resolve this complexity, we present an analysis using structural equation modeling (SEM). Unlike current methods that focus only on identifying direct associations between diseases and genetic variants such as single-nucleotide polymorphisms (SNPs), our method introduces the effects of intermediate phenotypes, which are related phenotypes distinct from the target, into the systematic genetic study of diseases. Moreover, we consider multiple diseases simultaneously in a single model. The procedure can be summarized in four steps: 1) selection of informative SNPs, 2) extraction of latent variables from the selected SNPs, 3) investigation of the relationships among intermediate phenotypes and diseases, and 4) construction of an SEM. As a result, a quantitative map can be drawn that simultaneously shows the relationship among multiple SNPs, phenotypes, and diseases. In this study, we considered two correlated diseases, hypertension and type 2 diabetes (T2D), which are known to have a substantial overlap in their disease mechanism and have significant public health implications. As intermediate phenotypes for these diseases, we considered three obesity-related phenotypes—subscapular skin fold thickness, body mass index, and waist circumference—as traits representing subcutaneous adiposity, overall adiposity, and abdominal adiposity, respectively. Using GWAS data collected from the Korea Association Resource (KARE) project, we applied the proposed SEM process. Among 327,872 SNPs, 24 informative SNPs were selected in the first step (p<1.0E-05). Ten latent variables were generated in step 2. After an exploratory analysis, we established a path diagram among phenotypes and diseases in step 3. Finally, in step 4, we produced a quantitative map with paths moving from specific SNPs to hypertension through intermediate phenotypes and T2D. The resulting model had high goodness-of-fit measures (χ2 = 536.52, NFI = 0.997, CFI = 0.998, GFI = 0.995, AGFI = 0.993, RMSEA = 0.012).
Klíčová slova:
Medicine and health sciences – Vascular medicine – Blood pressure – Hypertension – Endocrinology – Endocrine disorders – Metabolic disorders – Body weight – Body Mass Index – Biology and life sciences – Physiology – Physiological parameters – Obesity – Genetics – Molecular genetics – Phenotypes – Genomics – Genome analysis – Human genetics – Genetics of disease – Molecular biology – Computational biology – Genome-wide association studies
Zdroje
1. Organization, W.H., Global health risks: mortality and burden of disease attributable to selected major risks. 2009: Geneva: World Health Organization.
2. Lozano R., et al., Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. The lancet, 2012. 380(9859): p. 2095–2128.
3. De Boer I.H., et al., Diabetes and hypertension: a position statement by the American Diabetes Association. Diabetes Care, 2017. 40(9): p. 1273–1284. doi: 10.2337/dci17-0026 28830958
4. Ferrannini E. and Cushman W.C., Diabetes and hypertension: the bad companions. The Lancet, 2012. 380(9841): p. 601–610.
5. Tsimihodimos V., et al., Hypertension and diabetes mellitus: coprediction and time trajectories. Hypertension, 2018. 71(3): p. 422–428. doi: 10.1161/HYPERTENSIONAHA.117.10546 29335249
6. Sun D., et al., Type 2 Diabetes and Hypertension: A Study on Bidirectional Causality. Circulation research, 2019.
7. Cheung B.M. and Li C., Diabetes and hypertension: is there a common metabolic pathway? Current atherosclerosis reports, 2012. 14(2): p. 160–166. doi: 10.1007/s11883-012-0227-2 22281657
8. Karns R., et al., Modeling metabolic syndrome through structural equations of metabolic traits, comorbid diseases, and GWAS variants. Obesity (Silver Spring), 2013. 21(12): p. E745–54.
9. Visscher P.M., et al., Five years of GWAS discovery. Am J Hum Genet, 2012. 90(1): p. 7–24. doi: 10.1016/j.ajhg.2011.11.029 22243964
10. Ng F.L., Warren H.R., and Caulfield M.J., Hypertension genomics and cardiovascular prevention. Annals of translational medicine, 2018. 6(15): p. 291–291. doi: 10.21037/atm.2018.06.34 30211179
11. Mohlke K.L. and Boehnke M., Recent advances in understanding the genetic architecture of type 2 diabetes. Human Molecular Genetics, 2015. 24(R1): p. R85–R92. doi: 10.1093/hmg/ddv264 26160912
12. Taylor J.Y., et al., An overview of the genomics of metabolic syndrome. J Nurs Scholarsh, 2013. 45(1): p. 52–9. doi: 10.1111/j.1547-5069.2012.01484.x 23368731
13. VanderWeele T.J., Mediation Analysis: A Practitioner’s Guide. Annual Review of Public Health, 2016. 37(1): p. 17–32.
14. Agler R. and De Boeck P., On the Interpretation and Use of Mediation: Multiple Perspectives on Mediation Analysis. Front Psychol, 2017. 8: p. 1984. doi: 10.3389/fpsyg.2017.01984 29187828
15. Gunzler D., et al., Introduction to mediation analysis with structural equation modeling. Shanghai archives of psychiatry, 2013. 25(6): p. 390–394. 24991183
16. Stein C., Morris N., and Nock N., Structural Equation Modeling. Vol. 850. 2012. 495–512.
17. Hox J. and Bechger T., An Introduction to Structural Equation Modeling. Vol. 11. 1999.
18. MacCallum R.C. and Austin J.T., Applications of Structural Equation Modeling in Psychological Research. Annual Review of Psychology, 2000. 51(1): p. 201–226.
