Using multiple measures for quantitative trait association analyses: Application to estimated glomerular filtration rate

Journal of Human Genetics

Volumn 58, Issue 7, 2013, Pages 461-466

  Tin, Adrienne ^a   Colantuoni, Elizabeth ^a   Boerwinkle, Eric ^b   Kottgen, Anna ^c   Franceschini, Nora ^d   Astor, Brad C ^e   Coresh, Josef ^a   Kao, Wen Hong Linda ^a  

^a JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

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

^c UNIVERSITY OF FREIBURG   (Germany)

^d University of North Carolina at Chapel Hill   (United States)

^e UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

Author keywords

Biomarkers; Genome wide association study; Glomerular filtration rate; Kidney function; Repeated measures

Indexed keywords

ACCURACY; ADULT; ARTICLE; CONTROLLED STUDY; GENE LOCUS; GENETIC ASSOCIATION; GLOMERULUS FILTRATION RATE; HUMAN; MAJOR CLINICAL STUDY; MEASUREMENT ERROR; QUANTITATIVE TRAIT; RANDOM ERROR; SYSTEMATIC ERROR;

BIOLOGICAL MARKERS; EUROPEAN CONTINENTAL ANCESTRY GROUP; GENETIC ASSOCIATION STUDIES; GLOMERULAR FILTRATION RATE; HUMANS; KIDNEY; MIDDLE AGED; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; PROSPECTIVE STUDIES; QUANTITATIVE TRAIT LOCI;

EID: 84880872926     PISSN: 14345161     EISSN: 1435232X     Source Type: Journal    
DOI: 10.1038/jhg.2013.23     Document Type: Article

References (26)

1. Hindorff, L., MacArthur, J. E. B. I., Wise, A., Junkins, H., Hall, P., Klemm, A. et al. A Catalog of Published Genome-Wide Association Studies. Available at www.genome. gov/gwastudies (accessed on 12 June 2012
2. Liu, G. Jr. & Liang, K. Y. Sample size calculations for studies with correlated observations. Biometrics 53, 937-947 (1997
3. Diggle, P., Heagerty, P., Liang, K. Y. & Zeger, S. Analysis of Longitudinal Data. 2nd edn (Oxford University Press, Oxford, UK, 2002
4. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Am. J. Kidney Dis. 39 (suppl. 1), S1-S266 (2002
5. Stevens, L. A., Coresh, J., Schmid, C. H., Feldman, H. I., Froissart, M., Kusek, J. et al. Estimating GFR using serum cystatin C alone and in combination with serum creatinine: A pooled analysis of 3418 individuals with CKD. Am. J. Kidney Dis. 51, 395-406 (2008
6. Bianchi, C., Donadio, C., Tramonti, G., Consani, C., Lorusso, P. & Rossi, G. Reappraisal of serum beta2-microglobulin as marker of GFR. Ren. Fail. 23, 419-429 (2001
7. White, C. A., Akbari, A., Doucette, S., Fergusson, D., Hussain, N., Dinh, L. et al. Estimating GFR using serum beta trace protein: Accuracy and validation in kidney transplant and pediatric populations. Kidney Int. 76, 784-791 (2009
8. Astor, B. C., Shafi, T., Hoogeveen, R. C., Matsushita, K., Ballantyne, C. M., Inker, L. A. et al. Novel markers of kidney function as predictors of ESRD, cardiovascular disease, and mortality in the general population. Am. J. Kidney Dis. 59, 653-662 (2012
9. Kottgen, A., Pattaro, C., Boger, C. A., Fuchsberger, C., Olden, M., Glazer, N. L. et al. New loci associated with kidney function and chronic kidney disease. Nat. Genet. 42, 376-384 (2010
10. Levey, A. S. Measurement of renal function in chronic renal disease. Kidney Int. 38, 167-184 (1990
11. Myers, G. L., Miller, W. G., Coresh, J., Fleming, J., Greenberg, N., Greene, T. et al. Recommendations for improving serum creatinine measurement: A report from the Laboratory Working Group of the National Kidney Disease Education Program. Clin. Chem. 52, 5-18 (2006
12. Kwong, Y. T., Stevens, L. A., Selvin, E., Zhang, Y. L., Greene, T., Van Lente, F. et al. Imprecision of urinary iothalamate clearance as a gold-standard measure of GFR decreases the diagnostic accuracy of kidney function estimating equations. Am. J. Kidney Dis. 56, 39-49 (2010
13. Little, R J. A. & Rubin, D. B. Statistical Analysis with Missing Data (Wiley, New York, NY, USA,2002
14. ARIC. The Atherosclerosis Risk in Communities (ARIC) Study: Design and objectives. The ARIC investigators. Am. J. Epidemiol. 129, 687-702 (1989
15. Aulchenko, Y. S., Struchalin, M. V. & van Duijn, C. M. ProbABEL package for genomewide association analysis of imputed data. BMC Bioinform. 11, 134 (2010
16. Devlin, B., Roeder, K. & Wasserman, L. Genomic control, a new approach to geneticbased association studies. Theor. Popul. Biol. 60, 155-166 (2001
17. Sandell, L. L., Guan, X. J., Ingram, R. & Tilghman, S. M. Gatm, a creatine synthesis enzyme, is imprinted in mouse placenta. Proc. Natl Acad. Sci. USA 100, 4622-4627 (2003
18. Lui, K. J. & Cumberland, W. G. Sample size requirement for repeated measurements in continuous data. Stat. Med. 11, 633-641 (1992
19. Wojczynski, M. K. & Tiwari, H. K. Definition of phenotype. Adv. Genet. 60, 75-105 (2008
20. Rasmussen-Torvik, L. J., Alonso, A., Li, M., Kao, W., Kottgen, A., Yan, Y. et al. Impact of repeated measures and sample selection on genome-wide association studies of fasting glucose. Genet. Epidemiol. 34, 665-673 (2010
21. Malhotra, A., Kobes, S., Knowler, W. C., Baier, L. J., Bogardus, C. & Hanson, R. L. A genome-wide association study of BMI in American Indians. Obesity (Silver Spring, MD) 19, 2102-2106 (2011
22. Ferreira, M. A. & Purcell, S. M. A multivariate test of association. Bioinformatics 25, 132-133 (2009
23. Coin, L., O'Reilly, P., Pompyen, Y. & Hoggart, C. F. C. MultiPhen: Joint model of multiple phenotype increases discovery in GWAS. Presented at ASHG/ICHG. 13 October 2011, Montreal, Canada
24. Lander, E. S. Initial impact of the sequencing of the human genome. Nature 470, 187-197 (2011
25. Hirschhorn, J. N. Genomewide association studies-illuminating biologic pathways. N. Engl. J. Med. 360, 1699-1701 (2009
26. Park, J. H., Wacholder, S., Gail, M. H., Peters, U., Jacobs, K. B., Chanock, S. J. et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat. Genet. 42, 570-575 (2010