A powerful latent variable method for detecting and characterizing gene-based gene-gene interaction on multiple quantitative traits

BMC Genetics

Volumn 14, Issue , 2013, Pages

  Li, Fangyu ^a   Zhao, Jinghua ^b   Yuan, Zhongshang ^a   Zhang, Xiaoshuai ^a   Ji, Jiadong ^a   Xue, Fuzhong ^a  

^a SHANDONG UNIVERSITY   (China)

^b ADDENBROOKE S HOSPITAL   (United Kingdom)

Author keywords

Gene based gene gene interaction; mPLSPM statistic; Quantitative traits; Thinking quantitatively for complex diseases

Indexed keywords

BRAIN DERIVED NEUROTROPHIC FACTOR;

ARTICLE; BODY BUILD; BODY FAT DISTRIBUTION; BODY MASS; GENE INTERACTION; OBESITY; PARTIAL LEAST SQUARES REGRESSION; QUANTITATIVE TRAIT; SIMULATION; WAIST HIP RATIO;

BODY MASS INDEX; BODY SIZE; BRAIN-DERIVED NEUROTROPHIC FACTOR; EPISTASIS, GENETIC; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMANS; LINEAR MODELS; MEMBRANE PROTEINS; OBESITY; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; PRINCIPAL COMPONENT ANALYSIS; QUANTITATIVE TRAIT LOCI; SOFTWARE;

EID: 84884390374     PISSN: None     EISSN: 14712156     Source Type: Journal    
DOI: 10.1186/1471-2156-14-89     Document Type: Article

References (48)

1. Stranger BE SE, Raj T. Progress and Promise of Genome-Wide Association Studies for Human Complex Trait Genetics. Genetics 2011, 187:367-383. 10.1534/genetics.110.120907, 3030483, 21115973.
2. Zhang Y, Liu JS. Bayesian inference of epistatic interactions in case-control studies. Nat Genet 2007, 39(9):1167-1173. 10.1038/ng2110, 17721534.
3. Gayan J, Gonzalez-Perez A, Bermudo F, Saez ME, Royo JL, Quintas A, Galan JJ, Moron FJ, Ramirez-Lorca R, Real LM, et al. A method for detecting epistasis in genome-wide studies using case-control multi-locus association analysis. Bmc Genomics 2008, 9:360. 10.1186/1471-2164-9-360, 2533022, 18667089.
4. Plomin R, Haworth CMA, Davis OSP. Common disorders are quantitative traits. Nat Rev Genet 2009, 10(12):872-878.
5. Rowe NGMP, Cumming RG, Wans JJ. Diabetes, fasting blood glucose and age-related cataract: the Blue Mountains Eye Study. Ophthalmic Epidemiol 2000, 7:103-114.
6. RA F. The correlation between relatives on the supposition of Mendelian inheritance. Am J Hum Genet 1968, 20(4):402-403.
7. Donahue RP, Abbott RD. Central obesity and coronary heart disease in men. Lancet 1987, 2(8569):1215.
8. Ducimetiere P, Richard J, Cambien F. The pattern of subcutaneous fat distribution in middle-aged men and the risk of coronary heart disease: the Paris Prospective Study. Int J Obes 1986, 10(3):229-240.
9. Bjorntorp P. Abdominal obesity and the development of noninsulin-dependent diabetes mellitus. Diabetes Metab Rev 1988, 4(6):615-622. 10.1002/dmr.5610040607, 3065014.
10. Yusuf S, Hawken S, Ounpuu S, Bautista L, Franzosi MG, Commerford P, Lang CC, Rumboldt Z, Onen CL, Liu LS, et al. Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study. Lancet 2005, 366(9497):1640-1649. 10.1016/S0140-6736(05)67663-5, 16271645.
11. Wells J. BMI compared with 3-dimensional bodyshape: the UK National Sizing Survey. Am J Clin Nutr 2007, 85:7.
12. Pare G, Cook NR, Ridker PM, Chasman DI. On the Use of Variance per Genotype as a Tool to Identify Quantitative Trait Interaction Effects: A Report from the Women's Genome Health Study. Plos Genetics 2010, 6(6):e1000981. 10.1371/journal.pgen.1000981, 2887471, 20585554.
13. Li M, Ye C, Fu W, Elston RC, Lu Q. Detecting Genetic Interactions for Quantitative Traits With U-Statistics. Genet Epidemiol 2011, 35(6):457-468. 3575166, 21618602.
14. Culverhouse R, Suarez BK, Lin J, Reich T. A perspective on epistasis: Limits of models displaying no main effect. Am J Hum Genet 2002, 70(2):461-471. 10.1086/338759, 384920, 11791213.
15. Moore JH, Hahn LW, Bass M, Martin ER. Detection of gene-gene interactions in general pedigrees. Am J Hum Genet 2003, 73(5):606.
16. Beyene J, Tritchler D, Asimit JL, Hamid JS. Gene- or Region-Based Analysis of Genome-Wide Association Studies. Genet Epidemiol 2009, 33:S105-S110. 10.1002/gepi.20481, 2911440, 19924708.
17. Buil A, Martinez-Perez A, Perera-Lluna A, Rib L, Caminal P, Soria JM. A new gene-based association test for genome-wide association studies. BMC Proc 2009, 3Suppl 7:S130.
18. Li M, Wang K, Grant SFA, Hakonarson H, Li C. ATOM: a powerful gene-based association test by combining optimally weighted markers. Bioinformatics 2008, 25(4):497-503. 2642636, 19074959.
19. He J, Wang K, Edmondson AC, Rader DJ, Li C, Li M. Gene-based interaction analysis by incorporating external linkage disequilibrium information. Eur J Hum Genet 2010, 19(2):164-172. 3025792, 20924406.
20. Li S, Cui Y. Gene-centric gene-gene interaction: a model-based kernel machine method. Ann Appl Stat 2012, 6(3):1134-1161.
21. Ma L, Andrew GC, Alon K. Gene-Based Testing of Interactions in Association Studies of Quantitative Traits. Plos Genetics 2012, 9(2):e1003321.
22. Rajapakse I, Perlman MD, Martin PJ, Hansen JA, Kooperberg C. Multivariate Detection of Gene-Gene Interactions. Genet Epidemiol 2012, 36(6):622-630. 10.1002/gepi.21656, 3556521, 22782518.
23. Tenenhaus M, Vinzi VE, Chatelin YM, Lauro C. PLS path modeling. Comput Stat Data Anal 2005, 48(1):159-205.
24. Esposito VV CW, Henseler J, Wang H. Handbook of Partial Least Squares: Concepts, Methdos and Applications 2010, Berlin Heidelberg: Springer.
25. Lohmöller J. Latent variable path modeling with partial least squares 1989, Heidelberg: Physica-Verlag Heidelberg.