19. Stegmann G., Review of A Beginner’s Guide to Structural Equation Modeling (4th ed.), by Randall E. Schumacker & Richard G. Lomax: New York, NY: Routledge, 2016. 351 pp. $65.91 (paperback). Vol. 24. 2017. 475–477.
20. Li R., et al., Structural model analysis of multiple quantitative traits. PLoS Genet, 2006. 2(7): p. e114. doi: 10.1371/journal.pgen.0020114 16848643
21. Rosa Guilherme JM, V. B.D., Gustavo de los Campos, Wu Xiao-Lin, Gianola Daniel, Silva Martinho A Inferring causal phenotype networks using structural equation models. Genetics Selection Evolution, 2011. 43(6): p. 13.
22. Kim J., et al., Application of Structural Equation Models to Genome-wide Association Analysis. Genomics Inform, 2010. 8(3): p. 150–158.
23. Nuzhdin S.V., Friesen M.L., and McIntyre L.M., Genotype-phenotype mapping in a post-GWAS world. Trends Genet, 2012. 28(9): p. 421–6. doi: 10.1016/j.tig.2012.06.003 22818580
24. Verhulst B., Maes H.H., and Neale M.C., GW-SEM: A Statistical Package to Conduct Genome-Wide Structural Equation Modeling. Behavior genetics, 2017. 47(3): p. 345–359. doi: 10.1007/s10519-017-9842-6 28299468
25. Stein C.M., et al., Structural equation model-based genome scan for the metabolic syndrome. BMC Genet, 2003. 4 Suppl 1: p. S99.
26. Song Y.E., Morris N.J., and Stein C.M., Structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees. BMC Proc, 2016. 10(Suppl 7): p. 303–307. doi: 10.1186/s12919-016-0047-4 27980653
27. Jesús Rosel I.P., Longitudinal Data Analysis with Structural Equations. European Journal of Research Methods for the Behavioral and Social Sciences, 2008. 4: p. 37–50.
28. Osborne J., What is Rotating in Exploratory Factor Analysis? Vol. 20. 2015. 1–8.
29. Cheung M.W.L., Comparison of methods for constructing confidence intervals of standardized indirect effects. Behavior Research Methods, 2009. 41(2): p. 425–438. doi: 10.3758/BRM.41.2.425 19363183
30. Shrout P.E. and Bolger N., Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 2002. 7(4): p. 422–445. 12530702
31. MacKinnon D.P., et al., Distribution of the product confidence limits for the indirect effect: program PRODCLIN. Behavior research methods, 2007. 39(3): p. 384–389. 17958149
32. Leth-Steensen C. and Gallitto E., Testing Mediation in Structural Equation Modeling: The Effectiveness of the Test of Joint Significance. Educational and psychological measurement, 2016. 76(2): p. 339–351. doi: 10.1177/0013164415593777 29795869
33. Hooper D., Coughlan J., Mullen M., Structural Equation Modelling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods, 2008. 6(1): p. 8.
34. Barrett P., Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 2007. 42(5): p. 815–824.
35. Cho Y.S., et al., A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet, 2009. 41(5): p. 527–34. doi: 10.1038/ng.357 19396169
36. Snijder M., et al., What aspects of body fat are particularly hazardous and how do we measure them? International Journal of Epidemiology, 2005. 35(1): p. 83–92. doi: 10.1093/ije/dyi253 16339600
37. Levy D., et al., Genome-wide association study of blood pressure and hypertension. Nat Genet, 2009. 41(6): p. 677–87. doi: 10.1038/ng.384 19430479
38. Vazquez G., et al., Comparison of Body Mass Index, Waist Circumference, and Waist/Hip Ratio in Predicting Incident Diabetes: A Meta-Analysis. Epidemiologic Reviews, 2007. 29(1): p. 115–128.
39. Cho N., et al., IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes research and clinical practice, 2018. 138: p. 271–281. doi: 10.1016/j.diabres.2018.02.023 29496507
40. Forouzanfar M.H., et al., Global burden of hypertension and systolic blood pressure of at least 110 to 115 mm Hg, 1990–2015. Jama, 2017. 317(2): p. 165–182. doi: 10.1001/jama.2016.19043 28097354
Článek vyšel v časopise
PLOS One
2019 Číslo 9
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy