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Application of Structural Equation Models to Genome-wide Association Analysis

  • Kim, Ji-Young (Department of Statistics, Seoul National University) ;
  • Namkung, Jung-Hyun (Department of Epidemiology and Biostatistics, Case Western Reserve University) ;
  • Lee, Seung-Mook (Department of Statistics, Seoul National University) ;
  • Park, Tae-Sung (Department of Statistics, Seoul National University)
  • Accepted : 2010.09.02
  • Published : 2010.09.30

Abstract

Genome-wise association studies (GWASs) have become popular approaches to identify genetic variants associated with human biological traits. In this study, we applied Structural Equation Models (SEMs) in order to model complex relationships between genetic networks and traits as risk factors. SEMs allow us to achieve a better understanding of biological mechanisms through identifying greater numbers of genes and pathways that are associated with a set of traits and the relationship among them. For efficient SEM analysis for GWASs, we developed a procedure, comprised of four stages. In the first stage, we conducted single-SNP analysis using regression models, where age, sex, and recruited area were included as adjusting covariates. In the second stage, Fisher's combination test was conducted for each gene to detect significant genes using p-values obtained from the single-SNP analysis. In the third stage, Fisher's exact test was adopted to determine which biological pathways were enriched with significant SNPs. Finally, based on a pathway that was associated with the four traits in common, a SEM was fit to model a causal relationship among the genetic factors and traits. We applied our SEM model to GWAS data with four central obesity related traits: suprailiac and subscapular measures for upper body fat, BMI, and hypertension. Study subjects were collected from two Korean cohort regions. After quality control, 327,872 SNPs for 8842 individuals were included in the analysis. After comparing two SEMs, we concluded that suprailiac and subscapular measures may indirectly affect hypertension susceptibility by influencing BMI. In conclusion, our analysis demonstrates that SEMs provide a better understanding of biological mechanisms by identifying greater numbers of genes and pathways.

Keywords

central obesity;suprailiac;subscapular;body mass index (BMI);hypertension;genome-wide association study (GWAS);structural equation model (SEM);gene-based analysis;pathway-based analysis

References

  1. Baranzini, S.E., Galwey, N.W., Wang, J., Khankhanian, P., Lindberg, R., Pelletier, D., Wu, W., Uitdehaag, B.M., Kappos, L., Polman, C.H., Matthews, P.M., Hauser, S.L., Gibson, R.A., Oksenberg, J.R., and Barnes, M.R. (2009). Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum. Mol. Genet. 18, 2078-2090. https://doi.org/10.1093/hmg/ddp120
  2. Berridge, M.J. (1994). The biology and medicine of calcium signalling. Mol. Cell Endocrinol. 98, 119-124. https://doi.org/10.1016/0303-7207(94)90129-5
  3. Bost, F., Aouadi, M., Caron, L., and Binetruy, B. (2005). The role of MAPKs in adipocyte differentiation and obesity. Biochimie 87, 51-56. https://doi.org/10.1016/j.biochi.2004.10.018
  4. Cho, Y.S., Go, M.J., Kim, Y.J., Heo, J.Y., Oh, J.H., Ban, H.J., Yoon, D., Lee, M.H., Kim, D.J., Park, M., Cha, S.H., Kim, J.W., Han, B.G., Min, H., Ahn, Y., Park, M.S., Han, H.R., Jang, H.Y., Cho, E.Y., Lee, J.E., Cho, N.H., Shin, C., Park, T., Park, J.W., Lee, J.K., Cardon, L., Clarke, G., McCarthy, M.I., Lee, J.Y., Oh, B., and Kim, H.L. (2009). A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat. Genet. 41, 527-534. https://doi.org/10.1038/ng.357
  5. Dustan, H. (1991). Obesity and hypertension. Diabetes Care 14, 488-504. https://doi.org/10.2337/diacare.14.6.488
  6. Fisher, R.A. (1925). Statistical methods for research workers. Oliver and Lloyd, London.
  7. Frayling, T.M., Timpson, N.J., Weedon, M.N., Zeggini, E., Freathy, R.M., Lindgren, C.M., Perry, J.R., Elliott, K.S., Lango, H., Rayner, N.W., Shields, B., Harries, L.W., Barrett, J.C., Ellard, S., Groves, C.J., Knight, B., Patch, A.M., Ness, A.R., Ebrahim, S., Lawlor, D.A., Ring, S.M., Ben-Shlomo, Y., Jarvelin, M.R., Sovio, U., Bennett, A.J., Melzer, D., Ferrucci, L., Loos, R.J., Barroso, I., Wareham, N.J., Karpe, F., Owen, K.R., Cardon, L.R., Walker, M., Hitman, G.A., Palmer, C.N., Doney, A.S., Morris, A.D., Smith, G.D., Hattersley, A.T., and McCarthy, M.I. (2007). A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316, 889-894. https://doi.org/10.1126/science.1141634
  8. Hirosumi, J., Tuncman, G., Chang, L., Gorgun, C.Z., Uysal, K.T., Maeda, K., Karin, M., and Hotamisligil, G.S. (2002). A central role for JNK in obesity and insulin resistance. Nature 420, 333-336. https://doi.org/10.1038/nature01137
  9. Hong, K.W., Go, M.J., Jin, H.S., Lim, J.E., Lee, J.Y., Han, B.G., Hwang, S.Y., Lee, S.H., Park, H.K., Cho, Y.S., and Oh, B. (2010). Genetic variations in ATP2B1, CSK, ARSG and CSMD1 loci are related to blood pressure and/or hypertension in two Korean cohorts. J. Hum. Hypertens. 24, 367-372. https://doi.org/10.1038/jhh.2009.86
  10. Hotta, K., Nakamura, M., Nakamura, T., Matsuo, T., Nakata, Y., Kamohara, S., Miyatake, N., Kotani, K., Komatsu, R., Itoh, N., Mineo, I., Wada, J., Masuzaki, H., Yoneda, M., Nakajima, A., Funahashi, T., Miyazaki, S., Tokunaga, K., Kawamoto, M., Ueno, T., Hamaguchi, K., Tanaka, K., Yamada, K., Hanafusa, T., Oikawa, S., Yoshimatsu, H., Nakao, K., Sakata, T., Matsuzawa, Y., Kamatani, N., and, Y.N. (2009). Association between obesity and polymorphisms in SEC16B, TMEM18, GNPDA2, BDNF, FAIM2 and MC4R in a Japanese population. J. Hum. Genet. 54, 727-731. https://doi.org/10.1038/jhg.2009.106
  11. Kolz, M. (2008). Association between variations in the TLR4 gene and incident type 2 diabetes is modified by the ratio of total cholesterol to HDL-cholesterol. BMC Med. Genet. 25, 9.
  12. Kraft, P., and Raychaudhuri, S. (2009). Complex diseases, complex genes: keeping pathways on the right track. Epidemiology 20, 508-511. https://doi.org/10.1097/EDE.0b013e3181a93b98
  13. Lesnick, T.G., Papapetropoulos, S., Mash, D.C., Ffrench- Mullen, J., Shehadeh, L., de Andrade, M., Henley, J.R., Rocca, W.A., Ahlskog, J.E., and Maraganore, D.M. (2007). A genomic pathway approach to a complex disease: axon guidance and Parkinson disease. PLoS Genet , 3, e98. https://doi.org/10.1371/journal.pgen.0030098
  14. Levy, D., Ehret, G.B., Rice, K., Verwoert, G.C., Launer, L.J., Dehghan, A., Glazer, N.L., Morrison, A.C., Johnson, A.D., Aspelund, T., Aulchenko, Y., Lumley, T., Kottgen, A., Vasan, R.S., Rivadeneira, F., Eiriksdottir, G., Guo, X., Arking, D.E., Mitchell, G.F., Mattace-Raso, F.U., Smith, A.V., Taylor, K., Scharpf, R.B., Hwang, S.J., Sijbrands, E.J., Bis, J., Harris, T.B., Ganesh, S.K., O'Donnell, C.J., Hofman, A., Rotter, J.I., Coresh, J., Benjamin, E.J., Uitterlinden, A.G., Heiss, G., Fox, C.S., Witteman, J.C., Boerwinkle, E., Wang, T.J., Gudnason, V., Larson, M.G., Chakravarti, A., Psaty, B.M., and van Duijn, C.M. (2009). Genome-wide association study of blood pressure and hypertension. Nat. Genet. 41, 677-687. https://doi.org/10.1038/ng.384
  15. Licata, G., Scaglione, R., Ganguzza, A., Corrao, S., Donatelli, M., Parrinello, G., Dichiara, M.A., Merlino, G., and Cecala, M.G. (1994). Central obesity and hypertension. Relationship between fasting serum insulin, plasma renin activity, and diastolic blood pressure in young obese subjects. Am. J. Hypertens. 7, 314-320. https://doi.org/10.1093/ajh/7.4.314
  16. Mariman, E.C., and Wang, P. (2010). Adipocyte extracellular matrix composition, dynamics and role in obesity. Cell Mol. Life Sci. 67, 1277-1292. https://doi.org/10.1007/s00018-010-0263-4
  17. Niskanen, L., Laaksonen, D.E., Nyyssonen, K., Punnonen, K., Valkonen, V.-P., Fuentes, R., Tuomainen, T.-P., Salonen, R., and Salonen, J.T. (2004). Inflammation, abdominal obesity, and smoking as predictors of hypertension. Hypertension 44, 859-865. https://doi.org/10.1161/01.HYP.0000146691.51307.84
  18. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., de Bakker, P.I., Daly, M.J., and Sham, P.C. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559-575. https://doi.org/10.1086/519795
  19. Rajagopalan, D., and Agarwal, P. (2005). Inferring pathways from gene lists using a literature-derived network of biological relationships. Bioinformatics 21, 788-793. https://doi.org/10.1093/bioinformatics/bti069
  20. Rioux, J.D., Xavier, R.J., Taylor, K.D., Silverberg, M.S., Goyette, P., Huett, A., Green, T., Kuballa, P., Barmada, M.M., Datta, L.W., Shugart, Y.Y., Griffiths, A.M., Targan, S.R., Ippoliti, A.F., Bernard, E.J., Mei, L., Nicolae, D.L., Regueiro, M., Schumm, L.P., Steinhart, A.H., Rotter, J.I., Duerr, R.H., Cho, J.H., Daly, M.J., and Brant, S.R. (2007). Genome-wide association study identifies new susceptibility loci for Crohn disease and implicates autophagy in disease pathogenesis. Nat. Genet. 39, 596-604. https://doi.org/10.1038/ng2032
  21. Saxena, R., Voight, B.F., Lyssenko, V., Burtt, N.P., de Bakker, P.I., Chen, H., Roix, J.J., Kathiresan, S., Hirschhorn, J.N., Daly, M.J., Hughes, T.E., Groop, L., Altshuler, D., Almgren, P., Florez, J.C., Meyer, J., Ardlie, K., Bengtsson Bostrom, K., Isomaa, B., Lettre, G., Lindblad, U., Lyon, H.N., Melander, O., Newton-Cheh, C., Nilsson, P., Orho-Melander, M., Rastam, L., Speliotes, E.K., Taskinen, M.R., Tuomi, T., Guiducci, C., Berglund, A., Carlson, J., Gianniny, L., Hackett, R., Hall, L., Holmkvist, J., Laurila, E., Sjogren, M., Sterner, M., Surti, A., Svensson, M., Tewhey, R., Blumenstiel, B., Parkin, M., Defelice, M., Barry, R., Brodeur, W., Camarata, J., Chia, N., Fava, M., Gibbons, J., Handsaker, B., Healy, C., Nguyen, K., Gates, C., Sougnez, C., Gage, D., Nizzari, M., Gabriel, S.B., Chirn, G.W., Ma, Q., Parikh, H., Richardson, D., Ricke, D., and Purcell, S. (2007). Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316, 1331-1336. https://doi.org/10.1126/science.1142358
  22. Scaglione, R., Ganguzza, A., Corrao, S., Parrinello, G., Merlino, G., Dichiara, M.A., Arnone, S., D'Aubert, M.D., and Licata, G. (1995). Central obesity and hypertension: pathophysiologic role of renal haemodynamics and function. Int J Obes Relat. Metab. Disord. 19, 403-409.
  23. Selby, J.V., Friedman, G.D., and Quesenberry, C.P., Jr. (1989). Precursors of essential hypertension. The role of body fat distribution pattern. Am. J. Epidemiol. 129, 43-53. https://doi.org/10.1093/oxfordjournals.aje.a115123
  24. Sergeev, I.N. (2009). Novel Mediators of Vitamin D Signaling in Cancer and Obesity, Immun., Endoc. & Metab. Agents in Med. Chem. 9, 153-158.
  25. Torkamani, A., Topol, E.J., and Schork, N.J. (2008). Pathway analysis of seven common diseases assessed by genome-wide association, Genomics 92, 265-272. https://doi.org/10.1016/j.ygeno.2008.07.011
  26. Willer, C.J., Speliotes, E.K., Loos, R.J., Li, S., Lindgren, C.M., Heid, I.M., Berndt, S.I., Elliott, A.L., Jackson, A.U., Lamina, C., Lettre, G., Lim, N., Lyon, H.N., McCarroll, S.A., Papadakis, K., Qi, L., Randall, J.C., Roccasecca, R.M., Sanna, S., Scheet, P., Weedon, M.N., Wheeler, E., Zhao, J.H., Jacobs, L.C., Prokopenko, I., Soranzo, N., Tanaka, T., Timpson, N.J., Almgren, P., Bennett, A., Bergman, R.N., Bingham, S.A., Bonnycastle, L.L., Brown, M., Burtt, N.P., Chines, P., Coin, L., Collins, F.S., Connell, J.M., Cooper, C., Smith, G.D., Dennison, E.M., Deodhar, P., Elliott, P., Erdos, M.R., Estrada, K., Evans, D.M., Gianniny, L., Gieger, C., Gillson, C.J., Guiducci, C., Hackett, R., Hadley, D., Hall, A.S., Havulinna, A.S., Hebebrand, J., Hofman, A., Isomaa, B., Jacobs, K.B., Johnson, T., Jousilahti, P., Jovanovic, Z., Khaw, K.T., Kraft, P., Kuokkanen, M., Kuusisto, J., Laitinen, J., Lakatta, E.G., Luan, J., Luben, R.N., Mangino, M., McArdle, W.L., Meitinger, T., Mulas, A., Munroe, P.B., Narisu, N., Ness, A.R., Northstone, K., O'Rahilly, S., Purmann, C., Rees, M.G., Ridderstrale, M., Ring, S.M., Rivadeneira, F., Ruokonen, A., Sandhu, M.S., Saramies, J., Scott, L.J., Scuteri, A., Silander, K., Sims, M.A., Song, K., Stephens, J., Stevens, S., Stringham, H.M., Tung, Y.C., Valle, T.T., Van Duijn, C.M., Vimaleswaran, K.S., Vollenweider, P., Waeber, G., Wallace, C., Watanabe, R.M., Waterworth, D.M., Watkins, N., Witteman, J.C., Zeggini, E., Zhai, G., Zillikens, M.C., Altshuler, D., Caulfield, M.J., Chanock, S.J., Farooqi, I.S., Ferrucci, L., Guralnik, J.M., Hattersley, A.T., Hu, F.B., Jarvelin, M.R., Laakso, M., Mooser, V., Ong, K.K., Ouwehand, W.H., Salomaa, V., Samani, N.J., Spector, T.D., Tuomi, T., Tuomilehto, J., Uda, M., Uitterlinden, A.G., Wareham, N.J., Deloukas, P., Frayling, T.M., Groop, L.C., Hayes, R.B., Hunter, D.J., Mohlke, K.L., Peltonen, L., Schlessinger, D., Strachan, D.P., Wichmann, H.E., McCarthy, M.I., Boehnke, M., Barroso, I., Abecasis, G.R., and Hirschhorn, J.N. (2009). Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat. Genet. 41, 25-34. https://doi.org/10.1038/ng.287
  27. WTCCC (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661-678. https://doi.org/10.1038/nature05911
  28. Zanke, B.W., Greenwood, C.M., Rangrej, J., Kustra, R., Tenesa, A., Farrington, S.M., Prendergast, J., Olschwang, S., Chiang, T., Crowdy, E., Ferretti, V., Laflamme, P., Sundararajan, S., Roumy, S., Olivier, J.F., Robidoux, F., Sladek, R., Montpetit, A., Campbell, P., Bezieau, S., O'Shea, A.M., Zogopoulos, G., Cotterchio, M., Newcomb, P., McLaughlin, J., Younghusband, B., Green, R., Green, J., Porteous, M.E., Campbell, H., Blanche, H., Sahbatou, M., Tubacher, E., Bonaiti-Pellie, C., Buecher, B., Riboli, E., Kury, S., Chanock, S.J., Potter, J., Thomas, G., Gallinger, S., Hudson, T.J., and Dunlop, M.G. (2007). Genome-wide association scan identifies a colorectal cancer susceptibility locus on chromosome 8q24. Nat. Genet. 39, 989-994. https://doi.org/10.1038/ng2089
  29. Zaykin, D.V., Zhivotovsky, L.A., Czika, W., Shao, S., and Wolfinger, R.D. (2007). Combining p-values in large-scale genomics experiments. Pharm. Stat. 6, 217-226. https://doi.org/10.1002/pst.304
  30. Zhang, S., Weinheimer, C., Courtois, M., Kovacs, A., Zhang, C.E., Cheng, A.M., Wang, Y., and Muslin, A.J. (2003). The role of the Grb2-p38 MAPK signaling pathway in cardiac hypertrophy and fibrosis. J. Clin. Invest. 111, 833-841. https://doi.org/10.1172/JCI16290

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