26. Wang KAD. A principal components regression approach to multilocus genetic association studies. Genet Epidemiol 2008, 32:108-118. 10.1002/gepi.20266, 17849491.
27. Gauderman WJ, Murcray C, Gilliland F, Conti DV. Testing association between disease and multiple SNPs in a candidate gene. Genet Epidemiol 2007, 31:383-395. 10.1002/gepi.20219, 17410554.
28. Klei L, Luca D, Devlin B, Roeder K. Pleiotropy and principal components of heritability combine to increase power for association analysis. Genet Epidemiol 2008, 32(1):9-19. 10.1002/gepi.20257, 17922480.
29. Markus MT, Groenen PJF. An introduction to the bootstrap. Psychometrika 1998, 63(1):97-101.
30. Linden A, Adams JL, Roberts N. Evaluating disease management program effectiveness - An introduction to the bootstrap technique. Dis Manage Health Outcomes 2005, 13:159-167.
31. Xue F, Li S, Luan J, Yuan Z, Luben RN, Khaw K-T, Wareham NJ, Loos RJF, Zhao JH. A Latent Variable Partial Least Squares Path Modeling Approach to Regional Association and Polygenic Effect with Applications to a Human Obesity Study. Plos One 2012, 7(2):e31927. 10.1371/journal.pone.0031927, 3288051, 22384102.
32. Li J, Chen Y. Generating samples for association studies based on HapMap data. Bmc Bioinformatics 2008, 9:44. 10.1186/1471-2105-9-44, 2375120, 18218094.
33. Riboli E, Kaaks R. The EPIC project: Rationale and study design. Int J Epidemiol 1997, 26(SUPPL. 1):S6-S14.
34. Day N, Oakes S, Luben R, Khaw KT, Bingham S, Welch A, Wareham N. EPIC-Norfolk: study design and characteristics of the cohort. Br J Cancer 1999, 80:95-103.
35. Rimm AA, Hartz AJ, Fischer ME. A weight shape index for assessing risk of disease in 44,820 women. J Clin Epidemiol 1988, 41(5):459-465. 10.1016/0895-4356(88)90047-9, 3367176.
36. Walsh P. Research profile. The apple shape. Causes and effects. Diabetes Forecast 2004, 57(2):73-75.
37. Walton C, Lees B, Crook D, Worthington M, Godsland IF, Stevenson JC. Body fat distribution, rather than overall adiposity, influences serum lipids and lipoproteins in healthy men independently of age. Am J Med 1995, 99(5):459-464. 10.1016/S0002-9343(99)80220-4, 7485201.
38. DALTON M, CAMERON AJ, ZIMMET PZ, SHAW JE, JOLLEY D, DUNSTAN DW, WELBORN TA. Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults. J Intern Med 2003, 254:555-563. 10.1111/j.1365-2796.2003.01229.x, 14641796.
39. Peter T, Peter T, Katzmarzyk SRS, Wei C, Malina RM, Claude B, Berenson GS. Body Mass Index, Waist Circumference, and Clustering of Cardiovascular Disease Risk Factors in a Biracial Sample of Children and Adolescents. Pediatrics 2004, 114:e198-e205. 10.1542/peds.114.2.e198, 15286257.
40. Li S, Zhao JH, Luan J, Luben RN, Rodwell SA, Khaw K-T, Ong KK, Wareham NJ, Loos RJF. Cumulative effects and predictive value of common obesity-susceptibility variants identified by genome-wide association studies. Am J Clin Nutr 2010, 91(1):184-190. 10.3945/ajcn.2009.28403, 19812171.
41. Bhattacharya K, McCarthy MI, Morris AP. Rapid testing of gene-gene interactions in genome-wide association studies of binary and quantitative phenotypes. Genet Epidemiol 2011, 35(8):800-808. 10.1002/gepi.20629, 3410530, 21948692.
42. Speliotes E. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 2010, 42:937-948. 10.1038/ng.686, 3014648, 20935630.
43. Obici S. Minireview: Molecular targets for obesity therapy in the brain. Endocrinology 2009, 150:2512-2517. 10.1210/en.2009-0409, 19372196.
44. Walley A. The genetic contribution to non-syndromic human obesity. Nat Rev Genet 2009, 10:431-442. 10.1038/nrg2594, 19506576.
45. Henseler JR. The Use of Partial Least Squares Path Modeling in International Marketing. Advin Intern Marketing 2009, 20:277-319.
46. Fornell C. A comparative analysis of two structural equation models: LISREL and PLS applied to market data. A second generation of multivariate analysis 1982, New York: Praeger, Fornell C, 1:289-324.
47. Chin W. The partial least squares approach in structural equation modeling. Modern methods for business research 1998, Lawrence Erlbaum, Marcoulides GA.
48. Fuzhong LPH, Duncan TE, Duncan SC, Alan A, Shawn B. Approaches to Testing Interaction Effects Using Structural Equation Modeling Methodology. Multivar Behav Res 1998, 33(1):1-39.